diff --git a/CMakeLists.txt b/CMakeLists.txt
index 89b9454ff0..47da622914 100755
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -45,8 +45,10 @@ if(NOT MSVC)
   endif()
 endif(NOT MSVC)
 
+include(${PROJECT_SOURCE_DIR}/cmake/build_tools.cmake)
 if(UNIX AND (NOT APPLE) AND (NOT ANDROID) AND (NOT WITH_TIMVX))
-  include(${PROJECT_SOURCE_DIR}/cmake/patchelf.cmake)
+  download_patchelf()
+  set(PATCHELF_EXE ${THIRD_PARTY_PATH}/patchelf/bin/patchelf)
 endif()
 
 
@@ -423,9 +425,17 @@ if(ENABLE_ENCRYPTION)
 endif()
 
 if(ENABLE_PADDLE2ONNX)
+  set(BUILD_PADDLE2ONNX ON)
   add_definitions(-DENABLE_PADDLE2ONNX)
-  include(${PROJECT_SOURCE_DIR}/cmake/paddle2onnx.cmake)
-  list(APPEND DEPEND_LIBS external_paddle2onnx)
+  if(BUILD_PADDLE2ONNX)
+    download_protobuf()
+    include(${PROJECT_SOURCE_DIR}/cmake/build_paddle2onnx.cmake)
+    list(APPEND ALL_DEPLOY_SRCS ${PADDLE2ONNX_ALL_SRCS})
+    list(APPEND DEPEND_LIBS p2o_paddle_proto onnx)
+  else()
+    include(${PROJECT_SOURCE_DIR}/cmake/paddle2onnx.cmake)
+    list(APPEND DEPEND_LIBS external_paddle2onnx)
+  endif()
 endif(ENABLE_PADDLE2ONNX)
 
 if(WITH_CAPI)
diff --git a/FastDeploy.cmake.in b/FastDeploy.cmake.in
index ff0d82e6c5..0e20fb68c8 100644
--- a/FastDeploy.cmake.in
+++ b/FastDeploy.cmake.in
@@ -30,6 +30,7 @@ set(ENABLE_OPENVINO_BACKEND @ENABLE_OPENVINO_BACKEND@)
 set(ENABLE_POROS_BACKEND @ENABLE_POROS_BACKEND@)
 set(ENABLE_TRT_BACKEND @ENABLE_TRT_BACKEND@)
 set(ENABLE_PADDLE2ONNX @ENABLE_PADDLE2ONNX@)
+set(BUILD_PADDLE2ONNX @BUILD_PADDLE2ONNX@)
 
 set(ENABLE_VISION @ENABLE_VISION@)
 set(ENABLE_FLYCV @ENABLE_FLYCV@)
@@ -346,8 +347,10 @@ if(ENABLE_PADDLE2ONNX)
   if(ANDROID)
     message(FATAL_ERROR "Not support fastdeploy-paddle2onnx APIs with Android now!")
   endif()
-  find_library(PADDLE2ONNX_LIB paddle2onnx  ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/paddle2onnx/lib NO_DEFAULT_PATH)
-  list(APPEND FASTDEPLOY_LIBS ${PADDLE2ONNX_LIB})
+  if(NOT BUILD_PADDLE2ONNX)
+    find_library(PADDLE2ONNX_LIB paddle2onnx  ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/paddle2onnx/lib NO_DEFAULT_PATH)
+    list(APPEND FASTDEPLOY_LIBS ${PADDLE2ONNX_LIB})
+  endif()
 endif()
 
 if(WITH_KUNLUNXIN)
diff --git a/cmake/build_paddle2onnx.cmake b/cmake/build_paddle2onnx.cmake
new file mode 100644
index 0000000000..c7cc152467
--- /dev/null
+++ b/cmake/build_paddle2onnx.cmake
@@ -0,0 +1,41 @@
+add_definitions(-DMAX_ONNX_OPSET_VERSION=16)
+add_definitions(-DPADDLE2ONNX_LIB)
+
+# Third dependency: onnx
+if(NOT TARGET onnx_proto)
+  if(NOT ONNX_NAMESPACE)
+    set(ONNX_NAMESPACE "paddle2onnx")
+  endif()
+  add_definitions("-DONNX_NAMESPACE=${ONNX_NAMESPACE}")
+
+  set(MSVC_STATIC_CRT ON)
+  if(ONNX_CUSTOM_PROTOC_PATH)
+    if(WIN32)
+      if(MSVC_STATIC_CRT)
+        # MT
+        set(ONNX_USE_MSVC_STATIC_RUNTIME ON)
+      else()
+        # MD
+        set(ONNX_USE_MSVC_STATIC_RUNTIME OFF)
+      endif()
+      set(ONNX_CUSTOM_PROTOC_PATH "${ONNX_CUSTOM_PROTOC_PATH};$ENV{PATH}")
+    else()
+      set(ONNX_CUSTOM_PROTOC_PATH "${ONNX_CUSTOM_PROTOC_PATH}:$ENV{PATH}")
+    endif()
+    set(ENV{PATH} ${ONNX_CUSTOM_PROTOC_PATH})
+  endif()
+
+  set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+  add_subdirectory(${PROJECT_SOURCE_DIR}/third_party/onnx)
+endif()
+
+include_directories(${PROJECT_SOURCE_DIR})
+include_directories(${CMAKE_CURRENT_BINARY_DIR})
+include_directories(${CMAKE_CURRENT_BINARY_DIR}/third_party/onnx)
+
+include_directories(${PROJECT_SOURCE_DIR}/third_party/optimizer)
+add_subdirectory(${PROJECT_SOURCE_DIR}/paddle2onnx/proto)
+
+file(GLOB_RECURSE PADDLE2ONNX_ALL_SRCS ${PROJECT_SOURCE_DIR}/paddle2onnx/*.cc ${PROJECT_SOURCE_DIR}/third_party/optimizer/onnxoptimizer/*.cc)
+list(REMOVE_ITEM PADDLE2ONNX_ALL_SRCS  ${PROJECT_SOURCE_DIR}/paddle2onnx/cpp2py_export.cc ${PROJECT_SOURCE_DIR}/third_party/optimizer/onnxoptimizer/cpp2py_export.cc)
+
diff --git a/cmake/build_tools.cmake b/cmake/build_tools.cmake
new file mode 100644
index 0000000000..2a8613c973
--- /dev/null
+++ b/cmake/build_tools.cmake
@@ -0,0 +1,57 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+function(download_patchelf)
+  if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
+    set(PATCHELF_EXE "patchelf")
+    if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/patchelf-0.15.0-aarch64.tar.gz)
+      download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/patchelf-0.15.0-aarch64.tar.gz ${THIRD_PARTY_PATH}/patchelf)
+    else()
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/patchelf-0.15.0-x86_64.tar.gz)
+      download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/patchelf-0.15.0-x86_64.tar.gz ${THIRD_PARTY_PATH}/patchelf)
+    endif()
+  endif()
+endfunction()
+
+function(download_protobuf)
+  if(WIN32)
+    if(NOT CMAKE_CL_64)
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/protobuf-win-x86-3.16.0.zip)
+    else()
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/protobuf-win-x64-3.16.0.zip)
+    endif()
+    set(ORIGIN_ENV_PATH "$ENV{PATH}")
+    download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/protobuf-win-3.16.0.tgz ${THIRD_PARTY_PATH}/protobuf)
+    set(ENV{PATH} "${THIRD_PARTY_PATH}\\protobuf\\bin;${ORIGIN_ENV_PATH}")
+  elseif(APPLE)
+    if(CURRENT_OSX_ARCH MATCHES "arm64")
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/protobuf-osx-arm64-3.16.0.tgz)
+    else()
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/protobuf-osx-x86_64-3.16.0.tgz)
+    endif()
+    set(ORIGIN_ENV_PATH "$ENV{PATH}")
+    download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/protobuf-osx-3.16.0.tgz ${THIRD_PARTY_PATH}/protobuf)
+    set(ENV{PATH} "${THIRD_PARTY_PATH}/protobuf/bin/:${ORIGIN_ENV_PATH}")
+  else()
+    if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/protobuf-linux-aarch64-3.16.0.tgz)
+    else()
+      set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/protobuf-linux-x64-3.16.0.tgz)
+    endif()
+    set(ORIGIN_ENV_PATH "$ENV{PATH}")
+    download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/protobuf-linux-3.16.0.tgz ${THIRD_PARTY_PATH}/protobuf)
+    set(ENV{PATH} "${THIRD_PARTY_PATH}/protobuf/bin/:${ORIGIN_ENV_PATH}")
+  endif()
+endfunction()
diff --git a/cmake/paddle_inference.cmake b/cmake/paddle_inference.cmake
index fc9cbd8e12..5c125fcc53 100755
--- a/cmake/paddle_inference.cmake
+++ b/cmake/paddle_inference.cmake
@@ -134,6 +134,7 @@ else()
 endif(PADDLEINFERENCE_DIRECTORY)
 
 if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
+  message("?????????????? ${PATCHELF_EXE}")
   add_custom_target(patchelf_paddle_inference ALL COMMAND  bash -c "PATCHELF_EXE=${PATCHELF_EXE} python ${PROJECT_SOURCE_DIR}/scripts/patch_paddle_inference.py ${PADDLEINFERENCE_INSTALL_DIR}/paddle/lib/libpaddle_inference.so" DEPENDS ${LIBRARY_NAME})
 endif()
 
diff --git a/cmake/patchelf.cmake b/cmake/patchelf.cmake
deleted file mode 100644
index b4b35ba350..0000000000
--- a/cmake/patchelf.cmake
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
-  set(PATCHELF_EXE "patchelf")
-  if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
-    set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/patchelf-0.15.0-aarch64.tar.gz)
-    download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/patchelf-0.15.0-aarch64.tar.gz ${THIRD_PARTY_PATH}/patchelf)
-    set(PATCHELF_EXE ${THIRD_PARTY_PATH}/patchelf/bin/patchelf)
-  else()
-    set(PATCHELF_URL https://bj.bcebos.com/fastdeploy/third_libs/patchelf-0.15.0-x86_64.tar.gz)
-    download_and_decompress(${PATCHELF_URL} ${CMAKE_CURRENT_BINARY_DIR}/patchelf-0.15.0-x86_64.tar.gz ${THIRD_PARTY_PATH}/patchelf)
-    set(PATCHELF_EXE ${THIRD_PARTY_PATH}/patchelf/bin/patchelf)
-  endif()
-endif()
diff --git a/examples/runtime/cpp/infer_paddle_paddle_inference.cc b/examples/runtime/cpp/infer_paddle_paddle_inference.cc
index 454534f930..d271d7a6f9 100644
--- a/examples/runtime/cpp/infer_paddle_paddle_inference.cc
+++ b/examples/runtime/cpp/infer_paddle_paddle_inference.cc
@@ -27,7 +27,6 @@ int main(int argc, char* argv[]) {
   // https://baidu-paddle.github.io/fastdeploy-api/cpp/html/structfastdeploy_1_1RuntimeOption.html
   fd::RuntimeOption runtime_option;
   runtime_option.SetModelPath(model_file, params_file);
-  runtime_option.UsePaddleInferBackend();
   runtime_option.UseCpu();
  
   // If need to configure Paddle Inference backend for more option, we can configure runtime_option.paddle_infer_option
diff --git a/paddle2onnx/CMakeLists.txt b/paddle2onnx/CMakeLists.txt
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/paddle2onnx/__init__.py b/paddle2onnx/__init__.py
new file mode 100755
index 0000000000..7d726e8b74
--- /dev/null
+++ b/paddle2onnx/__init__.py
@@ -0,0 +1,65 @@
+# Copyright (c) 2022  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle2onnx.utils import logging
+from . import command
+from .convert import dygraph2onnx
+from .convert import program2onnx
+from .version import version
+from .version import git_version
+
+__version__ = version
+__commit_id__ = git_version
+
+
+def run_convert(model, input_shape_dict=None, scope=None, opset_version=9):
+    logging.warning(
+        "[Deprecated] `paddle2onnx.run_convert` will be deprecated in the future version, the recommended usage is `paddle2onnx.export`"
+    )
+    from paddle2onnx.legacy import run_convert
+    return run_convert(model, input_shape_dict, scope, opset_version)
+
+
+def export(model_file,
+           params_file="",
+           save_file=None,
+           opset_version=11,
+           auto_upgrade_opset=True,
+           verbose=True,
+           enable_onnx_checker=True,
+           enable_experimental_op=True,
+           enable_optimize=True,
+           custom_op_info=None,
+           deploy_backend="onnxruntime",
+           calibration_file="",
+           external_file="",
+           export_fp16_model=False):
+    import paddle2onnx.paddle2onnx_cpp2py_export as c_p2o
+    deploy_backend = deploy_backend.lower()
+    if custom_op_info is None:
+        onnx_model_str = c_p2o.export(
+            model_file, params_file, opset_version, auto_upgrade_opset, verbose,
+            enable_onnx_checker, enable_experimental_op, enable_optimize, {},
+            deploy_backend, calibration_file, external_file, export_fp16_model)
+    else:
+        onnx_model_str = c_p2o.export(
+            model_file, params_file, opset_version, auto_upgrade_opset, verbose,
+            enable_onnx_checker, enable_experimental_op, enable_optimize,
+            custom_op_info, deploy_backend, calibration_file, external_file,
+            export_fp16_model)
+    if save_file is not None:
+        with open(save_file, "wb") as f:
+            f.write(onnx_model_str)
+    else:
+        return onnx_model_str
diff --git a/paddle2onnx/command.py b/paddle2onnx/command.py
new file mode 100755
index 0000000000..f72b60ffc5
--- /dev/null
+++ b/paddle2onnx/command.py
@@ -0,0 +1,273 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from six import text_type as _text_type
+import argparse
+import ast
+import sys
+import os
+from paddle2onnx.utils import logging
+
+
+def str2list(v):
+    if len(v) == 0:
+        return None
+    v = v.replace(" ", "")
+    v = eval(v)
+    return v
+
+
+def arg_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_dir",
+        "-m",
+        type=_text_type,
+        default=None,
+        help="PaddlePaddle model directory, if params stored in single file, you need define '--model_filename' and 'params_filename'."
+    )
+    parser.add_argument(
+        "--model_filename",
+        "-mf",
+        type=_text_type,
+        default=None,
+        help="PaddlePaddle model's network file name, which under directory seted by --model_dir"
+    )
+    parser.add_argument(
+        "--params_filename",
+        "-pf",
+        type=_text_type,
+        default=None,
+        help="PaddlePaddle model's param file name(param files combined in single file), which under directory seted by --model_dir."
+    )
+    parser.add_argument(
+        "--save_file",
+        "-s",
+        type=_text_type,
+        default=None,
+        help="file path to save onnx model")
+    parser.add_argument(
+        "--opset_version",
+        "-ov",
+        type=int,
+        default=9,
+        help="set onnx opset version to export")
+    parser.add_argument(
+       "--input_shape_dict",
+       "-isd",
+       type=_text_type,
+       default="None",
+       help="define input shapes, e.g --input_shape_dict=\"{'image':[1, 3, 608, 608]}\" or" \
+       "--input_shape_dict=\"{'image':[1, 3, 608, 608], 'im_shape': [1, 2], 'scale_factor': [1, 2]}\"")
+    parser.add_argument(
+        "--enable_dev_version",
+        type=ast.literal_eval,
+        default=True,
+        help="whether to use new version of Paddle2ONNX which is under developing, default True"
+    )
+    parser.add_argument(
+        "--deploy_backend",
+        "-d",
+        type=_text_type,
+        default="onnxruntime",
+        choices=["onnxruntime", "tensorrt", "rknn", "others"],
+        help="Quantize model deploy backend, default onnxruntime.")
+    parser.add_argument(
+        "--save_calibration_file",
+        type=_text_type,
+        default="calibration.cache",
+        help="The calibration cache for TensorRT deploy, default calibration.cache."
+    )
+    parser.add_argument(
+        "--enable_onnx_checker",
+        type=ast.literal_eval,
+        default=True,
+        help="whether check onnx model validity, default True")
+    parser.add_argument(
+        "--enable_paddle_fallback",
+        type=ast.literal_eval,
+        default=False,
+        help="whether use PaddleFallback for custom op, default is False")
+    parser.add_argument(
+        "--version",
+        "-v",
+        action="store_true",
+        default=False,
+        help="get version of paddle2onnx")
+    parser.add_argument(
+        "--output_names",
+        "-on",
+        type=str2list,
+        default=None,
+        help="define output names, e.g --output_names=\"[\"output1\"]\" or \
+       --output_names=\"[\"output1\", \"output2\", \"output3\"]\" or \
+       --output_names=\"{\"Paddleoutput\":\"Onnxoutput\"}\"")
+    parser.add_argument(
+        "--enable_auto_update_opset",
+        type=ast.literal_eval,
+        default=True,
+        help="whether enable auto_update_opset, default is True")
+    parser.add_argument(
+        "--external_filename",
+        type=_text_type,
+        default=None,
+        help="The filename of external_data when the model is bigger than 2G.")
+    parser.add_argument(
+        "--export_fp16_model",
+        type=ast.literal_eval,
+        default=False,
+        help="Whether export FP16 model for ORT-GPU, default False")
+    return parser
+
+
+def c_paddle_to_onnx(model_file,
+                     params_file="",
+                     save_file=None,
+                     opset_version=7,
+                     auto_upgrade_opset=True,
+                     verbose=True,
+                     enable_onnx_checker=True,
+                     enable_experimental_op=True,
+                     enable_optimize=True,
+                     deploy_backend="onnxruntime",
+                     calibration_file="",
+                     external_file="",
+                     export_fp16_model=False):
+    import paddle2onnx.paddle2onnx_cpp2py_export as c_p2o
+    onnx_model_str = c_p2o.export(
+        model_file, params_file, opset_version, auto_upgrade_opset, verbose,
+        enable_onnx_checker, enable_experimental_op, enable_optimize, {},
+        deploy_backend, calibration_file, external_file, export_fp16_model)
+    if save_file is not None:
+        with open(save_file, "wb") as f:
+            f.write(onnx_model_str)
+    else:
+        return onnx_model_str
+
+
+def program2onnx(model_dir,
+                 save_file,
+                 model_filename=None,
+                 params_filename=None,
+                 opset_version=9,
+                 enable_onnx_checker=False,
+                 operator_export_type="ONNX",
+                 input_shape_dict=None,
+                 output_names=None,
+                 auto_update_opset=True):
+    logging.warning(
+        "[Deprecated] `paddle2onnx.command.program2onnx` will be deprecated in the future version, the recommended usage is `paddle2onnx.export`"
+    )
+    from paddle2onnx.legacy.command import program2onnx
+    return program2onnx(model_dir, save_file, model_filename, params_filename,
+                        opset_version, enable_onnx_checker,
+                        operator_export_type, input_shape_dict, output_names,
+                        auto_update_opset)
+
+
+def main():
+    if len(sys.argv) < 2:
+        logging.info("Use \"paddle2onnx -h\" to print the help information")
+        logging.info(
+            "For more information, please follow our github repo below:")
+        logging.info("Github: https://github.com/PaddlePaddle/paddle2onnx.git")
+        return
+
+    parser = arg_parser()
+    args = parser.parse_args()
+
+    if args.version:
+        import paddle2onnx
+        logging.info("paddle2onnx-{} with python>=3.6, paddlepaddle>=2.0.0".
+                     format(paddle2onnx.__version__))
+        return
+
+    assert args.model_dir is not None, "--model_dir should be defined while translating paddle model to onnx"
+    assert args.save_file is not None, "--save_file should be defined while translating paddle model to onnx"
+
+    input_shape_dict = eval(args.input_shape_dict)
+
+    operator_export_type = "ONNX"
+    if args.enable_paddle_fallback:
+        logging.warning(
+            "[Deprecated] The flag `--enable_paddle_fallback` will be deprecated, and only works while `--enable_dev_version False` now."
+        )
+        operator_export_type = "PaddleFallback"
+
+    if args.output_names is not None and args.enable_dev_version:
+        logging.warning(
+            "[Deprecated] The flag `--output_names` is deprecated, if you need to modify the output name, please refer to this tool https://github.com/jiangjiajun/PaddleUtils/tree/main/onnx "
+        )
+        if not isinstance(args.output_names, (list, dict)):
+            raise TypeError(
+                "The output_names should be 'list' or 'dict', but received type is %s."
+                % type(args.output_names))
+
+    if input_shape_dict is not None and args.enable_dev_version:
+        logging.warning(
+            "[Deprecated] The flag `--input_shape_dict` is deprecated, if you need to modify the input shape of PaddlePaddle model, please refer to this tool https://github.com/jiangjiajun/PaddleUtils/tree/main/paddle "
+        )
+
+    if args.enable_dev_version:
+        model_file = os.path.join(args.model_dir, args.model_filename)
+        if args.params_filename is None:
+            params_file = ""
+        else:
+            params_file = os.path.join(args.model_dir, args.params_filename)
+
+        if args.external_filename is None:
+            args.external_filename = "external_data"
+
+        base_path = os.path.dirname(args.save_file)
+        if base_path and not os.path.exists(base_path):
+            os.mkdir(base_path)
+        external_file = os.path.join(base_path, args.external_filename)
+
+        calibration_file = args.save_calibration_file
+        c_paddle_to_onnx(
+            model_file=model_file,
+            params_file=params_file,
+            save_file=args.save_file,
+            opset_version=args.opset_version,
+            auto_upgrade_opset=args.enable_auto_update_opset,
+            verbose=True,
+            enable_onnx_checker=args.enable_onnx_checker,
+            enable_experimental_op=True,
+            enable_optimize=True,
+            deploy_backend=args.deploy_backend,
+            calibration_file=calibration_file,
+            external_file=external_file,
+            export_fp16_model=args.export_fp16_model)
+        logging.info("===============Make PaddlePaddle Better!================")
+        logging.info("A little survey: https://iwenjuan.baidu.com/?code=r8hu2s")
+        return
+
+    program2onnx(
+        args.model_dir,
+        args.save_file,
+        args.model_filename,
+        args.params_filename,
+        opset_version=args.opset_version,
+        enable_onnx_checker=args.enable_onnx_checker,
+        operator_export_type=operator_export_type,
+        input_shape_dict=input_shape_dict,
+        output_names=args.output_names,
+        auto_update_opset=args.enable_auto_update_opset)
+    logging.info("===============Make PaddlePaddle Better!================")
+    logging.info("A little survey: https://iwenjuan.baidu.com/?code=r8hu2s")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/paddle2onnx/convert.py b/paddle2onnx/convert.py
new file mode 100755
index 0000000000..50c8db2da3
--- /dev/null
+++ b/paddle2onnx/convert.py
@@ -0,0 +1,83 @@
+# Copyright (c) 2022  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle2onnx.utils import logging
+
+
+def export_onnx(paddle_graph,
+                save_file,
+                opset_version=9,
+                enable_onnx_checker=False,
+                operator_export_type="ONNX",
+                verbose=False,
+                auto_update_opset=True,
+                output_names=None):
+    from paddle2onnx.legacy.convert import export_onnx
+    return export_onnx(paddle_graph, save_file, opset_version, opset_version,
+                       enable_onnx_checker, operator_export_type, verbose,
+                       auto_update_opset, output_names)
+
+
+def dygraph2onnx(layer, save_file, input_spec=None, opset_version=9, **configs):
+    if "enable_dev_version" in configs and not configs["enable_dev_version"]:
+        from paddle2onnx.legacy.convert import dygraph2onnx
+        return dygraph2onnx(layer, save_file, input_spec, opset_version,
+                            **configs)
+
+    import os
+    import paddle2onnx
+    import paddle
+    dirname = os.path.split(save_file)[0]
+    paddle_model_dir = os.path.join(dirname,
+                                    "paddle_model_static_onnx_temp_dir")
+    model_file = os.path.join(paddle_model_dir, "model.pdmodel")
+    params_file = os.path.join(paddle_model_dir, "model.pdiparams")
+
+    if os.path.exists(paddle_model_dir):
+        if os.path.isfile(paddle_model_dir):
+            logging.info("File {} exists, will remove it.".format(
+                paddle_model_dir))
+            os.remove(paddle_model_dir)
+        if os.path.isfile(model_file):
+            os.remove(model_file)
+        if os.path.isfile(params_file):
+            os.remove(params_file)
+    paddle.jit.save(layer, os.path.join(paddle_model_dir, "model"), input_spec)
+    logging.info("Static PaddlePaddle model saved in {}.".format(
+        paddle_model_dir))
+    if not os.path.isfile(params_file):
+        params_file = ""
+
+    if save_file is None:
+        return paddle2onnx.export(model_file, params_file, save_file,
+                                  opset_version)
+    else:
+        paddle2onnx.export(model_file, params_file, save_file, opset_version)
+    logging.info("ONNX model saved in {}.".format(save_file))
+
+
+def program2onnx(program,
+                 scope,
+                 save_file,
+                 feed_var_names=None,
+                 target_vars=None,
+                 opset_version=9,
+                 enable_onnx_checker=False,
+                 operator_export_type="ONNX",
+                 auto_update_opset=True,
+                 **configs):
+    from paddle2onnx.legacy.convert import program2onnx
+    return program2onnx(program, scope, save_file, feed_var_names, target_vars,
+                        opset_version, enable_onnx_checker,
+                        operator_export_type, auto_update_opset, **configs)
diff --git a/paddle2onnx/convert_to_fp16.py b/paddle2onnx/convert_to_fp16.py
new file mode 100755
index 0000000000..b0321f5ae1
--- /dev/null
+++ b/paddle2onnx/convert_to_fp16.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+
+import argparse
+import sys
+from paddle2onnx.utils import logging
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input_model_path',
+        required=True,
+        help='The path of input onnx model file.')
+    parser.add_argument(
+        '--output_model_path',
+        required=True,
+        help='The file path to write optimized onnx model file.')
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_arguments()
+    import paddle2onnx.paddle2onnx_cpp2py_export as c_p2o
+    c_p2o.convert_to_fp16(args.input_model_path, args.output_model_path)
+    logging.info("FP16 model saved in {}.".format(args.output_model_path))
diff --git a/paddle2onnx/converter.cc b/paddle2onnx/converter.cc
new file mode 100644
index 0000000000..53e5d4817e
--- /dev/null
+++ b/paddle2onnx/converter.cc
@@ -0,0 +1,287 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/converter.h"
+
+#include <fstream>
+#include <iostream>
+#include <set>
+#include <string>
+
+#include "paddle2onnx/mapper/exporter.h"
+#include "paddle2onnx/optimizer/convert_fp32_to_fp16.h"
+
+namespace paddle2onnx {
+
+PADDLE2ONNX_DECL bool IsExportable(const char* model, const char* params,
+                                   int32_t opset_version,
+                                   bool auto_upgrade_opset, bool verbose,
+                                   bool enable_onnx_checker,
+                                   bool enable_experimental_op,
+                                   bool enable_optimize, CustomOp* ops,
+                                   int op_count, const char* deploy_backend) {
+  auto parser = PaddleParser();
+  if (!parser.Init(model, params)) {
+    return false;
+  }
+  paddle2onnx::ModelExporter me;
+  std::set<std::string> unsupported_ops;
+  if (!me.CheckIfOpSupported(parser, &unsupported_ops,
+                             enable_experimental_op)) {
+    return false;
+  }
+
+  // Add custom operator information
+  if (ops != nullptr && op_count > 0) {
+    for (int i = 0; i < op_count; ++i) {
+      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
+      std::string export_op_name(ops[i].export_op_name,
+                                 strlen(ops[i].export_op_name));
+      if (export_op_name == "paddle2onnx_null") {
+        export_op_name = op_name;
+      }
+      me.custom_ops[op_name] = export_op_name;
+    }
+  }
+
+  if (me.GetMinOpset(parser, false) < 0) {
+    return false;
+  }
+  std::string calibration_str;
+  std::string onnx_model =
+      me.Run(parser, opset_version, auto_upgrade_opset, verbose,
+             enable_onnx_checker, enable_experimental_op, enable_optimize,
+             deploy_backend, &calibration_str);
+  if (onnx_model.empty()) {
+    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
+    return false;
+  }
+  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
+      calibration_str.empty()) {
+    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
+                          "deploy backend when export quantize model."
+                       << std::endl;
+    return false;
+  }
+  return true;
+}
+
+PADDLE2ONNX_DECL bool IsExportable(const void* model_buffer, int model_size,
+                                   const void* params_buffer, int params_size,
+                                   int32_t opset_version,
+                                   bool auto_upgrade_opset, bool verbose,
+                                   bool enable_onnx_checker,
+                                   bool enable_experimental_op,
+                                   bool enable_optimize, CustomOp* ops,
+                                   int op_count, const char* deploy_backend) {
+  auto parser = PaddleParser();
+  if (!parser.Init(model_buffer, model_size, params_buffer, params_size)) {
+    return false;
+  }
+  paddle2onnx::ModelExporter me;
+  std::set<std::string> unsupported_ops;
+  if (!me.CheckIfOpSupported(parser, &unsupported_ops,
+                             enable_experimental_op)) {
+    return false;
+  }
+
+  // Add custom operator information
+  if (ops != nullptr && op_count > 0) {
+    for (int i = 0; i < op_count; ++i) {
+      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
+      std::string export_op_name(ops[i].export_op_name,
+                                 strlen(ops[i].export_op_name));
+      if (export_op_name == "paddle2onnx_null") {
+        export_op_name = op_name;
+      }
+      me.custom_ops[op_name] = export_op_name;
+    }
+  }
+
+  if (me.GetMinOpset(parser, false) < 0) {
+    return false;
+  }
+  std::string calibration_str;
+  std::string onnx_model =
+      me.Run(parser, opset_version, auto_upgrade_opset, verbose,
+             enable_onnx_checker, enable_experimental_op, enable_optimize,
+             deploy_backend, &calibration_str);
+  if (onnx_model.empty()) {
+    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
+    return false;
+  }
+  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
+      calibration_str.empty()) {
+    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
+                          "deploy backend when export quantize model."
+                       << std::endl;
+    return false;
+  }
+  return true;
+}
+
+PADDLE2ONNX_DECL bool Export(
+    const char* model, const char* params, char** out, int* out_size,
+    int32_t opset_version, bool auto_upgrade_opset, bool verbose,
+    bool enable_onnx_checker, bool enable_experimental_op, bool enable_optimize,
+    CustomOp* ops, int op_count, const char* deploy_backend,
+    char** calibration_cache, int* calibration_size, const char* external_file,
+    bool* save_external, bool export_fp16_model) {
+  auto parser = PaddleParser();
+  P2OLogger(verbose) << "Start to parsing Paddle model..." << std::endl;
+  if (!parser.Init(model, params)) {
+    P2OLogger(verbose) << "Paddle model parsing failed." << std::endl;
+    return false;
+  }
+  paddle2onnx::ModelExporter me;
+
+  // Add custom operator information
+  if (ops != nullptr && op_count > 0) {
+    for (int i = 0; i < op_count; ++i) {
+      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
+      std::string export_op_name(ops[i].export_op_name,
+                                 strlen(ops[i].export_op_name));
+      if (export_op_name == "paddle2onnx_null") {
+        export_op_name = op_name;
+      }
+      me.custom_ops[op_name] = export_op_name;
+    }
+  }
+
+  std::string calibration_str;
+  std::string result = me.Run(
+      parser, opset_version, auto_upgrade_opset, verbose, enable_onnx_checker,
+      enable_experimental_op, enable_optimize, deploy_backend, &calibration_str,
+      external_file, save_external, export_fp16_model);
+  if (result.empty()) {
+    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
+    return false;
+  }
+  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
+      calibration_str.empty()) {
+    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
+                          "deploy backend when export quantize model."
+                       << std::endl;
+    return false;
+  }
+  *out_size = result.size();
+  *out = new char[*out_size]();
+  memcpy(*out, result.data(), *out_size);
+  if (calibration_str.size()) {
+    *calibration_size = calibration_str.size();
+    *calibration_cache = new char[*calibration_size]();
+    memcpy(*calibration_cache, calibration_str.data(), *calibration_size);
+  }
+  return true;
+}
+
+PADDLE2ONNX_DECL bool Export(
+    const void* model_buffer, int64_t model_size, const void* params_buffer,
+    int64_t params_size, char** out, int* out_size, int32_t opset_version,
+    bool auto_upgrade_opset, bool verbose, bool enable_onnx_checker,
+    bool enable_experimental_op, bool enable_optimize, CustomOp* ops,
+    int op_count, const char* deploy_backend, char** calibration_cache,
+    int* calibration_size, const char* external_file, bool* save_external,
+    bool export_fp16_model) {
+  auto parser = PaddleParser();
+  P2OLogger(verbose) << "Start to parsing Paddle model..." << std::endl;
+  if (!parser.Init(model_buffer, model_size, params_buffer, params_size)) {
+    P2OLogger(verbose) << "Paddle model parsing failed." << std::endl;
+    return false;
+  }
+  paddle2onnx::ModelExporter me;
+
+  // Add custom operator information
+  if (ops != nullptr && op_count > 0) {
+    for (int i = 0; i < op_count; ++i) {
+      std::string op_name(ops[i].op_name, strlen(ops[i].op_name));
+      std::string export_op_name(ops[i].export_op_name,
+                                 strlen(ops[i].export_op_name));
+      if (export_op_name == "paddle2onnx_null") {
+        export_op_name = op_name;
+      }
+      me.custom_ops[op_name] = export_op_name;
+    }
+  }
+  std::string calibration_str;
+  std::string result = me.Run(
+      parser, opset_version, auto_upgrade_opset, verbose, enable_onnx_checker,
+      enable_experimental_op, enable_optimize, deploy_backend, &calibration_str,
+      external_file, save_external, export_fp16_model);
+  if (result.empty()) {
+    P2OLogger(verbose) << "The exported ONNX model is invalid!" << std::endl;
+    return false;
+  }
+
+  if (parser.is_quantized_model && "tensorrt" == std::string(deploy_backend) &&
+      calibration_str.empty()) {
+    P2OLogger(verbose) << "Can not generate calibration cache for TensorRT "
+                          "deploy backend when export quantize model."
+                       << std::endl;
+    return false;
+  }
+  *out_size = result.size();
+  *out = new char[*out_size]();
+  memcpy(*out, result.data(), *out_size);
+  if (calibration_str.size()) {
+    *calibration_size = calibration_str.size();
+    *calibration_cache = new char[*calibration_size]();
+    memcpy(*calibration_cache, calibration_str.data(), *calibration_size);
+  }
+  return true;
+}
+
+PADDLE2ONNX_DECL bool ConvertFP32ToFP16(const char* onnx_model, int model_size,
+                                        char** out_model, int* out_model_size) {
+  std::string onnx_proto(onnx_model, onnx_model + model_size);
+  ONNX_NAMESPACE::ModelProto model;
+  model.ParseFromString(onnx_proto);
+
+  P2OLogger(true) << "Convert FP32 ONNX model to FP16." << std::endl;
+  ConvertFp32ToFp16 convert;
+  convert.Convert(&model);
+  // save external data file for big model
+  std::string external_data_file;
+  if (model.ByteSizeLong() > INT_MAX) {
+    external_data_file = "external_data";
+  }
+  paddle2onnx::ModelExporter me;
+  if (external_data_file.size()) {
+    me.SaveExternalData(model.mutable_graph(), external_data_file);
+  }
+  // check model
+  me.ONNXChecker(model, true);
+
+  std::string result;
+  if (!model.SerializeToString(&result)) {
+    P2OLogger(true)
+        << "Error happenedd while optimizing the exported ONNX model."
+        << std::endl;
+    return false;
+  }
+
+  *out_model_size = result.size();
+  *out_model = new char[*out_model_size]();
+  memcpy(*out_model, result.data(), *out_model_size);
+  return true;
+}
+
+ModelTensorInfo::~ModelTensorInfo() {
+  if (shape != nullptr) {
+    delete[] shape;
+    shape = nullptr;
+    rank = 0;
+  }
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/converter.h b/paddle2onnx/converter.h
new file mode 100755
index 0000000000..0fbed1c986
--- /dev/null
+++ b/paddle2onnx/converter.h
@@ -0,0 +1,130 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <stdint.h>
+
+#if defined(_WIN32)
+#ifdef PADDLE2ONNX_LIB
+#define PADDLE2ONNX_DECL __declspec(dllexport)
+#else
+#define PADDLE2ONNX_DECL __declspec(dllimport)
+#endif  // PADDLE2ONNX_LIB
+#else
+#define PADDLE2ONNX_DECL __attribute__((visibility("default")))
+#endif  // _WIN32
+
+namespace paddle2onnx {
+
+struct PADDLE2ONNX_DECL CustomOp {
+  char op_name[100] = "null";
+  // if export_op_name set as "paddle2onnx_null"
+  // it will automaticly change to `op_name`
+  char export_op_name[100] = "paddle2onnx_null";
+};
+
+PADDLE2ONNX_DECL bool IsExportable(
+    const char* model, const char* params, int32_t opset_version = 11,
+    bool auto_upgrade_opset = true, bool verbose = false,
+    bool enable_onnx_checker = true, bool enable_experimental_op = false,
+    bool enable_optimize = true, CustomOp* ops = nullptr, int op_count = 0,
+    const char* deploy_backend = "onnxruntime");
+
+PADDLE2ONNX_DECL bool IsExportable(
+    const void* model_buffer, int model_size, const void* params_buffer,
+    int params_size, int32_t opset_version = 11, bool auto_upgrade_opset = true,
+    bool verbose = false, bool enable_onnx_checker = true,
+    bool enable_experimental_op = false, bool enable_optimize = true,
+    CustomOp* ops = nullptr, int op_count = 0,
+    const char* deploy_backend = "onnxruntime");
+
+PADDLE2ONNX_DECL bool Export(
+    const char* model, const char* params, char** out, int* out_size,
+    int32_t opset_version = 11, bool auto_upgrade_opset = true,
+    bool verbose = false, bool enable_onnx_checker = true,
+    bool enable_experimental_op = false, bool enable_optimize = true,
+    CustomOp* ops = nullptr, int op_count = 0,
+    const char* deploy_backend = "onnxruntime",
+    char** calibration_cache = nullptr, int* calibration_size = 0,
+    const char* external_file = "", bool* save_external = nullptr,
+    bool export_fp16_model = false);
+
+PADDLE2ONNX_DECL bool Export(
+    const void* model_buffer, int64_t model_size, const void* params_buffer,
+    int64_t params_size, char** out, int* out_size, int32_t opset_version = 11,
+    bool auto_upgrade_opset = true, bool verbose = false,
+    bool enable_onnx_checker = true, bool enable_experimental_op = false,
+    bool enable_optimize = true, CustomOp* ops = nullptr, int op_count = 0,
+    const char* deploy_backend = "onnxruntime",
+    char** calibration_cache = nullptr, int* calibration_size = 0,
+    const char* external_file = "", bool* save_external = nullptr,
+    bool export_fp16_model = false);
+
+// Following are inside usage, will remove it maybe
+struct PADDLE2ONNX_DECL ModelTensorInfo {
+  char name[100] = "";
+  int64_t* shape = nullptr;
+  int32_t rank = 0;
+  // 0: float32
+  // 1: double
+  // 2: uint8
+  // 3: int8
+  // 4: int32
+  // 5: int64
+  // 6: float16
+  int32_t dtype = 0;
+  ~ModelTensorInfo();
+};
+
+struct PADDLE2ONNX_DECL NMSParameters {
+  int64_t background_label = -1;
+  int64_t keep_top_k = 300;
+  float nms_eta = 1.0;
+  float nms_threshold = 0.7;
+  float score_threshold = 0.01;
+  int64_t nms_top_k = 10000;
+  bool normalized = true;
+};
+
+struct PADDLE2ONNX_DECL OnnxReader {
+  OnnxReader(const char* model_buffer, int buffer_size);
+  // suppose the maximum number of inputs/outputs is 100
+  // suppose the longest string of inputs/outputs is 200
+  // suppose the biggest rank will be less than 10
+  ModelTensorInfo inputs[100];
+  ModelTensorInfo outputs[100];
+  int num_inputs;
+  int num_outputs;
+};
+
+PADDLE2ONNX_DECL bool RemoveMultiClassNMS(const char* onnx_model,
+                                          int model_size, char** out_model,
+                                          int* out_model_size);
+
+PADDLE2ONNX_DECL bool ConvertFP32ToFP16(const char* onnx_model, int model_size,
+                                        char** out_model, int* out_model_size);
+
+struct PADDLE2ONNX_DECL PaddleReader {
+  PaddleReader(const char* model_buffer, int buffer_size);
+  // suppose the maximum number of inputs/outputs is 100
+  // suppose the longest string of inputs/outputs is 200
+  ModelTensorInfo inputs[100];
+  ModelTensorInfo outputs[100];
+  int num_inputs;
+  int num_outputs;
+  bool has_nms = false;
+  bool is_quantize_model = false;
+  NMSParameters nms_params;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/cpp2py_export.cc b/paddle2onnx/cpp2py_export.cc
new file mode 100644
index 0000000000..0537d3c567
--- /dev/null
+++ b/paddle2onnx/cpp2py_export.cc
@@ -0,0 +1,128 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/converter.h"
+#include "paddle2onnx/mapper/exporter.h"
+#include "paddle2onnx/optimizer/paddle2onnx_optimizer.h"
+
+namespace paddle2onnx {
+
+typedef std::map<std::string, std::string> CustomOpInfo;
+PYBIND11_MODULE(paddle2onnx_cpp2py_export, m) {
+  m.doc() = "Paddle2ONNX: export PaddlePaddle to ONNX";
+  m.def("export", [](const std::string& model_filename,
+                     const std::string& params_filename, int opset_version = 9,
+                     bool auto_upgrade_opset = true, bool verbose = true,
+                     bool enable_onnx_checker = true,
+                     bool enable_experimental_op = true,
+                     bool enable_optimize = true,
+                     const CustomOpInfo& info = CustomOpInfo(),
+                     const std::string& deploy_backend = "onnxruntime",
+                     const std::string& calibration_file = "",
+                     const std::string& external_file = "",
+                     const bool& export_fp16_model = false) {
+    P2OLogger(verbose) << "Start to parse PaddlePaddle model..." << std::endl;
+    P2OLogger(verbose) << "Model file path: " << model_filename << std::endl;
+    P2OLogger(verbose) << "Paramters file path: " << params_filename
+                       << std::endl;
+    if (info.size() == 0) {
+      char* out = nullptr;
+      int size = 0;
+      char* calibration_cache = nullptr;
+      int cache_size = 0;
+      bool save_external;
+      if (!Export(model_filename.c_str(), params_filename.c_str(), &out, &size,
+                  opset_version, auto_upgrade_opset, verbose,
+                  enable_onnx_checker, enable_experimental_op, enable_optimize,
+                  nullptr, 0, deploy_backend.c_str(), &calibration_cache,
+                  &cache_size, external_file.c_str(), &save_external,
+                  export_fp16_model)) {
+        P2OLogger(verbose) << "Paddle model convert failed." << std::endl;
+        return pybind11::bytes("");
+      }
+      if (cache_size) {
+        std::string calibration_cache_str(calibration_cache,
+                                          calibration_cache + cache_size);
+        std::ofstream cache_file;
+        cache_file.open(calibration_file, std::ios::out);
+        cache_file << calibration_cache_str;
+        delete calibration_cache;
+        calibration_cache = nullptr;
+        P2OLogger(verbose) << "TensorRT calibration cache path: "
+                           << calibration_file << std::endl;
+      }
+      std::string onnx_proto(out, out + size);
+      delete out;
+      out = nullptr;
+      return pybind11::bytes(onnx_proto);
+    }
+
+    std::vector<CustomOp> ops;
+    ops.resize(info.size());
+    int index = 0;
+    for (auto& item : info) {
+      strcpy(ops[index].op_name, item.first.c_str());
+      strcpy(ops[index].export_op_name, item.second.c_str());
+      index += 1;
+    }
+    char* out = nullptr;
+    int size = 0;
+    char* calibration_cache = nullptr;
+    int cache_size = 0;
+    bool save_external;
+    if (!Export(model_filename.c_str(), params_filename.c_str(), &out, &size,
+                opset_version, auto_upgrade_opset, verbose, enable_onnx_checker,
+                enable_experimental_op, enable_optimize, ops.data(),
+                info.size(), deploy_backend.c_str(), &calibration_cache,
+                &cache_size, external_file.c_str(), &save_external,
+                export_fp16_model)) {
+      P2OLogger(verbose) << "Paddle model convert failed." << std::endl;
+      return pybind11::bytes("");
+    }
+    if (cache_size) {
+      std::string calibration_cache_str(calibration_cache,
+                                        calibration_cache + cache_size);
+      std::ofstream cache_file;
+      cache_file.open(calibration_file, std::ios::out);
+      cache_file << calibration_cache_str;
+      delete calibration_cache;
+      calibration_cache = nullptr;
+      P2OLogger(verbose) << "TensorRT calibration cache path: "
+                         << calibration_file << std::endl;
+    }
+    std::string onnx_proto(out, out + size);
+    delete out;
+    out = nullptr;
+    return pybind11::bytes(onnx_proto);
+  });
+  m.def(
+      "optimize",
+      [](const std::string& model_path, const std::string& optimized_model_path,
+         const std::map<std::string, std::vector<int>>& shape_infos) {
+        ONNX_NAMESPACE::optimization::OptimizePaddle2ONNX(
+            model_path, optimized_model_path, shape_infos);
+      });
+  m.def("convert_to_fp16", [](const std::string& fp32_model_path,
+                              const std::string& fp16_model_path) {
+    paddle2onnx::optimization::Paddle2ONNXFP32ToFP16(fp32_model_path,
+                                                     fp16_model_path);
+  });
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/legacy/__init__.py b/paddle2onnx/legacy/__init__.py
new file mode 100755
index 0000000000..12737e8595
--- /dev/null
+++ b/paddle2onnx/legacy/__init__.py
@@ -0,0 +1,128 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+
+__version__ = "0.9.6"
+
+import paddle
+from .convert import dygraph2onnx, program2onnx
+from .op_mapper import register_op_mapper
+from typing import TypeVar
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.op_mapper import OpMapper
+from . import command
+
+OP_WITHOUT_KERNEL_SET = {
+    'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
+    'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
+    'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
+    'gen_bkcl_id', 'c_gen_bkcl_id', 'gen_nccl_id', 'c_gen_nccl_id',
+    'c_comm_init', 'c_sync_calc_stream', 'c_sync_comm_stream',
+    'queue_generator', 'dequeue', 'enqueue', 'heter_listen_and_serv',
+    'c_wait_comm', 'c_wait_compute', 'c_gen_hccl_id', 'c_comm_init_hccl',
+    'copy_cross_scope'
+}
+
+
+def process_old_ops_desc(model):
+    for i in range(len(model.blocks[0].ops)):
+        if model.blocks[0].ops[i].type == "matmul":
+            if not model.blocks[0].ops[i].has_attr("head_number"):
+                model.blocks[0].ops[i]._set_attr("head_number", 1)
+        elif model.blocks[0].ops[i].type == "yolo_box":
+            if not model.blocks[0].ops[i].has_attr("iou_aware"):
+                model.blocks[0].ops[i]._set_attr("iou_aware", False)
+            if not model.blocks[0].ops[i].has_attr("iou_aware_factor"):
+                model.blocks[0].ops[i]._set_attr("iou_aware_factor", 0.5)
+
+
+def get_all_registered_ops(save_file=None):
+    ops = list(OpMapper.OPSETS.keys())
+    logging.warning("The number of all registered OPs is: {}".format(len(ops)))
+    if save_file is None:
+        return
+    with open(save_file, "w") as f:
+        logging.warning("All registered OPs will be written to the file: {}".
+                        format(save_file))
+        f.write("Total OPs num: {} \n".format(len(ops)))
+        for index in range(len(ops)):
+            op = ops[index]
+            f.write(str(index + 1) + ". " + op + "\n")
+        return
+
+
+def run_convert(model, input_shape_dict=None, scope=None, opset_version=9):
+    paddle_version = paddle.__version__
+    if isinstance(model, paddle.static.Program):
+        process_old_ops_desc(model)
+        if input_shape_dict is not None:
+            model_version = model.desc._version()
+            major_ver = model_version // 1000000
+            minor_ver = (model_version - major_ver * 1000000) // 1000
+            patch_ver = model_version - major_ver * 1000000 - minor_ver * 1000
+            model_version = "{}.{}.{}".format(major_ver, minor_ver, patch_ver)
+            if model_version != paddle_version:
+                logging.warning(
+                    "The model is saved by paddlepaddle v{}, but now your paddlepaddle is version of {}, this difference may cause error, it is recommend you reinstall a same version of paddlepaddle for this model".
+                    format(model_version, paddle_version))
+            for k, v in input_shape_dict.items():
+                model.blocks[0].var(k).desc.set_shape(v)
+            for i in range(len(model.blocks[0].ops)):
+                if model.blocks[0].ops[i].type in OP_WITHOUT_KERNEL_SET:
+                    continue
+                model.blocks[0].ops[i].desc.infer_shape(model.blocks[0].desc)
+        if scope is None:
+            scope = paddle.static.global_scope()
+        input_names = list()
+        output_vars = list()
+        for i in range(len(model.blocks[0].ops)):
+            if model.blocks[0].ops[i].type == "feed":
+                input_names.append(model.blocks[0].ops[i].output("Out")[0])
+            if model.blocks[0].ops[i].type == "fetch":
+                output_vars.append(model.blocks[0].var(model.blocks[0].ops[i]
+                                                       .input("X")[0]))
+        return program2onnx(
+            model,
+            scope,
+            save_file=None,
+            feed_var_names=input_names,
+            target_vars=output_vars,
+            opset_version=opset_version,
+            enable_onnx_checker=True)
+    elif isinstance(model, paddle.jit.TranslatedLayer):
+        process_old_ops_desc(model.program())
+        model_version = model.program().desc._version()
+        major_ver = model_version // 1000000
+        minor_ver = (model_version - major_ver * 1000000) // 1000
+        patch_ver = model_version - major_ver * 1000000 - minor_ver * 1000
+        model_version = "{}.{}.{}".format(major_ver, minor_ver, patch_ver)
+        if model_version != paddle_version:
+            logging.warning(
+                "The model is saved by paddlepaddle v{}, but now your paddlepaddle is version of {}, this difference may cause error, it is recommend you reinstall a same version of paddlepaddle for this model".
+                format(model_version, paddle_version))
+
+        if input_shape_dict is not None:
+            for k, v in input_shape_dict.items():
+                model.program().blocks[0].var(k).desc.set_shape(v)
+            for i in range(len(model.program().blocks[0].ops)):
+                if model.program().blocks[0].ops[
+                        i].type in OP_WITHOUT_KERNEL_SET:
+                    continue
+                model.program().blocks[0].ops[i].desc.infer_shape(model.program(
+                ).blocks[0].desc)
+        return dygraph2onnx(model, save_file=None, opset_version=opset_version)
+    else:
+        raise Exception(
+            "Only support model loaded from paddle.static.load_inference_model() or paddle.jit.load()"
+        )
diff --git a/paddle2onnx/legacy/command.py b/paddle2onnx/legacy/command.py
new file mode 100755
index 0000000000..eb67b6d9fe
--- /dev/null
+++ b/paddle2onnx/legacy/command.py
@@ -0,0 +1,287 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from six import text_type as _text_type
+import argparse
+import ast
+import sys
+import os
+import paddle.fluid as fluid
+from paddle2onnx.utils import logging
+
+
+def str2list(v):
+    if len(v) == 0:
+        return None
+    v = v.replace(" ", "")
+    v = eval(v)
+    return v
+
+
+def arg_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_dir",
+        "-m",
+        type=_text_type,
+        default=None,
+        help="PaddlePaddle model directory, if params stored in single file, you need define '--model_filename' and 'params_filename'."
+    )
+    parser.add_argument(
+        "--model_filename",
+        "-mf",
+        type=_text_type,
+        default=None,
+        help="PaddlePaddle model's network file name, which under directory seted by --model_dir"
+    )
+    parser.add_argument(
+        "--params_filename",
+        "-pf",
+        type=_text_type,
+        default=None,
+        help="PaddlePaddle model's param file name(param files combined in single file), which under directory seted by --model_dir."
+    )
+    parser.add_argument(
+        "--save_file",
+        "-s",
+        type=_text_type,
+        default=None,
+        help="file path to save onnx model")
+    parser.add_argument(
+        "--opset_version",
+        "-ov",
+        type=int,
+        default=9,
+        help="set onnx opset version to export")
+    parser.add_argument(
+       "--input_shape_dict",
+       "-isd",
+       type=_text_type,
+       default="None",
+       help="define input shapes, e.g --input_shape_dict=\"{'image':[1, 3, 608, 608]}\" or" \
+       "--input_shape_dict=\"{'image':[1, 3, 608, 608], 'im_shape': [1, 2], 'scale_factor': [1, 2]}\"")
+    parser.add_argument(
+        "--enable_dev_version",
+        type=ast.literal_eval,
+        default=False,
+        help="whether to use new version of Paddle2ONNX which is under developing, default False"
+    )
+    parser.add_argument(
+        "--enable_onnx_checker",
+        type=ast.literal_eval,
+        default=True,
+        help="whether check onnx model validity, default True")
+    parser.add_argument(
+        "--enable_paddle_fallback",
+        type=ast.literal_eval,
+        default=False,
+        help="whether use PaddleFallback for custom op, default is False")
+    parser.add_argument(
+        "--version",
+        "-v",
+        action="store_true",
+        default=False,
+        help="get version of paddle2onnx")
+    parser.add_argument(
+        "--output_names",
+        "-on",
+        type=str2list,
+        default=None,
+        help="define output names, e.g --output_names=\"[\"output1\"]\" or \
+       --output_names=\"[\"output1\", \"output2\", \"output3\"]\" or \
+       --output_names=\"{\"Paddleoutput\":\"Onnxoutput\"}\"")
+    parser.add_argument(
+        "--enable_auto_update_opset",
+        type=ast.literal_eval,
+        default=True,
+        help="whether enable auto_update_opset, default is True")
+    return parser
+
+
+def c_paddle_to_onnx(model_file,
+                     params_file="",
+                     save_file=None,
+                     opset_version=7,
+                     auto_upgrade_opset=True,
+                     verbose=True,
+                     enable_onnx_checker=True,
+                     enable_experimental_op=True,
+                     enable_optimize=True):
+    import paddle2onnx.paddle2onnx_cpp2py_export as c_p2o
+    onnx_model_str = c_p2o.export(
+        model_file, params_file, opset_version, auto_upgrade_opset, verbose,
+        enable_onnx_checker, enable_experimental_op, enable_optimize)
+    if save_file is not None:
+        with open(save_file, "wb") as f:
+            f.write(onnx_model_str)
+    else:
+        return onnx_model_str
+
+
+def program2onnx(model_dir,
+                 save_file,
+                 model_filename=None,
+                 params_filename=None,
+                 opset_version=9,
+                 enable_onnx_checker=False,
+                 operator_export_type="ONNX",
+                 input_shape_dict=None,
+                 output_names=None,
+                 auto_update_opset=True):
+    try:
+        import paddle
+    except:
+        logging.error(
+            "paddlepaddle not installed, use \"pip install paddlepaddle\"")
+
+    v0, v1, v2 = paddle.__version__.split('.')
+    if v0 == '0' and v1 == '0' and v2 == '0':
+        logging.warning("You are use develop version of paddlepaddle")
+    elif int(v0) <= 1 and int(v1) < 8:
+        raise ImportError("paddlepaddle>=1.8.0 is required")
+
+    import paddle2onnx as p2o
+    # convert model save with 'paddle.fluid.io.save_inference_model'
+    if hasattr(paddle, 'enable_static'):
+        paddle.enable_static()
+    exe = fluid.Executor(fluid.CPUPlace())
+    if model_filename is None and params_filename is None:
+        [program, feed_var_names, fetch_vars] = fluid.io.load_inference_model(
+            model_dir, exe)
+    else:
+        [program, feed_var_names, fetch_vars] = fluid.io.load_inference_model(
+            model_dir,
+            exe,
+            model_filename=model_filename,
+            params_filename=params_filename)
+
+    OP_WITHOUT_KERNEL_SET = {
+        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
+        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
+        'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
+        'gen_bkcl_id', 'c_gen_bkcl_id', 'gen_nccl_id', 'c_gen_nccl_id',
+        'c_comm_init', 'c_sync_calc_stream', 'c_sync_comm_stream',
+        'queue_generator', 'dequeue', 'enqueue', 'heter_listen_and_serv',
+        'c_wait_comm', 'c_wait_compute', 'c_gen_hccl_id', 'c_comm_init_hccl',
+        'copy_cross_scope'
+    }
+    if input_shape_dict is not None:
+        import paddle2onnx
+        paddle2onnx.legacy.process_old_ops_desc(program)
+        paddle_version = paddle.__version__
+        model_version = program.desc._version()
+        major_ver = model_version // 1000000
+        minor_ver = (model_version - major_ver * 1000000) // 1000
+        patch_ver = model_version - major_ver * 1000000 - minor_ver * 1000
+        model_version = "{}.{}.{}".format(major_ver, minor_ver, patch_ver)
+        if model_version != paddle_version:
+            logging.warning(
+                "The model is saved by paddlepaddle v{}, but now your paddlepaddle is version of {}, this difference may cause error, it is recommend you reinstall a same version of paddlepaddle for this model".
+                format(model_version, paddle_version))
+
+        for k, v in input_shape_dict.items():
+            program.blocks[0].var(k).desc.set_shape(v)
+        for i in range(len(program.blocks[0].ops)):
+            if program.blocks[0].ops[i].type in OP_WITHOUT_KERNEL_SET:
+                continue
+            program.blocks[0].ops[i].desc.infer_shape(program.blocks[0].desc)
+    p2o.program2onnx(
+        program,
+        fluid.global_scope(),
+        save_file,
+        feed_var_names=feed_var_names,
+        target_vars=fetch_vars,
+        opset_version=opset_version,
+        enable_onnx_checker=enable_onnx_checker,
+        operator_export_type=operator_export_type,
+        auto_update_opset=auto_update_opset,
+        output_names=output_names)
+
+
+def main():
+    if len(sys.argv) < 2:
+        logging.info("Use \"paddle2onnx -h\" to print the help information")
+        logging.info(
+            "For more information, please follow our github repo below:")
+        logging.info("Github: https://github.com/PaddlePaddle/paddle2onnx.git")
+        return
+
+    parser = arg_parser()
+    args = parser.parse_args()
+
+    if args.version:
+        import paddle2onnx
+        logging.info("paddle2onnx-{} with python>=2.7, paddlepaddle>=1.8.0".
+                     format(paddle2onnx.__version__))
+        return
+
+    assert args.model_dir is not None, "--model_dir should be defined while translating paddle model to onnx"
+    assert args.save_file is not None, "--save_file should be defined while translating paddle model to onnx"
+
+    input_shape_dict = eval(args.input_shape_dict)
+
+    operator_export_type = "ONNX"
+    if args.enable_paddle_fallback:
+        operator_export_type = "PaddleFallback"
+
+    if args.output_names is not None:
+        if not isinstance(args.output_names, (list, dict)):
+            raise TypeError(
+                "The output_names should be 'list' or 'dict', but received type is %s."
+                % type(args.output_names))
+
+    if args.enable_dev_version:
+        if args.enable_paddle_fallback:
+            logging.warn(
+                "--enable_paddle_fallback is deprecated while --enable_dev_version=True."
+            )
+        if args.output_names is not None:
+            logging.warn(
+                "--output_names is deprecated while --enable_dev_version=True.")
+        if input_shape_dict is not None:
+            logging.warn(
+                "--input_shape_dict is deprecated while --enable_dev_version=True."
+            )
+        model_file = os.path.join(args.model_dir, args.model_filename)
+        if args.params_filename is None:
+            params_file = ""
+        else:
+            params_file = os.path.join(args.model_dir, args.params_filename)
+        return c_paddle_to_onnx(
+            model_file=model_file,
+            params_file=params_file,
+            save_file=args.save_file,
+            opset_version=args.opset_version,
+            auto_upgrade_opset=args.enable_auto_update_opset,
+            verbose=True,
+            enable_onnx_checker=args.enable_onnx_checker,
+            enable_experimental_op=True,
+            enable_optimize=True)
+
+    program2onnx(
+        args.model_dir,
+        args.save_file,
+        args.model_filename,
+        args.params_filename,
+        opset_version=args.opset_version,
+        enable_onnx_checker=args.enable_onnx_checker,
+        operator_export_type=operator_export_type,
+        input_shape_dict=input_shape_dict,
+        output_names=args.output_names,
+        auto_update_opset=args.enable_auto_update_opset)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/paddle2onnx/legacy/constant/__init__.py b/paddle2onnx/legacy/constant/__init__.py
new file mode 100644
index 0000000000..4361cadef1
--- /dev/null
+++ b/paddle2onnx/legacy/constant/__init__.py
@@ -0,0 +1,15 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .constant import PRODUCER
+from .constant import NodeDomain
diff --git a/paddle2onnx/legacy/constant/constant.py b/paddle2onnx/legacy/constant/constant.py
new file mode 100644
index 0000000000..b8e4fa4fe6
--- /dev/null
+++ b/paddle2onnx/legacy/constant/constant.py
@@ -0,0 +1,24 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+PRODUCER = 'PaddlePaddle'
+
+ONNX_HELPER_VERSION = '1.7.0'
+
+
+class NodeDomain():
+    ONNX = 'onnx'
+    PADDLE = 'paddle'
+    CUSTOM = 'custom'
+    RAW = 'raw'
diff --git a/paddle2onnx/legacy/constant/dtypes.py b/paddle2onnx/legacy/constant/dtypes.py
new file mode 100644
index 0000000000..74fc6fe063
--- /dev/null
+++ b/paddle2onnx/legacy/constant/dtypes.py
@@ -0,0 +1,84 @@
+#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle.fluid.core as core
+from onnx import helper
+from onnx import TensorProto
+
+ONNX = TensorProto
+
+DTYPE_PADDLE_ONNX_MAP = {
+    TensorProto.FLOAT16: core.VarDesc.VarType.FP16,
+    TensorProto.FLOAT: core.VarDesc.VarType.FP32,
+    TensorProto.DOUBLE: core.VarDesc.VarType.FP64,
+    TensorProto.INT16: core.VarDesc.VarType.INT16,
+    TensorProto.INT32: core.VarDesc.VarType.INT32,
+    TensorProto.INT64: core.VarDesc.VarType.INT64,
+    TensorProto.BOOL: core.VarDesc.VarType.BOOL,
+    TensorProto.UINT8: core.VarDesc.VarType.UINT8,
+    core.VarDesc.VarType.FP16: TensorProto.FLOAT16,
+    core.VarDesc.VarType.FP32: TensorProto.FLOAT,
+    core.VarDesc.VarType.FP64: TensorProto.DOUBLE,
+    core.VarDesc.VarType.INT16: TensorProto.INT16,
+    core.VarDesc.VarType.INT32: TensorProto.INT32,
+    core.VarDesc.VarType.INT64: TensorProto.INT64,
+    core.VarDesc.VarType.BOOL: TensorProto.BOOL,
+    core.VarDesc.VarType.UINT8: TensorProto.UINT8,
+}
+
+DTYPE_PADDLE_NUMPY_MAP = {
+    np.float32: core.VarDesc.VarType.FP32,
+    np.float64: core.VarDesc.VarType.FP64,
+    np.int16: core.VarDesc.VarType.INT16,
+    np.int32: core.VarDesc.VarType.INT32,
+    np.int64: core.VarDesc.VarType.INT64,
+    np.bool_: core.VarDesc.VarType.BOOL,
+    core.VarDesc.VarType.FP32: np.float32,
+    core.VarDesc.VarType.FP64: np.float64,
+    core.VarDesc.VarType.INT16: np.int16,
+    core.VarDesc.VarType.INT32: np.int32,
+    core.VarDesc.VarType.INT64: np.int64,
+    core.VarDesc.VarType.BOOL: np.bool_
+}
+
+DTYPE_PADDLE_STR_MAP = {
+    core.VarDesc.VarType.FP32: 'float32',
+    core.VarDesc.VarType.FP64: 'float64',
+    core.VarDesc.VarType.INT16: 'int16',
+    core.VarDesc.VarType.INT32: 'int32',
+    core.VarDesc.VarType.INT64: 'int64',
+    core.VarDesc.VarType.BOOL: 'bool',
+    'float32': core.VarDesc.VarType.FP32,
+    'float64': core.VarDesc.VarType.FP64,
+    'int16': core.VarDesc.VarType.INT16,
+    'int32': core.VarDesc.VarType.INT32,
+    'int64': core.VarDesc.VarType.INT64,
+    'bool': core.VarDesc.VarType.BOOL
+}
+
+DTYPE_ONNX_STR_MAP = {
+    TensorProto.FLOAT: 'float32',
+    TensorProto.DOUBLE: 'float64',
+    TensorProto.INT16: 'int16',
+    TensorProto.INT32: 'int32',
+    TensorProto.INT64: 'int64',
+    TensorProto.BOOL: 'bool',
+    'float32': TensorProto.FLOAT,
+    'float64': TensorProto.DOUBLE,
+    'int16': TensorProto.INT16,
+    'int32': TensorProto.INT32,
+    'int64': TensorProto.INT64,
+    'bool': TensorProto.BOOL,
+}
diff --git a/paddle2onnx/legacy/constant/op_mapping_status.py b/paddle2onnx/legacy/constant/op_mapping_status.py
new file mode 100644
index 0000000000..0f9e074cda
--- /dev/null
+++ b/paddle2onnx/legacy/constant/op_mapping_status.py
@@ -0,0 +1,19 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+OP_MAPPING_WAITTING = 0
+OP_MAPPING_NO_REGISTER = 1
+OP_MAPPING_NO_VERSION = 2
+OP_MAPPING_SUCCESSED = 3
+OP_MAPPING_FAILED = 4
diff --git a/paddle2onnx/legacy/convert.py b/paddle2onnx/legacy/convert.py
new file mode 100755
index 0000000000..86e7d4f6c5
--- /dev/null
+++ b/paddle2onnx/legacy/convert.py
@@ -0,0 +1,206 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import os
+import six
+import paddle
+import numpy as np
+from paddle.fluid.framework import Variable
+from paddle2onnx.utils import check_model, logging
+from paddle2onnx.legacy.graph import PaddleGraph, ONNXGraph
+from paddle2onnx.legacy.passes import PassManager
+
+
+def export_onnx(paddle_graph,
+                save_file,
+                opset_version=9,
+                enable_onnx_checker=False,
+                operator_export_type="ONNX",
+                verbose=False,
+                auto_update_opset=True,
+                output_names=None):
+    onnx_graph = ONNXGraph.build(paddle_graph, opset_version,
+                                 operator_export_type, verbose,
+                                 auto_update_opset)
+    onnx_graph = PassManager.run_pass(
+        onnx_graph, ['dumplicate_names_pass', 'inplace_node_pass'])
+    onnx_proto = onnx_graph.export_proto(enable_onnx_checker, output_names)
+
+    if save_file is None:
+        return onnx_proto
+
+    path, _ = os.path.split(save_file)
+    if path != '' and not os.path.isdir(path):
+        os.makedirs(path)
+    with open(save_file, 'wb') as f:
+        f.write(onnx_proto.SerializeToString())
+    logging.info("ONNX model saved in {}".format(save_file))
+
+
+def program2onnx(program,
+                 scope,
+                 save_file,
+                 feed_var_names=None,
+                 target_vars=None,
+                 opset_version=9,
+                 enable_onnx_checker=False,
+                 operator_export_type="ONNX",
+                 auto_update_opset=True,
+                 **configs):
+    from paddle import fluid
+    if hasattr(paddle, 'enable_static'):
+        paddle.enable_static()
+    if isinstance(program, paddle.fluid.framework.Program):
+        if feed_var_names is not None:
+            if isinstance(feed_var_names, six.string_types):
+                feed_var_names = [feed_var_names]
+            else:
+                if not (bool(feed_var_names) and all(
+                        isinstance(name, six.string_types)
+                        for name in feed_var_names)):
+                    raise TypeError("'feed_var_names' should be a list of str.")
+
+        if target_vars is not None:
+            if isinstance(target_vars, Variable):
+                target_vars = [target_vars]
+            else:
+                if not (bool(target_vars) and
+                        all(isinstance(var, Variable) for var in target_vars)):
+                    raise TypeError(
+                        "'target_vars' should be a list of variable.")
+
+        paddle_graph = PaddleGraph.build_from_program(program, feed_var_names,
+                                                      target_vars, scope)
+        output_names = None
+        if 'output_names' in configs:
+            output_names = configs['output_names']
+            if output_names is not None and not isinstance(output_names,
+                                                           (list, dict)):
+                raise TypeError(
+                    "The output_names should be 'list' or dict, but received type is %s."
+                    % type(output_names))
+        return export_onnx(
+            paddle_graph,
+            save_file,
+            opset_version,
+            enable_onnx_checker,
+            operator_export_type,
+            auto_update_opset=auto_update_opset,
+            output_names=output_names)
+    else:
+        raise TypeError(
+            "the input 'program' should be 'Program', but received type is %s."
+            % type(program))
+
+
+def dygraph2onnx(layer, save_file, input_spec=None, opset_version=9, **configs):
+    from paddle.nn import Layer
+    from paddle.fluid import core
+    from paddle.fluid.framework import Variable
+    from paddle.fluid.dygraph.dygraph_to_static import program_translator
+    from paddle.fluid import dygraph
+    if not isinstance(layer, Layer):
+        raise TypeError(
+            "the input 'layer' should be 'Layer', 'TranslatedLayer', but received type is %s."
+            % type(layer))
+
+    inner_input_spec = None
+    if input_spec is not None:
+        if not isinstance(input_spec, list):
+            raise TypeError(
+                "The input input_spec should be 'list', but received type is %s."
+                % type(input_spec))
+        inner_input_spec = []
+        for var in input_spec:
+            if isinstance(var, paddle.static.InputSpec):
+                inner_input_spec.append(var)
+            elif isinstance(var, (core.VarBase, Variable)):
+                inner_input_spec.append(
+                    paddle.static.InputSpec.from_tensor(var))
+            else:
+                raise TypeError(
+                    "The element in input_spec list should be 'Variable' or `paddle.static.InputSpec`, but received element's type is %s."
+                    % type(var))
+
+    output_spec = None
+    if 'output_spec' in configs:
+        output_spec = configs['output_spec']
+        if not isinstance(output_spec, list):
+            raise TypeError(
+                "The output_spec should be 'list', but received type is %s." %
+                type(output_spec))
+        for var in output_spec:
+            if not isinstance(var, (core.VarBase, Variable)):
+                raise TypeError(
+                    "The element in output_spec list should be 'Variable', but received element's type is %s."
+                    % type(var))
+
+    verbose = False
+    if 'verbose' in configs:
+        if isinstance(configs['verbose'], bool):
+            verbose = configs['verbose']
+        else:
+            raise TypeError(
+                "The verbose should be 'bool', but received type is %s." %
+                type(configs['verbose']))
+
+    enable_onnx_checker = False
+    if 'enable_onnx_checker' in configs:
+        if isinstance(configs['enable_onnx_checker'], bool):
+            enable_onnx_checker = configs['enable_onnx_checker']
+        else:
+            raise TypeError(
+                "The 'enable_onnx_checker' should be 'bool', but received type is %s."
+                % type(configs['enable_onnx_checker']))
+
+    operator_export_type = "ONNX"
+    enable_paddle_fallback = False
+    if 'enable_paddle_fallback' in configs:
+        if isinstance(configs['enable_paddle_fallback'], bool):
+            enable_paddle_fallback = configs['enable_paddle_fallback']
+            if enable_paddle_fallback:
+                operator_export_type = "PaddleFallback"
+        else:
+            raise TypeError(
+                "The 'enable_paddle_fallback' should be 'bool', but received type is %s."
+                % type(configs['enable_paddle_fallback']))
+
+    paddle_graph = PaddleGraph.build_from_dygraph(layer, inner_input_spec,
+                                                  output_spec)
+
+    if 'get_paddle_graph' in configs:
+        return paddle_graph
+
+    auto_update_opset = True
+    if 'auto_update_opset' in configs:
+        if isinstance(configs['auto_update_opset'], bool):
+            auto_update_opset = configs['auto_update_opset']
+        else:
+            raise TypeError(
+                "The auto_update_opset should be 'bool', but received type is %s."
+                % type(configs['auto_update_opset']))
+
+    output_names = None
+    if 'output_names' in configs:
+        output_names = configs['output_names']
+        if not isinstance(output_names, (list, dict)):
+            raise TypeError(
+                "The output_names should be 'list' or dict, but received type is %s."
+                % type(output_names))
+
+    return export_onnx(paddle_graph, save_file, opset_version,
+                       enable_onnx_checker, operator_export_type, verbose,
+                       auto_update_opset, output_names)
diff --git a/paddle2onnx/legacy/graph/__init__.py b/paddle2onnx/legacy/graph/__init__.py
new file mode 100644
index 0000000000..fb87f22543
--- /dev/null
+++ b/paddle2onnx/legacy/graph/__init__.py
@@ -0,0 +1,17 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .graph import Graph, Node
+from .paddle_graph import PaddleGraph, PaddleNode
+from .onnx_graph import ONNXGraph, ONNXNode
diff --git a/paddle2onnx/legacy/graph/dygraph_helper.py b/paddle2onnx/legacy/graph/dygraph_helper.py
new file mode 100755
index 0000000000..1d4b6accbf
--- /dev/null
+++ b/paddle2onnx/legacy/graph/dygraph_helper.py
@@ -0,0 +1,271 @@
+#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import os
+import numpy as np
+import inspect
+import six
+import paddle
+from paddle.fluid.io import _get_valid_program
+from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator, StaticFunction
+from paddle.fluid.layers.utils import flatten, pack_sequence_as
+from collections import OrderedDict
+from paddle.fluid import dygraph
+from paddle.fluid.dygraph.jit import declarative
+from paddle.fluid import core
+from paddle.fluid import layers
+from paddle.nn import Layer
+from paddle.fluid.framework import Block, ParamBase, Program, Variable, Parameter, program_guard
+from paddle.fluid.dygraph.layers import Layer
+
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.graph.graph_helper import prepend_feed_ops, append_fetch_ops
+
+
+def _get_input_var_names(inputs, input_spec):
+    name_none_error = "The %s's name is None. " \
+        "When using jit.save, please set InputSepc's name in " \
+        "to_static(input_spec=[]) and jit.save(input_spec=[]) " \
+        "and make sure they are consistent."
+    name_no_exists_error = "The tensor `%s` does not exists. " \
+        "Please make sure the name of InputSpec or example Tensor " \
+        "in input_spec is the same as the name of InputSpec in " \
+        "`to_static` decorated on the Layer.forward method."
+    result_list = []
+    input_var_names = [
+        var.name for var in flatten(inputs) if isinstance(var, Variable)
+    ]
+    if input_spec is None:
+        # no prune
+        return input_var_names
+    else:
+        # fileter out non-tensor type spec infos.
+        input_spec = [
+            spec for spec in input_spec
+            if isinstance(spec, paddle.static.InputSpec)
+        ]
+
+    if len(input_spec) == len(input_var_names):
+        # no prune
+        result_list = input_var_names
+        # if input spec name not in input_var_names, only raise warning
+        for spec in input_spec:
+            if spec.name is None:
+                warnings.warn(name_none_error % spec)
+            elif spec.name not in input_var_names:
+                warnings.warn(name_no_exists_error % spec.name)
+            else:
+                # do nothing
+                pass
+    else:
+        # prune
+        for spec in input_spec:
+            if spec.name is None:
+                # name is None, the input_spec only can be InputSpec
+                raise ValueError(name_none_error % spec)
+            elif spec.name not in input_var_names:
+                # the input_spec can be `InputSpec` or `VarBase`
+                raise ValueError(name_no_exists_error % spec.name)
+            else:
+                result_list.append(spec.name)
+
+    return result_list
+
+
+def _get_output_vars(outputs, output_spec):
+    name_no_exists_error = "The tensor `%s` does not exists. " \
+        "Please make sure the name of example Tensor " \
+        "in configs.output_spec is the output tensor of " \
+        "Layer.forward method."
+    result_list = []
+    output_vars_dict = OrderedDict()
+    for var in flatten(outputs):
+        if isinstance(var, Variable):
+            output_vars_dict[var.name] = var
+    if output_spec is None:
+        result_list = output_vars_dict.values()
+    elif output_spec is not None and len(output_spec) == len(output_vars_dict):
+        result_list = output_vars_dict.values()
+        for var in output_spec:
+            if var.name not in output_vars_dict:
+                warnings.warn(name_no_exists_error % var.name)
+    else:
+        for var in output_spec:
+            if var.name not in output_vars_dict:
+                raise ValueError(name_no_exists_error % var.name)
+            else:
+                result_list.append(output_vars_dict[var.name])
+    return result_list
+
+
+@dygraph.base.switch_to_static_graph
+def get_program(layer, input_spec, output_spec, **configs):
+    paddle.jit.set_verbosity(0)
+    prog_translator = ProgramTranslator()
+    if not prog_translator.enable_to_static:
+        raise RuntimeError(
+            "The Paddle2onnx doesn't work when setting ProgramTranslator.enable to False."
+        )
+
+    if not isinstance(layer, Layer):
+        raise TypeError(
+            "The input of paddle2onnx should be 'Layer', but received input type is %s."
+            % type(layer))
+
+    if isinstance(layer, paddle.DataParallel):
+        inner_layer = layer._layers
+    else:
+        inner_layer = layer
+
+    # avoid change user given input_spec
+    inner_input_spec = None
+    if input_spec is not None:
+        for attr_func in dir(inner_layer):
+            static_func = getattr(inner_layer, attr_func, None)
+            if isinstance(static_func,
+                          StaticFunction) and 'forward' != attr_func:
+                raise ValueError(
+                    "If there are static functions other than 'forward' that need to be saved, the input 'input_spec' should be None, but received the type of 'input_spec' is %s."
+                    % type(input_spec))
+
+        if not isinstance(input_spec, (list, tuple)):
+            raise TypeError(
+                "The input input_spec should be 'list', but received input_spec's type is %s."
+                % type(input_spec))
+        inner_input_spec = []
+        for var in flatten(input_spec):
+            if isinstance(var, paddle.static.InputSpec):
+                inner_input_spec.append(var)
+            elif isinstance(var, (core.VarBase, core.eager.Tensor, Variable)):
+                inner_input_spec.append(
+                    paddle.static.InputSpec.from_tensor(var))
+            else:
+                # NOTE(Aurelius84): Support non-Tensor type in `input_spec`.
+                inner_input_spec.append(var)
+
+    extra_var_info = dict()
+    functions = dir(inner_layer)
+    for attr_func in functions:
+        static_func = getattr(inner_layer, attr_func, None)
+        if isinstance(static_func, StaticFunction):
+            concrete_program = static_func.concrete_program_specify_input_spec(
+                inner_input_spec)
+        elif 'forward' == attr_func:
+            # transform in jit.save, if input_spec is incomplete, declarative will throw error
+            # inner_input_spec is list[InputSpec], it should be packed with same structure
+            # as original input_spec here.
+            if inner_input_spec:
+                inner_input_spec = pack_sequence_as(input_spec,
+                                                    inner_input_spec)
+            static_forward = declarative(
+                inner_layer.forward, input_spec=inner_input_spec)
+            concrete_program = static_forward.concrete_program
+            # the input_spec has been used in declarative, which is equal to
+            # @declarative with input_spec and jit.save without input_spec,
+            # avoid needless warning
+            inner_input_spec = None
+        else:
+            continue
+
+        input_var_names = _get_input_var_names(concrete_program.inputs,
+                                               inner_input_spec)
+
+        # NOTE(chenweihang): [ Get output variables ]
+        # the rule is like [ Get input variables name ]. For output var,
+        # we only support VarBase spec, and actually, we only need the
+        # var name of output, and we don't recommended to use output_spec
+        output_vars = _get_output_vars(concrete_program.outputs, output_spec)
+
+    feeded_var_names = input_var_names
+    target_vars = output_vars
+    main_program = concrete_program.main_program.clone()
+    export_for_deployment = True
+
+    if isinstance(feeded_var_names, six.string_types):
+        feeded_var_names = [feeded_var_names]
+    elif export_for_deployment:
+        if len(feeded_var_names) > 0:
+            # TODO(paddle-dev): polish these code blocks
+            if not (bool(feeded_var_names) and all(
+                    isinstance(name, six.string_types)
+                    for name in feeded_var_names)):
+                raise ValueError("'feed_var_names' should be a list of str.")
+
+    if isinstance(target_vars, Variable):
+        target_vars = [target_vars]
+    elif export_for_deployment:
+        if not (bool(target_vars) and
+                all(isinstance(var, Variable) for var in target_vars)):
+            raise ValueError("'target_vars' should be a list of Variable.")
+
+    main_program = _get_valid_program(main_program)
+
+    # remind user to set auc_states to zeros if the program contains auc op
+    all_ops = main_program.global_block().ops
+    for op in all_ops:
+        # clear device of Op
+        device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
+        op._set_attr(device_attr_name, "")
+        if op.type == 'auc':
+            warnings.warn(
+                "please ensure that you have set the auc states to zeros before saving inference model"
+            )
+            break
+
+    with program_guard(main_program):
+        uniq_target_vars = []
+        for i, var in enumerate(target_vars):
+            uniq_target_vars.append(var)
+        target_vars = uniq_target_vars
+    target_var_name_list = [var.name for var in target_vars]
+
+    origin_program = main_program.clone()
+
+    main_program = main_program.clone()
+    global_block = main_program.global_block()
+    need_to_remove_op_index = []
+    for i, op in enumerate(global_block.ops):
+        op.desc.set_is_target(False)
+        if op.type == "feed" or op.type == "fetch":
+            need_to_remove_op_index.append(i)
+
+    for index in need_to_remove_op_index[::-1]:
+        global_block._remove_op(index)
+
+    main_program.desc.flush()
+
+    main_program = main_program._prune_with_input(
+        feeded_var_names=feeded_var_names, targets=target_vars)
+    main_program = main_program._inference_optimize(prune_read_op=True)
+    fetch_var_names = [v.name for v in target_vars]
+
+    for target_v in target_vars:
+        if not main_program.global_block().has_var(target_v.name):
+            main_program.global_block().create_var(
+                name=target_v.name,
+                shape=target_v.shape,
+                dtype=target_v.dtype,
+                persistable=target_v.persistable)
+
+    prepend_feed_ops(main_program, feeded_var_names)
+    append_fetch_ops(main_program, fetch_var_names)
+
+    main_program.desc._set_version()
+    paddle.fluid.core.save_op_version_info(main_program.desc)
+
+    main_program._copy_dist_param_info_from(origin_program)
+
+    return main_program, feeded_var_names, target_vars
diff --git a/paddle2onnx/legacy/graph/graph.py b/paddle2onnx/legacy/graph/graph.py
new file mode 100755
index 0000000000..a490e3d750
--- /dev/null
+++ b/paddle2onnx/legacy/graph/graph.py
@@ -0,0 +1,287 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import os
+import copy
+import six
+import collections
+from paddle2onnx.legacy.constant import NodeDomain
+
+
+class Node(object):
+    def __init__(self,
+                 op_type,
+                 inputs,
+                 outputs,
+                 attrs,
+                 layer_name,
+                 domain=NodeDomain.RAW):
+        self.domain = domain
+        self.type = op_type
+        self.attrs = attrs
+        self.layer_name = layer_name
+        self.set_inputs(inputs)
+        self.set_outputs(outputs)
+
+    def __hash__(self):
+        return hash(self.layer_name)
+
+    def __eq__(self, other):
+        if self.layer_name == other.layer_name:
+            return True
+        return False
+
+    def __str__(self):
+        node_str = ''
+        attrs = ''
+        for key, value in self.attrs.items():
+            attrs += ', ' + key + '=' + str(value)
+        node_str += "  {} = {}::{}(inputs={}{}) \n".format(
+            self.outputs, self.domain, self.type, self.inputs, attrs)
+        return node_str
+
+    def input(self, idx=None):
+        if idx is None:
+            return self.inputs
+        return self.inputs[idx]
+
+    def output(self, idx=None):
+        if idx is None:
+            return self.outputs
+        return self.outputs[idx]
+
+    def attr(self, name):
+        if name in self.attrs:
+            return self.attrs[name]
+        return None
+
+    def set_inputs(self, inputs):
+        if isinstance(inputs, list):
+            self.inputs = [
+                ipt.layer_name if isinstance(ipt, Node) else ipt
+                for ipt in inputs
+            ]
+        elif isinstance(inputs, six.string_types):
+            self.inputs = [inputs]
+        elif isinstance(inputs, Node):
+            self.inputs = [inputs.layer_name]
+        else:
+            raise TypeError(
+                'Inputs of node must be type: list, Node, or String but got {}'.
+                format(type(inputs)))
+
+    def set_outputs(self, outputs):
+        if isinstance(outputs, list):
+            self.outputs = [
+                opt.layer_name if isinstance(opt, Node) else opt
+                for opt in outputs
+            ]
+        elif isinstance(outputs, six.string_types):
+            self.outputs = [outputs]
+        elif isinstance(outputs, Node):
+            self.outputs = [outputs.layer_name]
+        else:
+            raise TypeError(
+                'Outputs of node must be type: list, Node, or String but got {}'.
+                format(type(outputs)))
+
+
+class Graph(object):
+    def __init__(self):
+        self.parameters = {}
+        self.node_map = collections.OrderedDict()
+        self.input_nodes = list()
+        self.output_nodes = list()
+        self.op_type_count = dict()
+
+    def __hash__(self):
+        return hash(self.id)
+
+    def __eq__(self, other):
+        if self.id == other.id:
+            return True
+        return False
+
+    def __str__(self):
+        graph_str = 'graph { \n'
+        for node in self.input_nodes:
+            graph_str += " input: {} \n".format(node.layer_name)
+        for node in self.output_nodes:
+            graph_str += " output: {} \n \n".format(node.layer_name)
+        for name, node in self.node_map.items():
+            graph_str += node.__str__()
+        graph_str += ' }'
+        return graph_str
+
+    def set_output_nodes(self, node_list):
+        if isinstance(node_list, list):
+            self.output_nodes = node_list
+        else:
+            raise TypeError(
+                'output_nodes of Graph must be type: list, but got {}'.format(
+                    type(node_list)))
+
+    def set_node_map(self, node_map):
+        if isinstance(node_map, dict):
+            self.node_map = node_map
+            self.generate_topo_sort()
+        else:
+            raise TypeError('node_map of Graph must be type: list, but got {}'.
+                            format(type(node_map)))
+
+    def set_input_nodes(self, node_list):
+        if isinstance(node_list, list):
+            self.input_nodes = node_list
+        else:
+            raise TypeError(
+                'input_nodes of Graph must be type: list, but got {}'.format(
+                    type(node_list)))
+
+    def set_parameters(self, parameters):
+        if isinstance(parameters, dict):
+            self.parameters = parameters
+        else:
+            raise TypeError(
+                'parameters of Graph must be type: dict, but got {}'.format(
+                    type(parameters)))
+
+    def generate_node_name(self, op_type):
+        if op_type in self.op_type_count:
+            self.op_type_count[op_type] += 1
+        else:
+            self.op_type_count[op_type] = 1
+        # layer_name need follow https://github.com/onnx/onnx/blob/master/docs/OpConventions.md
+        layer_name = op_type + '_' + str(self.op_type_count[op_type] - 1)
+        return layer_name
+
+    def insert_node(self, node):
+        if node.type not in ['feed', 'fetch']:
+            self.node_map[node.layer_name] = node
+
+    def make_node(self,
+                  op_type,
+                  inputs=None,
+                  outputs=None,
+                  attrs=None,
+                  layer_name=None,
+                  domain=None,
+                  **kw):
+        if layer_name is None:
+            layer_name = self.generate_node_name(op_type)
+
+        if attrs is None:
+            attrs = kw
+        attrs.update(kw)
+
+        if inputs is None:
+            inputs = []
+        if outputs is None:
+            outputs = [layer_name]
+        node = Node(op_type, layer_name, inputs, outputs, attrs, domain)
+        self.insert_node(node)
+        return node
+
+    def update_node(self,
+                    node,
+                    op_type=None,
+                    inputs=None,
+                    outputs=None,
+                    attrs=None,
+                    block=None,
+                    move_to_end=True,
+                    domain=None,
+                    **kw):
+        if op_type is not None:
+            node.type = op_type
+        if inputs is not None:
+            node.set_inputs(inputs)
+        if outputs is not None:
+            node.set_outputs(outputs)
+        if attrs is None:
+            attrs = kw
+        attrs.update(kw)
+        node.attrs = attrs
+        if domain is not None:
+            node.domain = domain
+        if move_to_end:
+            self.node_map.pop(node.layer_name)
+        self.node_map[node.layer_name] = node
+        return node
+
+    def get_node(self, name, copy=False):
+        if name not in self.node_map:
+            raise TypeError('Node with name:{} not in graph'.format(name))
+        if copy:
+            node = copy.copy(self.node_map[name])
+        else:
+            node = self.node_map[name]
+        return node
+
+    def remove_node_by_name(self, name):
+        if name in self.node_map:
+            node = self.node_map.pop(name)
+            return node
+        raise TypeError('Node with name:{} not in graph'.format(name))
+
+    def remove_node(self, node):
+        if isinstance(node, Node):
+            node = self.remove_node_by_name(node.layer_name)
+            return node
+        else:
+            node = self.remove_node_by_name(node)
+            return node
+
+    def get_output_nodes_of_node(self, node):
+        if node in self.edge_map:
+            return self.edge_map[node]
+        elif self.get_node(node.layer_name, copy=False):
+            return []
+        else:
+            raise KeyError('Node with layer_name {} not in graph.egde_map'.
+                           format(node.layer_name))
+
+    def get_adjacency_map(self):
+        adjacency_map = {}
+        for layer_name, current_node in self.node_map.items():
+            inputs = current_node.inputs
+            for ipt in inputs:
+                for layer_name, node in self.node_map.items():
+                    if current_node == node:
+                        continue
+                    outputs = node.outputs
+                    if ipt in outputs:
+                        if node not in adjacency_map:
+                            adjacency_map[node] = set([current_node])
+                        else:
+                            adjacency_map[node].add(current_node)
+        return adjacency_map
+
+    def get_topo_sort_list(self):
+        topo_sort_list = list()
+        adjacency_map = self.get_adjacency_map()
+        for layer_name, node in self.node_map.items():
+            if node not in adjacency_map:
+                topo_sort_list.append(node)
+        idx = 0
+        while idx < len(topo_sort_list):
+            current_node = topo_sort_list[idx]
+            for input_node, output_nodes in adjacency_map.items():
+                if current_node in output_nodes:
+                    adjacency_map[input_node].remove(current_node)
+                    if len(adjacency_map[input_node]) == 0:
+                        topo_sort_list.append(input_node)
+            idx += 1
+        return topo_sort_list[::-1]
diff --git a/paddle2onnx/legacy/graph/graph_helper.py b/paddle2onnx/legacy/graph/graph_helper.py
new file mode 100644
index 0000000000..a62594b5cc
--- /dev/null
+++ b/paddle2onnx/legacy/graph/graph_helper.py
@@ -0,0 +1,83 @@
+#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import os
+import paddle
+import numpy as np
+from paddle.fluid import core
+from paddle.fluid.framework import Variable, program_guard
+from paddle2onnx.utils import logging
+
+
+def prepend_feed_ops(inference_program,
+                     feed_target_names,
+                     feed_holder_name='feed'):
+    if len(feed_target_names) == 0:
+        return
+    global_block = inference_program.global_block()
+    feed_var = global_block.create_var(
+        name=feed_holder_name,
+        type=core.VarDesc.VarType.FEED_MINIBATCH,
+        persistable=True)
+    for i, name in enumerate(feed_target_names):
+        if not global_block.has_var(name):
+            raise ValueError(
+                "The feed_var_names[{i}]: '{name}' doesn't exist in pruned inference program. "
+                "Please check whether '{name}' is a valid feed_var name, or remove it from feed_var_names "
+                "if '{name}' is not involved in the fetch_vars calculation.".
+                format(
+                    i=i, name=name))
+        out = global_block.var(name)
+        global_block._prepend_op(
+            type='feed',
+            inputs={'X': [feed_var]},
+            outputs={'Out': [out]},
+            attrs={'col': i})
+
+
+def append_fetch_ops(inference_program,
+                     fetch_target_names,
+                     fetch_holder_name='fetch'):
+    global_block = inference_program.global_block()
+    fetch_var = global_block.create_var(
+        name=fetch_holder_name,
+        type=core.VarDesc.VarType.FETCH_LIST,
+        persistable=True)
+    for i, name in enumerate(fetch_target_names):
+        global_block.append_op(
+            type='fetch',
+            inputs={'X': [name]},
+            outputs={'Out': [fetch_var]},
+            attrs={'col': i})
+
+
+def get_program(program, feed_var_names, fetch_vars):
+    global_block = program.global_block()
+    need_to_remove_op_index = []
+    for i, op in enumerate(global_block.ops):
+        op.desc.set_is_target(False)
+        if op.type == "feed" or op.type == "fetch":
+            need_to_remove_op_index.append(i)
+    for index in need_to_remove_op_index[::-1]:
+        global_block._remove_op(index)
+    program.desc.flush()
+    program = program._prune_with_input(
+        feeded_var_names=feed_var_names, targets=fetch_vars)
+    program = program._inference_optimize(prune_read_op=True)
+    fetch_var_names = [v.name for v in fetch_vars]
+    prepend_feed_ops(program, feed_var_names)
+    append_fetch_ops(program, fetch_var_names)
+    return program
diff --git a/paddle2onnx/legacy/graph/onnx_graph.py b/paddle2onnx/legacy/graph/onnx_graph.py
new file mode 100755
index 0000000000..ac7ae9f597
--- /dev/null
+++ b/paddle2onnx/legacy/graph/onnx_graph.py
@@ -0,0 +1,333 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import os
+import copy
+import collections
+import numpy as np
+from paddle2onnx.legacy.graph import Node, Graph
+from paddle2onnx.legacy.constant import NodeDomain, PRODUCER, dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper
+from onnx import helper
+from paddle2onnx.utils import check_model, logging
+
+
+class ONNXNode(Node):
+    def __init__(self, op_type, inputs, outputs, attrs, layer_name, domain):
+        super(ONNXNode, self).__init__(op_type, inputs, outputs, attrs,
+                                       layer_name, domain)
+        self.domain = domain
+        self.onnx_node = self.make_onnx_node()
+
+    def make_onnx_constant_node(self):
+        dtype = self.attr('dtype')
+        value = self.attr('value')
+        if isinstance(value, list):
+            dims = (len(value), )
+        elif value is None:
+            dims = ()
+            value = []
+        else:
+            dims = ()
+            value = [value]
+
+        if 'dims' in self.attrs:
+            dims = self.attrs['dims']
+
+        tensor = helper.make_tensor(
+            name=self.layer_name, data_type=dtype, dims=dims, vals=value)
+
+        onnx_node = helper.make_node(
+            self.type, inputs=self.inputs, outputs=self.outputs, value=tensor)
+
+        return onnx_node
+
+    def make_onnx_node(self):
+        if self.type in ['Constant', 'ConstantOfShape']:
+            onnx_node = self.make_onnx_constant_node()
+        else:
+            onnx_node = helper.make_node(
+                self.type,
+                inputs=self.inputs,
+                outputs=self.outputs,
+                name=self.layer_name,
+                domain=self.domain,
+                **self.attrs)
+        return onnx_node
+
+
+class ONNXGraph(Graph):
+    def __init__(self,
+                 paddle_graph,
+                 opset_version,
+                 operator_export_type="ONNX",
+                 block=None,
+                 auto_update_opset=True):
+        super(ONNXGraph, self).__init__()
+        self.opset_version = opset_version
+        self.operator_export_type = operator_export_type
+        self.ctx = paddle_graph
+        self.custom = []
+        if auto_update_opset:
+            self.update_opset_version()
+
+    def __str__(self):
+        graph_str = 'graph { \n'
+        for node in self.input_nodes:
+            graph_str += " input: {} \n".format(node)
+        for node in self.output_nodes:
+            graph_str += " output: {} \n \n".format(node)
+        for name, node in self.node_map.items():
+            graph_str += node.__str__()
+        graph_str += ' }'
+        return graph_str
+
+    def make_node(self,
+                  op_type,
+                  inputs=[],
+                  outputs=[],
+                  attrs=None,
+                  layer_name=None,
+                  domain=None,
+                  **kw):
+        if layer_name is None:
+            layer_name = self.generate_node_name(op_type)
+
+        if domain is not None:
+            if domain not in self.custom:
+                self.custom.append(domain)
+
+        if attrs is None:
+            attrs = kw
+        attrs.update(kw)
+
+        if inputs is None:
+            inputs = []
+
+        real_outputs = None
+        if outputs is None:
+            real_outputs = [layer_name]
+        elif isinstance(outputs, int):
+            real_outputs = []
+            for i in range(outputs):
+                real_outputs.append(self.generate_node_name(op_type))
+        elif isinstance(outputs, list):
+            real_outputs = []
+            if len(outputs) == 0:
+                real_outputs = [layer_name]
+            else:
+                for opt in outputs:
+                    if isinstance(opt, Node):
+                        real_outputs.append(opt.layer_name)
+                    elif isinstance(opt, int):
+                        real_outputs.append(self.generate_node_name(op_type))
+                    else:
+                        real_outputs.append(opt)
+        else:
+            real_outputs = outputs
+
+        node = ONNXNode(op_type, inputs, real_outputs, attrs, layer_name,
+                        domain)
+
+        self.insert_node(node)
+        if len(node.outputs) == 1:
+            return node.outputs[0]
+        else:
+            return node.outputs
+
+    def update_node(self,
+                    node,
+                    op_type=None,
+                    inputs=None,
+                    outputs=None,
+                    attrs=None,
+                    **kw):
+        if op_type is None:
+            op_type = node.type
+        if inputs is None:
+            inputs = node.inputs
+        if outputs is None:
+            outputs = node.outputs
+        if attrs is None:
+            attrs = node.attrs
+        attrs.update(kw)
+
+        node = ONNXNode(op_type, inputs, outputs, attrs, node.layer_name,
+                        node.domain)
+        self.insert_node(node)
+        return node
+
+    def build_parameters(self, parameters):
+        # build weight nodes
+        for name, param in parameters.items():
+            weight = param['data']
+            if weight is not np.ndarray:
+                weight = np.array(weight)
+            tensor = helper.make_tensor(
+                name=name,
+                dims=param['shape'],
+                data_type=dtypes.DTYPE_PADDLE_ONNX_MAP[param['dtype']],
+                vals=weight.flatten().tolist())
+            node = helper.make_node(
+                'Constant', inputs=[], outputs=[name], value=tensor)
+            self.parameters[name] = node
+
+    def build_input_nodes(self, input_nodes):
+        # build input nodes
+        for ipt in input_nodes:
+            self.add_input_node(ipt.layer_name,
+                                ipt.attr('shape'), ipt.attr('dtype'))
+
+    def build_output_nodes(self, output_nodes):
+        # build output nodes
+        for opt in output_nodes:
+            self.add_output_node(opt.layer_name,
+                                 opt.attr('shape'), opt.attr('dtype'))
+
+    def update_opset_version(self):
+        node_map = self.ctx.node_map
+        self.opset_version = OpMapper.get_recommend_opset_version(
+            node_map, self.opset_version)
+
+    def build_op_nodes(self, node_map):
+        OpMapper.check_support_status(node_map, self.opset_version)
+        # build op nodes
+        for name, node in list(node_map.items()):
+            OpMapper.mapping(self, node, self.operator_export_type)
+
+    def make_value_info(self, name, shape, dtype):
+        tensor_info = helper.make_tensor_value_info(
+            name=name,
+            shape=shape,
+            elem_type=dtypes.DTYPE_PADDLE_ONNX_MAP[dtype])
+        return tensor_info
+
+    def add_input_node(self, name, shape, dtype):
+        vi = self.make_value_info(name, shape, dtype)
+        self.input_nodes.append(vi)
+
+    def add_output_node(self, name, shape, dtype):
+        vi = self.make_value_info(name, shape, dtype)
+        self.output_nodes.append(vi)
+
+    def find_index(self, node_inout, name):
+        for i in range(len(node_inout)):
+            if node_inout[i] == name:
+                return i
+        return -1
+
+    def change_output_names(self, onnx_proto, output_names):
+        logging.info("The output of the ONNX model is set to: {}".format(
+            output_names))
+        if isinstance(output_names, list):
+            assert len(output_names) == len(
+                onnx_proto.graph.output
+            ), "The provided output names are inconsistent with the output number of the onnx model when output_names is list"
+            origin_output_names = []
+            for i in range(len(onnx_proto.graph.output)):
+                origin_output_names.append(onnx_proto.graph.output[i].name)
+                onnx_proto.graph.output[i].name = output_names[i]
+
+            for i in range(len(onnx_proto.graph.node)):
+                node = onnx_proto.graph.node[i]
+                # Prevent changed names from being changed again
+                output_visited_node = []
+                input_visited_node = []
+                for j in range(len(origin_output_names)):
+                    if origin_output_names[j] in node.output:
+                        index = self.find_index(node.output,
+                                                origin_output_names[j])
+                        if index in output_visited_node:
+                            continue
+                        output_visited_node.append(index)
+                        onnx_proto.graph.node[i].output[index] = output_names[j]
+                    if origin_output_names[j] in node.input:
+                        index = self.find_index(node.input,
+                                                origin_output_names[j])
+                        if index in input_visited_node:
+                            continue
+                        input_visited_node.append(index)
+                        onnx_proto.graph.node[i].input[index] = output_names[j]
+        if isinstance(output_names, dict):
+            for i in range(len(onnx_proto.graph.output)):
+                for key, value in output_names.items():
+                    if onnx_proto.graph.output[i].name == key:
+                        onnx_proto.graph.output[i].name = value
+                        break
+
+            for i in range(len(onnx_proto.graph.node)):
+                node = onnx_proto.graph.node[i]
+                # Prevent changed names from being changed again
+                output_visited_node = []
+                input_visited_node = []
+                for key, value in output_names.items():
+                    if key in node.output:
+                        index = self.find_index(node.output, key)
+                        if index in output_visited_node:
+                            continue
+                        output_visited_node.append(index)
+                        onnx_proto.graph.node[i].output[index] = value
+                    if key in node.input:
+                        index = self.find_index(node.input, key)
+                        if index in input_visited_node:
+                            continue
+                        input_visited_node.append(index)
+                        onnx_proto.graph.node[i].input[index] = value
+
+        return onnx_proto
+
+    def export_proto(self, enable_onnx_checker=False, output_names=None):
+
+        op_nodes = [node.onnx_node for node in self.node_map.values()]
+        weight_nodes = [node for node in self.parameters.values()]
+
+        onnx_graph = helper.make_graph(
+            nodes=weight_nodes + op_nodes,
+            name='paddle-onnx',
+            initializer=[],
+            inputs=self.input_nodes,
+            outputs=self.output_nodes)
+
+        opset_imports = [helper.make_opsetid("", self.opset_version)]
+        for custom_domain in self.custom:
+            opset_imports.append(helper.make_opsetid(custom_domain, 1))
+        onnx_proto = helper.make_model(
+            onnx_graph, producer_name=PRODUCER, opset_imports=opset_imports)
+        if output_names is not None:
+            onnx_proto = self.change_output_names(onnx_proto, output_names)
+
+        if enable_onnx_checker:
+            check_model(onnx_proto)
+
+        return onnx_proto
+
+    @staticmethod
+    def build(paddle_graph,
+              opset_version,
+              operator_export_type="ONNX",
+              verbose=False,
+              auto_update_opset=True):
+        onnx_graph = ONNXGraph(
+            paddle_graph,
+            opset_version=opset_version,
+            operator_export_type=operator_export_type,
+            auto_update_opset=auto_update_opset)
+        onnx_graph.build_parameters(paddle_graph.parameters)
+        onnx_graph.build_input_nodes(paddle_graph.input_nodes)
+        onnx_graph.build_output_nodes(paddle_graph.output_nodes)
+        onnx_graph.build_op_nodes(paddle_graph.node_map)
+
+        return onnx_graph
diff --git a/paddle2onnx/legacy/graph/paddle_graph.py b/paddle2onnx/legacy/graph/paddle_graph.py
new file mode 100755
index 0000000000..7e2f2be7c4
--- /dev/null
+++ b/paddle2onnx/legacy/graph/paddle_graph.py
@@ -0,0 +1,303 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import os
+import copy
+import collections
+import numpy as np
+import paddle
+from paddle import fluid
+from paddle.fluid import dygraph
+from paddle.fluid.framework import Operator
+from paddle2onnx.legacy.graph import Node, Graph
+from paddle2onnx.legacy.constant import NodeDomain
+from paddle2onnx.utils import logging
+
+
+class PaddleNode(Node):
+    def __init__(self, paddle_op, inputs, outputs, attrs, layer_name, block):
+        super(PaddleNode, self).__init__(paddle_op.type, inputs, outputs, attrs,
+                                         layer_name, NodeDomain.PADDLE)
+        self.paddle_op = paddle_op
+        self.block = block
+
+    def __str__(self):
+        node_str = ''
+        attrs = ''
+        for key, value in self.attrs.items():
+            if key == 'op_callstack':
+                continue
+            attrs += ', ' + key + '=' + str(value)
+        node_str += "  {} = {}::{}(inputs={}{}) \n".format(
+            self.outputs, self.domain, self.type, self.inputs, attrs)
+        return node_str
+
+    @property
+    def input_names(self):
+        return [name for name in self.inputs.keys()]
+
+    @property
+    def output_names(self):
+        return [name for name in self.outputs.keys()]
+
+    def input(self, name, idx=None):
+        if name not in self.inputs:
+            return None
+        if idx is None:
+            return self.inputs[name]
+        if len(self.inputs[name]) <= idx:
+            return None
+        return self.inputs[name][idx]
+
+    def output(self, name, idx=None):
+        if idx is None:
+            return self.outputs[name]
+        return self.outputs[name][idx]
+
+    def output_shape(self, name, idx):
+        return self.block.var(self.output(name, idx)).shape
+
+    def input_shape(self, name, idx):
+        return self.block.var(self.input(name, idx)).shape
+
+    def input_var(self, name, idx):
+        return self.block.var(self.input(name, idx))
+
+    def input_dtype(self, name, idx):
+        return self.block.var(self.input(name, idx)).dtype
+
+    def output_dtype(self, name, idx):
+        return self.block.var(self.output(name, idx)).dtype
+
+    def attr(self, name, default=None):
+        if name in self.attrs:
+            return self.attrs[name]
+        return default
+
+    def set_inputs(self, inputs):
+        if isinstance(inputs, dict):
+            # input of node in paddle, which stored by dict 
+            self.inputs = inputs
+        else:
+            raise TypeError('Inputs of node must be type: dict, but got {}'.
+                            format(type(inputs)))
+
+    def set_outputs(self, outputs):
+        if isinstance(outputs, dict):
+            # output of node in paddle, which stored by dict 
+            self.outputs = outputs
+        else:
+            raise TypeError('Outputs of node must be type: dict, but got {}'.
+                            format(type(outputs)))
+
+
+class PaddleGraph(Graph):
+    def __init__(self, program, parameters, feed_var_names, fetch_vars):
+        super(PaddleGraph, self).__init__()
+        self.build_graph(program, parameters, feed_var_names, fetch_vars)
+
+    def make_node(self,
+                  op,
+                  inputs=None,
+                  outputs=None,
+                  attrs=None,
+                  block=None,
+                  layer_name=None,
+                  **kw):
+        if layer_name is None:
+            layer_name = self.generate_node_name(op.type)
+
+        if attrs is None:
+            attrs = kw
+        attrs.update(kw)
+
+        if inputs is None:
+            inputs = {}
+        if outputs is None:
+            outputs = {'Out': layer_name}
+        node = PaddleNode(op, inputs, outputs, attrs, layer_name, block)
+        self.insert_node(node)
+        return node
+
+    def add_input_node(self, inputs, block=None):
+        for ipt in inputs:
+            # parse feed_names
+            layer_name = ipt
+            var = block.var(ipt)
+            attrs = {}
+            attrs['shape'] = var.shape
+            attrs['dtype'] = var.dtype
+            node = Node('feed', [], [layer_name], attrs, layer_name)
+            self.input_nodes.append(node)
+
+    def add_output_node(self, outputs, block=None):
+        from paddle.fluid.framework import Variable
+        for opt in outputs:
+            # parse fetch_target_vars 
+            layer_name = opt.name
+            attrs = {}
+            attrs['shape'] = opt.shape
+            attrs['dtype'] = opt.dtype
+            node = Node('fetch', [layer_name], [], attrs, layer_name)
+            self.output_nodes.append(node)
+
+    def get_adjacency_map(self):
+        adjacency_map = {}
+        for layer_name, current_node in self.node_map.items():
+            inputs = current_node.inputs.values()
+            inputs = [x for j in inputs for x in j]
+            for ipt in inputs:
+                for layer_name, node in self.node_map.items():
+                    if current_node == node:
+                        continue
+                    outputs = node.outputs.values()
+                    outputs = [x for j in outputs for x in j]
+                    if ipt in outputs:
+                        if node not in adjacency_map:
+                            adjacency_map[node] = set([current_node])
+                        else:
+                            adjacency_map[node].add(current_node)
+        return adjacency_map
+
+    def build_graph(self,
+                    program,
+                    parameters,
+                    feed_var_names=None,
+                    target_vars=None):
+        self.program = program
+        self.set_parameters(parameters)
+        self.add_input_node(feed_var_names, program.global_block())
+        self.add_output_node(target_vars, program.global_block())
+        for block in program.blocks:
+            for i, op in enumerate(block.ops):
+                if op.type in ['feed', 'fetch']:
+                    continue
+                else:
+                    inputs = {}
+                    outputs = {}
+                    for ipt in op.input_names:
+                        inputs[ipt] = op.input(ipt)
+                    for opt in op.output_names:
+                        outputs[opt] = op.output(opt)
+                    node = self.make_node(op, inputs, outputs,
+                                          op.all_attrs(), block)
+
+    @staticmethod
+    def build_from_program(program,
+                           feed_var_names=None,
+                           fetch_vars=None,
+                           scope=None):
+        parameters_dict = {}
+        vars = program.global_block().vars
+        for name in vars:
+            var = program.global_block().var(name)
+            if name.endswith('feed') or name.endswith('fetch'):
+                continue
+            if not var.persistable:
+                continue
+            parameters_dict[name] = {
+                'data': np.array(scope.var(name).get_tensor()),
+                'dtype': var.dtype,
+                'shape': var.shape
+            }
+
+        graph = PaddleGraph(program, parameters_dict, feed_var_names,
+                            fetch_vars)
+        return graph
+
+    @staticmethod
+    def build_from_dygraph(layer, input_spec=None, output_spec=None):
+        from paddle.nn import Layer
+        from paddle.fluid import core
+        from paddle.fluid.framework import Variable
+        from paddle2onnx.legacy.graph import dygraph_helper as dg_helper
+        if isinstance(layer, dygraph.TranslatedLayer):
+            program = layer.program()
+            parameters_dict = {}
+            pruned_vars = program.global_block().vars
+            for param in layer.parameters():
+                if param.name.endswith('feed') or param.name.endswith('fetch'):
+                    continue
+                if not param.persistable:
+                    continue
+                if param.name in pruned_vars:
+                    parameters_dict[param.name] = {
+                        'data': np.array(param.value().get_tensor()),
+                        'dtype': param.dtype,
+                        'shape': param.shape
+                    }
+            for param in layer.buffers():
+                if param.name.endswith('feed') or param.name.endswith('fetch'):
+                    continue
+                if not param.value().get_tensor()._is_initialized():
+                    continue
+                if param.name in pruned_vars:
+                    parameters_dict[param.name] = {
+                        'data': np.array(param.value().get_tensor()),
+                        'dtype': param.dtype,
+                        'shape': param.shape
+                    }
+            if input_spec is not None:
+                logging.warning(
+                    "Although input_spec is specified, TranslatedLayer is not support prune. An Complete network will be exported."
+                )
+                input_spec = layer._input_spec()
+            if output_spec is not None:
+                logging.warning(
+                    "Although output_spec is specified, TranslatedLayer is not support prune. An Complete network will be exported."
+                )
+            feed_var_names = [ipt.name for ipt in layer._input_spec()]
+            fetch_vars = [
+                program.global_block().var(opt.name)
+                for opt in layer._output_spec()
+            ]
+            graph = PaddleGraph(program, parameters_dict, feed_var_names,
+                                fetch_vars)
+            return graph
+        elif isinstance(layer, Layer):
+            program, feed_var_names, fetch_vars = dg_helper.get_program(
+                layer, input_spec, output_spec)
+            parameters_dict = {}
+            pruned_vars = program.global_block().vars
+            for param in layer.parameters():
+                if param.name.endswith('feed') or param.name.endswith('fetch'):
+                    continue
+                if not param.persistable:
+                    continue
+                if param.name in pruned_vars:
+                    parameters_dict[param.name] = {
+                        'data': np.array(param.value().get_tensor()),
+                        'dtype': param.dtype,
+                        'shape': param.shape
+                    }
+            for param in layer.buffers():
+                if param.name.endswith('feed') or param.name.endswith('fetch'):
+                    continue
+                if not param.value().get_tensor()._is_initialized():
+                    continue
+                if param.name in pruned_vars:
+                    parameters_dict[param.name] = {
+                        'data': np.array(param.value().get_tensor()),
+                        'dtype': param.dtype,
+                        'shape': param.shape
+                    }
+            graph = PaddleGraph(program, parameters_dict, feed_var_names,
+                                fetch_vars)
+            return graph
+        else:
+            raise TypeError(
+                "The input Layer should be 'Layer' or 'TranslatedLayer', but received  type is %s."
+                % type(layer))
diff --git a/paddle2onnx/legacy/op_mapper/__init__.py b/paddle2onnx/legacy/op_mapper/__init__.py
new file mode 100644
index 0000000000..78792e8f2d
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/__init__.py
@@ -0,0 +1,39 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+from .op_mapper import OpMapper, register_op_mapper, CustomPaddleOp, register_custom_paddle_op
+
+from . import nn
+from . import math
+from . import activation
+from . import tensor
+from . import logic
+from . import search
+
+from .detection import yolo_box
+from .detection import multiclass_nms
+from .detection import prior_box
+from .detection import density_prior_box
+from .detection import box_coder
+from .sequence import im2sequence
+
+from .custom_paddle_op import deformable_conv
+from .custom_paddle_op import anchor_generator
+from .custom_paddle_op import generate_proposals
+from .custom_paddle_op import collect_fpn_proposals
+from .custom_paddle_op import distribute_fpn_proposals
+from .custom_paddle_op import box_clip
+from .custom_paddle_op import grid_sampler
diff --git a/paddle2onnx/legacy/op_mapper/activation.py b/paddle2onnx/legacy/op_mapper/activation.py
new file mode 100755
index 0000000000..bda17016ee
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/activation.py
@@ -0,0 +1,269 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import math
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+import paddle
+
+
+@op_mapper(
+    ['relu', 'tanh', 'log', 'sigmoid', 'sqrt'],
+    mapper_dict={
+        'relu': 'Relu',
+        'tanh': 'Tanh',
+        'log': 'Log',
+        'sigmoid': 'Sigmoid',
+        'sqrt': 'Sqrt',
+    })
+class ActivationOps():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        onnx_type = kw['mapper_dict'][node.type]
+        onnx_node = graph.make_node(
+            onnx_type, inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('silu')
+class Silu():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        x = node.input('X')[0]
+        out = graph.make_node('Sigmoid', inputs=[x])
+        graph.make_node('Mul', inputs=[x, out], outputs=node.output('Out'))
+
+@op_mapper('leaky_relu')
+class LeakyRelu():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        onnx_node = graph.make_node(
+            'LeakyRelu',
+            inputs=[node.input('X')[0]],
+            outputs=node.output('Out'),
+            alpha=node.attr('alpha'))
+
+
+@op_mapper('softplus')
+class Softplus():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        beta = node.attr('beta')
+        threshold = node.attr('threshold')
+        if np.isclose(beta, 1.0, 1e-06, 1e-06) and \
+            np.isclose(threshold, 20.0, 1e-06, 1e-06):
+            onnx_node = graph.make_node(
+                'Softplus',
+                inputs=[node.input('X')[0]],
+                outputs=node.output('Out'))
+        else:
+            raise Exception("[ERROR] Operator softplus " \
+            "only supported while beta==1.0 and threshold==20.0")
+
+
+@op_mapper('prelu')
+class PRelu():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        slope_shape = node.input_shape('Alpha', 0)
+        input_shape = node.input_shape('X', 0)
+
+        slope_node = node.input('Alpha')[0]
+        if len(input_shape) != len(slope_shape):
+            assert len(
+                slope_shape) == 1, "Slope shape is not expected for prelu"
+            broadcast_shape = [-1] + [1] * (len(input_shape) - 2)
+            broadcast_shape = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=broadcast_shape)
+            slope_node = graph.make_node(
+                'Reshape', inputs=[node.input('Alpha')[0], broadcast_shape])
+        x = node.input('X')[0]
+        x_dtype = node.input_dtype('X', 0)
+        slope_dtype = node.input_dtype('Alpha', 0)
+        if slope_dtype != paddle.float32:
+            slope_node = graph.make_node(
+                'Cast', inputs=[slope_node], to=dtypes.ONNX.FLOAT)
+        if x_dtype != paddle.float32:
+            x = graph.make_node('Cast', inputs=[x], to=dtypes.ONNX.FLOAT)
+            onnx_node = graph.make_node('PRelu', inputs=[x, slope_node])
+            graph.make_node(
+                'Cast',
+                inputs=[onnx_node],
+                outputs=node.output('Out'),
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+        else:
+            onnx_node = graph.make_node(
+                'PRelu', inputs=[x, slope_node], outputs=node.output('Out'))
+
+
+@op_mapper('relu6')
+class Relu6():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        mapper_helper.clip_helper(graph, node,
+                                  node.input('X', 0),
+                                  node.attr('threshold'), 0.0,
+                                  node.output('Out', 0))
+
+
+@op_mapper('gelu')
+class Gelu():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        input = node.input('X', 0)
+        x_dtype = node.input_dtype('X', 0)
+        # onnxruntime only support float32 Erf
+        if x_dtype != paddle.float32:
+            input = graph.make_node(
+                'Cast', inputs=[input], to=dtypes.ONNX.FLOAT)
+        sqrt2 = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.FLOAT, value=[1.4142135623730951])
+        zero_point_five = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.FLOAT, value=[0.5])
+        one = graph.make_node('Constant', dtype=dtypes.ONNX.FLOAT, value=[1])
+        x = graph.make_node('Div', inputs=[input, sqrt2])
+        x = graph.make_node('Erf', inputs=x)
+        x = graph.make_node('Add', inputs=[x, one])
+        x = graph.make_node('Mul', inputs=[input, x])
+        if x_dtype != paddle.float32:
+            mul_node = graph.make_node('Mul', inputs=[x, zero_point_five])
+            graph.make_node(
+                'Cast',
+                inputs=[mul_node],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype],
+                outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Mul', inputs=[x, zero_point_five], outputs=node.output('Out'))
+
+
+@op_mapper('selu')
+class Selu():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        graph.make_node(
+            'Selu',
+            inputs=node.input('X'),
+            alpha=node.attr('alpha'),
+            gamma=node.attr('scale'),
+            outputs=node.output('Out'))
+
+
+@op_mapper('hard_sigmoid')
+class HardSigmoid():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        slope = node.attr('slope')
+        offset = node.attr('offset')
+        graph.make_node(
+            'HardSigmoid',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            alpha=slope,
+            beta=offset)
+
+
+@op_mapper('swish')
+class Swish():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        x = node.input('X')[0]
+        if math.fabs(node.attr("beta") - 1.0) > 1e-05:
+            beta_node = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.FLOAT,
+                       'value': [node.attr('beta')]})
+            x = graph.make_node(
+                'Mul', inputs=[x, beta_node])
+        sigmoid_node = graph.make_node('Sigmoid', inputs=[x])
+        graph.make_node(
+            'Mul',
+            inputs=[x, sigmoid_node],
+            outputs=node.output('Out'))
+
+
+@op_mapper('mish')
+class Mish():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        inputs = node.input('X', 0)
+        dtype = node.input_dtype("X", 0)
+        if dtype != paddle.float32:
+            inputs = graph.make_node(
+                'Cast', inputs=[inputs], to=dtypes.ONNX.FLOAT)
+            dtype = paddle.float32
+        threshold = node.attr('threshold')
+        assert np.fabs(
+            threshold - 20
+        ) < 1e-4, "In mish OP, the threshold only supports 20, no other values are supported"
+        softplus_node = graph.make_node('Softplus', inputs=[inputs])
+        tanh_node = graph.make_node('Tanh', inputs=[softplus_node])
+        if node.input_dtype("X", 0) != paddle.float32:
+            mul_node = graph.make_node('Mul', inputs=[inputs, tanh_node])
+            inputs = graph.make_node(
+                'Cast',
+                inputs=[mul_node],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype("X", 0)],
+                outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Mul', inputs=[inputs, tanh_node], outputs=node.output('Out'))
+
+
+@op_mapper('hard_swish')
+class HardSwish():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        scale_node = graph.make_node(
+            'Constant',
+            attrs={'dtype': dtypes.ONNX.FLOAT,
+                   'value': node.attr('scale')})
+        offset_node = graph.make_node(
+            'Constant',
+            attrs={'dtype': dtypes.ONNX.FLOAT,
+                   'value': node.attr('offset')})
+
+        node0 = graph.make_node('Add', inputs=[node.input('X')[0], offset_node])
+        node1 = mapper_helper.clip_helper(graph, node, node0,
+                                          node.attr('threshold'), 0.0)
+        node2 = graph.make_node('Mul', inputs=[node.input('X')[0], node1])
+        node3 = graph.make_node(
+            'Div', inputs=[node2, scale_node], outputs=node.output('Out'))
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/__init__.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/__init__.py
new file mode 100644
index 0000000000..847ddc47ac
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/__init__.py
@@ -0,0 +1,13 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/anchor_generator.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/anchor_generator.py
new file mode 100755
index 0000000000..3f7517a798
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/anchor_generator.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+class AnchorGenerator(CustomPaddleOp):
+    def __init__(self, node, **kw):
+        super(AnchorGenerator, self).__init__(node)
+        #self.x_shape = node.input_shape('Input', 0)
+        self.anchor_sizes = node.attr('anchor_sizes')
+        self.aspect_ratios = node.attr('aspect_ratios')
+        self.offset = node.attr('offset')
+        self.strides = node.attr('stride')
+        self.variances = node.attr('variances')
+        self.shapes = self.compute_shapes()
+
+    def compute_shapes(self):
+        shapes = list()
+        for r in range(len(self.aspect_ratios)):
+            ar = self.aspect_ratios[r]
+            for s in range(len(self.anchor_sizes)):
+                anchor_size = self.anchor_sizes[s]
+                area = self.strides[0] * self.strides[1]
+                area_ratios = area / ar
+                base_w = np.floor(np.sqrt(area_ratios) + 0.5)
+                base_h = np.floor(base_w * ar + 0.5)
+                scale_w = anchor_size / self.strides[0]
+                scale_h = anchor_size / self.strides[1]
+                w = scale_w * base_w
+                h = scale_h * base_h
+                shapes.append([
+                    -0.5 * (w - 1), -0.5 * (h - 1), 0.5 * (w - 1), 0.5 * (h - 1)
+                ])
+        return shapes
+
+    def forward(self):
+        input_feature = self.input('Input', 0)
+        input_shape = paddle.shape(input_feature)
+        n, c, h, w = paddle.tensor.split(input_shape, num_or_sections=4)
+        x_ctr = paddle.arange(start=0, end=w, step=1, dtype=input_feature.dtype)
+        y_ctr = paddle.arange(start=0, end=h, step=1, dtype=input_feature.dtype)
+        x_ctr = x_ctr * self.strides[0] + self.offset * (self.strides[0] - 1)
+        y_ctr = y_ctr * self.strides[1] + self.offset * (self.strides[1] - 1)
+        tensor_one = paddle.ones(shape=[1], dtype='int64')
+        tensor_len_shape = paddle.full(
+            shape=[1], fill_value=len(self.shapes), dtype='int64')
+        x_ctr = paddle.reshape(x_ctr, shape=(1, -1))
+        y_ctr = paddle.reshape(y_ctr, shape=(1, -1))
+        x_ctr = paddle.tile(x_ctr, repeat_times=(h, tensor_one))
+        y_ctr = paddle.tile(y_ctr, repeat_times=(w, tensor_one))
+        y_ctr = paddle.transpose(y_ctr, perm=[1, 0])
+        centers = paddle.stack([x_ctr, y_ctr], axis=-1)
+        centers = paddle.tensor.unsqueeze(centers, axis=[2])
+        centers = paddle.tile(centers, repeat_times=(1, 1, len(self.shapes), 2))
+        shape_tensor = paddle.assign(np.array(self.shapes).astype('float32'))
+        anchors = centers + shape_tensor
+        variance_tensor = paddle.assign(
+            np.asarray(self.variances).astype('float32'))
+        vars = paddle.reshape(variance_tensor, shape=[1, 1, 1, -1])
+        vars = paddle.tile(
+            vars, repeat_times=(h, w, tensor_len_shape, tensor_one))
+        return {'Anchors': [anchors], 'Variances': [vars]}
+
+@op_mapper('anchor_generator')
+class Anchors_generator:
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'anchor_generator',
+            inputs=node.input('Input'),
+            outputs=node.output('Anchors') + node.output('Variances'),
+            anchor_sizes = node.attr('anchor_sizes'),
+            aspect_ratios = node.attr('aspect_ratios'),
+            offset = node.attr('offset'),
+            strides = node.attr('stride'),
+            variances = node.attr('variances'),
+            domain = 'custom')
+
+register_custom_paddle_op('anchor_generator', AnchorGenerator)
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/box_clip.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/box_clip.py
new file mode 100755
index 0000000000..e201c63483
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/box_clip.py
@@ -0,0 +1,56 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+class BoxClip(CustomPaddleOp):
+    def __init__(self, node, **kw):
+        super(BoxClip, self).__init__(node)
+
+    def forward(self):
+        input = self.input('Input', 0)
+        im_info = self.input('ImInfo', 0)
+        im_info = paddle.reshape(im_info, shape=[3])
+        h, w, s = paddle.tensor.split(im_info, axis=0, num_or_sections=3)
+        tensor_one = paddle.full(shape=[1], dtype='float32', fill_value=1.0)
+        tensor_zero = paddle.full(shape=[1], dtype='float32', fill_value=0.0)
+        h = paddle.subtract(h, tensor_one)
+        w = paddle.subtract(w, tensor_one)
+        xmin, ymin, xmax, ymax = paddle.tensor.split(
+            input, axis=-1, num_or_sections=4)
+        xmin = paddle.maximum(paddle.minimum(xmin, w), tensor_zero)
+        ymin = paddle.maximum(paddle.minimum(ymin, h), tensor_zero)
+        xmax = paddle.maximum(paddle.minimum(xmax, w), tensor_zero)
+        ymax = paddle.maximum(paddle.minimum(ymax, h), tensor_zero)
+        cliped_box = paddle.concat([xmin, ymin, xmax, ymax], axis=-1)
+
+        return {'Output': [cliped_box]}
+
+@op_mapper('box_clip')
+class Boxclip:
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'box_clip',
+            inputs=node.input('Input')+node.input('ImInfo'),
+            outputs=node.output('Output'),
+            domain = 'custom')
+register_custom_paddle_op('box_clip', BoxClip)
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/collect_fpn_proposals.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/collect_fpn_proposals.py
new file mode 100755
index 0000000000..1911939800
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/collect_fpn_proposals.py
@@ -0,0 +1,55 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+class CollectFpnProposals(CustomPaddleOp):
+    def __init__(self, node, **kw):
+        super(CollectFpnProposals, self).__init__(node)
+        self.post_nms_top_n = node.attr('post_nms_topN')
+
+    def forward(self):
+        multi_level_rois = self.input('MultiLevelRois')
+        multi_level_scores = self.input('MultiLevelScores')
+        multi_level_rois = paddle.concat(multi_level_rois, axis=0)
+        multi_level_scores = paddle.concat(multi_level_scores, axis=0)
+        proposal_num = paddle.shape(multi_level_scores)[0]
+        post_nms_top_n_tensor = paddle.assign(
+            np.array([self.post_nms_top_n]).astype('int32'))
+        k_candidate = paddle.concat([proposal_num, post_nms_top_n_tensor])
+        k = paddle.min(k_candidate)
+        scores, index = paddle.topk(multi_level_scores, k=k, axis=0)
+        rois = paddle.gather(multi_level_rois, index, axis=0)
+        return {"FpnRois": [rois]}
+
+@op_mapper('collect_fpn_proposals')
+class Collectfpnproposals:
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'collect_fpn_proposals',
+            inputs=node.input('MultiLevelRois')+ node.input('MultiLevelScores'),
+            outputs=node.output('FpnRois'),
+            post_nms_top_n = node.attr('post_nms_topN'),
+            domain = 'custom')
+            
+register_custom_paddle_op('collect_fpn_proposals', CollectFpnProposals)
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/deformable_conv.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/deformable_conv.py
new file mode 100755
index 0000000000..0042cd758d
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/deformable_conv.py
@@ -0,0 +1,296 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx import utils
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+class DeformConv2d(CustomPaddleOp):
+    def check_attribute(self, node):
+        utils.compare_attr_between_dims(
+            node.attr('strides'), (0, 1), 'strides', 'equal')
+        utils.compare_attr_between_dims(
+            node.attr('paddings'), (0, 1), 'paddings', 'equal')
+        utils.compare_attr_between_dims(
+            node.input_shape('Offset', 0), (2, 3), 'Offset', 'equal')
+        utils.compare_attr(
+            node.attr('deformable_groups'), 1, 'deformable_groups', 'equal')
+
+    def __init__(self, node, **kw):
+        super(DeformConv2d, self).__init__(node)
+        self.check_attribute(node)
+        self.in_channel = node.input_shape('Input', 0)[1]
+        self.offset_channel = node.input_shape('Offset', 0)[1]
+        self.stride = node.attr('strides')[0]
+        self.padding = node.attr('paddings')
+        if len(self.padding) == 2:
+            self.padding += self.padding
+        self.groups = node.attr('groups')
+        self.dilation = node.attr('dilations')[0]
+        self.padded_x_h = node.input_shape('Input', 0)[2]
+        self.padded_x_w = node.input_shape('Input', 0)[3]
+        if self.padded_x_h > 0:
+            self.padded_x_h = self.padded_x_h + self.padding[0] + self.padding[1]
+        if self.padded_x_w > 0:
+            self.padded_x_w = self.padded_x_w + self.padding[2] + self.padding[3]
+
+        self.kernel_size = node.input_shape('Filter', 0)[2]
+        self.N = self.kernel_size**2
+        self.num_filters = node.input_shape('Filter', 0)[0]
+
+    def forward(self):
+        input = self.input('Input', 0)
+        weight = self.input('Filter', 0)
+        mask = self.input('Mask', 0)
+        offset = self.input('Offset', 0)
+
+        input = layers.pad2d(input, self.padding)
+        input_shape = paddle.shape(input)
+        if self.padded_x_h < 0 or self.padded_x_w < 0:
+            self.padded_x_h = input_shape[2]
+            self.padded_x_w = input_shape[3]
+
+        offset_x = paddle.strided_slice(
+            offset,
+            axes=[1],
+            starts=[0],
+            ends=[self.offset_channel],
+            strides=[2])
+        offset_y = paddle.strided_slice(
+            offset,
+            axes=[1],
+            starts=[1],
+            ends=[self.offset_channel],
+            strides=[2])
+        offset = paddle.concat([offset_x, offset_y], axis=1)
+        offset_shape = paddle.shape(offset)
+        offset_h = offset_shape[2]
+        offset_w = offset_shape[3]
+
+        coordinate = self.get_offset_coordinate(offset, 'float32', offset_shape)
+
+        coordinate = coordinate.transpose((0, 2, 3, 1))
+        coord_lt, coord_rb, coord_lb, coord_rt = self.get_bilinear_corner_coordinate(
+            coordinate, self.padded_x_h, self.padded_x_w)
+
+        # clip coordinate
+        coordinate = paddle.concat(
+            [
+                paddle.clip(coordinate[:, :, :, :self.N], 0,
+                            self.padded_x_h - 1),
+                paddle.clip(coordinate[:, :, :, self.N:], 0,
+                            self.padded_x_w - 1)
+            ],
+            axis=-1)
+
+        cof_lt, cof_rb, cof_lb, cof_rt = self.get_bilinear_coefficient(
+            coord_lt, coord_rb, coord_lb, coord_rt, coordinate)
+
+        feature_lt = self.get_feature_by_coordinate(input, coord_lt, offset_h,
+                                                    offset_w, self.padded_x_w)
+        feature_rb = self.get_feature_by_coordinate(input, coord_rb, offset_h,
+                                                    offset_w, self.padded_x_w)
+        feature_lb = self.get_feature_by_coordinate(input, coord_lb, offset_h,
+                                                    offset_w, self.padded_x_w)
+        feature_rt = self.get_feature_by_coordinate(input, coord_rt, offset_h,
+                                                    offset_w, self.padded_x_w)
+
+        feature_after_deformation = paddle.unsqueeze(cof_lt, 1) * feature_lt + \
+                   paddle.unsqueeze(cof_rb, 1) * feature_rb + \
+                   paddle.unsqueeze(cof_lb, 1) * feature_lb + \
+                   paddle.unsqueeze(cof_rt, 1) * feature_rt
+
+        # modulation
+        if mask is not None:
+            mask = paddle.transpose(mask, (0, 2, 3, 1))
+            mask = paddle.unsqueeze(mask, 1)
+            mask = paddle.tile(mask, [1, self.in_channel, 1, 1, 1])
+            feature_after_deformation *= mask
+
+        feature_after_deformation = self.reshape_feature(
+            feature_after_deformation, offset_h, offset_w)
+
+        out = paddle.nn.functional.conv2d(
+            feature_after_deformation,
+            weight,
+            stride=self.kernel_size,
+            groups=self.groups)
+
+        return {'Output': [out]}
+
+    def get_offset_coordinate(self, offset, dtype, offset_shape):
+        kernel_grid_origin_x = paddle.arange(
+            0,
+            self.kernel_size + (self.kernel_size - 1) * (self.dilation - 1),
+            step=self.dilation,
+            dtype=dtype)
+        kernel_grid_origin_x = kernel_grid_origin_x.unsqueeze(1)
+        kernel_grid_origin_x = paddle.tile(kernel_grid_origin_x,
+                                           [1, self.kernel_size])
+        kernel_grid_origin_y = paddle.arange(
+            0,
+            self.kernel_size + (self.kernel_size - 1) * (self.dilation - 1),
+            step=self.dilation,
+            dtype=dtype)
+        kernel_grid_origin_y = kernel_grid_origin_y.unsqueeze(0)
+        kernel_grid_origin_y = paddle.tile(kernel_grid_origin_y,
+                                           [self.kernel_size, 1])
+        kernel_grid_origin_x = paddle.reshape(kernel_grid_origin_x, [-1])
+        kernel_grid_origin_y = paddle.reshape(kernel_grid_origin_y, [-1])
+        kernel_grid_origin = paddle.concat(
+            [kernel_grid_origin_x, kernel_grid_origin_y], -1)
+        kernel_grid_origin = paddle.reshape(kernel_grid_origin,
+                                            (1, 2 * self.N, 1, 1))
+
+        kernel_offset_x = paddle.arange(
+            0, offset_shape[2] * self.stride, step=self.stride, dtype=dtype)
+        kernel_offset_x = kernel_offset_x.unsqueeze(1)
+        kernel_offset_x = paddle.expand(kernel_offset_x, offset_shape[2:])
+        kernel_offset_y = paddle.arange(
+            0, offset_shape[3] * self.stride, step=self.stride, dtype=dtype)
+        kernel_offset_y = kernel_offset_y.unsqueeze(0)
+        kernel_offset_y = paddle.expand(kernel_offset_y, offset_shape[2:])
+        kernel_offset_x = kernel_offset_x.unsqueeze([0, 1])
+        kernel_offset_x = paddle.tile(kernel_offset_x, (1, self.N, 1, 1))
+        kernel_offset_y = kernel_offset_y.unsqueeze([0, 1])
+        kernel_offset_y = paddle.tile(kernel_offset_y, (1, self.N, 1, 1))
+
+        kernel_offset = paddle.concat([kernel_offset_x, kernel_offset_y], 1)
+        offset = offset + paddle.cast(kernel_offset, 'float32') + paddle.cast(
+            kernel_grid_origin, 'float32')
+
+        return offset
+
+    def get_bilinear_corner_coordinate(self, coord, padded_h, padded_w):
+        coord_lt = coord.floor()
+        coord_rb = coord_lt + 1
+        coord_lt = paddle.cast(
+            paddle.concat(
+                [
+                    paddle.clip(coord_lt[:, :, :, :self.N], 0, padded_h - 1),
+                    paddle.clip(coord_lt[:, :, :, self.N:], 0, padded_w - 1)
+                ],
+                axis=-1),
+            dtype='int64')
+        coord_rb = paddle.cast(
+            paddle.concat(
+                [
+                    paddle.clip(coord_rb[:, :, :, :self.N], 0, padded_h - 1),
+                    paddle.clip(coord_rb[:, :, :, self.N:], 0, padded_w - 1)
+                ],
+                axis=-1),
+            dtype='int64')
+        coord_lb = paddle.concat(
+            [coord_lt[:, :, :, :self.N], coord_rb[:, :, :, self.N:]], axis=-1)
+        coord_rt = paddle.concat(
+            [coord_rb[:, :, :, :self.N], coord_lt[:, :, :, self.N:]], axis=-1)
+
+        return coord_lt, coord_rb, coord_lb, coord_rt
+
+    def get_bilinear_coefficient(self, coord_lt, coord_rb, coord_lb, coord_rt,
+                                 p):
+        cof_lt = (1 + (paddle.cast(
+            coord_lt[:, :, :, :self.N], dtype='float32') - p[:, :, :, :self.N])
+                  ) * (1 + paddle.cast(
+                      coord_lt[:, :, :, self.N:], dtype='float32') -
+                       p[:, :, :, self.N:])
+        cof_rb = (1 - (paddle.cast(
+            coord_rb[:, :, :, :self.N], dtype='float32') - p[:, :, :, :self.N])
+                  ) * (1 - (paddle.cast(
+                      coord_rb[:, :, :, self.N:], dtype='float32') -
+                            p[:, :, :, self.N:]))
+        cof_lb = (1 + (paddle.cast(
+            coord_lb[:, :, :, :self.N], dtype='float32') - p[:, :, :, :self.N])
+                  ) * (1 - (paddle.cast(
+                      coord_lb[:, :, :, self.N:], dtype='float32') -
+                            p[:, :, :, self.N:]))
+        cof_rt = (1 - (paddle.cast(
+            coord_rt[:, :, :, :self.N], dtype='float32') - p[:, :, :, :self.N])
+                  ) * (1 + paddle.cast(
+                      coord_rt[:, :, :, self.N:], dtype='float32') -
+                       p[:, :, :, self.N:])
+
+        return cof_lt, cof_rb, cof_lb, cof_rt
+
+    def get_feature_by_coordinate(self, x, coord, offset_h, offset_w,
+                                  padded_x_w):
+        x = paddle.reshape(x, [0, 0, -1])
+        index = paddle.cast(
+            coord[:, :, :, :self.N] * padded_x_w,
+            dtype='int64') + coord[:, :, :, self.N:]  # offset_x*w + offset_y
+        index = paddle.unsqueeze(index, 1)
+        index = paddle.tile(index, [1, self.in_channel, 1, 1, 1])
+        index = paddle.reshape(index, (0, 0, -1))
+        x_range = list(range(3))
+        dim = 2
+        x_range[0] = dim
+        x_range[dim] = 0
+        x_swaped = paddle.transpose(x, perm=x_range)
+        index_range = list(range(3))
+        index_range[0] = dim
+        index_range[dim] = 0
+        index_swaped = paddle.transpose(index, perm=index_range)
+        x_shape = layers.shape(x_swaped)
+        index_shape = layers.shape(index_swaped)
+        prod = paddle.prod(x_shape[1:], keepdim=True)
+        x_swaped_flattend = paddle.reshape(x_swaped, [-1])
+        index_swaped_flattend = paddle.reshape(index_swaped, [-1])
+        index_swaped_flattend *= prod
+        bias = paddle.arange(start=0, end=prod, step=1, dtype='float32')
+        bias = paddle.tile(bias, index_shape[0])
+        index_swaped_flattend += bias
+        gathered = paddle.gather(x_swaped_flattend, index_swaped_flattend)
+        gathered = paddle.reshape(gathered, layers.shape(index_swaped))
+        x_offset = paddle.transpose(gathered, perm=x_range)
+        x_offset = paddle.reshape(
+            x_offset, (-1, self.in_channel, offset_h, offset_w, self.N))
+        return x_offset
+
+    def reshape_feature(self, x_offset, offset_h, offset_w):
+        x_offset = paddle.concat(
+            [
+                paddle.reshape(x_offset[:, :, :, :, s:s + self.kernel_size], (
+                    -1, self.in_channel, offset_h, offset_w * self.kernel_size))
+                for s in range(0, self.N, self.kernel_size)
+            ],
+            axis=-1)
+        x_offset = paddle.reshape(x_offset, (-1, self.in_channel,
+                                             offset_h * self.kernel_size,
+                                             offset_w * self.kernel_size))
+        return x_offset
+
+@op_mapper('deformable_conv')
+class Deformconv2d:
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'deformable_conv',
+            inputs=node.input('Input')+node.input('Filter')+node.input('Mask')+node.input('Offset'),
+            outputs=node.output('Output'),
+            stride = node.attr('strides'),
+            padding = node.attr('paddings'),
+            groups = node.attr('groups'),
+            dilation = node.attr('dilations'),
+            deformable_groups = node.attr('deformable_groups'),
+            domain = 'custom')
+            
+register_custom_paddle_op('deformable_conv', DeformConv2d)
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/distribute_fpn_proposals.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/distribute_fpn_proposals.py
new file mode 100755
index 0000000000..eaefd0d4f0
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/distribute_fpn_proposals.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+class DistributeFpnProposals(CustomPaddleOp):
+    def __init__(self, node, **kw):
+        super(DistributeFpnProposals, self).__init__(node)
+        self.max_level = node.attr('max_level')
+        self.min_level = node.attr('min_level')
+        self.refer_level = node.attr('refer_level')
+        self.refer_scale = node.attr('refer_scale')
+        self.pixel_offset = node.attr('pixel_offset')
+
+    def bbox_area(self, boxes):
+        offset = 1 if self.pixel_offset else 0
+        xmin, ymin, xmax, ymax = paddle.tensor.split(
+            boxes, axis=1, num_or_sections=4)
+        width = xmax - xmin + offset
+        height = ymax - ymin + offset
+        areas = width * height
+        return areas
+
+    def forward(self):
+        fpn_rois = self.input('FpnRois', 0)
+        areas = self.bbox_area(fpn_rois)
+        scale = paddle.sqrt(areas)
+        num_level = self.max_level - self.min_level + 1
+        target_level = paddle.log(scale / self.refer_scale + 1e-06) / np.log(2)
+        target_level = paddle.floor(self.refer_level + target_level)
+        target_level = paddle.clip(
+            target_level, min=self.min_level, max=self.max_level)
+
+        rois = list()
+        rois_idx_order = list()
+        rois_num_per_level = list()
+
+        for level in range(self.min_level, self.max_level + 1):
+            level_tensor = paddle.full_like(target_level, fill_value=level)
+            res = paddle.equal(target_level, level_tensor)
+            res = paddle.squeeze(res, axis=1)
+            res = paddle.cast(res, dtype='int32')
+            index = paddle.nonzero(res)
+            roi = paddle.gather(fpn_rois, index, axis=0)
+            rois.append(roi)
+            rois_idx_order.append(index)
+            rois_num_per_level.append(paddle.shape(roi)[0])
+        rois_idx_order = paddle.concat(rois_idx_order, axis=0)
+        size = paddle.shape(rois_idx_order)[0]
+        _, rois_idx_restore = paddle.topk(
+            rois_idx_order, axis=0, sorted=True, largest=False, k=size)
+
+        rois_idx_restore = paddle.cast(rois_idx_restore, dtype='int32')
+        if len(self.input('RoisNum')) > 0:
+            # trick: to keep rois num
+            rois_num_per_level[0] += self.input('RoisNum', 0) * 0
+            return {
+                'MultiFpnRois': rois,
+                'RestoreIndex': [rois_idx_restore],
+                'MultiLevelRoIsNum': rois_num_per_level
+            }
+        else:
+            return {'MultiFpnRois': rois, 'RestoreIndex': [rois_idx_restore]}
+
+
+@op_mapper('distribute_fpn_proposals')
+class Distributefpnproposals:
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'distribute_fpn_proposals',
+            inputs=node.input('FpnRois'),
+            outputs=node.output('MultiFpnRois') + node.output('RestoreIndex'),
+            max_level=node.attr('max_level'),
+            min_level=node.attr('min_level'),
+            refer_level=node.attr('refer_level'),
+            refer_scale=node.attr('refer_scale'),
+            domain='custom')
+
+
+register_custom_paddle_op('distribute_fpn_proposals', DistributeFpnProposals)
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/generate_proposals.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/generate_proposals.py
new file mode 100755
index 0000000000..f1ae448d42
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/generate_proposals.py
@@ -0,0 +1,223 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+import math
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+BBOX_CLIP_DEFAULT = math.log(1000.0 / 16.0)
+
+
+class GenerateProposals(CustomPaddleOp):
+    def __init__(self, node, **kw):
+        paddle.enable_static()
+        super(GenerateProposals, self).__init__(node)
+        self.eta = node.attr('eta')
+        self.min_size = node.attr('min_size')
+        self.nms_thresh = node.attr('nms_thresh')
+        self.post_nms_topN = node.attr('post_nms_topN')
+        self.pre_nms_topN = node.attr('pre_nms_topN')
+        self.type = node.type
+        if self.type == 'generate_proposals_v2':
+            self.pixel_offset = node.attr('pixel_offset')
+        else:
+            self.pixel_offset = True
+
+    def filter_boxes(self, boxes, im_w, im_h, im_s, min_size):
+        min_size = max(min_size, 1.0)
+        xmin, ymin, xmax, ymax = paddle.tensor.split(
+            boxes, axis=1, num_or_sections=4)
+        x_ctr = (xmax + xmin) / 2 + 0.5
+        y_ctr = (ymax + ymin) / 2 + 0.5
+        ws = (xmax - xmin) / im_s + 1
+        hs = (ymax - ymin) / im_s + 1
+
+        min_size = np.asarray([min_size], dtype='float32')
+        min_size = paddle.assign(min_size)
+        valid_flag_ws = paddle.greater_equal(ws, min_size)
+        valid_flag_hs = paddle.greater_equal(hs, min_size)
+        valid_flag_x = paddle.less_equal(x_ctr, im_w)
+        valid_flag_y = paddle.less_equal(y_ctr, im_h)
+        valid_flag = paddle.logical_and(valid_flag_ws, valid_flag_hs)
+        valid_flag = paddle.logical_and(valid_flag, valid_flag_x)
+        valid_flag = paddle.logical_and(valid_flag, valid_flag_y)
+        valid_flag = paddle.squeeze(valid_flag, axis=1)
+        valid_inds = paddle.nonzero(valid_flag)
+
+        return valid_inds
+
+    def filter_boxes_v2(self, boxes, im_w, im_h, min_size, pixel_offset=True):
+        min_size = max(min_size, 1.0)
+        xmin, ymin, xmax, ymax = paddle.tensor.split(
+            boxes, axis=1, num_or_sections=4)
+
+        offset = 1 if pixel_offset else 0
+        ws = (xmax - xmin) + offset
+        hs = (ymax - ymin) + offset
+
+        min_size = np.asarray([min_size], dtype='float32')
+        min_size = paddle.assign(min_size)
+        valid_flag_ws = paddle.greater_equal(ws, min_size)
+        valid_flag_hs = paddle.greater_equal(hs, min_size)
+        valid_flag = paddle.logical_and(valid_flag_ws, valid_flag_hs)
+        if pixel_offset:
+            x_ctr = xmin + ws / 2
+            y_ctr = ymin + hs / 2
+            valid_flag_x = paddle.less_equal(x_ctr, im_w)
+            valid_flag_y = paddle.less_equal(y_ctr, im_h)
+            valid_flag = paddle.logical_and(valid_flag, valid_flag_x)
+            valid_flag = paddle.logical_and(valid_flag, valid_flag_y)
+
+        valid_flag = paddle.squeeze(valid_flag, axis=1)
+        valid_inds = paddle.nonzero(valid_flag)
+        return valid_inds
+
+    def clip_tiled_boxes(self, im_w, im_h, input_boxes, pixel_offset=True):
+        offset = 1 if pixel_offset else 0
+        xmin, ymin, xmax, ymax = paddle.tensor.split(
+            input_boxes, axis=1, num_or_sections=4)
+        xmin = paddle.clip(xmin, max=im_w - offset, min=0)
+        ymin = paddle.clip(ymin, max=im_h - offset, min=0)
+        xmax = paddle.clip(xmax, max=im_w - offset, min=0)
+        ymax = paddle.clip(ymax, max=im_h - offset, min=0)
+        input_boxes = paddle.concat([xmin, ymin, xmax, ymax], axis=1)
+        return input_boxes
+
+    def box_encode(self, anchors, bbox_deltas, variances, pixel_offset=True):
+        offset = 1 if pixel_offset else 0
+        anchor_xmin, anchor_ymin, anchor_xmax, anchor_ymax = paddle.tensor.split(
+            anchors, axis=1, num_or_sections=4)
+        anchor_width = anchor_xmax - anchor_xmin + offset
+        anchor_height = anchor_ymax - anchor_ymin + offset
+        anchor_center_x = anchor_xmin + 0.5 * anchor_width
+        anchor_center_y = anchor_ymin + 0.5 * anchor_height
+        var_center_x, var_center_y, var_width, var_height = paddle.tensor.split(
+            variances, axis=1, num_or_sections=4)
+        delta_center_x, delta_center_y, delta_width, delta_height = paddle.tensor.split(
+            bbox_deltas, axis=1, num_or_sections=4)
+
+        bbox_center_x = var_center_x * delta_center_x * anchor_width + anchor_center_x
+        bbox_center_y = var_center_y * delta_center_y * anchor_height + anchor_center_y
+        bbox_width = paddle.exp(
+            paddle.clip(
+                var_width * delta_width, max=BBOX_CLIP_DEFAULT)) * anchor_width
+        bbox_height = paddle.exp(
+            paddle.clip(
+                var_height * delta_height,
+                max=BBOX_CLIP_DEFAULT)) * anchor_height
+
+        proposal_xmin = bbox_center_x - bbox_width / 2
+        proposal_ymin = bbox_center_y - bbox_height / 2
+        proposal_xmax = bbox_center_x + bbox_width / 2 - offset
+        proposal_ymax = bbox_center_y + bbox_height / 2 - offset
+        proposal = paddle.concat(
+            [proposal_xmin, proposal_ymin, proposal_xmax, proposal_ymax],
+            axis=1)
+        return proposal
+
+    def proposal_for_single_sample(self, anchors, bbox_deltas, im_info, scores,
+                                   variances):
+        proposal_num = paddle.shape(scores)[0]
+        pre_nms_top_n_tensor = paddle.assign(
+            np.asarray(
+                [self.pre_nms_topN], dtype='int32'))
+        k_candidate = paddle.concat([proposal_num, pre_nms_top_n_tensor])
+        k = paddle.min(k_candidate)
+        scores, index = paddle.topk(scores, k=k, axis=0)
+        bbox_deltas = paddle.gather(bbox_deltas, index, axis=0)
+        anchors = paddle.gather(anchors, index, axis=0)
+        variances = paddle.gather(variances, index, axis=0)
+
+        proposal = self.box_encode(anchors, bbox_deltas, variances,
+                                   self.pixel_offset)
+        if self.type == "generate_proposals_v2":
+            im_h, im_w = paddle.tensor.split(im_info, axis=1, num_or_sections=2)
+        else:
+            im_h, im_w, im_s = paddle.tensor.split(
+                im_info, axis=1, num_or_sections=3)
+        proposal = self.clip_tiled_boxes(im_w, im_h, proposal,
+                                         self.pixel_offset)
+
+        if self.type == "generate_proposals_v2":
+            keep = self.filter_boxes_v2(proposal, im_w, im_h, self.min_size,
+                                        self.pixel_offset)
+        else:
+            keep = self.filter_boxes(proposal, im_w, im_h, im_s, self.min_size)
+
+        tail_proposal = paddle.zeros(shape=[1, 4], dtype=proposal.dtype)
+        proposal_num = paddle.shape(proposal)[0]
+        tail_keep = paddle.reshape(proposal_num, shape=[1, 1])
+        tail_keep = paddle.cast(tail_keep, dtype=keep.dtype)
+        tail_scores = paddle.zeros(shape=[1, 1], dtype=scores.dtype)
+        # proposal = paddle.concat([proposal, tail_proposal])
+        # keep = paddle.concat([keep, tail_keep])
+        # scores = paddle.concat([scores, tail_scores])
+
+        bbox_sel = paddle.gather(proposal, keep, axis=0)
+        scores_sel = paddle.gather(scores, keep, axis=0)
+        proposal = paddle.unsqueeze(bbox_sel, axis=0)
+        scores = paddle.transpose(scores_sel, perm=[1, 0])
+        scores = paddle.unsqueeze(scores, axis=0)
+        out = layers.multiclass_nms(
+            proposal,
+            scores,
+            background_label=-1,
+            nms_top_k=self.pre_nms_topN,
+            score_threshold=-10000.,
+            keep_top_k=self.post_nms_topN,
+            nms_threshold=self.nms_thresh,
+            normalized=False if self.pixel_offset else True,
+            nms_eta=self.eta)
+        label, scores, proposal = paddle.tensor.split(
+            out, axis=1, num_or_sections=[1, 1, 4])
+        return scores, proposal
+
+    def forward(self):
+        anchors = self.input('Anchors', 0)
+        bboxdeltas = self.input('BboxDeltas', 0)
+        if self.type == 'generate_proposals_v2':
+            iminfo = self.input('ImShape', 0)
+        else:
+            iminfo = self.input('ImInfo', 0)
+        scores = self.input('Scores', 0)
+        variances = self.input('Variances', 0)
+
+        bboxdeltas = paddle.transpose(bboxdeltas, perm=[0, 2, 3, 1])
+        bboxdeltas = paddle.reshape(bboxdeltas, [-1, 4])
+        scores = paddle.transpose(scores, perm=[0, 2, 3, 1])
+        scores = paddle.reshape(scores, [-1, 1])
+        anchors = paddle.reshape(anchors, [-1, 4])
+        variances = paddle.reshape(variances, [-1, 4])
+
+        new_scores, proposals = self.proposal_for_single_sample(
+            anchors, bboxdeltas, iminfo, scores, variances)
+        if len(self.node.outputs) == 3:
+            rois_num = paddle.shape(new_scores)[0]
+            return {
+                'RpnRoiProbs': [new_scores],
+                'RpnRois': [proposals],
+                'RpnRoisNum': [rois_num]
+            }
+        else:
+            return {'RpnRoiProbs': [new_scores], 'RpnRois': [proposals]}
+
+
+register_custom_paddle_op('generate_proposals', GenerateProposals)
+register_custom_paddle_op('generate_proposals_v2', GenerateProposals)
diff --git a/paddle2onnx/legacy/op_mapper/custom_paddle_op/grid_sampler.py b/paddle2onnx/legacy/op_mapper/custom_paddle_op/grid_sampler.py
new file mode 100755
index 0000000000..4191cbb53b
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/custom_paddle_op/grid_sampler.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import paddle
+from paddle.fluid import layers
+from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+class GridSampler(CustomPaddleOp):
+    def __init__(self, node, **kw):
+        super(GridSampler, self).__init__(node)
+        self.padding_mode = node.attr('padding_mode')
+        self.mode = node.attr('mode')
+        self.align_corners = node.attr('align_corners')
+
+    def paddle_bilinear_grid_sample(self, im, grid, align_corners=False):
+        # this code reference: https://mmcv.readthedocs.io/en/latest/_modules/mmcv/ops/point_sample.html
+        im_shape = paddle.shape(im)
+        n, c, h, w = paddle.split(im_shape, num_or_sections=4)
+        grid_shape = paddle.shape(grid)
+        gn, gh, gw, _ = paddle.split(grid_shape, num_or_sections=4)
+
+        # n, c, h, w = im.shape
+        # gn, gh, gw, _ = grid.shape
+        # assert n == gn
+
+        x = grid[:, :, :, 0]
+        y = grid[:, :, :, 1]
+
+        if align_corners:
+            x = ((x + 1) / 2) * (w - 1)
+            y = ((y + 1) / 2) * (h - 1)
+        else:
+            x = ((x + 1) * w - 1) / 2
+            y = ((y + 1) * h - 1) / 2
+
+        x = paddle.reshape(x, [n, -1])
+        y = paddle.reshape(y, [n, -1])
+
+        x0 = paddle.floor(x).astype('int64')
+        y0 = paddle.floor(y).astype('int64')
+        x1 = x0 + 1
+        y1 = y0 + 1
+
+        x1_cast = x1.astype(grid.dtype)
+        x0_cast = x0.astype(grid.dtype)
+        y1_cast = y1.astype(grid.dtype)
+        y0_cast = y0.astype(grid.dtype)
+        wa = paddle.unsqueeze(((x1_cast - x) * (y1_cast - y)), 1)
+        wb = paddle.unsqueeze(((x1_cast - x) * (y - y0_cast)), 1)
+        wc = paddle.unsqueeze(((x - x0_cast) * (y1_cast - y)), 1)
+        wd = paddle.unsqueeze(((x - x0_cast) * (y - y0_cast)), 1)
+
+        # Apply default for grid_sample function zero padding
+        im_padded = paddle.nn.functional.pad(im,
+                                             pad=[1, 1, 1, 1],
+                                             mode='constant',
+                                             value=0)
+        if im_padded.dtype != im.dtype:
+            im_padded = paddle.cast(im_padded, im.dtype)
+        padded_h = h + 2
+        padded_w = w + 2
+        # save points positions after padding
+        x0, x1, y0, y1 = x0 + 1, x1 + 1, y0 + 1, y1 + 1
+
+        # Clip coordinates to padded image size
+        tensor_zero = paddle.full(shape=[1], dtype='int64', fill_value=0.0)
+        tensor_padded_w = paddle.full(
+            shape=[1], dtype='int64', fill_value=padded_w - 1)
+        tensor_padded_h = paddle.full(
+            shape=[1], dtype='int64', fill_value=padded_h - 1)
+        x0 = paddle.where(x0 < 0, tensor_zero, x0)
+        x0 = paddle.where(x0 > padded_w - 1, tensor_padded_w, x0)
+        x1 = paddle.where(x1 < 0, tensor_zero, x1)
+        x1 = paddle.where(x1 > padded_w - 1, tensor_padded_w, x1)
+        y0 = paddle.where(y0 < 0, tensor_zero, y0)
+        y0 = paddle.where(y0 > padded_h - 1, tensor_padded_h, y0)
+        y1 = paddle.where(y1 < 0, tensor_zero, y1)
+        y1 = paddle.where(y1 > padded_h - 1, tensor_padded_h, y1)
+        im_padded = paddle.reshape(im_padded, [n, c, -1])
+
+        x0_y0 = paddle.expand(
+            paddle.unsqueeze((x0 + y0 * padded_w), 1), [-1, c, -1])
+        x0_y1 = paddle.expand(
+            paddle.unsqueeze((x0 + y1 * padded_w), 1), [-1, c, -1])
+        x1_y0 = paddle.expand(
+            paddle.unsqueeze((x1 + y0 * padded_w), 1), [-1, c, -1])
+        x1_y1 = paddle.expand(
+            paddle.unsqueeze((x1 + y1 * padded_w), 1), [-1, c, -1])
+
+        Ia = self.paddle_gather(im_padded, 2, x0_y0)
+        Ib = self.paddle_gather(im_padded, 2, x0_y1)
+        Ic = self.paddle_gather(im_padded, 2, x1_y0)
+        Id = self.paddle_gather(im_padded, 2, x1_y1)
+
+        return paddle.reshape((Ia * wa + Ib * wb + Ic * wc + Id * wd),
+                              [n, c, gh, gw])
+
+    def paddle_gather(self, x, dim, index):
+        # index_shape = index.shape
+        index_shape = paddle.shape(index)
+        x_shape = paddle.shape(x)
+        index_flatten = index.flatten()
+        if dim < 0:
+            dim = len(x.shape) + dim
+        nd_index = []
+        for k in range(len(x.shape)):
+            if k == dim:
+                nd_index.append(index_flatten)
+            else:
+                reshape_shape = [1] * len(x.shape)
+                x_shape_k = x_shape[k]
+                # x_shape_k = x.shape[k]
+                reshape_shape[k] = x_shape_k
+                x_arange = paddle.arange(x_shape_k, dtype=index.dtype)
+                x_arange = x_arange.reshape(reshape_shape)
+                dim_index = paddle.expand(x_arange, index_shape).flatten()
+                nd_index.append(dim_index)
+        ind2 = paddle.transpose(paddle.stack(nd_index), [1, 0]).astype("int64")
+        paddle_out = paddle.gather_nd(x, ind2).reshape(index_shape)
+        return paddle_out
+
+    def forward(self):
+        input = self.input('X', 0)
+        grid = self.input('Grid', 0)
+        if self.mode != 'bilinear' or self.padding_mode != 'zeros':
+            raise Exception(
+                "grid_sample only is supported with mode should be 'bilinear' and padding_mode should be 'zeros'"
+            )
+        res = self.paddle_bilinear_grid_sample(
+            input, grid, align_corners=self.align_corners)
+        return {'Output': [res]}
+
+
+register_custom_paddle_op('grid_sampler', GridSampler)
diff --git a/paddle2onnx/legacy/op_mapper/detection/__init__.py b/paddle2onnx/legacy/op_mapper/detection/__init__.py
new file mode 100644
index 0000000000..847ddc47ac
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/detection/__init__.py
@@ -0,0 +1,13 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/paddle2onnx/legacy/op_mapper/detection/box_coder.py b/paddle2onnx/legacy/op_mapper/detection/box_coder.py
new file mode 100755
index 0000000000..6e8df722bc
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/detection/box_coder.py
@@ -0,0 +1,363 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+@op_mapper('box_coder')
+class BoxCoder():
+    """
+    we use the decode the prior box to target box,
+    we just use the decode mode to transform this op.
+    """
+    support_opset_verison_range = (7, 12)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        input_names = node.input_names
+
+        t_size = node.input_shape('TargetBox', 0)
+        p_size = node.input_shape('PriorBox', 0)
+
+        # get the outout_name
+        result_name = node.output('OutputBox', 0)
+        # n is size of batch, m is boxes num of targe_boxes
+        n = t_size[0]
+        m = t_size[0]
+
+        axis = int(node.attr('axis'))
+
+        #norm
+        norm = bool(node.attr('box_normalized'))
+
+        name_slice_x1 = node.output('OutputBox')[0] + "@x1"
+        name_slice_y1 = node.output('OutputBox')[0] + "@y1"
+        name_slice_x2 = node.output('OutputBox')[0] + "@x2"
+        name_slice_y2 = node.output('OutputBox')[0] + "@y2"
+
+        #make onnx tensor to save the intermeidate reslut
+        name_slice_indices = [
+            [node.output('OutputBox')[0] + "@slice_" + str(i)]
+            for i in range(1, 3)
+        ]
+        node_slice_indices = [None for i in range(1, 3)]
+
+        # create the range(0, 4) const data to slice
+        for i in range(1, 3):
+            tmp_node = graph.make_node(
+                'Constant',
+                inputs=[],
+                outputs=name_slice_indices[i - 1],
+                dtype=dtypes.ONNX.FLOAT,
+                dims=(),
+                value=[i])
+        # make node split data
+        name_box_split = [
+            name_slice_x1, name_slice_y1, name_slice_x2, name_slice_y2
+        ]
+        split_shape = list(p_size)
+        split_shape[-1] = 1
+
+        node_split_prior_node = graph.make_node(
+            'Split',
+            inputs=node.input('PriorBox'),
+            outputs=name_box_split,
+            axis=1)
+
+        # make node get centor node for decode
+        final_outputs_vars = []
+        if not norm:
+            name_centor_w_tmp = [node.output('OutputBox')[0] + "@centor_w_tmp"]
+            name_centor_h_tmp = [node.output('OutputBox')[0] + "@centor_h_tmp"]
+            node_centor_w_tmp = None
+            node_centor_h_tmp = None
+            name_centor_tmp_list = [name_centor_w_tmp, name_centor_h_tmp]
+            node_centor_tmp_list = [node_centor_w_tmp, node_centor_h_tmp]
+
+            count = 2
+            for (name, op_node) in zip(name_centor_tmp_list,
+                                       node_centor_tmp_list):
+                tmp_node = graph.make_node('Add',
+                       inputs=[node.output('OutputBox')[0] + "@slice_" + str(1)]\
+                           + [name_box_split[count]],
+                       outputs=name)
+                count = count + 1
+        if not norm:
+            inputs_sub = [[name_centor_w_tmp[0], name_box_split[0]],
+                          [name_centor_h_tmp[0], name_box_split[1]]]
+        else:
+            inputs_sub = [[name_box_split[2], name_box_split[0]],
+                          [name_box_split[3], name_box_split[1]]]
+        outputs_sub = [result_name + "@pb_w", result_name + "@pb_h"]
+        for i in range(0, 2):
+            tmp_node = graph.make_node(
+                'Sub', inputs=inputs_sub[i], outputs=[outputs_sub[i]])
+        # according to prior_box height and weight to get centor x, y
+        name_half_value = [result_name + "@half_value"]
+        node_half_value = graph.make_node(
+            'Constant',
+            inputs=[],
+            outputs=name_half_value,
+            dtype=dtypes.ONNX.FLOAT,
+            dims=(),
+            value=[0.5])
+        outputs_half_wh = [[result_name + "@pb_w_half"],
+                           [result_name + "@pb_h_half"]]
+        inputs_half_wh = [[result_name + "@pb_w", name_half_value[0]],
+                          [result_name + "@pb_h", name_half_value[0]]]
+
+        for i in range(0, 2):
+            tmp_node = graph.make_node(
+                'Mul', inputs=inputs_half_wh[i], outputs=outputs_half_wh[i])
+
+        inputs_centor_xy = [[outputs_half_wh[0][0], name_slice_x1],
+                            [outputs_half_wh[1][0], name_slice_y1]]
+
+        outputs_centor_xy = [[result_name + "@pb_x"], [result_name + "@pb_y"]]
+
+        # final calc the centor x ,y
+        for i in range(0, 2):
+            tmp_node = graph.make_node(
+                'Add', inputs=inputs_centor_xy[i], outputs=outputs_centor_xy[i])
+        # reshape the data
+        shape = (1, split_shape[0]) if axis == 0 else (split_shape[0], 1)
+
+        # need to reshape the data
+        inputs_transpose_pb = [
+            [result_name + "@pb_w"],
+            [result_name + "@pb_h"],
+            [result_name + "@pb_x"],
+            [result_name + "@pb_y"],
+        ]
+        outputs_transpose_pb = [
+            [result_name + "@pb_w_transpose"],
+            [result_name + "@pb_h_transpose"],
+            [result_name + "@pb_x_transpose"],
+            [result_name + "@pb_y_transpose"],
+        ]
+        if axis == 0:
+            name_reshape_pb = [result_name + "@pb_transpose"]
+            # reshape the data
+            for i in range(0, 4):
+                tmp_node = graph.make_node(
+                    'Transpose',
+                    inputs=inputs_transpose_pb[i],
+                    outputs=outputs_transpose_pb[i])
+        # decoder the box according to the target_box and variacne
+        name_variance_raw = [result_name + "@variance_raw"]
+        name_variance_unsqueeze = [result_name + "@variance_unsqueeze"]
+        shape = []
+        # make node to extend the data
+        var_split_axis = 0
+        var_split_inputs_name = []
+        if 'PriorBoxVar' in input_names and len(node.input('PriorBoxVar')) > 0:
+            if axis == 1:
+                raise Exception(
+                    "The op box_coder has variable do not support aixs broadcast"
+                )
+            axes = []
+            var_split_inputs_name = [result_name + "@variance_split"]
+            tmp_node = graph.make_node(
+                'Transpose',
+                inputs=node.input('PriorBoxVar'),
+                outputs=var_split_inputs_name)
+            var_split_axis = 0
+        else:
+            variances = [1.0, 1.0, 1.0, 1.0]
+            if 'variance' in node.attrs and len(node.attr('variance')) > 0:
+                variances = [float(var) for var in node.attr('variance')]
+            node_variance_create = graph.make_node(
+                'Constant',
+                inputs=[],
+                outputs=name_variance_raw,
+                dtype=dtypes.ONNX.FLOAT,
+                dims=[len(variances)],
+                value=variances)
+            var_split_axis = 0
+            var_split_inputs_name = name_variance_raw
+
+        # decode the result
+        outputs_split_variance = [
+            result_name + "@variance_split" + str(i) for i in range(0, 4)
+        ]
+        outputs_split_targebox = [
+            result_name + "@targebox_split" + str(i) for i in range(0, 4)
+        ]
+        node_split_var = graph.make_node(
+            'Split',
+            inputs=var_split_inputs_name,
+            outputs=outputs_split_variance,
+            axis=var_split_axis)
+        node_split_target = graph.make_node(
+            'Split',
+            inputs=node.input('TargetBox'),
+            outputs=outputs_split_targebox,
+            axis=2)
+
+        outputs_squeeze_targebox = [
+            result_name + "@targebox_squeeze" + str(i) for i in range(0, 4)
+        ]
+        for (input_name, output_name) in zip(outputs_split_targebox,
+                                             outputs_squeeze_targebox):
+            tmp_node = mapper_helper.squeeze_helper(graph, input_name, [2],
+                                                    [output_name])
+
+        output_shape_step1 = list(t_size)[:-1]
+
+        inputs_tb_step1 = [
+            [outputs_squeeze_targebox[0], outputs_split_variance[0]],
+            [outputs_squeeze_targebox[1], outputs_split_variance[1]],
+            [outputs_squeeze_targebox[2], outputs_split_variance[2]],
+            [outputs_squeeze_targebox[3], outputs_split_variance[3]]
+        ]
+        outputs_tb_step1 = [[result_name + "@decode_x_step1"],
+                            [result_name + "@decode_y_step1"],
+                            [result_name + "@decode_w_step1"],
+                            [result_name + "@decode_h_step1"]]
+
+        for input_step1, output_step_1 in zip(inputs_tb_step1,
+                                              outputs_tb_step1):
+            tmp_node = graph.make_node(
+                'Mul', inputs=input_step1, outputs=output_step_1)
+        if axis == 0:
+            inputs_tbxy_step2 = [[
+                outputs_tb_step1[0][0], outputs_transpose_pb[0][0]
+            ], [outputs_tb_step1[1][0], outputs_transpose_pb[1][0]]]
+        else:
+            inputs_tbxy_step2 = [[
+                outputs_tb_step1[0][0], inputs_transpose_pb[0][0]
+            ], [outputs_tb_step1[1][0], inputs_transpose_pb[1][0]]]
+
+        outputs_tbxy_step2 = [[result_name + "@decode_x_step2"],
+                              [result_name + "@decode_y_step2"]]
+
+        for input_step2, output_step_2 in zip(inputs_tbxy_step2,
+                                              outputs_tbxy_step2):
+            tmp_node = graph.make_node(
+                'Mul', inputs=input_step2, outputs=output_step_2)
+        if axis == 0:
+            inputs_tbxy_step3 = [[
+                outputs_tbxy_step2[0][0], outputs_transpose_pb[2][0]
+            ], [outputs_tbxy_step2[1][0], outputs_transpose_pb[3][0]]]
+        else:
+            inputs_tbxy_step3 = [[
+                outputs_tbxy_step2[0][0], inputs_transpose_pb[2][0]
+            ], [outputs_tbxy_step2[1][0], inputs_transpose_pb[3][0]]]
+
+        outputs_tbxy_step3 = [[result_name + "@decode_x_step3"],
+                              [result_name + "@decode_y_step3"]]
+
+        for input_step3, output_step_3 in zip(inputs_tbxy_step3,
+                                              outputs_tbxy_step3):
+            tmp_node = graph.make_node(
+                'Add', inputs=input_step3, outputs=output_step_3)
+
+        # deal with width & height
+        inputs_tbwh_step2 = [outputs_tb_step1[2], outputs_tb_step1[3]]
+        outputs_tbwh_step2 = [[result_name + "@decode_w_step2"],
+                              [result_name + "@decode_h_step2"]]
+
+        for input_name, output_name in zip(inputs_tbwh_step2,
+                                           outputs_tbwh_step2):
+            tmp_node = graph.make_node(
+                'Exp', inputs=input_name, outputs=output_name)
+
+        if axis == 0:
+            inputs_tbwh_step3 = [[
+                outputs_tbwh_step2[0][0], outputs_transpose_pb[0][0]
+            ], [outputs_tbwh_step2[1][0], outputs_transpose_pb[1][0]]]
+        else:
+            inputs_tbwh_step3 = [[
+                outputs_tbwh_step2[0][0], inputs_transpose_pb[0][0]
+            ], [outputs_tbwh_step2[1][0], inputs_transpose_pb[1][0]]]
+
+        outputs_tbwh_step3 = [[result_name + "@decode_w_step3"],
+                              [result_name + "@decode_h_step3"]]
+
+        for input_name, output_name in zip(inputs_tbwh_step3,
+                                           outputs_tbwh_step3):
+            tmp_node = graph.make_node(
+                'Mul', inputs=input_name, outputs=output_name)
+
+        # final step to calc the result, and concat the result to output
+        # return the output box, [(x1, y1), (x2, y2)]
+
+        inputs_half_tbwh_step4 = [[
+            outputs_tbwh_step3[0][0], result_name + "@slice_2"
+        ], [outputs_tbwh_step3[1][0], result_name + "@slice_2"]]
+
+        outputs_half_tbwh_step4 = [[result_name + "@decode_half_w_step4"],
+                                   [result_name + "@decode_half_h_step4"]]
+        for inputs_name, outputs_name in zip(inputs_half_tbwh_step4,
+                                             outputs_half_tbwh_step4):
+            tmp_node = graph.make_node(
+                'Div', inputs=inputs_name, outputs=outputs_name)
+        inputs_output_point1 = [[
+            outputs_tbxy_step3[0][0], outputs_half_tbwh_step4[0][0]
+        ], [outputs_tbxy_step3[1][0], outputs_half_tbwh_step4[1][0]]]
+
+        outputs_output_point1 = [[result_name + "@ouput_x1"],
+                                 [result_name + "@output_y1"]]
+        for input_name, output_name in zip(inputs_output_point1,
+                                           outputs_output_point1):
+            tmp_node = graph.make_node(
+                'Sub', inputs=input_name, outputs=output_name)
+
+        inputs_output_point2 = [[
+            outputs_tbxy_step3[0][0], outputs_half_tbwh_step4[0][0]
+        ], [outputs_tbxy_step3[1][0], outputs_half_tbwh_step4[1][0]]]
+
+        outputs_output_point2 = [[result_name + "@ouput_x2"],
+                                 [result_name + "@output_y2"]]
+
+        for input_name, output_name in zip(inputs_output_point2,
+                                           outputs_output_point2):
+            tmp_node = graph.make_node(
+                'Add', inputs=input_name, outputs=output_name)
+        if not norm:
+            inputs_unnorm_point2 = [[
+                outputs_output_point2[0][0], result_name + "@slice_1"
+            ], [outputs_output_point2[1][0], result_name + "@slice_1"]]
+            outputs_unnorm_point2 = [[result_name + "@ouput_unnorm_x2"],
+                                     [result_name + "@ouput_unnorm_y2"]]
+
+            for input_name, output_name in zip(inputs_unnorm_point2,
+                                               outputs_unnorm_point2):
+                tmp_node = graph.make_node(
+                    'Sub', inputs=input_name, outputs=output_name)
+            outputs_output_point2 = outputs_unnorm_point2
+
+        outputs_output_point1.extend(outputs_output_point2)
+        ouputs_points_unsqueeze = [[result_name + "@points_unsqueeze_x1"],
+                                   [result_name + "points_unsqueeze_y1"],
+                                   [result_name + "points_unsqueeze_x2"],
+                                   [result_name + "points_unsqueeze_y2"]]
+
+        for input_name, output_name in zip(outputs_output_point1,
+                                           ouputs_points_unsqueeze):
+            tmp_node = mapper_helper.unsqueeze_helper(
+                graph, input_name, [len(output_shape_step1)], output_name)
+        outputs_points_unsqueeze_list = [
+            output[0] for output in ouputs_points_unsqueeze
+        ]
+        node_point_final = graph.make_node(
+            'Concat',
+            inputs=outputs_points_unsqueeze_list,
+            outputs=node.output('OutputBox'),
+            axis=len(output_shape_step1))
diff --git a/paddle2onnx/legacy/op_mapper/detection/density_prior_box.py b/paddle2onnx/legacy/op_mapper/detection/density_prior_box.py
new file mode 100644
index 0000000000..f9ecc61b11
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/detection/density_prior_box.py
@@ -0,0 +1,125 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import math
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.utils import require_fixed_shape
+
+
+@op_mapper('density_prior_box')
+class DensityPriorBox():
+    """
+    In this function, use the attribute to get the prior box, because we do not use
+    the image data and feature map, wo could the python code to create the varaible,
+    and to create the onnx tensor as output.
+    """
+    support_opset_verison_range = (1, 12)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        clip = bool(node.attr('clip'))
+        densities = node.attr('densities')
+        fixed_ratios = node.attr('fixed_ratios')
+        fixed_sizes = node.attr('fixed_sizes')
+        flatten_to_2d = bool(node.attr('flatten_to_2d'))
+        offset = node.attr('offset')
+        step_h = node.attr('step_h')
+        step_w = node.attr('step_w')
+        variances = node.attr('variances')
+
+        input_shape = node.input_shape('Input', 0)
+        image_shape = node.input_shape('Image', 0)
+
+        img_width = image_shape[3]
+        img_height = image_shape[2]
+        feature_width = input_shape[3]
+        feature_height = input_shape[2]
+
+        assert img_width > 0 and img_height > 0, require_fixed_shape(
+            cls.__name__)
+
+        if step_w == 0.0 or step_h == 0.0:
+            step_w = float(img_width / feature_width)
+            step_h = float(img_height / feature_height)
+
+        num_priors = 0
+        if len(fixed_sizes) > 0 and len(densities) > 0:
+            for density in densities:
+                if len(fixed_ratios) > 0:
+                    num_priors += len(fixed_ratios) * (pow(density, 2))
+
+        out_dim = (feature_height, feature_width, num_priors, 4)
+        out_boxes = np.zeros(out_dim).astype('float32')
+        out_var = np.zeros(out_dim).astype('float32')
+        step_average = int((step_w + step_h) * 0.5)
+
+        for h in range(feature_height):
+            for w in range(feature_width):
+                c_x = (w + offset) * step_w
+                c_y = (h + offset) * step_h
+                idx = 0
+
+                for density, fixed_size in zip(densities, fixed_sizes):
+                    if (len(fixed_ratios) > 0):
+                        for ar in fixed_ratios:
+                            shift = int(step_average / density)
+                            box_width_ratio = fixed_size * math.sqrt(ar)
+                            box_height_ratio = fixed_size / math.sqrt(ar)
+                            for di in range(density):
+                                for dj in range(density):
+                                    c_x_temp = c_x - step_average / 2.0 + shift / 2.0 + dj * shift
+                                    c_y_temp = c_y - step_average / 2.0 + shift / 2.0 + di * shift
+                                    out_boxes[h, w, idx, :] = [
+                                        max((c_x_temp - box_width_ratio / 2.0) /
+                                            img_width, 0),
+                                        max((c_y_temp - box_height_ratio / 2.0)
+                                            / img_height, 0),
+                                        min((c_x_temp + box_width_ratio / 2.0) /
+                                            img_width, 1),
+                                        min((c_y_temp + box_height_ratio / 2.0)
+                                            / img_height, 1)
+                                    ]
+                                    idx += 1
+
+        if clip:
+            out_boxes = np.clip(out_boxes, 0.0, 1.0)
+        # set the variance.
+        out_var = np.tile(variances,
+                          (feature_height, feature_width, num_priors, 1))
+
+        if flatten_to_2d:
+            out_boxes = out_boxes.reshape((-1, 4))
+            out_var = out_var.reshape((-1, 4))
+
+        #make node that
+
+        node_boxes = graph.make_node(
+            'Constant',
+            inputs=[],
+            outputs=node.output('Boxes'),
+            dtype=dtypes.ONNX.FLOAT,
+            dims=out_boxes.shape,
+            value=out_boxes.flatten().tolist())
+
+        node_vars = graph.make_node(
+            'Constant',
+            inputs=[],
+            outputs=node.output('Variances'),
+            dtype=dtypes.ONNX.FLOAT,
+            dims=out_var.shape,
+            value=out_var.flatten().tolist())
diff --git a/paddle2onnx/legacy/op_mapper/detection/multiclass_nms.py b/paddle2onnx/legacy/op_mapper/detection/multiclass_nms.py
new file mode 100755
index 0000000000..9e57ff8fc5
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/detection/multiclass_nms.py
@@ -0,0 +1,343 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+@op_mapper(
+    ['multiclass_nms', 'multiclass_nms2', 'matrix_nms', 'multiclass_nms3'])
+class MultiClassNMS():
+    support_opset_verision_range = (10, 16)
+    """
+    Convert the paddle multiclass_nms to onnx op.
+    This op is get the select boxes from origin boxes.
+    """
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        if node.input_shape("BBoxes", 0)[0] != 1:
+            logging.warning(
+                "Due to the operator:{}, the converted ONNX model will only supports input[batch_size] == 1.".
+                format(node.type))
+        scores = node.input('Scores', 0)
+        bboxes = node.input('BBoxes', 0)
+        num_class = node.input_shape('Scores', 0)[1]
+        if len(node.input_shape('Scores', 0)) == 2:
+            # inputs: scores & bboxes is lod tensor
+            scores = graph.make_node('Transpose', inputs=[scores], perm=[1, 0])
+            scores = mapper_helper.unsqueeze_helper(graph, scores, [0])
+            if graph.opset_version < 13:
+                scores_list = graph.make_node(
+                    'Split',
+                    inputs=scores,
+                    outputs=num_class,
+                    axis=1,
+                    split=[1] * num_class)
+            else:
+                split_const = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[1] * num_class)
+                scores_list = graph.make_node(
+                    "Split",
+                    inputs=[scores] + [split_const],
+                    outputs=num_class,
+                    axis=1)
+
+            bboxes = graph.make_node('Transpose', inputs=bboxes, perm=[1, 0, 2])
+            if graph.opset_version < 13:
+                bboxes_list = graph.make_node(
+                    'Split',
+                    inputs=bboxes,
+                    outputs=num_class,
+                    axis=0,
+                    split=[1] * num_class)
+            else:
+                split_const = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[1] * num_class)
+                bboxes_list = graph.make_node(
+                    "Split",
+                    inputs=[bboxes] + [split_const],
+                    outputs=num_class,
+                    axis=0)
+            bbox_ids = []
+            if not isinstance(scores_list, list):
+                scores_list = [scores_list]
+            if not isinstance(bboxes_list, list):
+                bboxes_list = [bboxes_list]
+            for i in range(num_class):
+                bbox_id = cls.nms(graph,
+                                  node,
+                                  scores_list[i],
+                                  bboxes_list[i],
+                                  class_id=i)
+                bbox_ids.append(bbox_id)
+            bbox_ids = graph.make_node('Concat', inputs=bbox_ids, axis=0)
+            const_shape = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=[1, -1, 4])
+            bboxes = graph.make_node('Reshape', inputs=[bboxes, const_shape])
+            cls.keep_top_k(
+                graph, node, bbox_ids, scores, bboxes, is_lod_input=True)
+        else:
+            bbox_ids = cls.nms(graph, node, scores, bboxes)
+            cls.keep_top_k(graph, node, bbox_ids, scores, bboxes)
+
+    @classmethod
+    def nms(cls, graph, node, scores, bboxes, class_id=None):
+        normalized = node.attr('normalized')
+        nms_top_k = node.attr('nms_top_k')
+        if node.type == 'matrix_nms':
+            iou_threshold = 0.5
+            logging.warning(
+                "Operator:{} is not supported completely, so we use traditional"
+                " NMS (nms_theshold={}) to instead it, which introduce some difference.".
+                format(node.type, str(iou_threshold)))
+        else:
+            iou_threshold = node.attr('nms_threshold')
+        if nms_top_k == -1:
+            nms_top_k = 100000
+
+        #convert the paddle attribute to onnx tensor
+        score_threshold = graph.make_node(
+            'Constant',
+            dtype=dtypes.ONNX.FLOAT,
+            value=[float(node.attr('score_threshold'))])
+        iou_threshold = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.FLOAT, value=[float(iou_threshold)])
+        nms_top_k = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[np.int64(nms_top_k)])
+
+        # the paddle data format is x1,y1,x2,y2
+        kwargs = {'center_point_box': 0}
+
+        if normalized:
+            select_bbox_indices = graph.make_node(
+                'NonMaxSuppression',
+                inputs=[
+                    bboxes, scores, nms_top_k, iou_threshold, score_threshold
+                ])
+        elif not normalized:
+            value_one = graph.make_node(
+                'Constant', dims=[1], dtype=dtypes.ONNX.FLOAT, value=1.0)
+            if graph.opset_version < 13:
+                new_bboxes = graph.make_node(
+                    'Split',
+                    inputs=[bboxes],
+                    outputs=4,
+                    axis=2,
+                    split=[1, 1, 1, 1])
+            else:
+                split_const = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[1, 1, 1, 1])
+                new_bboxes = graph.make_node(
+                    "Split", inputs=[bboxes] + [split_const], outputs=4, axis=2)
+            new_xmax = graph.make_node('Add', inputs=[new_bboxes[2], value_one])
+            new_ymax = graph.make_node('Add', inputs=[new_bboxes[3], value_one])
+            new_bboxes = graph.make_node(
+                'Concat',
+                inputs=[new_bboxes[0], new_bboxes[1], new_xmax, new_ymax],
+                axis=2)
+            select_bbox_indices = graph.make_node(
+                'NonMaxSuppression',
+                inputs=[
+                    new_bboxes, scores, nms_top_k, iou_threshold,
+                    score_threshold
+                ])
+
+        if class_id is not None and class_id != 0:
+            class_id = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=[0, class_id, 0])
+            class_id = mapper_helper.unsqueeze_helper(graph, class_id, [0])
+            select_bbox_indices = graph.make_node(
+                'Add', inputs=[select_bbox_indices, class_id])
+
+        return select_bbox_indices
+
+    @classmethod
+    def keep_top_k(cls,
+                   graph,
+                   node,
+                   select_bbox_indices,
+                   scores,
+                   bboxes,
+                   is_lod_input=False):
+        # step 1 nodes select the nms class
+        # create some const value to use
+        background = node.attr('background_label')
+        const_values = []
+        for value in [0, 1, 2, -1]:
+            const_value = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=[value])
+            const_values.append(const_value)
+
+        # In this code block, we will deocde the raw score data, reshape N * C * M to 1 * N*C*M
+        # and the same time, decode the select indices to 1 * D, gather the select_indices
+        class_id = graph.make_node(
+            'Gather', inputs=[select_bbox_indices, const_values[1]], axis=1)
+
+        squeezed_class_id = mapper_helper.squeeze_helper(graph, class_id, [1])
+
+        bbox_id = graph.make_node(
+            'Gather', inputs=[select_bbox_indices, const_values[2]], axis=1)
+
+        if background == 0:
+            nonzero = graph.make_node('NonZero', inputs=[squeezed_class_id])
+        else:
+            filter_cls_id = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT32, value=[background])
+            cast = graph.make_node(
+                'Cast', inputs=[squeezed_class_id], to=dtypes.ONNX.INT32)
+            filter_index = graph.make_node('Sub', inputs=[cast, filter_cls_id])
+            nonzero = graph.make_node('NonZero', inputs=[filter_index])
+
+        class_id = graph.make_node('Gather', inputs=[class_id, nonzero], axis=0)
+        class_id = graph.make_node(
+            'Cast', inputs=[class_id], to=dtypes.ONNX.INT64)
+
+        bbox_id = graph.make_node('Gather', inputs=[bbox_id, nonzero], axis=0)
+        bbox_id = graph.make_node(
+            'Cast', inputs=[bbox_id], to=dtypes.ONNX.INT64)
+
+        # get the shape of scores
+        shape_scores = graph.make_node('Shape', inputs=scores)
+
+        # gather the index: 2 shape of scores
+        class_num = graph.make_node(
+            'Gather', inputs=[shape_scores, const_values[2]], axis=0)
+
+        # reshape scores N * C * M to (N*C*M) * 1
+        scores = graph.make_node('Reshape', inputs=[scores, const_values[-1]])
+
+        # mul class * M
+        mul_classnum_boxnum = graph.make_node(
+            'Mul', inputs=[class_id, class_num])
+
+        # add class * M * index
+        add_class_indices = graph.make_node(
+            'Add', inputs=[mul_classnum_boxnum, bbox_id])
+
+        # Squeeze the indices to 1 dim
+        score_indices = mapper_helper.squeeze_helper(graph, add_class_indices,
+                                                     [0, 2])
+
+        # gather the data from flatten scores
+        scores = graph.make_node(
+            'Gather', inputs=[scores, score_indices], axis=0)
+
+        keep_top_k = node.attr('keep_top_k')
+        keep_top_k = graph.make_node(
+            'Constant',
+            dtype=dtypes.ONNX.INT64,
+            dims=[1, 1],
+            value=[node.attr('keep_top_k')])
+
+        # get min(topK, num_select)
+        shape_select_num = graph.make_node('Shape', inputs=[scores])
+        const_zero = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[0])
+        gather_select_num = graph.make_node(
+            'Gather', inputs=[shape_select_num, const_zero], axis=0)
+        unsqueeze_select_num = mapper_helper.unsqueeze_helper(
+            graph, gather_select_num, [0])
+
+        concat_topK_select_num = graph.make_node(
+            'Concat', inputs=[unsqueeze_select_num, keep_top_k], axis=0)
+        cast_concat_topK_select_num = graph.make_node(
+            'Cast', inputs=[concat_topK_select_num], to=6)
+        keep_top_k = graph.make_node(
+            'ReduceMin', inputs=[cast_concat_topK_select_num], keepdims=0)
+        # unsqueeze the indices to 1D tensor
+        keep_top_k = mapper_helper.unsqueeze_helper(graph, keep_top_k, [0])
+
+        # cast the indices to INT64
+        keep_top_k = graph.make_node('Cast', inputs=[keep_top_k], to=7)
+
+        # select topk scores  indices
+        keep_topk_scores, keep_topk_indices = graph.make_node(
+            'TopK', inputs=[scores, keep_top_k], outputs=2)
+
+        # gather topk label, scores, boxes
+        gather_topk_scores = graph.make_node(
+            'Gather', inputs=[scores, keep_topk_indices], axis=0)
+
+        gather_topk_class = graph.make_node(
+            'Gather', inputs=[class_id, keep_topk_indices], axis=1)
+
+        # gather the boxes need to gather the boxes id, then get boxes
+        if is_lod_input:
+            gather_topk_boxes_id = graph.make_node(
+                'Gather', [add_class_indices, keep_topk_indices], axis=1)
+        else:
+            gather_topk_boxes_id = graph.make_node(
+                'Gather', [bbox_id, keep_topk_indices], axis=1)
+
+        # squeeze the gather_topk_boxes_id to 1 dim
+        squeeze_topk_boxes_id = mapper_helper.squeeze_helper(
+            graph, gather_topk_boxes_id, [0, 2])
+
+        gather_select_boxes = graph.make_node(
+            'Gather', inputs=[bboxes, squeeze_topk_boxes_id], axis=1)
+
+        # concat the final result
+        # before concat need to cast the class to float
+        cast_topk_class = graph.make_node(
+            'Cast', inputs=[gather_topk_class], to=1)
+
+        unsqueeze_topk_scores = mapper_helper.unsqueeze_helper(
+            graph, gather_topk_scores, [0, 2])
+
+        inputs_concat_final_results = [
+            cast_topk_class, unsqueeze_topk_scores, gather_select_boxes
+        ]
+
+        sort_by_socre_results = graph.make_node(
+            'Concat', inputs=inputs_concat_final_results, axis=2)
+
+        # sort by class_id
+        squeeze_cast_topk_class = mapper_helper.squeeze_helper(
+            graph, cast_topk_class, [0, 2])
+
+        neg_squeeze_cast_topk_class = graph.make_node(
+            'Neg', inputs=[squeeze_cast_topk_class])
+
+        data, indices = graph.make_node(
+            'TopK', inputs=[neg_squeeze_cast_topk_class, keep_top_k], outputs=2)
+
+        concat_final_results = graph.make_node(
+            'Gather', inputs=[sort_by_socre_results, indices], axis=1)
+
+        concat_final_results = mapper_helper.squeeze_helper(
+            graph, concat_final_results, [0], node.output('Out'))
+
+        if node.type in ['multiclass_nms2', 'matrix_nms', 'multiclass_nms3']:
+            final_indices = mapper_helper.squeeze_helper(graph, bbox_id, [0],
+                                                         node.output('Index'))
+            if node.type in ['matrix_nms', 'multiclass_nms3']:
+                select_bboxes_shape = graph.make_node('Shape', inputs=[indices])
+                select_bboxes_shape1 = graph.make_node(
+                    'Cast', inputs=[select_bboxes_shape], to=dtypes.ONNX.INT32)
+                indices = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[0])
+                rois_num = None
+                if 'NmsRoisNum' in node.outputs:
+                    rois_num = node.output('NmsRoisNum')
+                elif 'RoisNum' in node.outputs:
+                    rois_num = node.output('RoisNum')
+                if rois_num is not None:
+                    graph.make_node(
+                        "Gather",
+                        inputs=[select_bboxes_shape1, indices],
+                        outputs=rois_num)
diff --git a/paddle2onnx/legacy/op_mapper/detection/prior_box.py b/paddle2onnx/legacy/op_mapper/detection/prior_box.py
new file mode 100644
index 0000000000..82cb404527
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/detection/prior_box.py
@@ -0,0 +1,176 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import math
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.utils import require_fixed_shape
+
+
+def expand_aspect_rations(input_aspect_ratior, flip):
+    expsilon = 1e-6
+    output_ratios = [1.0]
+    for input_ratio in input_aspect_ratior:
+        already_exis = False
+        for output_ratio in output_ratios:
+            if abs(input_ratio - output_ratio) < expsilon:
+                already_exis = True
+                break
+        if already_exis == False:
+            output_ratios.append(input_ratio)
+            if flip:
+                output_ratios.append(1.0 / input_ratio)
+    return output_ratios
+
+
+@op_mapper('prior_box')
+class PriorBox():
+    """
+    In this function, use the attribute to get the prior box, because we do not use
+    the image data and feature map, wo could the python code to create the varaible,
+    and to create the onnx tensor as output.
+    """
+    support_opset_verison_range = (1, 12)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        flip = bool(node.attr('flip'))
+        clip = bool(node.attr('clip'))
+        min_max_aspect_ratios_order = bool(
+            node.attr('min_max_aspect_ratios_order'))
+        min_sizes = [float(size) for size in node.attr('min_sizes')]
+        max_sizes = [float(size) for size in node.attr('max_sizes')]
+        if isinstance(node.attr('aspect_ratios'), list):
+            aspect_ratios = [
+                float(ratio) for ratio in node.attr('aspect_ratios')
+            ]
+        else:
+            aspect_ratios = [float(node.attr('aspect_ratios'))]
+        variances = [float(var) for var in node.attr('variances')]
+        # set min_max_aspect_ratios_order = false
+        output_ratios = expand_aspect_rations(aspect_ratios, flip)
+
+        step_w = float(node.attr('step_w'))
+        step_h = float(node.attr('step_h'))
+        offset = float(node.attr('offset'))
+
+        input_shape = node.input_shape('Input', 0)
+        image_shape = node.input_shape('Image', 0)
+
+        img_width = image_shape[3]
+        img_height = image_shape[2]
+        feature_width = input_shape[3]
+        feature_height = input_shape[2]
+        assert img_width > 0 and img_height > 0, require_fixed_shape(
+            cls.__name__)
+
+        step_width = 1.0
+        step_height = 1.0
+
+        if step_w == 0.0 or step_h == 0.0:
+            step_w = float(img_width / feature_width)
+            step_h = float(img_height / feature_height)
+
+        num_priors = len(output_ratios) * len(min_sizes)
+        if len(max_sizes) > 0:
+            num_priors += len(max_sizes)
+        out_dim = (feature_height, feature_width, num_priors, 4)
+        out_boxes = np.zeros(out_dim).astype('float32')
+        out_var = np.zeros(out_dim).astype('float32')
+
+        idx = 0
+        for h in range(feature_height):
+            for w in range(feature_width):
+                c_x = (w + offset) * step_w
+                c_y = (h + offset) * step_h
+                idx = 0
+                for s in range(len(min_sizes)):
+                    min_size = min_sizes[s]
+                    if not min_max_aspect_ratios_order:
+                        # rest of priors
+                        for r in range(len(output_ratios)):
+                            ar = output_ratios[r]
+                            c_w = min_size * math.sqrt(ar) / 2
+                            c_h = (min_size / math.sqrt(ar)) / 2
+                            out_boxes[h, w, idx, :] = [(c_x - c_w) / img_width,
+                                                       (c_y - c_h) / img_height,
+                                                       (c_x + c_w) / img_width,
+                                                       (c_y + c_h) / img_height]
+                            idx += 1
+
+                        if len(max_sizes) > 0:
+                            max_size = max_sizes[s]
+                            # second prior: aspect_ratio = 1,
+                            c_w = c_h = math.sqrt(min_size * max_size) / 2
+                            out_boxes[h, w, idx, :] = [(c_x - c_w) / img_width,
+                                                       (c_y - c_h) / img_height,
+                                                       (c_x + c_w) / img_width,
+                                                       (c_y + c_h) / img_height]
+                            idx += 1
+                    else:
+                        c_w = c_h = min_size / 2.
+                        out_boxes[h, w, idx, :] = [
+                            (c_x - c_w) / img_width, (c_y - c_h) / img_height,
+                            (c_x + c_w) / img_width, (c_y + c_h) / img_height
+                        ]
+                        idx += 1
+                        if len(max_sizes) > 0:
+                            max_size = max_sizes[s]
+                            # second prior: aspect_ratio = 1,
+                            c_w = c_h = math.sqrt(min_size * max_size) / 2
+                            out_boxes[h, w, idx, :] = [(c_x - c_w) / img_width,
+                                                       (c_y - c_h) / img_height,
+                                                       (c_x + c_w) / img_width,
+                                                       (c_y + c_h) / img_height]
+                            idx += 1
+
+                        # rest of priors
+                        for r in range(len(output_ratios)):
+                            ar = output_ratios[r]
+                            if abs(ar - 1.) < 1e-6:
+                                continue
+                            c_w = min_size * math.sqrt(ar) / 2
+                            c_h = (min_size / math.sqrt(ar)) / 2
+                            out_boxes[h, w, idx, :] = [(c_x - c_w) / img_width,
+                                                       (c_y - c_h) / img_height,
+                                                       (c_x + c_w) / img_width,
+                                                       (c_y + c_h) / img_height]
+                            idx += 1
+
+        if clip:
+            out_boxes = np.clip(out_boxes, 0.0, 1.0)
+        # set the variance.
+        out_var = np.tile(variances,
+                          (feature_height, feature_width, num_priors, 1))
+
+        #make node that
+
+        node_boxes = graph.make_node(
+            'Constant',
+            inputs=[],
+            outputs=node.output('Boxes'),
+            dtype=dtypes.ONNX.FLOAT,
+            dims=out_boxes.shape,
+            value=out_boxes.flatten().tolist())
+
+        node_vars = graph.make_node(
+            'Constant',
+            inputs=[],
+            outputs=node.output('Variances'),
+            dtype=dtypes.ONNX.FLOAT,
+            dims=out_var.shape,
+            value=out_var.flatten().tolist())
diff --git a/paddle2onnx/legacy/op_mapper/detection/yolo_box.py b/paddle2onnx/legacy/op_mapper/detection/yolo_box.py
new file mode 100755
index 0000000000..023dabc81e
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/detection/yolo_box.py
@@ -0,0 +1,506 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import sys
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+from onnx import TensorProto
+import paddle
+
+MAX_FLOAT32 = 3.402823466E+38
+
+
+@op_mapper('yolo_box')
+class YOLOBox():
+    support_opset_verison_range = (9, 12)
+
+    node_pred_box_x1_decode = None
+    node_pred_box_y1_decode = None
+    node_pred_box_x2_decode = None
+    node_pred_box_y2_decode = None
+    node_pred_box_x2_sub_w = None
+    node_pred_box_y2_sub_h = None
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        model_name = node.output('Boxes', 0)
+        input_shape = node.input_shape('X', 0)
+        mapper_helper.is_static_shape(input_shape)
+        image_size = node.input('ImgSize')
+        input_height = input_shape[2]
+        input_width = input_shape[3]
+        class_num = node.attr('class_num')
+        anchors = node.attr('anchors')
+        num_anchors = int(len(anchors)) // 2
+        scale_x_y = node.attr('scale_x_y')
+        downsample_ratio = node.attr('downsample_ratio')
+        input_size = input_height * downsample_ratio
+        conf_thresh = node.attr('conf_thresh')
+        conf_thresh_mat = [conf_thresh
+                           ] * num_anchors * input_height * input_width
+
+        cls.score_shape = [
+            1, input_height * input_width * int(num_anchors), class_num
+        ]
+
+        im_outputs = []
+
+        x_shape = [1, num_anchors, 5 + class_num, input_height, input_width]
+        node_x_shape = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                               'value': x_shape})
+
+        node_x_reshape = graph.make_node(
+            'Reshape', inputs=[node.input('X')[0], node_x_shape])
+        node_x_transpose = graph.make_node(
+            'Transpose', inputs=[node_x_reshape], perm=[0, 1, 3, 4, 2])
+
+        range_x = []
+        range_y = []
+        for i in range(0, input_width):
+            range_x.append(i)
+        for j in range(0, input_height):
+            range_y.append(j)
+
+        node_range_x = graph.make_node(
+            'Constant',
+            attrs={
+                'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+                'value': range_x,
+            })
+
+        node_range_y = graph.make_node(
+            'Constant',
+            inputs=[],
+            attrs={
+                'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+                'value': range_y,
+            })
+
+        range_x_new_shape = [1, input_width]
+        range_y_new_shape = [input_height, 1]
+
+        node_range_x_new_shape = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.ONNX.INT64,
+            value=range_x_new_shape)
+        node_range_y_new_shape = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.ONNX.INT64,
+            value=range_y_new_shape)
+
+        node_range_x_reshape = graph.make_node(
+            'Reshape', inputs=[node_range_x, node_range_x_new_shape])
+        node_range_y_reshape = graph.make_node(
+            'Reshape', inputs=[node_range_y, node_range_y_new_shape])
+
+        node_grid_x = graph.make_node(
+            "Tile", inputs=[node_range_x_reshape, node_range_y_new_shape])
+
+        node_grid_y = graph.make_node(
+            "Tile", inputs=[node_range_y_reshape, node_range_x_new_shape])
+
+        node_box_x = model_name + "@box_x"
+        node_box_y = model_name + "@box_y"
+        node_box_w = model_name + "@box_w"
+        node_box_h = model_name + "@box_h"
+        node_conf = model_name + "@conf"
+        node_prob = model_name + "@prob"
+        output = [
+            node_box_x, node_box_y, node_box_w, node_box_h, node_conf, node_prob
+        ]
+        node_split_input = mapper_helper.split_helper(
+            graph, [node_x_transpose],
+            output,
+            -1, [1, 1, 1, 1, 1, class_num],
+            dtype=node.input_dtype('X', 0))
+
+        node_box_x_sigmoid = graph.make_node("Sigmoid", inputs=[node_box_x])
+
+        node_box_y_sigmoid = graph.make_node("Sigmoid", inputs=[node_box_y])
+
+        if scale_x_y is not None:
+            bias_x_y = -0.5 * (scale_x_y - 1.0)
+            scale_x_y_node = graph.make_node(
+                'Constant',
+                attrs={
+                    'dtype':
+                    dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+                    'value': scale_x_y
+                })
+
+            bias_x_y_node = graph.make_node(
+                'Constant',
+                attrs={
+                    'dtype':
+                    dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+                    'value': bias_x_y
+                })
+            node_box_x_sigmoid = graph.make_node(
+                "Mul", inputs=[node_box_x_sigmoid, scale_x_y_node])
+            node_box_x_sigmoid = graph.make_node(
+                "Add", inputs=[node_box_x_sigmoid, bias_x_y_node])
+            node_box_y_sigmoid = graph.make_node(
+                "Mul", inputs=[node_box_y_sigmoid, scale_x_y_node])
+            node_box_y_sigmoid = graph.make_node(
+                "Add", inputs=[node_box_y_sigmoid, bias_x_y_node])
+        node_box_x_squeeze = mapper_helper.squeeze_helper(
+            graph, node_box_x_sigmoid, [4])
+
+        node_box_y_squeeze = mapper_helper.squeeze_helper(
+            graph, node_box_y_sigmoid, [4])
+
+        node_box_x_add_grid = graph.make_node(
+            "Add", inputs=[node_grid_x, node_box_x_squeeze])
+
+        node_box_y_add_grid = graph.make_node(
+            "Add", inputs=[node_grid_y, node_box_y_squeeze])
+
+        node_input_h = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=[input_height])
+
+        node_input_w = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=[input_width])
+
+        node_box_x_encode = graph.make_node(
+            'Div', inputs=[node_box_x_add_grid, node_input_w])
+
+        node_box_y_encode = graph.make_node(
+            'Div', inputs=[node_box_y_add_grid, node_input_h])
+
+        node_anchor_tensor = graph.make_node(
+            "Constant",
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=anchors)
+
+        anchor_shape = [int(num_anchors), 2]
+        node_anchor_shape = graph.make_node(
+            "Constant", inputs=[], dtype=dtypes.ONNX.INT64, value=anchor_shape)
+
+        node_anchor_tensor_reshape = graph.make_node(
+            "Reshape", inputs=[node_anchor_tensor, node_anchor_shape])
+
+        node_input_size = graph.make_node(
+            "Constant",
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=[input_size])
+
+        node_anchors_div_input_size = graph.make_node(
+            "Div", inputs=[node_anchor_tensor_reshape, node_input_size])
+
+        node_anchor_w = model_name + "@anchor_w"
+        node_anchor_h = model_name + "@anchor_h"
+
+        node_anchor_split = mapper_helper.split_helper(
+            graph,
+            inputs=node_anchors_div_input_size,
+            axis=1,
+            split=[1, 1],
+            outputs=[node_anchor_w, node_anchor_h],
+            dtype=node.input_dtype('X', 0))
+
+        new_anchor_shape = [1, int(num_anchors), 1, 1]
+        node_new_anchor_shape = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.ONNX.INT64,
+            value=new_anchor_shape)
+
+        node_anchor_w_reshape = graph.make_node(
+            'Reshape', inputs=[node_anchor_w, node_new_anchor_shape])
+
+        node_anchor_h_reshape = graph.make_node(
+            'Reshape', inputs=[node_anchor_h, node_new_anchor_shape])
+
+        node_box_w_squeeze = mapper_helper.squeeze_helper(graph, node_box_w,
+                                                          [4])
+        node_box_h_squeeze = mapper_helper.squeeze_helper(graph, node_box_h,
+                                                          [4])
+
+        node_box_w_exp = graph.make_node("Exp", inputs=[node_box_w_squeeze])
+        node_box_h_exp = graph.make_node("Exp", inputs=[node_box_h_squeeze])
+
+        node_box_w_encode = graph.make_node(
+            'Mul', inputs=[node_box_w_exp, node_anchor_w_reshape])
+
+        node_box_h_encode = graph.make_node(
+            'Mul', inputs=[node_box_h_exp, node_anchor_h_reshape])
+
+        node_conf_sigmoid = graph.make_node('Sigmoid', inputs=[node_conf])
+
+        node_conf_thresh = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=conf_thresh_mat)
+
+        conf_shape = [1, int(num_anchors), input_height, input_width, 1]
+        node_conf_shape = graph.make_node(
+            'Constant', inputs=[], dtype=dtypes.ONNX.INT64, value=conf_shape)
+
+        node_conf_thresh_reshape = graph.make_node(
+            'Reshape', inputs=[node_conf_thresh, node_conf_shape])
+
+        node_conf_sub = graph.make_node(
+            'Sub', inputs=[node_conf_sigmoid, node_conf_thresh_reshape])
+
+        node_conf_clip = mapper_helper.clip_helper(graph, node, node_conf_sub,
+                                                   float(MAX_FLOAT32), 0.0)
+
+        node_zeros = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=[0])
+
+        node_conf_clip_bool = graph.make_node(
+            'Greater', inputs=[node_conf_clip, node_zeros])
+
+        node_conf_clip_cast = graph.make_node(
+            'Cast',
+            inputs=[node_conf_clip_bool],
+            to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+
+        node_conf_set_zero = graph.make_node(
+            'Mul', inputs=[node_conf_sigmoid, node_conf_clip_cast])
+
+        node_prob_sigmoid = graph.make_node('Sigmoid', inputs=[node_prob])
+
+        new_shape = [1, int(num_anchors), input_height, input_width, 1]
+        node_new_shape = graph.make_node(
+            'Constant',
+            inputs=[],
+            dtype=dtypes.ONNX.INT64,
+            dims=[len(new_shape)],
+            value=new_shape)
+
+        node_conf_new_shape = graph.make_node(
+            'Reshape', inputs=[node_conf_set_zero, node_new_shape])
+
+        cls.node_score = graph.make_node(
+            'Mul', inputs=[node_prob_sigmoid, node_conf_new_shape])
+
+        node_conf_bool = graph.make_node(
+            'Greater', inputs=[node_conf_new_shape, node_zeros])
+
+        node_box_x_new_shape = graph.make_node(
+            'Reshape', inputs=[node_box_x_encode, node_new_shape])
+
+        node_box_y_new_shape = graph.make_node(
+            'Reshape', inputs=[node_box_y_encode, node_new_shape])
+
+        node_box_w_new_shape = graph.make_node(
+            'Reshape', inputs=[node_box_w_encode, node_new_shape])
+
+        node_box_h_new_shape = graph.make_node(
+            'Reshape', inputs=[node_box_h_encode, node_new_shape])
+
+        node_pred_box = graph.make_node(
+            'Concat',
+            inputs=[node_box_x_new_shape, node_box_y_new_shape, \
+                   node_box_w_new_shape, node_box_h_new_shape],
+            axis=4)
+
+        node_conf_cast = graph.make_node(
+            'Cast',
+            inputs=[node_conf_bool],
+            to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+
+        node_pred_box_mul_conf = graph.make_node(
+            'Mul', inputs=[node_pred_box, node_conf_cast])
+
+        box_shape = [1, int(num_anchors) * input_height * input_width, 4]
+        node_box_shape = graph.make_node(
+            'Constant', inputs=[], dtype=dtypes.ONNX.INT64, value=box_shape)
+
+        node_pred_box_new_shape = graph.make_node(
+            'Reshape', inputs=[node_pred_box_mul_conf, node_box_shape])
+
+        node_pred_box_x = model_name + "@_pred_box_x"
+        node_pred_box_y = model_name + "@_pred_box_y"
+        node_pred_box_w = model_name + "@_pred_box_w"
+        node_pred_box_h = model_name + "@_pred_box_h"
+        if node.input_dtype('X', 0) == paddle.float64:
+            node_pred_box_new_shape = graph.make_node(
+                'Cast', inputs=[node_pred_box_new_shape], to=TensorProto.FLOAT)
+        node_pred_box_split = mapper_helper.split_helper(
+            graph,
+            inputs=node_pred_box_new_shape,
+            axis=2,
+            split=[1, 1, 1, 1],
+            outputs=[
+                node_pred_box_x, node_pred_box_y, node_pred_box_w,
+                node_pred_box_h
+            ],
+            dtype=node.input_dtype('X', 0))
+
+        if node.input_dtype('X', 0) == paddle.float64:
+            node_pred_box_x = graph.make_node(
+                'Cast',
+                inputs=[node_pred_box_x],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+            node_pred_box_y = graph.make_node(
+                'Cast',
+                inputs=[node_pred_box_y],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+            node_pred_box_w = graph.make_node(
+                'Cast',
+                inputs=[node_pred_box_w],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+            node_pred_box_h = graph.make_node(
+                'Cast',
+                inputs=[node_pred_box_h],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+        node_number_two = graph.make_node(
+            "Constant",
+            inputs=[],
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+            value=[2])
+
+        node_half_w = graph.make_node(
+            "Div", inputs=[node_pred_box_w, node_number_two])
+
+        node_half_h = graph.make_node(
+            "Div", inputs=[node_pred_box_h, node_number_two])
+
+        node_pred_box_x1 = graph.make_node(
+            'Sub', inputs=[node_pred_box_x, node_half_w])
+
+        node_pred_box_y1 = graph.make_node(
+            'Sub', inputs=[node_pred_box_y, node_half_h])
+
+        node_pred_box_x2 = graph.make_node(
+            'Add', inputs=[node_pred_box_x, node_half_w])
+
+        node_pred_box_y2 = graph.make_node(
+            'Add', inputs=[node_pred_box_y, node_half_h])
+
+        node_sqeeze_image_size = mapper_helper.squeeze_helper(
+            graph, image_size[0], [0])
+
+        node_img_height = model_name + "@img_height"
+        node_img_width = model_name + "@img_width"
+        node_image_size_split = mapper_helper.split_helper(
+            graph, [node_sqeeze_image_size], [node_img_height, node_img_width],
+            -1, [1, 1],
+            dtype=node.input_dtype('X', 0))
+
+        node_img_width_cast = graph.make_node(
+            'Cast',
+            inputs=[node_img_width],
+            to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+
+        node_img_height_cast = graph.make_node(
+            'Cast',
+            inputs=[node_img_height],
+            to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
+
+        cls.node_pred_box_x1_decode = graph.make_node(
+            'Mul',
+            inputs=[node_pred_box_x1, node_img_width_cast])  #boxes[box_idx]
+
+        cls.node_pred_box_y1_decode = graph.make_node(
+            'Mul', inputs=[node_pred_box_y1,
+                           node_img_height_cast])  #boxes[box_idx + 1]
+
+        cls.node_pred_box_x2_decode = graph.make_node(
+            'Mul',
+            inputs=[node_pred_box_x2, node_img_width_cast])  #boxes[box_idx + 2]
+
+        cls.node_pred_box_y2_decode = graph.make_node(
+            'Mul', inputs=[node_pred_box_y2,
+                           node_img_height_cast])  #boxes[box_idx + 3]
+
+        if node.attr('clip_bbox'):
+            node_number_one = graph.make_node(
+                'Constant',
+                inputs=[],
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+                value=[1])
+
+            node_new_img_height = graph.make_node(
+                'Sub', inputs=[node_img_height_cast, node_number_one])
+
+            node_new_img_width = graph.make_node(
+                'Sub', inputs=[node_img_width_cast, node_number_one])
+
+            cls.node_pred_box_x2_sub_w = graph.make_node(
+                'Sub',
+                inputs=[cls.node_pred_box_x2_decode, node_new_img_width])
+
+            cls.node_pred_box_y2_sub_h = graph.make_node(
+                'Sub',
+                inputs=[cls.node_pred_box_y2_decode, node_new_img_height])
+
+            node_pred_box_x1_clip = mapper_helper.clip_helper(
+                graph, node, cls.node_pred_box_x1_decode,
+                float(MAX_FLOAT32), 0.0)
+            node_pred_box_y1_clip = mapper_helper.clip_helper(
+                graph, node, cls.node_pred_box_y1_decode,
+                float(MAX_FLOAT32), 0.0)
+            node_pred_box_x2_clip = mapper_helper.clip_helper(
+                graph, node, cls.node_pred_box_x2_sub_w,
+                float(MAX_FLOAT32), 0.0)
+            node_pred_box_y2_clip = mapper_helper.clip_helper(
+                graph, node, cls.node_pred_box_y2_sub_h,
+                float(MAX_FLOAT32), 0.0)
+            node_pred_box_x2_res = graph.make_node(
+                'Sub',
+                inputs=[cls.node_pred_box_x2_decode, node_pred_box_x2_clip])
+
+            node_pred_box_y2_res = graph.make_node(
+                'Sub',
+                inputs=[cls.node_pred_box_y2_decode, node_pred_box_y2_clip])
+
+            node_pred_box_result = graph.make_node(
+                'Concat',
+                inputs=[
+                    node_pred_box_x1_clip, node_pred_box_y1_clip,
+                    node_pred_box_x2_res, node_pred_box_y2_res
+                ],
+                outputs=node.output('Boxes'),
+                axis=-1)
+        else:
+            node_pred_box_result = graph.make_node(
+                'Concat',
+                inputs=[
+                    cls.node_pred_box_x1_decode, cls.node_pred_box_y1_decode,
+                    cls.node_pred_box_x2_decode, cls.node_pred_box_y2_decode
+                ],
+                outputs=node.output('Boxes'),
+                axis=-1)
+        node_score_shape = graph.make_node(
+            "Constant",
+            inputs=[],
+            dtype=dtypes.ONNX.INT64,
+            value=cls.score_shape)
+
+        node_score_new_shape = graph.make_node(
+            'Reshape',
+            inputs=[cls.node_score, node_score_shape],
+            outputs=node.output('Scores'))
diff --git a/paddle2onnx/legacy/op_mapper/logic.py b/paddle2onnx/legacy/op_mapper/logic.py
new file mode 100755
index 0000000000..6e2c198fa3
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/logic.py
@@ -0,0 +1,280 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+import paddle
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.op_mapper import mapper_helper
+
+
+@op_mapper('greater_equal')
+class GreaterOrEqual():
+    support_opset_version_range = (12, 15)
+
+    @classmethod
+    def opset_12(cls, graph, node, **kw):
+        onnx_node = graph.make_node(
+            'GreaterOrEqual',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('equal')
+class Equal():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        if node.input_dtype('X', 0) in [paddle.float32, paddle.float64]:
+            warning_info = "Operator 'Equal' only support input with dtype of int/bool, now the dtype of input is {}, this may cause wrong results, it is more recommend converting this model with opset version >= 11.".format(
+                node.input_dtype('X', 0))
+            logging.warning(warning_info)
+            x_node = graph.make_node(
+                'Cast', inputs=node.input('X'), to=dtypes.ONNX.INT32)
+            y_node = graph.make_node(
+                'Cast', inputs=node.input('Y'), to=dtypes.ONNX.INT32)
+            onnx_node = graph.make_node(
+                'Equal', inputs=[x_node, y_node], outputs=node.output('Out'))
+        else:
+            onnx_node = graph.make_node(
+                'Equal',
+                inputs=[node.input('X', 0), node.input('Y', 0)],
+                outputs=node.output('Out'))
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        onnx_node = graph.make_node(
+            'Equal',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('not_equal')
+class NotEqual():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        equal_val = None
+        if node.input_dtype('X', 0) in [paddle.float32, paddle.float64]:
+            warning_info = "Operator 'not_equal' only support input with dtype of int/bool, now the dtype of input is {}, this may cause wrong results, it is more recommend converting this model with opset version >= 11.".format(
+                node.input_dtype('X', 0))
+            logging.warning(warning_info)
+            x_node = graph.make_node(
+                'Cast', inputs=node.input('X'), to=dtypes.ONNX.INT32)
+            y_node = graph.make_node(
+                'Cast', inputs=node.input('Y'), to=dtypes.ONNX.INT32)
+            equal_val = graph.make_node(
+                'Equal', inputs=[x_node, y_node], outputs=node.output('Out'))
+        else:
+            equal_val = graph.make_node(
+                'Equal',
+                inputs=[node.input('X', 0), node.input('Y', 0)],
+                outputs=node.output('Out'))
+        k_node = graph.make_node(
+            'Cast', inputs=[equal_val], to=dtypes.ONNX.INT64)
+        const = graph.make_node('Constant', dtype=dtypes.ONNX.INT64, value=1)
+        sub_ = graph.make_node('Sub', inputs=[const, k_node])
+        graph.make_node(
+            'Cast',
+            inputs=[sub_],
+            outputs=node.output('Out'),
+            to=dtypes.ONNX.BOOL)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        equal_val = graph.make_node(
+            'Equal', inputs=[node.input('X', 0), node.input('Y', 0)])
+        k_node = graph.make_node(
+            'Cast', inputs=[equal_val], to=dtypes.ONNX.INT64)
+        const = graph.make_node('Constant', dtype=dtypes.ONNX.INT64, value=1)
+        sub_ = graph.make_node('Sub', inputs=[const, k_node])
+        graph.make_node(
+            'Cast',
+            inputs=[sub_],
+            outputs=node.output('Out'),
+            to=dtypes.ONNX.BOOL)
+
+
+@op_mapper('greater_than')
+class GreaterThan():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        if node.input_dtype('X', 0) in [paddle.int32, paddle.int64]:
+            warning_info = "Operator 'greater_than' only support input with dtype of float/double, now the dtype of input is {}, this may cause wrong results, it is more recommend converting this model with opset version >= 11.".format(
+                node.input_dtype('X', 0))
+            logging.warning(warning_info)
+            x_node = graph.make_node(
+                'Cast', inputs=node.input('X'), to=dtypes.ONNX.INT32)
+            y_node = graph.make_node(
+                'Cast', inputs=node.input('Y'), to=dtypes.ONNX.INT32)
+            graph.make_node(
+                'Greater',
+                inputs=[node.input('X', 0), node.input('Y', 0)],
+                outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Greater',
+                inputs=[node.input('X', 0), node.input('Y', 0)],
+                outputs=node.output('Out'))
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        onnx_node = graph.make_node(
+            'Greater',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('logical_and')
+class LogicalAnd():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        onnx_node = graph.make_node(
+            'And',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('logical_not')
+class LogicalNot():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Not', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('logical_or')
+class LogicalOr():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Or',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('logical_xor')
+class LogicalXOr():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Xor',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('less_equal')
+class LessOrEqual():
+    support_opset_version_range = (12, 15)
+
+    @classmethod
+    def opset_12(cls, graph, node, **kw):
+        onnx_node = graph.make_node(
+            'LessOrEqual',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('less_than')
+class Less_than():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        if node.input_dtype('X', 0) in [paddle.int32, paddle.int64]:
+            warning_info = "Operator 'less_than' only support input with dtype of float/double, now the dtype of input is {}, this may cause wrong results, it is more recommend converting this model with opset version >= 11.".format(
+                node.input_dtype('X', 0))
+            logging.warning(warning_info)
+            x_node = graph.make_node(
+                'Cast', inputs=node.input('X'), to=dtypes.ONNX.INT32)
+            y_node = graph.make_node(
+                'Cast', inputs=node.input('Y'), to=dtypes.ONNX.INT32)
+            graph.make_node(
+                'Less',
+                inputs=[node.input('X', 0), node.input('Y', 0)],
+                outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Less',
+                inputs=[node.input('X', 0), node.input('Y', 0)],
+                outputs=node.output('Out'))
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'Less',
+            inputs=[node.input('X', 0), node.input('Y', 0)],
+            outputs=node.output('Out'), )
+
+
+@op_mapper('isfinite_v2')
+class Isfinite():
+    support_opset_version_range = (10, 15)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        is_inf = graph.make_node('IsInf', inputs=node.input('X', 0))
+        is_nan = graph.make_node('IsNaN', inputs=node.input('X', 0))
+        finite = graph.make_node('Or', inputs=[is_inf, is_nan])
+        graph.make_node('Not', inputs=[finite], outputs=node.output('Out'))
+
+
+@op_mapper('isinf_v2')
+class IsInf():
+    support_opset_version_range = (10, 15)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        graph.make_node(
+            'IsInf', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('isnan_v2')
+class IsNaN():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'IsNaN', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('isnan')
+class IsNaN():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        isnan = graph.make_node('IsNaN', inputs=node.input('X'))
+        cast_node = graph.make_node(
+            'Cast', inputs=isnan, attrs={'to': dtypes.ONNX.FLOAT})
+        reduce_node = graph.make_node(
+            'ReduceMax', inputs=[cast_node], keepdims=False)
+        mapper_helper.unsqueeze_helper(graph, reduce_node, [0],
+                                       node.output('Out'))
diff --git a/paddle2onnx/legacy/op_mapper/mapper_helper.py b/paddle2onnx/legacy/op_mapper/mapper_helper.py
new file mode 100755
index 0000000000..9850be9012
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/mapper_helper.py
@@ -0,0 +1,432 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid.core as core
+import six
+import copy
+from paddle2onnx.legacy.constant import dtypes
+import paddle
+from onnx import TensorProto
+
+
+def is_static_shape(shape):
+    if len(shape) > 1 and shape[1:].count(-1) > 0:
+        raise Exception(
+            "Converting this model to ONNX need with static input shape," \
+            " please fix input shape of this model, see doc Q2 in" \
+            " https://github.com/PaddlePaddle/paddle2onnx/blob/develop/docs/en/FAQ.md."
+        )
+
+
+def shape_helper(graph, input, dim=None):
+    if dim is None:
+        shape_node = graph.make_node('Shape', inputs=[input])
+        return shape_node
+    full_shape = graph.make_node('Shape', inputs=[input])
+    shape_node = slice_helper(graph, full_shape, [0], [dim], [dim + 1])
+    return shape_node
+
+
+def unsqueeze_helper(graph, input, axes, outputs=None):
+    inputs = []
+    if not isinstance(input, list):
+        input = [input]
+    inputs.append(input[0])
+    if not isinstance(axes, list):
+        axes = [axes]
+    if outputs is not None and isinstance(outputs, six.string_types):
+        outputs = [outputs]
+
+    if graph.opset_version < 13:
+        unsqueeze_node = graph.make_node(
+            "Unsqueeze", inputs=inputs, outputs=outputs, axes=axes)
+        return unsqueeze_node
+    else:
+        axes_node = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=axes)
+        inputs = inputs + [axes_node]
+        unsqueeze_node = graph.make_node(
+            "Unsqueeze", inputs=inputs, outputs=outputs)
+        return unsqueeze_node
+
+
+def split_helper(graph, input, axis=0, split=None, outputs=None):
+    assert outputs is not None, "outputs can not be None in split_helper."
+    inputs = []
+    if not isinstance(input, list):
+        input = [input]
+    inputs.append(input[0])
+    if split is not None and not isinstance(split, list):
+        split = [split]
+    if split is None:
+        split_node = graph.make_node(
+            "Split", inputs=inputs, outputs=outputs, axis=axis)
+        return split_node
+    if graph.opset_version < 13:
+        split_node = graph.make_node(
+            "Split", inputs=inputs, outputs=outputs, axis=axis, split=split)
+        return split_node
+    else:
+        split = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=split)
+        inputs = inputs + [split]
+        split_node = graph.make_node(
+            "Split", inputs=inputs, axis=axis, outputs=outputs)
+        return split_node
+    
+    
+def slice_helper(graph,
+                 input,
+                 axes,
+                 starts,
+                 ends,
+                 outputs=None,
+                 dtype=dtypes.ONNX.INT64):
+    inputs = []
+    if not isinstance(input, list):
+        input = [input]
+    inputs.append(input[0])
+    if axes is not None and not isinstance(axes, list):
+        axes = [axes]
+    if starts is not None and not isinstance(starts, (list, six.string_types)):
+        starts = [starts]
+    if ends is not None and not isinstance(ends, (list, six.string_types)):
+        ends = [ends]
+
+    if graph.opset_version < 10:
+        attrs = {
+            'starts': starts,
+            'ends': ends,
+        }
+        if axes not in [None, []]:
+            attrs['axes'] = axes
+        slice_node = graph.make_node(
+            "Slice", inputs=inputs, outputs=outputs, attrs=attrs)
+        return slice_node
+    else:
+        if not isinstance(starts, six.string_types):
+            starts = graph.make_node('Constant', dtype=dtype, value=starts)
+        if not isinstance(ends, six.string_types):
+            ends = graph.make_node('Constant', dtype=dtype, value=ends)
+        inputs = inputs + [starts, ends]
+        if axes not in [None, []]:
+            axes_node = graph.make_node('Constant', dtype=dtype, value=axes)
+            inputs.append(axes_node)
+        slice_node = graph.make_node("Slice", inputs=inputs, outputs=outputs)
+        return slice_node
+
+
+def squeeze_helper(graph, input, axes=None, outputs=None):
+    inputs = []
+    if not isinstance(input, list):
+        input = [input]
+    inputs.append(input[0])
+    if axes is not None and not isinstance(axes, list):
+        axes = [axes]
+    if graph.opset_version < 13:
+        squeeze_node = graph.make_node(
+            "Squeeze", inputs=inputs, axes=axes, outputs=outputs)
+        return squeeze_node
+    else:
+        if axes is not None:
+            axes_node = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=axes)
+            inputs.append(axes_node)
+        squeeze_node = graph.make_node(
+            "Squeeze", inputs=inputs, outputs=outputs)
+        return squeeze_node
+
+
+def unsqueeze_helper(graph, input, axes, outputs=None):
+    inputs = []
+    if isinstance(input, list):
+        input = input[0]
+    inputs.append(input)
+    if not isinstance(axes, list):
+        axes = [axes]
+    if graph.opset_version < 13:
+        unsqueeze_node = graph.make_node(
+            'Unsqueeze', inputs=inputs, axes=axes, outputs=outputs)
+    else:
+        axes_node = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                               'value': axes})
+        inputs.append(axes_node)
+        unsqueeze_node = graph.make_node(
+            'Unsqueeze', inputs=inputs, outputs=outputs)
+    return unsqueeze_node
+
+
+def split_helper(graph, inputs, outputs, axis, split, dtype=paddle.float32):
+    if not isinstance(inputs, (list, tuple)):
+        inputs = [inputs]
+
+    if not isinstance(outputs, int) and not isinstance(outputs, (list, tuple)):
+        outputs = [outputs]
+
+    if dtype == paddle.float64:
+        cast_inputs = []
+        for i in range(len(inputs)):
+            one = graph.make_node(
+                'Cast', inputs=[inputs[i]], to=TensorProto.FLOAT)
+            cast_inputs.append(one)
+        if graph.opset_version < 13:
+            split_node = graph.make_node(
+                "Split",
+                inputs=cast_inputs,
+                outputs=outputs,
+                axis=axis,
+                split=split)
+        else:
+            split_const = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=split)
+            split_node = graph.make_node(
+                "Split",
+                inputs=cast_inputs + [split_const],
+                outputs=outputs,
+                axis=axis)
+        casted_output = []
+        for i in range(len(outputs)):
+            one = graph.make_node(
+                'Cast',
+                inputs=[split_node[i]],
+                outputs=[outputs[i]],
+                to=TensorProto.DOUBLE)
+            casted_output.append(one)
+        return casted_output
+    else:
+        if graph.opset_version < 13:
+            split_node = graph.make_node(
+                "Split", inputs=inputs, outputs=outputs, axis=axis, split=split)
+        else:
+            split_const = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=split)
+            split_node = graph.make_node(
+                "Split",
+                inputs=inputs + [split_const],
+                outputs=outputs,
+                axis=axis)
+        return split_node
+
+
+def constant_helper(graph, dtype, value, shape=None, outputs=[]):
+    constant = graph.make_node(
+        'Constant',
+        inputs=[],
+        outputs=outputs,
+        attrs={
+            'dims': shape,
+            'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[dtype],
+            'value': value
+        })
+    return constant
+
+
+def clip_helper(graph, node, input, max, min, output=[]):
+    x_dtype = node.input_dtype('X', 0)
+    if (isinstance(min, six.string_types) or
+            isinstance(max, six.string_types)) and graph.opset_version < 11:
+        raise Exception(
+            "min or max of Clip is Tensor, please try with higher onnx opset_version."
+        )
+    if graph.opset_version < 11:
+        if x_dtype != paddle.float32:
+            input = graph.make_node(
+                'Cast', inputs=[input], to=dtypes.ONNX.FLOAT)
+            clip = graph.make_node('Clip', inputs=input, max=max, min=min)
+            clip = graph.make_node(
+                'Cast',
+                inputs=[clip],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype],
+                outputs=output)
+        else:
+            clip = graph.make_node(
+                'Clip', inputs=input, max=max, min=min, outputs=output)
+    else:
+        if x_dtype != paddle.float32:
+            input = graph.make_node(
+                'Cast', inputs=[input], to=dtypes.ONNX.FLOAT)
+
+        if not isinstance(min, six.string_types):
+            min = graph.make_node(
+                'Constant',
+                attrs={
+                    'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[paddle.float32],
+                    'value': min
+                })
+        else:
+            if node.input_dtype('Min', 0) != paddle.float32:
+                min = graph.make_node(
+                    'Cast',
+                    inputs=min,
+                    attrs={'to': dtypes.DTYPE_PADDLE_ONNX_MAP[paddle.float32]})
+            min = graph.make_node('Squeeze', min)
+
+        if not isinstance(max, six.string_types):
+            max = graph.make_node(
+                'Constant',
+                attrs={
+                    'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[paddle.float32],
+                    'value': max
+                })
+        else:
+            if node.input_dtype('Max', 0) != paddle.float32:
+                max = graph.make_node(
+                    'Cast',
+                    inputs=max,
+                    attrs={'to': dtypes.DTYPE_PADDLE_ONNX_MAP[paddle.float32]})
+            max = graph.make_node('Squeeze', max)
+        if x_dtype != paddle.float32:
+            clip_pre = graph.make_node('Clip', inputs=[input, min, max])
+            clip = graph.make_node(
+                'Cast',
+                inputs=[clip_pre],
+                outputs=output,
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+        else:
+            clip = graph.make_node(
+                'Clip', inputs=[input, min, max], outputs=output)
+    return clip
+
+
+def dtype_alignment(graph, nodes, node_dtypes, to=None):
+    assert len(nodes) == len(
+        node_dtypes), "Length of nodes and node_dtypes should be equal."
+    dtype_order = [
+        core.VarDesc.VarType.BOOL,
+        core.VarDesc.VarType.INT16,
+        core.VarDesc.VarType.INT32,
+        core.VarDesc.VarType.INT64,
+        core.VarDesc.VarType.FP16,
+        core.VarDesc.VarType.FP32,
+        core.VarDesc.VarType.FP64,
+    ]
+    max_index = -1
+    for dtype in node_dtypes:
+        index = dtype_order.index(dtype)
+        if index > max_index:
+            max_index = index
+
+    if max_index < 0:
+        return nodes
+
+    casted_nodes = list()
+    cast_dtype = dtype_order[max_index]
+    cast_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[cast_dtype]
+    for i, dtype in enumerate(node_dtypes):
+        index = dtype_order.index(dtype)
+        if to is not None:
+            cast_dtype = to
+            condition = dtypes.DTYPE_PADDLE_ONNX_MAP[index] != cast_dtype
+        else:
+            condition = index != max_index
+        if condition:
+            cast_node = graph.make_node(
+                'Cast', inputs=[nodes[i]], to=cast_dtype)
+            casted_nodes.append(cast_node)
+        else:
+            casted_nodes.append(nodes[i])
+    return casted_nodes
+
+
+def cast(graph, input, origin_dtype, target_dtype):
+    if not isinstance(origin_dtype, six.string_types):
+        origin_dtype = dtypes.DTYPE_PADDLE_STR_MAP[origin_dtype]
+    if origin_dtype != target_dtype:
+        cast_node = graph.make_node(
+            'Cast', inputs=input, to=dtypes.DTYPE_ONNX_STR_MAP[target_dtype])
+        return cast_node
+    return input
+
+
+def shape_alignment(graph, nodes, node_shapes):
+    assert len(nodes) == len(
+        node_shapes), "Length of nodes and node_shapes should be equal."
+    max_dim = -1
+    for shape in node_shapes:
+        dim = len(shape)
+        if dim > max_dim:
+            max_dim = dim
+
+    if max_dim < 0:
+        return nodes
+
+    assert max_dim == 1 or max_dim == 0, "max_dim is only supported when max_dim is 1 or 0."
+    max_dim = 1 if max_dim == 0 else max_dim
+    unsqueeze_nodes = list()
+    for i, shape in enumerate(node_shapes):
+        dim = len(shape)
+        if dim != max_dim:
+            unsqueeze_node = nodes[i]
+            for j in range(max_dim - dim):
+                unsqueeze_node = unsqueeze_helper(graph, unsqueeze_node, [0])
+            unsqueeze_nodes.append(unsqueeze_node)
+        else:
+            unsqueeze_nodes.append(nodes[i])
+    return unsqueeze_nodes
+
+
+def get_tensor_list_node(graph, node, name, dtype=None):
+    node_list = node.input(name)
+    node_dtypes = [node.input_dtype(name, i) for i in range(len(node_list))]
+    node_list = dtype_alignment(graph, node_list, node_dtypes, dtype)
+
+    node_shapes = [node.input_shape(name, i) for i in range(len(node_list))]
+    node_list = shape_alignment(graph, node_list, node_shapes)
+    node = graph.make_node("Concat", inputs=node_list, axis=0)
+    return node
+
+
+def get_value_from_parameters(graph, input_node):
+    assert input_node in graph.parameters, "{} is not in graph.parameters".format(
+        input_node)
+    data = graph.parameters[input_node].attribute[0].t.int32_data
+    if data is None or len(data) < 1:
+        data = graph.parameters[input_node].attribute[0].t.int64_data
+    value = [val for _, val in enumerate(data)]
+    return value
+
+
+# return value
+# arg1: attr_value
+# arg2: attr_value is tensor or not
+def get_node_attr_value(graph,
+                        node,
+                        attr_name=None,
+                        attr_tensor_name=None,
+                        attr_tensor_list_name=None,
+                        return_list=False,
+                        dtype=None):
+    attr_tensor = node.input(attr_tensor_name)
+    attr_tensor_list = node.input(attr_tensor_list_name)
+    if attr_tensor is not None and len(attr_tensor) > 0:
+        value = node.input(attr_tensor_name)[0]
+        if return_list:
+            try:
+                value = get_value_from_parameters(graph, value)
+                return value, False  # value, is_tensor
+            except Exception:
+                return value, True
+        else:
+            input_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype(
+                attr_tensor_name, 0)]
+            if input_dtype != dtype:
+                value = graph.make_node('Cast', inputs=[value], to=dtype)
+            return value, True
+    elif attr_tensor_list is not None and len(attr_tensor_list) > 0:
+        value = get_tensor_list_node(graph, node, attr_tensor_list_name, dtype)
+        return value, True
+    else:
+        value = node.attr(attr_name)
+        return value, False
diff --git a/paddle2onnx/legacy/op_mapper/math.py b/paddle2onnx/legacy/op_mapper/math.py
new file mode 100755
index 0000000000..ff94f93ff9
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/math.py
@@ -0,0 +1,1273 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+import paddle
+
+
+@op_mapper('matmul')
+class MatMul():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        x = node.input('X', idx=0)
+        y = node.input('Y', idx=0)
+        if node.attr('transpose_X'):
+            perm = list(range(len(node.input_shape('X', 0))))
+            perm[-1], perm[-2] = perm[-2], perm[-1]
+            if node.input_dtype('X', 0) == paddle.float64:
+                x = graph.make_node('Cast', inputs=x, to=dtypes.ONNX.FLOAT)
+            x = graph.make_node('Transpose', inputs=[x], perm=perm)
+        if node.attr('transpose_Y'):
+            perm = list(range(len(node.input_shape('Y', 0))))
+            perm[-1], perm[-2] = perm[-2], perm[-1]
+            if node.input_dtype('Y', 0) == paddle.float64:
+                y = graph.make_node('Cast', inputs=y, to=dtypes.ONNX.FLOAT)
+            y = graph.make_node('Transpose', inputs=[y], perm=perm)
+        if node.attr('alpha') == 1.0:
+            if node.input_dtype('X', 0) == paddle.float64:
+                output_node = graph.make_node('MatMul', inputs=[x, y])
+                graph.make_node(
+                    'Cast',
+                    inputs=output_node,
+                    to=dtypes.ONNX.DOUBLE,
+                    outputs=node.output('Out'))
+            else:
+                graph.make_node(
+                    'MatMul', inputs=[x, y], outputs=node.output('Out'))
+        else:
+            if node.input_dtype('X', 0) == paddle.float64:
+                output_node = graph.make_node('MatMul', inputs=[x, y])
+                matmul = graph.make_node(
+                    'Cast', inputs=output_node, to=dtypes.ONNX.DOUBLE)
+                scale = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.ONNX.DOUBLE,
+                    value=node.attr('alpha'))
+            else:
+                matmul = graph.make_node('MatMul', inputs=[x, y])
+                scale = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.ONNX.FLOAT,
+                    value=node.attr('alpha'))
+
+            onnx_node = graph.make_node(
+                'Mul', inputs=[matmul, scale], outputs=node.output('Out'))
+
+
+@op_mapper('matmul_v2')
+class MatMul():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        x = node.input('X', idx=0)
+        y = node.input('Y', idx=0)
+        out = node.output('Out')
+        ## TODO(wangjunjie06): The current addition of cast op is only for onnxruntime optimization, after onnxruntime is repaired, remove this logic
+        if node.attr('trans_x'):
+            perm = list(range(len(node.input_shape('X', 0))))
+            perm[-1], perm[-2] = perm[-2], perm[-1]
+            if node.input_dtype('X', 0) == paddle.float64:
+                x = graph.make_node('Cast', inputs=x, to=dtypes.ONNX.FLOAT)
+            x = graph.make_node('Transpose', inputs=[x], perm=perm)
+        if node.attr('trans_y'):
+            perm = list(range(len(node.input_shape('Y', 0))))
+            perm[-1], perm[-2] = perm[-2], perm[-1]
+            if node.input_dtype('Y', 0) == paddle.float64:
+                y = graph.make_node('Cast', inputs=y, to=dtypes.ONNX.FLOAT)
+            y = graph.make_node('Transpose', inputs=[y], perm=perm)
+        if node.input_dtype('X', 0) == paddle.float64:
+            output_node = graph.make_node('MatMul', inputs=[x, y])
+            graph.make_node(
+                'Cast', inputs=output_node, to=dtypes.ONNX.DOUBLE, outputs=out)
+        else:
+            graph.make_node('MatMul', inputs=[x, y], outputs=out)
+
+
+@op_mapper('exp')
+class Exp():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Exp', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('abs')
+class Abs:
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Abs', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('erf')
+class Erf():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        x_dtype = node.input_dtype('X', 0)
+        x = node.input('X', 0)
+        # onnxruntime only support float32 Erf
+        if x_dtype != paddle.float32:
+            x = graph.make_node('Cast', inputs=x, to=dtypes.ONNX.FLOAT)
+            erf_node = graph.make_node('Erf', inputs=[x])
+            graph.make_node(
+                'Cast',
+                inputs=[erf_node],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype],
+                outputs=node.output('Out'))
+        else:
+            graph.make_node('Erf', inputs=[x], outputs=node.output('Out'))
+
+
+@op_mapper('acos')
+class Acos():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Acos', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('asin')
+class Asin():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Asin', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('sinh')
+class Sinh():
+    supports_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'Sinh', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('sin')
+class Sin():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Sin', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('atan')
+class Atan():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Atan', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('tan')
+class Tan():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Tan', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('ceil')
+class Ceil():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        graph.make_node(
+            'Ceil', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('cos')
+class Cos():
+    supports_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Cos', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('cosh')
+class Cosh():
+    supports_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'Cosh', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('log2')
+class Log2():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        _ln2 = 0.693147180559945309
+        dtype = dtypes.ONNX.FLOAT
+        if node.input_dtype('X', 0) == paddle.float64:
+            dtype = dtypes.ONNX.DOUBLE
+        _ln2 = graph.make_node('Constant', dtype=dtype, value=_ln2)
+        lnx = graph.make_node('Log', inputs=node.input('X'))
+        graph.make_node('Div', inputs=[lnx, _ln2], outputs=node.output('Out'))
+
+
+@op_mapper('logsumexp')
+class LogSumExp():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+
+        if node.attr('reduce_all'):
+            if not node.attr('keepdim'):
+                reduce_node = graph.make_node(
+                    'ReduceLogSumExp',
+                    inputs=node.input('X'),
+                    keepdims=node.attr('keepdim'))
+                mapper_helper.unsqueeze_helper(graph, reduce_node, [0],
+                                               node.output('Out'))
+            else:
+                graph.make_node(
+                    'ReduceLogSumExp',
+                    inputs=node.input('X'),
+                    keepdims=node.attr('keepdim'),
+                    outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'ReduceLogSumExp',
+                inputs=node.input('X'),
+                keepdims=node.attr('keepdim'),
+                axes=node.attr('axis'),
+                outputs=node.output('Out'))
+
+
+@op_mapper(
+    [
+        'elementwise_add', 'elementwise_sub', 'elementwise_div',
+        'elementwise_mul', 'elementwise_min', 'elementwise_max',
+        'elementwise_pow'
+    ],
+    mapper_dict={
+        'elementwise_add': 'Add',
+        'elementwise_sub': 'Sub',
+        'elementwise_div': 'Div',
+        'elementwise_mul': 'Mul',
+        'elementwise_min': 'Min',
+        'elementwise_max': 'Max',
+        'elementwise_pow': 'Pow'
+    })
+class ElementwiseOps():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        x_shape = node.input_shape('X', 0)
+        y_shape = node.input_shape('Y', 0)
+        if node.type in ["elementwise_min", "elementwise_max"]:
+            assert False, "{} op is not supported when opset version < 8".format(
+                node.type)
+
+        op_type = kw['mapper_dict'][node.type]
+        axis = node.attr('axis')
+        x = node.input('X', 0)
+        y = node.input('Y', 0)
+
+        if axis == -1 or axis == (len(x_shape) - 1
+                                  ) or len(x_shape) == len(y_shape):
+            onnx_node = graph.make_node(
+                op_type, inputs=[x, y], outputs=node.output('Out'))
+        else:
+            broadcast_shape = [1] * len(x_shape)
+            broadcast_shape[axis:axis + len(y_shape)] = y_shape
+            broadcast_shape_node = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=list(broadcast_shape))
+            y_node = graph.make_node(
+                'Reshape', inputs=[y, broadcast_shape_node])
+            onnx_node = graph.make_node(
+                op_type, inputs=[x, y_node], outputs=node.output('Out'))
+
+    @classmethod
+    def opset_8(cls, graph, node, **kw):
+        op_type = kw['mapper_dict'][node.type]
+        x = node.input('X', 0)
+        y = node.input('Y', 0)
+        axis = node.attr('axis')
+        x_shape = node.input_shape('X', 0)
+        y_shape = node.input_shape('Y', 0)
+        if axis == -1 or axis == (len(x_shape) - 1
+                                  ) or len(x_shape) == len(y_shape):
+            onnx_node = graph.make_node(
+                op_type, inputs=[x, y], outputs=node.output('Out'))
+        else:
+            broadcast_shape = [1] * len(x_shape)
+            broadcast_shape[axis:axis + len(y_shape)] = y_shape
+            broadcast_shape_node = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=list(broadcast_shape))
+            y_node = graph.make_node(
+                'Reshape', inputs=[y, broadcast_shape_node])
+            onnx_node = graph.make_node(
+                op_type, inputs=[x, y_node], outputs=node.output('Out'))
+
+
+@op_mapper('elementwise_mod')
+class ElementWiseMod():
+    support_opset_version_range = (10, 15)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        x_shape = node.input_shape('X', 0)
+        y_shape = node.input_shape('Y', 0)
+        axis = node.attr('axis')
+        x = node.input('X', 0)
+        y = node.input('Y', 0)
+
+        if node.input_dtype('Y', 0) == paddle.int32 or node.input_dtype(
+                'Y', 0) == paddle.int64:
+            onnx_node = graph.make_node(
+                "Mod", inputs=[x, y], outputs=node.output('Out'))
+            return
+
+        fmod = 1
+
+        abs_x_node = graph.make_node("Abs", inputs=[x])
+        abs_y_node = graph.make_node("Abs", inputs=[y])
+
+        dtype = dtypes.ONNX.FLOAT
+        val_0 = [0.0]
+        val_1 = [-1.0]
+        if node.input_dtype('Y', 0) == paddle.float64:
+            dtype = dtypes.ONNX.DOUBLE
+        if node.input_dtype('Y', 0) == paddle.int32:
+            dtype = dtypes.ONNX.INT32
+            val_0 = [0]
+            val_1 = [-1]
+        if node.input_dtype('Y', 0) == paddle.int64:
+            dtype = dtypes.ONNX.INT64
+            val_0 = [0]
+            val_1 = [-1]
+        zero_node = graph.make_node('Constant', dtype=dtype, value=val_0)
+        one_node = graph.make_node('Constant', dtype=dtype, value=val_1)
+
+        mod_node = graph.make_node(
+            "Mod", inputs=[abs_x_node, abs_y_node], fmod=fmod)
+
+        minus_node = graph.make_node("Mul", inputs=[mod_node, one_node])
+
+        condition_dtype = graph.make_node("Less", inputs=[x, zero_node])
+        condition = graph.make_node(
+            'Cast', inputs=[condition_dtype], to=dtypes.ONNX.BOOL)
+
+        mod_res = graph.make_node(
+            "Where", inputs=[condition, minus_node, mod_node])
+
+        add_node = graph.make_node("Add", inputs=[mod_res, y])
+
+        mod_y_mul_node = graph.make_node("Mul", inputs=[mod_res, y])
+        condition_dtype_1 = graph.make_node(
+            "Less", inputs=[mod_y_mul_node, zero_node])
+        condition_1 = graph.make_node(
+            'Cast', inputs=[condition_dtype_1], to=dtypes.ONNX.BOOL)
+
+        graph.make_node(
+            "Where",
+            inputs=[condition_1, add_node, mod_res],
+            outputs=node.output('Out'))
+
+
+@op_mapper('elementwise_floordiv')
+class ElementWiseFloorDiv():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        y = node.input('Y', 0)
+        axis = node.attr('axis')
+        x_shape = node.input_shape('X', 0)
+        y_shape = node.input_shape('Y', 0)
+        x_dtype = node.input_dtype('X', 0)
+        y_dtype = node.input_dtype('Y', 0)
+        x_dtype = dtypes.DTYPE_PADDLE_STR_MAP[x_dtype]
+        y_dtype = dtypes.DTYPE_PADDLE_STR_MAP[y_dtype]
+        is_int = False
+        if x_dtype.count('int') > 0 and y_dtype.count('int') > 0:
+            is_int = True
+        if axis == -1 or axis == (len(x_shape) - 1
+                                  ) or len(x_shape) == len(y_shape):
+            if is_int:
+                graph.make_node(
+                    'Div', inputs=[x, y], outputs=node.output('Out'))
+            else:
+                div_node = graph.make_node('Div', inputs=[x, y])
+                graph.make_node(
+                    'Floor', inputs=[div_node], outputs=node.output('Out'))
+        else:
+            broadcast_shape = [1] * len(x_shape)
+            broadcast_shape[axis:axis + len(y_shape)] = y_shape
+            broadcast_shape_node = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=list(broadcast_shape))
+            y_node = graph.make_node(
+                'Reshape', inputs=[y, broadcast_shape_node])
+            if is_int:
+                div_node = graph.make_node(
+                    'Div', inputs=[x, y_node], outputs=node.output('Out'))
+            else:
+                div_node = graph.make_node('Div', inputs=[x, y_node])
+                graph.make_node(
+                    'Floor', inputs=[div_node], outputs=node.output('Out'))
+
+
+@op_mapper('pow')
+class Pow():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        x_dtype = node.input_dtype('X', 0)
+        factor = node.attr('factor')
+        # Pow-7 Only support input type as float and double
+        if x_dtype == paddle.int32 or x_dtype == paddle.int64:
+            x = graph.make_node('Cast', inputs=[x], to=dtypes.ONNX.FLOAT)
+            factor_node = graph.make_node(
+                'Constant',
+                inputs=[],
+                dims=[1],
+                dtype=dtypes.ONNX.FLOAT,
+                value=factor)
+        else:
+            factor_node = graph.make_node(
+                'Constant',
+                inputs=[],
+                dims=[1],
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype],
+                value=factor)
+        if x_dtype == paddle.int32 or x_dtype == paddle.int64:
+            pow_node = graph.make_node('Pow', inputs=[x, factor_node])
+            graph.make_node(
+                'Cast',
+                inputs=[pow_node],
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype],
+                outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Pow', inputs=[x, factor_node], outputs=node.output('Out'))
+
+    @classmethod
+    def opset_12(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        factor = node.attr('factor')
+        factor_node = graph.make_node(
+            'Constant',
+            inputs=[],
+            dims=[1],
+            dtype=dtypes.ONNX.FLOAT,
+            value=factor)
+        pow_node = graph.make_node(
+            'Pow', inputs=[x, factor_node], outputs=node.output('Out'))
+
+
+@op_mapper('square')
+class Square():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        onnx_node = graph.make_node(
+            'Mul', inputs=[x, x], outputs=node.output('Out'))
+
+
+@op_mapper('cumsum')
+class CumSum():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+
+        axis = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=node.attr('axis'))
+        graph.make_node(
+            'CumSum',
+            inputs=[node.input('X', 0), axis],
+            outputs=node.output('Out'))
+
+
+@op_mapper('mul')
+class Mul():
+    support_opset_version_range = (5, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        y = node.input('Y', 0)
+        out = node.output('Out', 0)
+        x_num_col_dims = node.attr('x_num_col_dims')
+        y_num_col_dims = node.attr('y_num_col_dims')
+        flatten_x = graph.make_node(
+            'Flatten', inputs=node.input('X'), attrs={'axis': x_num_col_dims})
+        flatten_y = graph.make_node(
+            'Flatten', inputs=node.input('Y'), attrs={'axis': y_num_col_dims})
+        mul_node = graph.make_node('MatMul', inputs=[flatten_x, flatten_y])
+
+        x_shape = graph.make_node('Shape', inputs=[x])
+        l_shape = mapper_helper.slice_helper(
+            graph, x_shape, axes=[0], starts=[0], ends=[x_num_col_dims])
+        y_shape = graph.make_node('Shape', inputs=[y])
+        y_rank = len(node.input_shape('Y', 0))
+        r_shape = mapper_helper.slice_helper(
+            graph, y_shape, axes=[0], starts=[y_num_col_dims], ends=[y_rank])
+
+        out_shape = graph.make_node('Concat', inputs=[l_shape, r_shape], axis=0)
+        graph.make_node('Reshape', [mul_node, out_shape], node.output('Out'))
+
+
+@op_mapper('affine_channel')
+class AffineChannel():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        if "data_layout" in node.attrs.keys():
+            assert node.attrs['data_layout'] == 'NCHW' or node.attrs['data_layout'] == "AnyLayout",  \
+                                "The affine_channel data format should be 'NCHW', but received data format " \
+                                "is %s." % node.attrs['data_layout']
+        x = node.input('X', 0)
+        bias = node.input('Bias', 0)
+        scale = node.input('Scale', 0)
+        scale = mapper_helper.unsqueeze_helper(graph, scale, [0, 2, 3])
+        bias = mapper_helper.unsqueeze_helper(graph, bias, [0, 2, 3])
+        x = graph.make_node('Mul', inputs=[x, scale])
+        x = graph.make_node('Add', inputs=[x, bias], outputs=node.output('Out'))
+
+
+@op_mapper('bmm')
+class BMM():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        y = node.input('Y', 0)
+        mul_node = graph.make_node(
+            'MatMul', inputs=[x, y], outputs=node.output('Out'))
+
+
+@op_mapper('p_norm')
+class PNorm():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        axis = node.attr('axis')
+        if isinstance(axis, (int, float)):
+            axis = [axis]
+        p = node.attr('porder')
+        keepdim = node.attr('keepdim')
+        dtype = dtypes.ONNX.FLOAT
+        if node.input_dtype('X', 0) == paddle.float64:
+            dtype = dtypes.ONNX.DOUBLE
+
+        pnode = graph.make_node('Constant', dtype=dtype, value=[p])
+
+        abs_node = graph.make_node('Abs', inputs=[x])
+        pow_node = graph.make_node('Pow', inputs=[abs_node, pnode])
+        reduce_sum = graph.make_node(
+            'ReduceSum', inputs=[pow_node], axes=axis, keepdims=keepdim)
+        pnode1 = graph.make_node('Constant', dtype=dtype, value=[1.0 / p])
+        graph.make_node(
+            'Pow', inputs=[reduce_sum, pnode1], outputs=node.output('Out'))
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        x = node.input('X', 0)
+        axis = node.attr('axis')
+        if isinstance(axis, (int, float)):
+            axis = [axis]
+        p = node.attr('porder')
+        keepdim = node.attr('keepdim')
+        pnode = graph.make_node('Constant', dtype=dtypes.ONNX.FLOAT, value=[p])
+        abs_node = graph.make_node('Abs', inputs=[x])
+        pow_node = graph.make_node('Pow', inputs=[abs_node, pnode])
+        axes = graph.make_node('Constant', dtype=dtypes.ONNX.INT64, value=axis)
+        reduce_sum = graph.make_node(
+            'ReduceSum', inputs=[pow_node, axes], keepdims=keepdim)
+        pnode1 = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.FLOAT, value=[1.0 / p])
+        graph.make_node(
+            'Pow', inputs=[reduce_sum, pnode1], outputs=node.output('Out'))
+
+
+@op_mapper('sum')
+class Sum():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Sum', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('floor')
+class Floor():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Floor', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('log10')
+class Log10():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        _ln10 = 2.30258509299404568401
+        dtype = dtypes.ONNX.FLOAT
+        if node.input_dtype('X', 0) == paddle.float64:
+            dtype = dtypes.ONNX.DOUBLE
+        _ln10 = graph.make_node('Constant', dtype=dtype, value=_ln10)
+        lnx = graph.make_node('Log', inputs=node.input('X'))
+        graph.make_node('Div', inputs=[lnx, _ln10], outputs=node.output('Out'))
+
+
+@op_mapper('log1p')
+class Log1p():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        dtype = dtypes.ONNX.FLOAT
+        if node.input_dtype('X', 0) == paddle.float64:
+            dtype = dtypes.ONNX.DOUBLE
+        one = graph.make_node('Constant', attrs={'dtype': dtype, 'value': [1]})
+        add_node = graph.make_node('Add', inputs=[node.input('X', 0), one])
+        graph.make_node('Log', inputs=add_node, outputs=node.output('Out'))
+
+
+@op_mapper(
+    ['reduce_all', 'reduce_any'],
+    mapper_dict={'reduce_all': 'ReduceMin',
+                 'reduce_any': 'ReduceMax'})
+class ReduceAll():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        op_type = kw['mapper_dict'][node.type]
+        input_dtype = node.block.vars[node.input('X', 0)].dtype
+        input_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[input_dtype]
+        all_node = graph.make_node(
+            'Cast', inputs=[node.input('X', 0)], to=dtypes.ONNX.INT32)
+
+        attrs = {'keepdims': node.attr('keep_dim'), }
+        if not node.attr('reduce_all'):
+            attrs['axes'] = node.attr('dim')
+        output_node = graph.make_node(op_type, inputs=[all_node], attrs=attrs)
+
+        if node.attr('reduce_all') and not node.attr('keep_dim'):
+            output_node = mapper_helper.unsqueeze_helper(graph, output_node,
+                                                         [0])
+        graph.make_node(
+            'Cast',
+            inputs=[output_node],
+            to=input_dtype,
+            outputs=node.output('Out'))
+
+
+@op_mapper(
+    ['reduce_mean', 'reduce_sum', 'reduce_min', 'reduce_max', 'reduce_prod'],
+    mapper_dict={
+        'reduce_mean': 'ReduceMean',
+        'reduce_sum': 'ReduceSum',
+        'reduce_min': 'ReduceMin',
+        'reduce_max': 'ReduceMax',
+        'reduce_prod': 'ReduceProd'
+    })
+class ReduceMean():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        op_type = kw['mapper_dict'][node.type]
+
+        output_shape = node.output_shape('Out', 0)
+        reduce_all = node.attr("reduce_all")
+        axes = node.attr("dim")
+        if reduce_all:
+            axes = list(range(len(node.input_shape("X", 0))))
+        if len(axes) == len(node.input_shape("X", 0)):
+            reduce_all = True
+        keepdims = node.attr('keep_dim')
+        if keepdims:
+            cls.create_reduce_node(graph, op_type,
+                                   node.input("X"), node.output("Out"), axes, 1)
+        else:
+            if reduce_all:
+                shape = graph.make_node(
+                    "Constant", dtype=dtypes.ONNX.INT64, value=[-1])
+                flatten_node = graph.make_node(
+                    "Reshape", inputs=[node.input("X")[0], shape])
+                cls.create_reduce_node(graph, op_type, [flatten_node],
+                                       node.output("Out"), [0], 1)
+            else:
+                cls.create_reduce_node(graph, op_type,
+                                       node.input("X"),
+                                       node.output("Out"), axes, 0)
+
+    @classmethod
+    def create_reduce_node(cls, graph, op_type, inputs, outputs, axes,
+                           keepdims):
+        if graph.opset_version >= 13 and op_type == "ReduceSum":
+            axes = graph.make_node(
+                "Constant", dtype=dtypes.ONNX.INT64, value=axes)
+            output = graph.make_node(
+                "ReduceSum",
+                inputs=inputs + [axes],
+                outputs=outputs,
+                keepdims=keepdims)
+        else:
+            output = graph.make_node(
+                op_type,
+                inputs=inputs,
+                outputs=outputs,
+                axes=axes,
+                keepdims=keepdims)
+
+
+@op_mapper('mean')
+class Mean():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        shape = graph.make_node("Constant", dtype=dtypes.ONNX.INT64, value=[-1])
+        flatten_node = graph.make_node(
+            "Reshape", inputs=[node.input("X")[0], shape])
+        mean_node = graph.make_node(
+            'ReduceMean',
+            inputs=flatten_node,
+            outputs=node.output("Out"),
+            keepdims=1)
+
+
+@op_mapper('arg_max')
+class ArgMax():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        if node.attr('dtype') and node.attr('dtype') == 2:
+            arg_node = graph.make_node(
+                'ArgMax',
+                inputs=node.input('X'),
+                attrs={
+                    'axis': node.attr('axis'),
+                    'keepdims': node.attr('keepdims')
+                })
+            graph.make_node(
+                'Cast',
+                inputs=arg_node,
+                attrs={'to': dtypes.ONNX.INT32},
+                outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'ArgMax',
+                inputs=node.input('X'),
+                outputs=node.output('Out'),
+                attrs={
+                    'axis': node.attr('axis'),
+                    'keepdims': node.attr('keepdims')
+                })
+
+
+@op_mapper('arg_min')
+class ArgMin():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        if node.attr('flatten'):
+            flatten = graph.make_node('Flatten', inputs=node.input('X'), axis=0)
+            squeeze_node = graph.make_node('Squeeze', inputs=flatten)
+            graph.make_node(
+                'ArgMin', inputs=squeeze_node, outputs=node.output('Out'))
+        else:
+            if node.attr('keepdims'):
+                graph.make_node(
+                    'ArgMin',
+                    inputs=node.input('X'),
+                    outputs=node.output('Out'),
+                    axis=node.attr('axis'),
+                    keepdims=1)
+            else:
+                graph.make_node(
+                    'ArgMin',
+                    inputs=node.input('X'),
+                    outputs=node.output('Out'),
+                    axis=node.attr('axis'),
+                    keepdims=0)
+
+
+@op_mapper('brelu')
+class Hardtanh():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        mapper_helper.clip_helper(graph, node,
+                                  node.input('X', 0),
+                                  node.attr('t_max'),
+                                  node.attr('t_min'), node.output('Out', 0))
+
+
+@op_mapper('mv')
+class Mv():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'MatMul',
+            inputs=[node.input('X', 0), node.input('Vec', 0)],
+            outputs=node.output('Out'))
+
+
+@op_mapper('dot')
+class Dot():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        mul_node = graph.make_node(
+            'Mul', inputs=[node.input('X', 0), node.input('Y', 0)])
+        graph.make_node(
+            'ReduceSum',
+            inputs=[mul_node],
+            axes=[len(node.input_shape('X', 0)) - 1],
+            outputs=node.output('Out'))
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        mul_node = graph.make_node(
+            'Mul', inputs=[node.input('X', 0), node.input('Y', 0)])
+        one = graph.make_node(
+            'Constant',
+            dtype=dtypes.ONNX.INT64,
+            value=[len(node.input_shape('X', 0)) - 1])
+        graph.make_node(
+            'ReduceSum', inputs=[mul_node, one], outputs=node.output('Out'))
+
+
+@op_mapper('dist')
+class Dist():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        sub_node = graph.make_node(
+            'Sub', inputs=[node.input('X', 0), node.input('Y', 0)])
+        abs_node = graph.make_node('Abs', inputs=sub_node)
+        if node.attr('p') == 0:
+            assert graph.opset_version >= 9, "When p is 0, onnx opset should be (onnx_opset>=9)."
+            sign_node = graph.make_node('Sign', inputs=abs_node)
+            sum_node = graph.make_node(
+                'ReduceSum', inputs=sign_node, keepdims=0)
+            mapper_helper.unsqueeze_helper(graph, sum_node, [0],
+                                           node.output('Out'))
+        elif node.attr('p') == float('inf'):
+            max_node = graph.make_node('ReduceMax', inputs=abs_node, keepdims=0)
+            mapper_helper.unsqueeze_helper(graph, max_node, [0],
+                                           node.output('Out'))
+        elif node.attr('p') == float('-inf'):
+            min_node = graph.make_node('ReduceMin', inputs=abs_node, keepdims=0)
+            mapper_helper.unsqueeze_helper(graph, min_node, [0],
+                                           node.output('Out'))
+        else:
+            x_dtype = node.input_dtype('X', 0)
+            p = graph.make_node(
+                'Constant',
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype],
+                value=node.attr('p'))
+            pow_node = graph.make_node(
+                'Pow',
+                inputs=[abs_node, p], )
+            sum_node = graph.make_node('ReduceSum', inputs=pow_node, keepdims=0)
+            sum_node = mapper_helper.unsqueeze_helper(graph, sum_node, [0])
+            p_1 = graph.make_node('Reciprocal', inputs=p)
+            graph.make_node(
+                'Pow', inputs=[sum_node, p_1], outputs=node.output('Out'))
+
+
+@op_mapper('round')
+class Round():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        graph.make_node(
+            'Round', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('rsqrt')
+class Rsqrt():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        sqrt_node = graph.make_node('Sqrt', inputs=node.input('X'))
+        graph.make_node(
+            'Reciprocal', inputs=sqrt_node, outputs=node.output('Out'))
+
+
+@op_mapper('sign')
+class Sign():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'Sign', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('scale')
+class Scale():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        scale = node.attr('scale')
+        bias = node.attr('bias')
+        if len(node.input('ScaleTensor')) == 0 and np.fabs(
+                scale - 1.0) < 1e-06 and np.fabs(bias - 0.0) < 1e-06:
+            graph.make_node(
+                'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+        else:
+            input_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)]
+            if input_dtype in [
+                    dtypes.ONNX.INT16, dtypes.ONNX.INT32, dtypes.ONNX.INT64
+            ]:
+                outputs = None
+                data_type = dtypes.ONNX.FLOAT
+                cast_node = graph.make_node(
+                    'Cast', inputs=node.input('X'), attrs={'to': data_type})
+            else:
+                outputs = node.output('Out')
+                data_type = input_dtype
+                cast_node = node.input('X')[0]
+
+            if len(node.input('ScaleTensor')) > 0:
+                scale_node = node.input('ScaleTensor')[0]
+                scale_type = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype(
+                    'ScaleTensor', 0)]
+                if scale_type != data_type:
+                    scale_node = graph.make_node(
+                        'Cast', inputs=[scale_node], attrs={'to': data_type})
+            else:
+                scale_node = graph.make_node(
+                    'Constant', attrs={'dtype': data_type,
+                                       'value': [scale]})
+            bias_node = graph.make_node(
+                'Constant', attrs={'dtype': data_type,
+                                   'value': [bias]})
+
+            if node.attr('bias_after_scale'):
+                node1 = graph.make_node('Mul', inputs=[cast_node, scale_node])
+                node2 = graph.make_node(
+                    'Add', inputs=[node1, bias_node], outputs=outputs)
+            else:
+                node1 = graph.make_node('Add', inputs=[cast_node, bias_node])
+                node2 = graph.make_node(
+                    'Mul', inputs=[node1, scale_node], outputs=outputs)
+
+            if input_dtype in [
+                    dtypes.ONNX.INT16, dtypes.ONNX.INT32, dtypes.ONNX.INT64
+            ]:
+                cast_node = graph.make_node(
+                    'Cast',
+                    inputs=node2,
+                    outputs=node.output('Out'),
+                    attrs={'to': input_dtype})
+
+
+@op_mapper('softmax')
+class Softmax():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        axis = node.attr('axis')
+        shape = node.output_shape('Out', 0)
+        if axis is None:
+            axis = -1
+        if axis < 0:
+            axis += len(shape)
+        if axis == len(shape) - 1:
+            node = graph.make_node(
+                'Softmax',
+                inputs=node.input('X'),
+                outputs=node.output('Out'),
+                attrs={'axis': axis})
+        else:
+            perm = [i for i in range(len(shape))]
+            perm[-1] = axis
+            perm[axis] = len(shape) - 1
+            transpose_node = graph.make_node(
+                'Transpose', inputs=node.input('X'), attrs={'perm': perm})
+            softmax_node = graph.make_node(
+                'Softmax', inputs=[transpose_node], axis=-1)
+            transpose_node1 = graph.make_node(
+                'Transpose',
+                inputs=[softmax_node],
+                outputs=node.output('Out'),
+                attrs={'perm': perm})
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        graph.make_node(
+            'Softmax',
+            inputs=node.input('X'),
+            axis=node.attr('axis'),
+            outputs=node.output('Out'))
+
+
+@op_mapper('unfold')
+class Unfold():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+
+        strides = node.attr('strides')
+        stride_h = strides[0]
+        stride_w = strides[1]
+
+        paddings = node.attr('paddings')
+        padding_h_1 = paddings[0]
+        padding_w_1 = paddings[1]
+        padding_h_2 = paddings[2]
+        padding_w_2 = paddings[3]
+
+        dilations = node.attr('dilations')
+        dilation_h = dilations[0]
+        dilation_w = dilations[1]
+
+        kernel_sizes = node.attr('kernel_sizes')
+        kernel_h = kernel_sizes[0]
+        kernel_w = kernel_sizes[1]
+
+        input_w = mapper_helper.shape_helper(graph, node.input('X', 0), 3)
+        blocks_row_indices_node = cls._get_im2col_indices_along_dim(
+            graph, node, 2, kernel_h, dilation_h, padding_h_1, padding_h_2,
+            stride_h)
+        blocks_col_indices_node = cls._get_im2col_indices_along_dim(
+            graph, node, 3, kernel_w, dilation_w, padding_w_1, padding_w_2,
+            stride_w)
+
+        output_shape = cls._get_im2col_output_shape(graph, node, kernel_h,
+                                                    kernel_w)
+        padded_input = cls._get_im2col_padded_input(
+            graph, node, padding_h_1, padding_h_2, padding_w_1, padding_w_2)
+
+        output = graph.make_node(
+            'Gather', inputs=[padded_input, blocks_row_indices_node], axis=2)
+
+        output = graph.make_node(
+            'Gather', inputs=[output, blocks_col_indices_node], axis=4)
+        output = graph.make_node(
+            'Transpose', inputs=[output], perm=[0, 1, 2, 4, 3, 5])
+
+        graph.make_node(
+            'Reshape', inputs=[output, output_shape], outputs=node.output('Y'))
+
+    @classmethod
+    def _get_im2col_indices_along_dim(cls, graph, node, index, kernel_size_d,
+                                      dilation_d, padding_d_1, padding_d_2,
+                                      stride_d):
+        input_shape = node.input_shape('X', 0)
+        if input_shape[index] == -1:
+            input_d_node = mapper_helper.shape_helper(graph,
+                                                      node.input('X', 0), index)
+
+            padding_d_node = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=[padding_d_1 + padding_d_2])
+            blocks_d_node = graph.make_node(
+                'Add', inputs=[input_d_node, padding_d_node])
+
+            dilation_kernel_size_node = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=[dilation_d * (kernel_size_d - 1)])
+            blocks_d_node = graph.make_node(
+                'Sub', inputs=[blocks_d_node, dilation_kernel_size_node])
+
+            zero_node = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=[0])
+            stride_node = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=[stride_d])
+            blocks_d_indices_node = graph.make_node(
+                'Range', inputs=[zero_node, blocks_d_node, stride_node])
+        else:
+            end = input_shape[
+                index] + padding_d_1 + padding_d_2 - dilation_d * (kernel_size_d
+                                                                   - 1)
+            stride = stride_d
+            blocks_d_indices = np.arange(0, end, stride)
+            blocks_d_indices_node = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=blocks_d_indices.flatten().tolist())
+
+        kernel_grid = np.arange(0, kernel_size_d * dilation_d, dilation_d)
+        kernel_grid_node = graph.make_node(
+            'Constant',
+            dtype=dtypes.ONNX.INT64,
+            value=kernel_grid.flatten().tolist())
+
+        shape_node = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[-1, 1])
+        kernel_mask_node = graph.make_node(
+            'Reshape', inputs=[kernel_grid_node, shape_node])
+
+        block_mask_node = graph.make_node(
+            'Add', inputs=[blocks_d_indices_node, kernel_mask_node])
+        return block_mask_node
+
+    @classmethod
+    def _get_im2col_output_shape(cls, graph, node, kernel_h, kernel_w):
+        batch_dim = mapper_helper.shape_helper(graph, node.input('X', 0), 0)
+        channel_dim = mapper_helper.shape_helper(graph, node.input('X', 0), 1)
+
+        constant_node = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[kernel_h * kernel_w])
+        channel_unfolded = graph.make_node(
+            'Mul', inputs=[channel_dim, constant_node])
+
+        concat_const_node = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[-1])
+        result_node = graph.make_node(
+            'Concat',
+            inputs=[batch_dim, channel_unfolded, concat_const_node],
+            axis=0)
+
+        return result_node
+
+    @classmethod
+    def _get_im2col_padded_input(cls, graph, node, padding_h_1, padding_h_2,
+                                 padding_w_1, padding_w_2):
+        pad_const_node = graph.make_node(
+            'Constant',
+            dtype=dtypes.ONNX.INT64,
+            value=[
+                0, 0, padding_h_1, padding_w_1, 0, 0, padding_h_2, padding_w_2
+            ])
+        result_node = graph.make_node(
+            'Pad', inputs=[node.input('X', 0), pad_const_node])
+        return result_node
+
+
+@op_mapper('softmax_with_cross_entropy')
+class SoftmaxCrossEntropyLoss():
+    support_opset_version_range = (12, 15)
+
+    @classmethod
+    def opset_12(cls, graph, node, **kw):
+        if node.attr('soft_label'):
+            raise Exception(
+                "SoftmaxCrossEntropyLoss in onnx not support soft label.")
+        scores = node.input('Logits', 0)
+        labels = node.input('Label', 0)
+        # Whether return_softmax is True or False, the model will have two outputs
+        outputs = [node.output('Loss', 0), node.output('Softmax', 0)]
+
+        shape = node.input_shape('Logits', 0)
+        if len(shape) < 2:
+            raise Exception(
+                "SoftmaxCrossEntropyLoss in onnx not support 1D logits.")
+        axis = node.attr('axis')
+        if axis < 0:
+            axis += len(shape)
+        if axis == 1:
+            squeeze_node = mapper_helper.squeeze_helper(graph, labels, [axis])
+            loss_node, softmax_node = graph.make_node(
+                'SoftmaxCrossEntropyLoss',
+                inputs=[scores, squeeze_node],
+                outputs=2,
+                ignore_index=node.attr('ignore_index'),
+                reduction='none')
+            loss_node = mapper_helper.unsqueeze_helper(graph, loss_node,
+                                                       [axis], outputs[0])
+            # onnx output is log(softmax), but paddle output is softmax
+            graph.make_node('Exp', inputs=[softmax_node], outputs=outputs[1])
+        else:
+            perm = [i for i in range(len(shape))]
+            perm[1] = axis
+            perm[axis] = 1
+            transpose_scores = graph.make_node(
+                'Transpose', inputs=[scores], perm=perm)
+            transpose_labels = graph.make_node(
+                'Transpose', inputs=[labels], perm=perm)
+            squeeze_labels = mapper_helper.squeeze_helper(
+                graph, transpose_labels, [1])
+
+            loss_node, softmax_node = graph.make_node(
+                'SoftmaxCrossEntropyLoss',
+                inputs=[transpose_scores, squeeze_labels],
+                ignore_index=node.attr('ignore_index'),
+                outputs=2,
+                reduction='none')
+            output_node = mapper_helper.unsqueeze_helper(graph, loss_node, [1])
+            graph.make_node(
+                'Transpose', inputs=output_node, outputs=outputs[0], perm=perm)
+            softmax_node = graph.make_node(
+                'Transpose', inputs=softmax_node, perm=perm)
+            # onnx output is log(softmax), but paddle output is softmax
+            graph.make_node('Exp', inputs=[softmax_node], outputs=outputs[1])
diff --git a/paddle2onnx/legacy/op_mapper/nn.py b/paddle2onnx/legacy/op_mapper/nn.py
new file mode 100755
index 0000000000..37e2f1fabc
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/nn.py
@@ -0,0 +1,952 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+import math
+import collections
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+from paddle2onnx import utils
+import paddle
+
+
+@op_mapper(['conv2d', 'depthwise_conv2d', 'conv3d'])
+class Conv():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        kernel_shape = node.input_shape('Filter', 0)
+        dilations = node.attr('dilations')
+        kernel_shape = kernel_shape[2:]
+        strides = node.attr('strides')
+        group = node.attr('groups')
+        pads = node.attr('paddings')
+        assert node.attrs['data_format'] == 'NCHW' or node.attrs['data_format'] == 'NCDHW' or node.attrs['data_format'] == "AnyLayout",  \
+                            "The conv data format should be 'NCHW' or 'NCDHW', but received data format " \
+                            "is %s." % node.attrs['data_format']
+        # onnx padding is [x1_begin, x2_begin...x1_end, x2_end, ...]
+        if len(pads) == 2 or len(pads) == 3:
+            pads = pads + pads
+        elif len(pads) == 4:
+            pads = [pads[i] for i in [0, 2, 1, 3]]
+        elif len(pads) == 6:
+            pads = [pads[i] for i in [0, 2, 4, 1, 3, 5]]
+        attrs = {
+            'dilations': dilations,
+            'kernel_shape': kernel_shape,
+            'strides': strides,
+            'group': group
+        }
+        auto_pad = node.attr('padding_algorithm')
+        if auto_pad == 'SAME':
+            attrs['auto_pad'] = 'SAME_UPPER'
+        elif auto_pad == 'VALID':
+            attrs['auto_pad'] = 'VALID'
+        else:
+            attrs['pads'] = pads
+        graph.make_node(
+            'Conv',
+            inputs=node.input('Input') + node.input('Filter'),
+            outputs=node.output('Output'),
+            attrs=attrs)
+
+
+@op_mapper(
+    ['conv2d_transpose', 'depthwise_conv2d_transpose', 'conv3d_transpose'])
+class ConvTranspose():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        output_padding = node.attr('output_padding')
+        kernel_shape = node.input_shape('Filter', 0)
+        dilations = node.attr('dilations')
+        kernel_shape = kernel_shape[2:]
+        strides = node.attr('strides')
+        group = node.attr('groups')
+        pads = node.attr('paddings')
+        assert node.attrs['data_format'] == 'NCHW' or node.attrs['data_format'] == 'NCDHW', \
+            "The conv data format should be 'NCHW' or 'NCDHW', but received data format " \
+            "is %s." % node.attrs['data_format']
+
+        if len(pads) == 2 or len(pads) == 3:
+            pads = pads + pads
+        elif len(pads) == 4:
+            pads = [pads[i] for i in [0, 2, 1, 3]]
+        elif len(pads) == 6:
+            pads = [pads[i] for i in [0, 2, 4, 1, 3, 5]]
+
+        attrs = {
+            'dilations': dilations,
+            'kernel_shape': kernel_shape,
+            'strides': strides,
+            'group': group
+        }
+        auto_pad = node.attr('padding_algorithm')
+        if auto_pad == 'SAME':
+            attrs['auto_pad'] = 'SAME_UPPER'
+        elif auto_pad == 'VALID':
+            attrs['auto_pad'] = 'VALID'
+        else:
+            attrs['pads'] = pads
+        if output_padding and len(output_padding) > 0:
+            attrs['output_padding'] = output_padding
+        graph.make_node(
+            'ConvTranspose',
+            inputs=node.input('Input') + node.input('Filter'),
+            outputs=node.output('Output'),
+            attrs=attrs)
+
+
+@op_mapper('pool2d')
+class Pool():
+    support_opset_version_range = (1, 12)
+    pool_type = {
+        'max': ('MaxPool', 'GlobalMaxPool'),
+        'avg': ('AveragePool', 'GlobalAveragePool')
+    }
+
+    @classmethod
+    def is_same_span(cls, in_size, out_size):
+        spans = []
+        for i in range(out_size):
+            start = math.floor(i * (in_size / out_size))
+            end = math.ceil((i + 1) * (in_size / out_size))
+            spans.append(end - start)
+        if len(set(spans)) == 1:
+            return True
+        return False
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        assert node.attrs['data_format'] == 'NCHW' or node.attrs['data_format'] == "AnyLayout",  \
+                            "The conv data format should be 'NCHW', but received data format " \
+                            "is %s." % node.attrs['data_format']
+        x_dtype = node.input_dtype('X', 0)
+        need_dtype_convert = False
+        input_name = node.input('X', 0)
+        if x_dtype != paddle.float32:
+            need_dtype_convert = True
+            input_name = graph.make_node(
+                'Cast', inputs=node.input('X'), to=dtypes.ONNX.FLOAT)
+
+        if node.attr('global_pooling') or (node.attr('adaptive') and
+                                           node.attr('ksize') == [1, 1]):
+            if need_dtype_convert:
+                onnx_node = graph.make_node(
+                    cls.pool_type[node.attr('pooling_type')][1],
+                    inputs=[input_name])
+                graph.make_node(
+                    'Cast',
+                    inputs=[onnx_node],
+                    outputs=node.output('Out'),
+                    to=dtypes.ONNX.DOUBLE)
+            else:
+                onnx_node = graph.make_node(
+                    cls.pool_type[node.attr('pooling_type')][1],
+                    inputs=[input_name],
+                    outputs=node.output('Out'))
+        elif node.attr('adaptive'):
+            # if pool is adaptive, check if input shape of pool is fixed.
+            if node.input_shape('X', 0)[2:].count(-1) > 0:
+                raise Exception(
+                    "Converting this model to ONNX need with static input shape," \
+                    " please fix input shape of this model, see doc Q2 in" \
+                    " https://github.com/PaddlePaddle/paddle2onnx/blob/develop/docs/en/FAQ.md."
+                )
+            input_h, input_w = node.input_shape('X', 0)[2:]
+            output_h, output_w = node.output_shape('Out', 0)[2:]
+            stride_h = int(input_h / output_h)
+            stride_w = int(input_w / output_w)
+
+            kernel_h = input_h - (output_h - 1) * stride_h
+            kernel_w = input_w - (output_w - 1) * stride_w
+
+            #check if kernel_size is fixed.
+            if not cls.is_same_span(input_h, output_h) or not cls.is_same_span(
+                    input_w, output_w):
+                raise Exception(
+                    "Cannot convert adaptive pool with input_size: {}, output_size: {}"
+                    .format(
+                        node.input_shape('X', 0), node.output_shape('Out', 0)))
+            else:
+                attrs = {
+                    'kernel_shape': (kernel_h, kernel_w),
+                    'strides': (stride_h, stride_w),
+                }
+                if node.attr('ceil_mode') and graph.opset_version < 10:
+                    raise Exception(
+                        "Cannot convert pool with ceil_model == True to ONNX Opset version < 10."
+                    )
+                elif graph.opset_version > 10:
+                    attrs['ceil_mode'] = node.attr('ceil_mode')
+                auto_pad = node.attr('padding_algorithm')
+                if auto_pad == 'SAME':
+                    attrs['auto_pad'] = 'SAME_UPPER'
+                elif auto_pad == 'VALID':
+                    attrs['auto_pad'] = 'VALID'
+                if node.attr('pooling_type') == 'avg':
+                    attrs['count_include_pad'] = not node.attr('exclusive')
+                if need_dtype_convert:
+                    onnx_node = graph.make_node(
+                        cls.pool_type[node.attr('pooling_type')][0],
+                        inputs=[input_name],
+                        attrs=attrs)
+                    graph.make_node(
+                        'Cast',
+                        inputs=[onnx_node],
+                        outputs=node.output('Out'),
+                        to=dtypes.ONNX.DOUBLE)
+                else:
+                    onnx_node = graph.make_node(
+                        cls.pool_type[node.attr('pooling_type')][0],
+                        inputs=[input_name],
+                        outputs=node.output('Out'),
+                        attrs=attrs)
+        else:
+            input_shape = node.input_shape('X', 0)
+            k_size = node.attr('ksize')
+            pads = node.attr('paddings')
+            strides = node.attr('strides')
+
+            if len(pads) == 2:
+                pads = pads + pads
+            elif len(pads) == 4:
+                pads = [pads[i] for i in [0, 2, 1, 3]]
+
+            if input_shape[2] > 0 and input_shape[2] + pads[0] < k_size[0]:
+                k_size[0] = input_shape[2] + pads[0]
+            if input_shape[3] > 0 and input_shape[3] + pads[1] < k_size[1]:
+                k_size[1] = input_shape[3] + pads[1]
+
+            input_x = [input_name]
+            if max(k_size) <= max(pads):
+                onnx_paddings = [0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]]
+                attrs_pad = {'mode': 'constant', }
+                if graph.opset_version >= 11:
+                    pads_node = graph.make_node(
+                        'Constant',
+                        attrs={
+                            'dtype': dtypes.ONNX.INT64,
+                            'value': onnx_paddings
+                        })
+                    value_node = graph.make_node(
+                        'Constant',
+                        attrs={'dtype': dtypes.ONNX.FLOAT,
+                               'value': 0.0})
+                    input_x = input_x + [pads_node, value_node]
+                else:
+                    attrs_pad['pads'] = onnx_paddings
+                    attrs_pad['value'] = 0.0
+                input_x = graph.make_node(
+                    'Pad', inputs=input_x, attrs=attrs_pad)
+                pads = [0, 0, 0, 0]
+
+            attrs = {
+                'kernel_shape': k_size,
+                'strides': strides,
+            }
+            auto_pad = node.attr('padding_algorithm')
+            if auto_pad == 'SAME':
+                attrs['auto_pad'] = 'SAME_UPPER'
+            elif auto_pad == 'VALID':
+                attrs['auto_pad'] = 'VALID'
+            else:
+                attrs['pads'] = pads
+            if node.attr('ceil_mode') and graph.opset_version < 10:
+                raise Exception(
+                    "Cannot convert pool with ceil_model == True to ONNX Opset version < 10"
+                )
+            elif graph.opset_version >= 10:
+                attrs['ceil_mode'] = node.attr('ceil_mode')
+
+            if node.attr('pooling_type') == 'avg':
+                attrs['count_include_pad'] = not node.attr('exclusive')
+            if need_dtype_convert:
+                onnx_node = graph.make_node(
+                    cls.pool_type[node.attr('pooling_type')][0],
+                    inputs=input_x,
+                    attrs=attrs)
+                graph.make_node(
+                    'Cast',
+                    inputs=[onnx_node],
+                    outputs=node.output('Out'),
+                    to=dtypes.ONNX.DOUBLE)
+            else:
+                onnx_node = graph.make_node(
+                    cls.pool_type[node.attr('pooling_type')][0],
+                    inputs=input_x,
+                    outputs=node.output('Out'),
+                    attrs=attrs)
+
+
+@op_mapper('pool3d')
+class Pool3D():
+    support_opset_version_range = (1, 12)
+    pool_type = {
+        'max': ('MaxPool', 'GlobalMaxPool'),
+        'avg': ('AveragePool', 'GlobalAveragePool')
+    }
+
+    @classmethod
+    def is_same_span(cls, in_size, out_size):
+        spans = []
+        for i in range(out_size):
+            start = math.floor(i * (in_size / out_size))
+            end = math.ceil((i + 1) * (in_size / out_size))
+            spans.append(end - start)
+        if len(set(spans)) == 1:
+            return True
+        return False
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        assert node.attrs['data_format'] == 'NCDHW' or node.attrs['data_format'] == "AnyLayout",  \
+                            "The conv data format should be 'NCDHW', but received data format " \
+                            "is %s." % node.attrs['data_format']
+
+        if node.attr('global_pooling') or (node.attr('adaptive') and
+                                           node.attr('ksize') == [1, 1, 1]):
+            onnx_node = graph.make_node(
+                cls.pool_type[node.attr('pooling_type')][1],
+                inputs=node.input('X'),
+                outputs=node.output('Out'))
+        elif node.attr('adaptive'):
+            # if pool is adaptive, check if input shape of pool is fixed.
+            if node.input_shape('X', 0)[2:].count(-1) > 0:
+                raise Exception(
+                    "Converting this model to ONNX need with static input shape," \
+                    " please fix input shape of this model, see doc Q2 in" \
+                    " https://github.com/PaddlePaddle/paddle2onnx/blob/develop/docs/en/FAQ.md."
+                )
+            input_d, input_h, input_w = node.input_shape('X', 0)[2:]
+            output_d, output_h, output_w = node.output_shape('Out', 0)[2:]
+            stride_d = int(input_d / output_d)
+            stride_h = int(input_h / output_h)
+            stride_w = int(input_w / output_w)
+
+            kernel_d = input_d - (output_d - 1) * stride_d
+            kernel_h = input_h - (output_h - 1) * stride_h
+            kernel_w = input_w - (output_w - 1) * stride_w
+
+            #check if kernel_size is fixed.
+            if not cls.is_same_span(input_h, output_h) or not cls.is_same_span(
+                    input_w, output_w) or not cls.is_same_span(input_d,
+                                                               output_d):
+                raise Exception(
+                    "Cannot convert adaptive pool with input_size: {}, output_size: {}"
+                    .format(
+                        node.input_shape('X', 0), node.output_shape('Out', 0)))
+            else:
+                attrs = {
+                    'kernel_shape': (kernel_d, kernel_h, kernel_w),
+                    'strides': (stride_d, stride_h, stride_w),
+                }
+                if node.attr('ceil_mode') and graph.opset_version < 10:
+                    raise Exception(
+                        "Cannot convert pool with ceil_model == True to ONNX Opset version < 10."
+                    )
+                elif graph.opset_version > 10:
+                    attrs['ceil_mode'] = node.attr('ceil_mode')
+                auto_pad = node.attr('padding_algorithm')
+                if auto_pad == 'SAME':
+                    attrs['auto_pad'] = 'SAME_UPPER'
+                elif auto_pad == 'VALID':
+                    attrs['auto_pad'] = 'VALID'
+                if node.attr('pooling_type') == 'avg':
+                    attrs['count_include_pad'] = not node.attr('exclusive')
+                onnx_node = graph.make_node(
+                    cls.pool_type[node.attr('pooling_type')][0],
+                    inputs=node.input('X'),
+                    outputs=node.output('Out'),
+                    attrs=attrs)
+        else:
+            input_shape = node.input_shape('X', 0)
+            k_size = node.attr('ksize')
+            paddings = node.attr('paddings')
+            if input_shape[2] > 0 and input_shape[2] + paddings[0] < k_size[0]:
+                k_size[0] = input_shape[2] + paddings[0]
+            if input_shape[3] > 0 and input_shape[3] + paddings[1] < k_size[1]:
+                k_size[1] = input_shape[3] + paddings[1]
+            if input_shape[4] > 0 and input_shape[4] + paddings[2] < k_size[2]:
+                k_size[2] = input_shape[4] + paddings[2]
+            attrs = {
+                'kernel_shape': k_size,
+                'strides': node.attr('strides'),
+                'pads': node.attr('paddings') + node.attr('paddings'),
+            }
+            if node.attr('ceil_mode') and graph.opset_version < 10:
+                raise Exception(
+                    "Cannot convert pool with ceil_model == True to ONNX Opset version < 10"
+                )
+            elif graph.opset_version >= 10:
+                attrs['ceil_mode'] = node.attr('ceil_mode')
+
+            if node.attr('pooling_type') == 'avg':
+                attrs['count_include_pad'] = not node.attr('exclusive')
+            onnx_node = graph.make_node(
+                cls.pool_type[node.attr('pooling_type')][0],
+                inputs=node.input('X'),
+                outputs=node.output('Out'),
+                attrs=attrs)
+
+
+@op_mapper('elu')
+class ELU():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'Elu',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            alpha=node.attr('alpha'))
+
+
+@op_mapper('softsign')
+class SoftSign():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Softsign', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('hard_shrink')
+class Hardshrink():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        node = graph.make_node(
+            'Shrink',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            lambd=node.attr('threshold'))
+
+
+@op_mapper('logsigmoid')
+class LogSigmoid():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        sigmoid_node = graph.make_node('Sigmoid', inputs=node.input('X'))
+        graph.make_node('Log', inputs=sigmoid_node, outputs=node.output('Out'))
+
+
+@op_mapper('norm')
+class Norm():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        node = graph.make_node(
+            'LpNormalization',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            axis=node.attr('axis'))
+
+
+@op_mapper('softshrink')
+class SoftShrink():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'Shrink',
+            inputs=node.input('X'),
+            bias=node.attr('lambda'),
+            lambd=node.attr('lambda'),
+            outputs=node.output('Out'))
+
+
+@op_mapper('tanh_shrink')
+class TanhShrink():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        tanh_node = graph.make_node(
+            'Tanh',
+            inputs=node.input('X', 0), )
+        graph.make_node(
+            'Sub',
+            inputs=[node.input('X', 0), tanh_node],
+            outputs=node.output('Out'))
+
+
+@op_mapper('log_softmax')
+class LogSoftmax():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        axis = node.attr('axis')
+        shape = node.output_shape('Out', 0)
+        if axis is None:
+            axis = -1
+        if axis < 0:
+            axis += len(shape)
+        if axis == len(shape) - 1:
+            node = graph.make_node(
+                'LogSoftmax',
+                inputs=node.input('X'),
+                outputs=node.output('Out'),
+                attrs={'axis': axis})
+        else:
+            perm = [i for i in range(len(shape))]
+            perm[-1] = axis
+            perm[axis] = len(shape) - 1
+            transpose_node = graph.make_node(
+                'Transpose', inputs=node.input('X'), attrs={'perm': perm})
+            softmax_node = graph.make_node(
+                'LogSoftmax', inputs=[transpose_node], axis=-1)
+            transpose_node1 = graph.make_node(
+                'Transpose',
+                inputs=[softmax_node],
+                outputs=node.output('Out'),
+                attrs={'perm': perm})
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        graph.make_node(
+            'LogSoftmax',
+            inputs=node.input('X'),
+            axis=node.attr('axis'),
+            outputs=node.output('Out'))
+
+
+@op_mapper('layer_norm')
+class LayerNorm():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        ipt = node.input('X', 0)
+        ipt_dims = len(node.input_shape('X', 0))
+        normalized_shape = node.attr('begin_norm_axis')
+        axes = None
+        if isinstance(normalized_shape, collections.Iterable):
+            axes = [-i for i in range(len(normalized_shape), 0, -1)]
+        else:
+            axes = [i for i in range(normalized_shape, ipt_dims)]
+        dtype = node.block.vars[node.input('X', 0)].dtype
+        dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype]
+        epsilon = graph.make_node(
+            'Constant', dtype=dtype, value=node.attr('epsilon'))
+        two = graph.make_node('Constant', dtype=dtype, value=2.0)
+        mean = graph.make_node("ReduceMean", inputs=[ipt], axes=axes)
+        numerator = graph.make_node("Sub", inputs=[ipt, mean])
+        pow_num = graph.make_node("Pow", inputs=[numerator, two])
+        variance = graph.make_node("ReduceMean", inputs=[pow_num], axes=axes)
+        add_eps = graph.make_node("Add", inputs=[variance, epsilon])
+        denominator = graph.make_node("Sqrt", inputs=[add_eps])
+
+        ipt_shape = graph.make_node("Shape", inputs=[ipt])
+        weight_shape = mapper_helper.slice_helper(
+            graph, ipt_shape, [0], [ipt_dims - len(axes)], [ipt_dims])
+        if 'Bias' in node.inputs and 'Scale' in node.inputs and len(
+                node.input('Scale')) > 0 and len(node.input('Bias')) > 0:
+            if normalized_shape == ipt_dims - 1:
+                shape_const = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[-1])
+                scale = graph.make_node(
+                    "Reshape", inputs=[node.input('Scale', 0), shape_const])
+                bias = graph.make_node(
+                    "Reshape", inputs=[node.input('Bias', 0), shape_const])
+            else:
+                scale = graph.make_node(
+                    "Reshape", inputs=[node.input('Scale', 0), weight_shape])
+                bias = graph.make_node(
+                    "Reshape", inputs=[node.input('Bias', 0), weight_shape])
+            layer_norm = graph.make_node("Div", inputs=[numerator, denominator])
+            layer_norm = graph.make_node("Mul", inputs=[layer_norm, scale])
+            graph.make_node(
+                "Add", inputs=[layer_norm, bias], outputs=node.output('Y'))
+        elif 'Bias' in node.inputs and len(node.input('Bias')) > 0:
+            if normalized_shape == ipt_dims - 1:
+                shape_const = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[-1])
+                bias = graph.make_node(
+                    "Reshape", inputs=[node.input('Bias', 0), shape_const])
+            else:
+                bias = graph.make_node(
+                    "Reshape", inputs=[node.input('Bias', 0), weight_shape])
+            layer_norm = graph.make_node("Div", inputs=[numerator, denominator])
+            graph.make_node(
+                "Add", inputs=[layer_norm, bias], outputs=node.output('Y'))
+        elif 'Scale' in node.inputs and len(node.input('Scale')) > 0:
+            if normalized_shape == ipt_dims - 1:
+                shape_const = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[-1])
+                scale = graph.make_node(
+                    "Reshape", inputs=[node.input('Scale', 0), shape_const])
+            else:
+                scale = graph.make_node(
+                    "Reshape", inputs=[node.input('Scale', 0), weight_shape])
+            layer_norm = graph.make_node("Div", inputs=[numerator, denominator])
+            graph.make_node(
+                "Mul", inputs=[layer_norm, scale], outputs=node.output('Y'))
+        else:
+            layer_norm = graph.make_node(
+                "Div",
+                inputs=[numerator, denominator],
+                outputs=node.output('Y'))
+
+
+@op_mapper('batch_norm')
+class BatchNorm():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def make_attrs_and_inputs(cls, graph, node, **kw):
+        onnx_attr = {
+            'epsilon': node.attr('epsilon'),
+            'momentum': node.attr('momentum')
+        }
+        inputs = node.input('X') + node.input('Scale') + node.input(
+            'Bias') + node.input('Mean') + node.input('Variance')
+        return onnx_attr, inputs
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        onnx_attr, inputs = cls.make_attrs_and_inputs(graph, node, **kw)
+        onnx_node = graph.make_node(
+            'BatchNormalization',
+            inputs=inputs,
+            outputs=node.output('Y'),
+            **onnx_attr)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        onnx_attr, inputs = cls.make_attrs_and_inputs(graph, node, **kw)
+        onnx_attr['spatial'] = 1
+        onnx_node = graph.make_node(
+            'BatchNormalization',
+            inputs=inputs,
+            outputs=node.output('Y'),
+            **onnx_attr)
+
+
+@op_mapper('group_norm')
+class GroupNorm():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        num_groups = node.attr('groups')
+        epsilon = node.attr('epsilon')
+        ipt = node.input('X')[0]
+
+        ipt_shape = node.input_shape('X', 0)
+        assert len(
+            ipt_shape) == 4, "Only support 4D-Tensor as input for GroupNorm"
+
+        dtype = node.block.vars[node.input('X', 0)].dtype
+        dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype]
+
+        shape = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[0, num_groups, -1])
+        reshape_input = graph.make_node('Reshape', inputs=[ipt, shape])
+        scale_ = graph.make_node(
+            'Constant', dtype=dtype, value=[1.0] * num_groups)
+        bias_ = graph.make_node(
+            'Constant', dtype=dtype, value=[0.0] * num_groups)
+        reshaped_output = graph.make_node(
+            'InstanceNormalization',
+            inputs=[reshape_input, scale_, bias_],
+            epsilon=epsilon)
+        origin_shape = graph.make_node('Shape', inputs=[ipt])
+
+        if len(node.input('Scale')) > 0 and len(node.input('Bias')) > 0:
+            output = graph.make_node(
+                'Reshape', inputs=[reshaped_output, origin_shape])
+            unsqueezed_scale = mapper_helper.unsqueeze_helper(
+                graph, node.input('Scale', 0), [1, 2])
+            unsqueezed_bias = mapper_helper.unsqueeze_helper(
+                graph, node.input('Bias', 0), [1, 2])
+            part0 = graph.make_node('Mul', inputs=[output, unsqueezed_scale])
+            graph.make_node(
+                'Add',
+                inputs=[part0, unsqueezed_bias],
+                outputs=node.output('Y'))
+        else:
+            output = graph.make_node(
+                'Reshape',
+                inputs=[reshaped_output, origin_shape],
+                outputs=node.output('Y'))
+
+
+@op_mapper('instance_norm')
+class InstanceNorm():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        onnx_attr = {'epsilon': node.attr('epsilon'), }
+        num_groups = node.block.vars[node.input('X')[0]].shape[1]
+
+        dtype = node.block.vars[node.input('X', 0)].dtype
+        dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype]
+
+        if len(node.input('Scale')) == 0:
+            scale_ = graph.make_node(
+                'Constant', dtype=dtype, value=[1.0] * num_groups)
+        else:
+            scale_ = node.input('Scale')[0]
+        if len(node.input('Bias')) == 0:
+            bias_ = graph.make_node(
+                'Constant', dtype=dtype, value=[0.0] * num_groups)
+        else:
+            bias_ = node.input('Bias')[0]
+
+        inputs = node.input('X') + [scale_] + [bias_]
+        onnx_node = graph.make_node(
+            'InstanceNormalization',
+            inputs=inputs,
+            outputs=node.output('Y'),
+            **onnx_attr)
+
+
+@op_mapper('dropout')
+class Dropout():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        dropout_mode = node.attr('dropout_implementation')
+        dropout_prob = node.attr('dropout_prob')
+        if dropout_mode == 'upscale_in_train':
+            onnx_node = graph.make_node(
+                'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+        elif dropout_mode == 'downgrade_in_infer':
+            scale_node = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.FLOAT,
+                       'value': 1 - dropout_prob})
+            graph.make_node(
+                "Mul",
+                inputs=[node.input('X')[0], scale_node],
+                outputs=node.output('Out'))
+        else:
+            raise Exception("Unexpected situation happend")
+
+
+@op_mapper('roi_align')
+class RoiAlign():
+    support_opset_version_range = (10, 16)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        if node.attr('aligned') and graph.opset_version < 16:
+            raise Exception(
+                'when aligned is true, onnx opset should be (onnx_opset>= 16)')
+        rois_shape = graph.make_node('Shape', inputs=[node.input('ROIs', 0)])
+        starts = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                               'value': [0]})
+        ends = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                               'value': [1]})
+        num_rois = graph.make_node('Slice', inputs=[rois_shape, starts, ends])
+        zero = graph.make_node(
+            'Constant', dims=[1], dtype=dtypes.ONNX.INT64, value=[0])
+        batch_indices = graph.make_node('Expand', inputs=[zero, num_rois])
+        node = graph.make_node(
+            'RoiAlign',
+            inputs=[node.input('X', 0), node.input('ROIs', 0), batch_indices],
+            outputs=node.output('Out'),
+            mode='avg',
+            output_height=node.attr('pooled_height'),
+            output_width=node.attr('pooled_width'),
+            sampling_ratio=node.attr('sampling_ratio'),
+            spatial_scale=node.attr('spatial_scale'))
+
+
+@op_mapper('rnn')
+class RNN():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def make_param_inputs(cls, graph, node, layer, hidden_size, num_layers):
+        # weight assign order:
+        # (F_whi F_whh B_whi B_whh）* layer_num  + (F_bias_hi F_bias_hh B_bias_hi  B_bias_hi)* layer_num
+        def reform_weights(g, w, n, intervals):
+            slices = [
+                mapper_helper.slice_helper(
+                    g, w, axes=[1], starts=[x * n], ends=[y * n])
+                for x, y in intervals
+            ]
+            return g.make_node('Concat', slices, axis=1)
+
+        def transform_weight_with_bias(g, weights, n, intervals):
+            return [reform_weights(g, w, n, intervals) for w in weights]
+
+        if node.attr('mode') == 'LSTM':
+            reform_permutation = [(0, 1), (3, 4), (1, 3)]
+        elif node.attr('mode') == 'GRU':
+            reform_permutation = [(1, 2), (0, 1), (2, 3)]
+        bidirect_len = 4 if node.attr('is_bidirec') else 2
+        all_layer_param_len = len(node.input('WeightList'))
+        weight_list = node.input('WeightList')[:all_layer_param_len // 2]
+        bias_list = node.input('WeightList')[all_layer_param_len // 2:]
+        single_layer_param_len = all_layer_param_len // num_layers
+
+        unsqueeze_weights = []
+        layer_weight_list = weight_list[layer * bidirect_len:layer *
+                                        bidirect_len + bidirect_len]
+        layer_bias_list = bias_list[layer * bidirect_len:layer * bidirect_len +
+                                    bidirect_len]
+        param_list = layer_weight_list + layer_bias_list
+        param_list_len = len(param_list)
+        for i in range(param_list_len):
+            weight = mapper_helper.unsqueeze_helper(graph, param_list[i], [0])
+            unsqueeze_weights.append(weight)
+
+        input_weights = unsqueeze_weights[0:param_list_len // 2:2]
+        hidden_weights = unsqueeze_weights[1:param_list_len // 2:2]
+
+        input_weight = graph.make_node('Concat', inputs=input_weights, axis=0)
+        hidden_weight = graph.make_node('Concat', inputs=hidden_weights, axis=0)
+        input_bias = unsqueeze_weights[param_list_len // 2:param_list_len:2]
+        hidden_bias = unsqueeze_weights[param_list_len // 2 + 1:param_list_len:
+                                        2]
+
+        input_bias = graph.make_node('Concat', inputs=input_bias, axis=0)
+        hidden_bias = graph.make_node('Concat', inputs=hidden_bias, axis=0)
+        input_weight, hidden_weight, input_bias, hidden_bias = transform_weight_with_bias(
+            graph, [input_weight, hidden_weight, input_bias, hidden_bias],
+            hidden_size, reform_permutation)
+        bias = graph.make_node(
+            'Concat', inputs=[input_bias, hidden_bias], axis=1)
+        return [input_weight, hidden_weight, bias, '']
+
+    @classmethod
+    def make_init_param_inputs(cls, graph, node, layer):
+        if node.attr('mode') == 'LSTM':
+            all_init_h, all_init_c = node.input('PreState')
+            bidirect_len = 2 if node.attr('is_bidirec') else 1
+            init_h = mapper_helper.slice_helper(
+                graph, all_init_h, [0], [layer * bidirect_len],
+                [layer * bidirect_len + bidirect_len])
+            init_c = mapper_helper.slice_helper(
+                graph, all_init_c, [0], [layer * bidirect_len],
+                [layer * bidirect_len + bidirect_len])
+            return [init_h, init_c]
+        elif node.attr('mode') == 'GRU':
+            all_init_h = node.input('PreState', 0)
+            bidirect_len = 2 if node.attr('is_bidirec') else 1
+            init_h = mapper_helper.slice_helper(
+                graph, all_init_h, [0], [layer * bidirect_len],
+                [layer * bidirect_len + bidirect_len])
+            return [init_h]
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        mode = node.attr('mode')
+        hidden_size = node.attr('hidden_size')
+        num_layers = node.attr('num_layers')
+        prev_output = node.input('Input', 0)
+        if node.attr('mode') == 'LSTM':
+            for layer in range(num_layers):
+                param_inputs = cls.make_param_inputs(graph, node, layer,
+                                                     hidden_size, num_layers)
+                init_param_inputs = cls.make_init_param_inputs(graph, node,
+                                                               layer)
+                if layer + 1 < num_layers:
+                    rnn_outputs = 3
+                    output_y = None
+                else:
+                    rnn_outputs = [1] + node.output('State')
+                    output_y = node.output('Out')
+                prev_output, h_out, c_out = graph.make_node(
+                    node.attr('mode'),
+                    inputs=[prev_output] + param_inputs + init_param_inputs,
+                    outputs=rnn_outputs,
+                    direction='bidirectional'
+                    if node.attr('is_bidirec') else 'forward',
+                    hidden_size=node.attr('hidden_size'))
+                prev_output = graph.make_node(
+                    'Transpose', inputs=[prev_output], perm=[0, 2, 1, 3])
+
+                prev_shape = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[0, 0, -1])
+                prev_output = graph.make_node(
+                    'Reshape',
+                    inputs=[prev_output, prev_shape],
+                    outputs=output_y)
+        elif node.attr('mode') == 'GRU':
+            for layer in range(num_layers):
+                param_inputs = cls.make_param_inputs(graph, node, layer,
+                                                     hidden_size, num_layers)
+                init_param_inputs = cls.make_init_param_inputs(graph, node,
+                                                               layer)
+                if layer + 1 < num_layers:
+                    rnn_outputs = 2
+                    output_y = None
+                else:
+                    rnn_outputs = [1] + node.output('State')
+                    output_y = node.output('Out')
+                attrs = {
+                    'direction': 'bidirectional'
+                    if node.attr('is_bidirec') else 'forward',
+                    'hidden_size': node.attr('hidden_size'),
+                    'linear_before_reset': 1,
+                }
+                prev_output, h_out = graph.make_node(
+                    node.attr('mode'),
+                    inputs=[prev_output] + param_inputs + init_param_inputs,
+                    outputs=rnn_outputs,
+                    attrs=attrs)
+                prev_output = graph.make_node(
+                    'Transpose', inputs=[prev_output], perm=[0, 2, 1, 3])
+                prev_shape = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[0, 0, -1])
+                prev_output = graph.make_node(
+                    'Reshape',
+                    inputs=[prev_output, prev_shape],
+                    outputs=output_y)
+
+
+@op_mapper('thresholded_relu')
+class ThresholdedRelu():
+    support_opset_version_range = (10, 15)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        x_dtype = node.input_dtype('X', 0)
+        if x_dtype != paddle.float32:
+            x = graph.make_node(
+                'Cast', inputs=node.input('X'), to=dtypes.ONNX.FLOAT)
+            threshholdedrelu_node = graph.make_node(
+                'ThresholdedRelu', inputs=[x], alpha=node.attr('threshold'))
+            graph.make_node(
+                'Cast',
+                inputs=[threshholdedrelu_node],
+                outputs=node.output('Out'),
+                to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+        else:
+            graph.make_node(
+                'ThresholdedRelu',
+                inputs=node.input('X'),
+                alpha=node.attr('threshold'),
+                outputs=node.output('Out'))
diff --git a/paddle2onnx/legacy/op_mapper/op_mapper.py b/paddle2onnx/legacy/op_mapper/op_mapper.py
new file mode 100755
index 0000000000..beff194ae4
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/op_mapper.py
@@ -0,0 +1,305 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import inspect
+import six
+import numpy as np
+import paddle
+from paddle import fluid
+from paddle.fluid import layers
+
+from paddle2onnx.legacy.graph import graph_helper, PaddleGraph
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.constant.op_mapping_status import *
+
+
+REGISTER_CUSTOM_PADDLE_OP = {}
+
+
+def get_max_support_version(versions, opset_version):
+    max_version = -1
+    for vs in sorted(versions):
+        if vs <= opset_version:
+            max_version = vs
+    return max_version
+
+
+def register_op_mapper(paddle_op, mapper_obj):
+    paddle_op_list = []
+
+    if isinstance(paddle_op, six.string_types):
+        paddle_op_list.append(paddle_op)
+    elif isinstance(paddle_op, list):
+        paddle_op_list = paddle_op
+    else:
+        raise ValueError('paddle_op must be List or string, but got type {}.'.
+                         format(type(paddle_op)))
+
+    if not isinstance(mapper_obj, six.class_types):
+        raise ValueError('mapper_obj must be Class, but got type {}.'.format(
+            type(mapper_obj)))
+
+    valid_register_func = 0
+    for k, v in inspect.getmembers(mapper_obj, inspect.ismethod):
+        if k.startswith("opset_"):
+            version = int(k.replace("opset_", ""))
+            if version > 13 or version < 1:
+                raise Exception(
+                    'the specific method of operator mapper must be named opset_[number](1<=number<=13), such as opset_9, but got {}.'.
+                    format(k))
+            valid_register_func += 1
+
+    if valid_register_func == 0:
+        raise Exception(
+            'the specific method of operator mapper must be classmethod, which named opset_[number](1<=number<=13), such as opset_9, but none achieved.'
+        )
+
+    mapper = OpMapper(paddle_op_list)
+    mapper(mapper_obj)
+
+
+class OpMapper(object):
+    OPSETS = {}
+    REGISTER_CUSTOM_PADDLE_OP = {}
+
+    def __init__(self, paddle_op, **kwargs):
+        if not isinstance(paddle_op, list):
+            paddle_op = [paddle_op]
+        self.paddle_op = paddle_op
+        self.kwargs = kwargs
+
+    def __call__(self, cls):
+        for k, v in inspect.getmembers(cls, inspect.ismethod):
+            if k.startswith("opset_"):
+                version = int(k.replace("opset_", ""))
+                for op in self.paddle_op:
+                    if op not in OpMapper.OPSETS:
+                        OpMapper.OPSETS[op] = {}
+                    opset_dict = OpMapper.OPSETS[op]
+                    opset_dict[version] = (v, self.kwargs)
+
+    @staticmethod
+    def mapping(graph, node, operator_export_type="ONNX"):
+        try:
+            if node.type in OpMapper.REGISTER_CUSTOM_PADDLE_OP:
+                if operator_export_type in ["PaddleFallback"]:
+                    opsets = OpMapper.OPSETS[node.type]
+                    versions = list(opsets.keys())
+                    convert_version = get_max_support_version(
+                        versions, graph.opset_version)
+                    mapper_func, kw = opsets[convert_version]
+                    mapper_func(graph, node, **kw)
+                else:
+                    custom_paddle_op = OpMapper.REGISTER_CUSTOM_PADDLE_OP[
+                        node.type](node)
+                    custom_paddle_graph, output_results = custom_paddle_op.get_paddle_graph(
+                    )
+                    OpMapper.check_support_status(custom_paddle_graph.node_map,
+                                                  graph.opset_version)
+                    graph.build_op_nodes(custom_paddle_graph.node_map)
+
+                    node_output_results = dict()
+                    for k in node.output_names:
+                        custom_outs = output_results[k]
+                        node_outs = node.output(k)
+                        assert len(custom_outs) == len(
+                            node_outs
+                        ), "Length of custom implementation operator's outputs is not same with the length of original operator's outputs."
+                        for i in range(len(custom_outs)):
+                            graph.make_node(
+                                "Identity",
+                                inputs=[custom_outs[i]],
+                                outputs=[node_outs[i]])
+            else:
+                opsets = OpMapper.OPSETS[node.type]
+                versions = list(opsets.keys())
+                convert_version = get_max_support_version(versions,
+                                                          graph.opset_version)
+                mapper_func, kw = opsets[convert_version]
+                mapper_func(graph, node, **kw)
+        except Exception as e:
+            raise Exception(
+                "Error happened when mapping node ['{}'] to onnx, which op_type is '{}' with inputs: {} and outputs: {}, specific error: ".
+                format(node.layer_name, node.type, node.inputs,
+                       node.outputs) + str(e))
+
+    @staticmethod
+    def get_recommend_opset_version(node_map, opset_version):
+        recommend_opset_version = OpMapper.check_support_status(
+            node_map, opset_version, True)
+        for name, node in list(node_map.items()):
+            if node.type in OpMapper.REGISTER_CUSTOM_PADDLE_OP:  #如果是custom的op，获�custom的推�op
+                custom_paddle_op = OpMapper.REGISTER_CUSTOM_PADDLE_OP[
+                    node.type](node)
+                custom_paddle_graph, output_results = custom_paddle_op.get_paddle_graph(
+                )
+                custom_recommend_opset_version = OpMapper.check_support_status(
+                    custom_paddle_graph.node_map, opset_version, True)
+                recommend_opset_version = max(recommend_opset_version,
+                                              custom_recommend_opset_version)
+        if opset_version != recommend_opset_version:
+            warning_info = "\n======================\n"
+            warning_info += "\nFor a successful conversion, set the recommended opset version : {}\n".format(
+                recommend_opset_version)
+            warning_info += "\n======================\n"
+            logging.warning(warning_info)
+        return recommend_opset_version
+
+    @staticmethod
+    def check_support_status(node_map, opset_version, for_check=False):
+        op_mapping_status = {
+            OP_MAPPING_NO_REGISTER: [],
+            OP_MAPPING_NO_VERSION: [],
+        }
+        for name, node in list(node_map.items()):
+            if node.type in OpMapper.REGISTER_CUSTOM_PADDLE_OP:
+                continue
+            if node.type not in OpMapper.OPSETS:
+                op_mapping_status[OP_MAPPING_NO_REGISTER].append(node)
+            else:
+                opsets = OpMapper.OPSETS[node.type]
+                versions = list(opsets.keys())
+                convert_version = get_max_support_version(versions,
+                                                          opset_version)
+                if convert_version == -1:
+                    op_mapping_status[OP_MAPPING_NO_VERSION].append(node)
+
+        if len(op_mapping_status[OP_MAPPING_NO_REGISTER]) > 0:
+            unsupported_op_types = set([
+                node.type for node in op_mapping_status[OP_MAPPING_NO_REGISTER]
+            ])
+            error_info = "\nThere's {} ops are not supported yet\n".format(
+                len(unsupported_op_types))
+            for op_type in unsupported_op_types:
+                error_info += "=========== {} ===========\n".format(op_type)
+            raise NotImplementedError(error_info)
+
+        if len(op_mapping_status[OP_MAPPING_NO_VERSION]) > 0:
+            unsupported_op_types = set([
+                node.type for node in op_mapping_status[OP_MAPPING_NO_VERSION]
+            ])
+
+            recommend_opset_version = -1
+            for op_type in unsupported_op_types:
+                opsets = OpMapper.OPSETS[op_type]
+                if min(opsets.keys()) > recommend_opset_version:
+                    recommend_opset_version = min(opsets.keys())
+            warning_info = "\nThere are {} ops that are not supported in opset version {}, please set opset version >= {}.\n".format(
+                len(unsupported_op_types), opset_version,
+                recommend_opset_version)
+
+            for op_type in unsupported_op_types:
+                warning_info += "=========== {} ===========\n".format(op_type)
+            if for_check:
+                logging.warning(warning_info)
+                return recommend_opset_version
+            raise NotImplementedError(warning_info)
+        return opset_version
+
+
+class CustomPaddleOp(object):
+    CREATE_TIMES = {}
+
+    def __init__(self, node):
+        self.main_program = paddle.static.Program()
+        self.startup_program = paddle.static.Program()
+        self.inputs = self.create_place_holder(node)
+        self.node = node
+
+    def generate_scope_name(self, node):
+        if node.type in CustomPaddleOp.CREATE_TIMES:
+            CustomPaddleOp.CREATE_TIMES[node.type] += 1
+        else:
+            CustomPaddleOp.CREATE_TIMES[node.type] = 1
+        scope_prefix = node.type + str(CustomPaddleOp.CREATE_TIMES[node.type] -
+                                       1) + '_'
+        return scope_prefix
+
+    def create_place_holder(self, node):
+        place_holders = {}
+        with paddle.static.program_guard(self.main_program,
+                                         self.startup_program):
+            for arg_name, idxs in node.inputs.items():
+                place_holders[arg_name] = []
+                for idx in range(len(idxs)):
+                    shape = node.input_shape(arg_name, idx)
+                    dtype = node.input_dtype(arg_name, idx)
+                    name = node.input(arg_name, idx)
+                    data = paddle.static.data(
+                        name=name, shape=shape, dtype=dtype)
+                    place_holders[arg_name].append(data)
+        return place_holders
+
+    def input(self, name, idx=None):
+        if name not in self.inputs:
+            return None
+        if idx is None:
+            return self.inputs[name]
+        if len(self.inputs[name]) <= idx:
+            return None
+        return self.inputs[name][idx]
+
+    def get_paddle_graph(self):
+        scope_prefix = self.generate_scope_name(self.node)
+        scope = paddle.static.Scope()
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(self.main_program,
+                                             self.startup_program):
+                with paddle.utils.unique_name.guard(scope_prefix):
+                    res = self.forward()
+                    feed_var_names = [
+                        var.name for vars in self.inputs.values()
+                        for var in vars
+                    ]
+                    fetch_vars = [var for vars in res.values() for var in vars]
+                    inference_program = graph_helper.get_program(
+                        self.main_program, feed_var_names, fetch_vars)
+                    paddle_graph = PaddleGraph.build_from_program(
+                        inference_program,
+                        feed_var_names,
+                        fetch_vars,
+                        scope=scope)
+
+        output_results = dict()
+        for arg_name, outs in res.items():
+            output_results[arg_name] = [out.name for out in outs]
+        return paddle_graph, output_results
+
+
+def register_custom_paddle_op(paddle_op, custom_op):
+    paddle_op_list = []
+
+    if isinstance(paddle_op, six.string_types):
+        paddle_op_list.append(paddle_op)
+    elif isinstance(paddle_op, list):
+        paddle_op_list = paddle_op
+    else:
+        raise ValueError("paddle_op' must be List or string, but got type {}.".
+                         format(type(paddle_op)))
+
+    if not isinstance(custom_op, six.class_types):
+        raise ValueError("'custom_op' must be Class, but got type {}.".format(
+            type(custom_op)))
+
+    forward = getattr(custom_op, "forward", None)
+    if not callable(forward):
+        raise Exception(
+            "Custom paddle operators must be implemented in function named 'forward'."
+        )
+
+    for op in paddle_op_list:
+        if op not in OpMapper.REGISTER_CUSTOM_PADDLE_OP:
+            OpMapper.REGISTER_CUSTOM_PADDLE_OP[op] = custom_op
diff --git a/paddle2onnx/legacy/op_mapper/search.py b/paddle2onnx/legacy/op_mapper/search.py
new file mode 100755
index 0000000000..38848b5ebb
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/search.py
@@ -0,0 +1,233 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+
+
+@op_mapper('where_index')
+class WhereIndex():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        nonzero_node = graph.make_node(
+            'NonZero', inputs=node.input('Condition'))
+        graph.make_node(
+            'Transpose',
+            inputs=[nonzero_node],
+            outputs=node.output('Out'),
+            perm=[1, 0])
+
+
+@op_mapper('top_k_v2')
+class TopKV2():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        sorted = node.attr('sorted')
+        # for paddle, In gpu device, it always return the sorted value
+        # if not sorted:
+        #     sorted = True
+        if 'K' in node.inputs and len(node.input('K')) > 0:
+            k_node = node.input('K', 0)
+            k_node_dtype = node.input_dtype('K', 0)
+            if dtypes.DTYPE_PADDLE_STR_MAP[k_node_dtype] != 'int64':
+                k_node = graph.make_node(
+                    'Cast', inputs=[k_node], to=dtypes.ONNX.INT64)
+            graph.make_node(
+                'TopK',
+                inputs=[node.input('X', 0), k_node],
+                outputs=[node.output('Out', 0), node.output('Indices', 0)],
+                largest=node.attr('largest'),
+                sorted=sorted,
+                axis=node.attr('axis'))
+        else:
+            k = node.attr('k')
+            k_node = graph.make_node(
+                'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                                   'value': [k]})
+            graph.make_node(
+                'TopK',
+                inputs=[node.input('X', 0), k_node],
+                outputs=[node.output('Out', 0), node.output('Indices', 0)],
+                largest=node.attr('largest'),
+                sorted=sorted,
+                axis=node.attr('axis'))
+
+
+@op_mapper('top_k')
+class TopK():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        if 'K' in node.inputs and len(node.input('K')) > 0:
+            k_node = node.input('K', 0)
+            k_node_dtype = node.input_dtype('K', 0)
+            if dtypes.DTYPE_PADDLE_STR_MAP[k_node_dtype] != 'int64':
+                k_node = graph.make_node(
+                    'Cast', inputs=[k_node], to=dtypes.ONNX.INT64)
+            graph.make_node(
+                'TopK',
+                inputs=[node.input('X', 0), k_node],
+                outputs=[node.output('Out', 0), node.output('Indices', 0)])
+        else:
+            k = node.attr('k')
+            k_node = graph.make_node(
+                'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                                   'value': [k]})
+            graph.make_node(
+                'TopK',
+                inputs=[node.input('X', 0), k_node],
+                outputs=[node.output('Out', 0), node.output('Indices', 0)])
+
+
+@op_mapper('argsort')
+class ArgSort():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        shape = graph.make_node('Shape', inputs=node.input('X', 0))
+        from paddle2onnx.legacy.op_mapper import mapper_helper
+        axis = node.attr('axis')
+        if axis < 0:
+            axis = axis + len(node.input_shape('X', 0))
+        dim_size = mapper_helper.slice_helper(
+            graph, shape, axes=[0], starts=[axis], ends=[axis + 1])
+        if graph.opset_version > 10:
+            if not node.attr('descending'):
+                graph.make_node(
+                    'TopK',
+                    inputs=[node.input('X', 0), dim_size],
+                    outputs=[node.output('Out', 0), node.output('Indices', 0)],
+                    axis=node.attr('axis'),
+                    largest=0)
+            else:
+                graph.make_node(
+                    'TopK',
+                    inputs=[node.input('X', 0), dim_size],
+                    outputs=[node.output('Out', 0), node.output('Indices', 0)],
+                    axis=node.attr('axis'),
+                    largest=1)
+        else:
+            if not node.attr('descending'):
+                raise Exception(
+                    "descending=False only support opset version>=11.")
+            else:
+                graph.make_node(
+                    'TopK',
+                    inputs=[node.input('X', 0), dim_size],
+                    outputs=[node.output('Out', 0), node.output('Indices', 0)],
+                    axis=node.attr('axis'))
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        shape = node.input_shape('X', 0)
+        k = shape[node.attr('axis')]
+        assert k > 0, "while input shape is dynamic, it only support opset version>=10."
+        input_dtype = node.input_dtype('X', 0)
+        dtype = dtypes.DTYPE_PADDLE_STR_MAP[input_dtype]
+        inputs = node.input('X', 0)
+        if dtype in ["int32", "int64"]:
+            inputs = graph.make_node(
+                'Cast', inputs=inputs, to=dtypes.ONNX.FLOAT)
+        if not node.attr('descending'):
+            raise Exception("descending=False only support opset version>=11.")
+        else:
+            output_node = node.output('Out', 0)
+            graph.make_node(
+                'TopK',
+                inputs=[inputs],
+                outputs=[output_node, node.output('Indices', 0)],
+                axis=node.attr('axis'),
+                k=k)
+            if dtype in ["int32", "int64"]:
+                graph.make_node(
+                    'Cast',
+                    inputs=[output_node],
+                    to=dtypes.DTYPE_PADDLE_ONNX_MAP[input_dtype],
+                    outputs=[output_node])
+
+
+@op_mapper('index_select')
+class IndexSelect():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Gather',
+            inputs=[node.input('X', 0), node.input('Index', 0)],
+            axis=node.attr('dim'),
+            outputs=node.output('Out'))
+
+
+@op_mapper('unique')
+class Unique():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        if node.attr('axis') == []:
+            graph.make_node(
+                'Unique',
+                inputs=node.input('X'),
+                outputs=[
+                    node.output('Out', 0), node.output('Indices', 0),
+                    node.output('Index', 0), node.output('Counts', 0)
+                ])
+        else:
+            graph.make_node(
+                'Unique',
+                inputs=node.input('X'),
+                axis=node.attr('axis')[0],
+                outputs=[
+                    node.output('Out', 0), node.output('Indices', 0),
+                    node.output('Index', 0), node.output('Counts', 0)
+                ])
+
+
+@op_mapper('where')
+class Where():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        graph.make_node(
+            'Where',
+            inputs=[
+                node.input('Condition', 0), node.input('X', 0),
+                node.input('Y', 0)
+            ],
+            outputs=node.output('Out'))
+
+
+@op_mapper('masked_select')
+class MaskSelect():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        index = graph.make_node('NonZero', inputs=node.input('Mask', 0))
+        index = graph.make_node('Transpose', inputs=[index], perm=[1, 0])
+        graph.make_node(
+            'GatherND',
+            inputs=[node.input('X', 0), index],
+            outputs=node.output('Y'))
diff --git a/paddle2onnx/legacy/op_mapper/sequence/__init__.py b/paddle2onnx/legacy/op_mapper/sequence/__init__.py
new file mode 100644
index 0000000000..847ddc47ac
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/sequence/__init__.py
@@ -0,0 +1,13 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/paddle2onnx/legacy/op_mapper/sequence/im2sequence.py b/paddle2onnx/legacy/op_mapper/sequence/im2sequence.py
new file mode 100644
index 0000000000..accd6f13d3
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/sequence/im2sequence.py
@@ -0,0 +1,78 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+
+
+@op_mapper('im2sequence')
+class Im2Sequence():
+    support_opset_verison_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        n, c, h, w = node.input_shape('X', 0)
+        assert h > 0 and w > 0, "Only supported fixed input shape for im2sequence operator."
+        stride_h, stride_w = node.attr('strides')
+        paddings = node.attr('paddings')
+        assert node.attr(
+            'out_stride'
+        ) != 1, "Only out_stride==1 is supported for im2sequence operator."
+        h = h + paddings[0] + paddings[1]
+        w = w + paddings[1] + paddings[2]
+        kernel_h, kernel_w = node.attr('kernels')
+        out_h = 1 + (h - kernel_h + stride_h - 1) // stride_h
+        out_w = 1 + (w - kernel_w + stride_w - 1) // stride_w
+        h_steps = list()
+        for i in range(out_h):
+            h_steps.append([i * stride_h, i * stride_h + kernel_h])
+        w_steps = list()
+        for i in range(out_w):
+            w_steps.append([i * stride_w, i * stride_w + kernel_w])
+
+        slice_node_blocks = list()
+        for i in range(out_h):
+            for j in range(out_w):
+                starts_node = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.ONNX.INT64,
+                    dims=[4],
+                    value=[0, 0, h_steps[i][0], w_steps[j][0]])
+                ends_node = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.ONNX.INT64,
+                    dims=[4],
+                    value=[999999, 999999, h_steps[i][1], w_steps[j][1]])
+                nodes.extend([starts_node, ends_node])
+
+                slice_block_node = graph.make_node(
+                    'Slice',
+                    inputs=[node.input('X', 0), starts_node, ends_node])
+                flatten_block_node = graph.make_node(
+                    "Flatten", inputs=[slice_block_node], axis=0)
+                nodes.extend([slice_block_node, flatten_block_node])
+        concat_block_node = graph.make_node(
+            "Concat",
+            inputs=slice_node_blocks,
+            outputs=node.output('Out'),
+            axis=0)
+        logging.info("==========Importance Notice===========")
+        logging.info(
+            "Since im2sequence operator is used in your paddlepaddle model, the translated onnx model only support input data with batch_size=1."
+        )
+        logging.info("======================================")
diff --git a/paddle2onnx/legacy/op_mapper/tensor.py b/paddle2onnx/legacy/op_mapper/tensor.py
new file mode 100755
index 0000000000..081cbbfd21
--- /dev/null
+++ b/paddle2onnx/legacy/op_mapper/tensor.py
@@ -0,0 +1,2155 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+from paddle2onnx.legacy.op_mapper import mapper_helper
+import copy
+import six
+import paddle
+
+
+@op_mapper('set_value')
+class SetValue():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        axes = node.attr('axes')
+        steps, is_steps_tensor = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'steps',
+            'StepsTensor',
+            'StepsTensorList',
+            return_list=True,
+            dtype=dtypes.ONNX.INT64)
+
+        starts, is_starts_tensor = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'starts',
+            'StartsTensor',
+            'StartsTensorList',
+            return_list=True,
+            dtype=dtypes.ONNX.INT64)
+
+        ends, is_ends_tensor = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'ends',
+            'EndsTensor',
+            'EndsTensorList',
+            return_list=True,
+            dtype=dtypes.ONNX.INT64)
+
+        contain_step_bigger_than_1 = False
+        for i in steps:
+            contain_step_bigger_than_1 = i > 1
+            if not isinstance(i, int) or contain_step_bigger_than_1:
+                contain_step_bigger_than_1 = True
+                break
+        condition = is_steps_tensor or is_starts_tensor or is_ends_tensor or contain_step_bigger_than_1
+        assert not condition, "Currently not supported convert now"
+
+        input_x_shape = node.input_shape('Input', 0)
+        onnx_paddings = [0] * len(input_x_shape) * 2
+        value_shape = list(copy.copy(node.input_shape('Input', 0)))
+        for i in range(len(axes)):
+            axis = axes[i]
+            if starts[i] < 0:
+                starts[i] = starts[i] + input_x_shape[i]
+            if ends[i] < 0:
+                ends[i] = ends[i] + input_x_shape[i]
+            onnx_paddings[axis] = starts[i]
+            value_shape[axis] = value_shape[axis] - onnx_paddings[axis]
+            onnx_paddings[axis + len(input_x_shape)] = input_x_shape[
+                axis] - ends[i]
+            if onnx_paddings[axis + len(input_x_shape)] < 0:
+                onnx_paddings[axis + len(input_x_shape)] = 0
+            value_shape[axis] = value_shape[axis] - onnx_paddings[axis + len(
+                input_x_shape)]
+        dtype_paddle = node.input_dtype('Input', 0)
+        dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype_paddle]
+        value_tensor = None
+        shape = node.attr('shape')
+        if len(shape) > 0:
+            dtypes_list = [
+                'fp32_values', 'fp64_values', 'int32_values', 'int64_values',
+                'bool_values'
+            ]
+            for i in range(len(dtypes_list)):
+                value = node.attr(dtypes_list[i])
+                if value is not None:
+                    break
+            if len(value) == 1:
+                total_nums = 1
+                for i in value_shape:
+                    total_nums *= i
+                value = value * total_nums
+                value_tensor = mapper_helper.constant_helper(
+                    graph, dtype_paddle, value, shape=value_shape)
+            else:
+                value_tensor = mapper_helper.constant_helper(
+                    graph, dtype_paddle, value, shape=shape)
+        else:
+            value_tensor = node.input('ValueTensor', 0)
+        MAX_FLOAT32 = 3.402823466E+38
+        max_node = graph.make_node(
+            'Constant', attrs={'dtype': dtype,
+                               'value': [MAX_FLOAT32]})
+        pads_node = graph.make_node(
+            'Constant',
+            attrs={'dtype': dtypes.ONNX.INT64,
+                   'value': onnx_paddings})
+        value_pad_node = graph.make_node(
+            'Pad', inputs=[value_tensor, pads_node, max_node])
+
+        condition_dtype = graph.make_node(
+            "Equal", inputs=[value_pad_node, max_node])
+        condition_node = graph.make_node(
+            'Cast', inputs=[condition_dtype], to=dtypes.ONNX.BOOL)
+        graph.make_node(
+            "Where",
+            inputs=[condition_node, node.input('Input', 0), value_pad_node],
+            outputs=node.output('Out'))
+
+
+@op_mapper('one_hot_v2')
+class OneHotV2():
+    support_opset_version_range = (9, )
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        allow_out_of_range = node.attr('allow_out_of_range')
+        assert not allow_out_of_range, "allow_out_of_range can not be true in one_hot_v2."
+        in_dtype_paddle = node.input_dtype('X', 0)
+        in_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[in_dtype_paddle]
+        out_dtype = node.output_dtype('Out', 0)
+        out_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[out_dtype]
+        inputs = node.input('X', 0)
+        if in_dtype_paddle == paddle.int32:
+            inputs = graph.make_node(
+                'Cast', inputs=[inputs], to=dtypes.ONNX.INT64)
+            in_dtype = dtypes.ONNX.INT64
+        value_node = graph.make_node('Constant', dtype=out_dtype, value=[0, 1])
+        depth = node.attr('depth')
+        if node.input('depth_tensor', 0) is not None:
+            depth_node = node.input('depth_tensor', 0)
+        else:
+            depth_node = graph.make_node(
+                'Constant', dtype=in_dtype, value=[depth])
+        reshaped_input_node = graph.make_node(
+            'OneHot',
+            inputs=[inputs, depth_node, value_node],
+            outputs=node.output('Out'))
+
+
+@op_mapper('concat')
+class Concat():
+    support_opset_version_range = (4, 15)
+
+    @classmethod
+    def opset_4(cls, graph, node, **kw):
+        inputs = node.input('X')
+
+        input_dtypes = [node.input_dtype('X', i) for i in range(len(inputs))]
+        inputs = mapper_helper.dtype_alignment(graph, inputs, input_dtypes)
+        node_axis = node.input('AxisTensor')
+        if node_axis is not None and len(node_axis) > 0:
+            axis_node = node.input('AxisTensor')[0]
+            try:
+                axis = mapper_helper.get_value_from_parameters(graph,
+                                                               axis_node)[0]
+            except Exception as e:
+                raise Exception(
+                    "Currently does not support the axis parameter as input tensor"
+                    + str(e))
+        else:
+            axis = node.attr('axis')
+        if axis < 0:
+            axis = axis + len(node.input_shape('X', 0))
+
+        node = graph.make_node(
+            'Concat', inputs=inputs, outputs=node.output('Out'), axis=axis)
+
+
+@op_mapper('assign')
+class Assign():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        inputs = node.input('X')
+        graph.make_node('Identity', inputs=inputs, outputs=node.output('Out'))
+
+
+@op_mapper('lod_reset')
+class LodReset():
+    support_opset_version_range = (1, )
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('eye')
+class Eye():
+    support_opset_version_range = (9, )
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        num_rows = node.attr('num_rows')
+        num_columns = node.attr('num_columns')
+        dtype = node.output_dtype('Out', 0)
+        value = [0] * num_rows * num_columns
+        value_tensor = mapper_helper.constant_helper(
+            graph, dtype, value, shape=[num_rows, num_columns])
+        graph.make_node(
+            'EyeLike', inputs=[value_tensor], outputs=node.output('Out'))
+
+
+@op_mapper('stack')
+class Stack():
+    support_opset_version_range = (4, 15)
+
+    @classmethod
+    def opset_4(cls, graph, node, **kw):
+        inputs = node.input('X')
+        input_dtypes = [node.input_dtype('X', i) for i in range(len(inputs))]
+        inputs = mapper_helper.dtype_alignment(graph, inputs, input_dtypes)
+        axis = node.attr('axis')
+
+        unsqueezed_inputs = list()
+        for ipt in inputs:
+            unsqueezed_ipt = mapper_helper.unsqueeze_helper(graph, ipt, [axis])
+            unsqueezed_inputs.append(unsqueezed_ipt)
+        graph.make_node(
+            'Concat',
+            inputs=unsqueezed_inputs,
+            outputs=node.output('Y'),
+            axis=axis)
+
+
+@op_mapper('unstack')
+class Unstack():
+    support_opset_version_range = (2, 15)
+
+    @classmethod
+    def opset_2(cls, graph, node, **kw):
+        axis = node.attr('axis')
+        ndim = node.block.vars[node.input('X')[0]].ndim
+        axis = axis + ndim if axis < 0 else axis
+        output_y = mapper_helper.split_helper(
+            graph,
+            node.input('X'),
+            axis=axis,
+            split=[1] * len(node.output('Y')),
+            outputs=len(node.output('Y')))
+
+        if isinstance(output_y, six.string_types):
+            output_y = [output_y]
+
+        for i in range(len(output_y)):
+            mapper_helper.squeeze_helper(graph, output_y[i], [axis],
+                                         node.output('Y', i))
+
+
+@op_mapper('expand_as_v2')
+class ExpandAsV2():
+    support_opset_version_range = (8, 15)
+
+    @classmethod
+    def opset_8(cls, graph, node, **kw):
+        target_shape = node.attr('target_shape')
+        if node.input('target_tensor', 0) is not None:
+            target_shape = graph.make_node(
+                'Shape', inputs=[node.input('target_tensor', 0)])
+        elif target_shape is not None:
+            target_shape = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': target_shape})
+        else:
+            raise Exception(
+                "Not find attribute: 'target_shape' or tensor 'target_tensor'")
+        node = graph.make_node(
+            'Expand',
+            inputs=[node.input('X', 0), target_shape],
+            outputs=node.output('Out'))
+
+
+@op_mapper('expand_v2')
+class ExpandV2():
+    support_opset_version_range = (8, 15)
+
+    @classmethod
+    def opset_8(cls, graph, node, **kw):
+        expand_shape, _ = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'shape',
+            'Shape',
+            'expand_shapes_tensor',
+            dtype=dtypes.ONNX.INT64)
+
+        input_shape = node.input_shape('X', 0)
+        input_shape_node = graph.make_node('Shape', inputs=node.input('X', 0))
+
+        node_shape = node.attr('shape')
+        node_shape_tensor = node.input('Shape')
+        node_shape_tensor_list = node.input('expand_shapes_tensor')
+        if node_shape_tensor is not None and len(node_shape_tensor) > 0:
+            diff = node.input_shape('Shape', 0)[0] - len(input_shape)
+        elif node_shape_tensor_list is not None and \
+                len(node_shape_tensor_list) > 0:
+            diff = len(node_shape_tensor_list) - len(input_shape)
+        elif node_shape is not None and len(node_shape) > 0:
+            diff = len(node_shape) - len(input_shape)
+            expand_shape = graph.make_node(
+                'Constant', dtype=dtypes.ONNX.INT64, value=expand_shape)
+
+        if diff > 0:
+            one_node = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': [1] * diff})
+            input_shape_node = graph.make_node(
+                'Concat', inputs=[one_node, input_shape_node], axis=0)
+
+        if graph.opset_version < 12:
+            input_shape_node = graph.make_node(
+                'Cast', inputs=[input_shape_node], to=dtypes.ONNX.FLOAT)
+            expand_shape = graph.make_node(
+                'Cast', inputs=[expand_shape], to=dtypes.ONNX.FLOAT)
+            shape = graph.make_node(
+                'Max', inputs=[input_shape_node, expand_shape])
+            shape = graph.make_node(
+                'Cast', inputs=[shape], to=dtypes.ONNX.INT64)
+        else:
+            shape = graph.make_node(
+                'Max', inputs=[input_shape_node, expand_shape])
+        node = graph.make_node(
+            'Expand',
+            inputs=[node.input('X', 0), shape],
+            outputs=node.output('Out'))
+
+
+@op_mapper('shape')
+class Shape():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        shape_node = graph.make_node('Shape', inputs=node.input('Input'))
+        graph.make_node(
+            'Cast',
+            inputs=[shape_node],
+            outputs=node.output('Out'),
+            to=dtypes.ONNX.INT32)
+
+
+@op_mapper('size')
+class Numel():
+    supports_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        size_node = graph.make_node('Size', inputs=node.input('Input'))
+        mapper_helper.unsqueeze_helper(graph, size_node, [0],
+                                       node.output('Out'))
+
+
+@op_mapper('split')
+class Split():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        sections = node.attr('sections')
+        axis = cls.get_axis(graph, node)
+        if isinstance(sections, list) and len(sections) == 1:
+            graph.make_node(
+                'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+        else:
+            if len(sections) > 0:
+                input_shape = node.block.vars[node.input('X')[0]].shape
+                section_index = [
+                    i for i, val in enumerate(sections) if val == -1
+                ]
+                if input_shape[axis] != -1 and len(section_index) == 1:
+                    sections[section_index[0]] = input_shape[axis] - sum(
+                        sections) - 1
+                mapper_helper.split_helper(
+                    graph,
+                    node.input('X'),
+                    axis=axis,
+                    split=sections,
+                    outputs=node.output('Out'))
+            else:
+                graph.make_node(
+                    'Split',
+                    inputs=node.input('X'),
+                    outputs=node.output('Out'),
+                    axis=axis)
+
+    @classmethod
+    def get_axis(cls, graph, node):
+        if len(node.input('AxisTensor')) > 0:
+            axis_node = node.input('AxisTensor')[0]
+            # When axis is tensor, only int32 and int64 are supported
+            if axis_node not in graph.parameters:
+                raise Exception(
+                    "Currently does not support the axis parameter as input tensor!"
+                )
+            else:
+                axis = graph.parameters[axis_node].attribute[0].t.int32_data
+                if axis is None or len(axis) < 1:
+                    axis = graph.parameters[axis_node].attribute[
+                        0].t.int64_data[0]
+        else:
+            axis = node.attr('axis')
+        return axis
+
+
+@op_mapper(['roll'])
+class Roll():
+    support_opset_version_range = (4, 15)
+
+    @classmethod
+    def roll(cls, graph, node, input_x, dims, shifts):
+        for i in range(len(dims)):
+            if graph.opset_version >= 10 and isinstance(shifts,
+                                                        six.string_types):
+                to_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype(
+                    'ShiftsTensor', 0)]
+                const_i = graph.make_node('Constant', dtype=to_dtype, value=i)
+                const_0 = graph.make_node('Constant', dtype=to_dtype, value=0)
+                shift_node = graph.make_node(
+                    'Gather', inputs=[shifts, const_i], axis=0)
+                shift_node = graph.make_node(
+                    "Sub", inputs=[const_0, shift_node])
+                shift_node = mapper_helper.unsqueeze_helper(graph, shift_node,
+                                                            [0])
+            elif graph.opset_version < 10 and isinstance(shifts,
+                                                         six.string_types):
+                raise Exception(
+                    "shifts of roll is Tensor, please try with higher onnx opset_version>=10."
+                )
+            else:
+                shift_node = [-shifts[i]]
+                to_dtype = dtypes.ONNX.INT64
+            shapes = []
+            shape = mapper_helper.slice_helper(
+                graph, input_x, [dims[i]], shift_node, [60000], dtype=to_dtype)
+            shapes.append(shape)
+            shape = mapper_helper.slice_helper(
+                graph, input_x, [dims[i]], [0], shift_node, dtype=to_dtype)
+            shapes.append(shape)
+            input_x = graph.make_node('Concat', inputs=shapes, axis=dims[i])
+        return input_x
+
+    @classmethod
+    def flatten(cls, graph, node):
+        dims = len(node.input_shape('X', 0))
+        start_axis = 0
+        end_axis = dims - 1
+        shape_node = graph.make_node('Shape', inputs=node.input('X'))
+        if end_axis < dims - 1:
+            slice1 = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[0], ends=[start_axis])
+            slice3 = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[end_axis + 1],
+                ends=[dims])
+            slices = [
+                slice1, graph.make_node(
+                    'Constant', value=[-1], dtype=dtypes.ONNX.INT64), slice3
+            ]
+        else:
+            slice1 = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[0], ends=[start_axis])
+            slices = [
+                slice1, graph.make_node(
+                    'Constant', value=[-1], dtype=dtypes.ONNX.INT64)
+            ]
+        final_shape = graph.make_node('Concat', inputs=slices, axis=0)
+        output = graph.make_node(
+            'Reshape', inputs=[node.input('X')[0], final_shape])
+        return output
+
+    @classmethod
+    def opset_4(cls, graph, node, **kw):
+        dims = node.attr('axis')
+        shifts = node.attr('shifts')
+        input_x = node.input('X')[0]
+        input_shape = node.input_shape('X', 0)
+        shifts_node = node.input('ShiftsTensor')
+        if len(dims) > 0:
+            axes = [
+                axis + len(input_shape) if axis < 0 else axis
+                for i, axis in enumerate(dims)
+            ]
+            if shifts_node is not None and len(shifts_node) > 0:
+                shifts = shifts_node[0]
+            else:
+                for i in range(0, len(axes)):
+                    if input_shape[axes[i]] > 0:
+                        assert -input_shape[axes[i]] <= shifts[i] <= input_shape[axes[i]], \
+                            "the value of shifts in axis is less than the value of input_shape in axis."
+
+            input_x = cls.roll(graph, node, input_x, axes, shifts)
+            graph.make_node(
+                'Identity', inputs=[input_x], outputs=node.output('Out'))
+        else:
+            if shifts_node is not None and len(shifts_node) > 0:
+                shifts = shifts_node[0]
+            input_x = cls.flatten(graph, node)
+            input_x = cls.roll(graph, node, input_x, [0], shifts)
+            shape_node = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': list(input_shape)})
+            graph.make_node(
+                'Reshape',
+                inputs=[input_x, shape_node],
+                outputs=node.output('Out'))
+
+
+@op_mapper(['slice', 'strided_slice'])
+class Slice():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def decrease_axis(cls, node):
+        # tensor[i,:] will decrease rank of origin input, example:
+        # paddle.slice() will not decrease rank of origin input
+        # if input shape is [2, 3], input[0, :] will generate output with shape [3], not [1, 3].
+        # paddle.slice(input, 0, 1, 0) will  generate output with shape [1, 3], not [3].
+
+        decrease_axis = node.attr('decrease_axis')
+        if len(decrease_axis) == 0:
+            return None
+        if node.output_shape('Out', 0) == [0]:
+            return decrease_axis
+        if len(node.input_shape('Input', 0)) > len(node.output_shape('Out', 0)):
+            return decrease_axis
+        return None
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        axes = node.attr('axes')
+        strides, strides_is_tensor = mapper_helper.get_node_attr_value(
+            graph, node, 'strides', 'StridesTensor', 'StridesTensorList', True)
+        strides = [1] * len(axes) if strides is None else strides
+        steps = [i for i, val in enumerate(strides) if val == 1]
+        assert len(steps) == len(axes), \
+            "Slice in onnx(opset<10) not support attribute 'step', Try converting with opset_version >=10"
+
+        starts, start_is_tensor = mapper_helper.get_node_attr_value(
+            graph, node, 'starts', 'StartsTensor', 'StartsTensorList', True)
+        ends, end_is_tensor = mapper_helper.get_node_attr_value(
+            graph, node, 'ends', 'EndsTensor', 'EndsTensorList', True)
+
+        assert not strides_is_tensor and not start_is_tensor and not end_is_tensor, \
+            "Slice in onnx(opset<10) not support attribute 'steps','starts' or 'ends' which have tensor value, " \
+            "Try converting with opset_version >=10 "
+
+        decrease_axis = cls.decrease_axis(node)
+        if decrease_axis is None:
+            graph.make_node(
+                "Slice",
+                inputs=[node.input('Input')[0]],
+                outputs=node.output('Out'),
+                axes=axes,
+                starts=starts,
+                ends=ends)
+        else:
+            sliced = graph.make_node(
+                "Slice",
+                inputs=[node.input('Input')[0]],
+                axes=axes,
+                starts=starts,
+                ends=ends)
+            mapper_helper.squeeze_helper(graph, sliced, decrease_axis,
+                                         node.output('Out'))
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        axes = node.attr('axes')
+        strides, _ = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'strides',
+            'StridesTensor',
+            'StridesTensorList',
+            dtype=dtypes.ONNX.INT64)
+        strides = [1] * len(axes) if strides is None else strides
+
+        starts, _ = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'starts',
+            'StartsTensor',
+            'StartsTensorList',
+            dtype=dtypes.ONNX.INT64)
+        ends, _ = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'ends',
+            'EndsTensor',
+            'EndsTensorList',
+            dtype=dtypes.ONNX.INT64)
+
+        if isinstance(starts, list):
+            starts_node = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': starts})
+        else:
+            starts_node = starts
+        if isinstance(ends, list):
+            ends_node = graph.make_node(
+                'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                                   'value': ends})
+        else:
+            ends_node = ends
+
+        if isinstance(strides, list):
+            strides_node = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': strides})
+        else:
+            strides_node = strides
+
+        steps_node = strides_node
+        axes_node = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                               'value': axes})
+
+        decrease_axis = cls.decrease_axis(node)
+        if decrease_axis is None:
+            sliced = graph.make_node(
+                "Slice",
+                inputs=[
+                    node.input('Input')[0], starts_node, ends_node, axes_node,
+                    steps_node
+                ],
+                outputs=node.output('Out'))
+        else:
+            sliced = graph.make_node(
+                "Slice",
+                inputs=[
+                    node.input('Input')[0], starts_node, ends_node, axes_node,
+                    steps_node
+                ])
+            mapper_helper.squeeze_helper(graph, sliced, decrease_axis,
+                                         node.output('Out'))
+
+
+@op_mapper(['sequence_expand'])
+class SequenceExpand():
+    support_opset_version_range = ()
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper(['expand'])
+class Expand():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        expand_times, _ = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'expand_times',
+            'ExpandTimes',
+            'expand_times_tensor',
+            dtype=dtypes.ONNX.INT64)
+
+        if isinstance(expand_times, list):
+            expand_times = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': expand_times})
+
+        graph.make_node(
+            "Tile",
+            inputs=[node.input('X', 0), expand_times],
+            outputs=node.output('Out'))
+
+
+@op_mapper(['tile'])
+class Tile():
+    support_opset_version_range = (6, 15)
+
+    @classmethod
+    def opset_6(cls, graph, node, **kw):
+        repeat_times, _ = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'repeat_times',
+            'RepeatTimes',
+            'repeat_times_tensor',
+            dtype=dtypes.ONNX.INT64)
+
+        if isinstance(repeat_times, list):
+            repeat_times = graph.make_node(
+                'Constant',
+                attrs={'dtype': dtypes.ONNX.INT64,
+                       'value': repeat_times})
+
+        graph.make_node(
+            "Tile",
+            inputs=[node.input('X', 0), repeat_times],
+            outputs=node.output('Out'))
+
+
+@op_mapper('range')
+class Range():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        start = node.input('Start', 0)
+        end = node.input('End', 0)
+        step = node.input('Step', 0)
+        start_t = mapper_helper.squeeze_helper(graph, start, [0])
+        end_t = mapper_helper.squeeze_helper(graph, end, [0])
+        step_t = mapper_helper.squeeze_helper(graph, step, [0])
+        graph.make_node(
+            "Range",
+            inputs=[start_t, end_t, step_t],
+            outputs=node.output('Out'))
+
+
+@op_mapper('fill_constant')
+class Constant():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def check_int_type(cls, dtype):
+        if dtype in [dtypes.ONNX.INT16, dtypes.ONNX.INT32, dtypes.ONNX.INT64]:
+            return True
+        return False
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        value = node.attr('value')
+        dtype = node.attr('dtype')
+        value_is_scalar_tensor = False
+        if 'ValueTensor' in node.inputs and len(node.input('ValueTensor')) > 0:
+            rank = len(node.input_shape("ValueTensor", 0))
+            if rank == 1 and node.input_shape("ValueTensor", 0)[0] == 1:
+                value_is_scalar_tensor = True
+                value = node.input("ValueTensor")[0]
+            else:
+                raise Exception(
+                    "paddle.full with tensor value parameter is not supported yet."
+                )
+
+        shape, is_shape_tensor = mapper_helper.get_node_attr_value(
+            graph,
+            node,
+            'shape',
+            'ShapeTensor',
+            'ShapeTensorList',
+            dtype=dtypes.ONNX.INT64)
+
+        if graph.opset_version >= 9 and (is_shape_tensor or
+                                         value_is_scalar_tensor):
+            if not is_shape_tensor:
+                shape = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=shape)
+            input_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype]
+            if not value_is_scalar_tensor and cls.check_int_type(input_dtype):
+                to_dtype = dtypes.ONNX.DOUBLE
+                outputs = None
+            else:
+                to_dtype = input_dtype
+                outputs = node.output('Out')
+
+            if value_is_scalar_tensor:
+                base_value = graph.make_node(
+                    'ConstantOfShape',
+                    inputs=shape,
+                    attrs={'dims': [1],
+                           'dtype': to_dtype,
+                           'value': 0})
+                node2 = graph.make_node(
+                    "Add", inputs=[base_value, value], outputs=outputs)
+            else:
+                node2 = graph.make_node(
+                    'ConstantOfShape',
+                    inputs=shape,
+                    outputs=outputs,
+                    attrs={'dims': [1],
+                           'dtype': to_dtype,
+                           'value': value})
+
+            if not value_is_scalar_tensor and cls.check_int_type(input_dtype):
+                graph.make_node(
+                    'Cast',
+                    inputs=node2,
+                    outputs=node.output('Out'),
+                    attrs={'to': input_dtype})
+        else:
+            assert not is_shape_tensor and not value_is_scalar_tensor, \
+                "Currently op ['fill_constant'] does not support in onnx(opset<9) when 'shape' or 'fill_value' has " \
+                "tensor, Try converting with opset_version >=9 "
+
+            value = np.ones(shape) * value
+            value = value.astype(dtypes.DTYPE_PADDLE_NUMPY_MAP[dtype])
+            value = value.flatten().tolist()
+
+            graph.make_node(
+                'Constant',
+                inputs=[],
+                outputs=node.output('Out'),
+                attrs={
+                    'dims': shape,
+                    'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[dtype],
+                    'value': value
+                })
+
+
+@op_mapper(['lookup_table_v2', 'lookup_table'])
+class Embedding():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        ids = node.input('Ids', 0)
+        if node.type == 'lookup_table' and node.input_shape('Ids', 0)[-1] == 1:
+            ids = mapper_helper.squeeze_helper(graph,
+                                               node.input('Ids', 0), [-1])
+        padding_idx = node.attr('padding_idx')
+        input_shape = node.input_shape('W', 0)
+        if padding_idx != -1:
+            key = node.input('W', 0)
+            if -1 in input_shape:
+                assert False, "opset version < 11 do not support padding_idx !=-1 and weight is tensor with dynamic shape, please set opset version > 11 or use input_spec to set input shape"
+            else:
+                data = np.ones(shape=input_shape, dtype=np.float32)
+                data[padding_idx] = 0.0
+                dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('W', 0)]
+                constant = graph.make_node(
+                    'Constant',
+                    dtype=dtype,
+                    dims=input_shape,
+                    value=data.flatten().tolist())
+                weight_node = graph.make_node(
+                    'Mul', inputs=[node.input('W', 0), constant])
+                graph.make_node(
+                    'Gather',
+                    inputs=[weight_node, ids],
+                    outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Gather',
+                inputs=[node.input('W', 0), ids],
+                outputs=node.output('Out'))
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        ids = node.input('Ids', 0)
+        if node.type == 'lookup_table' and node.input_shape('Ids', 0)[-1] == 1:
+            ids = mapper_helper.squeeze_helper(graph,
+                                               node.input('Ids', 0), [-1])
+
+        padding_idx = node.attr('padding_idx')
+        input_shape = node.input_shape('W', 0)
+        if padding_idx != -1:
+            if -1 in input_shape:
+                replace_shape = list(copy.copy(input_shape))
+                del (replace_shape[0])
+                replace_data = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('W',
+                                                                        0)],
+                    dims=replace_shape,
+                    value=[0.0] * np.prod(replace_shape))
+                index = graph.make_node(
+                    'Constant', dtype=dtypes.ONNX.INT64, value=[padding_idx])
+                Scatter_node = graph.make_node(
+                    'ScatterND',
+                    inputs=[node.input('W', 0), index, replace_data])
+                graph.make_node(
+                    'Gather',
+                    inputs=[Scatter_node, ids],
+                    outputs=node.output('Out'))
+            else:
+                data = np.ones(shape=input_shape, dtype=np.float32)
+                data[padding_idx] = 0.0
+                dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('W', 0)]
+                constant = graph.make_node(
+                    'Constant',
+                    dtype=dtype,
+                    dims=input_shape,
+                    value=data.flatten().tolist())
+                weight_node = graph.make_node(
+                    'Mul', inputs=[node.input('W', 0), constant])
+                graph.make_node(
+                    'Gather',
+                    inputs=[weight_node, ids],
+                    outputs=node.output('Out'))
+        else:
+            graph.make_node(
+                'Gather',
+                inputs=[node.input('W', 0), ids],
+                outputs=node.output('Out'))
+
+
+@op_mapper('fill_constant_batch_size_like')
+class FillConstantBatchSizeLike():
+    support_opset_version_range = (9, 12)
+
+    @classmethod
+    def opset_10(cls, graph, node, **kw):
+        out_shape = node.attr('shape')
+        input_dim_idx = node.attr('input_dim_idx')
+        output_dim_idx = node.attr('output_dim_idx')
+
+        del out_shape[output_dim_idx]
+        out_shape.insert(0, 1)
+
+        dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('dtype')]
+        if node.attr("str_value") is not None and node.attr("str_value") != "":
+            value = eval(node.attr("str_value"))
+        else:
+            value = node.attr('value')
+        input_shape = node.input_shape('Input', 0)
+        constant = graph.make_node(
+            'Constant',
+            dtype=dtype,
+            dims=out_shape,
+            value=[value] * np.prod(out_shape))
+
+        shape = graph.make_node('Shape', inputs=node.input('Input'))
+        start = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[input_dim_idx])
+        end = graph.make_node(
+            'Constant', dtype=dtypes.ONNX.INT64, value=[input_dim_idx + 1])
+        batch = graph.make_node('Slice', inputs=[shape, start, end])
+        repeat = batch
+        if len(out_shape) > 1:
+            repeat = graph.make_node(
+                'Constant',
+                dtype=dtypes.ONNX.INT64,
+                value=[1] * (len(out_shape) - 1))
+            repeat = graph.make_node('Concat', inputs=[batch, repeat], axis=-1)
+        if output_dim_idx == 0:
+            graph.make_node(
+                'Tile', inputs=[constant, repeat], outputs=node.output('Out'))
+        else:
+            out = graph.make_node('Tile', inputs=[constant, repeat])
+            perm = list(range(len(out_shape)))
+            del perm[0]
+            perm.insert(output_dim_idx, 0)
+            graph.make_node(
+                'Transpose',
+                inputs=[out],
+                perm=perm,
+                outputs=node.output('Out'))
+
+
+@op_mapper('fill_any_like')
+class FullLike():
+    '''
+    fill_any_like is kernel for paddle op::full_like & ones_like
+    '''
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        shape_node = graph.make_node('Shape', inputs=node.input('X'))
+        value = node.attr('value')
+        dtype = node.attr('dtype')
+        input_dtype = node.input_dtype('X', 0)
+        if dtype is None:
+            dtype = input_dtype
+        np_dtype = dtypes.DTYPE_PADDLE_STR_MAP[dtype]
+        onnx_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype]
+        graph.make_node(
+            'ConstantOfShape',
+            inputs=[shape_node],
+            outputs=node.output('Out'),
+            dims=[1],
+            dtype=onnx_dtype,
+            value=np.array(value).astype(np_dtype).tolist())
+
+
+@op_mapper('fill_zeros_like')
+class FullZeroLike():
+    '''
+    fill_zeros_like is kernel for paddle op::zeros_like
+    '''
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        shape_node = graph.make_node('Shape', inputs=node.input('X'))
+        value = 0
+        dtype = node.attr('dtype')
+        input_dtype = node.input_dtype('X', 0)
+        if dtype is None:
+            dtype = input_dtype
+        np_dtype = dtypes.DTYPE_PADDLE_STR_MAP[dtype]
+        onnx_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[dtype]
+        graph.make_node(
+            'ConstantOfShape',
+            inputs=[shape_node],
+            outputs=node.output('Out'),
+            dims=[1],
+            dtype=onnx_dtype,
+            value=np.array(value).astype(np_dtype).tolist())
+
+
+@op_mapper('gather_nd')
+class Gather_nd():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        data = node.input('X', 0)
+        index = node.input('Index', 0)
+        index_dtype = node.input_dtype('Index', 0)
+        index_node = None
+        if index_dtype != paddle.int64:
+            index_node = graph.make_node(
+                'Cast', inputs=[node.input('Index', 0)], to=dtypes.ONNX.INT64)
+        else:
+            index_node = index
+        graph.make_node(
+            'GatherND', inputs=[data, index_node], outputs=node.output('Out'))
+
+
+@op_mapper('gather')
+class Gather():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        axis = node.attr('axis')
+        if node.input('Axis', 0) != None:
+            axis_node = node.input('Axis', 0)
+            try:
+                axis = mapper_helper.get_value_from_parameters(graph,
+                                                               axis_node)[0]
+            except Exception as e:
+                raise Exception(
+                    "Currently does not support the axis parameter as input tensor"
+                    + str(e))
+        if axis is None:
+            axis = 0
+        if len(node.input_shape('Index', 0)) == 1:
+            # gather
+            graph.make_node(
+                'Gather',
+                inputs=[node.input('X', 0), node.input('Index', 0)],
+                outputs=node.output('Out'),
+                attrs={'axis': axis})
+        else:
+            raise Exception(
+                "please try to convert OP:gather(indices's rank >1) with opset_version >= 11."
+            )
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        axis = node.attr('axis')
+        if node.input('Axis', 0) != None:
+            axis_node = node.input('Axis', 0)
+            try:
+                axis = mapper_helper.get_value_from_parameters(graph,
+                                                               axis_node)[0]
+            except Exception as e:
+                raise Exception(
+                    "Currently does not support the axis parameter as input tensor"
+                    + str(e))
+        if axis is None:
+            axis = 0
+        if len(node.input_shape('Index', 0)) == 1:
+            # gather
+            graph.make_node(
+                'Gather',
+                inputs=[node.input('X', 0), node.input('Index', 0)],
+                outputs=node.output('Out'),
+                attrs={'axis': axis})
+        else:
+            # gather_nd
+            index_dtype = node.input_dtype('Index', 0)
+            if index_dtype != paddle.int64:
+                index_node = graph.make_node(
+                    'Cast',
+                    inputs=[node.input('Index', 0)],
+                    to=dtypes.ONNX.INT64)
+                graph.make_node(
+                    'GatherND',
+                    inputs=[node.input('X', 0), index_node],
+                    outputs=node.output('Out'))
+            else:
+                graph.make_node(
+                    'GatherND',
+                    inputs=[node.input('X', 0), node.input('Index', 0)],
+                    outputs=node.output('Out'))
+
+
+@op_mapper('squeeze2')
+class Squeeze():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        shape = node.input_shape('X', 0)
+        ret = [i for i, val in enumerate(shape) if val > 1]
+        if len(ret) == len(shape):
+            graph.make_node(
+                'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+        else:
+            axes = cls.compute_axes(graph, node)
+            if len(axes) > 0:
+                axes.sort()
+                mapper_helper.squeeze_helper(graph,
+                                             node.input('X', 0), axes,
+                                             node.output('Out'))
+            else:
+                graph.make_node(
+                    'Squeeze',
+                    inputs=[node.input('X', 0)],
+                    outputs=node.output('Out'))
+
+    @classmethod
+    def compute_axes(cls, graph, node):
+        shape = node.input_shape('X', 0)
+        axes = node.attr('axes')
+        if len(axes) > 0:
+            axes = [
+                axis + len(shape) if axis < 0 else axis
+                for i, axis in enumerate(axes)
+            ]
+        return axes
+
+
+@op_mapper('assign_value')
+class Assign():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        if len(node.input_names) > 0:
+            graph.make_node(
+                'Identity', inputs=node.input('X'), outputs=node.output('Out'))
+        else:
+            parameters = {}
+            value = np.array(node.attr('fp32_values'))
+            if value is None or value.size < 1:
+                value = np.array(node.attr('int32_values'))
+            if value is None or value.size < 1:
+                value = np.array(node.attr('int64_values'))
+            parameter = {
+                'data': value,
+                'dtype': node.output_dtype("Out", 0),
+                'shape': node.attr('shape')
+            }
+            parameters[node.output('Out', 0)] = parameter
+            graph.build_parameters(parameters)
+
+
+@op_mapper('transpose2')
+class Transpose():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        graph.make_node(
+            'Transpose',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            perm=node.attr('axis'))
+
+
+@op_mapper('flatten2')
+class Flatten():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        input_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)]
+        if input_dtype in [dtypes.ONNX.INT32, dtypes.ONNX.INT64
+                           ] and graph.opset_version < 9:
+            raise Exception(
+                "int32 or int64 not supported in onnx <9, please try with higher onnx opset_version>=9."
+            )
+
+        graph.make_node(
+            'Flatten',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            axis=node.attr('axis'))
+
+
+@op_mapper('flatten_contiguous_range')
+class FlattenContiguousRange():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        dims = len(node.input_shape('X', 0))
+        start_axis = node.attr('start_axis')
+        end_axis = node.attr('stop_axis')
+        shape_node = graph.make_node('Shape', inputs=node.input('X'))
+        if start_axis < 0:
+            start_axis += dims
+        if end_axis < 0:
+            end_axis += dims
+        if start_axis == 0 and end_axis == dims - 1:
+            final_shape = graph.make_node(
+                'Constant', value=[-1], dtype=dtypes.ONNX.INT64)
+        elif start_axis == 0:
+            slice_end = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[end_axis + 1],
+                ends=[dims])
+            slices = [
+                graph.make_node(
+                    'Constant', value=[-1], dtype=dtypes.ONNX.INT64), slice_end
+            ]
+            final_shape = graph.make_node('Concat', inputs=slices, axis=0)
+        elif end_axis == dims - 1:
+            slice_start = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[0], ends=[start_axis])
+            slices = [
+                slice_start, graph.make_node(
+                    'Constant', value=[-1], dtype=dtypes.ONNX.INT64)
+            ]
+            final_shape = graph.make_node('Concat', inputs=slices, axis=0)
+        else:
+            slice_start = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[0], ends=[start_axis])
+            slice_end = mapper_helper.slice_helper(
+                graph, shape_node, axes=[0], starts=[end_axis + 1],
+                ends=[dims])
+            slices = [
+                slice_start, graph.make_node(
+                    'Constant', value=[-1], dtype=dtypes.ONNX.INT64), slice_end
+            ]
+            final_shape = graph.make_node('Concat', inputs=slices, axis=0)
+        graph.make_node(
+            'Reshape',
+            inputs=[node.input('X')[0], final_shape],
+            outputs=node.output('Out'))
+
+
+@op_mapper('reshape2')
+class Reshape():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        shape_name = 'ShapeTensor'
+        if shape_name not in node.inputs or len(node.input(shape_name)) == 0:
+            shape_name = 'Shape'
+        if shape_name not in node.inputs or len(node.input(shape_name)) == 0:
+            if node.attr('shape') is None or len(node.attr('shape')) == 0:
+                raise Exception("shape tensor and shape attrubite all unkown.")
+        if len(node.input(shape_name)) > 1:
+            dims = []
+            for i in range(len(node.input(shape_name))):
+                dim = node.input(shape_name)[i]
+                dim = graph.make_node(
+                    'Cast', inputs=[dim], to=dtypes.ONNX.INT64)
+                dims.append(dim)
+            shape = graph.make_node('Concat', inputs=dims, axis=-1)
+            graph.make_node(
+                'Reshape',
+                inputs=[node.input('X')[0], shape],
+                outputs=node.output('Out'))
+        elif len(node.input(shape_name)) == 1:
+            cast_shape_node = graph.make_node(
+                'Cast', inputs=node.input(shape_name), to=dtypes.ONNX.INT64)
+            graph.make_node(
+                'Reshape',
+                inputs=[node.input('X')[0], cast_shape_node],
+                outputs=node.output('Out'))
+        elif node.attr('shape') is not None and len(node.attr('shape')) > 0:
+            shape_node = graph.make_node(
+                'Constant',
+                attrs={
+                    'dtype': dtypes.ONNX.INT64,
+                    'value': node.attr('shape')
+                })
+            reshape_node = graph.make_node(
+                'Reshape',
+                inputs=[node.input('X')[0], shape_node],
+                outputs=node.output('Out'))
+
+
+@op_mapper('unsqueeze2')
+class Unsqueeze():
+    support_opset_version_range = (1, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        axes = cls.get_axes(graph, node)
+        mapper_helper.unsqueeze_helper(graph,
+                                       node.input('X'), axes,
+                                       node.output('Out'))
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        axes_node = cls.get_axes(graph, node, return_node=True)
+        graph.make_node(
+            'Unsqueeze',
+            inputs=node.input('X') + [axes_node],
+            outputs=node.output('Out'))
+
+    @classmethod
+    def get_axes(cls, graph, node, return_node=False):
+        axes_node = None
+        ndim = node.block.vars[node.input('X')[0]].ndim
+        if len(node.attr('axes')) > 0:
+            axes = node.attr('axes')
+        else:
+            axes_node = node.input('AxesTensor')[0]
+            if axes_node is not None and graph.opset_version > 12 and return_node:
+                return axes_node
+            try:
+                axes = mapper_helper.get_value_from_parameters(graph, axes_node)
+            except Exception as e:
+                raise Exception(
+                    "Currently does not support the axes parameter as input tensor in onnx(opset<13), "
+                    "Try converting with opset_version >=13 " + str(e))
+        # axes is list of non-negative integers
+        axes = [
+            axis + ndim + i + 1 if axis < 0 else axis
+            for i, axis in enumerate(axes)
+        ]
+
+        axes_copy = axes.copy()
+        assert sorted(
+            axes) == axes_copy, "axes must be arranged in the following order"
+        assert len(set(axes)) == len(axes), "axes have duplicate axis"
+
+        if return_node:
+            if axes_node is None:
+                axes_node = graph.make_node(
+                    'Constant',
+                    attrs={'dtype': dtypes.ONNX.INT64,
+                           'value': axes})
+            return axes_node
+        return axes
+
+
+@op_mapper('reciprocal')
+class Reciprocal():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Reciprocal', inputs=node.input('X'), outputs=node.output('Out'))
+
+
+@op_mapper('cast')
+class Cast():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'Cast',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('out_dtype')])
+
+
+@op_mapper('linspace')
+class Linspace():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        start = node.input('Start', 0)
+        stop = node.input('Stop', 0)
+        num = node.input('Num', 0)
+        dtype = node.attr('dtype')
+
+        start = graph.make_node('Cast', inputs=[start], to=dtypes.ONNX.FLOAT)
+        stop = graph.make_node('Cast', inputs=[stop], to=dtypes.ONNX.FLOAT)
+
+        sub_a_node = graph.make_node('Sub', inputs=[stop, start])
+
+        one_node = graph.make_node(
+            'Constant',
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('Num', 0)],
+            value=[1])
+
+        sub_b_node = graph.make_node('Sub', inputs=[num, one_node])
+
+        sub_b_float_node = graph.make_node(
+            'Cast', inputs=[sub_b_node], to=dtypes.ONNX.FLOAT)
+
+        step = graph.make_node('Div', inputs=[sub_a_node, sub_b_float_node])
+
+        range_tensor = graph.make_node(
+            'Cast', inputs=[num], to=dtypes.ONNX.INT64)
+
+        one_like_node = graph.make_node(
+            'ConstantOfShape',
+            inputs=[range_tensor],
+            dtype=dtypes.ONNX.FLOAT,
+            value=[1])
+
+        none_zero_node = graph.make_node('NonZero', inputs=[one_like_node])
+
+        trans_none_zero_node = graph.make_node(
+            'Transpose', inputs=[none_zero_node], perm=[1, 0])
+
+        trans_squeeze = mapper_helper.squeeze_helper(graph,
+                                                     trans_none_zero_node, [1])
+
+        trans_squeeze = graph.make_node(
+            'Cast', inputs=[trans_squeeze], to=dtypes.ONNX.FLOAT)
+
+        mul_node = graph.make_node('Mul', inputs=[trans_squeeze, step])
+
+        add_node = graph.make_node('Add', inputs=[mul_node, start])
+        graph.make_node(
+            'Cast',
+            inputs=[add_node],
+            outputs=node.output('Out'),
+            to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('Start', 0)])
+
+
+@op_mapper('clip')
+class Clip():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        min_value = node.attr('min')
+        max_value = node.attr('max')
+        if node.input('Max', 0) is None or len(node.input('Max')) == 0:
+            max_ = max_value
+        else:
+            max_ = node.input('Max', 0)
+        if node.input('Min', 0) is None or len(node.input('Min')) == 0:
+            min_ = min_value
+        else:
+            min_ = node.input('Min', 0)
+        mapper_helper.clip_helper(graph, node,
+                                  node.input('X', 0), max_, min_,
+                                  node.output('Out', 0))
+
+
+@op_mapper(['pad2d', 'pad3d'])
+class Pad():
+    support_opset_version_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        if node.attr('mode') == 'replicate':
+            mode = 'edge'
+        elif node.attr('mode') == 'circular':
+            raise Exception("The padding mode = circular is not supported, " \
+                            "Please try the other three ways")
+        else:
+            mode = node.attr('mode')
+        pads = cls.convert_padding(node, **kw)
+        if pads is None:
+            key = node.input('Paddings', 0)
+            padding = None
+            if key in graph.parameters.keys():
+                paddings = graph.parameters[key].attribute[0].t.int32_data
+                if node.attr('data_format') == 'NCHW':
+                    pads = [
+                        0, 0, paddings[0], paddings[2], 0, 0, paddings[1],
+                        paddings[3]
+                    ]
+                elif node.attr('data_format') == 'NHWC':
+                    pads = [
+                        0, paddings[0], paddings[2], 0, 0, paddings[1],
+                        paddings[3], 0
+                    ]
+                elif node.attr('data_format') == 'NCDHW':
+                    pads = [
+                        0, 0, paddings[4], paddings[2], paddings[0], 0, 0,
+                        paddings[5], paddings[3], paddings[1]
+                    ]
+                elif node.attr('data_format') == 'NDHWC':
+                    pads = [
+                        0, paddings[4], paddings[2], paddings[0], 0, 0,
+                        paddings[5], paddings[3], paddings[1], 0
+                    ]
+            else:
+                raise Exception("In Pad op, padding can not be tensor" \
+                                "Please set opset version >= 11")
+
+        value = None
+        if node.attr('pad_value') is not None:
+            value = node.attr('pad_value')
+        elif node.attr('value') is not None:
+            value = node.attr('value')
+        graph.make_node(
+            'Pad',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            mode=mode,
+            value=value,
+            pads=pads)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        pads = cls.convert_padding(node, **kw)
+        if node.attr('mode') == 'replicate':
+            mode = 'edge'
+        elif node.attr('mode') == 'circular':
+            raise Exception("The padding mode = circular is not supported, " \
+                            "Please try the other three ways")
+        else:
+            mode = node.attr('mode')
+        pads_node = None
+        if isinstance(pads, list):
+            pads_node = graph.make_node(
+                'Constant', attrs={'dtype': dtypes.ONNX.INT64,
+                                   'value': pads})
+        else:
+            key = node.input('Paddings', 0)
+            padding = None
+            if key in graph.parameters.keys():
+                paddings = graph.parameters[key].attribute[0].t.int32_data
+                onnx_paddings = None
+                if node.attr('data_format') == 'NCHW':
+                    onnx_paddings = [
+                        0, 0, paddings[0], paddings[2], 0, 0, paddings[1],
+                        paddings[3]
+                    ]
+                elif node.attr('data_format') == 'NHWC':
+                    onnx_paddings = [
+                        0, paddings[0], paddings[2], 0, 0, paddings[1],
+                        paddings[3], 0
+                    ]
+                elif node.attr('data_format') == 'NCDHW':
+                    onnx_paddings = [
+                        0, 0, paddings[4], paddings[2], paddings[0], 0, 0,
+                        paddings[5], paddings[3], paddings[1]
+                    ]
+                elif node.attr('data_format') == 'NDHWC':
+                    onnx_paddings = [
+                        0, paddings[4], paddings[2], paddings[0], 0, 0,
+                        paddings[5], paddings[3], paddings[1], 0
+                    ]
+
+                pads_node = graph.make_node(
+                    'Constant',
+                    attrs={'dtype': dtypes.ONNX.INT64,
+                           'value': onnx_paddings})
+            else:
+                padding_node = node.input('Paddings', 0)
+                casted_padding_node = graph.make_node(
+                    'Cast', inputs=[padding_node], to=dtypes.ONNX.FLOAT)
+                zero_node = None
+                if node.attr('data_format') == 'NCHW' or node.attr(
+                        'data_format') == 'NHWC':
+                    zero_node = graph.make_node(
+                        'Constant', dtype=dtypes.ONNX.FLOAT, value=[0] * 8)
+                else:
+                    zero_node = graph.make_node(
+                        'Constant', dtype=dtypes.ONNX.FLOAT, value=[0] * 10)
+                index = None
+                if node.attr('data_format') == 'NCHW':
+                    index = graph.make_node(
+                        'Constant', dtype=dtypes.ONNX.INT32,
+                        value=[2, 6, 3, 7])
+                elif node.attr('data_format') == 'NHWC':
+                    index = graph.make_node(
+                        'Constant', dtype=dtypes.ONNX.INT32,
+                        value=[1, 5, 2, 6])
+                elif node.attr('data_format') == 'NCDHW':
+                    index = graph.make_node(
+                        'Constant',
+                        dtype=dtypes.ONNX.INT32,
+                        value=[4, 9, 3, 8, 2, 7])
+                elif node.attr('data_format') == 'NDHWC':
+                    index = graph.make_node(
+                        'Constant',
+                        dtype=dtypes.ONNX.INT32,
+                        value=[3, 8, 2, 7, 1, 6])
+
+                float_paddle_node = graph.make_node(
+                    'ScatterElements',
+                    inputs=[zero_node, index, casted_padding_node])
+                paddle_node = graph.make_node(
+                    'Cast', inputs=[float_paddle_node], to=dtypes.ONNX.INT64)
+                pads_node = paddle_node
+
+        value = None
+        if node.attr('pad_value') is not None:
+            value = node.attr('pad_value')
+        elif node.attr('value') is not None:
+            value = node.attr('value')
+        value_node = graph.make_node(
+            'Constant',
+            attrs={
+                'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
+                'value': value
+            })
+
+        graph.make_node(
+            'Pad',
+            inputs=node.input('X') + [pads_node, value_node],
+            outputs=node.output('Out'),
+            mode=mode)
+
+    @classmethod
+    def convert_padding(cls, node, **kw):
+        x_shape = node.input_shape('X', 0)
+        paddings = node.attr('paddings')
+        if paddings == []:
+            return None
+        onnx_paddings = None
+        if node.attr('data_format') == 'NCHW':
+            onnx_paddings = [
+                0, 0, paddings[0], paddings[2], 0, 0, paddings[1], paddings[3]
+            ]
+        elif node.attr('data_format') == 'NHWC':
+            onnx_paddings = [
+                0, paddings[0], paddings[2], 0, 0, paddings[1], paddings[3], 0
+            ]
+        elif node.attr('data_format') == 'NCDHW':
+            onnx_paddings = [
+                0, 0, paddings[4], paddings[2], paddings[0], 0, 0, paddings[5],
+                paddings[3], paddings[1]
+            ]
+        elif node.attr('data_format') == 'NDHWC':
+            onnx_paddings = [
+                0, paddings[4], paddings[2], paddings[0], 0, 0, paddings[5],
+                paddings[3], paddings[1], 0
+            ]
+        return onnx_paddings
+
+
+@op_mapper('gaussian_random')
+class GaussianRandom():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        shape_input_list = node.input('ShapeTensorList')
+        shape_input = None
+        if len(shape_input_list) == 0:
+            shape_input = node.input('ShapeTensor')
+        else:
+            shape_input = graph.make_node(
+                "Concat", inputs=node.input('ShapeTensorList'), axis=0)
+        if shape_input is None or len(shape_input) == 0:
+            graph.make_node(
+                'RandomNormal',
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('dtype')],
+                outputs=node.output('Out'),
+                shape=node.attr('shape'),
+                seed=float(node.attr('seed')),
+                mean=node.attr('mean'),
+                scale=node.attr('std'))
+        else:
+            cast_input_shape = graph.make_node(
+                'Cast', inputs=shape_input, to=dtypes.ONNX.INT64)
+            zero_like_node = graph.make_node(
+                'ConstantOfShape',
+                inputs=cast_input_shape,
+                dims=[1],
+                dtype=dtypes.ONNX.FLOAT,
+                value=[0])
+            graph.make_node(
+                'RandomNormalLike',
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('dtype')],
+                outputs=node.output('Out'),
+                inputs=zero_like_node,
+                seed=float(node.attr('seed')),
+                mean=node.attr('mean'),
+                scale=node.attr('std'))
+
+
+@op_mapper('uniform_random_batch_size_like')
+class UniformRandom():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        graph.make_node(
+            'RandomUniformLike',
+            inputs=node.input('Input'),
+            outputs=node.output('Out'),
+            high=node.attr('max'),
+            dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('dtype')],
+            low=node.attr('min'),
+            seed=float(node.attr('seed')), )
+
+
+@op_mapper('uniform_random')
+class UniformRandom():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        shape_input_list = node.input('ShapeTensorList')
+        shape_input = None
+        if len(shape_input_list) == 0:
+            shape_input = node.input('ShapeTensor')
+        else:
+            shape_input = graph.make_node(
+                "Concat", inputs=node.input('ShapeTensorList'), axis=0)
+        if shape_input is None or len(shape_input) == 0:
+            graph.make_node(
+                'RandomUniform',
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('dtype')],
+                outputs=node.output('Out'),
+                shape=node.attr('shape'),
+                seed=float(node.attr('seed')),
+                low=node.attr('min'),
+                high=node.attr('max'))
+        else:
+            cast_input_shape = graph.make_node(
+                'Cast', inputs=shape_input, to=dtypes.ONNX.INT64)
+            zero_like_node = graph.make_node(
+                'ConstantOfShape',
+                inputs=cast_input_shape,
+                dtype=dtypes.ONNX.FLOAT,
+                value=[0])
+            graph.make_node(
+                'RandomUniformLike',
+                dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.attr('dtype')],
+                outputs=node.output('Out'),
+                inputs=zero_like_node,
+                seed=float(node.attr('seed')),
+                low=node.attr('min'),
+                high=node.attr('max'))
+
+
+# 'bilinear_interp', 'nearest_interp', scale only support 2, 4, 6, 8, 10
+@op_mapper(
+    [
+        'bilinear_interp', 'nearest_interp', 'bilinear_interp_v2',
+        'nearest_interp_v2', 'bicubic_interp_v2', 'linear_interp_v2',
+        'trilinear_interp_v2', 'trilinear_interp', 'linear_interp'
+    ],
+    mapper_dict={
+        'bilinear_interp': 'linear',
+        'nearest_interp': 'nearest',
+        'bilinear_interp_v2': 'linear',
+        'nearest_interp_v2': 'nearest',
+        'bicubic_interp_v2': 'cubic',
+        'linear_interp_v2': 'linear',
+        'trilinear_interp_v2': 'linear',
+        'trilinear_interp': 'linear',
+        'linear_interp': 'linear',
+    },
+    opset_op_dict={
+        9: 'Upsample',
+        10: 'Resize',
+    })
+class Resize():
+    support_opset_version_range = (9, 15)
+
+    @classmethod
+    def opset_9(cls, graph, node, **kw):
+        inputs = [node.input('X')[0]]
+        resize_type = kw['mapper_dict'][node.type]
+        cls.waringInfo(graph, node, resize_type)
+        if len(node.input('OutSize')) > 0 or len(node.input('SizeTensor')) > 0:
+            output_node = cls.compute_outsize_node(
+                graph, node, return_scale=True)
+        elif 'Scale' in node.inputs and len(node.input('Scale')) > 0:
+            output_node = cls.compute_scale_node(graph, node)
+        else:
+            output_node = cls.compute_attrs_node(graph, node, return_scale=True)
+
+        inputs = inputs + output_node
+        op = kw['opset_op_dict'][graph.opset_version]
+        graph.make_node(
+            op, inputs=inputs, outputs=node.output('Out'), mode=resize_type)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        inputs = [node.input('X')[0]]
+        resize_type = kw['mapper_dict'][node.type]
+        cls.waringInfo(graph, node, resize_type)
+        if node.attr('align_corners'):
+            coordinate_transformation_mode = 'align_corners'
+        elif node.attr('align_mode') == 1 and resize_type is not 'cubic':
+            coordinate_transformation_mode = 'asymmetric'
+        elif resize_type == 'nearest':
+            coordinate_transformation_mode = 'asymmetric'
+        else:
+            coordinate_transformation_mode = 'half_pixel'
+        roi_node = graph.make_node(
+            'Constant',
+            attrs={
+                'dtype': dtypes.ONNX.FLOAT,
+                'value': [1, 1, 1, 1, 1, 1, 1, 1]
+            })
+
+        inputs.append(roi_node)
+        if len(node.input('OutSize')) > 0 or len(node.input('SizeTensor')) > 0:
+            output_node = cls.compute_outsize_node(graph, node)
+        elif 'Scale' in node.inputs and len(node.input('Scale')) > 0:
+            output_node = cls.compute_scale_node(graph, node)
+        else:
+            output_node = cls.compute_attrs_node(graph, node)
+        inputs = inputs + output_node
+        attrs = {
+            'mode': resize_type,
+            'coordinate_transformation_mode': coordinate_transformation_mode
+        }
+        if resize_type == 'nearest' and coordinate_transformation_mode == 'asymmetric':
+            attrs['nearest_mode'] = 'floor'
+        graph.make_node(
+            'Resize', inputs=inputs, outputs=node.output('Out'), attrs=attrs)
+
+    @classmethod
+    def compute_outsize_node(cls, graph, node, return_scale=False):
+        dtype = dtypes.ONNX.INT64
+        if return_scale:
+            dtype = dtypes.ONNX.FLOAT
+        input_shape_node = graph.make_node('Shape', inputs=node.input('X'))
+        if dtype != dtypes.ONNX.INT64:
+            input_shape_node = graph.make_node(
+                'Cast', inputs=[input_shape_node], to=dtype)
+        shape_pre_node = mapper_helper.slice_helper(
+            graph, input_shape_node, axes=[], starts=[0], ends=[2])
+
+        out_size = [node.attr('out_d'), node.attr('out_h'), node.attr('out_w')]
+        out_size = [val for val in out_size if val > 0]
+        use_tensor = False
+        if len(node.input('OutSize')) > 0 or len(node.input('SizeTensor')) > 0:
+            use_tensor = True
+        if len(out_size) > 0 and not use_tensor:
+            out_size_node = graph.make_node(
+                'Constant', attrs={'dtype': dtype,
+                                   'value': out_size})
+        else:
+            out_size_node, _ = mapper_helper.get_node_attr_value(
+                graph, node, None, 'OutSize', 'SizeTensor', dtype=dtype)
+        out_size_node = graph.make_node(
+            'Concat', inputs=[shape_pre_node, out_size_node], axis=0)
+
+        if return_scale:
+            scale_node = graph.make_node(
+                'Div', inputs=[out_size_node, input_shape_node])
+            return [scale_node]
+
+        scale_empty_node = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.FLOAT,
+                               'value': []})
+        return [scale_empty_node, out_size_node]
+
+    @classmethod
+    def compute_scale_node(cls, graph, node):
+        cast_scale = graph.make_node(
+            'Cast', inputs=node.input('Scale'), to=dtypes.ONNX.FLOAT)
+        inputs_cocat = []
+        const_node = graph.make_node(
+            'Constant', attrs={'dtype': dtypes.ONNX.FLOAT,
+                               'value': [1, 1]})
+        inputs_cocat.append(const_node)
+        scale = node.attr('scale')
+        if isinstance(scale, (float, int)):
+            cast_scale = [cast_scale] * (len(node.input_shape('X', 0)) - 2)
+            inputs_cocat = inputs_cocat + cast_scale
+        else:
+            inputs_cocat = inputs_cocat + [cast_scale]
+        scale_node = graph.make_node('Concat', inputs=inputs_cocat, axis=0)
+        return [scale_node]
+
+    @classmethod
+    def compute_attrs_node(cls, graph, node, return_scale=False):
+        out_size = [node.attr('out_d'), node.attr('out_h'), node.attr('out_w')]
+        scale = node.attr('scale')
+        if isinstance(scale, (float, int)):
+            scale = [scale] * (len(node.input_shape('X', 0)) - 2)
+
+        out_size = [val for val in out_size if val > 0]
+        if len(out_size) > 0:
+            output_node = cls.compute_outsize_node(
+                graph, node, return_scale=return_scale)
+            return output_node
+
+        assert len(scale) > 0, Exception("scale size should > 0!")
+        scale_node = graph.make_node(
+            'Constant',
+            attrs={'dtype': dtypes.ONNX.FLOAT,
+                   'value': [1, 1] + scale})
+        return [scale_node]
+
+    @classmethod
+    def waringInfo(cls, graph, node, resize_type):
+        assert node.attrs['data_layout'] == 'NCHW', \
+            "The conv data layout should be 'NCHW' , but received data format " \
+            "is %s." % node.attrs['data_format']
+
+        if graph.opset_version < 11:
+            if node.attr('align_corners') or resize_type in ["cubic"]:
+                raise Exception(
+                    "When align_corners is true or resize_type is 'cubic', the case isn't supported in onnx(opset<=10), "
+                    "Try converting with opset_version>= 11 ")
+            if node.attr('align_mode') == 0 and resize_type in [
+                    "bilinear", "linear", "trilinear"
+            ]:
+                raise Exception(
+                    "When align_mode == 0 and resize_type is 'bilinear' or 'linear or 'trilinear', the case isn't "
+                    "supported in onnx(opset<=10), Try converting with opset_version>= 11 "
+                )
+
+
+@op_mapper('pixel_shuffle')
+class PixelShuffle():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        upscale_factor = node.attr('upscale_factor')
+
+        node = graph.make_node(
+            'DepthToSpace',
+            inputs=node.input('X'),
+            outputs=node.output('Out'),
+            blocksize=upscale_factor,
+            mode='CRD')
+
+
+@op_mapper('scatter')
+class Scatter():
+    support_opset_version_range = (11, 15)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        ids = node.input('Ids', 0)
+        input_dtype = dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('Ids', 0)]
+        if input_dtype != dtypes.ONNX.INT64:
+            ids = graph.make_node('Cast', inputs=[ids], to=dtypes.ONNX.INT64)
+
+        shape = graph.make_node(
+            'Constant',
+            value=[node.input_shape('Ids', 0)[0], 1],
+            dtype=dtypes.ONNX.INT64)
+        reshape_index = graph.make_node('Reshape', inputs=[ids, shape])
+        if not node.attr('overwrite'):
+            raise Exception("overwrite = False not support yet.")
+        else:
+            graph.make_node(
+                'ScatterND',
+                inputs=[
+                    node.input('X', 0), reshape_index, node.input('Updates', 0)
+                ],
+                outputs=node.output('Out'))
+
+
+@op_mapper('scatter_nd_add')
+class ScatterndAdd():
+    support_opset_version_range = (11, 12)
+
+    @classmethod
+    def opset_11(cls, graph, node, **kw):
+        shape = graph.make_node('Shape', inputs=node.input('X', 0))
+        zero_like_node = graph.make_node(
+            'ConstantOfShape',
+            inputs=[shape],
+            dims=[1],
+            dtype=dtypes.ONNX.FLOAT,
+            value=[0])
+        add_node = graph.make_node(
+            'ScatterND',
+            inputs=[
+                zero_like_node, node.input('Index', 0), node.input('Updates', 0)
+            ], )
+        graph.make_node(
+            'Add',
+            inputs=[node.input('X', 0), add_node],
+            outputs=node.output('Out'))
+
+
+@op_mapper('meshgrid')
+class Meshgrid():
+    support_opset_version_range = (8, 15)
+
+    @classmethod
+    def opset_8(cls, graph, node, **kw):
+        tensors = [t for t in list(node.input('X'))]
+        tensors_shape = [graph.make_node('Shape', inputs=t) for t in tensors]
+        out_shape = graph.make_node('Concat', inputs=tensors_shape, axis=0)
+        out = []
+        for i, t in enumerate(tensors):
+            shape_i = [
+                graph.make_node(
+                    'Constant',
+                    attrs={'dtype': dtypes.ONNX.INT64,
+                           'value': [1]})
+            ] * len(tensors)
+            shape_i[i] = tensors_shape[i]
+            t_reshaped = graph.make_node(
+                'Reshape',
+                inputs=[t, graph.make_node(
+                    'Concat', inputs=shape_i, axis=0)])
+            out.append(
+                graph.make_node(
+                    'Expand',
+                    inputs=[t_reshaped, out_shape],
+                    outputs=node.output('Out')[i]))
+
+
+@op_mapper('flip')
+class Flip():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        inputs = node.input('X')
+        x_dtype = node.input_dtype('X', 0)
+        if x_dtype == paddle.bool or x_dtype == paddle.float64:
+            inputs = [
+                graph.make_node(
+                    "Cast", inputs=inputs, to=dtypes.ONNX.FLOAT)
+            ]
+        axes = node.attr("axis")
+        if not isinstance(axes, list):
+            axes = [axes]
+        input_shape = node.input_shape('X', 0)
+
+        for i, axis in enumerate(axes):
+            if axis < 0:
+                axes[i] += len(input_shape)
+            assert input_shape[
+                axis] > 0, "The dimension in axis of input must be fixed for flip operator, but now the input shape({}) in axis({}) is unknow.".format(
+                    input_shape, axis)
+
+        temp_input = inputs[0]
+        for i, axis in enumerate(axes):
+            if input_shape[axis] == 1:
+                if i != len(axes) - 1:
+                    continue
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        graph.make_node(
+                            "Cast",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Identity",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"))
+            else:
+                splits = graph.make_node(
+                    "Split",
+                    inputs=[temp_input],
+                    outputs=input_shape[axis],
+                    axis=axis,
+                    split=[1] * input_shape[axis])
+                reversed_splits = splits[::-1]
+                if i != len(axes) - 1:
+                    temp_input = graph.make_node(
+                        "Concat", inputs=reversed_splits, axis=axis)
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        out = graph.make_node(
+                            "Concat", inputs=reversed_splits, axis=axis)
+                        graph.make_node(
+                            "Cast",
+                            inputs=[out],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Concat",
+                            inputs=reversed_splits,
+                            outputs=node.output("Out"),
+                            axis=axis)
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        inputs = node.input('X')
+        x_dtype = node.input_dtype('X', 0)
+        if x_dtype == paddle.bool or x_dtype == paddle.float64:
+            inputs = [
+                graph.make_node(
+                    "Cast", inputs=inputs, to=dtypes.ONNX.FLOAT)
+            ]
+        axes = node.attr("axis")
+        if not isinstance(axes, list):
+            axes = [axes]
+        input_shape = node.input_shape('X', 0)
+
+        for i, axis in enumerate(axes):
+            if axis < 0:
+                axes[i] += len(input_shape)
+            assert input_shape[
+                axis] > 0, "The dimension in axis of input must be fixed for flip operator, but now the input shape({}) in axis({}) is unknow.".format(
+                    input_shape, axis)
+
+        temp_input = inputs[0]
+        for i, axis in enumerate(axes):
+            if input_shape[axis] == 1:
+                if i != len(axes) - 1:
+                    continue
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        graph.make_node(
+                            "Cast",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Identity",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"))
+            else:
+                split = graph.make_node(
+                    'Constant',
+                    attrs={
+                        'dtype': dtypes.ONNX.INT64,
+                        'value': [1] * input_shape[axis]
+                    })
+                splits = graph.make_node(
+                    "Split",
+                    inputs=[temp_input, split],
+                    outputs=input_shape[axis],
+                    axis=axis)
+                reversed_splits = splits[::-1]
+                if i != len(axes) - 1:
+                    temp_input = graph.make_node(
+                        "Concat", inputs=reversed_splits, axis=axis)
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        out = graph.make_node(
+                            "Concat", inputs=reversed_splits, axis=axis)
+                        graph.make_node(
+                            "Cast",
+                            inputs=[out],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Concat",
+                            inputs=reversed_splits,
+                            outputs=node.output("Out"),
+                            axis=axis)
diff --git a/paddle2onnx/legacy/passes/__init__.py b/paddle2onnx/legacy/passes/__init__.py
new file mode 100755
index 0000000000..9ac316d841
--- /dev/null
+++ b/paddle2onnx/legacy/passes/__init__.py
@@ -0,0 +1,17 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .pass_manager import PassManager
+from .inplace_node_pass import InplaceNodePass
+from .dumplicate_names_pass import DumplicateNamesPass
\ No newline at end of file
diff --git a/paddle2onnx/legacy/passes/dumplicate_names_pass.py b/paddle2onnx/legacy/passes/dumplicate_names_pass.py
new file mode 100755
index 0000000000..82d17a129d
--- /dev/null
+++ b/paddle2onnx/legacy/passes/dumplicate_names_pass.py
@@ -0,0 +1,91 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle2onnx.legacy.passes import PassManager
+from paddle2onnx.utils import logging
+
+
+@PassManager('dumplicate_names_pass')
+class DumplicateNamesPass(object):
+
+    name_count = dict()
+
+    @classmethod
+    def generate_new_name(cls, name):
+        for saved_name in cls.name_count:
+            if name.startswith(saved_name):
+                cls.name_count[saved_name] += 1
+                new_name = saved_name + '.' + str(cls.name_count[saved_name])
+                return new_name
+        cls.name_count[name] = 1
+        new_name = name + '.' + str(cls.name_count[name])
+        return new_name
+
+    @classmethod
+    def run_pass(cls, onnx_graph):
+        renamer = {}
+        tensor_names = set()
+        for name, node in onnx_graph.parameters.items():
+            output = node.output
+            for opt in output:
+                assert opt not in tensor_names, "There's dumplicate names in parameters."
+                tensor_names.add(opt)
+
+        for ipt in onnx_graph.input_nodes:
+            assert ipt.name not in tensor_names, "There's dumplicate names in exported parameters and inputs."
+            tensor_names.add(ipt.name)
+
+        for name, node in onnx_graph.node_map.items():
+            inputs = node.inputs
+            outputs = node.outputs
+            update_node = False
+            for idx in range(len(inputs)):
+                ipt = inputs[idx]
+                if ipt not in renamer:
+                    continue
+                updated_name = renamer[ipt]
+                while updated_name in renamer:
+                    updated_name = renamer[updated_name]
+                inputs[idx] = updated_name
+                update_node = True
+
+            for idx in range(len(outputs)):
+                opt = outputs[idx]
+                if opt not in tensor_names:
+                    tensor_names.add(opt)
+                    continue
+                renamed_tensor_name = opt
+                while renamed_tensor_name in renamer:
+                    renamed_tensor_name = renamer[renamed_tensor_name]
+                new_name = cls.generate_new_name(renamed_tensor_name)
+                logging.warning("[Renamer Pass] Will rename {}, to {}".format(
+                    renamed_tensor_name, new_name))
+                outputs[idx] = new_name
+                update_node = True
+                renamer[renamed_tensor_name] = new_name
+
+            if update_node:
+                node.set_inputs(inputs)
+                node.set_outputs(outputs)
+                onnx_graph.update_node(node)
+
+        for opt in onnx_graph.output_nodes:
+            if opt.name not in renamer:
+                continue
+            updated_name = renamer[opt.name]
+            while updated_name in renamer:
+                updated_name = renamer[updated_name]
+            opt.name = updated_name
+
+        return onnx_graph
diff --git a/paddle2onnx/legacy/passes/inplace_node_pass.py b/paddle2onnx/legacy/passes/inplace_node_pass.py
new file mode 100755
index 0000000000..e9fc6f84be
--- /dev/null
+++ b/paddle2onnx/legacy/passes/inplace_node_pass.py
@@ -0,0 +1,62 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle2onnx.legacy.passes import PassManager
+
+
+def get_repeated_output(inputs, outputs):
+    repeated_output = {}
+    for idx in range(len(outputs)):
+        opt = outputs[idx]
+        if opt in inputs:
+            repeated_output[opt] = idx
+    return repeated_output
+
+
+@PassManager('inplace_node_pass')
+class InplaceNodePass(object):
+
+    name_count = dict()
+
+    @classmethod
+    def generate_new_name(cls, name):
+        if name in cls.name_count:
+            cls.name_count[name] += 1
+        else:
+            cls.name_count[name] = 1
+        new_name = name + '.' + str(cls.name_count[name])
+        return new_name
+
+    @classmethod
+    def run_pass(cls, onnx_graph):
+        node_map = list(onnx_graph.node_map.items())
+        name_mapping = {}
+        for idx in range(len(node_map)):
+            name, node = node_map[idx]
+            inputs = node.inputs
+            outputs = node.outputs
+            for idx in range(len(inputs)):
+                ipt = inputs[idx]
+                if ipt in name_mapping:
+                    inputs[idx] = name_mapping[ipt]
+            repeated_output = get_repeated_output(inputs, outputs)
+            if len(repeated_output) != 0:
+                for opt, idx in repeated_output.items():
+                    name_mapping[opt] = cls.generate_new_name(opt)
+                    outputs[idx] = name_mapping[opt]
+            node.set_inputs(inputs)
+            node.set_outputs(outputs)
+            onnx_graph.update_node(node)
+
+        return onnx_graph
diff --git a/paddle2onnx/legacy/passes/pass_manager.py b/paddle2onnx/legacy/passes/pass_manager.py
new file mode 100644
index 0000000000..ca8813c449
--- /dev/null
+++ b/paddle2onnx/legacy/passes/pass_manager.py
@@ -0,0 +1,39 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+
+
+class PassManager(object):
+    PASSES = {}
+
+    def __init__(self, name, **kwargs):
+        self.name = name
+        self.kwargs = kwargs
+
+    def __call__(self, cls):
+        for k, v in inspect.getmembers(cls, inspect.ismethod):
+            if k == 'run_pass':
+                self.PASSES[self.name] = (v, self.kwargs)
+
+    @staticmethod
+    def run_pass(graph, custom_pass_list):
+        for pass_name in custom_pass_list:
+            try:
+                pass_func, kw = PassManager.PASSES[pass_name]
+                pass_func(graph, **kw)
+            except:
+                raise Exception("Error happened when excute pass: {}".format(
+                    pass_name))
+        return graph
diff --git a/paddle2onnx/mapper/activation.cc b/paddle2onnx/mapper/activation.cc
new file mode 100644
index 0000000000..fe391be687
--- /dev/null
+++ b/paddle2onnx/mapper/activation.cc
@@ -0,0 +1,453 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/activation.h"
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(relu, ActivationMapper)
+REGISTER_MAPPER(relu6, Relu6Mapper)
+REGISTER_MAPPER(tanh, ActivationMapper)
+REGISTER_MAPPER(log, ActivationMapper)
+REGISTER_MAPPER(sigmoid, ActivationMapper)
+REGISTER_MAPPER(sqrt, ActivationMapper)
+REGISTER_MAPPER(softplus, ActivationMapper)
+REGISTER_MAPPER(exp, ActivationMapper)
+REGISTER_MAPPER(floor, ActivationMapper)
+REGISTER_MAPPER(cos, ActivationMapper)
+REGISTER_MAPPER(sin, ActivationMapper)
+REGISTER_MAPPER(round, ActivationMapper)
+REGISTER_MAPPER(abs, ActivationMapper)
+REGISTER_MAPPER(acos, ActivationMapper)
+REGISTER_MAPPER(asin, ActivationMapper)
+REGISTER_MAPPER(atan, ActivationMapper)
+REGISTER_MAPPER(sinh, ActivationMapper)
+REGISTER_MAPPER(tan, ActivationMapper)
+REGISTER_MAPPER(ceil, ActivationMapper)
+REGISTER_MAPPER(cosh, ActivationMapper)
+REGISTER_MAPPER(softsign, ActivationMapper)
+REGISTER_MAPPER(sign, ActivationMapper)
+REGISTER_MAPPER(erf, ActivationMapper)
+REGISTER_MAPPER(reciprocal, ActivationMapper)
+REGISTER_MAPPER(leaky_relu, LeakyReluMapper)
+REGISTER_MAPPER(gelu, GeluMapper)
+REGISTER_MAPPER(selu, SeluMapper)
+REGISTER_MAPPER(prelu, PReluMapper)
+REGISTER_MAPPER(hard_sigmoid, HardSigmoidMapper)
+REGISTER_MAPPER(swish, SwishMapper)
+REGISTER_MAPPER(hard_swish, HardSwishMapper)
+REGISTER_MAPPER(softmax, SoftMaxMapper)
+REGISTER_MAPPER(brelu, BReluMapper)
+REGISTER_MAPPER(elu, EluMapper)
+REGISTER_MAPPER(hard_shrink, HardShrinkMapper)
+REGISTER_MAPPER(softshrink, SoftShrinkMapper)
+REGISTER_MAPPER(mish, MishMapper)
+REGISTER_MAPPER(square, SquareMapper)
+REGISTER_MAPPER(size, SizeMapper)
+REGISTER_MAPPER(rsqrt, RsqrtMapper)
+REGISTER_MAPPER(logsigmoid, LogSigmoidMapper)
+REGISTER_MAPPER(log_softmax, LogSoftmaxMapper)
+REGISTER_MAPPER(tanh_shrink, TanhShrinkMapper)
+REGISTER_MAPPER(thresholded_relu, ThresholdedReluMapper)
+REGISTER_MAPPER(log1p, Log1PMapper)
+REGISTER_MAPPER(log2, Log2Mapper)
+REGISTER_MAPPER(log10, Log10Mapper)
+REGISTER_MAPPER(silu, SiluMapper)
+
+int32_t ActivationMapper::GetMinOpset(bool verbose) {
+  if (OpType() == "softplus") {
+    float beta = 0.0;
+    float threshold = 20.0;
+    GetAttr("beta", &beta);
+    GetAttr("threshold", &threshold);
+    if ((beta - 1.0) > 1e-06 || (beta - 1.0) < -1e-06 ||
+        (threshold - 20.0) > 1e-06 || (threshold - 20.0) < -1e-06) {
+      Error() << "Only support softplus with beta == 1.0 and threshold == 20.0."
+              << std::endl;
+      return -1;
+    }
+  }
+  if (OpType() == "round") {
+    Logger(verbose, 11) << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  if (OpType() == "sinh" || OpType() == "cosh" || OpType() == "sign") {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  return 7;
+}
+
+void ActivationMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto iter = op_mapper_.find(OpType());
+  Assert(op_mapper_.end() != iter,
+         "Cannot find " + OpType() + " in activation op_mapper.");
+  if (OpType() == "erf") {
+    auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+    auto output = helper_->MakeNode(iter->second, {input})->output(0);
+    helper_->AutoCast(output, output_info[0].name, P2ODataType::FP32,
+                      output_info[0].dtype);
+  } else {
+    helper_->MakeNode(iter->second, {input_info[0].name},
+                      {output_info[0].name});
+  }
+}
+
+void Relu6Mapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  float min = 0.0;
+  helper_->Clip(input_info[0].name, output_info[0].name, min, threshold_,
+                input_info[0].dtype);
+}
+
+int32_t PReluMapper::GetMinOpset(bool verbose) {
+  auto input_info = GetInput("X");
+  auto slope_info = GetInput("Alpha");
+  if (input_info[0].Rank() != slope_info[0].Rank()) {
+    if (slope_info[0].Rank() > 1) {
+      Error()
+          << "Only support rank of alpha <=1 while Rank(alpha) != Rank(input)."
+          << std::endl;
+      return -1;
+    }
+  }
+  return 7;
+}
+
+void PReluMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto slope_info = GetInput("Alpha");
+  auto output_info = GetOutput("Out");
+
+  std::string slope_cast_name = slope_info[0].name;
+  if (slope_info[0].dtype == P2ODataType::FP64) {
+    slope_cast_name = helper_->AutoCast({slope_info[0].name}, P2ODataType::FP64,
+                                        P2ODataType::FP32);
+  }
+
+  if (slope_info[0].Rank() != input_info[0].Rank()) {
+    Assert(slope_info[0].Rank() <= 1,
+           "Paddle2ONNX: Only support rank of alpha <= 1 while rank of alpha "
+           "is not equal with rank of input for operator prelu.");
+    Assert(
+        input_info[0].Rank() > 1,
+        "Paddle2ONNX: Rank of input should greater than 2 for operator prelu.");
+    std::vector<int64_t> shape_value(input_info[0].Rank() - 1, 1);
+    shape_value[0] = -1;
+    slope_cast_name = helper_->Reshape(slope_cast_name, shape_value);
+  }
+
+  if (input_info[0].dtype == P2ODataType::FP64) {
+    std::string x_cast_name = helper_->AutoCast(
+        {input_info[0].name}, P2ODataType::FP64, P2ODataType::FP32);
+    auto node = helper_->MakeNode("PRelu", {x_cast_name, slope_cast_name});
+    helper_->AutoCast(node->output(0), {output_info[0].name}, P2ODataType::FP32,
+                      P2ODataType::FP64);
+  } else {
+    helper_->MakeNode("PRelu", {input_info[0].name, slope_cast_name},
+                      {output_info[0].name});
+  }
+}
+
+void SeluMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node =
+      helper_->MakeNode("Selu", {input_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "alpha", alpha_);
+  AddAttribute(node, "gamma", scale_);
+}
+
+void HardSigmoidMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node = helper_->MakeNode("HardSigmoid", {input_info[0].name},
+                                {output_info[0].name});
+  AddAttribute(node, "alpha", alpha_);
+  AddAttribute(node, "beta", beta_);
+}
+
+void SwishMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string beta_node =
+      helper_->Constant({1}, GetOnnxDtype(input_info[0].dtype), beta_);
+  // TODO(jiangjiajun) eliminate multiply with a constant of value 1
+  // TODO(jiangjiajun) eliminate add with a constant of value 0
+  auto beta_x_node = helper_->MakeNode("Mul", {input_info[0].name, beta_node});
+  auto sigmod_node = helper_->MakeNode("Sigmoid", {beta_x_node->output(0)});
+  helper_->MakeNode("Mul", {input_info[0].name, sigmod_node->output(0)},
+                    {output_info[0].name});
+}
+
+void HardSwishMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string scale_node =
+      helper_->Constant({1}, GetOnnxDtype(input_info[0].dtype), scale_);
+  std::string offset_node =
+      helper_->Constant({1}, GetOnnxDtype(input_info[0].dtype), offset_);
+
+  auto add_node = helper_->MakeNode("Add", {input_info[0].name, offset_node});
+  auto clip_node =
+      helper_->Clip(add_node->output(0), 0.0, threshold_, input_info[0].dtype);
+
+  auto mul_node = helper_->MakeNode("Mul", {input_info[0].name, clip_node});
+  helper_->MakeNode("Div", {mul_node->output(0), scale_node},
+                    {output_info[0].name});
+}
+
+void HardSwishMapper::Opset14() {
+  if (fabs(offset_ - 3.0) > 1e-05 || fabs(scale_ - 6.0) > 1e-05 ||
+      fabs(threshold_ - 6.0) > 1e-05) {
+    return Opset7();
+  }
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  helper_->MakeNode("HardSwish", {input_info[0].name}, {output_info[0].name});
+}
+
+void LeakyReluMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node = helper_->MakeNode("LeakyRelu", {input_info[0].name},
+                                {output_info[0].name});
+  AddAttribute(node, "alpha", alpha_);
+}
+
+void GeluMapper::Opset9() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto input_onnx_dtype = GetOnnxDtype(input_info[0].dtype);
+  double sqrt_2_value = 1.4142135623730951;
+  double scale_value = 0.5;
+  double const_1_value = 1.0;
+  auto sqrt_2 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::FLOAT, sqrt_2_value);
+  auto scale =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::FLOAT, scale_value);
+  auto const_1 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::FLOAT, const_1_value);
+
+  auto input_name = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                      P2ODataType::FP32);
+
+  // the computation formula follows
+  // https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/functional/gelu_cn.html#gelu
+  auto erf0 = helper_->MakeNode("Div", {input_name, sqrt_2});
+  auto erf1 = helper_->MakeNode("Erf", {erf0->output(0)});
+  auto gelu0 = helper_->MakeNode("Add", {erf1->output(0), const_1});
+  auto gelu1 = helper_->MakeNode("Mul", {input_name, gelu0->output(0)});
+
+  if (input_info[0].dtype != P2ODataType::FP32) {
+    auto out = helper_->MakeNode("Mul", {gelu1->output(0), scale});
+    auto cast_out =
+        helper_->MakeNode("Cast", {out->output(0)}, {output_info[0].name});
+    AddAttribute(cast_out, "to", GetOnnxDtype(input_info[0].dtype));
+  } else {
+    helper_->MakeNode("Mul", {gelu1->output(0), scale}, {output_info[0].name});
+  }
+}
+
+void SoftMaxMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  if (axis_ < 0) {
+    axis_ = axis_ + output_info[0].Rank();
+  }
+  if (axis_ == output_info[0].Rank() - 1) {
+    auto node = helper_->MakeNode("Softmax", {input_info[0].name},
+                                  {output_info[0].name});
+    AddAttribute(node, "axis", axis_);
+  } else {
+    std::vector<int64_t> perm = Arange(0, output_info[0].Rank());
+    perm[output_info[0].Rank() - 1] = axis_;
+    perm[axis_] = output_info[0].Rank() - 1;
+    auto transpose_node = helper_->MakeNode("Transpose", {input_info[0].name});
+    AddAttribute(transpose_node, "perm", perm);
+    auto softmax_node =
+        helper_->MakeNode("Softmax", {transpose_node->output(0)});
+    int64_t axis_last = -1;
+    AddAttribute(softmax_node, "axis", axis_last);
+    auto transpose_node_last = helper_->MakeNode(
+        "Transpose", {softmax_node->output(0)}, {output_info[0].name});
+    AddAttribute(transpose_node_last, "perm", perm);
+  }
+}
+
+void SoftMaxMapper::Opset13() {
+  int64_t axis;
+  GetAttr("axis", &axis);
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node =
+      helper_->MakeNode("Softmax", {input_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "axis", axis);
+}
+
+void BReluMapper::Opset7() {
+  auto x_info = GetInput("X");
+  helper_->Clip(x_info[0].name, GetOutput("Out")[0].name, t_min_, t_max_,
+                x_info[0].dtype);
+}
+
+void EluMapper::Opset7() {
+  auto node = helper_->MakeNode("Elu", {GetInput("X")[0].name},
+                                {GetOutput("Out")[0].name});
+  AddAttribute(node, "alpha", alpha_);
+}
+
+void HardShrinkMapper::Opset9() {
+  auto node = helper_->MakeNode("Shrink", {GetInput("X")[0].name},
+                                {GetOutput("Out")[0].name});
+  AddAttribute(node, "lambd", threshold_);
+  AddAttribute(node, "bias", float(0.0));
+}
+
+int32_t MishMapper::GetMinOpset(bool verbose) {
+  if (fabs(threshold_ - 20.0) > 1e-05) {
+    Error() << "Only support threshold = 20.0." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void MishMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                 P2ODataType::FP32);
+  auto softplus = helper_->MakeNode("Softplus", {input})->output(0);
+  auto tanh = helper_->MakeNode("Tanh", {softplus})->output(0);
+  auto output = helper_->MakeNode("Mul", {input, tanh})->output(0);
+  helper_->AutoCast(output, out_info[0].name, P2ODataType::FP32,
+                    out_info[0].dtype);
+}
+
+void SquareMapper::Opset7() {
+  auto input_info = GetInput("X");
+  helper_->MakeNode("Mul", {input_info[0].name, input_info[0].name},
+                    {GetOutput("Out")[0].name});
+}
+
+void SoftShrinkMapper::Opset9() {
+  auto node = helper_->MakeNode("Shrink", {GetInput("X")[0].name},
+                                {GetOutput("Out")[0].name});
+  AddAttribute(node, "lambd", lambda_);
+  AddAttribute(node, "bias", lambda_);
+}
+
+void SizeMapper::Opset7() {
+  auto out_info = GetOutput("Out");
+  auto output =
+      helper_->MakeNode("Size", {GetInput("Input")[0].name})->output(0);
+  output = helper_->Reshape(output, {-1});
+  output = helper_->AutoCast(output, out_info[0].name, P2ODataType::INT64,
+                             out_info[0].dtype);
+}
+
+void RsqrtMapper::Opset7() {
+  auto output = helper_->MakeNode("Sqrt", {GetInput("X")[0].name})->output(0);
+  helper_->MakeNode("Reciprocal", {output}, {GetOutput("Out")[0].name});
+}
+
+void TanhShrinkMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto tanh = helper_->MakeNode("Tanh", {x_info[0].name})->output(0);
+  helper_->MakeNode("Sub", {x_info[0].name, tanh}, {GetOutput("Out")[0].name});
+}
+
+void LogSigmoidMapper::Opset7() {
+  auto output =
+      helper_->MakeNode("Sigmoid", {GetInput("X")[0].name})->output(0);
+  helper_->MakeNode("Log", {output}, {GetOutput("Out")[0].name});
+}
+
+void LogSoftmaxMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto axis = axis_;
+  if (axis < 0) {
+    axis += input_info[0].Rank();
+  }
+  if (axis == input_info[0].Rank() - 1) {
+    auto node = helper_->MakeNode("LogSoftmax", {input_info[0].name},
+                                  {GetOutput("Out")[0].name});
+    AddAttribute(node, "axis", axis);
+  } else {
+    auto perm = Arange(0, input_info[0].Rank());
+    perm[input_info[0].Rank() - 1] = axis;
+    perm[axis] = input_info[0].Rank() - 1;
+    auto output = helper_->Transpose(input_info[0].name, perm);
+    auto node = helper_->MakeNode("LogSoftmax", {output});
+    AddAttribute(node, "axis", int64_t(-1));
+    helper_->Transpose(node->output(0), GetOutput("Out")[0].name, perm);
+  }
+}
+
+void ThresholdedReluMapper::Opset10() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  auto input = x_info[0].name;
+  if (x_info[0].dtype != P2ODataType::FP32) {
+    input = helper_->AutoCast(input, x_info[0].dtype, P2ODataType::FP32);
+    auto node = helper_->MakeNode("ThresholdedRelu", {input});
+    AddAttribute(node, "alpha", threshold_);
+    helper_->AutoCast(node->output(0), out_info[0].name, P2ODataType::FP32,
+                      out_info[0].dtype);
+  } else {
+    auto node =
+        helper_->MakeNode("ThresholdedRelu", {input}, {out_info[0].name});
+    AddAttribute(node, "alpha", threshold_);
+  }
+}
+
+void Log1PMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  auto one = helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), float(1.0));
+  auto input = helper_->MakeNode("Add", {x_info[0].name, one})->output(0);
+  helper_->MakeNode("Log", {input}, {out_info[0].name});
+}
+
+void Log2Mapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  double ln2 = 0.693147180559945309;
+  auto ln2_tensor = helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), ln2);
+  auto output = helper_->MakeNode("Log", {x_info[0].name})->output(0);
+  helper_->MakeNode("Div", {output, ln2_tensor}, {out_info[0].name});
+}
+
+void Log10Mapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  double ln10 = 2.30258509299404568401;
+  auto ln10_tensor =
+      helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), ln10);
+  auto output = helper_->MakeNode("Log", {x_info[0].name})->output(0);
+  helper_->MakeNode("Div", {output, ln10_tensor}, {out_info[0].name});
+}
+
+void SiluMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  auto out = helper_->MakeNode("Sigmoid", {x_info[0].name})->output(0);
+  helper_->MakeNode("Mul", {x_info[0].name, out}, {out_info[0].name});
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/activation.h b/paddle2onnx/mapper/activation.h
new file mode 100644
index 0000000000..d1143c5fbb
--- /dev/null
+++ b/paddle2onnx/mapper/activation.h
@@ -0,0 +1,372 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <cmath>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ActivationMapper : public Mapper {
+ public:
+  ActivationMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    op_mapper_["relu"] = "Relu";
+    op_mapper_["tanh"] = "Tanh";
+    op_mapper_["log"] = "Log";
+    op_mapper_["sigmoid"] = "Sigmoid";
+    op_mapper_["sqrt"] = "Sqrt";
+    op_mapper_["softplus"] = "Softplus";
+    op_mapper_["exp"] = "Exp";
+    op_mapper_["floor"] = "Floor";
+    op_mapper_["cos"] = "Cos";
+    op_mapper_["sin"] = "Sin";
+    op_mapper_["round"] = "Round";
+    op_mapper_["abs"] = "Abs";
+    op_mapper_["acos"] = "Acos";
+    op_mapper_["asin"] = "Asin";
+    op_mapper_["atan"] = "Atan";
+    op_mapper_["sinh"] = "Sinh";
+    op_mapper_["tan"] = "Tan";
+    op_mapper_["ceil"] = "Ceil";
+    op_mapper_["cosh"] = "Cosh";
+    op_mapper_["erf"] = "Erf";
+    op_mapper_["sign"] = "Sign";
+    op_mapper_["softsign"] = "Softsign";
+    op_mapper_["reciprocal"] = "Reciprocal";
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::map<std::string, std::string> op_mapper_;
+};
+
+class Relu6Mapper : public Mapper {
+ public:
+  Relu6Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("threshold", &threshold_);
+  }
+
+  void Opset7();
+
+ private:
+  float threshold_;
+};
+
+class PReluMapper : public Mapper {
+ public:
+  PReluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+};
+
+class SeluMapper : public Mapper {
+ public:
+  SeluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("alpha", &alpha_);
+    GetAttr("scale", &scale_);
+  }
+
+  void Opset7();
+
+ private:
+  float alpha_;
+  float scale_;
+};
+
+class HardSigmoidMapper : public Mapper {
+ public:
+  HardSigmoidMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("slope", &alpha_);
+    GetAttr("offset", &beta_);
+  }
+
+  void Opset7();
+
+ private:
+  float alpha_;
+  float beta_;
+};
+
+class SwishMapper : public Mapper {
+ public:
+  SwishMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("beta", &beta_);
+  }
+
+  void Opset7();
+
+ private:
+  float beta_;
+};
+
+class HardSwishMapper : public Mapper {
+ public:
+  HardSwishMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("scale", &scale_);
+    GetAttr("offset", &offset_);
+    GetAttr("threshold", &threshold_);
+  }
+
+  void Opset7();
+  void Opset14();
+
+ private:
+  float scale_;
+  float offset_;
+  float threshold_;
+};
+
+class LeakyReluMapper : public Mapper {
+ public:
+  LeakyReluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("alpha", &alpha_);
+  }
+
+  void Opset7();
+
+ private:
+  float alpha_;
+};
+
+class GeluMapper : public Mapper {
+ public:
+  GeluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+
+  void Opset9();
+};
+
+class SoftMaxMapper : public Mapper {
+ public:
+  SoftMaxMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+
+  void Opset7();
+  void Opset13();
+
+ private:
+  int64_t axis_ = -1;
+};
+
+class BReluMapper : public Mapper {
+ public:
+  BReluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("t_min", &t_min_);
+    GetAttr("t_max", &t_max_);
+  }
+
+  void Opset7();
+
+ private:
+  float t_min_;
+  float t_max_;
+};
+
+class EluMapper : public Mapper {
+ public:
+  EluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("alpha", &alpha_);
+  }
+  void Opset7();
+
+ private:
+  float alpha_;
+};
+
+class HardShrinkMapper : public Mapper {
+ public:
+  HardShrinkMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("threshold", &threshold_);
+  }
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  void Opset9();
+
+ private:
+  float threshold_;
+};
+
+class MishMapper : public Mapper {
+ public:
+  MishMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("threshold", &threshold_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  float threshold_;
+};
+
+class SquareMapper : public Mapper {
+ public:
+  SquareMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class SizeMapper : public Mapper {
+ public:
+  SizeMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class LogSigmoidMapper : public Mapper {
+ public:
+  LogSigmoidMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class RsqrtMapper : public Mapper {
+ public:
+  RsqrtMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class LogSoftmaxMapper : public Mapper {
+ public:
+  LogSoftmaxMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+class SoftShrinkMapper : public Mapper {
+ public:
+  SoftShrinkMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("lambda", &lambda_);
+  }
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  void Opset9();
+
+ private:
+  float lambda_;
+};
+
+class ThresholdedReluMapper : public Mapper {
+ public:
+  ThresholdedReluMapper(const PaddleParser& p, OnnxHelper* helper,
+                        int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("threshold", &threshold_);
+  }
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 10) << RequireOpset(10) << std::endl;
+    return 10;
+  }
+  void Opset10();
+
+ private:
+  float threshold_;
+};
+
+class TanhShrinkMapper : public Mapper {
+ public:
+  TanhShrinkMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class Log1PMapper : public Mapper {
+ public:
+  Log1PMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class Log2Mapper : public Mapper {
+ public:
+  Log2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class Log10Mapper : public Mapper {
+ public:
+  Log10Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+class SiluMapper : public Mapper {
+ public:
+  SiluMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/data_helper.h b/paddle2onnx/mapper/data_helper.h
new file mode 100644
index 0000000000..df488194b7
--- /dev/null
+++ b/paddle2onnx/mapper/data_helper.h
@@ -0,0 +1,30 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <vector>
+#include "paddle2onnx/utils/utils.h"
+
+namespace paddle2onnx {
+
+inline std::vector<int64_t> Arange(int64_t start, int64_t end) {
+  Assert(end > start, "In arrange(), end must be greater than start.");
+  std::vector<int64_t> res;
+  res.resize(end - start);
+  for (auto i = start; i < end; ++i) {
+    res[i - start] = i;
+  }
+  return res;
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/detection/multiclass_nms.cc b/paddle2onnx/mapper/detection/multiclass_nms.cc
new file mode 100644
index 0000000000..3c262b6b88
--- /dev/null
+++ b/paddle2onnx/mapper/detection/multiclass_nms.cc
@@ -0,0 +1,360 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/detection/multiclass_nms.h"
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(multiclass_nms3, NMSMapper);
+
+int32_t NMSMapper::GetMinOpset(bool verbose) {
+  auto boxes_info = GetInput("BBoxes");
+  auto score_info = GetInput("Scores");
+  if (score_info[0].Rank() != 3) {
+    Error() << "Lod Tensor input is not supported, which means the shape of "
+               "input(scores) is [M, C] now, but Paddle2ONNX only support [N, "
+               "C, M]."
+            << std::endl;
+    return -1;
+  }
+  if (boxes_info[0].Rank() != 3) {
+    Error() << "Only support input boxes as 3-D Tensor, but now it's rank is "
+            << boxes_info[0].Rank() << "." << std::endl;
+    return -1;
+  }
+  if (score_info[0].shape[1] <= 0) {
+    Error() << "The 2nd-dimension of score should be fixed(means the number of "
+               "classes), but now it's "
+            << score_info[0].shape[1] << "." << std::endl;
+    return -1;
+  }
+
+  if (export_as_custom_op || this->deploy_backend == "tensorrt") {
+    return 7;
+  }
+
+  Logger(verbose, 10) << RequireOpset(10) << std::endl;
+  return 10;
+}
+
+void NMSMapper::KeepTopK(const std::string& selected_indices) {
+  auto boxes_info = GetInput("BBoxes");
+  auto score_info = GetInput("Scores");
+  auto out_info = GetOutput("Out");
+  auto index_info = GetOutput("Index");
+  auto num_rois_info = GetOutput("NmsRoisNum");
+  auto value_0 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(0));
+  auto value_1 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(1));
+  auto value_2 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(2));
+  auto value_neg_1 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(-1));
+
+  auto class_id = helper_->MakeNode("Gather", {selected_indices, value_1});
+  AddAttribute(class_id, "axis", int64_t(1));
+
+  auto box_id = helper_->MakeNode("Gather", {selected_indices, value_2});
+  AddAttribute(box_id, "axis", int64_t(1));
+
+  auto filtered_class_id = class_id->output(0);
+  auto filtered_box_id = box_id->output(0);
+  if (background_label_ >= 0) {
+    auto filter_indices = MapperHelper::Get()->GenName("nms.filter_background");
+    auto squeezed_class_id =
+        helper_->Squeeze(class_id->output(0), std::vector<int64_t>(1, 1));
+    if (background_label_ > 0) {
+      auto background = helper_->Constant(
+          {1}, ONNX_NAMESPACE::TensorProto::INT64, background_label_);
+      auto diff = helper_->MakeNode("Sub", {squeezed_class_id, background});
+      helper_->MakeNode("NonZero", {diff->output(0)}, {filter_indices});
+    } else if (background_label_ == 0) {
+      helper_->MakeNode("NonZero", {squeezed_class_id}, {filter_indices});
+    }
+    auto new_class_id =
+        helper_->MakeNode("Gather", {filtered_class_id, filter_indices});
+    AddAttribute(new_class_id, "axis", int64_t(0));
+    auto new_box_id =
+        helper_->MakeNode("Gather", {box_id->output(0), filter_indices});
+    AddAttribute(new_box_id, "axis", int64_t(0));
+    filtered_class_id = new_class_id->output(0);
+    filtered_box_id = new_box_id->output(0);
+  }
+
+  // Here is a little complicated
+  // Since we need to gather all the scores for the final boxes to filter the
+  // top-k boxes Now we have the follow inputs
+  //    - scores: [N, C, M] N means batch size(but now it will be regarded as
+  //    1); C means number of classes; M means number of boxes for each classes
+  //    - selected_indices: [num_selected_indices, 3], and 3 means [batch,
+  //    class_id, box_id]. We will use this inputs to gather score
+  // So now we will first flatten `scores` to shape of [1 * C * M], then we
+  // gather scores by each elements in `selected_indices` The index need be
+  // calculated as
+  //    `gather_index = class_id * M + box_id`
+  auto flatten_score = helper_->Flatten(score_info[0].name);
+  auto num_boxes_each_class = helper_->Constant(
+      {1}, ONNX_NAMESPACE::TensorProto::INT64, score_info[0].shape[2]);
+  auto gather_indices_0 =
+      helper_->MakeNode("Mul", {filtered_class_id, num_boxes_each_class});
+  auto gather_indices_1 =
+      helper_->MakeNode("Add", {gather_indices_0->output(0), filtered_box_id});
+  auto gather_indices = helper_->Flatten(gather_indices_1->output(0));
+  auto gathered_scores =
+      helper_->MakeNode("Gather", {flatten_score, gather_indices});
+  AddAttribute(gathered_scores, "axis", int64_t(0));
+
+  // Now we will perform keep_top_k process
+  // First we need to check if the number of remaining boxes is greater than
+  // keep_top_k Otherwise, we will downgrade the keep_top_k to number of
+  // remaining boxes
+  auto final_classes = filtered_class_id;
+  auto final_boxes_id = filtered_box_id;
+  auto final_scores = gathered_scores->output(0);
+  if (keep_top_k_ > 0) {
+    // get proper topk
+    auto shape_of_scores = helper_->MakeNode("Shape", {final_scores});
+    auto num_of_boxes =
+        helper_->Slice(shape_of_scores->output(0), std::vector<int64_t>(1, 0),
+                       std::vector<int64_t>(1, 0), std::vector<int64_t>(1, 1));
+    auto top_k =
+        helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, keep_top_k_);
+    auto ensemble_value = helper_->MakeNode("Concat", {num_of_boxes, top_k});
+    AddAttribute(ensemble_value, "axis", int64_t(0));
+    auto new_top_k =
+        helper_->MakeNode("ReduceMin", {ensemble_value->output(0)});
+    AddAttribute(new_top_k, "axes", std::vector<int64_t>(1, 0));
+    AddAttribute(new_top_k, "keepdims", int64_t(1));
+
+    // the output is topk_scores, topk_score_indices
+    auto topk_node =
+        helper_->MakeNode("TopK", {final_scores, new_top_k->output(0)}, 2);
+    auto topk_scores =
+        helper_->MakeNode("Gather", {final_scores, topk_node->output(1)});
+    AddAttribute(topk_scores, "axis", int64_t(0));
+    filtered_class_id =
+        helper_->MakeNode("Squeeze", {filtered_class_id})->output(0);
+    auto topk_classes =
+        helper_->MakeNode("Gather", {filtered_class_id, topk_node->output(1)});
+    AddAttribute(topk_classes, "axis", int64_t(0));
+    filtered_box_id =
+        helper_->MakeNode("Squeeze", {filtered_box_id})->output(0);
+    auto topk_boxes_id =
+        helper_->MakeNode("Gather", {filtered_box_id, topk_node->output(1)});
+    AddAttribute(topk_boxes_id, "axis", int64_t(0));
+
+    final_boxes_id = topk_boxes_id->output(0);
+    final_scores = topk_scores->output(0);
+    final_classes = topk_classes->output(0);
+  }
+
+  auto flatten_boxes_id = helper_->Flatten({final_boxes_id});
+  auto gathered_selected_boxes =
+      helper_->MakeNode("Gather", {boxes_info[0].name, flatten_boxes_id});
+  AddAttribute(gathered_selected_boxes, "axis", int64_t(1));
+
+  auto float_classes = helper_->MakeNode("Cast", {final_classes});
+  AddAttribute(float_classes, "to", ONNX_NAMESPACE::TensorProto::FLOAT);
+
+  std::vector<int64_t> shape{1, -1, 1};
+  auto unsqueezed_scores = helper_->Reshape({final_scores}, shape);
+
+  auto unsqueezed_class = helper_->Reshape({float_classes->output(0)}, shape);
+
+  auto box_result =
+      helper_->MakeNode("Concat", {unsqueezed_class, unsqueezed_scores,
+                                   gathered_selected_boxes->output(0)});
+  AddAttribute(box_result, "axis", int64_t(2));
+  helper_->Squeeze({box_result->output(0)}, {out_info[0].name},
+                   std::vector<int64_t>(1, 0));
+
+  // other outputs, we don't use sometimes
+  // there's lots of Cast in exporting
+  // TODO(jiangjiajun) A pass to eleminate all the useless Cast is needed
+  auto reshaped_index_result =
+      helper_->Reshape({flatten_boxes_id}, {int64_t(-1), int64_t(1)});
+  auto index_result =
+      helper_->MakeNode("Cast", {reshaped_index_result}, {index_info[0].name});
+  AddAttribute(index_result, "to", GetOnnxDtype(index_info[0].dtype));
+
+  auto out_box_shape = helper_->MakeNode("Shape", {out_info[0].name});
+  auto num_rois_result =
+      helper_->Slice({out_box_shape->output(0)}, std::vector<int64_t>(1, 0),
+                     std::vector<int64_t>(1, 0), std::vector<int64_t>(1, 1));
+  auto int32_num_rois_result =
+      helper_->AutoCast(num_rois_result, num_rois_info[0].name,
+                        P2ODataType::INT64, num_rois_info[0].dtype);
+}
+
+void NMSMapper::Opset10() {
+  if (this->deploy_backend == "tensorrt") {
+    return ExportForTensorRT();
+  }
+  auto boxes_info = GetInput("BBoxes");
+  auto score_info = GetInput("Scores");
+  if (boxes_info[0].shape[0] != 1) {
+    Warn()
+        << "[WARNING] Due to the operator multiclass_nms3, the exported ONNX "
+           "model will only supports inference with input batch_size == 1."
+        << std::endl;
+  }
+  int64_t num_classes = score_info[0].shape[1];
+  auto score_threshold = helper_->Constant(
+      {1}, ONNX_NAMESPACE::TensorProto::FLOAT, score_threshold_);
+  auto nms_threshold = helper_->Constant(
+      {1}, ONNX_NAMESPACE::TensorProto::FLOAT, nms_threshold_);
+  auto nms_top_k =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, nms_top_k_);
+
+  auto selected_box_index = MapperHelper::Get()->GenName("nms.selected_index");
+  if (normalized_) {
+    helper_->MakeNode("NonMaxSuppression",
+                      {boxes_info[0].name, score_info[0].name, nms_top_k,
+                       nms_threshold, score_threshold},
+                      {selected_box_index});
+  } else {
+    auto value_1 =
+        helper_->Constant({1}, GetOnnxDtype(boxes_info[0].dtype), float(1.0));
+    auto split_boxes = helper_->Split(boxes_info[0].name,
+                                      std::vector<int64_t>(4, 1), int64_t(2));
+    auto xmax = helper_->MakeNode("Add", {split_boxes[2], value_1});
+    auto ymax = helper_->MakeNode("Add", {split_boxes[3], value_1});
+    auto new_boxes = helper_->MakeNode(
+        "Concat",
+        {split_boxes[0], split_boxes[1], xmax->output(0), ymax->output(0)});
+    AddAttribute(new_boxes, "axis", int64_t(2));
+    helper_->MakeNode("NonMaxSuppression",
+                      {new_boxes->output(0), score_info[0].name, nms_top_k,
+                       nms_threshold, score_threshold},
+                      {selected_box_index});
+  }
+  KeepTopK(selected_box_index);
+}
+
+void NMSMapper::ExportAsCustomOp() {
+  auto boxes_info = GetInput("BBoxes");
+  auto score_info = GetInput("Scores");
+  auto out_info = GetOutput("Out");
+  auto index_info = GetOutput("Index");
+  auto num_rois_info = GetOutput("NmsRoisNum");
+  auto node = helper_->MakeNode(
+      custom_op_name, {boxes_info[0].name, score_info[0].name},
+      {out_info[0].name, index_info[0].name, num_rois_info[0].name});
+  node->set_domain("Paddle");
+  int64_t normalized = normalized_ ? 1 : 0;
+  AddAttribute(node, "normalized", normalized);
+  AddAttribute(node, "nms_threshold", nms_threshold_);
+  AddAttribute(node, "score_threshold", score_threshold_);
+  AddAttribute(node, "nms_eta", nms_eta_);
+  AddAttribute(node, "nms_top_k", nms_top_k_);
+  AddAttribute(node, "background_label", background_label_);
+  AddAttribute(node, "keep_top_k", keep_top_k_);
+  helper_->MakeValueInfo(boxes_info[0].name, boxes_info[0].dtype,
+                         boxes_info[0].shape);
+  helper_->MakeValueInfo(score_info[0].name, score_info[0].dtype,
+                         score_info[0].shape);
+  helper_->MakeValueInfo(out_info[0].name, out_info[0].dtype,
+                         out_info[0].shape);
+  helper_->MakeValueInfo(index_info[0].name, index_info[0].dtype,
+                         index_info[0].shape);
+  helper_->MakeValueInfo(num_rois_info[0].name, num_rois_info[0].dtype,
+                         num_rois_info[0].shape);
+}
+
+void NMSMapper::ExportForTensorRT() {
+  auto boxes_info = GetInput("BBoxes");
+  auto score_info = GetInput("Scores");
+  auto out_info = GetOutput("Out");
+  auto index_info = GetOutput("Index");
+  auto num_rois_info = GetOutput("NmsRoisNum");
+
+  auto scores = helper_->Transpose(score_info[0].name, {0, 2, 1});
+  auto boxes = helper_->Unsqueeze(boxes_info[0].name, {2});
+  int64_t num_classes = score_info[0].shape[1];
+  auto repeats =
+      helper_->Constant(GetOnnxDtype(P2ODataType::INT64),
+                        std::vector<int64_t>({1, 1, num_classes, 1}));
+  boxes = helper_->MakeNode("Tile", {boxes, repeats})->output(0);
+
+  auto nms_node =
+      helper_->MakeNode("BatchedNMSDynamic_TRT", {boxes, scores}, 4);
+  AddAttribute(nms_node, "shareLocation", int64_t(0));
+  AddAttribute(nms_node, "backgroundLabelId", background_label_);
+  AddAttribute(nms_node, "numClasses", num_classes);
+  int64_t nms_top_k = nms_top_k_;
+  int64_t keep_top_k = keep_top_k_;
+  if (nms_top_k > 4096) {
+    Warn()
+        << "Paramter nms_top_k:" << nms_top_k
+        << " is exceed limit in TensorRT BatchedNMS plugin, will force to 4096."
+        << std::endl;
+    nms_top_k = 4096;
+  }
+  if (keep_top_k > 4096) {
+    Warn()
+        << "Parameter keep_top_k:" << keep_top_k
+        << " is exceed limit in TensorRT BatchedNMS plugin, will force to 4096."
+        << std::endl;
+    keep_top_k = 4096;
+  }
+  AddAttribute(nms_node, "topK", nms_top_k);
+  AddAttribute(nms_node, "keepTopK", keep_top_k);
+  AddAttribute(nms_node, "scoreThreshold", score_threshold_);
+  AddAttribute(nms_node, "iouThreshold", nms_threshold_);
+  if (normalized_) {
+    AddAttribute(nms_node, "isNormalized", int64_t(1));
+  } else {
+    AddAttribute(nms_node, "isNormalized", int64_t(0));
+  }
+  AddAttribute(nms_node, "clipBoxes", int64_t(0));
+  nms_node->set_domain("Paddle");
+
+  auto num_rois = helper_->Reshape(nms_node->output(0), {-1});
+  helper_->AutoCast(num_rois, num_rois_info[0].name, P2ODataType::INT32,
+                    num_rois_info[0].dtype);
+
+  auto out_classes = helper_->Reshape(nms_node->output(3), {-1, 1});
+  auto out_scores = helper_->Reshape(nms_node->output(2), {-1, 1});
+  auto out_boxes = helper_->Reshape(nms_node->output(1), {-1, 4});
+  out_classes =
+      helper_->AutoCast(out_classes, P2ODataType::INT32, P2ODataType::FP32);
+  helper_->Concat({out_classes, out_scores, out_boxes}, {out_info[0].name}, 1);
+
+  //  EfficientNMS_TRT cannot get the same result, so disable now
+  //  auto nms_node = helper_->MakeNode("EfficientNMS_TRT", {boxes_info[0].name,
+  //  score}, 4);
+  //  AddAttribute(nms_node, "plugin_version", "1");
+  //  AddAttribute(nms_node, "background_class", background_label_);
+  //  AddAttribute(nms_node, "max_output_boxes", nms_top_k_);
+  //  AddAttribute(nms_node, "score_threshold", score_threshold_);
+  //  AddAttribute(nms_node, "iou_threshold", nms_threshold_);
+  //  AddAttribute(nms_node, "score_activation", int64_t(0));
+  //  AddAttribute(nms_node, "box_coding", int64_t(0));
+  //  nms_node->set_domain("Paddle");
+  //
+  //  auto num_rois = helper_->Reshape(nms_node->output(0), {-1});
+  //  helper_->AutoCast(num_rois, num_rois_info[0].name, P2ODataType::INT32,
+  //  num_rois_info[0].dtype);
+  //
+  //  auto out_classes = helper_->Reshape(nms_node->output(3), {-1, 1});
+  //  auto out_scores = helper_->Reshape(nms_node->output(2), {-1, 1});
+  //  auto out_boxes = helper_->Reshape(nms_node->output(1), {-1, 4});
+  //  out_classes = helper_->AutoCast(out_classes, P2ODataType::INT32,
+  //  P2ODataType::FP32);
+  //  helper_->Concat({out_classes, out_scores, out_boxes}, {out_info[0].name},
+  //  1);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/detection/multiclass_nms.h b/paddle2onnx/mapper/detection/multiclass_nms.h
new file mode 100644
index 0000000000..84711a5ad2
--- /dev/null
+++ b/paddle2onnx/mapper/detection/multiclass_nms.h
@@ -0,0 +1,62 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class NMSMapper : public Mapper {
+ public:
+  NMSMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    // NMS is a post process operators for object detection
+    // We have found there're difference between `multi_class_nms3` in
+    // PaddlePaddle and `NonMaxSuppresion` in ONNX
+    MarkAsExperimentalOp();
+    GetAttr("normalized", &normalized_);
+    GetAttr("nms_threshold", &nms_threshold_);
+    GetAttr("score_threshold", &score_threshold_);
+    GetAttr("nms_eta", &nms_eta_);
+    // The `nms_top_k` in Paddle and `max_output_boxes_per_class` in ONNX share
+    // the same meaning But the filter process may not be same Since NMS is just
+    // a post process for Detection, we are not going to export it with exactly
+    // same result. We will make a precision performance in COCO or Pascal VOC
+    // data later.
+    GetAttr("nms_top_k", &nms_top_k_);
+    GetAttr("background_label", &background_label_);
+    GetAttr("keep_top_k", &keep_top_k_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void KeepTopK(const std::string& selected_indices);
+  void Opset10();
+  void ExportForTensorRT();
+  void ExportAsCustomOp();
+
+ private:
+  bool normalized_;
+  float nms_threshold_;
+  float score_threshold_;
+  float nms_eta_;
+  int64_t nms_top_k_;
+  int64_t background_label_;
+  int64_t keep_top_k_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/detection/roi_align.cc b/paddle2onnx/mapper/detection/roi_align.cc
new file mode 100755
index 0000000000..88629a2819
--- /dev/null
+++ b/paddle2onnx/mapper/detection/roi_align.cc
@@ -0,0 +1,43 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/detection/roi_align.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(roi_align, RoiAlignMapper)
+
+void RoiAlignMapper::Opset10() {
+  auto x_info = GetInput("X");
+  auto rois_info = GetInput("ROIs");
+  auto out_info = GetOutput("Out");
+
+  auto roi_shape = helper_->MakeNode("Shape", {rois_info[0].name})->output(0);
+  auto num_rois =
+      helper_->Slice(roi_shape, std::vector<int64_t>(1, 0),
+                     std::vector<int64_t>(1, 0), std::vector<int64_t>(1, 1));
+  auto value_zero = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                      std::vector<int64_t>(1, 0));
+  auto batch_indices =
+      helper_->MakeNode("Expand", {value_zero, num_rois})->output(0);
+  auto roi_align_node = helper_->MakeNode(
+      "RoiAlign", {x_info[0].name, rois_info[0].name, batch_indices},
+      {out_info[0].name});
+  AddAttribute(roi_align_node, "output_height", pooled_height_);
+  AddAttribute(roi_align_node, "output_width", pooled_width_);
+  AddAttribute(roi_align_node, "sampling_ratio", sampling_ratio_);
+  AddAttribute(roi_align_node, "spatial_scale", spatial_scale_);
+  AddAttribute(roi_align_node, "mode", "avg");
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/detection/roi_align.h b/paddle2onnx/mapper/detection/roi_align.h
new file mode 100755
index 0000000000..68e2c27f92
--- /dev/null
+++ b/paddle2onnx/mapper/detection/roi_align.h
@@ -0,0 +1,47 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class RoiAlignMapper : public Mapper {
+ public:
+  RoiAlignMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    MarkAsExperimentalOp();
+    GetAttr("pooled_height", &pooled_height_);
+    GetAttr("pooled_width", &pooled_width_);
+    GetAttr("spatial_scale", &spatial_scale_);
+    GetAttr("sampling_ratio", &sampling_ratio_);
+    GetAttr("aligned", &aligned_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 10) << RequireOpset(10) << std::endl;
+    return 10;
+  }
+  void Opset10();
+
+ private:
+  int64_t pooled_height_;
+  int64_t pooled_width_;
+  float spatial_scale_;
+  int64_t sampling_ratio_;
+  bool aligned_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/detection/yolo_box.cc b/paddle2onnx/mapper/detection/yolo_box.cc
new file mode 100644
index 0000000000..e9f9289890
--- /dev/null
+++ b/paddle2onnx/mapper/detection/yolo_box.cc
@@ -0,0 +1,254 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/detection/yolo_box.h"
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(yolo_box, YoloBoxMapper)
+
+int32_t YoloBoxMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void YoloBoxMapper::Opset11() {
+  auto x_info_ori = GetInput("X");
+
+  // handle the float64 input
+  auto x_info = x_info_ori;
+  if (x_info_ori[0].dtype != P2ODataType::FP32) {
+    x_info[0].name = helper_->AutoCast(x_info_ori[0].name, x_info_ori[0].dtype,
+                                       P2ODataType::FP32);
+    x_info[0].dtype = P2ODataType::FP32;
+  }
+
+  auto im_size_info = GetInput("ImgSize");
+  auto boxes_info = GetOutput("Boxes");
+  auto scores_info = GetOutput("Scores");
+  int64_t max_int = 999999;
+
+  int64_t anchor_num = anchors_.size() / 2;
+
+  auto x_shape = helper_->MakeNode("Shape", {x_info[0].name});
+  std::vector<std::string> nchw = helper_->Split(
+      x_shape->output(0), std::vector<int64_t>(4, 1), int64_t(0));
+  std::string float_h =
+      helper_->AutoCast(nchw[2], P2ODataType::INT64, x_info[0].dtype);
+  std::string float_w =
+      helper_->AutoCast(nchw[3], P2ODataType::INT64, x_info[0].dtype);
+
+  auto anchor_num_tensor =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, anchor_num);
+
+  auto x_name = x_info[0].name;
+  if (iou_aware_) {
+    // Here we use the feature that while value is very large, it equals to the
+    // ends This is a standared definition in ONNX However not sure all the
+    // inference engines implements `Slice` this way Let's handle this issue
+    // later
+    x_name = helper_->Slice(x_name, {0, 1, 2, 3}, {0, 0, 0, 0},
+                            {max_int, anchor_num, max_int, max_int});
+  }
+
+  auto unknown_dim =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(-1));
+  auto shape_0 = helper_->MakeNode(
+      "Concat", {nchw[0], anchor_num_tensor, unknown_dim, nchw[2], nchw[3]});
+  AddAttribute(shape_0, "axis", int64_t(0));
+  auto reshaped_x = helper_->MakeNode("Reshape", {x_name, shape_0->output(0)});
+  auto transposed_x = helper_->MakeNode("Transpose", {reshaped_x->output(0)});
+  {
+    std::vector<int64_t> perm({0, 1, 3, 4, 2});
+    AddAttribute(transposed_x, "perm", perm);
+  }
+
+  // grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
+  // grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
+  auto float_value_0 =
+      helper_->Constant({}, GetOnnxDtype(x_info[0].dtype), float(0.0));
+  auto float_value_1 =
+      helper_->Constant({}, GetOnnxDtype(x_info[0].dtype), float(1.0));
+  auto scalar_float_w = helper_->Squeeze(float_w, {});
+  auto scalar_float_h = helper_->Squeeze(float_h, {});
+  auto grid_x_0 = helper_->MakeNode(
+      "Range", {float_value_0, scalar_float_w, float_value_1});  // shape is [w]
+  auto grid_y_0 = helper_->MakeNode(
+      "Range", {float_value_0, scalar_float_h, float_value_1});  // shape is [h]
+  auto grid_x_1 = helper_->MakeNode(
+      "Tile", {grid_x_0->output(0), nchw[2]});  // shape is [w*h]
+  auto grid_y_1 = helper_->MakeNode(
+      "Tile", {grid_y_0->output(0), nchw[3]});  // shape is [h*w]
+  auto int_value_1 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, float(1.0));
+  auto grid_shape_x =
+      helper_->MakeNode("Concat", {nchw[2], nchw[3], int_value_1});
+  auto grid_shape_y =
+      helper_->MakeNode("Concat", {nchw[3], nchw[2], int_value_1});
+  AddAttribute(grid_shape_x, "axis", int64_t(0));
+  AddAttribute(grid_shape_y, "axis", int64_t(0));
+  auto grid_x = helper_->MakeNode(
+      "Reshape", {grid_x_1->output(0), grid_shape_x->output(0)});
+  auto grid_y_2 = helper_->MakeNode(
+      "Reshape", {grid_y_1->output(0), grid_shape_y->output(0)});
+  auto grid_y = helper_->MakeNode("Transpose", {grid_y_2->output(0)});
+  {
+    std::vector<int64_t> perm({1, 0, 2});
+    AddAttribute(grid_y, "perm", perm);
+  }
+
+  auto grid =
+      helper_->MakeNode("Concat", {grid_x->output(0), grid_y->output(0)});
+  AddAttribute(grid, "axis", int64_t(2));
+
+  // pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0]) *
+  // scale_x_y + bias_x_y) / w pred_box[:, :, :, :, 1] = (grid_y +
+  // sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y + bias_x_y) / h
+  auto pred_box_xy =
+      helper_->Slice(transposed_x->output(0), {0, 1, 2, 3, 4}, {0, 0, 0, 0, 0},
+                     {max_int, max_int, max_int, max_int, 2});
+  auto scale_x_y =
+      helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), scale_x_y_);
+  float bias_x_y_value = (1.0 - scale_x_y_) / 2.0;
+  auto bias_x_y =
+      helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), bias_x_y_value);
+  auto wh = helper_->MakeNode("Concat", {float_w, float_h});
+  AddAttribute(wh, "axis", int64_t(0));
+  pred_box_xy = helper_->MakeNode("Sigmoid", {pred_box_xy})->output(0);
+  pred_box_xy = helper_->MakeNode("Mul", {pred_box_xy, scale_x_y})->output(0);
+  pred_box_xy = helper_->MakeNode("Add", {pred_box_xy, bias_x_y})->output(0);
+  pred_box_xy =
+      helper_->MakeNode("Add", {pred_box_xy, grid->output(0)})->output(0);
+  pred_box_xy =
+      helper_->MakeNode("Div", {pred_box_xy, wh->output(0)})->output(0);
+
+  // anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
+  // anchors_s = np.array(
+  //     [(an_w / input_w, an_h / input_h) for an_w, an_h in anchors])
+  // anchor_w = anchors_s[:, 0:1].reshape((1, an_num, 1, 1))
+  // anchor_h = anchors_s[:, 1:2].reshape((1, an_num, 1, 1))
+  std::vector<int64_t> valid_anchors(anchor_num);
+  valid_anchors.assign(anchors_.begin(), anchors_.begin() + anchor_num * 2);
+  auto anchors =
+      helper_->Constant(GetOnnxDtype(x_info[0].dtype), valid_anchors);
+  anchors = helper_->Reshape(anchors, {anchor_num, 2});
+
+  auto downsample =
+      helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), downsample_ratio_);
+  auto ori_wh =
+      helper_->MakeNode("Mul", {wh->output(0), downsample})->output(0);
+  anchors = helper_->MakeNode("Div", {anchors, ori_wh})->output(0);
+  // Following divide operation requires undirectional broadcast
+  // It satisfies the definition of ONNX, but now sure all the inference engines
+  // support this rule e.g TensorRT�OpenVINO anchor_w = anchors_s[:,
+  // 0:1].reshape((1, an_num, 1, 1)) anchor_h = anchors_s[:, 1:2].reshape((1,
+  // an_num, 1, 1)) pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) *
+  // anchor_w pred_box[:, :, :, :, 3] = np.exp(pred_box[:, :, :, :, 3]) *
+  // anchor_h
+  anchors = helper_->Reshape(anchors, {1, anchor_num, 1, 1, 2});
+  auto pred_box_wh =
+      helper_->Slice(transposed_x->output(0), {0, 1, 2, 3, 4}, {0, 0, 0, 0, 2},
+                     {max_int, max_int, max_int, max_int, 4});
+  pred_box_wh = helper_->MakeNode("Exp", {pred_box_wh})->output(0);
+  pred_box_wh = helper_->MakeNode("Mul", {pred_box_wh, anchors})->output(0);
+
+  // if iou_aware:
+  //     pred_conf = sigmoid(x[:, :, :, :, 4:5])**(
+  //         1 - iou_aware_factor) * sigmoid(ioup)**iou_aware_factor
+  // else:
+  //     pred_conf = sigmoid(x[:, :, :, :, 4:5])
+  auto confidence =
+      helper_->Slice(transposed_x->output(0), {0, 1, 2, 3, 4}, {0, 0, 0, 0, 4},
+                     {max_int, max_int, max_int, max_int, 5});
+  std::string pred_conf = helper_->MakeNode("Sigmoid", {confidence})->output(0);
+  if (iou_aware_) {
+    auto ioup = helper_->Slice(x_info[0].name, {0, 1, 2, 3}, {0, 0, 0, 0},
+                               {max_int, anchor_num, max_int, max_int});
+    ioup = helper_->Unsqueeze(ioup, {4});
+    ioup = helper_->MakeNode("Sigmoid", {ioup})->output(0);
+    float power_value_0 = 1 - iou_aware_factor_;
+    auto power_0 =
+        helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), power_value_0);
+    auto power_1 = helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype),
+                                     iou_aware_factor_);
+    ioup = helper_->MakeNode("Pow", {ioup, power_1})->output(0);
+    pred_conf = helper_->MakeNode("Pow", {pred_conf, power_0})->output(0);
+    pred_conf = helper_->MakeNode("Mul", {pred_conf, ioup})->output(0);
+  }
+
+  // pred_conf[pred_conf < conf_thresh] = 0.
+  // pred_score = sigmoid(x[:, :, :, :, 5:]) * pred_conf
+  // pred_box = pred_box * (pred_conf > 0.).astype('float32')
+  auto value_2 =
+      helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), float(2.0));
+  auto center = helper_->MakeNode("Div", {pred_box_wh, value_2})->output(0);
+  auto min_xy = helper_->MakeNode("Sub", {pred_box_xy, center})->output(0);
+  auto max_xy = helper_->MakeNode("Add", {pred_box_xy, center})->output(0);
+
+  auto conf_thresh =
+      helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), conf_thresh_);
+  auto filter =
+      helper_->MakeNode("Greater", {pred_conf, conf_thresh})->output(0);
+  filter = helper_->AutoCast(filter, P2ODataType::BOOL, x_info[0].dtype);
+  pred_conf = helper_->MakeNode("Mul", {pred_conf, filter})->output(0);
+  auto pred_score =
+      helper_->Slice(transposed_x->output(0), {0, 1, 2, 3, 4}, {0, 0, 0, 0, 5},
+                     {max_int, max_int, max_int, max_int, max_int});
+  pred_score = helper_->MakeNode("Sigmoid", {pred_score})->output(0);
+  pred_score = helper_->MakeNode("Mul", {pred_score, pred_conf})->output(0);
+  auto pred_box = helper_->Concat({min_xy, max_xy}, 4);
+  pred_box = helper_->MakeNode("Mul", {pred_box, filter})->output(0);
+
+  auto value_neg_1 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(-1));
+  auto value_4 =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(4));
+  auto new_shape = helper_->Concat({nchw[0], value_neg_1, value_4}, 0);
+  pred_box = helper_->MakeNode("Reshape", {pred_box, new_shape})->output(0);
+
+  auto float_img_size = helper_->AutoCast(
+      im_size_info[0].name, im_size_info[0].dtype, x_info[0].dtype);
+  float_img_size = helper_->Unsqueeze(float_img_size, {1});
+  auto split_im_hw = helper_->Split(float_img_size, {1, 1}, 2);
+  auto im_whwh = helper_->Concat(
+      {split_im_hw[1], split_im_hw[0], split_im_hw[1], split_im_hw[0]}, 2);
+
+  if (!clip_bbox_) {
+    auto out = helper_->MakeNode("Mul", {pred_box, im_whwh})->output(0);
+    helper_->AutoCast(out, boxes_info[0].name, x_info[0].dtype,
+                      boxes_info[0].dtype);
+  } else {
+    pred_box = helper_->MakeNode("Mul", {pred_box, im_whwh})->output(0);
+    auto im_wh = helper_->Concat({split_im_hw[1], split_im_hw[0]}, 2);
+    im_wh = helper_->MakeNode("Sub", {im_wh, float_value_1})->output(0);
+    auto pred_box_xymin_xymax = helper_->Split(pred_box, {2, 2}, 2);
+    pred_box_xymin_xymax[0] =
+        helper_->MakeNode("Relu", {pred_box_xymin_xymax[0]})->output(0);
+    pred_box_xymin_xymax[1] =
+        helper_->MakeNode("Min", {pred_box_xymin_xymax[1], im_wh})->output(0);
+    auto out = helper_->Concat(pred_box_xymin_xymax, 2);
+    helper_->AutoCast(out, boxes_info[0].name, x_info[0].dtype,
+                      boxes_info[0].dtype);
+  }
+
+  auto class_num =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, class_num_);
+  auto score_out_shape =
+      helper_->Concat({nchw[0], value_neg_1, class_num}, int64_t(0));
+  auto score_out =
+      helper_->MakeNode("Reshape", {pred_score, score_out_shape})->output(0);
+  helper_->AutoCast(score_out, scores_info[0].name, x_info[0].dtype,
+                    scores_info[0].dtype);
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/detection/yolo_box.h b/paddle2onnx/mapper/detection/yolo_box.h
new file mode 100644
index 0000000000..1c4055f5e2
--- /dev/null
+++ b/paddle2onnx/mapper/detection/yolo_box.h
@@ -0,0 +1,53 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class YoloBoxMapper : public Mapper {
+ public:
+  YoloBoxMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    MarkAsExperimentalOp();
+    GetAttr("clip_bbox", &clip_bbox_);
+    GetAttr("iou_aware", &iou_aware_);
+    GetAttr("conf_thresh", &conf_thresh_);
+    GetAttr("iou_aware_factor", &iou_aware_factor_);
+    GetAttr("class_num", &class_num_);
+    GetAttr("downsample_ratio", &downsample_ratio_);
+    GetAttr("scale_x_y", &scale_x_y_);
+    GetAttr("anchors", &anchors_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+
+ private:
+  bool clip_bbox_;
+  bool iou_aware_;
+  float conf_thresh_;
+  float iou_aware_factor_;
+  float scale_x_y_;
+  int64_t class_num_;
+  int64_t downsample_ratio_;
+  std::vector<int64_t> anchors_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/elementwise.cc b/paddle2onnx/mapper/elementwise.cc
new file mode 100644
index 0000000000..6fc42dc601
--- /dev/null
+++ b/paddle2onnx/mapper/elementwise.cc
@@ -0,0 +1,188 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/elementwise.h"
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(elementwise_add, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_sub, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_div, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_mul, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_min, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_max, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_pow, ElementwiseMapper)
+REGISTER_MAPPER(elementwise_mod, ElementWiseModMapper)
+REGISTER_MAPPER(elementwise_floordiv, ElementWiseFloordivMapper)
+
+int32_t ElementwiseMapper::GetMinOpset(bool verbose) {
+  if (OpType() == "elementwise_min" || OpType() == "elementwise_max") {
+    Logger(verbose, 8) << RequireOpset(8) << std::endl;
+    return 8;
+  }
+  return 7;
+}
+
+void ElementwiseMapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+  auto iter = op_mapper_.find(OpType());
+  Assert(op_mapper_.end() != iter,
+         "Cannot find " + OpType() + " in elementwise op_mapper.");
+
+  auto x_name = input_x_info[0].name;
+  auto y_name = input_y_info[0].name;
+  if (input_x_info[0].dtype == P2ODataType::BOOL &&
+      input_y_info[0].dtype == P2ODataType::BOOL) {
+    x_name =
+        helper_->AutoCast(x_name, input_x_info[0].dtype, P2ODataType::INT32);
+    y_name =
+        helper_->AutoCast(y_name, input_y_info[0].dtype, P2ODataType::INT32);
+  }
+
+  std::string output_name;
+  if (axis_ == -1 || axis_ == (input_x_info[0].Rank() - 1) ||
+      input_x_info[0].Rank() == input_y_info[0].Rank()) {
+    output_name = helper_->MakeNode(iter->second, {x_name, y_name})->output(0);
+  } else {
+    std::vector<int64_t> broadcast_shape(input_x_info[0].Rank(), 1);
+    for (int i = axis_; i < axis_ + input_y_info[0].Rank(); ++i) {
+      broadcast_shape[i] = input_y_info[0].shape[i - axis_];
+    }
+    std::string broadcast_shape_node =
+        helper_->Constant(GetOnnxDtype(P2ODataType::INT64), broadcast_shape);
+    auto y_node = helper_->MakeNode("Reshape", {y_name, broadcast_shape_node});
+    output_name =
+        helper_->MakeNode(iter->second, {x_name, y_node->output(0)})->output(0);
+  }
+
+  if (input_x_info[0].dtype == P2ODataType::BOOL &&
+      input_y_info[0].dtype == P2ODataType::BOOL) {
+    helper_->AutoCast(output_name, output_info[0].name, P2ODataType::INT32,
+                      P2ODataType::BOOL);
+  } else {
+    helper_->MakeNode("Identity", {output_name}, {output_info[0].name});
+  }
+}
+
+void ElementWiseModMapper::Opset10() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+  int64_t fmod = 0;
+  if (input_y_info[0].dtype == P2ODataType::INT32 ||
+      input_y_info[0].dtype == P2ODataType::INT64) {
+    if (this->deploy_backend == "tensorrt") {
+      auto x = helper_->AutoCast(input_x_info[0].name, input_x_info[0].dtype,
+                                 input_y_info[0].dtype);
+      auto times =
+          helper_->MakeNode("Div", {input_x_info[0].name, input_y_info[0].name})
+              ->output(0);
+      auto result =
+          helper_->MakeNode("Mul", {input_y_info[0].name, times})->output(0);
+      helper_->MakeNode("Sub", {input_x_info[0].name, result},
+                        {output_info[0].name});
+      return;
+    }
+    auto mod_node =
+        helper_->MakeNode("Mod", {input_x_info[0].name, input_y_info[0].name},
+                          {output_info[0].name});
+    AddAttribute(mod_node, "fmod", fmod);
+    return;
+  }
+
+  fmod = 1;
+
+  auto abs_x_node = helper_->MakeNode("Abs", {input_x_info[0].name});
+  auto abs_y_node = helper_->MakeNode("Abs", {input_y_info[0].name});
+
+  auto dtype = input_y_info[0].dtype;
+  std::vector<float> val_0 = {0.0};
+
+  std::string zero_node = helper_->Constant(GetOnnxDtype(dtype), val_0);
+
+  auto mod_node =
+      helper_->MakeNode("Mod", {abs_x_node->output(0), abs_y_node->output(0)});
+  AddAttribute(mod_node, "fmod", fmod);
+
+  auto neg_node = helper_->MakeNode("Neg", {mod_node->output(0)});
+
+  auto less_node = helper_->MakeNode("Less", {input_x_info[0].name, zero_node});
+
+  std::string condition_node =
+      helper_->AutoCast(less_node->output(0), dtype, P2ODataType::BOOL);
+
+  auto mod_res_node = helper_->MakeNode(
+      "Where", {condition_node, neg_node->output(0), mod_node->output(0)});
+
+  auto mod_y_add_node =
+      helper_->MakeNode("Add", {mod_res_node->output(0), input_y_info[0].name});
+
+  auto mod_y_mul_node =
+      helper_->MakeNode("Mul", {mod_res_node->output(0), input_y_info[0].name});
+
+  auto mod_y_mul_less_node =
+      helper_->MakeNode("Less", {mod_y_mul_node->output(0), zero_node});
+
+  std::string mod_y_mul_condition_node = helper_->AutoCast(
+      mod_y_mul_less_node->output(0), dtype, P2ODataType::BOOL);
+
+  helper_->MakeNode("Where",
+                    {mod_y_mul_condition_node, mod_y_add_node->output(0),
+                     mod_res_node->output(0)},
+                    {output_info[0].name});
+}
+
+void ElementWiseFloordivMapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+
+  bool is_int = false;
+  if (input_x_info[0].dtype <= 3 || input_x_info[0].dtype == 20 ||
+      input_y_info[0].dtype <= 3 || input_y_info[0].dtype == 20) {
+    is_int = true;
+  }
+  if (axis_ == -1 || axis_ == input_x_info[0].Rank() - 1 ||
+      input_x_info[0].Rank() == input_y_info[0].Rank()) {
+    if (is_int) {
+      helper_->MakeNode("Div", {input_x_info[0].name, input_y_info[0].name},
+                        {output_info[0].name});
+    } else {
+      auto div_node = helper_->MakeNode(
+          "Div", {input_x_info[0].name, input_y_info[0].name});
+      helper_->MakeNode("Floor", {div_node->output(0)}, {output_info[0].name});
+    }
+  } else {
+    std::vector<int64_t> broadcast_shape;
+    broadcast_shape.resize(axis_ + input_x_info[0].Rank(), 1);
+    for (auto i = 0; i < input_y_info[0].Rank(); ++i) {
+      broadcast_shape[axis_ + i] = input_y_info[0].shape[i];
+    }
+    std::string broadcast_shape_node =
+        helper_->Constant(GetOnnxDtype(P2ODataType::INT64), broadcast_shape);
+    auto y_node = helper_->MakeNode(
+        "Reshape", {input_y_info[0].name, broadcast_shape_node});
+    if (is_int) {
+      helper_->MakeNode("Div", {input_x_info[0].name, y_node->output(0)},
+                        {output_info[0].name});
+    } else {
+      auto div_node =
+          helper_->MakeNode("Div", {input_x_info[0].name, y_node->output(0)});
+      helper_->MakeNode("Floor", {div_node->output(0)}, {output_info[0].name});
+    }
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/elementwise.h b/paddle2onnx/mapper/elementwise.h
new file mode 100644
index 0000000000..4a6ec28f91
--- /dev/null
+++ b/paddle2onnx/mapper/elementwise.h
@@ -0,0 +1,75 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ElementwiseMapper : public Mapper {
+ public:
+  ElementwiseMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+
+    op_mapper_["elementwise_add"] = "Add";
+    op_mapper_["elementwise_sub"] = "Sub";
+    op_mapper_["elementwise_div"] = "Div";
+    op_mapper_["elementwise_mul"] = "Mul";
+    op_mapper_["elementwise_min"] = "Min";
+    op_mapper_["elementwise_max"] = "Max";
+    op_mapper_["elementwise_pow"] = "Pow";
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::map<std::string, std::string> op_mapper_;
+  int64_t axis_;
+};
+
+class ElementWiseModMapper : public Mapper {
+ public:
+  ElementWiseModMapper(const PaddleParser& p, OnnxHelper* helper,
+                       int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 10) << RequireOpset(10) << std::endl;
+    return 10;
+  }
+
+  void Opset10();
+};
+
+class ElementWiseFloordivMapper : public Mapper {
+ public:
+  ElementWiseFloordivMapper(const PaddleParser& p, OnnxHelper* helper,
+                            int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/exporter.cc b/paddle2onnx/mapper/exporter.cc
new file mode 100644
index 0000000000..e2d158e768
--- /dev/null
+++ b/paddle2onnx/mapper/exporter.cc
@@ -0,0 +1,548 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/exporter.h"
+
+#include <google/protobuf/message.h>
+#include <onnx/checker.h>
+
+#include <array>
+
+#include "onnxoptimizer/optimize.h"
+#include "paddle2onnx/optimizer/convert_fp32_to_fp16.h"
+#include "paddle2onnx/optimizer/eliminate_non_transpose.h"
+#include "paddle2onnx/optimizer/fuse_constant_cast.h"
+#include "paddle2onnx/optimizer/fuse_constant_reshape.h"
+#include "paddle2onnx/optimizer/fuse_constant_unsqueeze.h"
+#include "paddle2onnx/optimizer/fuse_paddle_conv_bias.h"
+#include "paddle2onnx/optimizer/fuse_unsqueeze_conv2d_squeeze.h"
+
+namespace paddle2onnx {
+MapperHelper* MapperHelper::helper = nullptr;
+
+void ModelExporter::ExportParameters(
+    const std::map<std::string, Weight>& params, bool use_initializer) {
+  for (auto& item : params) {
+    // TODO(jiangjiajun) I'm not handling use_initializer now, but some day I
+    // will
+    auto node = MakeConstant(item.first, item.second);
+    parameters.push_back(std::move(node));
+  }
+}
+
+void ModelExporter::UpdateParameters(
+    const std::map<std::string, Weight>& params) {
+  for (auto& item : params) {
+    auto node = MakeConstant(item.first, item.second);
+    bool updated = false;
+    for (int i = 0; i < parameters.size(); ++i) {
+      auto old_node = parameters[i];
+      if (old_node->output(0) == item.first) {
+        parameters.erase(parameters.begin() + i);
+        parameters.push_back(std::move(node));
+        updated = true;
+        break;
+      }
+    }
+    if (!updated) {
+      parameters.push_back(std::move(node));
+    }
+  }
+}
+
+void ModelExporter::ExportInputOutputs(
+    const std::vector<TensorInfo>& input_infos,
+    const std::vector<TensorInfo>& output_infos) {
+  for (auto& item : input_infos) {
+    auto value_info = MakeValueInfo(item);
+    inputs.push_back(std::move(value_info));
+  }
+  for (auto& item : output_infos) {
+    auto value_info = MakeValueInfo(item);
+    outputs.push_back(std::move(value_info));
+  }
+}
+
+void ModelExporter::ExportOp(const PaddleParser& parser, OnnxHelper* helper,
+                             int32_t opset_version, int64_t block_id,
+                             int64_t op_id, bool verbose) {
+  _current_exported_num += 1;
+  auto op = parser.GetOpDesc(block_id, op_id);
+#ifdef PADDLE2ONNX_DEBUG
+  P2OLogger(true) << "---Converting operator: " << op.type() << " ---"
+                  << std::endl;
+#endif
+  if (op.type() == "while") {
+    return ExportLoop(parser, helper, opset_version, block_id, op_id, verbose);
+  }
+
+  auto mapper = MapperHelper::Get()->CreateMapper(op.type(), parser, helper,
+                                                  block_id, op_id);
+  mapper->deploy_backend = _deploy_backend;
+#ifdef PADDLE2ONNX_DEBUG
+  P2OLogger(true) << "Mapper Name: " << mapper->Name() << std::endl;
+#endif
+  // Some operators will export as custom operator
+  auto iter = custom_ops.find(op.type());
+  if (iter != custom_ops.end()) {
+    mapper->export_as_custom_op = true;
+    mapper->custom_op_name = iter->second;
+  }
+  mapper->Run();
+  delete mapper;
+
+#ifdef PADDLE2ONNX_DEBUG
+  P2OLogger(true) << "---Converting operator: " << op.type() << " done---"
+                  << std::endl;
+#endif
+}
+
+void ModelExporter::ProcessGraphDumplicateNames(
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* parameters,
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* inputs,
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* outputs,
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* nodes,
+    std::map<std::string, QuantizeInfo>* quantize_info) {
+  // process dumplicate tensor names
+  std::map<std::string, std::string> renamer;
+  std::set<std::string> tensor_names;
+  for (auto& item : *parameters) {
+    for (size_t i = 0; i < item->output_size(); ++i) {
+      if (tensor_names.find(item->output(i)) != tensor_names.end()) {
+        Assert(false, "There's dumplicate names in exported parameters.");
+      }
+      tensor_names.insert(item->output(i));
+    }
+  }
+  for (auto& item : *inputs) {
+    if (tensor_names.find(item->name()) != tensor_names.end()) {
+      Assert(false, "There's dumplicate names:" + item->name() +
+                        " in exported parameters and inputs.");
+    }
+    tensor_names.insert(item->name());
+  }
+  for (auto& item : *nodes) {
+    // update node inputs
+    for (size_t i = 0; i < item->input_size(); ++i) {
+      if (renamer.find(item->input(i)) != renamer.end()) {
+        auto updated_name = renamer[item->input(i)];
+        while (renamer.find(updated_name) != renamer.end()) {
+          updated_name = renamer[updated_name];
+        }
+        *(item->mutable_input(i)) = updated_name;
+      }
+    }
+    // if there's dumplicate name
+    // will generate new name and replace it
+    for (size_t i = 0; i < item->output_size(); ++i) {
+      if (tensor_names.find(item->output(i)) != tensor_names.end()) {
+        std::string renamed_tensor_name = item->output(i);
+        while (renamer.find(renamed_tensor_name) != renamer.end()) {
+          renamed_tensor_name = renamer[renamed_tensor_name];
+        }
+        auto new_tensor_name =
+            MapperHelper::Get()->GenName(renamed_tensor_name);
+        P2OLogger() << "Find dumplicate output name '" << renamed_tensor_name
+                    << "', it will rename to '" << new_tensor_name << "'."
+                    << std::endl;
+        if (quantize_info &&
+            quantize_info->find(renamed_tensor_name) != quantize_info->end()) {
+          (*quantize_info)[new_tensor_name] =
+              (*quantize_info)[renamed_tensor_name];
+        }
+        *(item->mutable_output(i)) = new_tensor_name;
+        renamer[renamed_tensor_name] = new_tensor_name;
+      }
+      tensor_names.insert(item->output(i));
+    }
+  }
+
+  for (auto& item : *outputs) {
+    if (renamer.find(item->name()) != renamer.end()) {
+      auto updated_name = renamer[item->name()];
+      while (renamer.find(updated_name) != renamer.end()) {
+        updated_name = renamer[updated_name];
+      }
+      item->set_name(updated_name);
+    }
+  }
+}
+
+void ModelExporter::SaveExternalData(::paddle2onnx::GraphProto* graph,
+                                     const std::string& external_file_path,
+                                     bool* save_external) {
+  P2OLogger() << "The exported ONNX model is bigger than 2G, external data "
+                 "will save to file: "
+              << external_file_path << std::endl;
+  std::string file_name = GetFilenameFromPath(external_file_path);
+  if (save_external) {
+    *save_external = true;
+  }
+  std::fstream f(external_file_path, std::ios::out);
+  Assert(f.is_open(), "Failed to open: " + external_file_path +
+                          " file to save external data");
+  for (auto index = 0; index < graph->node_size(); index++) {
+    auto node = graph->mutable_node(index);
+    if (node->op_type() != "Constant") {
+      continue;
+    }
+    for (auto i = 0; i < node->attribute_size(); i++) {
+      auto attr = node->mutable_attribute(i);
+      if (attr->name() != "value") {
+        continue;
+      }
+      auto tensor = attr->mutable_t();
+
+      if (tensor->raw_data().size() <= 128) {
+        continue;
+      }
+
+      tensor->set_data_location(TensorProto::EXTERNAL);
+      auto external_data = tensor->add_external_data();
+      external_data->set_key("location");
+      external_data->set_value(file_name);
+
+      external_data = tensor->add_external_data();
+      external_data->set_key("offset");
+      f.seekg(0, std::ios::end);
+      int64_t offset = f.tellg();
+      external_data->set_value(std::to_string(offset));
+      auto raw_data = tensor->raw_data();
+      f << raw_data;
+      external_data = tensor->add_external_data();
+      external_data->set_key("length");
+      int64_t raw_datas_size = raw_data.size();
+      external_data->set_value(std::to_string(raw_datas_size));
+      tensor->clear_raw_data();
+    }
+  }
+  f.close();
+}
+void ModelExporter::ONNXChecker(const ONNX_NAMESPACE::ModelProto& model,
+                                const bool& verbose) {
+  // TODO(jiangjiajun)
+  // If we need to integrate with framework
+  // this check will return a information
+  // to let framework know the conversion is
+  // pass or fail
+  try {
+    // ONNX_NAMESPACE::checker::check_model(*(model.get()));
+    ONNX_NAMESPACE::checker::check_model(model);
+  } catch (const std::exception& e) {
+    P2OLogger(verbose) << "The exported ONNX model is invalid." << std::endl;
+    P2OLogger(verbose) << "Model checker error log: " << e.what() << std::endl;
+  }
+  P2OLogger(verbose) << "PaddlePaddle model is exported as ONNX format now."
+                     << std::endl;
+}
+
+std::string ModelExporter::Run(
+    const PaddleParser& parser, int opset_version, bool auto_upgrade_opset,
+    bool verbose, bool enable_onnx_checker, bool enable_experimental_op,
+    bool enable_optimize, const std::string& deploy_backend,
+    std::string* calibration_cache, const std::string& external_file,
+    bool* save_external, bool export_fp16_model) {
+  _deploy_backend = deploy_backend;
+  _helper.SetOpsetVersion(opset_version);
+  _total_ops_num = 0;
+  _current_exported_num = 0;
+  for (auto i = 0; i < parser.NumOfBlocks(); ++i) {
+    _total_ops_num += parser.NumOfOps(i);
+  }
+  _helper.nodes.reserve(_total_ops_num * 3);
+  Assert(opset_version <= MAX_ONNX_OPSET_VERSION && opset_version >= 7,
+         "Paddle2ONNX now only support opset version in range of [7, " +
+             std::to_string(MAX_ONNX_OPSET_VERSION) + "].");
+  _helper.Clear();
+  inputs.clear();
+  outputs.clear();
+  parameters.clear();
+
+  // clear name_counter
+  // this use to generate unique name
+  // for intermdiate
+  // while converting all the op
+  MapperHelper::Get()->ClearNameCounter();
+
+  std::set<std::string> unsupported_ops;
+  if (!CheckIfOpSupported(parser, &unsupported_ops, enable_experimental_op)) {
+    auto logger = P2OLogger();
+    logger << "Oops, there are some operators not supported yet, including ";
+    for (auto& item : unsupported_ops) {
+      logger << item << ",";
+    }
+    logger << std::endl;
+    Assert(1 == 0,
+           "Due to the unsupported operators, the conversion is aborted.");
+  }
+
+  int32_t min_opset = GetMinOpset(parser, verbose);
+  if (min_opset < 0) {
+    Assert(false,
+           "Model exporting failed, you can report this problem to "
+           "https://github.com/PaddlePaddle/Paddle2ONNX.git.");
+  }
+  if (!auto_upgrade_opset) {
+    if (min_opset > opset_version) {
+      P2OLogger() << "This PaddlePaddle model is not able to export to ONNX "
+                     "with opset_version="
+                  << opset_version << ", please set the opset_version to "
+                  << min_opset << " or higher for successfully conversion."
+                  << std::endl;
+      Assert(false,
+             "Due to opset version, the model exporting is aborted, please set "
+             "a higher opset_version or set auto_upgrade_opset=true.");
+    }
+  } else {
+    if (min_opset > opset_version) {
+      P2OLogger() << "Opset version will change to " << min_opset << " from "
+                  << opset_version << std::endl;
+      opset_version = min_opset;
+    }
+  }
+  _helper.SetOpsetVersion(opset_version);
+  P2OLogger(verbose) << "Use opset_version = " << _helper.GetOpsetVersion()
+                     << " for ONNX export." << std::endl;
+  ExportParameters(parser.params);
+  ExportInputOutputs(parser.inputs, parser.outputs);
+
+  // Only convert blocks 0 now
+  // because control flow is not supported yet
+  for (auto i = 0; i < parser.NumOfOps(0); ++i) {
+    auto op = parser.GetOpDesc(0, i);
+    if (op.type() == "feed") {
+      continue;
+    } else if (op.type() == "fetch") {
+      continue;
+    }
+    ExportOp(parser, &_helper, opset_version, 0, i, verbose);
+  }
+
+  // construct a onnx model proto
+  auto model = std::make_shared<ONNX_NAMESPACE::ModelProto>();
+  // TODO(jiangjiajun) ir version is related to onnx version
+  model->set_ir_version(ONNX_NAMESPACE::IR_VERSION);
+  auto graph = model->mutable_graph();
+  graph->set_name("Model from PaddlePaddle.");
+  auto opset_id = model->add_opset_import();
+  opset_id->set_domain("");
+  opset_id->set_version(opset_version);
+  if (custom_ops.size()) {
+    auto opset_paddle_id = model->add_opset_import();
+    opset_paddle_id->set_domain("Paddle");
+    opset_paddle_id->set_version(1);
+  }
+
+  ProcessGraphDumplicateNames(&parameters, &inputs, &outputs, &_helper.nodes,
+                              &_helper.quantize_info);
+  if (parser.is_quantized_model) {
+    quantize_model_processer.ProcessQuantizeModel(
+        &parameters, &inputs, &outputs, &_helper.nodes, &_helper,
+        deploy_backend, parser, calibration_cache);
+    // Update int8 weights in quantized OP to float32
+    UpdateParameters(_helper.updated_params);
+  }
+
+  for (auto& item : parameters) {
+    *(graph->add_node()) = *(item.get());
+  }
+  for (auto& item : inputs) {
+    *(graph->add_input()) = *(item.get());
+  }
+  for (auto& item : _helper.nodes) {
+    *(graph->add_node()) = (*item.get());
+  }
+  for (auto& item : outputs) {
+    *(graph->add_output()) = (*item.get());
+  }
+  for (auto& item : _helper.value_infos) {
+    *(graph->add_value_info()) = (*item.get());
+  }
+
+  ONNX_NAMESPACE::ModelProto onnx_model;
+  std::string out;
+  if (enable_optimize) {
+    onnx_model = Optimize(*(model.get()));
+  } else {
+    onnx_model = *model.get();
+  }
+
+  // convert fp32 model to fp16
+  if (export_fp16_model) {
+    P2OLogger(verbose) << "Convert FP32 ONNX model to FP16." << std::endl;
+    ConvertFp32ToFp16 convert;
+    convert.SetCustomOps(custom_ops);
+    convert.Convert(&onnx_model);
+  }
+
+  // save external data file for big model
+  std::string external_data_file;
+  if (onnx_model.ByteSizeLong() > INT_MAX) {
+    if (external_file.empty()) {
+      external_data_file = "external_data";
+    } else {
+      external_data_file = external_file;
+    }
+  }
+  if (external_data_file.size()) {
+    SaveExternalData(onnx_model.mutable_graph(), external_data_file,
+                     save_external);
+  }
+  // check model
+  if (enable_onnx_checker) {
+    ONNXChecker(onnx_model, verbose);
+  }
+
+  if (!onnx_model.SerializeToString(&out)) {
+    P2OLogger(verbose)
+        << "Error happenedd while optimizing the exported ONNX model."
+        << std::endl;
+    return "";
+  }
+  return out;
+}
+
+bool ModelExporter::CheckIfOpSupported(const PaddleParser& parser,
+                                       std::set<std::string>* unsupported_ops,
+                                       bool enable_experimental_op) {
+  unsupported_ops->clear();
+  for (auto i = 0; i < parser.NumOfBlocks(); ++i) {
+    for (auto j = 0; j < parser.NumOfOps(i); ++j) {
+      auto op = parser.GetOpDesc(i, j);
+      if (op.type() == "feed" || op.type() == "fetch") {
+        continue;
+      }
+      if (op.type() == "while" && enable_experimental_op) {
+        if (!IsLoopSupported(parser, i, j)) {
+          unsupported_ops->insert("while");
+        }
+        continue;
+      }
+      if (!MapperHelper::Get()->IsRegistered(op.type())) {
+        unsupported_ops->insert(op.type());
+      } else if (!enable_experimental_op) {
+        auto mapper = MapperHelper::Get()->CreateMapper(op.type(), parser,
+                                                        &_helper, i, j);
+        if (mapper->IsExperimentalOp()) {
+          unsupported_ops->insert(op.type());
+        }
+        delete mapper;
+      }
+    }
+  }
+  return (unsupported_ops->size() == 0);
+}
+
+int32_t ModelExporter::GetMinOpset(const PaddleParser& parser, bool verbose) {
+  int32_t opset_version = _helper.GetOpsetVersion();
+  int32_t max_opset = 7;
+  bool exportable = true;
+  // Record the number of ops that need to be converted
+  int converted_op_num = 0;
+  std::set<std::string> verbose_log;
+  for (auto i = 0; i < parser.NumOfBlocks(); ++i) {
+    for (auto j = 0; j < parser.NumOfOps(i); ++j) {
+      auto op = parser.GetOpDesc(i, j);
+      if (custom_ops.find(op.type()) != custom_ops.end()) {
+        continue;
+      }
+      if (op.type() == "feed" || op.type() == "fetch") {
+        continue;
+      }
+      converted_op_num += 1;
+      int current_min_opset = 7;
+      if (op.type() == "while") {
+        P2OLogger() << "Detected there's control flow 'while' op in your "
+                       "model, this requires the minimal opset version of 13."
+                    << std::endl;
+        current_min_opset = 13;
+      } else {
+        auto mapper = MapperHelper::Get()->CreateMapper(op.type(), parser,
+                                                        &_helper, i, j);
+        auto iter = custom_ops.find(op.type());
+        if (iter != custom_ops.end()) {
+          mapper->export_as_custom_op = true;
+        }
+        current_min_opset = mapper->GetMinOpset(verbose);
+        delete mapper;
+      }
+      if (current_min_opset < 0) {
+        exportable = false;
+        P2OLogger(verbose) << "Due to the operator: " << op.type()
+                           << ", this model cannot be exported to ONNX."
+                           << std::endl;
+      } else if (current_min_opset > max_opset) {
+        max_opset = current_min_opset;
+        if (verbose && current_min_opset > opset_version) {
+          verbose_log.insert("Due to the operator: " + op.type() +
+                             ", requires opset_version >= " +
+                             std::to_string(current_min_opset) + ".");
+        }
+      }
+    }
+  }
+  if (verbose) {
+    for (auto iter = verbose_log.begin(); iter != verbose_log.end(); ++iter) {
+      P2OLogger() << *iter << std::endl;
+    }
+  }
+
+  // Here we put some checks to make sure
+  // paddle2onnx could compatible with
+  // other version of onnx
+  int32_t max_support_opset = MAX_ONNX_OPSET_VERSION;
+  if (exportable && (max_opset > MAX_ONNX_OPSET_VERSION)) {
+    exportable = false;
+    P2OLogger() << "[ERROR] The compiled ONNX version only supports opset 7~"
+                << MAX_ONNX_OPSET_VERSION
+                << ", but now this model need as least opset " << max_opset
+                << ", please compile with higher version of ONNX." << std::endl;
+  }
+  if (exportable) {
+    return max_opset;
+  }
+
+  return -1;
+}
+
+ONNX_NAMESPACE::ModelProto ModelExporter::Optimize(
+    const ONNX_NAMESPACE::ModelProto& model) {
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantReshape>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantUnsqueeze>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FusePaddleConvBias>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseUnsqueezeConv2dSqueeze>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::EliminateNonTranspose>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantCast>();
+  std::vector<std::string> passes = {"eliminate_identity",
+                                     "eliminate_deadend",
+                                     "eliminate_deadend",
+                                     "fuse_constant_reshape",
+                                     "fuse_constant_unsqueeze",
+                                     "fuse_paddle_conv_bias",
+                                     "fuse_consecutive_transposes",
+                                     "eliminate_non_transpose",
+                                     "fuse_matmul_add_bias_into_gemm",
+                                     "eliminate_identity",
+                                     "eliminate_deadend",
+                                     "eliminate_unused_initializer"};
+  return ONNX_NAMESPACE::optimization::Optimize(model, passes);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/exporter.h b/paddle2onnx/mapper/exporter.h
new file mode 100755
index 0000000000..484c763591
--- /dev/null
+++ b/paddle2onnx/mapper/exporter.h
@@ -0,0 +1,122 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <onnx/onnx_pb.h>
+
+#include <algorithm>
+#include <set>
+
+#include "paddle2onnx/mapper/mapper.h"
+#include "paddle2onnx/mapper/quantize_helper.h"
+#include "paddle2onnx/parser/parser.h"
+
+#ifdef _MSC_VER
+#define PATH_SEP "\\"
+#else
+#define PATH_SEP "/"
+#endif
+
+inline std::string GetFilenameFromPath(const std::string& path) {
+  auto pos = path.find_last_of(PATH_SEP);
+  if (pos == std::string::npos) {
+    return path;
+  }
+  return path.substr(pos + 1);
+}
+
+namespace paddle2onnx {
+
+struct ModelExporter {
+ private:
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> parameters;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>> inputs;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>> outputs;
+  // The _deploy_backend will pass to Mapper to influence the conversion
+  std::string _deploy_backend = "onnxruntime";
+  OnnxHelper _helper;
+  int32_t _total_ops_num = 0;
+  int32_t _current_exported_num = 0;
+
+  void ExportParameters(const std::map<std::string, Weight>& params,
+                        bool use_initializer = false);
+
+  // Update constant node in parameters. When process quantize model, the weight
+  // dtype may be int8, it should be convet to float32 and use this function to
+  // update converted params.
+  void UpdateParameters(const std::map<std::string, Weight>& params);
+  void ExportInputOutputs(const std::vector<TensorInfo>& input_infos,
+                          const std::vector<TensorInfo>& output_infos);
+  void ExportOp(const PaddleParser& parser, OnnxHelper* helper,
+                int32_t opset_version, int64_t block_id, int64_t op_id,
+                bool verbose);
+  bool IsLoopSupported(const PaddleParser& parser, const int64_t& block_id,
+                       const int64_t& op_id);
+  void ExportLoop(const PaddleParser& parser, OnnxHelper* helper,
+                  int32_t opset_version, int64_t block_id, int64_t op_id,
+                  bool verbose);
+
+  ONNX_NAMESPACE::ModelProto Optimize(const ONNX_NAMESPACE::ModelProto& model);
+
+ public:
+  // custom operators for export
+  // <key: op_name, value:[exported_op_name, domain]>
+  std::map<std::string, std::string> custom_ops;
+
+  QuantizeModelProcessor quantize_model_processer;
+  // Get a proper opset version in range of [7, 16]
+  // Also will check the model is convertable, this will include 2 parts
+  //    1. is the op convert function implemented
+  //    2. is the op convertable(some cases may not be able to convert)
+  // If the model is not convertable, return -1
+  int32_t GetMinOpset(const PaddleParser& parser, bool verbose = false);
+
+  //  // Remove isolated nodes in onnx model
+  //  void RemoveIsolatedNodes(
+  //      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* parameters,
+  //      std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* inputs,
+  //      std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* outputs,
+  //      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* nodes);
+  // Process dumplicate tensor names in paddle model
+  void ProcessGraphDumplicateNames(
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* parameters,
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* inputs,
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* outputs,
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* nodes,
+      std::map<std::string, QuantizeInfo>* quantize_info = nullptr);
+
+  bool CheckIfOpSupported(const PaddleParser& parser,
+                          std::set<std::string>* unsupported_ops,
+                          bool enable_experimental_op);
+
+  void SaveExternalData(::paddle2onnx::GraphProto* graph,
+                        const std::string& external_file_path,
+                        bool* save_external = nullptr);
+
+  void ONNXChecker(const ONNX_NAMESPACE::ModelProto& model,
+                   const bool& verbose);
+
+  std::string Run(const PaddleParser& parser, int opset_version = 9,
+                  bool auto_upgrade_opset = true, bool verbose = false,
+                  bool enable_onnx_checker = true,
+                  bool enable_experimental_op = false,
+                  bool enable_optimize = true,
+                  const std::string& deploy_backend = "onnxruntime",
+                  std::string* calibration_cache = nullptr,
+                  const std::string& external_file = "",
+                  bool* save_external = nullptr,
+                  bool export_fp16_model = false);
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/loop.cc b/paddle2onnx/mapper/loop.cc
new file mode 100644
index 0000000000..00d72b3233
--- /dev/null
+++ b/paddle2onnx/mapper/loop.cc
@@ -0,0 +1,195 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/exporter.h"
+
+namespace paddle2onnx {
+
+bool ModelExporter::IsLoopSupported(const PaddleParser& parser,
+                                    const int64_t& block_id,
+                                    const int64_t& op_id) {
+  auto x_info = parser.GetOpInput(block_id, op_id, "X");
+  auto out_info = parser.GetOpOutput(block_id, op_id, "Out");
+  auto cond_info = parser.GetOpInput(block_id, op_id, "Condition");
+  std::set<std::string> input_names;
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    input_names.insert(x_info[i].name);
+  }
+  input_names.insert(cond_info[0].name);
+
+  for (size_t i = 0; i < out_info.size(); ++i) {
+    auto iter = input_names.find(out_info[i].name);
+    if (iter == input_names.end()) {
+      P2OLogger() << "Cannot find output:" << out_info[i].name << " in input tensors while converting operator 'while', Paddle2ONNX doesn't support this situation now." << std::endl;
+      return false;
+    }
+  }
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    if (x_info[i].is_tensor_array) {
+      P2OLogger() << "LodTensorArray is not supported." << std::endl;
+      return false;
+    }
+  }
+  return true;
+}
+
+void ModelExporter::ExportLoop(const PaddleParser& parser, OnnxHelper* helper,
+                               int32_t opset_version, int64_t block_id,
+                               int64_t op_id, bool verbose) {
+  auto op = parser.GetOpDesc(block_id, op_id);
+  int32_t sub_block_idx = -1;
+  for (size_t i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == "sub_block") {
+      sub_block_idx = op.attrs(i).block_idx();
+      break;
+    }
+  }
+  Assert(sub_block_idx > 0, "Cannot find sub_block in while operator.");
+  auto x_info = parser.GetOpInput(block_id, op_id, "X");
+  auto cond_info = parser.GetOpInput(block_id, op_id, "Condition");
+
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>> inputs;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>> outputs;
+
+  // make loop iter
+  auto iter_name = MapperHelper::Get()->GenName("loop.iter");
+  TensorInfo iter_info(iter_name, std::vector<int64_t>(1, 1),
+                       P2ODataType::INT64);
+  inputs.push_back(std::move(MakeValueInfo(iter_info)));
+
+  std::set<std::string> input_names;
+  // make cond
+  inputs.push_back(std::move(MakeValueInfo(cond_info[0])));
+  input_names.insert(cond_info[0].name);
+  // other inputs
+  outputs.push_back(std::move(std::move(MakeValueInfo(cond_info[0]))));
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    if (x_info[i].is_tensor_array) {
+      continue;
+    }
+    if (input_names.find(x_info[i].name) != input_names.end()) {
+      continue;
+    }
+    input_names.insert(x_info[i].name);
+    inputs.push_back(std::move(MakeValueInfo(x_info[i])));
+    outputs.push_back(std::move(MakeValueInfo(x_info[i])));
+  }
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    if (x_info[i].is_tensor_array) {
+      if (input_names.find(x_info[i].name) != input_names.end()) {
+        continue;
+      }
+      input_names.insert(x_info[i].name);
+      outputs.push_back(std::move(MakeValueInfo(x_info[i])));
+    }
+  }
+
+  // make op nodes
+  OnnxHelper loop_helper;
+  loop_helper.SetOpsetVersion(opset_version);
+
+  for (auto i = 0; i < parser.NumOfOps(sub_block_idx); ++i) {
+    auto op = parser.GetOpDesc(sub_block_idx, i);
+    ExportOp(parser, &loop_helper, opset_version, sub_block_idx, i, verbose);
+  }
+
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> parameters;
+  ProcessGraphDumplicateNames(&parameters, &inputs, &outputs,
+                              &loop_helper.nodes);
+  std::map<std::string, std::string> renamer;
+  for (auto& item : inputs) {
+    auto name = MapperHelper::Get()->GenName("loop.input");
+    renamer[item->name()] = name;
+    item->set_name(name);
+  }
+  for (auto& item : loop_helper.nodes) {
+    for (size_t i = 0; i < item->input_size(); ++i) {
+      if (renamer.find(item->input(i)) != renamer.end()) {
+        auto updated_name = renamer[item->input(i)];
+        while (renamer.find(updated_name) != renamer.end()) {
+          updated_name = renamer[updated_name];
+        }
+        *(item->mutable_input(i)) = updated_name;
+      }
+    }
+  }
+  for (auto& item : outputs) {
+    if (renamer.find(item->name()) != renamer.end()) {
+      auto updated_name = renamer[item->name()];
+      while (renamer.find(updated_name) != renamer.end()) {
+        updated_name = renamer[updated_name];
+      }
+      item->set_name(updated_name);
+    }
+  }
+
+  //  // construct a onnx model proto
+  //  // consider to optimize the subgraph
+  //  auto model = std::make_shared<ONNX_NAMESPACE::ModelProto>();
+  //  model->set_ir_version(ONNX_NAMESPACE::IR_VERSION);
+  //  auto graph = model->mutable_graph();
+  //  auto graph_name = MapperHelper::Get()->GenName("Model from
+  //  PaddlePaddle(Loop).");
+  //  graph->set_name(graph_name);
+  //  auto opset_id = model->add_opset_import();
+  //  opset_id->set_domain("");
+  //  opset_id->set_version(loop_helper->GetOpsetVersion());
+
+  auto graph_name = MapperHelper::Get()->GenName("paddle.loop");
+  auto graph = std::make_shared<ONNX_NAMESPACE::GraphProto>();
+  graph->set_name(graph_name);
+  for (auto& item : inputs) {
+    *(graph->add_input()) = *(item.get());
+  }
+  for (auto& item : loop_helper.nodes) {
+    *(graph->add_node()) = (*item.get());
+  }
+  for (auto& item : outputs) {
+    *(graph->add_output()) = (*item.get());
+  }
+
+  // fake iter
+  auto fake_iter = helper->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                    std::vector<int64_t>(1, 1024));
+  std::vector<std::string> x_names;
+  x_names.push_back(fake_iter);
+  x_names.push_back(cond_info[0].name);
+  std::vector<std::string> out_names;
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    if (x_info[i].is_tensor_array) {
+      continue;
+    }
+    if (std::find(x_names.begin(), x_names.end(), x_info[i].name) != x_names.end()) {
+      continue;
+    }
+    x_names.push_back(x_info[i].name);
+    out_names.push_back(x_info[i].name);
+  }
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    if (x_info[i].is_tensor_array) {
+      if (std::find(x_names.begin(), x_names.end(), x_info[i].name) != x_names.end()) {
+        continue;
+      }
+      out_names.push_back(x_info[i].name);
+    }
+  }
+
+  auto loop_node = helper->MakeNode("Loop", x_names, out_names);
+  auto attr = loop_node->add_attribute();
+  attr->set_name("body");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::GRAPH);
+  *(attr->mutable_g()) = *(graph.get());
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/mapper.h b/paddle2onnx/mapper/mapper.h
new file mode 100755
index 0000000000..1cb72d59f3
--- /dev/null
+++ b/paddle2onnx/mapper/mapper.h
@@ -0,0 +1,246 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <vector>
+
+#include "paddle2onnx/mapper/data_helper.h"
+#include "paddle2onnx/mapper/onnx_helper.h"
+#include "paddle2onnx/mapper/register_mapper.h"
+#include "paddle2onnx/parser/parser.h"
+
+namespace paddle2onnx {
+
+class Mapper {
+ public:
+  Mapper() {}
+  Mapper(const PaddleParser& p, OnnxHelper* helper, int32_t block_id,
+         int32_t op_id, std::string name = {})
+      : parser_(&p) {
+    block_idx_ = block_id;
+    op_idx_ = op_id;
+    helper_ = helper;
+    name_ = name;
+  }
+
+  // The flag will control if the op is exported as a custom operator
+  // if export_as_custom_op = true, will exported as description in
+  // custom_op_info
+  bool export_as_custom_op = false;
+  // [exported_op_name, domain]
+  std::string custom_op_name;
+  std::string deploy_backend;
+
+  P2OLogger Logger(const bool& verbose, const int32_t& opset_version = 100) {
+    bool v = verbose;
+    if (opset_version <= helper_->GetOpsetVersion()) {
+      v = false;
+    }
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    std::string output_name = "";
+    if (op.outputs(0).arguments_size() > 0) {
+      output_name = op.outputs(0).arguments(0);
+    }
+    std::string op_type = op.type();
+    std::string prefix = "[Paddle2ONNX] [" + op_type + ": " + output_name + "]";
+    return P2OLogger(v, prefix);
+  }
+
+  P2OLogger Error() {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    std::string output_name = "";
+    if (op.outputs(0).arguments_size() > 0) {
+      output_name = op.outputs(0).arguments(0);
+    }
+    std::string op_type = op.type();
+    std::string prefix =
+        "[ERROR][Paddle2ONNX] [" + op_type + ": " + output_name + "]";
+    return P2OLogger(true, prefix);
+  }
+
+  P2OLogger Warn() {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    std::string output_name = "";
+    if (op.outputs(0).arguments_size() > 0) {
+      output_name = op.outputs(0).arguments(0);
+    }
+    std::string op_type = op.type();
+    std::string prefix =
+        "[WARN][Paddle2ONNX] [" + op_type + ": " + output_name + "]";
+    return P2OLogger(true, prefix);
+  }
+
+  // Some operators is not implement very well, e.g the output may not be same
+  // We mark these operators as experimental, these operators requires double
+  // checking after model exported.
+  virtual void MarkAsExperimentalOp() { is_experimental_op_ = true; }
+  virtual bool IsExperimentalOp() const { return is_experimental_op_; }
+  // the return value in [7, MAX_ONNX_OPSET_VERSION], represent the minimum
+  // opset_version
+  // if return value < 0, means the op is not supported.
+  virtual int32_t GetMinOpset(bool verbose = false) { return 7; }
+
+  virtual bool IsExportAsCustomOp() { return export_as_custom_op; }
+
+  void Run() {
+    int32_t opset_version = helper_->GetOpsetVersion();
+    Assert(opset_version >= 7 && opset_version <= MAX_ONNX_OPSET_VERSION,
+           "[Paddle2ONNX] Only support opset_version in range of [7, " +
+               std::to_string(MAX_ONNX_OPSET_VERSION) + "].");
+    if (IsExportAsCustomOp()) {
+      return ExportAsCustomOp();
+    }
+    if (opset_version == 16) {
+      Opset16();
+    } else if (opset_version == 15) {
+      Opset15();
+    } else if (opset_version == 14) {
+      Opset14();
+    } else if (opset_version == 13) {
+      Opset13();
+    } else if (opset_version == 12) {
+      Opset12();
+    } else if (opset_version == 11) {
+      Opset11();
+    } else if (opset_version == 10) {
+      Opset10();
+    } else if (opset_version == 9) {
+      Opset9();
+    } else if (opset_version == 8) {
+      Opset8();
+    } else {
+      Opset7();
+    }
+  }
+
+  virtual void ExportAsCustomOp() {
+    Assert(false,
+           "Operator " + name_ + "doesn't support export as custom operator.");
+  }
+
+  virtual void Opset16() { Opset15(); }
+
+  virtual void Opset15() { Opset14(); }
+
+  virtual void Opset14() { Opset13(); }
+
+  virtual void Opset13() { Opset12(); }
+
+  virtual void Opset12() { Opset11(); }
+
+  virtual void Opset11() { Opset10(); }
+
+  virtual void Opset10() { Opset9(); }
+
+  virtual void Opset9() { Opset8(); }
+
+  virtual void Opset8() { Opset7(); }
+
+  virtual void Opset7() {
+    Assert(false,
+           "This error shouldn't happend, please report to "
+           "https://github.com/PaddlePaddle/Paddle2ONNX.git.");
+  }
+
+  virtual ~Mapper() = default;
+  bool is_experimental_op_ = false;
+  const PaddleParser* parser_;
+  OnnxHelper* helper_;
+  int32_t block_idx_;
+  int32_t op_idx_;
+  std::string name_;  // op transform name
+
+  std::string OpType() const {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    return op.type();
+  }
+
+  std::string Name() const { return name_; }
+
+  bool HasInput(const std::string& name) const {
+    return parser_->OpHasInput(block_idx_, op_idx_, name);
+  }
+  bool HasOutput(const std::string& name) const {
+    return parser_->OpHasOutput(block_idx_, op_idx_, name);
+  }
+  std::vector<TensorInfo> GetInput(const std::string& name) const {
+    return parser_->GetOpInput(block_idx_, op_idx_, name);
+  }
+  std::vector<TensorInfo> GetOutput(const std::string& name) const {
+    return parser_->GetOpOutput(block_idx_, op_idx_, name);
+  }
+  // Judge whether Attribute(name)'s type is Var or Vars.
+  bool IsAttrVar(const std::string& name) const {
+    return parser_->OpIsAttrVar(block_idx_, op_idx_, name);
+  }
+
+  // Get TensorInfo(s) from Attribute Var or Vars.
+  std::vector<TensorInfo> GetAttrVar(const std::string& name) const {
+    return parser_->GetOpAttrVar(block_idx_, op_idx_, name);
+  }
+
+  bool HasAttr(const std::string& name) const {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    return parser_->OpHasAttr(op, name);
+  }
+  void GetAttr(const std::string& name, int64_t* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+  void GetAttr(const std::string& name, float* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+  void GetAttr(const std::string& name, bool* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+  void GetAttr(const std::string& name, std::string* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+  void GetAttr(const std::string& name, std::vector<int64_t>* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+  void GetAttr(const std::string& name, std::vector<float>* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+  void GetAttr(const std::string& name, std::vector<double>* val) {
+    auto& op = parser_->GetOpDesc(block_idx_, op_idx_);
+    parser_->GetOpAttr(op, name, val);
+  }
+
+  bool IsConstantInput(const std::string& input_key) const {
+    auto input_info = GetInput(input_key);
+    return parser_->IsConstantTensor(block_idx_, input_info[0].name);
+  }
+
+  bool IsConstant(const TensorInfo& info) const {
+    return parser_->IsConstantTensor(block_idx_, info.name);
+  }
+
+  template <typename T>
+  bool TryGetInputValue(const std::string& input_key, std::vector<T>* data) {
+    auto input_info = GetInput(input_key);
+    return parser_->TryGetTensorValue(block_idx_, input_info[0].name, data);
+  }
+
+  template <typename T>
+  bool TryGetValue(const TensorInfo& info, std::vector<T>* data) {
+    return parser_->TryGetTensorValue(block_idx_, info.name, data);
+  }
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/affine_channel.cc b/paddle2onnx/mapper/nn/affine_channel.cc
new file mode 100644
index 0000000000..87b5efd802
--- /dev/null
+++ b/paddle2onnx/mapper/nn/affine_channel.cc
@@ -0,0 +1,44 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/affine_channel.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(affine_channel, AffineChannelMapper)
+
+int32_t AffineChannelMapper::GetMinOpset(bool verbose) {
+  if (data_layout_ == "NHWC") {
+    Error() << "Data format NHWC is not supported." << std::endl;
+    return false;
+  }
+  return 7;
+}
+
+void AffineChannelMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto scale_info = GetInput("Scale");
+  auto bias_info = GetInput("Bias");
+  auto out_info = GetOutput("Out");
+
+  auto scale = scale_info[0].name;
+  auto bias = bias_info[0].name;
+  if (scale_info[0].shape.size() <= 1) {
+    scale = helper_->Reshape(scale, {1, -1, 1, 1});
+    bias = helper_->Reshape(bias, {1, -1, 1, 1});
+  }
+  auto out = helper_->MakeNode("Mul", {x_info[0].name, scale})->output(0);
+  helper_->MakeNode("Add", {out, bias}, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/affine_channel.h b/paddle2onnx/mapper/nn/affine_channel.h
new file mode 100644
index 0000000000..fcb14920cc
--- /dev/null
+++ b/paddle2onnx/mapper/nn/affine_channel.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class AffineChannelMapper : public Mapper {
+ public:
+  AffineChannelMapper(const PaddleParser& p, OnnxHelper* helper,
+                      int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("data_layout", &data_layout_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::string data_layout_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/batch_norm.cc b/paddle2onnx/mapper/nn/batch_norm.cc
new file mode 100644
index 0000000000..dd81622e08
--- /dev/null
+++ b/paddle2onnx/mapper/nn/batch_norm.cc
@@ -0,0 +1,45 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/batch_norm.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(batch_norm, BatchNormMapper)
+
+void BatchNormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto scale_info = GetInput("Scale");
+  auto bias_info = GetInput("Bias");
+  auto mean_info = GetInput("Mean");
+  auto variance_info = GetInput("Variance");
+  auto output_info = GetOutput("Y");
+
+  auto node = helper_->MakeNode(
+      "BatchNormalization",
+      {input_info[0].name, scale_info[0].name, bias_info[0].name,
+       mean_info[0].name, variance_info[0].name},
+      {output_info[0].name});
+  if (helper_->GetOpsetVersion() < 9) {
+    int64_t spatial = 1;
+    AddAttribute(node, "spatial", spatial);
+  }
+
+  AddAttribute(node, "epsilon", epsilon_);
+  AddAttribute(node, "momentum", momentum_);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/batch_norm.h b/paddle2onnx/mapper/nn/batch_norm.h
new file mode 100644
index 0000000000..abc649e022
--- /dev/null
+++ b/paddle2onnx/mapper/nn/batch_norm.h
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class BatchNormMapper : public Mapper {
+ public:
+  BatchNormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("epsilon", &epsilon_);
+    GetAttr("momentum", &momentum_);
+  }
+
+  void Opset7();
+
+ private:
+  float epsilon_;
+  float momentum_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/conv2d.cc b/paddle2onnx/mapper/nn/conv2d.cc
new file mode 100644
index 0000000000..7c763a0662
--- /dev/null
+++ b/paddle2onnx/mapper/nn/conv2d.cc
@@ -0,0 +1,81 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/conv2d.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(conv2d, Conv2dMapper)
+REGISTER_MAPPER(depthwise_conv2d, Conv2dMapper)
+
+int32_t Conv2dMapper::GetMinOpset(bool verbose) {
+  // NHWC is not supported
+  if (data_format_ == "NHWC") {
+    Error() << "Cannot support input with NHWC format." << std::endl;
+    return -1;
+  }
+  if (padding_algorithm_ == "EXPLICIT") {
+    if (paddings_.size() != 2 && paddings_.size() != 4) {
+      Error() << "While padding_algorithm is EXPLICIT, size of paddings should "
+                 "be 2 or 4."
+              << std::endl;
+      return -1;
+    }
+  }
+  if (dilations_[0] != 1 || dilations_[1] != 1) {
+    if (padding_algorithm_ == "SAME") {
+      Error() << "While dilations != 1, cannot support padding = 'SAME'."
+              << std::endl;
+      return -1;
+    }
+  }
+  return 7;
+}
+
+void Conv2dMapper::Opset7() {
+  auto kernel_info = GetInput("Filter");
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Output");
+
+  auto node = helper_->MakeNode(
+      "Conv", {input_info[0].name, kernel_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "dilations", dilations_);
+  std::vector<int64_t> kernel_shape = {kernel_info[0].shape[2],
+                                       kernel_info[0].shape[3]};
+  AddAttribute(node, "kernel_shape", kernel_shape);
+  AddAttribute(node, "strides", strides_);
+  AddAttribute(node, "group", groups_);
+  if (padding_algorithm_ == "SAME") {
+    std::string auto_pad = "SAME_UPPER";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else if (padding_algorithm_ == "VALID") {
+    std::string auto_pad = "VALID";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else {
+    std::vector<int64_t> paddings;
+    if (paddings_.size() == 2) {
+      paddings.insert(paddings.begin(), paddings_.begin(), paddings_.end());
+      paddings.insert(paddings.begin(), paddings_.begin(), paddings_.end());
+    } else {
+      paddings.assign(paddings_.begin(), paddings_.end());
+      paddings[1] = paddings_[2];
+      paddings[2] = paddings_[1];
+    }
+    AddAttribute(node, "pads", paddings);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/conv2d.h b/paddle2onnx/mapper/nn/conv2d.h
new file mode 100644
index 0000000000..06efdf4ef0
--- /dev/null
+++ b/paddle2onnx/mapper/nn/conv2d.h
@@ -0,0 +1,48 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Conv2dMapper : public Mapper {
+ public:
+  Conv2dMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("groups", &groups_);
+    GetAttr("dilations", &dilations_);
+    GetAttr("strides", &strides_);
+    GetAttr("paddings", &paddings_);
+    GetAttr("padding_algorithm", &padding_algorithm_);
+    GetAttr("data_format", &data_format_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<int64_t> dilations_;
+  std::vector<int64_t> strides_;
+  std::vector<int64_t> paddings_;
+  std::string padding_algorithm_;
+  std::string data_format_;
+  int64_t groups_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/conv2d_transpose.cc b/paddle2onnx/mapper/nn/conv2d_transpose.cc
new file mode 100755
index 0000000000..b397c6f978
--- /dev/null
+++ b/paddle2onnx/mapper/nn/conv2d_transpose.cc
@@ -0,0 +1,66 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/conv2d_transpose.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(conv2d_transpose, Conv2dTransposeMapper)
+REGISTER_MAPPER(depthwise_conv2d_transpose, Conv2dTransposeMapper)
+
+int32_t Conv2dTransposeMapper::GetMinOpset(bool verbose) {
+  // NHWC is not supported
+  if (data_format_ == "NHWC") {
+    Error() << "[ERROR] Cannot support NHWC format for operator "
+               "conv2d_transpose/depthwise_conv2d_transpose."
+            << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void Conv2dTransposeMapper::Opset7() {
+  auto kernel_info = GetInput("Filter");
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Output");
+  auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                 P2ODataType::FP32);
+  auto kernel = helper_->AutoCast(kernel_info[0].name, kernel_info[0].dtype,
+                                  P2ODataType::FP32);
+  auto node = helper_->MakeNode("ConvTranspose", {input, kernel});
+  AddAttribute(node, "dilations", dilations_);
+  std::vector<int64_t> kernel_shape = {kernel_info[0].shape[2],
+                                       kernel_info[0].shape[3]};
+  AddAttribute(node, "kernel_shape", kernel_shape);
+  AddAttribute(node, "strides", strides_);
+  AddAttribute(node, "group", groups_);
+  if (padding_algorithm_ == "SAME") {
+    std::string auto_pad = "SAME_UPPER";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else if (padding_algorithm_ == "VALID") {
+    std::string auto_pad = "VALID";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else {
+    AddAttribute(node, "pads", paddings_);
+  }
+  if (output_padding_.size() > 0) {
+    AddAttribute(node, "output_padding", output_padding_);
+  }
+  helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                    output_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/conv2d_transpose.h b/paddle2onnx/mapper/nn/conv2d_transpose.h
new file mode 100755
index 0000000000..3a8b472fd2
--- /dev/null
+++ b/paddle2onnx/mapper/nn/conv2d_transpose.h
@@ -0,0 +1,58 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Conv2dTransposeMapper : public Mapper {
+ public:
+  Conv2dTransposeMapper(const PaddleParser& p, OnnxHelper* helper,
+                        int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("groups", &groups_);
+    GetAttr("dilations", &dilations_);
+    GetAttr("strides", &strides_);
+    GetAttr("paddings", &paddings_);
+    GetAttr("padding_algorithm", &padding_algorithm_);
+    GetAttr("output_padding", &output_padding_);
+    GetAttr("data_format", &data_format_);
+    if (paddings_.size() == 2) {
+      paddings_.push_back(paddings_[0]);
+      paddings_.push_back(paddings_[1]);
+    } else if (paddings_.size() == 4) {
+      int32_t tmp = paddings_[1];
+      paddings_[1] = paddings_[2];
+      paddings_[2] = tmp;
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<int64_t> dilations_;
+  std::vector<int64_t> strides_;
+  std::vector<int64_t> paddings_;
+  std::vector<int64_t> output_padding_;
+  std::string padding_algorithm_;
+  std::string data_format_;
+  int64_t groups_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/conv3d.cc b/paddle2onnx/mapper/nn/conv3d.cc
new file mode 100644
index 0000000000..44e457f9b1
--- /dev/null
+++ b/paddle2onnx/mapper/nn/conv3d.cc
@@ -0,0 +1,82 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/conv3d.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(conv3d, Conv3dMapper)
+
+int32_t Conv3dMapper::GetMinOpset(bool verbose) {
+  // NDHWC is not supported
+  if (data_format_ == "NDHWC") {
+    Error() << "Cannot support input with NDHWC format." << std::endl;
+    return -1;
+  }
+  if (padding_algorithm_ == "EXPLICIT") {
+    if (paddings_.size() != 3 && paddings_.size() != 6) {
+      Error() << "While padding_algorithm is EXPLICIT, size of paddings should "
+                 "be 3 or 6."
+              << std::endl;
+      return -1;
+    }
+  }
+  if (dilations_[0] != 1 || dilations_[1] != 1 || dilations_[2] != 1) {
+    if (padding_algorithm_ == "SAME") {
+      Error() << "While dilations != 1, cannot support padding = 'SAME'."
+              << std::endl;
+      return -1;
+    }
+  }
+  return 7;
+}
+
+void Conv3dMapper::Opset7() {
+  auto kernel_info = GetInput("Filter");
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Output");
+
+  auto node = helper_->MakeNode(
+      "Conv", {input_info[0].name, kernel_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "dilations", dilations_);
+  std::vector<int64_t> kernel_shape = {kernel_info[0].shape[2],
+                                       kernel_info[0].shape[3],
+                                       kernel_info[0].shape[4]};
+  AddAttribute(node, "kernel_shape", kernel_shape);
+  AddAttribute(node, "strides", strides_);
+  AddAttribute(node, "group", groups_);
+  if (padding_algorithm_ == "SAME") {
+    std::string auto_pad = "SAME_UPPER";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else if (padding_algorithm_ == "VALID") {
+    std::string auto_pad = "VALID";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else {
+    std::vector<int64_t> paddings;
+    if (paddings_.size() == 3) {
+      paddings.insert(paddings.begin(), paddings_.begin(), paddings_.end());
+      paddings.insert(paddings.begin(), paddings_.begin(), paddings_.end());
+    } else {
+      std::vector<int64_t> index = {0, 2, 4, 1, 3, 5};
+      for (auto &i : index) {
+        paddings.push_back(paddings_[i]);
+      }
+    }
+    AddAttribute(node, "pads", paddings);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/conv3d.h b/paddle2onnx/mapper/nn/conv3d.h
new file mode 100755
index 0000000000..310edd5cc0
--- /dev/null
+++ b/paddle2onnx/mapper/nn/conv3d.h
@@ -0,0 +1,48 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Conv3dMapper : public Mapper {
+ public:
+  Conv3dMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("groups", &groups_);
+    GetAttr("dilations", &dilations_);
+    GetAttr("strides", &strides_);
+    GetAttr("paddings", &paddings_);
+    GetAttr("padding_algorithm", &padding_algorithm_);
+    GetAttr("data_format", &data_format_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<int64_t> dilations_;
+  std::vector<int64_t> strides_;
+  std::vector<int64_t> paddings_;
+  std::string padding_algorithm_;
+  std::string data_format_;
+  int64_t groups_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/data_norm.cc b/paddle2onnx/mapper/nn/data_norm.cc
new file mode 100644
index 0000000000..2cea544f4c
--- /dev/null
+++ b/paddle2onnx/mapper/nn/data_norm.cc
@@ -0,0 +1,43 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/data_norm.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(data_norm, DataNormMapper)
+
+int32_t DataNormMapper::GetMinOpset(bool verbose) {
+  if (slot_dim_ > 0) {
+    Error() << "slot_dim > 0 is not supported." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void DataNormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto batch_size_info = GetInput("BatchSize");
+  auto batch_sum_info = GetInput("BatchSum");
+  auto batch_square_sum_info = GetInput("BatchSquareSum");
+  auto output_info = GetOutput("Y");
+
+  Assert(slot_dim_ <= 0, "slot_dim > 0 is not supported.");
+  auto mean_arr = helper_->MakeNode("Div", {batch_sum_info[0].name, batch_size_info[0].name})->output(0);
+  auto scale_arr = helper_->MakeNode("Div", {batch_size_info[0].name, batch_square_sum_info[0].name})->output(0);
+  scale_arr = helper_->MakeNode("Sqrt", {scale_arr})->output(0);
+  auto out = helper_->MakeNode("Sub", {input_info[0].name, mean_arr})->output(0);
+  helper_->MakeNode("Mul" ,{out, scale_arr}, {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/data_norm.h b/paddle2onnx/mapper/nn/data_norm.h
new file mode 100644
index 0000000000..576a53d9dc
--- /dev/null
+++ b/paddle2onnx/mapper/nn/data_norm.h
@@ -0,0 +1,44 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class DataNormMapper : public Mapper {
+ public:
+  DataNormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("data_layout", &data_layout_);
+    GetAttr("epsilon", &epsilon_);
+    if (HasAttr("slot_dim")) {
+      GetAttr("slot_dim", &slot_dim_);
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::string data_layout_;
+  float epsilon_;
+  int64_t slot_dim_ = -1;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/dropout.cc b/paddle2onnx/mapper/nn/dropout.cc
new file mode 100644
index 0000000000..3f10ad1f87
--- /dev/null
+++ b/paddle2onnx/mapper/nn/dropout.cc
@@ -0,0 +1,66 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/nn/dropout.h"
+
+#include <vector>
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(dropout, DropoutMapper)
+
+int32_t DropoutMapper::GetMinOpset(bool verbose) {
+  if (dropout_implementation_ != "downgrade_in_infer" &&
+      dropout_implementation_ != "upscale_in_train") {
+    Error() << "Drop out type: " << dropout_implementation_
+            << " is not supported yet." << std::endl;
+    return -1;
+  }
+  if (dropout_implementation_ == "downgrade_in_infer") {
+    if (IsAttrVar("dropout_prob") &&
+        !IsConstant(GetAttrVar("dropout_prob")[0])) {
+      Error() << "While Attribute(dropout_prob)'s type is Tensor, it's not "
+                 "supported "
+                 "unless it's a constant tensor when dropout_implementation is "
+                 "downgrade_in_infer."
+              << std::endl;
+      return -1;
+    }
+  }
+  return 7;
+}
+
+void DropoutMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  if (dropout_implementation_ == "upscale_in_train") {
+    helper_->MakeNode("Identity", {input_info[0].name}, {output_info[0].name});
+  } else {
+    if (IsAttrVar("dropout_prob")) {
+      auto prob_info = GetAttrVar("dropout_prob");
+      std::vector<float> temp;
+      TryGetValue(prob_info[0], &temp);
+      dropout_prob_ = temp[0];
+    } else {
+      GetAttr("dropout_prob", &dropout_prob_);
+    }
+    std::vector<float> value = {1 - dropout_prob_};
+    std::string scale_node =
+        helper_->Constant(GetOnnxDtype(input_info[0].dtype), value);
+    helper_->MakeNode("Mul", {input_info[0].name, scale_node},
+                      {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/dropout.h b/paddle2onnx/mapper/nn/dropout.h
new file mode 100755
index 0000000000..048d9b6603
--- /dev/null
+++ b/paddle2onnx/mapper/nn/dropout.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class DropoutMapper : public Mapper {
+ public:
+  DropoutMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("dropout_implementation", &dropout_implementation_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  float dropout_prob_ = 0.0;
+  std::string dropout_implementation_ = "upscale_in_train";
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/group_norm.cc b/paddle2onnx/mapper/nn/group_norm.cc
new file mode 100644
index 0000000000..717401d270
--- /dev/null
+++ b/paddle2onnx/mapper/nn/group_norm.cc
@@ -0,0 +1,75 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/group_norm.h"
+
+#include <cmath>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(group_norm, GroupNormMapper)
+
+int32_t GroupNormMapper::GetMinOpset(bool verbose) {
+  auto input_info = GetInput("X");
+  if (input_info[0].Rank() != 4) {
+    Error() << "Only support 4D-Tensor as input for GroupNorm" << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void GroupNormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Y");
+
+  std::vector<int64_t> shape_val = {0, groups_, -1};
+  std::string shape =
+      helper_->Constant(GetOnnxDtype(P2ODataType::INT64), shape_val);
+
+  auto reshape_input =
+      helper_->MakeNode("Reshape", {input_info[0].name, shape});
+
+  std::string scale_ = helper_->Constant(GetOnnxDtype(input_info[0].dtype),
+                                         std::vector<float>(groups_, 1.0));
+
+  std::string bias_ = helper_->Constant(GetOnnxDtype(input_info[0].dtype),
+                                        std::vector<float>(groups_, 0.0));
+
+  auto reshaped_output = helper_->MakeNode(
+      "InstanceNormalization", {reshape_input->output(0), scale_, bias_});
+  AddAttribute(reshaped_output, "epsilon", epsilon_);
+
+  auto origin_shape = helper_->MakeNode("Shape", {input_info[0].name});
+
+  if (HasInput("Scale") && HasInput("Bias")) {
+    auto scale_info = GetInput("Scale");
+    auto bias_info = GetInput("Bias");
+    auto output = helper_->MakeNode(
+        "Reshape", {reshaped_output->output(0), origin_shape->output(0)});
+    std::string unsqueezed_scale =
+        helper_->Unsqueeze(scale_info[0].name, {1, 2});
+    std::string unsqueezed_bias = helper_->Unsqueeze(bias_info[0].name, {1, 2});
+    auto scale_output =
+        helper_->MakeNode("Mul", {output->output(0), unsqueezed_scale});
+    helper_->MakeNode("Add", {scale_output->output(0), unsqueezed_bias},
+                      {output_info[0].name});
+  } else {
+    helper_->MakeNode("Reshape",
+                      {reshaped_output->output(0), origin_shape->output(0)},
+                      {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/group_norm.h b/paddle2onnx/mapper/nn/group_norm.h
new file mode 100755
index 0000000000..a2bd1bd8d6
--- /dev/null
+++ b/paddle2onnx/mapper/nn/group_norm.h
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GroupNormMapper : public Mapper {
+ public:
+  GroupNormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("groups", &groups_);
+    GetAttr("epsilon", &epsilon_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  int64_t groups_;
+  float epsilon_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/instance_norm.cc b/paddle2onnx/mapper/nn/instance_norm.cc
new file mode 100644
index 0000000000..edddac10f4
--- /dev/null
+++ b/paddle2onnx/mapper/nn/instance_norm.cc
@@ -0,0 +1,59 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/instance_norm.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(instance_norm, InstanceNormMapper)
+
+int32_t InstanceNormMapper::GetMinOpset(bool verbose) {
+  auto input_info = GetInput("X");
+  int num_groups = input_info[0].shape[1];
+  if (num_groups < 0) {
+    Error() << "The dimension in axis=1 of input tensor must be known, but now it's unknown." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void InstanceNormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Y");
+  int num_groups = input_info[0].shape[1];
+
+  std::string scale = "";
+  if (HasInput("Scale")) {
+    scale = GetInput("Scale")[0].name;
+  } else {
+    scale = helper_->Constant(GetOnnxDtype(input_info[0].dtype), std::vector<float>(num_groups, 1.0));
+  }
+  
+  std::string bias = "";
+  if (HasInput("Bias")) {
+    bias = GetInput("Bias")[0].name;
+  } else {
+    bias = helper_->Constant(GetOnnxDtype(input_info[0].dtype), std::vector<float>(num_groups, 0.0));
+  }
+
+  auto node = helper_->MakeNode(
+      "InstanceNormalization",
+      {input_info[0].name, scale, bias},
+      {output_info[0].name});
+  AddAttribute(node, "epsilon", epsilon_);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/instance_norm.h b/paddle2onnx/mapper/nn/instance_norm.h
new file mode 100644
index 0000000000..c569b1f92c
--- /dev/null
+++ b/paddle2onnx/mapper/nn/instance_norm.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class InstanceNormMapper : public Mapper {
+ public:
+  InstanceNormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("epsilon", &epsilon_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  float epsilon_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/interpolate.cc b/paddle2onnx/mapper/nn/interpolate.cc
new file mode 100755
index 0000000000..49523a8a69
--- /dev/null
+++ b/paddle2onnx/mapper/nn/interpolate.cc
@@ -0,0 +1,136 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/interpolate.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(bilinear_interp, InterpolateMapper)
+REGISTER_MAPPER(bilinear_interp_v2, InterpolateMapper)
+REGISTER_MAPPER(nearest_interp_v2, InterpolateMapper)
+REGISTER_MAPPER(bicubic_interp_v2, InterpolateMapper)
+REGISTER_MAPPER(linear_interp_v2, InterpolateMapper)
+REGISTER_MAPPER(trilinear_interp_v2, InterpolateMapper)
+
+int32_t InterpolateMapper::GetMinOpset(bool verbose) {
+  if (data_layout_ == "NHWC") {
+    Error() << "Data format of NHWC is not supported." << std::endl;
+    return -1;
+  }
+  auto x_info = GetInput("X");
+  if (x_info[0].Rank() > 5 && x_info[0].Rank() < 3) {
+    Error() << "Only support 3D/4D/5D tensor, but now its dimension is "
+            << x_info[0].Rank() << std::endl;
+    return -1;
+  }
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+std::string InterpolateMapper::ComputeOutSize() {
+  bool has_out_size = HasInput("OutSize");
+  bool has_size_tensor = HasInput("SizeTensor");
+  if (has_out_size) {
+    auto out_size_info = GetInput("OutSize");
+    return helper_->AutoCast(out_size_info[0].name, out_size_info[0].dtype,
+                             P2ODataType::INT64);
+  } else {
+    auto size_tensor_info = GetInput("SizeTensor");
+    return helper_->ConcatIndices(size_tensor_info);
+  }
+}
+
+std::string InterpolateMapper::ComputeScale() {
+  auto scale_info = GetInput("Scale");
+  auto scale = helper_->AutoCast(scale_info[0].name, scale_info[0].dtype,
+                                 P2ODataType::FP32);
+  auto padding = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                                   std::vector<float>(2, 1.0));
+  scale = helper_->Concat({padding, scale}, 0);
+  return scale;
+}
+
+void InterpolateMapper::Opset11() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  std::string coordinate_transformation_mode = "half_pixel";
+  auto resize_type = resize_mapper_[method_];
+  if (align_corners_) {
+    coordinate_transformation_mode = "align_corners";
+  } else if (resize_type == "nearest") {
+    coordinate_transformation_mode = "asymmetric";
+  } else if (align_mode_ == 1 && resize_type != "cubic") {
+    coordinate_transformation_mode = "asymmetric";
+  }
+  std::string scale = "";
+  std::string size = "";
+  bool has_out_size = HasInput("OutSize");
+  bool has_size_tensor = HasInput("SizeTensor");
+  bool has_scale_tensor = HasInput("Scale");
+  if (has_out_size || has_size_tensor) {
+    size = ComputeOutSize();
+  } else if (has_scale_tensor) {
+    scale = ComputeScale();
+  } else {
+    // get size or scale from attribute
+    if (out_d_ > 0 || out_w_ > 0 || out_h_ > 0) {
+      std::vector<int64_t> out_size;
+      if (x_info[0].Rank() == 5) {
+        out_size.push_back(out_d_);
+        out_size.push_back(out_h_);
+      }
+      if (x_info[0].Rank() == 4) {
+        out_size.push_back(out_h_);
+      }
+      out_size.push_back(out_w_);
+      size = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, out_size);
+    } else {
+      std::vector<float> scale_;
+      GetAttr("scale", &scale_);
+      float padding = 1.0;
+      scale_.insert(scale_.begin(), padding);
+      scale_.insert(scale_.begin(), padding);
+      scale = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, scale_);
+    }
+  }
+  std::string roi = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, std::vector<float>());
+  if (scale == "") {
+    // has to generate a empty tensor for resize
+    scale = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                              std::vector<float>());
+  }
+  if (size != "") {
+    auto ipt_shape = helper_->MakeNode("Shape", {x_info[0].name})->output(0);
+    auto nc = helper_->Slice(ipt_shape, {0}, {0}, {2});
+    size = helper_->Concat({nc, size}, 0);
+  }
+  std::shared_ptr<ONNX_NAMESPACE::NodeProto> node;
+  if (size != "") {
+    node = helper_->MakeNode("Resize", {x_info[0].name, roi, scale, size},
+                             {out_info[0].name});
+  } else {
+    node = helper_->MakeNode("Resize", {x_info[0].name, roi, scale},
+                             {out_info[0].name});
+  }
+  Assert(resize_mapper_.find(OpType()) != resize_mapper_.end(),
+         "Cannot find " + OpType() + " in resize_mapper.");
+  AddAttribute(node, "mode", resize_mapper_[OpType()]);
+  AddAttribute(node, "coordinate_transformation_mode",
+               coordinate_transformation_mode);
+  if (resize_mapper_[OpType()] == "nearest" &&
+      coordinate_transformation_mode == "asymmetric") {
+    AddAttribute(node, "nearest_mode", "floor");
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/interpolate.h b/paddle2onnx/mapper/nn/interpolate.h
new file mode 100755
index 0000000000..d3b8a7a7a8
--- /dev/null
+++ b/paddle2onnx/mapper/nn/interpolate.h
@@ -0,0 +1,57 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class InterpolateMapper : public Mapper {
+ public:
+  InterpolateMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("data_layout", &data_layout_);
+    GetAttr("align_corners", &align_corners_);
+    GetAttr("align_mode", &align_mode_);
+    GetAttr("out_d", &out_d_);
+    GetAttr("out_h", &out_h_);
+    GetAttr("out_w", &out_w_);
+    method_ = OpType();
+
+    resize_mapper_["bilinear_interp"] = "linear";
+    resize_mapper_["bilinear_interp_v2"] = "linear";
+    resize_mapper_["nearest_interp_v2"] = "nearest";
+    resize_mapper_["bicubic_interp_v2"] = "cubic";
+    resize_mapper_["linear_interp_v2"] = "linear";
+    resize_mapper_["trilinear_interp_v2"] = "linear";
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+
+ private:
+  std::string ComputeOutSize();
+  std::string ComputeScale();
+  std::map<std::string, std::string> resize_mapper_;
+  std::string method_;
+  std::string data_layout_;
+  int64_t align_mode_;
+  int64_t out_d_;
+  int64_t out_h_;
+  int64_t out_w_;
+  bool align_corners_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/layer_norm.cc b/paddle2onnx/mapper/nn/layer_norm.cc
new file mode 100644
index 0000000000..5fab8da22c
--- /dev/null
+++ b/paddle2onnx/mapper/nn/layer_norm.cc
@@ -0,0 +1,146 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/layer_norm.h"
+
+#include <cmath>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(layer_norm, LayerNormMapper)
+
+void LayerNormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Y");
+
+  std::string input_name = helper_->AutoCast(
+      input_info[0].name, input_info[0].dtype, P2ODataType::FP32);
+
+  std::vector<int64_t> input_shape = input_info[0].shape;
+  std::vector<int64_t> axes;
+  for (auto i = begin_norm_axis_; i < input_shape.size(); i++) {
+    axes.push_back(i);
+  }
+  if (begin_norm_axis_ == input_shape.size() - 1) {
+    axes[0] = -1;
+  }
+
+  float epsilon = epsilon_;
+  std::string epsilon_node =
+      helper_->Constant({}, GetOnnxDtype(P2ODataType::FP32), epsilon);
+  std::string two_node =
+      helper_->Constant({}, GetOnnxDtype(P2ODataType::FP32), float(2.0));
+
+  auto mean_node = helper_->MakeNode("ReduceMean", {input_name});
+  AddAttribute(mean_node, "axes", axes);
+
+  auto numerator_node =
+      helper_->MakeNode("Sub", {input_name, mean_node->output(0)});
+  auto pow_num_node =
+      helper_->MakeNode("Pow", {numerator_node->output(0), two_node});
+
+  auto variance_node =
+      helper_->MakeNode("ReduceMean", {pow_num_node->output(0)});
+  AddAttribute(variance_node, "axes", axes);
+
+  auto add_eps_node =
+      helper_->MakeNode("Add", {variance_node->output(0), epsilon_node});
+
+  auto denominator_node = helper_->MakeNode("Sqrt", {add_eps_node->output(0)});
+
+  auto ipt_shape_node = helper_->MakeNode("Shape", {input_name});
+  std::vector<int64_t> slice_axes = {0};
+  std::vector<int64_t> start = {
+      static_cast<int64_t>(input_shape.size() - axes.size())};
+  std::vector<int64_t> end = {static_cast<int64_t>(input_shape.size())};
+  std::string weight_shape_node =
+      helper_->Slice(ipt_shape_node->output(0), slice_axes, start, end);
+
+  bool has_input_Bias = HasInput("Bias");
+  bool has_input_Scale = HasInput("Scale");
+
+  if (has_input_Bias && has_input_Scale) {
+    auto scale_info = GetInput("Scale");
+    auto bias_info = GetInput("Bias");
+    std::string scale_name = helper_->AutoCast(
+        scale_info[0].name, scale_info[0].dtype, P2ODataType::FP32);
+    std::string bias_name = helper_->AutoCast(
+        bias_info[0].name, bias_info[0].dtype, P2ODataType::FP32);
+    std::string scale_node = "";
+    std::string bias_node = "";
+    if (begin_norm_axis_ == input_shape.size() - 1) {
+      scale_node = helper_->Reshape(scale_name, {-1});
+      bias_node = helper_->Reshape(bias_name, {-1});
+    } else {
+      scale_node = helper_->MakeNode("Reshape", {scale_name, weight_shape_node})
+                       ->output(0);
+      bias_node = helper_->MakeNode("Reshape", {bias_name, weight_shape_node})
+                      ->output(0);
+    }
+    auto layer_norm_pre_node = helper_->MakeNode(
+        "Div", {numerator_node->output(0), denominator_node->output(0)});
+    auto layer_norm_node =
+        helper_->MakeNode("Mul", {layer_norm_pre_node->output(0), scale_node});
+    auto pre_cast_node =
+        helper_->MakeNode("Add", {layer_norm_node->output(0), bias_node});
+    helper_->AutoCast(pre_cast_node->output(0), output_info[0].name,
+                      P2ODataType::FP32, output_info[0].dtype);
+    return;
+  }
+  if (has_input_Bias) {
+    auto bias_info = GetInput("Bias");
+    std::string bias_name = helper_->AutoCast(
+        bias_info[0].name, bias_info[0].dtype, P2ODataType::FP32);
+    std::string bias_node = "";
+    if (begin_norm_axis_ == input_shape.size() - 1) {
+      bias_node = helper_->Reshape(bias_name, {-1});
+    } else {
+      bias_node = helper_->MakeNode("Reshape", {bias_name, weight_shape_node})
+                      ->output(0);
+    }
+    auto layer_norm_node = helper_->MakeNode(
+        "Div", {numerator_node->output(0), denominator_node->output(0)});
+    auto pre_cast_node =
+        helper_->MakeNode("Add", {layer_norm_node->output(0), bias_node});
+    helper_->AutoCast(pre_cast_node->output(0), output_info[0].name,
+                      P2ODataType::FP32, output_info[0].dtype);
+    return;
+  }
+  if (has_input_Scale) {
+    auto scale_info = GetInput("Scale");
+    std::string scale_name = helper_->AutoCast(
+        scale_info[0].name, scale_info[0].dtype, P2ODataType::FP32);
+    std::string scale_node = "";
+    if (begin_norm_axis_ == input_shape.size() - 1) {
+      scale_node = helper_->Reshape(scale_name, {-1});
+    } else {
+      scale_node = helper_->MakeNode("Reshape", {scale_name, weight_shape_node})
+                       ->output(0);
+    }
+    auto layer_norm_node = helper_->MakeNode(
+        "Div", {numerator_node->output(0), denominator_node->output(0)});
+    auto pre_cast_node =
+        helper_->MakeNode("Mul", {layer_norm_node->output(0), scale_node});
+    helper_->AutoCast(pre_cast_node->output(0), output_info[0].name,
+                      P2ODataType::FP32, output_info[0].dtype);
+    return;
+  }
+  auto pre_cast_node = helper_->MakeNode(
+      "Div", {numerator_node->output(0), denominator_node->output(0)});
+  helper_->AutoCast(pre_cast_node->output(0), output_info[0].name,
+                    P2ODataType::FP32, output_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/layer_norm.h b/paddle2onnx/mapper/nn/layer_norm.h
new file mode 100644
index 0000000000..6eebe062e0
--- /dev/null
+++ b/paddle2onnx/mapper/nn/layer_norm.h
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LayerNormMapper : public Mapper {
+ public:
+  LayerNormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("begin_norm_axis", &begin_norm_axis_);
+    GetAttr("epsilon", &epsilon_);
+  }
+
+  void Opset7();
+
+ private:
+  int64_t begin_norm_axis_;
+  float epsilon_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/norm.cc b/paddle2onnx/mapper/nn/norm.cc
new file mode 100755
index 0000000000..abe188f274
--- /dev/null
+++ b/paddle2onnx/mapper/nn/norm.cc
@@ -0,0 +1,28 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/norm.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(norm, NormMapper)
+
+void NormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node = helper_->MakeNode("LpNormalization", {input_info[0].name},
+                                {output_info[0].name});
+  AddAttribute(node, "axis", axis_);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/norm.h b/paddle2onnx/mapper/nn/norm.h
new file mode 100644
index 0000000000..f3eb01bfd5
--- /dev/null
+++ b/paddle2onnx/mapper/nn/norm.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class NormMapper : public Mapper {
+ public:
+  NormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+
+  void Opset7();
+
+ private:
+  int64_t axis_ = -1;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/pad3d.cc b/paddle2onnx/mapper/nn/pad3d.cc
new file mode 100644
index 0000000000..3040247364
--- /dev/null
+++ b/paddle2onnx/mapper/nn/pad3d.cc
@@ -0,0 +1,114 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/pad3d.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(pad3d, Pad3DMapper)
+
+int32_t Pad3DMapper::GetMinOpset(bool verbose) {
+  if (data_format_ == "NDHWC") {
+    Error() << "NDHWC format is not supported." << std::endl;
+    return -1;
+  }
+  if (mode_ == "circular") {
+    Error() << "Padding mode `circular` is not supported." << std::endl;
+    return -1;
+  }
+  if (HasInput("Paddings")) {
+    if (!IsConstantInput("Paddings")) {
+      Logger(verbose, 11) << "While Paddings is input and it's not a constant tensor, " << RequireOpset(11) << std::endl;
+      return 11;
+    }
+    std::vector<int64_t> paddings;
+    if (!TryGetInputValue("Paddings", &paddings)) {
+      Logger(verbose, 11) << "Cannot get constant value from input of Paddings, " << RequireOpset(11) << std::endl;
+      return 11;
+    } else {
+      if (paddings.size() != 6) {
+       Error() << "Size of paddings should be equal to 6, but now it's " << paddings.size() << std::endl;
+       return -1;
+      }
+    }
+  } else {
+    if (paddings_.size() != 6) {
+      Error() << "Size of paddings should be equal to 6, but now it's " << paddings_.size() << std::endl;
+      return -1;
+    }
+  }
+  return 7;
+}
+
+std::vector<int64_t> Pad3DMapper::ConvertPaddingParameter(const std::vector<int64_t>& paddings) {
+  std::vector<int64_t> new_paddings(10, 0);
+  new_paddings[2] = paddings[4];
+  new_paddings[3] = paddings[2];
+  new_paddings[4] = paddings[0];
+  new_paddings[7] = paddings[5];
+  new_paddings[8] = paddings[3];
+  new_paddings[9] = paddings[1];
+  return new_paddings;
+}
+
+void Pad3DMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto mode = mode_;
+  if (mode == "replicate") {
+    mode = "edge";
+  }
+  std::vector<int64_t> paddings;
+  if (HasInput("Paddings")) {
+    Assert(TryGetInputValue("Paddings", &paddings), "Cannot get constant value from input of Paddings, " + RequireOpset(11));
+  } else {
+    paddings.assign(paddings_.begin(), paddings_.end());
+  }
+  std::vector<int64_t> new_paddings = ConvertPaddingParameter(paddings);
+  auto node = helper_->MakeNode("Pad", {input_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "mode", mode);
+  AddAttribute(node, "value", value_);
+  AddAttribute(node, "pads", new_paddings);
+}
+
+void Pad3DMapper::Opset11() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto mode = mode_;
+  if (mode == "replicate") {
+    mode = "edge";
+  }
+
+  std::string paddings = "";
+  if (HasInput("Paddings")) {
+    std::vector<int64_t> paddings_value;
+    if (TryGetInputValue("Paddings", &paddings_value)) {
+      std::vector<int64_t> new_paddings = ConvertPaddingParameter(paddings_value);
+      paddings = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, new_paddings);
+    } else {
+      auto pad_info = GetInput("Paddings");
+      auto cast_pad = helper_->AutoCast(pad_info[0].name, pad_info[0].dtype, P2ODataType::INT64);
+      auto split_pads = helper_->Split(cast_pad, std::vector<int64_t>(6, 1), 0);
+      auto zero = helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(0));
+      paddings = helper_->Concat({zero, zero, split_pads[4], split_pads[2], split_pads[0], zero, zero, split_pads[5], split_pads[3], split_pads[1]}, 0);
+    }
+  } else {
+    std::vector<int64_t> new_paddings = ConvertPaddingParameter(paddings_);
+    paddings = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, new_paddings);
+  }
+  auto value = helper_->Constant({}, GetOnnxDtype(input_info[0].dtype), value_);
+  auto node = helper_->MakeNode("Pad", {input_info[0].name, paddings, value}, {output_info[0].name});
+  AddAttribute(node, "mode", mode);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/pad3d.h b/paddle2onnx/mapper/nn/pad3d.h
new file mode 100644
index 0000000000..3e9f50599d
--- /dev/null
+++ b/paddle2onnx/mapper/nn/pad3d.h
@@ -0,0 +1,45 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Pad3DMapper : public Mapper {
+ public:
+  Pad3DMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("data_format", &data_format_);
+    GetAttr("mode", &mode_);
+    GetAttr("value", &value_);
+    GetAttr("paddings", &paddings_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset11();
+
+ private:
+  std::vector<int64_t> ConvertPaddingParameter(const std::vector<int64_t>& paddings);
+  std::string data_format_;
+  std::string mode_;
+  std::vector<int64_t> paddings_;
+  float value_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/pool2d.cc b/paddle2onnx/mapper/nn/pool2d.cc
new file mode 100644
index 0000000000..435e3cabaf
--- /dev/null
+++ b/paddle2onnx/mapper/nn/pool2d.cc
@@ -0,0 +1,332 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/pool2d.h"
+
+#include <algorithm>
+#include <cmath>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(pool2d, Pool2dMapper)
+REGISTER_MAPPER(max_pool2d_with_index, Pool2dMapper)
+
+bool Pool2dMapper::IsSameSpan(const int64_t& in_size, const int64_t& out_size) {
+  std::vector<int64_t> spans;
+  spans.reserve(out_size);
+  for (auto i = 0; i < out_size; ++i) {
+    int64_t start = std::floor(i * (in_size / out_size));
+    int64_t end = std::ceil((i + 1) * (in_size / out_size));
+    spans.push_back(end - start);
+  }
+  std::sort(spans.begin(), spans.end());
+  return spans[0] == spans[spans.size() - 1];
+}
+
+bool Pool2dMapper::IsExportAsCustomOp() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  GetAttr("ksize", &k_size_);
+  if (global_pooling_ || (k_size_[0] == 1 && k_size_[1] == 1)) {
+    return false;
+  }
+  if (export_as_custom_op && adaptive_) {
+    bool is_1x1_kernel = true;
+    for (auto i : k_size_) {
+      if (i != 1) {
+        is_1x1_kernel = false;
+      }
+    }
+    if (is_1x1_kernel) {
+      return false;
+    }
+    for (auto one_input : input_info) {
+      for (auto i = 2; i < one_input.shape.size(); ++i) {
+        if (one_input.shape[i] == -1) {
+          return true;
+        }
+      }
+    }
+    int64_t input_h = input_info[0].shape[2];
+    int64_t input_w = input_info[0].shape[3];
+    int64_t output_h = output_info[0].shape[2];
+    int64_t output_w = output_info[0].shape[3];
+    if (output_h == -1 || output_w == -1 || !IsSameSpan(input_h, output_h) ||
+        !IsSameSpan(input_w, output_w)) {
+      return true;
+    }
+  }
+  return false;
+}
+
+void Pool2dMapper::ExportAsCustomOp() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node = helper_->MakeNode(custom_op_name, {input_info[0].name},
+                                {output_info[0].name});
+  node->set_domain("Paddle");
+  AddAttribute(node, "pooling_type", pooling_type_);
+  for (auto i = 1; i < output_info[0].shape.size(); i++) {
+    if (output_info[0].shape[i] == -1) {
+      if (input_info[0].shape[i] == -1) {
+        Assert(false,
+               "Can not convert to AdaptivePool custom OP, because the shapes "
+               "of the input and output are unknown.");
+      } else {
+        output_info[0].shape[i] = input_info[0].shape[i];
+      }
+    }
+  }
+  AddAttribute(node, "output_size", output_info[0].shape);
+  Warn() << "Pool2d is exported as custom operator: " << custom_op_name
+         << std::endl;
+  helper_->MakeValueInfo(input_info[0].name, input_info[0].dtype,
+                         input_info[0].shape);
+  helper_->MakeValueInfo(output_info[0].name, output_info[0].dtype,
+                         output_info[0].shape);
+}
+
+void Pool2dMapper::AdaptivePool(const std::vector<TensorInfo>& input_info,
+                                const std::vector<TensorInfo>& output_info) {
+  int64_t input_h = input_info[0].shape[2];
+  int64_t input_w = input_info[0].shape[3];
+  int64_t output_h = output_info[0].shape[2];
+  int64_t output_w = output_info[0].shape[3];
+  int64_t stride_h = std::floor(input_h / output_h);
+  int64_t stride_w = std::floor(input_w / output_w);
+  int64_t kernel_h = input_h - (output_h - 1) * stride_h;
+  int64_t kernel_w = input_w - (output_w - 1) * stride_w;
+  std::string onnx_pool_type;
+  if (OpType() == "max_pool2d_with_index") {
+    onnx_pool_type = "MaxPool";
+  } else {
+    auto iter = op_mapper_.find(pooling_type_);
+    onnx_pool_type = iter->second[0];
+  }
+
+  std::shared_ptr<ONNX_NAMESPACE::NodeProto>* node_ptr;
+  auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                 P2ODataType::FP32);
+  auto node = helper_->MakeNode(onnx_pool_type, {input});
+  helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                    output_info[0].dtype);
+  std::vector<int64_t> kernel_size = {kernel_h, kernel_w};
+  AddAttribute(node, "kernel_shape", kernel_size);
+  std::vector<int64_t> strides = {stride_h, stride_w};
+  AddAttribute(node, "strides", strides);
+
+  if (helper_->GetOpsetVersion() > 10) {
+    AddAttribute(node, "ceil_mode", static_cast<int64_t>(ceil_mode_));
+  }
+
+  std::string auto_pad = "NOTSET";
+  if (padding_algorithm_ == "SAME") {
+    auto_pad = "SAME_UPPER";
+  } else if (padding_algorithm_ == "VALID") {
+    auto_pad = "VALID";
+  }
+  AddAttribute(node, "auto_pad", auto_pad);
+  if (pooling_type_ == "avg") {
+    AddAttribute(node, "count_include_pad", static_cast<int64_t>(exclusive_));
+  }
+}
+
+void Pool2dMapper::NoAdaptivePool(const std::vector<TensorInfo>& input_info,
+                                  const std::vector<TensorInfo>& output_info) {
+  std::vector<int64_t> input_shape = input_info[0].shape;
+  if (pads_.size() == 2) {
+    pads_.push_back(pads_[0]);
+    pads_.push_back(pads_[1]);
+  } else if (pads_.size() == 4) {
+    std::vector<int64_t> index = {0, 2, 1, 3};
+    std::vector<int64_t> copy = pads_;
+    for (auto i = 0; i < index.size(); ++i) {
+      pads_[i] = copy[index[i]];
+    }
+  }
+  if (input_shape[2] > 0 && input_shape[2] + pads_[0] < k_size_[0]) {
+    k_size_[0] = input_shape[2] + pads_[0];
+  }
+  if (input_shape[3] > 0 && input_shape[3] + pads_[1] < k_size_[1]) {
+    k_size_[1] = input_shape[3] + pads_[1];
+  }
+
+  int64_t max_ksize = *std::max_element(std::begin(k_size_), std::end(k_size_));
+  int64_t max_pads = *std::max_element(std::begin(pads_), std::end(pads_));
+  auto input_x = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+  if (max_ksize <= max_pads) {
+    std::vector<int64_t> onnx_paddings = {0, 0, pads_[0], pads_[1],
+                                          0, 0, pads_[2], pads_[3]};
+    std::vector<std::string> inputs_names = {input_x};
+    if (helper_->GetOpsetVersion() >= 11) {
+      std::string paddings_node =
+          helper_->Constant(GetOnnxDtype(P2ODataType::INT64), onnx_paddings);
+      inputs_names.push_back(paddings_node);
+      std::vector<float> val = {0.0};
+      std::string val_node =
+          helper_->Constant(GetOnnxDtype(P2ODataType::FP32), val);
+      inputs_names.push_back(val_node);
+    }
+    auto node = helper_->MakeNode("Pad", inputs_names);
+    std::string mode = "constant";
+    AddAttribute(node, "mode", mode);
+    if (helper_->GetOpsetVersion() < 11) {
+      AddAttribute(node, "pads", onnx_paddings);
+      float val = 0.0;
+      AddAttribute(node, "value", val);
+    }
+    input_x = node->output(0);
+    pads_.clear();
+    pads_.resize(4, 0);
+  }
+  std::string onnx_pool_type;
+  if (OpType() == "max_pool2d_with_index") {
+    onnx_pool_type = "MaxPool";
+  } else {
+    auto iter = op_mapper_.find(pooling_type_);
+    onnx_pool_type = iter->second[0];
+  }
+  auto node = helper_->MakeNode(onnx_pool_type, {input_x});
+  helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                    output_info[0].dtype);
+
+  AddAttribute(node, "kernel_shape", k_size_);
+  AddAttribute(node, "strides", strides_);
+  std::string auto_pad = "NOTSET";
+  if (padding_algorithm_ == "SAME") {
+    auto_pad = "SAME_UPPER";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else if (padding_algorithm_ == "VALID") {
+    auto_pad = "VALID";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else {
+    AddAttribute(node, "pads", pads_);
+  }
+  if (OpType() != "max_pool2d_with_index" && helper_->GetOpsetVersion() >= 10) {
+    AddAttribute(node, "ceil_mode", static_cast<int64_t>(ceil_mode_));
+  }
+  if (OpType() != "max_pool2d_with_index" && pooling_type_ == "avg") {
+    AddAttribute(node, "count_include_pad", static_cast<int64_t>(exclusive_));
+  }
+}
+
+int32_t Pool2dMapper::GetMinOpset(bool verbose) {
+  // NHWC is not supported
+  if (data_format_ == "NHWC") {
+    Error() << "NHWC format is not supported." << std::endl;
+    return -1;
+  }
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  if (IsAttrVar("ksize")) {
+    Error() << "While Attribute(ksize)'s type is Tensor, it's not "
+               "supported."
+            << std::endl;
+    return -1;
+  } else {
+    GetAttr("ksize", &k_size_);
+  }
+
+  if (global_pooling_ || (k_size_[0] == 1 && k_size_[1] == 1)) {
+    if (ceil_mode_) {
+      Logger(verbose, 10) << "While ceil_model is True, " << RequireOpset(10)
+                          << std::endl;
+      return 10;
+    }
+    return 7;
+  }
+
+  if (adaptive_) {
+    for (auto one_input : input_info) {
+      for (auto i = 2; i < one_input.shape.size(); ++i) {
+        if (one_input.shape[i] == -1) {
+          if (export_as_custom_op) {
+            return 7;
+          } else {
+            Error() << "Adaptive only support static input shape." << std::endl;
+            return -1;
+          }
+        }
+      }
+    }
+    int64_t input_h = input_info[0].shape[2];
+    int64_t input_w = input_info[0].shape[3];
+    int64_t output_h = output_info[0].shape[2];
+    int64_t output_w = output_info[0].shape[3];
+    if (output_h == -1 || output_w == -1 || !IsSameSpan(input_h, output_h) ||
+        !IsSameSpan(input_w, output_w)) {
+      if (export_as_custom_op) {
+        return 7;
+      } else {
+        Error() << "Cannot convert adaptive pool with input_size: " << input_h
+                << " " << input_h << " output_size: " << output_h << " "
+                << output_w << std::endl;
+        return -1;
+      }
+    }
+  }
+  if (OpType() == "max_pool2d_with_index") {
+    return 9;
+  }
+  auto iter = op_mapper_.find(pooling_type_);
+  if (op_mapper_.end() == iter) {
+    Error() << "Cannot find " << pooling_type_ << " in pool op_mapper."
+            << std::endl;
+    return -1;
+  }
+
+  if (ceil_mode_) {
+    Logger(verbose, 10) << "While ceil_model is True, " << RequireOpset(10)
+                        << std::endl;
+    return 10;
+  }
+  return 7;
+}
+
+void Pool2dMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  GetAttr("ksize", &k_size_);
+
+  bool is_1x1_kernel = true;
+  for (auto i : k_size_) {
+    if (i != 1) {
+      is_1x1_kernel = false;
+    }
+  }
+
+  if (global_pooling_ || (adaptive_ && is_1x1_kernel)) {
+    std::string onnx_pool_type;
+    if (OpType() == "max_pool2d_with_index") {
+      onnx_pool_type = "GlobalMaxPool";
+    } else {
+      auto iter = op_mapper_.find(pooling_type_);
+      onnx_pool_type = iter->second[1];
+    }
+    auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+    auto output = helper_->MakeNode(onnx_pool_type, {input})->output(0);
+    helper_->AutoCast(output, output_info[0].name, P2ODataType::FP32,
+                      output_info[0].dtype);
+  } else if (adaptive_) {
+    AdaptivePool(input_info, output_info);
+  } else {
+    NoAdaptivePool(input_info, output_info);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/pool2d.h b/paddle2onnx/mapper/nn/pool2d.h
new file mode 100644
index 0000000000..bbaf7c7879
--- /dev/null
+++ b/paddle2onnx/mapper/nn/pool2d.h
@@ -0,0 +1,67 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Pool2dMapper : public Mapper {
+ public:
+  Pool2dMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    op_mapper_["max"] = {"MaxPool", "GlobalMaxPool"};
+    op_mapper_["avg"] = {"AveragePool", "GlobalAveragePool"};
+    GetAttr("global_pooling", &global_pooling_);
+    GetAttr("adaptive", &adaptive_);
+    GetAttr("strides", &strides_);
+    GetAttr("paddings", &pads_);
+    if (OpType() != "max_pool2d_with_index") {
+      GetAttr("pooling_type", &pooling_type_);
+      GetAttr("data_format", &data_format_);
+      GetAttr("ceil_mode", &ceil_mode_);
+      GetAttr("padding_algorithm", &padding_algorithm_);
+      GetAttr("exclusive", &exclusive_);
+      exclusive_ = !exclusive_;
+    }
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void ExportAsCustomOp();
+  bool IsExportAsCustomOp();
+
+ private:
+  bool IsSameSpan(const int64_t& in_size, const int64_t& out_size);
+  void AdaptivePool(const std::vector<TensorInfo>& input_info,
+                    const std::vector<TensorInfo>& output_info);
+  void NoAdaptivePool(const std::vector<TensorInfo>& input_info,
+                      const std::vector<TensorInfo>& output_info);
+  bool ceil_mode_;
+  bool global_pooling_;
+  bool adaptive_;
+  bool exclusive_;
+  std::string data_format_;
+  std::string pooling_type_;
+  std::string padding_algorithm_;
+  std::vector<int64_t> k_size_;
+  std::vector<int64_t> pads_;
+  std::vector<int64_t> strides_;
+  std::map<std::string, std::vector<std::string>> op_mapper_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/pool3d.cc b/paddle2onnx/mapper/nn/pool3d.cc
new file mode 100644
index 0000000000..fb6916fa10
--- /dev/null
+++ b/paddle2onnx/mapper/nn/pool3d.cc
@@ -0,0 +1,262 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/pool3d.h"
+
+#include <algorithm>
+#include <cmath>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(pool3d, Pool3dMapper)
+REGISTER_MAPPER(max_pool3d_with_index, Pool3dMapper)
+
+bool Pool3dMapper::IsSameSpan(const int64_t& in_size, const int64_t& out_size) {
+  std::vector<int64_t> spans;
+  spans.reserve(out_size);
+  for (auto i = 0; i < out_size; ++i) {
+    int64_t start = std::floor(i * (in_size / out_size));
+    int64_t end = std::ceil((i + 1) * (in_size / out_size));
+    spans.push_back(end - start);
+  }
+  std::sort(spans.begin(), spans.end());
+  return spans[0] == spans[spans.size() - 1];
+}
+
+void Pool3dMapper::AdaptivePool(const std::vector<TensorInfo>& input_info,
+                                const std::vector<TensorInfo>& output_info) {
+  int64_t input_d = input_info[0].shape[2];
+  int64_t input_h = input_info[0].shape[3];
+  int64_t input_w = input_info[0].shape[4];
+  int64_t output_d = output_info[0].shape[2];
+  int64_t output_h = output_info[0].shape[3];
+  int64_t output_w = output_info[0].shape[4];
+  int64_t stride_d = std::floor(input_d / output_d);
+  int64_t stride_h = std::floor(input_h / output_h);
+  int64_t stride_w = std::floor(input_w / output_w);
+  int64_t kernel_d = input_d - (output_d - 1) * stride_d;
+  int64_t kernel_h = input_h - (output_h - 1) * stride_h;
+  int64_t kernel_w = input_w - (output_w - 1) * stride_w;
+  std::string onnx_pool_type;
+  if (OpType() == "max_pool3d_with_index") {
+    onnx_pool_type = "MaxPool";
+  } else {
+    auto iter = op_mapper_.find(pooling_type_);
+    onnx_pool_type = iter->second[0];
+  }
+  std::shared_ptr<ONNX_NAMESPACE::NodeProto>* node_ptr;
+  auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                 P2ODataType::FP32);
+  auto node = helper_->MakeNode(onnx_pool_type, {input});
+  helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                    output_info[0].dtype);
+  std::vector<int64_t> kernel_size = {kernel_d, kernel_h, kernel_w};
+  AddAttribute(node, "kernel_shape", kernel_size);
+  std::vector<int64_t> strides = {stride_d, stride_h, stride_w};
+  AddAttribute(node, "strides", strides);
+
+  if (helper_->GetOpsetVersion() > 10) {
+    AddAttribute(node, "ceil_mode", static_cast<int64_t>(ceil_mode_));
+  }
+
+  std::string auto_pad = "NOTSET";
+  if (padding_algorithm_ == "SAME") {
+    auto_pad = "SAME_UPPER";
+  } else if (padding_algorithm_ == "VALID") {
+    auto_pad = "VALID";
+  }
+  AddAttribute(node, "auto_pad", auto_pad);
+  if (pooling_type_ == "avg") {
+    AddAttribute(node, "count_include_pad", static_cast<int64_t>(exclusive_));
+  }
+}
+
+void Pool3dMapper::NoAdaptivePool(const std::vector<TensorInfo>& input_info,
+                                  const std::vector<TensorInfo>& output_info) {
+  std::vector<int64_t> input_shape = input_info[0].shape;
+  if (pads_.size() == 3) {
+    pads_.push_back(pads_[0]);
+    pads_.push_back(pads_[1]);
+    pads_.push_back(pads_[2]);
+  } else if (pads_.size() == 6) {
+    std::vector<int64_t> index = {0, 2, 4, 1, 3, 5};
+    std::vector<int64_t> copy = pads_;
+    for (auto i = 0; i < index.size(); ++i) {
+      pads_[i] = copy[index[i]];
+    }
+  }
+  if (input_shape[2] > 0 && input_shape[2] + pads_[0] < k_size_[0]) {
+    k_size_[0] = input_shape[2] + pads_[0];
+  }
+  if (input_shape[3] > 0 && input_shape[3] + pads_[1] < k_size_[1]) {
+    k_size_[1] = input_shape[3] + pads_[1];
+  }
+  if (input_shape[4] > 0 && input_shape[4] + pads_[2] < k_size_[2]) {
+    k_size_[2] = input_shape[4] + pads_[2];
+  }
+
+  int64_t max_ksize = *std::max_element(std::begin(k_size_), std::end(k_size_));
+  int64_t max_pads = *std::max_element(std::begin(pads_), std::end(pads_));
+  auto input_x = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+  if (max_ksize <= max_pads) {
+    std::vector<int64_t> onnx_paddings = {0, 0, pads_[0], pads_[1], pads_[2],
+                                          0, 0, pads_[3], pads_[4], pads_[5]};
+    std::vector<std::string> inputs_names = {input_x};
+    if (helper_->GetOpsetVersion() >= 11) {
+      std::string paddings_node =
+          helper_->Constant(GetOnnxDtype(P2ODataType::INT64), onnx_paddings);
+      inputs_names.push_back(paddings_node);
+      std::vector<float> val = {0.0};
+      std::string val_node =
+          helper_->Constant(GetOnnxDtype(P2ODataType::FP32), val);
+      inputs_names.push_back(val_node);
+    }
+    auto node = helper_->MakeNode("Pad", inputs_names);
+    std::string mode = "constant";
+    AddAttribute(node, "mode", mode);
+    if (helper_->GetOpsetVersion() < 11) {
+      AddAttribute(node, "pads", onnx_paddings);
+      float val = 0.0;
+      AddAttribute(node, "value", val);
+    }
+    input_x = node->output(0);
+    pads_.clear();
+    pads_.resize(6, 0);
+  }
+  std::string onnx_pool_type;
+  if (OpType() == "max_pool3d_with_index") {
+    onnx_pool_type = "MaxPool";
+  } else {
+    auto iter = op_mapper_.find(pooling_type_);
+    onnx_pool_type = iter->second[0];
+  }
+  auto node = helper_->MakeNode(onnx_pool_type, {input_x});
+  helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                    output_info[0].dtype);
+
+  AddAttribute(node, "kernel_shape", k_size_);
+  AddAttribute(node, "strides", strides_);
+  std::string auto_pad = "NOTSET";
+  if (padding_algorithm_ == "SAME") {
+    auto_pad = "SAME_UPPER";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else if (padding_algorithm_ == "VALID") {
+    auto_pad = "VALID";
+    AddAttribute(node, "auto_pad", auto_pad);
+  } else {
+    AddAttribute(node, "pads", pads_);
+  }
+  if (OpType() != "max_pool3d_with_index" && helper_->GetOpsetVersion() >= 10) {
+    AddAttribute(node, "ceil_mode", static_cast<int64_t>(ceil_mode_));
+  }
+  if (pooling_type_ == "avg") {
+    AddAttribute(node, "count_include_pad", static_cast<int64_t>(exclusive_));
+  }
+}
+
+int32_t Pool3dMapper::GetMinOpset(bool verbose) {
+  // NHWC is not supported
+  if (data_format_ == "NDHWC") {
+    Error() << "NDHWC format is not supported." << std::endl;
+    return -1;
+  }
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  if (global_pooling_ ||
+      (k_size_[0] == 1 && k_size_[1] == 1 && k_size_[2] == 1)) {
+    if (ceil_mode_) {
+      Logger(verbose, 10) << "While ceil_model is True, " << RequireOpset(10)
+                          << std::endl;
+      return 10;
+    }
+    return 7;
+  }
+
+  if (adaptive_) {
+    for (auto one_input : input_info) {
+      for (auto i = 2; i < one_input.shape.size(); ++i) {
+        if (one_input.shape[i] == -1) {
+          Error() << "Adaptive only support static input shape." << std::endl;
+          return -1;
+        }
+      }
+    }
+    int64_t input_d = input_info[0].shape[2];
+    int64_t input_h = input_info[0].shape[3];
+    int64_t input_w = input_info[0].shape[4];
+    int64_t output_d = output_info[0].shape[2];
+    int64_t output_h = output_info[0].shape[3];
+    int64_t output_w = output_info[0].shape[4];
+    if (!IsSameSpan(input_h, output_h) || !IsSameSpan(input_w, output_w) ||
+        !IsSameSpan(input_d, output_d)) {
+      Error() << "Cannot convert adaptive pool with input_size: " << input_d
+              << " " << input_h << " " << input_w
+              << " output_size: " << output_d << " " << output_h << " "
+              << output_w << std::endl;
+      return -1;
+    }
+  }
+  if (OpType() == "max_pool3d_with_index") {
+    return 9;
+  }
+  auto iter = op_mapper_.find(pooling_type_);
+  if (op_mapper_.end() == iter) {
+    Error() << "Cannot find " << pooling_type_ << " in pool op_mapper."
+            << std::endl;
+    return -1;
+  }
+
+  if (ceil_mode_) {
+    Logger(verbose, 10) << "While ceil_model is True, " << RequireOpset(10)
+                        << std::endl;
+    return 10;
+  }
+  return 7;
+}
+
+void Pool3dMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  bool is_1x1_kernel = true;
+  for (auto i : k_size_) {
+    if (i != 1) {
+      is_1x1_kernel = false;
+    }
+  }
+
+  if (global_pooling_ || (adaptive_ && is_1x1_kernel)) {
+    std::string onnx_pool_type;
+    if (OpType() == "max_pool3d_with_index") {
+      onnx_pool_type = "GlobalMaxPool";
+    } else {
+      auto iter = op_mapper_.find(pooling_type_);
+      onnx_pool_type = iter->second[1];
+    }
+    auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+    auto output = helper_->MakeNode(onnx_pool_type, {input})->output(0);
+    helper_->AutoCast(output, output_info[0].name, P2ODataType::FP32,
+                      output_info[0].dtype);
+  } else if (adaptive_) {
+    AdaptivePool(input_info, output_info);
+  } else {
+    NoAdaptivePool(input_info, output_info);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/pool3d.h b/paddle2onnx/mapper/nn/pool3d.h
new file mode 100644
index 0000000000..9be0c901df
--- /dev/null
+++ b/paddle2onnx/mapper/nn/pool3d.h
@@ -0,0 +1,66 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Pool3dMapper : public Mapper {
+ public:
+  Pool3dMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    op_mapper_["max"] = {"MaxPool", "GlobalMaxPool"};
+    op_mapper_["avg"] = {"AveragePool", "GlobalAveragePool"};
+    GetAttr("global_pooling", &global_pooling_);
+    GetAttr("adaptive", &adaptive_);
+    GetAttr("strides", &strides_);
+    GetAttr("paddings", &pads_);
+    GetAttr("ksize", &k_size_);
+    if (OpType() != "max_pool3d_with_index") {
+      GetAttr("pooling_type", &pooling_type_);
+      GetAttr("data_format", &data_format_);
+      GetAttr("ceil_mode", &ceil_mode_);
+      GetAttr("padding_algorithm", &padding_algorithm_);
+      GetAttr("exclusive", &exclusive_);
+      exclusive_ = !exclusive_;
+    }
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  bool IsSameSpan(const int64_t& in_size, const int64_t& out_size);
+  void AdaptivePool(const std::vector<TensorInfo>& input_info,
+                    const std::vector<TensorInfo>& output_info);
+  void NoAdaptivePool(const std::vector<TensorInfo>& input_info,
+                      const std::vector<TensorInfo>& output_info);
+  bool ceil_mode_;
+  bool global_pooling_;
+  bool adaptive_;
+  bool exclusive_;
+  std::string data_format_;
+  std::string pooling_type_;
+  std::string padding_algorithm_;
+  std::vector<int64_t> k_size_;
+  std::vector<int64_t> pads_;
+  std::vector<int64_t> strides_;
+  std::map<std::string, std::vector<std::string>> op_mapper_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/rnn.cc b/paddle2onnx/mapper/nn/rnn.cc
new file mode 100644
index 0000000000..1c6f609fb0
--- /dev/null
+++ b/paddle2onnx/mapper/nn/rnn.cc
@@ -0,0 +1,156 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/rnn.h"
+#include <cmath>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(rnn, RnnMapper)
+
+int32_t RnnMapper::GetMinOpset(bool verbose) {
+  return 7;
+}
+
+std::string RnnMapper::ReformWeight(const std::string& weight, const int64_t& size, const std::vector<int64_t>& perm) {
+  std::vector<std::string> items;
+  for (size_t i = 0; i < perm.size(); i += 2) {
+    auto item = helper_->Slice(weight, {1}, {perm[i] * size}, {perm[i + 1] * size});
+    items.push_back(item);
+  }
+  return helper_->Concat(items, 1);
+}
+
+std::vector<std::string> RnnMapper::MakeParamInputs(int64_t layer_index) {
+  auto weight_list_info = GetInput("WeightList");
+  int64_t bidirect_len = is_bidirec_ ? 4 : 2;
+  int64_t all_layer_param_len = weight_list_info.size();
+  int64_t single_layer_param_len = std::floor(all_layer_param_len / num_layers_);
+  int64_t weight_start_idx = layer_index * bidirect_len;
+  int64_t weight_end_idx = weight_start_idx + bidirect_len;
+  int64_t bias_start_idx = weight_start_idx + std::floor(all_layer_param_len / 2);
+  int64_t bias_end_idx = bias_start_idx + bidirect_len;
+
+  std::vector<std::string> unsqueezed_weights;
+  for (auto i = weight_start_idx; i < weight_end_idx; ++i) {
+    unsqueezed_weights.push_back(helper_->Unsqueeze(weight_list_info[i].name, {0}));
+  }
+  for (auto i = bias_start_idx; i < bias_end_idx; ++i) {
+    unsqueezed_weights.push_back(helper_->Unsqueeze(weight_list_info[i].name, {0}));
+  }
+  
+  std::vector<std::string> input_weight;
+  std::vector<std::string> hidden_weight;
+  for (size_t i = 0; i < bidirect_len; i += 2) {
+    input_weight.push_back(unsqueezed_weights[i]);
+  }
+  for (size_t i = 1; i < bidirect_len; i += 2) {
+    hidden_weight.push_back(unsqueezed_weights[i]);
+  }
+  std::vector<std::string> input_bias;
+  std::vector<std::string> hidden_bias;
+  for (size_t i = bidirect_len; i < 2 * bidirect_len; i += 2) {
+    input_bias.push_back(unsqueezed_weights[i]);
+  }
+  for (size_t i = bidirect_len + 1; i < 2 * bidirect_len; i += 2) {
+    hidden_bias.push_back(unsqueezed_weights[i]);
+  }
+
+  auto input_weight_tensor = helper_->Concat(input_weight, 0);
+  auto hidden_weight_tensor = helper_->Concat(hidden_weight, 0);
+  auto input_bias_tensor = helper_->Concat(input_bias, 0);
+  auto hidden_bias_tensor = helper_->Concat(hidden_bias, 0);
+
+  std::vector<int64_t> reform_permutation;
+  if (mode_ == "LSTM") {
+    std::vector<int64_t> perm({0, 1, 3, 4, 1, 3});
+    reform_permutation.assign(perm.begin(), perm.end());
+  } else if (mode_ == "GRU") {
+    std::vector<int64_t> perm({1, 2, 0, 1, 2, 3});
+    reform_permutation.assign(perm.begin(), perm.end());
+  }
+  input_weight_tensor = ReformWeight(input_weight_tensor, hidden_size_, reform_permutation);
+  hidden_weight_tensor = ReformWeight(hidden_weight_tensor, hidden_size_, reform_permutation);
+  input_bias_tensor = ReformWeight(input_bias_tensor, hidden_size_, reform_permutation);
+  hidden_bias_tensor = ReformWeight(hidden_bias_tensor, hidden_size_, reform_permutation);
+  
+  std::vector<std::string> outputs;
+  outputs.push_back(input_weight_tensor);
+  outputs.push_back(hidden_weight_tensor);
+  outputs.push_back(helper_->Concat({input_bias_tensor, hidden_bias_tensor}, 1));
+  outputs.push_back("");
+  return outputs;
+}
+
+std::vector<std::string> RnnMapper::MakeInitParamInputs(int64_t layer_index) {
+  std::vector<std::string> outputs;
+  auto prestate_info = GetInput("PreState");
+  int64_t bidirect_len = is_bidirec_ ? 2 : 1;
+  auto init_h = helper_->Slice(prestate_info[0].name, {0}, {layer_index * bidirect_len}, {layer_index * bidirect_len + bidirect_len});
+  outputs.push_back(init_h);
+  if (mode_ == "GRU") {
+    return outputs;
+  }
+  auto init_c = helper_->Slice(prestate_info[1].name, {0}, {layer_index * bidirect_len}, {layer_index * bidirect_len + bidirect_len});
+  outputs.push_back(init_c);
+  return outputs;
+}
+
+void RnnMapper::Opset7() {
+  auto input_info = GetInput("Input");
+  auto state_info = GetOutput("State");
+  auto out_info = GetOutput("Out");
+  auto input = input_info[0].name;
+  if (mode_ == "LSTM") {
+    std::string h_out = "";
+    std::string c_out = "";
+    for (auto i = 0; i < num_layers_; ++i) {
+      auto param_inputs = MakeParamInputs(i);
+      auto init_param_inputs = MakeInitParamInputs(i);
+      std::vector<std::string> inputs({input});
+      inputs.insert(inputs.end(), param_inputs.begin(), param_inputs.end());
+      inputs.insert(inputs.end(), init_param_inputs.begin(), init_param_inputs.end());
+      auto node = helper_->MakeNode("LSTM", inputs, 3);
+      std::string direction = is_bidirec_ ? "bidirectional" : "forward";
+      AddAttribute(node, "direction", direction);
+      AddAttribute(node, "hidden_size", hidden_size_);
+      input = helper_->Transpose(node->output(0), {0, 2, 1, 3});
+      input = helper_->Reshape(input, {0, 0, -1});
+      h_out = node->output(1);
+      c_out = node->output(2);
+    }
+    helper_->MakeNode("Identity", {h_out}, {state_info[0].name});
+    helper_->MakeNode("Identity", {c_out}, {state_info[1].name});
+    helper_->MakeNode("Identity", {input}, {out_info[0].name});
+  } else if (mode_ == "GRU") {
+    std::string h_out = "";
+    for (auto i = 0; i < num_layers_; ++i) {
+      auto param_inputs = MakeParamInputs(i);
+      auto init_param_inputs = MakeInitParamInputs(i);
+      std::vector<std::string> inputs({input});
+      inputs.insert(inputs.end(), param_inputs.begin(), param_inputs.end());
+      inputs.insert(inputs.end(), init_param_inputs.begin(), init_param_inputs.end());
+      auto node = helper_->MakeNode("GRU", inputs, 2);
+      std::string direction = is_bidirec_ ? "bidirectional" : "forward";
+      AddAttribute(node, "direction", direction);
+      AddAttribute(node, "hidden_size", hidden_size_);
+      AddAttribute(node, "linear_before_reset", int64_t(1));
+      input = helper_->Transpose(node->output(0), {0, 2, 1, 3});
+      input = helper_->Reshape(input, {0, 0, -1});
+      h_out = node->output(1);
+    }
+    helper_->MakeNode("Identity", {h_out}, {state_info[0].name});
+    helper_->MakeNode("Identity", {input}, {out_info[0].name});
+  }
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/rnn.h b/paddle2onnx/mapper/nn/rnn.h
new file mode 100644
index 0000000000..b2ef24df1f
--- /dev/null
+++ b/paddle2onnx/mapper/nn/rnn.h
@@ -0,0 +1,58 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class RnnMapper : public Mapper {
+ public:
+  RnnMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    MarkAsExperimentalOp();
+    GetAttr("num_layers", &num_layers_);
+    GetAttr("input_size", &input_size_);
+    GetAttr("hidden_size", &hidden_size_);
+    GetAttr("seed", &seed_);
+    GetAttr("dropout_prob", &dropout_prob_);
+    GetAttr("mode", &mode_);
+    GetAttr("is_bidirec", &is_bidirec_);
+    if (HasAttr("is_test")) {
+      GetAttr("is_test", &is_test_);
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<std::string> MakeParamInputs(int64_t layer_index);
+  std::vector<std::string> MakeInitParamInputs(int64_t layer_index);
+  std::string ReformWeight(const std::string& weight, const int64_t& size, const std::vector<int64_t>& perm);
+  int64_t num_layers_;
+  int64_t input_size_;
+  int64_t hidden_size_;
+  int64_t seed_;
+  float dropout_prob_;
+  std::string mode_;
+  bool is_test_ = false;
+  bool is_bidirec_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/shape.cc b/paddle2onnx/mapper/nn/shape.cc
new file mode 100644
index 0000000000..d82a1d00f4
--- /dev/null
+++ b/paddle2onnx/mapper/nn/shape.cc
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/shape.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(shape, ShapeMapper)
+
+void ShapeMapper::Opset7() {
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Out");
+
+  auto shape_out = helper_->MakeNode("Shape", {input_info[0].name})->output(0);
+  helper_->AutoCast(shape_out, output_info[0].name, P2ODataType::INT64,
+                    output_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/shape.h b/paddle2onnx/mapper/nn/shape.h
new file mode 100644
index 0000000000..6fd1867c66
--- /dev/null
+++ b/paddle2onnx/mapper/nn/shape.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ShapeMapper : public Mapper {
+ public:
+  ShapeMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/softmax_with_cross_entropy.cc b/paddle2onnx/mapper/nn/softmax_with_cross_entropy.cc
new file mode 100644
index 0000000000..14c64e9d07
--- /dev/null
+++ b/paddle2onnx/mapper/nn/softmax_with_cross_entropy.cc
@@ -0,0 +1,126 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/nn/softmax_with_cross_entropy.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(softmax_with_cross_entropy, SoftmaxCrossEntropyLossMapper)
+
+int32_t SoftmaxCrossEntropyLossMapper::GetMinOpset(bool verbose) {
+  auto logits = GetInput("Logits");
+  std::vector<int64_t> logits_shape = logits[0].shape;
+  if (logits_shape.size() < 2) {
+    Error() << "SoftmaxCrossEntropyLoss in onnx not support 1D logits."
+            << std::endl;
+    return -1;
+  }
+  Logger(verbose, 12) << RequireOpset(12) << std::endl;
+  return 12;
+}
+
+void SoftmaxCrossEntropyLossMapper::Opset12() {
+  auto logits = GetInput("Logits");
+  auto labels = GetInput("Label");
+
+  auto loss = GetOutput("Loss");
+  auto softmax = GetOutput("Softmax");
+  std::vector<int64_t> logits_shape = logits[0].shape;
+  auto dim = logits[0].Rank();
+  if (axis_ < 0) {
+    axis_ += dim;
+  }
+  if (soft_label_) {
+    std::vector<int64_t> split;
+    split.resize(logits_shape[axis_], 1);
+    std::vector<int64_t> axes_val = {axis_};
+    std::string axes_node =
+        helper_->Constant(GetOnnxDtype(P2ODataType::INT64), axes_val);
+    if (axis_ == dim - 1) {
+      auto logsoftmax_node = helper_->MakeNode("LogSoftmax", {logits[0].name});
+      AddAttribute(logsoftmax_node, "axis", axis_);
+      helper_->MakeNode("Exp", {logsoftmax_node->output(0)}, {softmax[0].name});
+      auto mul_result = helper_->MakeNode(
+          "Mul", {logsoftmax_node->output(0), labels[0].name});
+      if (helper_->GetOpsetVersion() < 13) {
+        auto reducesum_node =
+            helper_->MakeNode("ReduceSum", {mul_result->output(0)});
+        AddAttribute(reducesum_node, "axes", axes_val);
+        helper_->MakeNode("Neg", {reducesum_node->output(0)}, {loss[0].name});
+      } else {
+        auto reducesum_node =
+            helper_->MakeNode("ReduceSum", {mul_result->output(0), axes_node});
+        helper_->MakeNode("Neg", {reducesum_node->output(0)}, {loss[0].name});
+      }
+    } else {
+      auto perm = Arange(0, dim);
+      perm[dim - 1] = axis_;
+      perm[axis_] = dim - 1;
+      auto output = helper_->Transpose(logits[0].name, perm);
+      auto logsoftmax_node = helper_->MakeNode("LogSoftmax", {output});
+      AddAttribute(logsoftmax_node, "axis", int64_t(-1));
+      auto transpose_logsoftmax_node =
+          helper_->Transpose(logsoftmax_node->output(0), perm);
+      helper_->MakeNode("Exp", {transpose_logsoftmax_node}, {softmax[0].name});
+      auto mul_result =
+          helper_->MakeNode("Mul", {transpose_logsoftmax_node, labels[0].name});
+      if (helper_->GetOpsetVersion() < 13) {
+        auto reducesum_node =
+            helper_->MakeNode("ReduceSum", {mul_result->output(0)});
+        AddAttribute(reducesum_node, "axes", axes_val);
+        helper_->MakeNode("Neg", {reducesum_node->output(0)}, {loss[0].name});
+      } else {
+        auto reducesum_node =
+            helper_->MakeNode("ReduceSum", {mul_result->output(0), axes_node});
+        helper_->MakeNode("Neg", {reducesum_node->output(0)}, {loss[0].name});
+      }
+    }
+  } else {
+    if (axis_ == 1) {
+      auto squeeze_node = helper_->Squeeze(labels[0].name, {axis_});
+      auto node = helper_->MakeNode("SoftmaxCrossEntropyLoss",
+                                    {logits[0].name, squeeze_node}, 2);
+      AddAttribute(node, "ignore_index", ignore_index_);
+      AddAttribute(node, "reduction", "none");
+      auto loss_node =
+          helper_->Unsqueeze(node->output(0), loss[0].name, {axis_});
+      // onnx output is log(softmax), but paddle output is softmax
+      helper_->MakeNode("Exp", {node->output(1)}, {softmax[0].name});
+    } else {
+      std::vector<int64_t> perm = Arange(0, dim);
+      perm[1] = axis_;
+      perm[axis_] = 1;
+      auto transpose_logits = helper_->MakeNode("Transpose", {logits[0].name});
+      AddAttribute(transpose_logits, "perm", perm);
+      auto transpose_labels = helper_->MakeNode("Transpose", {labels[0].name});
+      AddAttribute(transpose_labels, "perm", perm);
+      auto squeeze_labels = helper_->Squeeze(transpose_labels->output(0), {1});
+      auto node =
+          helper_->MakeNode("SoftmaxCrossEntropyLoss",
+                            {transpose_logits->output(0), squeeze_labels}, 2);
+      AddAttribute(node, "ignore_index", ignore_index_);
+      AddAttribute(node, "reduction", "none");
+      auto unsqueeze_node = helper_->Unsqueeze(node->output(0), {1});
+      auto revert_transpose_logits =
+          helper_->MakeNode("Transpose", {unsqueeze_node}, {loss[0].name});
+      AddAttribute(revert_transpose_logits, "perm", perm);
+      auto revert_transpose_softmax =
+          helper_->MakeNode("Transpose", {node->output(1)});
+      AddAttribute(revert_transpose_softmax, "perm", perm);
+      // onnx output is log(softmax), but paddle output is softmax
+      helper_->MakeNode("Exp", {revert_transpose_softmax->output(0)},
+                        {softmax[0].name});
+    }
+  }
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/nn/softmax_with_cross_entropy.h b/paddle2onnx/mapper/nn/softmax_with_cross_entropy.h
new file mode 100644
index 0000000000..960f978de6
--- /dev/null
+++ b/paddle2onnx/mapper/nn/softmax_with_cross_entropy.h
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class SoftmaxCrossEntropyLossMapper : public Mapper {
+ public:
+  SoftmaxCrossEntropyLossMapper(const PaddleParser& p, OnnxHelper* helper,
+                                int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+    GetAttr("soft_label", &soft_label_);
+    GetAttr("ignore_index", &ignore_index_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset12();
+
+ private:
+  int64_t axis_ = -1;
+  bool soft_label_ = false;
+  int64_t ignore_index_ = -100;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/onnx_helper.cc b/paddle2onnx/mapper/onnx_helper.cc
new file mode 100644
index 0000000000..6f0b0142f3
--- /dev/null
+++ b/paddle2onnx/mapper/onnx_helper.cc
@@ -0,0 +1,534 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/onnx_helper.h"
+
+#include <fstream>
+
+namespace paddle2onnx {
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const int64_t& value) {
+  for (int i = 0; i < node->attribute_size(); ++i) {
+    if (node->attribute(i).name() == name) {
+      node->mutable_attribute(i)->set_i(value);
+      node->mutable_attribute(i)->set_type(ONNX_NAMESPACE::AttributeProto::INT);
+      return;
+    }
+  }
+  auto attr = node->add_attribute();
+  attr->set_name(name);
+  attr->set_i(value);
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::INT);
+}
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const float& value) {
+  for (int i = 0; i < node->attribute_size(); ++i) {
+    if (node->attribute(i).name() == name) {
+      node->mutable_attribute(i)->set_f(value);
+      node->mutable_attribute(i)->set_type(
+          ONNX_NAMESPACE::AttributeProto::FLOAT);
+      return;
+    }
+  }
+  auto attr = node->add_attribute();
+  attr->set_name(name);
+  attr->set_f(value);
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::FLOAT);
+}
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const std::string& value) {
+  auto attr = node->add_attribute();
+  attr->set_name(name);
+  attr->set_s(value);
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::STRING);
+}
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const std::vector<int64_t>& values) {
+  auto attr = node->add_attribute();
+  attr->set_name(name);
+  for (auto& item : values) {
+    attr->add_ints(item);
+  }
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::INTS);
+}
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const std::vector<float>& values) {
+  auto attr = node->add_attribute();
+  attr->set_name(name);
+  for (auto& item : values) {
+    attr->add_floats(item);
+  }
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::FLOAT);
+}
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name,
+                  ONNX_NAMESPACE::TensorProto_DataType dtype) {
+  auto attr = node->add_attribute();
+  attr->set_name(name);
+  attr->set_i(static_cast<int>(dtype));
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::INT);
+}
+
+ONNX_NAMESPACE::TensorProto_DataType GetOnnxDtype(int32_t paddle_dtype) {
+  Assert((paddle_dtype >= 0 && paddle_dtype <= 6) || paddle_dtype == 20 ||
+             paddle_dtype == 21,
+         "Unknow paddle data type: " + std::to_string(paddle_dtype) +
+             " While call GetOnnxDtype.");
+  auto onnx_dtype = ONNX_NAMESPACE::TensorProto::FLOAT;
+  if (paddle_dtype == P2ODataType::BOOL) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::BOOL;
+  } else if (paddle_dtype == P2ODataType::INT8) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::INT8;
+  } else if (paddle_dtype == P2ODataType::INT16) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::INT16;
+  } else if (paddle_dtype == P2ODataType::INT32) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::INT32;
+  } else if (paddle_dtype == P2ODataType::INT64) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::INT64;
+  } else if (paddle_dtype == P2ODataType::FP16) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::FLOAT16;
+  } else if (paddle_dtype == P2ODataType::FP32) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::FLOAT;
+  } else if (paddle_dtype == P2ODataType::FP64) {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::DOUBLE;
+  } else {
+    onnx_dtype = ONNX_NAMESPACE::TensorProto::UINT8;
+  }
+  return onnx_dtype;
+}
+
+std::shared_ptr<ONNX_NAMESPACE::NodeProto> MakeConstant(const std::string& name,
+                                                        const Weight& weight) {
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  node->set_op_type("Constant");
+  node->add_output(name);
+  auto attr = node->add_attribute();
+  attr->set_name("value");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+  auto tensor = attr->mutable_t();
+  tensor->set_name(name);
+  auto onnx_dtype = GetOnnxDtype(weight.dtype);
+  tensor->set_data_type(onnx_dtype);
+  for (auto& dim : weight.shape) {
+    tensor->add_dims(dim);
+  }
+  tensor->set_raw_data(std::string(weight.buffer.data(), weight.buffer.size()));
+  return node;
+}
+
+// std::shared_ptr<ONNX_NAMESPACE::NodeProto> OnnxHelper::MakeConstant(
+//    const Weight& weight) {
+//  auto node_name = MapperHelper::Get()->GenName("auto.constant");
+//  return MakeConstant(node_name, weight);
+//}
+//
+// std::shared_ptr<ONNX_NAMESPACE::NodeProto> OnnxHelper::MakeConstant(
+//    const std::string& name, const Weight& weight) {
+//  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+//  node->set_op_type("Constant");
+//  node->add_output(name);
+//  auto attr = node->add_attribute();
+//  attr->set_name("value");
+//  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+//  auto tensor = attr->mutable_t();
+//  tensor->set_name(name);
+//  auto onnx_dtype = GetOnnxDtype(weight.dtype);
+//  tensor->set_data_type(onnx_dtype);
+//  for (auto& dim : weight.shape) {
+//    tensor->add_dims(dim);
+//  }
+//  tensor->set_raw_data(std::string(weight.buffer.data(),
+//  weight.buffer.size()));
+//  nodes.push_back(node);
+//  return node;
+//}
+
+std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto> MakeValueInfo(
+    const TensorInfo& info) {
+  auto value_info = std::make_shared<ONNX_NAMESPACE::ValueInfoProto>();
+  value_info->set_name(info.name);
+  auto type_proto = value_info->mutable_type();
+  auto tensor_type_proto = type_proto->mutable_tensor_type();
+  tensor_type_proto->set_elem_type(GetOnnxDtype(info.dtype));
+  auto shape = tensor_type_proto->mutable_shape();
+  for (auto& dim : info.shape) {
+    if (dim < 0) {
+      auto dynamic_dim_name = MapperHelper::Get()->GenName("DynamicDimension");
+      shape->add_dim()->set_dim_param(dynamic_dim_name);
+    } else {
+      shape->add_dim()->set_dim_value(dim);
+    }
+  }
+  return value_info;
+}
+
+std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto> OnnxHelper::MakeValueInfo(
+    const std::string& name, const int32_t& dtype,
+    std::vector<int64_t>& shape) {
+  auto value_info = std::make_shared<ONNX_NAMESPACE::ValueInfoProto>();
+  value_info->set_name(name);
+  auto type_proto = value_info->mutable_type();
+  auto tensor_type_proto = type_proto->mutable_tensor_type();
+  tensor_type_proto->set_elem_type(GetOnnxDtype(dtype));
+  auto shape_proto = tensor_type_proto->mutable_shape();
+  for (auto& dim : shape) {
+    if (dim < 0) {
+      auto dynamic_dim_name = MapperHelper::Get()->GenName("DynamicDimension");
+      shape_proto->add_dim()->set_dim_param(dynamic_dim_name);
+    } else {
+      shape_proto->add_dim()->set_dim_value(dim);
+    }
+  }
+  value_infos.push_back(value_info);
+  return value_info;
+}
+
+std::shared_ptr<ONNX_NAMESPACE::NodeProto> OnnxHelper::MakeNode(
+    const std::string& op_type, const std::vector<std::string>& inputs,
+    const std::vector<std::string>& outputs) {
+#ifdef PADDLE2ONNX_DEBUG
+  P2OLogger(true) << "ONNX Node: " << op_type << std::endl;
+#endif
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  auto node_name = MapperHelper::Get()->GenName(op_type);
+  node->set_name(node_name);
+  node->set_op_type(op_type);
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    node->add_input(inputs[i]);
+  }
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    node->add_output(outputs[i]);
+  }
+  if (op_type == "Reshape" && GetOpsetVersion() >= 14) {
+    AddAttribute(node, "allowzero", int64_t(0));
+  }
+
+  nodes.push_back(node);
+  return node;
+}
+
+std::shared_ptr<ONNX_NAMESPACE::NodeProto> OnnxHelper::MakeNode(
+    const std::string& op_type, const std::vector<std::string>& inputs,
+    int num_outputs) {
+#ifdef PADDLE2ONNX_DEBUG
+  P2OLogger(true) << "ONNX Node: " << op_type << std::endl;
+#endif
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  auto node_name = MapperHelper::Get()->GenName(op_type);
+  node->set_name(node_name);
+  node->set_op_type(op_type);
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    node->add_input(inputs[i]);
+  }
+  std::vector<std::string> outputs;
+  for (auto i = 0; i < num_outputs; ++i) {
+    outputs.push_back(MapperHelper::Get()->GenName(op_type));
+  }
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    node->add_output(outputs[i]);
+  }
+  if (op_type == "Reshape" && GetOpsetVersion() >= 14) {
+    AddAttribute(node, "allowzero", int64_t(0));
+  }
+  nodes.push_back(node);
+  return node;
+}
+
+std::string OnnxHelper::AutoCast(const std::string& input,
+                                 int32_t input_paddle_dtype,
+                                 int32_t to_paddle_dtype) {
+  std::string output = MapperHelper::Get()->GenName("auto.cast");
+  if (input_paddle_dtype == to_paddle_dtype) {
+    MakeNode("Identity", {input}, {output});
+    return output;
+  }
+  auto cast_node = MakeNode("Cast", {input}, {output});
+  AddAttribute(cast_node, "to", GetOnnxDtype(to_paddle_dtype));
+  return cast_node->output(0);
+}
+
+std::string OnnxHelper::AutoCast(const std::string& input,
+                                 const std::string& output,
+                                 int32_t input_paddle_dtype,
+                                 int32_t to_paddle_dtype) {
+  if (input_paddle_dtype == to_paddle_dtype) {
+    auto node = MakeNode("Identity", {input}, {output});
+    return output;
+  }
+  auto cast_node = MakeNode("Cast", {input}, {output});
+  AddAttribute(cast_node, "to", GetOnnxDtype(to_paddle_dtype));
+  return cast_node->output(0);
+}
+
+std::string OnnxHelper::ConcatIndices(const std::vector<TensorInfo>& indices) {
+  std::vector<std::string> vars;
+  // make sure all the indices be 1-D tensor
+  for (size_t i = 0; i < indices.size(); ++i) {
+    std::string var = indices[i].name;
+    if (indices[i].Rank() != 1) {
+      var = Reshape(indices[i].name, {1});
+    }
+    vars.push_back(var);
+  }
+  // make sure all the indices be int64
+  for (size_t i = 0; i < indices.size(); ++i) {
+    if (indices[i].dtype != P2ODataType::INT64) {
+      auto node = MakeNode("Cast", {vars[i]});
+      AddAttribute(node, "to", ONNX_NAMESPACE::TensorProto::INT64);
+      vars[i] = node->output(0);
+    }
+  }
+  // concat and return
+  if (vars.size() > 1) {
+    return Concat(vars, 0);
+  }
+  return vars[0];
+}
+
+std::string OnnxHelper::Clip(const std::string& input,
+                             const std::string& output, const float& min,
+                             const float& max, const int32_t& in_dtype) {
+  // onnxruntime only supports float input
+  std::string input_name = AutoCast(input, in_dtype, P2ODataType::FP32);
+  if (opset_version < 11) {
+    auto node = MakeNode("Clip", {input_name});
+    AddAttribute(node, "max", max);
+    AddAttribute(node, "min", min);
+    auto res = AutoCast(node->output(0), output, P2ODataType::FP32, in_dtype);
+    return res;
+  } else {
+    int32_t dtype = P2ODataType::FP32;
+    std::string min_name = Constant({}, GetOnnxDtype(dtype), min);
+    std::string max_name;
+    max_name = Constant({}, GetOnnxDtype(dtype), max);
+    auto node = MakeNode("Clip", {input_name, min_name, max_name});
+    auto res = AutoCast(node->output(0), {output}, P2ODataType::FP32, in_dtype);
+    return res;
+  }
+}
+
+std::string OnnxHelper::Clip(const std::string& input, const float& min,
+                             const float& max, const int32_t& in_dtype) {
+  std::string output = MapperHelper::Get()->GenName("helper.clip");
+  return Clip(input, output, min, max, in_dtype);
+}
+
+std::string OnnxHelper::Squeeze(const std::string& input,
+                                const std::string& output,
+                                const std::vector<int64_t>& axes) {
+  if (axes.size() == 0) {
+    auto node = MakeNode("Squeeze", {input}, {output});
+  } else {
+    if (opset_version < 13) {
+      auto node = MakeNode("Squeeze", {input}, {output});
+      AddAttribute(node, "axes", axes);
+    } else {
+      auto axes_node = Constant(ONNX_NAMESPACE::TensorProto::INT64, axes);
+      auto node = MakeNode("Squeeze", {input, axes_node}, {output});
+    }
+  }
+  return output;
+}
+
+std::string OnnxHelper::Squeeze(const std::string& input,
+                                const std::vector<int64_t>& axes) {
+  std::string output = MapperHelper::Get()->GenName("helper.squeeze");
+  return Squeeze(input, output, axes);
+}
+
+std::string OnnxHelper::Unsqueeze(const std::string& input,
+                                  const std::string& output,
+                                  const std::vector<int64_t>& axes) {
+  Assert(axes.size() >= 0, "OnnxHelper::Unsqueeze Size of axes should > 0");
+  for (auto& item : axes) {
+    Assert(item >= 0,
+           "OnnxHelper::Unsqueeze All the elements in axes should >= 0");
+  }
+  if (opset_version < 13) {
+    auto node = MakeNode("Unsqueeze", {input}, {output});
+    AddAttribute(node, "axes", axes);
+  } else {
+    auto axes_node = Constant(ONNX_NAMESPACE::TensorProto::INT64, axes);
+    auto node = MakeNode("Unsqueeze", {input, axes_node}, {output});
+  }
+  return output;
+}
+
+std::string OnnxHelper::Unsqueeze(const std::string& input,
+                                  const std::vector<int64_t>& axes) {
+  std::string output = MapperHelper::Get()->GenName("helper.unsqueeze");
+  return Unsqueeze(input, output, axes);
+}
+
+std::string OnnxHelper::Reshape(const std::string& input,
+                                const std::string& output,
+                                const std::vector<int64_t>& shape) {
+  if (opset_version < 6) {
+    auto node = MakeNode("Reshape", {input}, {output});
+    AddAttribute(node, "shape", shape);
+  } else {
+    auto shape_node = Constant(ONNX_NAMESPACE::TensorProto::INT64, shape);
+    auto node = MakeNode("Reshape", {input, shape_node}, {output});
+    if (opset_version >= 14) {
+      AddAttribute(node, "allowzero", int64_t(0));
+    }
+  }
+  return output;
+}
+
+std::string OnnxHelper::Reshape(const std::string& input,
+                                const std::vector<int64_t>& shape) {
+  std::string output = MapperHelper::Get()->GenName("helper.reshape");
+  return Reshape(input, output, shape);
+}
+
+std::string OnnxHelper::Flatten(const std::string& input,
+                                const std::string& output) {
+  return Reshape(input, output, std::vector<int64_t>(1, -1));
+}
+
+std::string OnnxHelper::Flatten(const std::string& input) {
+  std::string output = MapperHelper::Get()->GenName("helper.flatten");
+  return Flatten(input, output);
+}
+
+std::string OnnxHelper::Slice(const std::string& input,
+                              const std::string& output,
+                              const std::vector<int64_t>& axes,
+                              const std::vector<int64_t>& starts,
+                              const std::vector<int64_t>& ends) {
+  if (opset_version < 10) {
+    auto node = MakeNode("Slice", {input}, {output});
+    AddAttribute(node, "axes", axes);
+    AddAttribute(node, "starts", starts);
+    AddAttribute(node, "ends", ends);
+  } else {
+    auto axes_node = Constant(ONNX_NAMESPACE::TensorProto::INT64, axes);
+    auto starts_node = Constant(ONNX_NAMESPACE::TensorProto::INT64, starts);
+    auto ends_node = Constant(ONNX_NAMESPACE::TensorProto::INT64, ends);
+    auto node =
+        MakeNode("Slice", {input, starts_node, ends_node, axes_node}, {output});
+  }
+  return output;
+}
+
+std::string OnnxHelper::Slice(const std::string& input,
+                              const std::vector<int64_t>& axes,
+                              const std::vector<int64_t>& starts,
+                              const std::vector<int64_t>& ends) {
+  std::string output = MapperHelper::Get()->GenName("helper.slice");
+  return Slice(input, output, axes, starts, ends);
+}
+
+std::string OnnxHelper::Concat(const std::vector<std::string>& input,
+                               const std::string& output, int64_t axis) {
+  auto node = MakeNode("Concat", input, {output});
+  AddAttribute(node, "axis", axis);
+  return output;
+}
+
+std::string OnnxHelper::Concat(const std::vector<std::string>& input,
+                               int64_t axis) {
+  auto output = MapperHelper::Get()->GenName("helper.concat");
+  return Concat(input, output, axis);
+}
+
+std::string OnnxHelper::Transpose(const std::string& input,
+                                  const std::string& output,
+                                  const std::vector<int64_t>& perm) {
+  auto node = MakeNode("Transpose", {input}, {output});
+  AddAttribute(node, "perm", perm);
+  return output;
+}
+
+std::string OnnxHelper::Transpose(const std::string& input,
+                                  const std::vector<int64_t>& perm) {
+  auto output = MapperHelper::Get()->GenName("helper.transpose");
+  return Transpose(input, output, perm);
+}
+
+std::vector<std::string> OnnxHelper::Split(
+    const std::string& input, const std::vector<std::string>& outputs,
+    const std::vector<int64_t>& split, int64_t axis) {
+  Assert(outputs.size() > 0 || split.size() > 0,
+         "OnnxHelper::Split requires the size of outputs or the size of split "
+         "> 0.");
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  auto node_name = MapperHelper::Get()->GenName("Split");
+  node->set_name(node_name);
+  node->set_op_type("Split");
+  node->add_input(input);
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    node->add_output(outputs[i]);
+  }
+  AddAttribute(node, "axis", axis);
+  if (split.size() > 0) {
+    Assert(outputs.size() == split.size(),
+           "OnnxHelper::Split While size of outputs and the size of split both "
+           "> 0, their size must be same.");
+    if (opset_version < 13) {
+      AddAttribute(node, "split", split);
+    } else {
+      auto split_const = Constant(ONNX_NAMESPACE::TensorProto::INT64, split);
+      node->add_input(split_const);
+    }
+  }
+  nodes.push_back(node);
+  return outputs;
+}
+
+std::vector<std::string> OnnxHelper::Split(const std::string& input,
+                                           const std::vector<int64_t>& split,
+                                           int64_t axis) {
+  Assert(split.size() > 0,
+         "OnnxHelper::Split requires the size of parameter split > 0.");
+  std::vector<std::string> outputs(split.size());
+  for (size_t i = 0; i < split.size(); ++i) {
+    outputs[i] = MapperHelper::Get()->GenName("helper.split");
+  }
+  return Split(input, outputs, split, axis);
+}
+std::vector<std::string> OnnxHelper::DtypeAlignment(
+    const std::vector<TensorInfo>& input_info, int32_t* out_dtype) {
+  Assert(input_info.size() > 0,
+         "OnnxHelper::DtypeAlignment requires the size of input info > 0.");
+  std::vector<int32_t> input_dtypes;
+  input_dtypes.reserve(input_info.size());
+  for (auto i = 0; i < input_info.size(); ++i) {
+    input_dtypes.push_back(input_info[i].dtype);
+  }
+  int32_t max_index = -1;
+  for (auto i : input_dtypes) {
+    if (i > max_index) {
+      max_index = i;
+    }
+  }
+  *out_dtype = max_index;
+  std::vector<std::string> casted_node;
+  casted_node.reserve(input_info.size());
+  for (auto i = 0; i < input_info.size(); ++i) {
+    std::string cast_name =
+        AutoCast(input_info[i].name, input_info[i].dtype, max_index);
+    casted_node.push_back(cast_name);
+  }
+  return casted_node;
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/onnx_helper.h b/paddle2onnx/mapper/onnx_helper.h
new file mode 100644
index 0000000000..5958eb38f6
--- /dev/null
+++ b/paddle2onnx/mapper/onnx_helper.h
@@ -0,0 +1,561 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <onnx/onnx_pb.h>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/register_mapper.h"
+#include "paddle2onnx/parser/parser.h"
+
+namespace paddle2onnx {
+
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const int64_t& value);
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const float& value);
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const std::string& value);
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const std::vector<int64_t>& values);
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name, const std::vector<float>& values);
+void AddAttribute(std::shared_ptr<ONNX_NAMESPACE::NodeProto> node,
+                  const std::string& name,
+                  ONNX_NAMESPACE::TensorProto_DataType dtype);
+
+ONNX_NAMESPACE::TensorProto_DataType GetOnnxDtype(int32_t paddle_dtype);
+std::shared_ptr<ONNX_NAMESPACE::NodeProto> MakeConstant(const std::string& name,
+                                                        const Weight& weight);
+std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto> MakeValueInfo(
+    const TensorInfo& info);
+
+struct QuantizeInfo {
+ public:
+  std::vector<float> scale_;
+  std::vector<int64_t> zeros_;
+  std::string zeros_node_;
+  std::string scale_node_;
+  int64_t quantize_axis_;
+
+  QuantizeInfo() {}
+  QuantizeInfo(const std::vector<float>& scale,
+               const std::vector<int64_t>& zeros, const std::string& scale_node,
+               const std::string& zeros_node, const int64_t& quantize_axis) {
+    zeros_node_ = zeros_node;
+    scale_node_ = scale_node;
+    quantize_axis_ = quantize_axis;
+    scale_.resize(scale.size());
+    memcpy(scale_.data(), scale.data(), scale.size() * sizeof(float));
+    zeros_.resize(zeros.size());
+    memcpy(zeros_.data(), zeros.data(), zeros.size() * sizeof(int64_t));
+  }
+};
+
+class OnnxHelper {
+ public:
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> nodes;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>> value_infos;
+  int32_t opset_version = 7;
+  // Use updated_params to store params that were changed during conversion
+  std::map<std::string, Weight> updated_params;
+  // Use quantize_info to record quantization-related information, scale and
+  // zero information corresponding to each tensor
+  std::map<std::string, QuantizeInfo> quantize_info;
+
+  void Clear() { nodes.clear(); }
+
+  void SetOpsetVersion(int32_t op_v) { opset_version = op_v; }
+
+  int32_t GetOpsetVersion() { return opset_version; }
+
+  template <typename T>
+  bool TryGetTensorValue(const std::string& name, std::vector<T>* value);
+
+  std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto> MakeValueInfo(
+      const std::string& name, const int32_t& dtype,
+      std::vector<int64_t>& shape);
+
+  std::shared_ptr<ONNX_NAMESPACE::NodeProto> MakeNode(
+      const std::string& op_type, const std::vector<std::string>& inputs,
+      const std::vector<std::string>& outputs);
+  // we use this function to generate some temporary node
+  // we do not need to define the outputs, because the outputs
+  // is generate by MapperHelper, which will make sure there's no
+  // name confict problem
+  // the parameter `num_outputs` will define the number of output names
+  std::shared_ptr<ONNX_NAMESPACE::NodeProto> MakeNode(
+      const std::string& op_type, const std::vector<std::string>& inputs,
+      int num_outputs = 1);
+
+  template <typename T>
+  std::string ConstOfShape(const std::string& input, const std::string& output,
+                           ONNX_NAMESPACE::TensorProto_DataType dtype, T value);
+  template <typename T>
+  std::string ConstOfShape(const std::string& input,
+                           ONNX_NAMESPACE::TensorProto_DataType dtype, T value);
+
+  std::string AutoCast(const std::string& input, int32_t input_paddle_dtype,
+                       int32_t to_paddle_dtype);
+  std::string AutoCast(const std::string& input, const std::string& output,
+                       int32_t input_paddle_dtype, int32_t to_paddle_dtype);
+
+  // Helper function for PaddlePaddle's shape tensor list inputs
+  // will cast all data type to int64
+  // will make sure all inputs to be 1-D tensor
+  // will concat them as output
+  std::string ConcatIndices(const std::vector<TensorInfo>& indices);
+  std::vector<std::string> DtypeAlignment(
+      const std::vector<TensorInfo>& input_info, int32_t* out_dtype);
+  std::string Clip(const std::string& input, const float& min, const float& max,
+                   const int32_t& in_dtype);
+  std::string Clip(const std::string& input, const std::string& output,
+                   const float& min, const float& max, const int32_t& in_dtype);
+  std::string Squeeze(const std::string& input,
+                      const std::vector<int64_t>& axes);
+  std::string Squeeze(const std::string& input, const std::string& output,
+                      const std::vector<int64_t>& axes);
+  std::string Unsqueeze(const std::string& input,
+                        const std::vector<int64_t>& axes);
+  std::string Unsqueeze(const std::string& input, const std::string& output,
+                        const std::vector<int64_t>& axes);
+  std::string Reshape(const std::string& input, const std::string& output,
+                      const std::vector<int64_t>& shape);
+  std::string Reshape(const std::string& input,
+                      const std::vector<int64_t>& shape);
+  std::string Flatten(const std::string& input, const std::string& output);
+  std::string Flatten(const std::string& input);
+  std::string Slice(const std::string& input, const std::string& output,
+                    const std::vector<int64_t>& axes,
+                    const std::vector<int64_t>& starts,
+                    const std::vector<int64_t>& ends);
+  std::string Slice(const std::string& input, const std::vector<int64_t>& axes,
+                    const std::vector<int64_t>& starts,
+                    const std::vector<int64_t>& ends);
+  std::string Concat(const std::vector<std::string>& input,
+                     const std::string& output, int64_t axis);
+  std::string Concat(const std::vector<std::string>& input, int64_t axis);
+
+  std::string Transpose(const std::string& input, const std::string& output,
+                        const std::vector<int64_t>& perm);
+  std::string Transpose(const std::string& input,
+                        const std::vector<int64_t>& perm);
+
+  std::vector<std::string> Split(const std::string& input,
+                                 const std::vector<std::string>& outputs,
+                                 const std::vector<int64_t>& split,
+                                 int64_t axis);
+  std::vector<std::string> Split(const std::string& input,
+                                 const std::vector<int64_t>& split,
+                                 int64_t axis);
+
+  template <typename T>
+  std::string Constant(const std::string& output,
+                       ONNX_NAMESPACE::TensorProto_DataType dtype,
+                       const std::vector<T>& value);
+  template <typename T>
+  std::string Constant(ONNX_NAMESPACE::TensorProto_DataType dtype,
+                       const std::vector<T>& value);
+  template <typename T>
+  std::string Constant(const std::string& output,
+                       const std::vector<int64_t>& shape,
+                       ONNX_NAMESPACE::TensorProto_DataType dtype, T value);
+  template <typename T>
+  std::string Constant(const std::vector<int64_t>& shape,
+                       ONNX_NAMESPACE::TensorProto_DataType dtype, T value);
+
+  template <typename T>
+  std::string Constant(const std::vector<int64_t>& shape,
+                       ONNX_NAMESPACE::TensorProto_DataType dtype,
+                       std::vector<T>& value);
+
+  template <typename T>
+  std::string Assign(const std::string& output,
+                     const ONNX_NAMESPACE::TensorProto_DataType& dtype,
+                     const std::vector<int64_t>& shape,
+                     const std::vector<T>& value);
+  template <typename T>
+  std::string Assign(const ONNX_NAMESPACE::TensorProto_DataType& dtype,
+                     const std::vector<int64_t>& shape,
+                     const std::vector<T>& value);
+};
+
+template <typename T>
+std::string OnnxHelper::Constant(const std::vector<int64_t>& shape,
+                                 ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                 std::vector<T>& value) {
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  node->set_op_type("Constant");
+  auto name = MapperHelper::Get()->GenName("const");
+  node->add_output(name);
+  auto attr = node->add_attribute();
+  attr->set_name("value");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+  auto tensor = attr->mutable_t();
+  tensor->set_name(name);
+
+  int numel = 1;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    tensor->add_dims(shape[i]);
+    numel *= shape[i];
+  }
+  Assert(numel == value.size(),
+         "numel and val number is not equal in Constant "
+         "function.");
+  tensor->set_data_type(dtype);
+  if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    std::vector<float> data;
+    data.reserve(numel);
+    for (auto& i : value) {
+      data.push_back(static_cast<float>(i));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    std::vector<double> data;
+    data.reserve(numel);
+    for (auto& i : value) {
+      data.push_back(static_cast<double>(i));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+    std::vector<int64_t> data;
+    data.reserve(numel);
+    for (auto& i : value) {
+      data.push_back(static_cast<int64_t>(i));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::BOOL) {
+    bool* data = new bool[numel];
+    for (size_t i = 0; i < numel; ++i) {
+      data[i] = static_cast<bool>(value[i]);
+    }
+    tensor->set_raw_data(std::string((const char*)(data), numel));
+    delete[] data;
+  } else {
+    Assert(false,
+           "Only support data type of BOOL/FLOAT/DOUBLE/INT64 in Constant "
+           "function.");
+  }
+  nodes.push_back(node);
+  return node->output(0);
+}
+
+template <typename T>
+std::string OnnxHelper::Constant(const std::string& output,
+                                 ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                 const std::vector<T>& value) {
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  node->set_op_type("Constant");
+  node->add_output(output);
+  auto attr = node->add_attribute();
+  attr->set_name("value");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+  auto tensor = attr->mutable_t();
+  tensor->set_name(output);
+
+  int numel = value.size();
+  tensor->add_dims(numel);
+  tensor->set_data_type(dtype);
+  if (value.size() == 0) {
+    nodes.push_back(node);
+    return output;
+  }
+  if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    std::vector<float> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<float>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    std::vector<double> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<double>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+    std::vector<int64_t> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<int64_t>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+    std::vector<int32_t> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<int32_t>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::BOOL) {
+    bool* data = new bool[numel];
+    for (size_t i = 0; i < numel; ++i) {
+      data[i] = static_cast<bool>(value[i]);
+    }
+    tensor->set_raw_data(std::string((const char*)(data), numel));
+    delete[] data;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT8) {
+    std::vector<int8_t> data;
+    data.reserve(numel);
+    for (auto& i : value) {
+      data.push_back(static_cast<int8_t>(i));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel));
+  } else {
+    Assert(false,
+           "Only support data type of BOOL/FLOAT/DOUBLE/INT32/INT64/INT8 in "
+           "Constant "
+           "function.");
+  }
+  nodes.push_back(node);
+  return output;
+}
+
+template <typename T>
+std::string OnnxHelper::Constant(ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                 const std::vector<T>& value) {
+  auto output = MapperHelper::Get()->GenName("helper.constant");
+  return Constant(output, dtype, value);
+}
+
+template <typename T>
+std::string OnnxHelper::Constant(const std::string& output,
+                                 const std::vector<int64_t>& shape,
+                                 ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                 T value) {
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  node->set_op_type("Constant");
+  node->add_output(output);
+  auto attr = node->add_attribute();
+  attr->set_name("value");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+  auto tensor = attr->mutable_t();
+  tensor->set_name(output);
+
+  int numel = 1;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    tensor->add_dims(shape[i]);
+    numel *= shape[i];
+  }
+  tensor->set_data_type(dtype);
+  if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    std::vector<float> data(numel, static_cast<float>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    std::vector<double> data(numel, static_cast<double>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+    std::vector<int64_t> data(numel, static_cast<int64_t>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+    std::vector<int32_t> data(numel, static_cast<int32_t>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT8) {
+    std::vector<int8_t> data(numel, static_cast<int8_t>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::BOOL) {
+    bool* data = new bool[numel];
+    for (size_t i = 0; i < numel; ++i) {
+      data[i] = static_cast<bool>(value);
+    }
+    tensor->set_raw_data(std::string((const char*)(data), numel));
+    delete[] data;
+  } else {
+    Assert(
+        false,
+        "Only support data type of BOOL/FLOAT/DOUBLE/INT32/INT64 in Constant "
+        "function.");
+  }
+  nodes.push_back(node);
+  return output;
+}
+
+template <typename T>
+std::string OnnxHelper::Constant(const std::vector<int64_t>& shape,
+                                 ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                 T value) {
+  auto output = MapperHelper::Get()->GenName("helper.constant");
+  return Constant(output, shape, dtype, value);
+}
+
+template <typename T>
+std::string OnnxHelper::ConstOfShape(const std::string& input,
+                                     ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                     T value) {
+  auto output = MapperHelper::Get()->GenName("helper.constofshape");
+  return ConstOfShape(input, output, dtype, value);
+}
+
+template <typename T>
+std::string OnnxHelper::ConstOfShape(const std::string& input,
+                                     const std::string& output,
+                                     ONNX_NAMESPACE::TensorProto_DataType dtype,
+                                     T value) {
+  auto node = MakeNode("ConstantOfShape", {input}, {output});
+  auto attr = node->add_attribute();
+  attr->set_name("value");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+  auto tensor = attr->mutable_t();
+  tensor->set_name("tensor_value");
+  std::vector<int64_t> shape = {1};
+  int numel = 1;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    tensor->add_dims(shape[i]);
+    numel *= shape[i];
+  }
+  tensor->set_data_type(dtype);
+  if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    std::vector<float> data(numel, static_cast<float>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    std::vector<double> data(numel, static_cast<double>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+    std::vector<int64_t> data(numel, static_cast<int64_t>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+    std::vector<int32_t> data(numel, static_cast<int32_t>(value));
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else {
+    Assert(false,
+           "Only support data type of FLOAT/DOUBLE/INT64/INT32 in ConstOfShape "
+           "function.");
+  }
+  return output;
+}
+
+template <typename T>
+std::string OnnxHelper::Assign(
+    const std::string& output,
+    const ONNX_NAMESPACE::TensorProto_DataType& dtype,
+    const std::vector<int64_t>& shape, const std::vector<T>& value) {
+  auto node = std::make_shared<ONNX_NAMESPACE::NodeProto>();
+  node->set_op_type("Constant");
+  node->add_output(output);
+  auto attr = node->add_attribute();
+  attr->set_name("value");
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+  auto tensor = attr->mutable_t();
+  tensor->set_name(output);
+
+  int numel = std::accumulate(std::begin(shape), std::end(shape), 1,
+                              std::multiplies<int64_t>());
+  Assert(numel == value.size(),
+         "Numel of value not satisfy the input shape while creating contant "
+         "tensor.");
+  for (size_t i = 0; i < shape.size(); ++i) {
+    tensor->add_dims(shape[i]);
+  }
+  tensor->set_data_type(dtype);
+  if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    std::vector<float> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<float>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    std::vector<double> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<double>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+    std::vector<int64_t> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<int64_t>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 8));
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+    std::vector<int32_t> data;
+    for (auto& item : value) {
+      data.push_back(static_cast<int32_t>(item));
+    }
+    tensor->set_raw_data(std::string((const char*)(data.data()), numel * 4));
+  } else {
+    Assert(false,
+           "Only support data type of FLOAT/DOUBLE/INT32/INT64 in Constant "
+           "function.");
+  }
+  nodes.push_back(node);
+  return output;
+}
+
+template <typename T>
+std::string OnnxHelper::Assign(
+    const ONNX_NAMESPACE::TensorProto_DataType& dtype,
+    const std::vector<int64_t>& shape, const std::vector<T>& value) {
+  auto output = MapperHelper::Get()->GenName("helper.constant");
+  return Assign(output, dtype, shape, value);
+}
+
+template <typename T>
+bool OnnxHelper::TryGetTensorValue(const std::string& name,
+                                   std::vector<T>* value) {
+  for (auto iter = nodes.begin(); iter != nodes.end(); iter++) {
+    auto node = *iter;
+    if (node->op_type() != "Constant") {
+      continue;
+    }
+    if (node->output(0) == name) {
+      for (auto i = 0; i < node->attribute_size(); i++) {
+        auto attr = node->attribute(i);
+        if (attr.name() == "value") {
+          auto tensor = attr.mutable_t();
+          auto dtype = tensor->data_type();
+          std::vector<int64_t> shape;
+          for (int64_t i = 0; i < tensor->dims_size(); i++) {
+            shape.push_back(tensor->dims(i));
+          }
+          int64_t nums = 1;
+          for (auto& i : shape) nums *= i;
+          value->resize(nums);
+          if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+            std::vector<int64_t> val(nums, 0);
+            memcpy(val.data(), tensor->raw_data().data(),
+                   nums * sizeof(int64_t));
+            value->assign(val.begin(), val.end());
+            return true;
+          } else if (dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+            std::vector<int32_t> val(nums, 0);
+            memcpy(val.data(), tensor->raw_data().data(),
+                   nums * sizeof(int32_t));
+            value->assign(val.begin(), val.end());
+            return true;
+          } else if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+            std::vector<float> val(nums, 0);
+            memcpy(val.data(), tensor->raw_data().data(), nums * sizeof(float));
+            value->assign(val.begin(), val.end());
+            return true;
+          } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+            std::vector<double> val(nums, 0);
+            memcpy(val.data(), tensor->raw_data().data(),
+                   nums * sizeof(double));
+            value->assign(val.begin(), val.end());
+            return true;
+          } else {
+            P2OLogger() << "[WARNING] OnnxHelper function TryGetTensorValue "
+                           "only support get int64_t/int32_t/float/double "
+                           "value from Constant now."
+                        << std::endl;
+            return false;
+          }
+        }
+      }
+    }
+  }
+  return false;
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/quantize/dequantize_linear.cc b/paddle2onnx/mapper/quantize/dequantize_linear.cc
new file mode 100644
index 0000000000..9bf7797fdc
--- /dev/null
+++ b/paddle2onnx/mapper/quantize/dequantize_linear.cc
@@ -0,0 +1,183 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/quantize/dequantize_linear.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(dequantize_linear, DequantizeLinearMapper)
+
+int32_t DequantizeLinearMapper::GetMinOpset(bool verbose) {
+  if (!IsConstantInput("Scale")) {
+    Error() << "Input `Scale` requires to be a constant tensor." << std::endl;
+    return -1;
+  }
+  std::vector<float> scales;
+  if (!TryGetInputValue("Scale", &scales)) {
+    Error() << "Failed to read tensor value of `Scale`." << std::endl;
+    return -1;
+  }
+  if (bit_length_ != 8) {
+    Error() << "Only support bit_length = 8." << std::endl;
+    return -1;
+  }
+  if (scales.size() > 1) {
+    auto x_info = GetInput("X");
+    if (x_info[0].shape[quant_axis_] != scales.size()) {
+      Error() << "Scale size must equal to the size of input quantize axis."
+              << std::endl;
+      return -1;
+    }
+    Logger(verbose, 13) << "While size of scales greater than 1, "
+                        << RequireOpset(13) << std::endl;
+    return 13;
+  }
+  auto x_info = GetInput("X");
+  auto x_shape = x_info[0].shape;
+  if (x_shape.size() == 2) {
+    if (quant_axis_ != 1) {
+      Error() << "When the rank of input is 2, the attribute quant_axis "
+                 "requires to be 1."
+              << std::endl;
+      return -1;
+    }
+  } else if (x_shape.size() == 4) {
+    if (!(quant_axis_ == 1 || quant_axis_ == 0)) {
+      Error() << "When the rank of input is 4, the attribute quant_axis "
+                 "requires to be 0 or 1."
+              << std::endl;
+      return -1;
+    }
+  }
+
+  Logger(verbose, 10) << RequireOpset(10) << std::endl;
+  return 10;
+}
+
+void DequantizeLinearMapper::ConvertInt8ToFp32(
+    const std::vector<float> &onnx_scales, std::vector<float> *weight) {
+  auto x_info = GetInput("X");
+  auto x_shape = x_info[0].shape;
+  if (x_shape.size() == 2) {
+    for (auto j = 0; j < x_shape[1]; ++j) {
+      float scale_value = 0;
+      if (onnx_scales.size() == 1) {
+        scale_value = onnx_scales[0];
+      } else {
+        scale_value = onnx_scales[j];
+      }
+      for (auto i = 0; i < x_shape[0]; ++i) {
+        auto offset = i * x_shape[1] + j;
+        (*weight)[offset] *= scale_value;
+      }
+    }
+  } else if (x_shape.size() == 4) {
+    if (quant_axis_ == 0) {
+      auto inner_offset = 1;
+      for (auto i : x_shape) {
+        inner_offset *= i;
+      }
+      inner_offset /= x_shape[0];
+      for (int i = 0; i < x_shape[0]; ++i) {
+        float scale_value = 0;
+        if (onnx_scales.size() == 1) {
+          scale_value = onnx_scales[0];
+        } else {
+          scale_value = onnx_scales[i];
+        }
+        for (auto j = 0; j < inner_offset; ++j) {
+          auto offset = i * inner_offset + j;
+          (*weight)[offset] *= scale_value;
+        }
+      }
+    } else {
+      auto inner_offset = x_shape[2] * x_shape[3];
+      auto outter_offset = x_shape[1] * inner_offset;
+      for (auto i = 0; i < x_shape[0]; ++i) {
+        for (auto j = 0; j < x_shape[1]; ++j) {
+          float scale_value = 0;
+          if (onnx_scales.size() == 1) {
+            scale_value = onnx_scales[0];
+          } else {
+            scale_value = onnx_scales[j];
+          }
+          for (auto k = 0; k < inner_offset; k++) {
+            auto offset = i * outter_offset + j * inner_offset + k;
+            (*weight)[offset] *= scale_value;
+          }
+        }
+      }
+    }
+  }
+}
+
+void DequantizeLinearMapper::Opset10() {
+  auto x_info = GetInput("X");
+  auto x_shape = x_info[0].shape;
+  std::vector<float> scales;
+  Assert(TryGetInputValue("Scale", &scales),
+         "Failed to read tensor value of `Scale`.");
+  std::vector<float> onnx_scales;
+  onnx_scales.reserve(scales.size());
+  for (auto &i : scales) {
+    onnx_scales.push_back(i / 127);
+  }
+  std::vector<int64_t> onnx_zeros(onnx_scales.size(), 0);
+  std::string scale_node, zero_node;
+  if (onnx_zeros.size() == 1) {
+    scale_node = helper_->Constant({}, ONNX_NAMESPACE::TensorProto::FLOAT,
+                                   onnx_scales[0]);
+    zero_node =
+        helper_->Constant({}, ONNX_NAMESPACE::TensorProto::INT8, onnx_zeros[0]);
+  } else {
+    scale_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, onnx_scales);
+    zero_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, onnx_zeros);
+  }
+
+  std::vector<float> weight;
+  TryGetInputValue("X", &weight);
+  if (weight.empty()) {
+    auto node = helper_->MakeNode("DequantizeLinear",
+                                  {x_info[0].name, scale_node, zero_node},
+                                  {GetOutput("Y")[0].name});
+    if (helper_->GetOpsetVersion() >= 13) {
+      AddAttribute(node, "axis", quant_axis_);
+    }
+    QuantizeInfo quantize_info(onnx_scales, onnx_zeros, scale_node, zero_node,
+                               quant_axis_);
+    helper_->quantize_info[GetOutput("Y")[0].name] = quantize_info;
+    return;
+  }
+  ConvertInt8ToFp32(onnx_scales, &weight);
+
+  QuantizeInfo quantize_info(onnx_scales, onnx_zeros, scale_node, zero_node,
+                             quant_axis_);
+  helper_->quantize_info[x_info[0].name] = quantize_info;
+  Weight fp32_weight;
+  fp32_weight.set(P2ODataType::FP32, x_shape, weight);
+  helper_->updated_params[x_info[0].name] = fp32_weight;
+  auto node = helper_->MakeNode("QuantizeLinear",
+                                {x_info[0].name, scale_node, zero_node});
+  if (helper_->GetOpsetVersion() >= 13) {
+    AddAttribute(node, "axis", quant_axis_);
+  }
+  auto dq_node = helper_->MakeNode("DequantizeLinear",
+                                   {node->output(0), scale_node, zero_node},
+                                   {GetOutput("Y")[0].name});
+  if (helper_->GetOpsetVersion() >= 13) {
+    AddAttribute(dq_node, "axis", quant_axis_);
+  }
+}
+}  // namespace paddle2onnx
\ No newline at end of file
diff --git a/paddle2onnx/mapper/quantize/dequantize_linear.h b/paddle2onnx/mapper/quantize/dequantize_linear.h
new file mode 100755
index 0000000000..e49ed1d224
--- /dev/null
+++ b/paddle2onnx/mapper/quantize/dequantize_linear.h
@@ -0,0 +1,42 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class DequantizeLinearMapper : public Mapper {
+ public:
+  DequantizeLinearMapper(const PaddleParser& p, OnnxHelper* helper,
+                         int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("quant_axis", &quant_axis_);
+    GetAttr("bit_length", &bit_length_);
+    if (quant_axis_ == -1) {
+      quant_axis_ = 1;
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset10();
+
+ private:
+  void ConvertInt8ToFp32(const std::vector<float>& onnx_scales,
+                         std::vector<float>* weight);
+  int64_t quant_axis_ = 1;
+  int64_t bit_length_ = 8;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/quantize/quantize_linear.cc b/paddle2onnx/mapper/quantize/quantize_linear.cc
new file mode 100755
index 0000000000..6ff1c18806
--- /dev/null
+++ b/paddle2onnx/mapper/quantize/quantize_linear.cc
@@ -0,0 +1,88 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/quantize/quantize_linear.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(quantize_linear, QuantizeLinearMapper)
+
+int32_t QuantizeLinearMapper::GetMinOpset(bool verbose) {
+  if (!IsConstantInput("Scale")) {
+    Error() << "Input `Scale` requires to be a constant tensor." << std::endl;
+    return -1;
+  }
+  std::vector<float> scales;
+  if (!TryGetInputValue("Scale", &scales)) {
+    Error() << "Failed to read tensor value of `Scale`." << std::endl;
+    return -1;
+  }
+  if (bit_length_ != 8) {
+    Error() << "Only support bit_length = 8." << std::endl;
+    return -1;
+  }
+  if (round_type_ != 0) {
+    Error() << "The round_type attr of quantize_linear must be 0." << std::endl;
+    return -1;
+  }
+  if (scales.size() > 1) {
+    auto x_info = GetInput("X");
+    if (x_info[0].shape[quant_axis_] != scales.size()) {
+      Error() << "Scale size must equal to the size of input quantize axis."
+              << std::endl;
+      return -1;
+    }
+    Logger(verbose, 13) << "While size of scales greater than 1, "
+                        << RequireOpset(13) << std::endl;
+    return 13;
+  }
+  Logger(verbose, 10) << RequireOpset(10) << std::endl;
+  return 10;
+}
+
+void QuantizeLinearMapper::Opset10() {
+  auto x_info = GetInput("X");
+  std::vector<float> scales;
+  Assert(TryGetInputValue("Scale", &scales),
+         "Failed to read tensor value of `Scale`.");
+  std::vector<float> onnx_scales;
+  onnx_scales.reserve(scales.size());
+  for (auto i : scales) {
+    onnx_scales.push_back(i / 127);
+  }
+  std::vector<int64_t> onnx_zeros(onnx_scales.size(), 0);
+
+  std::string scale_node, zero_node;
+  if (onnx_scales.size() == 1) {
+    scale_node = helper_->Constant({}, ONNX_NAMESPACE::TensorProto::FLOAT,
+                                   onnx_scales[0]);
+    zero_node =
+        helper_->Constant({}, ONNX_NAMESPACE::TensorProto::INT8, onnx_zeros[0]);
+  } else {
+    scale_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, onnx_scales);
+    zero_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, onnx_zeros);
+  }
+
+  auto node = helper_->MakeNode("QuantizeLinear",
+                                {x_info[0].name, scale_node, zero_node},
+                                {GetOutput("Y")[0].name});
+  if (helper_->GetOpsetVersion() >= 13) {
+    AddAttribute(node, "axis", quant_axis_);
+  }
+  QuantizeInfo quantize_info(onnx_scales, onnx_zeros, scale_node, zero_node,
+                             quant_axis_);
+  helper_->quantize_info[x_info[0].name] = quantize_info;
+}
+}  // namespace paddle2onnx
\ No newline at end of file
diff --git a/paddle2onnx/mapper/quantize/quantize_linear.h b/paddle2onnx/mapper/quantize/quantize_linear.h
new file mode 100644
index 0000000000..a209fdbb02
--- /dev/null
+++ b/paddle2onnx/mapper/quantize/quantize_linear.h
@@ -0,0 +1,45 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class QuantizeLinearMapper : public Mapper {
+ public:
+  QuantizeLinearMapper(const PaddleParser& p, OnnxHelper* helper,
+                       int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("quant_axis", &quant_axis_);
+    GetAttr("bit_length", &bit_length_);
+    if (quant_axis_ == -1) {
+      quant_axis_ = 1;
+    }
+    if (HasAttr("round_type")) {
+      GetAttr("round_type", &round_type_);
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset10();
+
+ private:
+  int64_t round_type_ = 0;  // 0: rounding to nearest ties to even. 1: rounding
+                            // to nearest ties away from zero.
+  int64_t quant_axis_ = 1;
+  int64_t bit_length_ = 8;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/quantize_helper.cc b/paddle2onnx/mapper/quantize_helper.cc
new file mode 100755
index 0000000000..f99e5171ad
--- /dev/null
+++ b/paddle2onnx/mapper/quantize_helper.cc
@@ -0,0 +1,1205 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/quantize_helper.h"
+
+namespace paddle2onnx {
+
+void QuantizeModelProcessor::RemoveNodeByName(const std::string& name,
+                                              const bool& update_io) {
+  if (name.empty()) {
+    return;
+  }
+  for (auto iter = nodes_->begin(); iter != nodes_->end(); iter++) {
+    if ((*iter)->name() == name) {
+      std::string input_name = (*iter)->input(0);
+      std::string output_name = (*iter)->output(0);
+      nodes_->erase(iter);
+      if (update_io) {
+        ReplaceInputOfAllNodes(output_name, input_name);
+      }
+      return;
+    }
+  }
+}
+
+void QuantizeModelProcessor::ReplaceInputOfAllNodes(
+    const std::string& old_name, const std::string& new_name,
+    const std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>&
+        except_nodes) {
+  auto iter = name2node_dict_.find(old_name);
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> need_rename_nodes;
+  // after replace all old_name to new_name, replace the quantize_info of new
+  // name with the quantize_info of old name
+  auto quantize_info_iter = helper_->quantize_info.find(old_name);
+  if (quantize_info_iter != helper_->quantize_info.end()) {
+    helper_->quantize_info[new_name] = helper_->quantize_info[old_name];
+  }
+
+  if (iter != name2node_dict_.end()) {
+    need_rename_nodes = iter->second;
+  }
+  for (auto& node : need_rename_nodes) {
+    auto iter = std::find(except_nodes.begin(), except_nodes.end(), node);
+    if (iter != except_nodes.end()) {
+      continue;
+    }
+    for (size_t i = 0; i < node->input_size(); ++i) {
+      if (node->input(i) == old_name) {
+        node->set_input(i, new_name);
+      }
+    }
+  }
+}
+
+void QuantizeModelProcessor::UpdateInputNameToNodes() {
+  name2node_dict_.clear();
+  for (auto& node : *nodes_) {
+    for (size_t i = 0; i < node->input_size(); ++i) {
+      std::string node_input = node->input(i);
+      if (name2node_dict_.find(node_input) != name2node_dict_.end()) {
+        name2node_dict_[node_input].push_back(node);
+      } else {
+        name2node_dict_[node_input] = {node};
+      }
+    }
+  }
+}
+
+void QuantizeModelProcessor::ProcessQuantizeModel(
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* parameters,
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* inputs,
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* outputs,
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* nodes,
+    OnnxHelper* helper, const std::string& deploy_backend,
+    const PaddleParser& parser, std::string* calibration_cache) {
+  // Determine whether the model contains quantization related OPs, if not, exit
+  // directly
+  bool quantized_model = false;
+  for (auto& node : *nodes) {
+    if (node->op_type() == "QuantizeLinear" ||
+        node->op_type() == "DequantizeLinear") {
+      quantized_model = true;
+      break;
+    }
+  }
+  if (!quantized_model) {
+    return;
+  }
+  parser_ = &parser;
+  helper_ = helper;
+  parameters_ = parameters;
+  inputs_ = inputs;
+  outputs_ = outputs;
+  nodes_ = nodes;
+  P2OLogger() << "[Info] Quantize model deploy backend is: " << deploy_backend
+              << std::endl;
+  // Determine the format of the exported ONNX quantization model according to
+  // the deploy_backend
+  if (deploy_backend == "others") {
+    // If deploy_backend is others, the quantization model is exported as a
+    // float model + quantization table.
+    RemoveAllQuantizeOps();
+    std::ofstream outfile;
+    outfile.open("max_range.txt", std::ios::out);
+    if (!outfile.is_open()) {
+      P2OLogger() << "[WARNING] Quantize model processer failed to write range "
+                     "information in current location."
+                  << std::endl;
+      return;
+    }
+    for (auto iter = helper_->quantize_info.begin();
+         iter != helper_->quantize_info.end(); iter++) {
+      std::string log = iter->first;
+      auto scale = iter->second.scale_;
+      if (scale.size() == 1) {
+        log = log + ": " + std::to_string(scale[0] * 127);
+        outfile << log << std::endl;
+      }
+    }
+    outfile.close();
+  } else if (deploy_backend == "onnxruntime") {
+    // When deploy_backend is ONNXRuntime, use the follow four steps to process:
+    // 1. broadcast quantize info
+    // 2. remove all quantize ops
+    // 3. merge conv and add
+    // 4. merge conv and bn
+    // 5. add Q and DQ according ONNXRuntime quantize OP fuse patten.
+    // 6. use topo sort in nodes
+    QuantizeInfoBroadcast();
+    RemoveAllQuantizeOps();
+    MergeConvAdd();
+    MergeConvBN();
+    AddQDQForORT();
+    SortNodes();
+  } else if (deploy_backend == "tensorrt") {
+    // When deploy_backend is TensorRT, use the follow four steps to process:
+    // For Explicit Quantization
+    // 1. broadcast quantize info
+    // 2. remove all quantize ops
+    // 3. add Q and DQ before conv and matmul.
+    // 4. use topo sort in nodes
+
+    // For Implicit Quantization
+    // 1. remove all quantize ops
+    // 2. broadcast quantize info
+    // 3. save float onnx model and alibration.cache
+    QuantizeInfoBroadcast();
+    RemoveAllQuantizeOps();
+    // Add qdq for Explicit Quantization
+    // AddTrtQDQ();
+    // SortNodes();
+
+    // Genarate calibration.cache for Implicit Quantization
+    // convert float to hex
+    GenerateCache(calibration_cache);
+  } else if (deploy_backend == "rknn") {
+    // When deploy_backend is RKNN, use the follow four steps to process:
+    // 1. broadcast quantize info
+    // 2. remove all quantize ops
+    // 3. merge conv and add
+    // 4. merge conv and bn
+    // 5. add Q and DQ
+    // 6. use topo sort in nodes
+    QuantizeInfoBroadcast();
+    RemoveAllQuantizeOps();
+    MergeConvAdd();
+    MergeConvBN();
+    AddQDQForRKNN();
+    SortNodes();
+  } else {
+    Assert(false,
+           "[QuantizeModelProcessor] Only support 'onnxruntime'  / 'tensorrt' "
+           "/ 'others' as "
+           "backend now, but now the backend is: " +
+               deploy_backend + ".");
+  }
+}
+
+void QuantizeModelProcessor::AddQDQForRKNN() {
+  UpdateInputNameToNodes();
+  supported_quantize_type_ = {"Abs",
+                              "Acos",
+                              "Add",
+                              "Asin",
+                              "Atan",
+                              "AveragePool",
+                              "BatchNormalization",
+                              "Ceil",
+                              "Clip",
+                              "Conv",
+                              "ConvTranspose",
+                              "Cos",
+                              "Cosh",
+                              "Concat",
+                              "Elu",
+                              "Erf",
+                              "Exp",
+                              "Floor",
+                              "Gemm",
+                              "HardSigmoid",
+                              "InstanceNormalization",
+                              "IsInf",
+                              "IsNaN",
+                              "Log",
+                              "MaxPool",
+                              "Mul",
+                              "Neg",
+                              "ReduceMean",
+                              "Relu",
+                              "Resize",
+                              "Round",
+                              "Sigmoid",
+                              "Sin",
+                              "Sinh",
+                              "Split",
+                              "Sqrt",
+                              "Tan",
+                              "Tanh"};
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    auto node = *iter;
+    auto type_iter = std::find(supported_quantize_type_.begin(),
+                               supported_quantize_type_.end(), node->op_type());
+    if (!supported_quantize_type_.empty() &&
+        type_iter == supported_quantize_type_.end()) {
+      continue;
+    }
+    // Add Miss scale for Mul and MatMul
+    if (node->op_type() == "Mul" || node->op_type() == "MatMul" ||
+        node->op_type() == "Add") {
+      for (size_t i = 0; i < node->input_size(); ++i) {
+        std::string node_input = node->input(i);
+        if (helper_->quantize_info.find(node_input) !=
+            helper_->quantize_info.end()) {
+          continue;
+        }
+        std::vector<float> weight;
+        if (!GetTensorByName(node_input, &weight)) {
+          continue;
+        }
+        std::vector<int64_t> weight_shape;
+        GetTensorShape(node_input, &weight_shape);
+        int64_t quantize_axis = 1;
+        if (node->op_type() == "Add") {
+          quantize_axis = 0;
+        }
+        std::vector<float> scale;
+        std::vector<int64_t> zeros;
+
+        if (node->op_type() == "Add" || weight_shape.size() == 1 ||
+            weight_shape.empty()) {
+          GetTensorWiseQuantizeInfo(weight, &scale, &zeros);
+        } else {
+          GetChannelWiseQuantizeInfo(weight, weight_shape, quantize_axis,
+                                     &scale, &zeros);
+        }
+        std::string weight_scale_node, weight_zero_node;
+        if (scale.size() == 1) {
+          weight_scale_node = helper_->Constant(
+              {}, ONNX_NAMESPACE::TensorProto::FLOAT, scale[0]);
+          weight_zero_node = helper_->Constant(
+              {}, ONNX_NAMESPACE::TensorProto::INT8, zeros[0]);
+        } else {
+          weight_scale_node =
+              helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, scale);
+          weight_zero_node =
+              helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, zeros);
+        }
+        QuantizeInfo weight_quantize_info(scale, zeros, weight_scale_node,
+                                          weight_zero_node, quantize_axis);
+        helper_->quantize_info[node_input] = weight_quantize_info;
+      }
+    }
+    std::vector<std::string> tensor_names;
+    for (size_t i = 0; i < node->input_size(); ++i) {
+      std::string node_input = node->input(i);
+      tensor_names.push_back(node_input);
+    }
+    for (size_t i = 0; i < node->output_size(); ++i) {
+      std::string node_output = node->output(i);
+      tensor_names.push_back(node_output);
+    }
+    if (!CanBeQuantize(tensor_names)) {
+      continue;
+    }
+    for (auto& name : tensor_names) {
+      AppendQuantizeTensor(name);
+    }
+  }
+
+  // update name2node_dict for the change of Relu op.
+  UpdateInputNameToNodes();
+  // Add QDQ in model
+  AddQDQInModel(tensors_to_be_quantize);
+}
+
+void QuantizeModelProcessor::GenerateCache(std::string* calibration_cache) {
+  union {
+    float f;
+    unsigned char farray[4];
+  } un;
+  *calibration_cache += "TRT-8XXX-EntropyCalibration2 \n";
+  for (auto iter = helper_->quantize_info.rbegin();
+       iter != helper_->quantize_info.rend(); iter++) {
+    std::string tensor_name = iter->first;
+    QuantizeInfo quantize_info = iter->second;
+    if (quantize_info.scale_.size() == 1) {
+      float val = quantize_info.scale_[0];
+      un.f = val;
+      *calibration_cache += (tensor_name + ": ");
+      std::stringstream enc;
+      for (int64_t i = 3; i >= 0; i--) {
+        enc << std::hex << std::setw(2) << std::setfill('0')
+            << (int)(un.farray[i]);
+      }
+      *calibration_cache = *calibration_cache + enc.str() + "\n";
+    }
+  }
+}
+// In TensorRT, all quantized op: Conv, ConvTranspose, liner(MatMul), MaxPool,
+// AvgPool, AdaptiveAvgPool, rnn(not support now)
+// https://github.com/NVIDIA/TensorRT/tree/main/tools/pytorch-quantization/pytorch_quantization/nn/modules
+void QuantizeModelProcessor::AddTrtQDQ() {
+  UpdateInputNameToNodes();
+  std::vector<std::string>
+      quantize_tensors;  // save the tensor names that need add quantize ops
+  std::vector<std::string> pool_types = {"MaxPool", "AvgPool",
+                                         "AdaptiveAvgPool"};
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    quantize_tensors.clear();
+    auto node = *iter;
+    if (node->op_type() == "Conv" || node->op_type() == "ConvTranspose") {
+      std::vector<std::string> tensor_names = {node->input(0), node->input(1)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      quantize_tensors = tensor_names;
+    }
+    if (node->op_type() == "MatMul") {
+      std::vector<std::string> tensor_names = {node->input(0), node->input(1)};
+      for (auto& name : tensor_names) {
+        if (helper_->quantize_info.find(name) != helper_->quantize_info.end()) {
+          continue;
+        }
+        std::vector<float> matmul_weight;
+        if (!GetTensorByName(name, &matmul_weight)) {
+          continue;
+        }
+        std::vector<int64_t> matmul_weight_shape;
+        if (!GetTensorShape(name, &matmul_weight_shape)) {
+          continue;
+        }
+        int64_t quantize_axis = 1;
+        std::vector<float> scale;
+        std::vector<int64_t> zeros;
+        GetChannelWiseQuantizeInfo(matmul_weight, matmul_weight_shape,
+                                   quantize_axis, &scale, &zeros);
+        auto scale_node =
+            helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, scale);
+        auto zero_node =
+            helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, zeros);
+        QuantizeInfo matmul_weight_quantize_info(scale, zeros, scale_node,
+                                                 zero_node, quantize_axis);
+        helper_->quantize_info[name] = matmul_weight_quantize_info;
+      }
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      quantize_tensors = tensor_names;
+    }
+    auto type_iter =
+        std::find(pool_types.begin(), pool_types.end(), node->op_type());
+    if (type_iter != pool_types.end()) {
+      std::vector<std::string> tensor_names = {node->input(0)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      quantize_tensors = tensor_names;
+    }
+
+    std::string negative_scale_tensor = "";
+    for (std::string& name : quantize_tensors) {
+      Assert(
+          helper_->quantize_info.find(name) != helper_->quantize_info.end(),
+          "[QuantizeModelProcessor] Can not find quantize info for tensor: " +
+              name);
+      QuantizeInfo quantize_info = helper_->quantize_info[name];
+      std::vector<float> scales = quantize_info.scale_;
+      for (auto& i : scales) {
+        if (i <= 1e-10) {
+          negative_scale_tensor = negative_scale_tensor + " " + name;
+        }
+      }
+    }
+    if (negative_scale_tensor.size() > 0) {
+      P2OLogger()
+          << "[Warning] The scale of tensors: [ " + negative_scale_tensor +
+                 " ] contains negative scale, so this OP will not be quantized."
+          << std::endl;
+      continue;
+    }
+    // An OP requires a separate quantize op
+    for (std::string& name : quantize_tensors) {
+      if (IsGraphOutput(name)) {
+        continue;
+      }
+      QuantizeInfo quantize_info = helper_->quantize_info[name];
+      std::string scale_node = quantize_info.scale_node_;
+      std::string zeros_node = quantize_info.zeros_node_;
+      int64_t quantize_axis = quantize_info.quantize_axis_;
+      auto q_node =
+          helper_->MakeNode("QuantizeLinear", {name, scale_node, zeros_node});
+      if (helper_->GetOpsetVersion() >= 13) {
+        AddAttribute(q_node, "axis", quantize_axis);
+      }
+      auto dq_node = helper_->MakeNode(
+          "DequantizeLinear", {q_node->output(0), scale_node, zeros_node});
+      if (helper_->GetOpsetVersion() >= 13) {
+        AddAttribute(dq_node, "axis", quantize_axis);
+      }
+      for (size_t i = 0; i < node->input_size(); ++i) {
+        if (node->input(i) == name) {
+          node->set_input(i, dq_node->output(0));
+        }
+      }
+    }
+  }
+}
+
+// According to:
+// https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc
+void QuantizeModelProcessor::AddQDQForORT() {
+  UpdateInputNameToNodes();
+  supported_quantize_type_ = {"Conv", "MatMul",    "Mul", "Sigmoid",
+                              "Add",  "LeakyRelu", "Relu"};
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    auto node = *iter;
+    auto type_iter = std::find(supported_quantize_type_.begin(),
+                               supported_quantize_type_.end(), node->op_type());
+    if (!supported_quantize_type_.empty() &&
+        type_iter == supported_quantize_type_.end()) {
+      continue;
+    }
+    // Here we only add Relu, Conv, mul and matmul, all tensors should add Q and
+    // DQ will be saved in tensors_to_be_quantize
+    if (node->op_type() == "Relu") {
+      std::vector<std::string> tensor_names = {node->input(0), node->output(0)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      node->set_op_type("LeakyRelu");
+      AddAttribute(node, "alpha", static_cast<float>(0.0));
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+    if (node->op_type() == "LeakyRelu") {
+      std::vector<std::string> tensor_names = {node->input(0), node->output(0)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+    if (node->op_type() == "Add") {
+      std::vector<std::string> tensor_names = {node->input(0), node->input(1),
+                                               node->output(0)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+    if (node->op_type() == "Sigmoid") {
+      std::vector<std::string> tensor_names = {node->input(0), node->output(0)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+    if (node->op_type() == "Conv") {
+      std::vector<std::string> tensor_names = {node->input(0), node->input(1),
+                                               node->output(0)};
+      if (node->input_size() == 3) {
+        tensor_names.push_back(node->input(2));
+      }
+      if (!CanBeQuantize(tensor_names, {2})) {
+        continue;
+      }
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+    if (node->op_type() == "MatMul") {
+      std::vector<std::string> tensor_names = {node->input(0), node->input(1),
+                                               node->output(0)};
+      for (auto& name : tensor_names) {
+        if (helper_->quantize_info.find(name) != helper_->quantize_info.end()) {
+          continue;
+        }
+        std::vector<float> matmul_weight;
+        if (!GetTensorByName(name, &matmul_weight)) {
+          continue;
+        }
+        std::vector<int64_t> matmul_weight_shape;
+        if (!GetTensorShape(name, &matmul_weight_shape)) {
+          continue;
+        }
+        int64_t quantize_axis = 1;
+        std::vector<float> scale;
+        std::vector<int64_t> zeros;
+        GetChannelWiseQuantizeInfo(matmul_weight, matmul_weight_shape,
+                                   quantize_axis, &scale, &zeros);
+        auto scale_node =
+            helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, scale);
+        auto zero_node =
+            helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, zeros);
+        QuantizeInfo matmul_weight_quantize_info(scale, zeros, scale_node,
+                                                 zero_node, quantize_axis);
+        helper_->quantize_info[name] = matmul_weight_quantize_info;
+      }
+      if (!CanBeQuantize(tensor_names)) {
+        tensor_names.pop_back();
+        if (!CanBeQuantize(tensor_names)) {
+          continue;
+        }
+      }
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+    if (node->op_type() == "Mul") {
+      std::vector<std::string> tensor_names = {node->input(0), node->input(1),
+                                               node->output(0)};
+      if (!CanBeQuantize(tensor_names)) {
+        continue;
+      }
+      for (auto& name : tensor_names) {
+        AppendQuantizeTensor(name);
+      }
+    }
+  }
+  // update name2node_dict for the change of Relu op.
+  UpdateInputNameToNodes();
+  // Add QDQ in model
+  AddQDQInModel(tensors_to_be_quantize);
+}
+
+void QuantizeModelProcessor::AddQDQInModel(
+    const std::vector<std::string>& tensors_to_be_quantize) {
+  // add Q and DQ according to tensors_to_be_quantize
+  for (auto& name : tensors_to_be_quantize) {
+    if (IsGraphOutput(name)) {
+      continue;
+    }
+    Assert(helper_->quantize_info.find(name) != helper_->quantize_info.end(),
+           "[QuantizeModelProcessor] Can not find quantize info for tensor: " +
+               name);
+    QuantizeInfo quantize_info = helper_->quantize_info[name];
+    std::string scale_node = quantize_info.scale_node_;
+    std::string zeros_node = quantize_info.zeros_node_;
+    int64_t quantize_axis = quantize_info.quantize_axis_;
+    auto iter = std::find(only_dequantize_tensors.begin(),
+                          only_dequantize_tensors.end(), name);
+    if (iter != only_dequantize_tensors.end()) {
+      // if only add DequantizeLinear
+      std::vector<float> scale = quantize_info.scale_;
+      std::vector<float> bias;
+      Assert(GetTensorByName(name, &bias),
+             "[QuantizeModelProcessor] Can not find bias value: " + name);
+      std::vector<int32_t> new_bias(scale.size(), 0);
+      for (int64_t i = 0; i < bias.size(); i++) {
+        float scale_val = scale.size() == 1 ? scale[0] : scale[i];
+        new_bias[i] = rint(bias[i] / scale_val);
+      }
+
+      Weight updated_bias;
+      std::vector<int64_t> bias_shape = {static_cast<int64_t>(new_bias.size())};
+      updated_bias.set(P2ODataType::INT32, bias_shape, new_bias);
+      helper_->updated_params[name] = updated_bias;
+      auto dq_node =
+          helper_->MakeNode("DequantizeLinear", {name, scale_node, zeros_node});
+      if (helper_->GetOpsetVersion() >= 13) {
+        AddAttribute(dq_node, "axis", quantize_axis);
+      }
+      ReplaceInputOfAllNodes(name, dq_node->output(0));
+    } else {
+      // Handle the following situations
+      //           conv                   conv
+      //         /  |  \         ->     /    \ 
+      //      conv conv scale         DQD   scale
+      //                             /   \       
+      //                           conv conv
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> except_nodes;
+      auto next_nodes = name2node_dict_[name];
+      if (next_nodes.size() > 1) {
+        for (auto& node : next_nodes) {
+          auto iter =
+              std::find(supported_quantize_type_.begin(),
+                        supported_quantize_type_.end(), node->op_type());
+          if (iter == supported_quantize_type_.end()) {
+            except_nodes.push_back(node);
+          }
+        }
+      }
+      // When all the outputs of this tensor cannot be renamed,
+      // it means that the quantization OP will be merged
+      if (next_nodes.size() == except_nodes.size()) {
+        except_nodes.clear();
+      }
+      auto q_node =
+          helper_->MakeNode("QuantizeLinear", {name, scale_node, zeros_node});
+      if (helper_->GetOpsetVersion() >= 13) {
+        AddAttribute(q_node, "axis", quantize_axis);
+      }
+      auto dq_node = helper_->MakeNode(
+          "DequantizeLinear", {q_node->output(0), scale_node, zeros_node});
+      if (helper_->GetOpsetVersion() >= 13) {
+        AddAttribute(dq_node, "axis", quantize_axis);
+      }
+      ReplaceInputOfAllNodes(name, dq_node->output(0), except_nodes);
+    }
+  }
+}
+
+void QuantizeModelProcessor::MergeConvBN() {
+  UpdateInputNameToNodes();
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    auto conv_node = *iter;
+    if (conv_node->op_type() != "Conv") {
+      continue;
+    }
+
+    bool act_has_quantize_info =
+        helper_->quantize_info.find(conv_node->input(0)) !=
+        helper_->quantize_info.end();
+    if (!act_has_quantize_info) {
+      continue;
+    }
+    auto next_nodes = name2node_dict_[conv_node->output(0)];
+
+    if (next_nodes.size() > 1 || IsGraphOutput(conv_node->output(0))) {
+      continue;
+    }
+
+    auto bn_node = next_nodes[0];
+    if (bn_node->op_type() != "BatchNormalization" ||
+        IsGraphOutput(bn_node->output(0))) {
+      continue;
+    }
+
+    std::vector<float> conv_weight;
+    Assert(GetTensorByName(conv_node->input(1), &conv_weight),
+           "Can not get " + conv_node->input(1) + " from Conv.");
+
+    std::vector<float> bn_scale;
+    Assert(GetTensorByName(bn_node->input(1), &bn_scale),
+           "Can not get " + bn_node->input(1) + " from BN.");
+
+    std::vector<float> bn_bias;
+    Assert(GetTensorByName(bn_node->input(2), &bn_bias),
+           "Can not get " + bn_node->input(2) + " from BN.");
+
+    std::vector<float> bn_mean;
+    Assert(GetTensorByName(bn_node->input(3), &bn_mean),
+           "Can not get " + bn_node->input(3) + " from BN.");
+
+    std::vector<float> bn_var;
+    Assert(GetTensorByName(bn_node->input(4), &bn_var),
+           "Can not get " + bn_node->input(4) + " from BN.");
+
+    float epsilon = 1;
+    for (auto i = 0; i < bn_node->attribute_size(); i++) {
+      auto attr = bn_node->attribute(i);
+      if (attr.name() == "epsilon") {
+        epsilon = attr.f();
+      }
+    }
+
+    std::vector<float> conv_bias(bn_bias.size(), 0);
+    std::string conv_bias_node = conv_node->input(1) + ".merged.bias";
+    if (conv_node->input_size() == 3) {
+      conv_bias_node = conv_node->input(2);
+      conv_bias.clear();
+      Assert(GetTensorByName(conv_bias_node, &conv_bias),
+             "Can not get " + conv_node->input(2) + " in Conv.");
+    }
+
+    // merge conv and bn
+    std::vector<float> alpha(bn_scale.size());
+    for (int64_t i = 0; i < bn_scale.size(); i++) {
+      alpha[i] = bn_scale[i] / sqrt(bn_var[i] + epsilon);
+    }
+
+    std::vector<float> new_bias(bn_scale.size());
+    for (int64_t i = 0; i < bn_scale.size(); i++) {
+      new_bias[i] =
+          conv_bias[i] * alpha[i] + (bn_bias[i] - bn_mean[i] * alpha[i]);
+    }
+    std::vector<float> new_weight(conv_weight.size());
+    int64_t offset = conv_weight.size() / bn_bias.size();
+    for (int64_t i = 0; i < bn_scale.size(); i++) {
+      int64_t outter_offset = i * offset;
+      for (int64_t j = 0; j < offset; j++) {
+        int64_t index = outter_offset + j;
+        new_weight[index] = conv_weight[index] * alpha[i];
+      }
+    }
+    // update weight
+    std::vector<int64_t> weight_shape;
+    Assert(GetTensorShape(conv_node->input(1), &weight_shape),
+           "Can not get the shape of " + conv_node->input(1) + " in Conv.");
+    Weight updated_conv_weight;
+    updated_conv_weight.set(P2ODataType::FP32, weight_shape, new_weight);
+    helper_->updated_params[conv_node->input(1)] = updated_conv_weight;
+    // update bias
+    Weight updated_bias_weight;
+    std::vector<int64_t> bias_shape = {static_cast<int64_t>(new_bias.size())};
+    updated_bias_weight.set(P2ODataType::FP32, bias_shape, new_bias);
+    helper_->updated_params[conv_bias_node] = updated_bias_weight;
+    AppendQuantizeTensor(conv_bias_node, true);
+    // update weight scale
+    auto quantize_info = helper_->quantize_info[conv_node->input(1)];
+    std::string scale_node = quantize_info.scale_node_;
+    std::string zero_node = quantize_info.zeros_node_;
+    int64_t quantize_axis = quantize_info.quantize_axis_;
+    RemoveNodeByName(scale_node);
+    RemoveNodeByName(zero_node);
+    std::vector<float> scale = quantize_info.scale_;
+    std::vector<float> new_scale;
+    std::vector<int64_t> new_zeros;
+    if (scale.size() == 1) {
+      GetTensorWiseQuantizeInfo(new_weight, &new_scale, &new_zeros);
+    } else {
+      GetChannelWiseQuantizeInfo(new_weight, weight_shape, quantize_axis,
+                                 &new_scale, &new_zeros);
+    }
+    auto weight_scale_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, new_scale);
+    auto weight_zero_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::INT8, new_zeros);
+    QuantizeInfo updated_weight_quantize_info(new_scale, new_zeros,
+                                              weight_scale_node,
+                                              weight_zero_node, quantize_axis);
+    helper_->quantize_info[conv_node->input(1)] = updated_weight_quantize_info;
+    // add bias scale and update bias
+    auto act_quantize_info = helper_->quantize_info[conv_node->input(0)];
+    std::vector<float> act_scale = act_quantize_info.scale_;
+    std::vector<float> bias_scale;
+    for (int64_t i = 0; i < new_scale.size(); i++) {
+      bias_scale.push_back(act_scale[0] * new_scale[i]);
+    }
+    std::vector<int64_t> bias_zeros(bias_scale.size(), 0);
+    auto bias_scale_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, bias_scale);
+    auto bias_zero_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::INT32, bias_zeros);
+    QuantizeInfo bias_quantize_info(bias_scale, bias_zeros, bias_scale_node,
+                                    bias_zero_node, 0);
+    helper_->quantize_info[conv_bias_node] = bias_quantize_info;
+    if (conv_node->input_size() == 2) {
+      conv_node->add_input(conv_bias_node);
+    }
+    // remove BN op
+    RemoveNodeByName(bn_node->name());
+  }
+}
+
+void QuantizeModelProcessor::MergeConvAdd() {
+  UpdateInputNameToNodes();
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    auto node = *iter;
+    if (node->op_type() != "Conv") {
+      continue;
+    }
+    // if act input of conv does not have quantize info, continue
+    bool act_has_quantize_info = helper_->quantize_info.find(node->input(0)) !=
+                                 helper_->quantize_info.end();
+    if (!act_has_quantize_info) {
+      continue;
+    }
+
+    // if weight of conv does not have quantize info, continue
+    bool weight_has_quantize_info =
+        helper_->quantize_info.find(node->input(1)) !=
+        helper_->quantize_info.end();
+    if (!weight_has_quantize_info) {
+      continue;
+    }
+    auto next_nodes = name2node_dict_[node->output(0)];
+
+    if (next_nodes.size() > 1 || IsGraphOutput(node->output(0))) {
+      continue;
+    }
+
+    auto next_node = next_nodes[0];
+    if (next_node->op_type() != "Add" || IsGraphOutput(next_node->output(0))) {
+      continue;
+    }
+    std::string reshape_node = node->output(0) == next_node->input(0)
+                                   ? next_node->input(1)
+                                   : next_node->input(0);
+    std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> before_nodes;
+    for (auto& node : *nodes_) {
+      for (size_t i = 0; i < node->output_size(); ++i) {
+        std::string node_output = node->output(i);
+        if (node_output == reshape_node) {
+          before_nodes.push_back(node);
+          break;
+        }
+      }
+    }
+
+    if (before_nodes.size() != 1 || before_nodes[0]->op_type() != "Reshape") {
+      continue;
+    }
+
+    std::string bias_node = before_nodes[0]->input(0);
+    // continue if bias is not a constant
+    std::vector<float> bias_val;
+    if (!GetTensorByName(bias_node, &bias_val)) {
+      continue;
+    }
+
+    // continue if shape tensor of reshape op is not a constant
+    std::vector<int64_t> shape_val;
+    if (!GetTensorByName(before_nodes[0]->input(1), &shape_val)) {
+      continue;
+    }
+    // continue if shape_val != [1, bias_val.size(), 1, 1]
+    std::vector<int64_t> target = {1, static_cast<int64_t>(bias_val.size()), 1,
+                                   1};
+    if (target != shape_val) {
+      continue;
+    }
+    // remove Reshape op
+    RemoveNodeByName(before_nodes[0]->name());
+    // add scale for bias
+    std::vector<float> weight_scale =
+        helper_->quantize_info[node->input(1)].scale_;
+    std::vector<float> act_scale =
+        helper_->quantize_info[node->input(0)].scale_;
+    std::vector<float> bias_scale;
+    for (int64_t i = 0; i < weight_scale.size(); i++) {
+      bias_scale.push_back(weight_scale[i] * act_scale[0]);
+    }
+    std::vector<int64_t> onnx_zeros(bias_scale.size(), 0);
+    auto scale_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT, bias_scale);
+    auto zero_node =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::INT32, onnx_zeros);
+
+    QuantizeInfo quantize_info(bias_scale, onnx_zeros, scale_node, zero_node,
+                               0);
+
+    helper_->quantize_info[bias_node] = quantize_info;
+    AppendQuantizeTensor(bias_node, true);
+    node->add_input(bias_node);
+    RemoveNodeByName(next_node->name());
+  }
+}
+
+void QuantizeModelProcessor::SortNodes() {
+  // return the topo sort of nodes;
+  // 1. Get i2o_mapper and  constant_nodes, i2o_mapper means the node map to its
+  // all output nodes, constant_nodes save all constant nodes.
+  // 2. Nodes without output nodes are first saved to new_nodes, and then
+  // cyclically delete the records of the node in i2o_mapper items, and nodes
+  // whose output nodes are empty are also saved to new_nodes in turn.
+  // 3. Store constant nodes in new_nodes.
+  // 4. Reverse new_nodes, then assign to nodes.
+  std::map<std::string, std::vector<std::string>> i2o_mapper;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> constant_nodes;
+  std::map<std::string, std::shared_ptr<ONNX_NAMESPACE::NodeProto>>
+      name2node_mapper;
+  for (int64_t i = 0; i < nodes_->size(); i++) {
+    auto node = (*nodes_)[i];
+    if (node->op_type() == "Constant") {
+      constant_nodes.push_back(node);
+      continue;
+    }
+    name2node_mapper[node->name()] = node;
+    for (int64_t in_index = 0; in_index < node->input_size(); in_index++) {
+      std::string input = node->input(in_index);
+      for (int64_t j = 0; j < nodes_->size(); j++) {
+        if (i == j) {
+          continue;
+        }
+        auto input_node = (*nodes_)[j];
+        if (input_node->op_type() == "Constant") {
+          continue;
+        }
+        for (int64_t out_index = 0; out_index < input_node->output_size();
+             out_index++) {
+          if (input == input_node->output(out_index)) {
+            if (i2o_mapper.find(input_node->name()) == i2o_mapper.end()) {
+              i2o_mapper[input_node->name()] = {node->name()};
+            } else {
+              auto iter =
+                  std::find(i2o_mapper[input_node->name()].begin(),
+                            i2o_mapper[input_node->name()].end(), node->name());
+              if (iter == i2o_mapper[input_node->name()].end()) {
+                i2o_mapper[input_node->name()].push_back(node->name());
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>> new_nodes;
+
+  for (int64_t i = 0; i < nodes_->size(); i++) {
+    auto node_name = (*nodes_)[i]->name();
+    auto node = (*nodes_)[i];
+    if (node->op_type() == "Constant") {
+      continue;
+    }
+    if (i2o_mapper.find(node_name) == i2o_mapper.end()) {
+      new_nodes.push_back(node);
+    }
+  }
+  int64_t index = 0;
+  while (index < new_nodes.size()) {
+    auto current_node = new_nodes[index];
+    std::string current_node_name = current_node->name();
+    for (auto iter = i2o_mapper.begin(); iter != i2o_mapper.end(); iter++) {
+      std::string input_node_name = iter->first;
+      std::vector<std::string>* output_nodes_name = &iter->second;
+      if (output_nodes_name->empty()) {
+        continue;
+      }
+      auto in_inter = std::find(output_nodes_name->begin(),
+                                output_nodes_name->end(), current_node_name);
+      if (in_inter != output_nodes_name->end()) {
+        output_nodes_name->erase(in_inter);
+      }
+      if (output_nodes_name->empty()) {
+        new_nodes.push_back(name2node_mapper[input_node_name]);
+      }
+    }
+    index++;
+  }
+
+  for (auto& node : constant_nodes) {
+    new_nodes.push_back(node);
+  }
+  std::reverse(new_nodes.begin(), new_nodes.end());
+  Assert(nodes_->size() == new_nodes.size(),
+         "The number of nodes after topological sorting is not equal to the "
+         "number before sorting");
+  *nodes_ = new_nodes;
+}
+
+void QuantizeModelProcessor::RemoveAllQuantizeOps() {
+  UpdateInputNameToNodes();
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    auto node = *iter;
+    if (node->op_type() != "QuantizeLinear") {
+      continue;
+    }
+    auto next_node_names = name2node_dict_[node->output(0)];
+
+    if (next_node_names.empty() || !next_node_names[0]->has_op_type() ||
+        next_node_names[0]->op_type() != "DequantizeLinear") {
+      continue;
+    }
+    std::string input_name = node->input(0);
+    RemoveNodeByName(node->name());
+    std::string output_name = next_node_names[0]->output(0);
+    RemoveNodeByName(next_node_names[0]->name());
+    ReplaceInputOfAllNodes(output_name, input_name);
+  }
+}
+
+// Broadcast quantize info between the input and output of the OPs that will not
+// change quantize info
+void QuantizeModelProcessor::QuantizeInfoBroadcast() {
+  UpdateInputNameToNodes();
+  for (auto iter = nodes_->begin(); iter < nodes_->end(); iter++) {
+    auto node = *iter;
+    if (node->op_type() != "Identity") {
+      continue;
+    }
+    std::string input_name = node->input(0);
+    std::string output_name = node->output(0);
+    auto input_quantize_info_iter = helper_->quantize_info.find(input_name);
+    auto output_quantize_info_iter = helper_->quantize_info.find(output_name);
+    // The input and output of Identity do not have quantize info
+    if (input_quantize_info_iter == helper_->quantize_info.end() &&
+        output_quantize_info_iter == helper_->quantize_info.end()) {
+      continue;
+    }
+    // The input and output of Identity have quantize info
+    if (input_quantize_info_iter != helper_->quantize_info.end() &&
+        output_quantize_info_iter != helper_->quantize_info.end()) {
+      continue;
+    }
+    if (input_quantize_info_iter != helper_->quantize_info.end()) {
+      helper_->quantize_info[output_name] = helper_->quantize_info[input_name];
+    } else if (output_quantize_info_iter != helper_->quantize_info.end()) {
+      helper_->quantize_info[input_name] = helper_->quantize_info[output_name];
+    }
+    if (ConnectToOutput(output_name)) {
+      continue;
+    }
+    RemoveNodeByName(node->name());
+    iter--;
+  }
+}
+
+bool QuantizeModelProcessor::IsGraphOutput(const std::string& name) {
+  for (auto& item : *outputs_) {
+    auto out_node = (*item.get());
+    if (name == out_node.name()) {
+      return true;
+    }
+  }
+  return false;
+}
+
+// Try get tensor shape value
+bool QuantizeModelProcessor::GetTensorShape(const std::string& name,
+                                            std::vector<int64_t>* shape) {
+  for (auto& item : *parameters_) {
+    auto node = *(item.get());
+    if (node.output(0) != name) {
+      continue;
+    }
+    for (auto i = 0; i < node.attribute_size(); i++) {
+      auto attr = node.attribute(i);
+      if (attr.name() == "value") {
+        auto tensor = attr.mutable_t();
+        for (int64_t i = 0; i < tensor->dims_size(); i++) {
+          shape->push_back(tensor->dims(i));
+        }
+      }
+    }
+  }
+  return !shape->empty();
+}
+
+void QuantizeModelProcessor::GetTensorWiseQuantizeInfo(
+    const std::vector<float>& tensor, std::vector<float>* scale,
+    std::vector<int64_t>* zero) {
+  float max_val = -1;
+  for (int64_t i = 0; i < tensor.size(); i++) {
+    if (fabs(tensor[i]) > max_val) {
+      max_val = fabs(tensor[i]);
+    }
+  }
+  Assert(max_val >= 0,
+         "[GetTensorWiseQuantizeInfo] Require the scale >= 0, but now it's " +
+             std::to_string(max_val) + ".");
+  scale->push_back(max_val / 127);
+  zero->push_back(0);
+}
+
+void QuantizeModelProcessor::GetChannelWiseQuantizeInfo(
+    const std::vector<float>& tensor, const std::vector<int64_t>& shape,
+    const int64_t& quant_axis, std::vector<float>* scale,
+    std::vector<int64_t>* zero) {
+  int64_t channel_count = shape[quant_axis];
+
+  for (int64_t i = 0; i < channel_count; i++) {
+    if (quant_axis == 0) {
+      float max_val = -1;
+      int64_t inner_offset = 1;
+      for (auto& j : shape) {
+        inner_offset *= j;
+      }
+      inner_offset /= channel_count;
+      int64_t index = i * inner_offset;
+      for (int64_t j = 0; j < inner_offset; j++) {
+        if (fabs(tensor[index + j]) > max_val) {
+          max_val = fabs(tensor[index + j]);
+        }
+      }
+      Assert(
+          max_val >= 0,
+          "[GetChannelWiseQuantizeInfo] Require the scale >= 0, but now it's " +
+              std::to_string(max_val) + ".");
+      scale->push_back(max_val / 127);
+      zero->push_back(0);
+    } else if (quant_axis == 1) {
+      float max_val = -1;
+      int64_t inner_offset = shape.size() == 4 ? shape[2] * shape[3] : 1;
+      for (int64_t outter = 0; outter < shape[0]; outter++) {
+        int64_t index = outter * channel_count * inner_offset;
+        for (int64_t inner = 0; inner < inner_offset; inner++) {
+          int64_t final_index = index + i * inner_offset + inner;
+          if (fabs(tensor[final_index]) > max_val) {
+            max_val = fabs(tensor[final_index]);
+          }
+        }
+      }
+      Assert(
+          max_val >= 0,
+          "[GetChannelWiseQuantizeInfo] Require the scale >= 0, but now it's " +
+              std::to_string(max_val) + ".");
+      scale->push_back(max_val / 127);
+      zero->push_back(0);
+    } else {
+      Assert(false,
+             "QuantizeModelProcessor::GetChannelWiseQuantizeInfo only supports "
+             "quant_axis equals to 0 or 1, but now it's " +
+                 std::to_string(quant_axis) + ".");
+    }
+  }
+}
+
+template <typename T>
+bool QuantizeModelProcessor::GetTensorByName(const std::string& name,
+                                             std::vector<T>* value) {
+  // Find tensor values in the following order, if found, store the data in
+  // value, and return true：
+  // 1. updated_parameters, the weight of conv or matmul.
+  // 2. parameters of original graph, the scale or bias of BN.
+  // 3. constant node in nodes, other vals.
+  auto updated_params_iter = helper_->updated_params.find(name);
+  if (updated_params_iter != helper_->updated_params.end()) {
+    (updated_params_iter->second).get(value);
+    return true;
+  }
+  for (int64_t block_index = 0; block_index < parser_->NumOfBlocks();
+       block_index++) {
+    if (parser_->TryGetTensorValue(block_index, name, value)) {
+      return true;
+    }
+  }
+  return helper_->TryGetTensorValue(name, value);
+}
+
+bool QuantizeModelProcessor::ConnectToOutput(const std::string& output_name) {
+  std::vector<std::string> names = {output_name};
+  while (!names.empty()) {
+    std::string name = names[names.size() - 1];
+    names.pop_back();
+    if (IsGraphOutput(name)) {
+      return true;
+    }
+    auto next_nodes = name2node_dict_[name];
+    for (auto& next : next_nodes) {
+      if (next->op_type() == "Identity") {
+        names.push_back(next->output(0));
+      }
+    }
+  }
+  return false;
+}
+
+bool QuantizeModelProcessor::CanBeQuantize(
+    const std::vector<std::string>& tensor_names,
+    const std::vector<int64_t>& output_index) {
+  for (auto& tensor : tensor_names) {
+    if (helper_->quantize_info.find(tensor) == helper_->quantize_info.end()) {
+      return false;
+    }
+  }
+  // If there is an OP linked to the output by identity, it needs to be skipped,
+  // do not quantize the OP
+  for (auto i = 0; i < output_index.size(); i++) {
+    int64_t index = output_index[i];
+    if (index == -1) {
+      index = tensor_names.size() - 1;
+    }
+
+    std::string output_name = tensor_names[index];
+    if (ConnectToOutput(output_name)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+void QuantizeModelProcessor::AppendQuantizeTensor(const std::string& tensor,
+                                                  const bool& only_dequantize) {
+  if (only_dequantize) {
+    if (std::find(only_dequantize_tensors.begin(),
+                  only_dequantize_tensors.end(),
+                  tensor) == only_dequantize_tensors.end()) {
+      only_dequantize_tensors.push_back(tensor);
+    }
+  } else {
+    if (std::find(tensors_to_be_quantize.begin(), tensors_to_be_quantize.end(),
+                  tensor) == tensors_to_be_quantize.end()) {
+      tensors_to_be_quantize.push_back(tensor);
+    }
+  }
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/quantize_helper.h b/paddle2onnx/mapper/quantize_helper.h
new file mode 100644
index 0000000000..c60a284f6d
--- /dev/null
+++ b/paddle2onnx/mapper/quantize_helper.h
@@ -0,0 +1,133 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <onnx/onnx_pb.h>
+
+#include <cmath>
+#include <fstream>
+#include <iomanip>
+
+#include "paddle2onnx/mapper/mapper.h"
+#include "paddle2onnx/parser/parser.h"
+namespace paddle2onnx {
+
+struct QuantizeModelProcessor {
+ public:
+  std::vector<QuantizeInfo> quantize_info;
+  const PaddleParser* parser_;
+  OnnxHelper* helper_;
+
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* parameters_;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* inputs_;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* outputs_;
+  std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* nodes_;
+  // All types that support quantization
+  std::vector<std::string> supported_quantize_type_;
+
+  std::map<std::string, std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>>
+      name2node_dict_;
+  std::vector<std::string> tensors_to_be_quantize;  // records those tensors
+                                                    // that need to add quantize
+                                                    // and dequantize op
+  std::vector<std::string> only_dequantize_tensors;  // records those tensors
+                                                     // that only need to add
+                                                     // the dequantize op
+  // Convert to different model formats based on backend, backend can be
+  // TensorRT, ONNXRuntime and Others
+  void ProcessQuantizeModel(
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* parameters,
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* inputs,
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::ValueInfoProto>>* outputs,
+      std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>* nodes,
+      OnnxHelper* helper, const std::string& deploy_backend,
+      const PaddleParser& parser, std::string* calibration_cache = nullptr);
+
+  // Remove all Quantize and Dequantize ops
+  void RemoveAllQuantizeOps();
+
+  // If all tensors in tensor_names have quantize info and all the next nodes
+  // can be quantized, return True, otherwise
+  // return false
+  bool CanBeQuantize(const std::vector<std::string>& tensor_names,
+                     const std::vector<int64_t>& output_index = {-1});
+  // only_dequantize records those tensors that only need to add the dequantize
+  // op
+  void AppendQuantizeTensor(const std::string& tensor,
+                            const bool& only_dequantize = false);
+
+  // Add QDQ for ORT according to:
+  // https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc
+  void AddQDQForORT();
+
+  // Determine if the tensor is directly linked to the output by identity
+  bool ConnectToOutput(const std::string& output_name);
+
+  // Generate cache file for TensorRT8.X int8 deploy
+  void GenerateCache(std::string* calibration_cache);
+
+  // Add QDQ for TRT according to:
+  // https://github.com/NVIDIA/TensorRT/tree/main/tools/pytorch-quantization/pytorch_quantization/nn/modules
+  void AddTrtQDQ();
+
+  // Add QDQ for RKNN
+  void AddQDQForRKNN();
+
+  // Add quantize related op in model according to tensor names
+  void AddQDQInModel(const std::vector<std::string>& tensors_to_be_quantize);
+
+  void QuantizeInfoBroadcast();
+
+  // merge conv + add
+  void MergeConvAdd();
+
+  // merge conv + BN
+  void MergeConvBN();
+
+  // Determine whether a tensor is an output
+  bool IsGraphOutput(const std::string& name);
+
+  // Because processing the quantize model will add new nodes, which will
+  // destroy the topo sorting of nodes, this function will sort the nodes again
+  void SortNodes();
+
+  bool GetTensorShape(const std::string& name, std::vector<int64_t>* shape);
+
+  // return the value of tensor by name
+  template <typename T>
+  bool GetTensorByName(const std::string& name, std::vector<T>* value);
+
+  // Perform tensor wise quantization, returning scale and zero
+  void GetTensorWiseQuantizeInfo(const std::vector<float>& tensor,
+                                 std::vector<float>* scale,
+                                 std::vector<int64_t>* zero);
+
+  // Perform channel wise quantization, returning scale and zero
+  void GetChannelWiseQuantizeInfo(const std::vector<float>& tensor,
+                                  const std::vector<int64_t>& shape,
+                                  const int64_t& quant_axis,
+                                  std::vector<float>* scale,
+                                  std::vector<int64_t>* zero);
+
+  // Generate name2node_dict to save input name and its related nodes
+  void UpdateInputNameToNodes();
+
+  void RemoveNodeByName(const std::string& name, const bool& update_io = true);
+
+  void ReplaceInputOfAllNodes(
+      const std::string& old_name, const std::string& new_name,
+      const std::vector<std::shared_ptr<ONNX_NAMESPACE::NodeProto>>&
+          except_nodes = {});
+};
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/register_mapper.h b/paddle2onnx/mapper/register_mapper.h
new file mode 100644
index 0000000000..1d2253e0f3
--- /dev/null
+++ b/paddle2onnx/mapper/register_mapper.h
@@ -0,0 +1,115 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <fstream>
+#include <map>
+#include <string>
+
+#include "paddle2onnx/utils/utils.h"
+// This code is modified from
+// https://blog.csdn.net/ZJU_fish1996/article/details/86515711
+namespace paddle2onnx {
+class Mapper;
+class PaddleParser;
+class OnnxHelper;
+#define REGISTER_MAPPER(op_name, class_name)                            \
+  class op_name##Generator : public Generator {                         \
+   public:                                                              \
+    op_name##Generator() { MapperHelper::Get()->Push(#op_name, this); } \
+    void Touch() {};                                                    \
+    Mapper* Create(const PaddleParser& p, OnnxHelper* h, int64_t b,     \
+                   int64_t o) {                                         \
+      auto m = new class_name(p, h, b, o);                              \
+      m->name_ = #class_name;                                           \
+      return m;                                                         \
+    }                                                                   \
+  };                                                                    \
+  op_name##Generator* op_name##inst = new op_name##Generator();         \
+  int Touch##op_name##class_name() {                                    \
+    op_name##inst->Touch();                                             \
+    return 0;                                                           \
+  }
+
+class Generator {
+ public:
+  virtual Mapper* Create(const PaddleParser&, OnnxHelper* helper, int64_t,
+                         int64_t) = 0;
+};
+
+class MapperHelper {
+ private:
+  std::map<std::string, Generator*> mappers;
+  std::map<std::string, int64_t> name_counter;
+  MapperHelper() {}
+
+ public:
+  static MapperHelper* helper;
+  static MapperHelper* Get() {
+    if (nullptr == helper) {
+      helper = new MapperHelper();
+    }
+    return helper;
+  }
+
+  int64_t GetAllOps(const std::string& file_path) {
+    std::ofstream outfile(file_path);
+    if (!outfile) {
+      std::cerr << "Failed to open file: " << file_path << std::endl;
+      return mappers.size();
+    }
+    for (auto iter = mappers.begin(); iter != mappers.end(); iter++) {
+      outfile << iter->first << std::endl;
+    }
+    outfile << "Total OPs: " << mappers.size() << std::endl;
+    std::cout << " [ * Paddle2ONNX * ] All Registered OPs saved in "
+              << file_path << std::endl;
+    outfile.close();
+    return mappers.size();
+  }
+
+  bool IsRegistered(const std::string& op_name) {
+    auto iter = mappers.find(op_name);
+    if (mappers.end() == iter) {
+      return false;
+    }
+    return true;
+  }
+
+  std::string GenName(const std::string& op_name) {
+    std::string key = "p2o." + op_name + ".";
+    if (name_counter.find(key) == name_counter.end()) {
+      name_counter[key] = 0;
+    } else {
+      name_counter[key] += 1;
+    }
+    return key + std::to_string(name_counter[key]);
+  }
+
+  void ClearNameCounter() { name_counter.clear(); }
+
+  Mapper* CreateMapper(const std::string& name, const PaddleParser& parser,
+                       OnnxHelper* helper, int64_t block_id, int64_t op_id) {
+    Assert(mappers.find(name) != mappers.end(),
+           name + " cannot be found in registered mappers.");
+    return mappers[name]->Create(parser, helper, block_id, op_id);
+  }
+
+  void Push(const std::string& name, Generator* generator) {
+    Assert(mappers.find(name) == mappers.end(),
+           name + " has been registered before.");
+    mappers[name] = generator;
+  }
+};
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/add_n.cc b/paddle2onnx/mapper/tensor/add_n.cc
new file mode 100644
index 0000000000..24c5c67da5
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/add_n.cc
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/add_n.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(sum, AddNMapper)
+
+void AddNMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  if (x_info.size() == 1) {
+    helper_->AutoCast(x_info[0].name, out_info[0].name, x_info[0].dtype,
+                      out_info[0].dtype);
+  } else {
+    std::vector<std::string> inputs;
+    for (auto i = 0; i < x_info.size(); ++i) {
+      inputs.push_back(helper_->AutoCast(x_info[i].name, x_info[0].dtype,
+                                         P2ODataType::FP32));
+    }
+    auto output = helper_->MakeNode("Sum", inputs)->output(0);
+    helper_->AutoCast(output, out_info[0].name, P2ODataType::FP32,
+                      out_info[0].dtype);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/add_n.h b/paddle2onnx/mapper/tensor/add_n.h
new file mode 100644
index 0000000000..b74191a464
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/add_n.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class AddNMapper : public Mapper {
+ public:
+  AddNMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/argmax.cc b/paddle2onnx/mapper/tensor/argmax.cc
new file mode 100644
index 0000000000..3327eea3d1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/argmax.cc
@@ -0,0 +1,73 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/argmax.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(arg_max, ArgMaxMapper)
+
+int32_t ArgMaxMapper::GetMinOpset(bool verbose) {
+  if (IsAttrVar("axis") && !IsConstant(GetAttrVar("axis")[0])) {
+    Error() << "While Attribute(axis)'s type is Tensor, it's not "
+               "supported "
+               "unless it's a constant tensor."
+            << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void ArgMaxMapper::Opset7() {
+  auto input_info = parser_->GetOpInput(block_idx_, op_idx_, "X");
+  auto output_info = parser_->GetOpOutput(block_idx_, op_idx_, "Out");
+  auto input = input_info[0].name;
+  if (flatten_) {
+    input = helper_->Flatten(input_info[0].name);
+  }
+
+  // Make sure the output tensor has to be 1D-Tensor
+  bool need_unsqueeze = false;
+  if (flatten_ || input_info[0].shape.size() <= 1) {
+    if (!keepdims_) {
+      need_unsqueeze = true;
+    }
+  }
+  if (IsAttrVar("axis")) {
+    auto axis_info = GetAttrVar("axis");
+    std::vector<int64_t> temp;
+    TryGetValue(axis_info[0], &temp);
+    axis_ = temp[0];
+  } else {
+    GetAttr("axis", &axis_);
+  }
+  if (input_info[0].dtype == P2ODataType::FP64) {
+    input = helper_->AutoCast(input, P2ODataType::FP64, P2ODataType::FP32);
+  }
+  if (input_info[0].dtype == P2ODataType::INT64) {
+    input = helper_->AutoCast(input, P2ODataType::INT64, P2ODataType::INT32);
+  }
+  auto arg_node = helper_->MakeNode("ArgMax", {input});
+  AddAttribute(arg_node, "axis", axis_);
+  AddAttribute(arg_node, "keepdims", static_cast<int64_t>(keepdims_));
+  if (!need_unsqueeze) {
+    helper_->AutoCast(arg_node->output(0), output_info[0].name,
+                      P2ODataType::INT64, output_info[0].dtype);
+  } else {
+    auto out = helper_->Unsqueeze(arg_node->output(0), {0});
+    helper_->AutoCast(out, output_info[0].name, P2ODataType::INT64,
+                      output_info[0].dtype);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/argmax.h b/paddle2onnx/mapper/tensor/argmax.h
new file mode 100755
index 0000000000..f7375e94e1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/argmax.h
@@ -0,0 +1,42 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ArgMaxMapper : public Mapper {
+ public:
+  ArgMaxMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("flatten", &flatten_);
+    GetAttr("keepdims", &keepdims_);
+    GetAttr("dtype", &dtype_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  bool flatten_;
+  bool keepdims_;
+  int64_t axis_;
+  int64_t dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/argmin.cc b/paddle2onnx/mapper/tensor/argmin.cc
new file mode 100644
index 0000000000..f0be6eda14
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/argmin.cc
@@ -0,0 +1,73 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/argmin.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(arg_min, ArgMinMapper)
+
+int32_t ArgMinMapper::GetMinOpset(bool verbose) {
+  if (IsAttrVar("axis") && !IsConstant(GetAttrVar("axis")[0])) {
+    Error() << "While Attribute(axis)'s type is Tensor, it's not "
+               "supported "
+               "unless it's a constant tensor."
+            << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void ArgMinMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto input = input_info[0].name;
+  if (flatten_) {
+    input = helper_->Flatten(input_info[0].name);
+  }
+
+  // Make sure the output tensor has to be 1D-Tensor
+  bool need_unsqueeze = false;
+  if (flatten_ || input_info[0].shape.size() <= 1) {
+    if (!keepdims_) {
+      need_unsqueeze = true;
+    }
+  }
+  if (IsAttrVar("axis")) {
+    auto axis_info = GetAttrVar("axis");
+    std::vector<int64_t> temp;
+    TryGetValue(axis_info[0], &temp);
+    axis_ = temp[0];
+  } else {
+    GetAttr("axis", &axis_);
+  }
+  if (input_info[0].dtype == P2ODataType::FP64) {
+    input = helper_->AutoCast(input, P2ODataType::FP64, P2ODataType::FP32);
+  }
+  if (input_info[0].dtype == P2ODataType::INT64) {
+    input = helper_->AutoCast(input, P2ODataType::INT64, P2ODataType::INT32);
+  }
+  auto arg_node = helper_->MakeNode("ArgMin", {input});
+  AddAttribute(arg_node, "axis", axis_);
+  AddAttribute(arg_node, "keepdims", static_cast<int64_t>(keepdims_));
+  if (!need_unsqueeze) {
+    helper_->AutoCast(arg_node->output(0), output_info[0].name,
+                      P2ODataType::INT64, output_info[0].dtype);
+  } else {
+    auto out = helper_->Unsqueeze(arg_node->output(0), {0});
+    helper_->AutoCast(out, output_info[0].name, P2ODataType::INT64,
+                      output_info[0].dtype);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/argmin.h b/paddle2onnx/mapper/tensor/argmin.h
new file mode 100755
index 0000000000..ca29d1a590
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/argmin.h
@@ -0,0 +1,42 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ArgMinMapper : public Mapper {
+ public:
+  ArgMinMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("flatten", &flatten_);
+    GetAttr("keepdims", &keepdims_);
+    GetAttr("dtype", &dtype_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  bool flatten_;
+  bool keepdims_;
+  int64_t axis_;
+  int64_t dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/argsort.cc b/paddle2onnx/mapper/tensor/argsort.cc
new file mode 100644
index 0000000000..bef1002137
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/argsort.cc
@@ -0,0 +1,77 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/argsort.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(argsort, ArgsortMapper)
+
+int32_t ArgsortMapper::GetMinOpset(bool verbose) {
+  if (!descending_) {
+    Logger(verbose, 11) << "While descending=False, " << RequireOpset(11)
+                        << std::endl;
+    return 11;
+  }
+
+  if (axis_ < 0) {
+    axis_ = axis_ + GetInput("X")[0].Rank();
+  }
+  if (GetInput("X")[0].shape[axis_] <= 0) {
+    Logger(verbose, 10) << "While input shape is dynamic, " << RequireOpset(10)
+                        << std::endl;
+    return 10;
+  }
+  return 7;
+}
+
+void ArgsortMapper::Opset10() {
+  auto x_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto indices_info = GetOutput("Indices");
+
+  auto shape = helper_->MakeNode("Shape", {x_info[0].name})->output(0);
+  if (axis_ < 0) {
+    axis_ = axis_ + x_info[0].Rank();
+  }
+  auto dim_size = helper_->Slice(shape, {0}, {axis_}, {axis_ + 1});
+
+  auto out_node =
+      helper_->MakeNode("TopK", {x_info[0].name, dim_size},
+                        {output_info[0].name, indices_info[0].name});
+  AddAttribute(out_node, "axis", axis_);
+  if (helper_->GetOpsetVersion() > 10) {
+    if (!descending_) {
+      AddAttribute(out_node, "largest", static_cast<int64_t>(0));
+    } else {
+      AddAttribute(out_node, "largest", static_cast<int64_t>(1));
+    }
+  }
+}
+
+void ArgsortMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto indices_info = GetOutput("Indices");
+
+  if (axis_ < 0) {
+    axis_ = axis_ + x_info[0].Rank();
+  }
+
+  auto out_node = helper_->MakeNode(
+      "TopK", {x_info[0].name}, {output_info[0].name, indices_info[0].name});
+  AddAttribute(out_node, "axis", axis_);
+  AddAttribute(out_node, "k", x_info[0].shape[axis_]);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/argsort.h b/paddle2onnx/mapper/tensor/argsort.h
new file mode 100644
index 0000000000..c339566729
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/argsort.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ArgsortMapper : public Mapper {
+ public:
+  ArgsortMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("descending", &descending_);
+    GetAttr("axis", &axis_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset10();
+  void Opset7();
+
+ private:
+  bool descending_;
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/assign.cc b/paddle2onnx/mapper/tensor/assign.cc
new file mode 100644
index 0000000000..a4d0d3553d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/assign.cc
@@ -0,0 +1,48 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/assign.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(assign, AssignMapper)
+REGISTER_MAPPER(share_data, AssignMapper)
+
+void AssignMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  if (block_idx_ != 0 && OpType() != "share_data") {
+    // Here's a trick for tensorrt
+    // Consider remove this trick
+    if (input_info[0].dtype == P2ODataType::BOOL) {
+      auto zero = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                    std::vector<int64_t>(1, 0));
+      auto cast_input = helper_->AutoCast(input_info[0].name, P2ODataType::BOOL,
+                                          P2ODataType::INT64);
+      auto result = helper_->MakeNode("Add", {cast_input, zero})->output(0);
+      helper_->AutoCast(result, output_info[0].name, P2ODataType::INT64,
+                        output_info[0].dtype);
+    } else {
+      auto zero = helper_->Constant(GetOnnxDtype(input_info[0].dtype),
+                                    std::vector<double>(1, 0.0));
+      auto new_input =
+          helper_->Unsqueeze(input_info[0].name, std::vector<int64_t>(1, 0));
+      auto result = helper_->MakeNode("Add", {new_input, zero})->output(0);
+      helper_->Squeeze(result, output_info[0].name, std::vector<int64_t>(1, 0));
+    }
+  } else {
+    helper_->MakeNode("Identity", {input_info[0].name}, {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/assign.h b/paddle2onnx/mapper/tensor/assign.h
new file mode 100644
index 0000000000..ad9585978f
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/assign.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class AssignMapper : public Mapper {
+ public:
+  AssignMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/assign_value.cc b/paddle2onnx/mapper/tensor/assign_value.cc
new file mode 100644
index 0000000000..6f476cd444
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/assign_value.cc
@@ -0,0 +1,49 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/assign_value.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(assign_value, AssignValueMapper)
+
+int32_t AssignValueMapper::GetMinOpset(bool verbose) {
+  int32_t dtype = static_cast<int32_t>(dtype_);
+  if (dtype != P2ODataType::INT32 && dtype != P2ODataType::INT64 &&
+      dtype != P2ODataType::FP32) {
+    Error() << "Only supports int32/int64/float32." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void AssignValueMapper::Opset7() {
+  auto output_info = GetOutput("Out");
+  int32_t dtype = static_cast<int32_t>(dtype_);
+  if (dtype == P2ODataType::INT32) {
+    helper_->Assign(output_info[0].name, GetOnnxDtype(output_info[0].dtype),
+                    shape_, int64_values_);
+  } else if (dtype == P2ODataType::FP32) {
+    helper_->Assign(output_info[0].name, GetOnnxDtype(output_info[0].dtype),
+                    shape_, fp32_values_);
+  } else if (dtype == P2ODataType::INT64) {
+    helper_->Assign(output_info[0].name, GetOnnxDtype(output_info[0].dtype),
+                    shape_, int64_values_);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/assign_value.h b/paddle2onnx/mapper/tensor/assign_value.h
new file mode 100644
index 0000000000..ebda258857
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/assign_value.h
@@ -0,0 +1,49 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class AssignValueMapper : public Mapper {
+ public:
+  AssignValueMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("dtype", &dtype_);
+    GetAttr("shape", &shape_);
+    int32_t dtype = static_cast<int32_t>(dtype_);
+    if (dtype == P2ODataType::INT32) {
+      GetAttr("int32_values", &int64_values_);
+    } else if (dtype == P2ODataType::FP32) {
+      GetAttr("fp32_values", &fp32_values_);
+    } else if (dtype == P2ODataType::INT64) {
+      GetAttr("int64_values", &int64_values_);
+    }
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<float> fp32_values_;
+  std::vector<int64_t> int64_values_;
+  std::vector<int64_t> shape_;
+  int64_t dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/bmm.cc b/paddle2onnx/mapper/tensor/bmm.cc
new file mode 100644
index 0000000000..7d8c0b76d2
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/bmm.cc
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/bmm.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(bmm, BmmMapper)
+
+void BmmMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  auto y = helper_->AutoCast(y_info[0].name, y_info[0].dtype, x_info[0].dtype);
+  helper_->MakeNode("MatMul", {x_info[0].name, y}, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/bmm.h b/paddle2onnx/mapper/tensor/bmm.h
new file mode 100644
index 0000000000..0f0057f78d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/bmm.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class BmmMapper : public Mapper {
+ public:
+  BmmMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/cast.cc b/paddle2onnx/mapper/tensor/cast.cc
new file mode 100644
index 0000000000..8e9c6c647d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/cast.cc
@@ -0,0 +1,28 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/cast.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(cast, CastMapper)
+
+void CastMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto node =
+      helper_->MakeNode("Cast", {input_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "to", GetOnnxDtype(out_dtype_));
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/cast.h b/paddle2onnx/mapper/tensor/cast.h
new file mode 100644
index 0000000000..d95c9c6582
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/cast.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class CastMapper : public Mapper {
+ public:
+  CastMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("out_dtype", &out_dtype_);
+  }
+  void Opset7();
+
+ private:
+  int64_t out_dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/clip.cc b/paddle2onnx/mapper/tensor/clip.cc
new file mode 100644
index 0000000000..6ef10525f3
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/clip.cc
@@ -0,0 +1,89 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/clip.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(clip, ClipMapper)
+
+int32_t ClipMapper::GetMinOpset(bool verbose) {
+  bool has_max_tensor_input = HasInput("Max");
+  bool has_min_tensor_input = HasInput("Min");
+  if (has_max_tensor_input || has_min_tensor_input) {
+    return 11;
+  }
+  return 7;
+}
+
+void ClipMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  bool has_max_tensor_input = HasInput("Max");
+  bool has_min_tensor_input = HasInput("Min");
+
+  if (has_max_tensor_input || has_min_tensor_input) {
+    bool dtype_converted = false;
+    std::string input_name = input_info[0].name;
+    int32_t dtype = input_info[0].dtype;
+    // onnxruntime only supports float input
+    if (input_info[0].dtype != P2ODataType::FP32) {
+      input_name = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                     P2ODataType::FP32);
+      dtype_converted = true;
+      dtype = P2ODataType::FP32;
+    }
+    std::string max_name;
+    if (has_max_tensor_input) {
+      auto max_info = GetInput("Max");
+      max_name = helper_->AutoCast(max_info[0].name, max_info[0].dtype, dtype);
+      if (max_info[0].Rank() > 0) {
+        max_name = helper_->Squeeze(max_name, {});
+      }
+    } else {
+      float max_val;
+      GetAttr("max", &max_val);
+      max_name = helper_->Constant({}, GetOnnxDtype(dtype), max_val);
+    }
+    std::string min_name;
+    if (has_min_tensor_input) {
+      auto min_info = GetInput("Min");
+      min_name = helper_->AutoCast(min_info[0].name, min_info[0].dtype, dtype);
+      if (min_info[0].Rank() > 0) {
+        min_name = helper_->Squeeze(min_name, {});
+      }
+    } else {
+      float min_val;
+      GetAttr("min", &min_val);
+      min_name = helper_->Constant({}, GetOnnxDtype(dtype), min_val);
+    }
+    if (dtype_converted) {
+      auto node = helper_->MakeNode("Clip", {input_name, min_name, max_name});
+      helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                        output_info[0].dtype);
+    } else {
+      helper_->MakeNode("Clip", {input_name, min_name, max_name},
+                        {output_info[0].name});
+    }
+  } else {
+    float max_val;
+    GetAttr("max", &max_val);
+    float min_val;
+    GetAttr("min", &min_val);
+    helper_->Clip(input_info[0].name, output_info[0].name, min_val, max_val,
+                  input_info[0].dtype);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/clip.h b/paddle2onnx/mapper/tensor/clip.h
new file mode 100644
index 0000000000..2e2019da69
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/clip.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ClipMapper : public Mapper {
+ public:
+  ClipMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/concat.cc b/paddle2onnx/mapper/tensor/concat.cc
new file mode 100644
index 0000000000..3b106bd5af
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/concat.cc
@@ -0,0 +1,72 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/concat.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(concat, ConcatMapper)
+
+int32_t ConcatMapper::GetMinOpset(bool verbose) {
+  if (HasInput("AxisTensor") && !IsConstantInput("AxisTensor")) {
+    Error() << "While AxisTensor as input exists, it's not supported unless "
+               "it's a constant tensor."
+            << std::endl;
+    return -1;
+  } else if (IsAttrVar("axis") && !IsConstant(GetAttrVar("axis")[0])) {
+    Error() << "While Attribute(axis)'s type is Tensor, it's not supported "
+               "unless it's a constant tensor."
+            << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void ConcatMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  int32_t casted_dtype;
+  std::vector<std::string> casted_names =
+      helper_->DtypeAlignment(input_info, &casted_dtype);
+
+  auto parse_axis_value = [&](const TensorInfo& tensor_info, int64_t& axis) {
+    std::vector<int64_t> value;
+    TryGetValue(tensor_info, &value);
+    axis = value[0];
+  };
+
+  bool has_axis_tensor_input = HasInput("AxisTensor");
+
+  int64_t axis = axis_;
+  // NOTE(Aurelius84): we need to deprecate this branch in the future.
+  if (has_axis_tensor_input) {
+    auto info = GetInput("AxisTensor");
+    parse_axis_value(info[0], axis);
+  } else if (IsAttrVar("axis")) {
+    auto info = GetAttrVar("axis");
+    parse_axis_value(info[0], axis);
+  }
+  if (axis < 0) {
+    axis = axis + input_info[0].Rank();
+  }
+  auto node = helper_->MakeNode("Concat", casted_names);
+  AddAttribute(node, "axis", axis);
+  helper_->AutoCast(node->output(0), output_info[0].name, casted_dtype,
+                    output_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/concat.h b/paddle2onnx/mapper/tensor/concat.h
new file mode 100644
index 0000000000..4a4a79df17
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/concat.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ConcatMapper : public Mapper {
+ public:
+  ConcatMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/cumsum.cc b/paddle2onnx/mapper/tensor/cumsum.cc
new file mode 100644
index 0000000000..f16ef65c76
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/cumsum.cc
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/cumsum.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(cumsum, CumsumMapper)
+
+int32_t CumsumMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void CumsumMapper::Opset11() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  std::string axis_node;
+  if (IsAttrVar("axis")) {
+    auto axis_info = GetAttrVar("axis");
+    axis_node = helper_->AutoCast(axis_info[0].name, axis_info[0].dtype,
+                                  P2ODataType::INT64);
+  } else {
+    GetAttr("axis", &axis_);
+    axis_node = helper_->Constant({1}, GetOnnxDtype(P2ODataType::INT64), axis_);
+  }
+  helper_->MakeNode("CumSum", {input_info[0].name, axis_node},
+                    {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/cumsum.h b/paddle2onnx/mapper/tensor/cumsum.h
new file mode 100644
index 0000000000..7f747f55c3
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/cumsum.h
@@ -0,0 +1,35 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class CumsumMapper : public Mapper {
+ public:
+  CumsumMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/dist.cc b/paddle2onnx/mapper/tensor/dist.cc
new file mode 100644
index 0000000000..b69bb3de70
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/dist.cc
@@ -0,0 +1,67 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/dist.h"
+
+#include <cmath>
+#include <vector>
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(dist, DistMapper)
+
+const int g_NegIntInfinity = 0xFF800000;
+const float g_NegFloatInfinity = *((float *)&g_NegIntInfinity);
+
+void DistMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+
+  auto sub_node = helper_->MakeNode("Sub", {x_info[0].name, y_info[0].name});
+  auto abs_node = helper_->MakeNode("Abs", {sub_node->output(0)});
+
+  if (fabs(p_) < 1e-6) {
+    auto sign_node = helper_->MakeNode("Sign", {abs_node->output(0)});
+    auto sum_node = helper_->MakeNode("ReduceSum", {sign_node->output(0)});
+    AddAttribute(sum_node, "keepdims", static_cast<int64_t>(0));
+    auto s_sum_node = helper_->Reshape(sum_node->output(0), {-1});
+    helper_->AutoCast(s_sum_node, output_info[0].name, x_info[0].dtype,
+                      output_info[0].dtype);
+  } else if (p_ == std::numeric_limits<float>::infinity()) {
+    auto max_node = helper_->MakeNode("ReduceMax", {abs_node->output(0)});
+    AddAttribute(max_node, "keepdims", static_cast<int64_t>(0));
+    auto s_max_node = helper_->Reshape(max_node->output(0), {-1});
+    helper_->AutoCast(s_max_node, output_info[0].name, x_info[0].dtype,
+                      output_info[0].dtype);
+  } else if (p_ == g_NegFloatInfinity) {
+    auto min_node = helper_->MakeNode("ReduceMin", {abs_node->output(0)});
+    AddAttribute(min_node, "keepdims", static_cast<int64_t>(0));
+    auto s_min_node = helper_->Reshape(min_node->output(0), {-1});
+    helper_->AutoCast(s_min_node, output_info[0].name, x_info[0].dtype,
+                      output_info[0].dtype);
+  } else {
+    std::string p = helper_->Constant({1}, GetOnnxDtype(x_info[0].dtype), p_);
+    auto pow_node = helper_->MakeNode("Pow", {abs_node->output(0), p});
+
+    auto sum_node = helper_->MakeNode("ReduceSum", {pow_node->output(0)});
+    AddAttribute(sum_node, "keepdims", static_cast<int64_t>(0));
+    auto s_node = helper_->Reshape(sum_node->output(0), {-1});
+
+    auto p_1 = helper_->MakeNode("Reciprocal", {p});
+    helper_->MakeNode("Pow", {s_node, p_1->output(0)}, {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/dist.h b/paddle2onnx/mapper/tensor/dist.h
new file mode 100644
index 0000000000..8d7eeedaec
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/dist.h
@@ -0,0 +1,34 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class DistMapper : public Mapper {
+ public:
+  DistMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("p", &p_);
+  }
+  void Opset7();
+
+ private:
+  float p_;
+};
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/dot.cc b/paddle2onnx/mapper/tensor/dot.cc
new file mode 100755
index 0000000000..0349b80c3b
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/dot.cc
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/dot.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(dot, DotMapper)
+
+void DotMapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+
+  auto mul_node =
+      helper_->MakeNode("Mul", {input_x_info[0].name, input_y_info[0].name});
+
+  if (helper_->GetOpsetVersion() >= 13) {
+    std::string axes_node = helper_->Constant(
+        {1}, GetOnnxDtype(P2ODataType::INT64), input_x_info[0].Rank() - 1);
+    helper_->MakeNode("ReduceSum", {mul_node->output(0), axes_node},
+                      {output_info[0].name});
+  } else {
+    auto reducesum_node = helper_->MakeNode("ReduceSum", {mul_node->output(0)},
+                                            {output_info[0].name});
+    std::vector<int64_t> axes = {input_x_info[0].Rank() - 1};
+    AddAttribute(reducesum_node, "axes", axes);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/dot.h b/paddle2onnx/mapper/tensor/dot.h
new file mode 100644
index 0000000000..a9575060fc
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/dot.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class DotMapper : public Mapper {
+ public:
+  DotMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/equal.cc b/paddle2onnx/mapper/tensor/equal.cc
new file mode 100644
index 0000000000..e06be2442e
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/equal.cc
@@ -0,0 +1,44 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/equal.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(equal, EqualMapper)
+
+int32_t EqualMapper::GetMinOpset(bool verbose) {
+  if (axis_ != -1) {
+    Error() << "axis attribute must be -1 in operator equal." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void EqualMapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+
+  std::string input_x = input_x_info[0].name;
+  std::string input_y = input_y_info[0].name;
+  if (helper_->GetOpsetVersion() < 11) {
+    input_x = helper_->AutoCast(input_x_info[0].name, input_x_info[0].dtype,
+                                P2ODataType::INT32);
+    input_y = helper_->AutoCast(input_y_info[0].name, input_y_info[0].dtype,
+                                P2ODataType::INT32);
+  }
+  helper_->MakeNode("Equal", {input_x, input_y}, {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/equal.h b/paddle2onnx/mapper/tensor/equal.h
new file mode 100644
index 0000000000..23a85b6493
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/equal.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class EqualMapper : public Mapper {
+ public:
+  EqualMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/expand.cc b/paddle2onnx/mapper/tensor/expand.cc
new file mode 100644
index 0000000000..f1a9cbf5ea
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/expand.cc
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/expand.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(expand, ExpandMapper)
+
+void ExpandMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string expand_times = "";
+  if (HasInput("expand_times_tensor")) {
+    auto info = GetInput("expand_times_tensor");
+    expand_times = helper_->ConcatIndices(info);
+  } else if (HasInput("ExpandTimes")) {
+    auto info = GetInput("ExpandTimes");
+    expand_times = helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
+  } else {
+    expand_times = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, expand_times_);
+  }
+
+  helper_->MakeNode("Tile",
+                      {input_info[0].name, expand_times},
+                      {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/expand.h b/paddle2onnx/mapper/tensor/expand.h
new file mode 100644
index 0000000000..5d45aa6afe
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/expand.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ExpandMapper : public Mapper {
+ public:
+  ExpandMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+        GetAttr("expand_times", &expand_times_);
+      }
+  void Opset7();
+
+  private:
+  std::vector<int64_t> expand_times_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/expand_as.cc b/paddle2onnx/mapper/tensor/expand_as.cc
new file mode 100644
index 0000000000..5a678aff71
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/expand_as.cc
@@ -0,0 +1,52 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/expand_as.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(expand_as_v2, ExpandAsMapper)
+
+int32_t ExpandAsMapper::GetMinOpset(bool verbose) {
+  if (target_shape_.size() == 0 && !HasInput("target_tensor")) {
+    Error() << "Attribute `target_shape` or input tensor `target_tensor` is "
+               "not exist"
+            << std::endl;
+    return -1;
+  }
+  Logger(verbose, 8) << RequireOpset(8) << std::endl;
+  return 8;
+};
+
+void ExpandAsMapper::Opset8() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string target_shape = "";
+  if (HasInput("target_tensor")) {
+    auto info = GetInput("target_tensor");
+    target_shape = helper_->MakeNode("Shape", {info[0].name})->output(0);
+  } else {
+    target_shape =
+        helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, target_shape_);
+  }
+
+  helper_->MakeNode("Expand", {input_info[0].name, target_shape},
+                    {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/expand_as.h b/paddle2onnx/mapper/tensor/expand_as.h
new file mode 100644
index 0000000000..b45a433786
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/expand_as.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ExpandAsMapper : public Mapper {
+ public:
+  ExpandAsMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("target_shape", &target_shape_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset8();
+
+ private:
+  std::vector<int64_t> target_shape_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/expand_v2.cc b/paddle2onnx/mapper/tensor/expand_v2.cc
new file mode 100644
index 0000000000..c8758bbb03
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/expand_v2.cc
@@ -0,0 +1,61 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/expand_v2.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(expand_v2, ExpandV2Mapper)
+
+void ExpandV2Mapper::Opset8() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+
+  int dim_diff = 0;
+  std::string shape = "";
+  if (HasInput("Shape")) {
+    auto shape_info = GetInput("Shape");
+    dim_diff = shape_info[0].shape[0] - x_info[0].Rank();
+    shape = helper_->AutoCast(shape_info[0].name, shape_info[0].dtype,
+                              P2ODataType::INT64);
+  } else if (HasInput("expand_shapes_tensor")) {
+    auto shape_info = GetInput("expand_shapes_tensor");
+    dim_diff = shape_info.size() - x_info[0].Rank();
+    shape = helper_->ConcatIndices(shape_info);
+  } else {
+    std::vector<int64_t> shape_value;
+    GetAttr("shape", &shape_value);
+    dim_diff = shape_value.size() - x_info[0].Rank();
+    shape = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, shape_value);
+  }
+
+  auto input_shape = helper_->MakeNode("Shape", {x_info[0].name})->output(0);
+  if (dim_diff > 0) {
+    auto padding_shape = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                           std::vector<int64_t>(dim_diff, 1));
+    input_shape = helper_->Concat({padding_shape, input_shape}, 0);
+  }
+  if (helper_->GetOpsetVersion() < 12) {
+    // While opset < 12, Max cannot support int64 datatype with onnxruntime
+    input_shape =
+        helper_->AutoCast(input_shape, P2ODataType::INT64, P2ODataType::FP32);
+    shape = helper_->AutoCast(shape, P2ODataType::INT64, P2ODataType::FP32);
+    shape = helper_->MakeNode("Max", {input_shape, shape})->output(0);
+    shape = helper_->AutoCast(shape, P2ODataType::FP32, P2ODataType::INT64);
+  } else {
+    shape = helper_->MakeNode("Max", {input_shape, shape})->output(0);
+  }
+  helper_->MakeNode("Expand", {x_info[0].name, shape}, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/expand_v2.h b/paddle2onnx/mapper/tensor/expand_v2.h
new file mode 100644
index 0000000000..00dc098c8f
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/expand_v2.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ExpandV2Mapper : public Mapper {
+ public:
+  ExpandV2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 8) << RequireOpset(8) << std::endl;
+    return 8;
+  }
+  void Opset8();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/eye.cc b/paddle2onnx/mapper/tensor/eye.cc
new file mode 100644
index 0000000000..5af3aa6757
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/eye.cc
@@ -0,0 +1,87 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/eye.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(eye, EyeMapper)
+
+void EyeMapper::ParseValue(const TensorInfo& tensor_info, int64_t* num_val) {
+  std::vector<int64_t> value;
+  TryGetValue(tensor_info, &value);
+  *num_val = value[0];
+}
+
+int32_t EyeMapper::GetMinOpset(bool verbose) {
+  if (IsAttrVar("num_rows")) {
+    if (!IsConstant(GetAttrVar("num_rows")[0])) {
+      Error()
+          << "While Attribute(num_rows)'s type is Tensor, it's not supported "
+             "unless it's a constant tensor."
+          << std::endl;
+      return -1;
+    } else {
+      auto info = GetAttrVar("num_rows");
+      ParseValue(info[0], &num_rows_);
+    }
+  } else {
+    GetAttr("num_rows", &num_rows_);
+  }
+  if (IsAttrVar("num_columns")) {
+    if (!IsConstant(GetAttrVar("num_columns")[0])) {
+      Error() << "While Attribute(num_columns)'s type is Tensor, it's not "
+                 "supported "
+                 "unless it's a constant tensor."
+              << std::endl;
+      return -1;
+    } else {
+      auto info = GetAttrVar("num_columns");
+      ParseValue(info[0], &num_columns_);
+    }
+  } else {
+    GetAttr("num_columns", &num_columns_);
+  }
+
+  if (num_rows_ <= 0 || num_columns_ <= 0) {
+    Error() << "Attribute `num_rows` or  `num_columns` must greater than 0. "
+            << std::endl;
+    return -1;
+  }
+  Logger(verbose, 9) << RequireOpset(9) << std::endl;
+  return 9;
+}
+
+void EyeMapper::Opset9() {
+  auto output_info = GetOutput("Out");
+  if (IsAttrVar("num_rows")) {
+    auto info = GetAttrVar("num_rows");
+    ParseValue(info[0], &num_rows_);
+  } else {
+    GetAttr("num_rows", &num_rows_);
+  }
+  if (IsAttrVar("num_columns")) {
+    auto info = GetAttrVar("num_columns");
+    ParseValue(info[0], &num_columns_);
+  } else {
+    GetAttr("num_columns", &num_columns_);
+  }
+
+  std::string constant_node = helper_->Constant(
+      {num_rows_, num_columns_}, GetOnnxDtype(output_info[0].dtype), 0);
+
+  auto node =
+      helper_->MakeNode("EyeLike", {constant_node}, {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/eye.h b/paddle2onnx/mapper/tensor/eye.h
new file mode 100644
index 0000000000..7aa8629b28
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/eye.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class EyeMapper : public Mapper {
+ public:
+  EyeMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset9();
+
+ private:
+  void ParseValue(const TensorInfo& tensor_info, int64_t* num_val);
+  int64_t num_rows_;
+  int64_t num_columns_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/fill_constant.cc b/paddle2onnx/mapper/tensor/fill_constant.cc
new file mode 100644
index 0000000000..13b68b5286
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/fill_constant.cc
@@ -0,0 +1,153 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/tensor/fill_constant.h"
+
+#include <sstream>
+#include <vector>
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(fill_constant, FillConstantMapper)
+
+int32_t FillConstantMapper::GetMinOpset(bool verbose) {
+  auto out_info = GetOutput("Out");
+  auto onnx_dtype = GetOnnxDtype(out_info[0].dtype);
+  if (onnx_dtype != ONNX_NAMESPACE::TensorProto::INT32 &&
+      onnx_dtype != ONNX_NAMESPACE::TensorProto::INT64 &&
+      onnx_dtype != ONNX_NAMESPACE::TensorProto::FLOAT &&
+      onnx_dtype != ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    Error() << "Only support int32/int64/float32/float64 data type in "
+               "fill_constant operator."
+            << std::endl;
+    return -1;
+  }
+  if (HasInput("ShapeTensorList")) {
+    Logger(verbose, 9) << "While ShapeTensorList as input, " << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  if (HasInput("ShapeTensor") && !IsConstantInput("ShapeTensor")) {
+    Logger(verbose, 9) << "While ShapeTensor as input and it's not a constant tensor, " << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  return 7;
+}
+
+float FillConstantMapper::GetFillValue() {
+  float value = 0;
+  if (str_value_.empty()) {
+    value = value_;
+  } else {
+    if (str_value_ == "inf") {
+      value = std::numeric_limits<float>::infinity();
+    } else if (str_value_ == "-inf") {
+      value = -std::numeric_limits<float>::infinity();
+    } else if (str_value_ == "nan") {
+      value = std::numeric_limits<float>::quiet_NaN();
+    } else {
+      std::stringstream convert_stream(str_value_);
+      convert_stream >> value;
+    }
+  }
+  if (HasInput("ValueTensor")) {
+    value = 0.0;
+  }
+  return value;
+}
+
+void FillConstantMapper::Opset7() {
+  auto out_info = GetOutput("Out");
+  Assert(!HasInput("ShapeTensorList"), "While ShapeTensorList as input, requires opset_version>=9 for op fill_constant.");
+  std::vector<int64_t> shape;
+  if (HasInput("ShapeTensor")) {
+    Assert(TryGetInputValue("ShapeTensor", &shape), "While ShapeTensor as input and it's not a constant tensor, requires opset_version>=9 for op fill_constant.");
+  } else {
+    GetAttr("shape", &shape);
+  }
+  float value = GetFillValue();
+  if (HasInput("ValueTensor")) {
+    auto value_info = GetInput("ValueTensor");
+    auto value_tensor = helper_->AutoCast(value_info[0].name, value_info[0].dtype, out_info[0].dtype);
+    auto out = helper_->Constant(shape, GetOnnxDtype(out_info[0].dtype), float(0.0));
+    helper_->MakeNode("Add", {out, value_tensor}, {out_info[0].name});
+  } else {
+    helper_->Constant(out_info[0].name, shape, GetOnnxDtype(out_info[0].dtype), value);
+  }
+}
+
+void FillConstantMapper::Opset9() {
+  if (GetMinOpset() == 7) {
+    return Opset7();
+  }
+  auto out_info = GetOutput("Out");
+  bool shape_is_tensor = HasInput("ShapeTensor") || HasInput("ShapeTensorList");
+  bool value_is_tensor = HasInput("ValueTensor");
+  auto onnx_dtype = GetOnnxDtype(out_info[0].dtype);
+  float value = GetFillValue();
+  std::string out;
+  if (shape_is_tensor) {
+    std::string shape_name;
+    if (HasInput("ShapeTensor")) {
+      auto shape_info = GetInput("ShapeTensor");
+      shape_name = helper_->AutoCast(shape_info[0].name, shape_info[0].dtype,
+                                     P2ODataType::INT64);
+    } else {
+      auto shape_info = GetInput("ShapeTensorList");
+      shape_name = helper_->ConcatIndices(shape_info);
+    }
+
+    auto node = helper_->MakeNode("ConstantOfShape", {shape_name});
+    auto attr = node->add_attribute();
+    attr->set_name("value");
+    attr->set_type(ONNX_NAMESPACE::AttributeProto::TENSOR);
+    auto tensor = attr->mutable_t();
+    tensor->set_name(out_info[0].name);
+    tensor->set_data_type(onnx_dtype);
+    tensor->add_dims(1);
+    if (onnx_dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+      std::vector<int32_t> data(1);
+      data[0] = static_cast<int32_t>(value);
+      auto ptr = reinterpret_cast<char*>(data.data());
+      tensor->set_raw_data(std::string(ptr, sizeof(int32_t)));
+    } else if (onnx_dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+      std::vector<int64_t> data(1);
+      data[0] = static_cast<int64_t>(value);
+      auto ptr = reinterpret_cast<char*>(data.data());
+      tensor->set_raw_data(std::string(ptr, sizeof(int64_t)));
+    } else if (onnx_dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+      std::vector<float> data(1, value_);
+      auto ptr = reinterpret_cast<char*>(data.data());
+      tensor->set_raw_data(std::string(ptr, sizeof(float)));
+    } else if (onnx_dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+      std::vector<double> data(1);
+      data[0] = static_cast<double>(value);
+      auto ptr = reinterpret_cast<char*>(data.data());
+      tensor->set_raw_data(std::string(ptr, sizeof(double)));
+    }
+    out = node->output(0);
+  } else {
+    std::vector<int64_t> shape;
+    GetAttr("shape", &shape);
+    out = helper_->Constant(shape, onnx_dtype, value);
+  }
+  if (value_is_tensor) {
+    auto value_info = GetInput("ValueTensor");
+    std::string cast_value = helper_->AutoCast(
+        value_info[0].name, value_info[0].dtype, out_info[0].dtype);
+    helper_->MakeNode("Add", {out, cast_value}, {out_info[0].name});
+  } else {
+    helper_->MakeNode("Identity", {out}, {out_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/fill_constant.h b/paddle2onnx/mapper/tensor/fill_constant.h
new file mode 100644
index 0000000000..231cb84021
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/fill_constant.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class FillConstantMapper : public Mapper {
+ public:
+  FillConstantMapper(const PaddleParser& p, OnnxHelper* helper,
+                     int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("str_value", &str_value_);
+    GetAttr("value", &value_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset9();
+
+ private:
+  float GetFillValue();
+  std::string str_value_;
+  float value_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/fill_constant_batch_size_like.cc b/paddle2onnx/mapper/tensor/fill_constant_batch_size_like.cc
new file mode 100644
index 0000000000..80fbc78772
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/fill_constant_batch_size_like.cc
@@ -0,0 +1,84 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/tensor/fill_constant_batch_size_like.h"
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(fill_constant_batch_size_like, FillConstantBatchSizeLikeMapper)
+
+int32_t FillConstantBatchSizeLikeMapper::GetMinOpset(bool verbose) {
+  auto out_info = GetOutput("Out");
+  if (out_info[0].dtype == P2ODataType::BOOL) {
+    Error() << "Dtype of boolean is not supported." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void FillConstantBatchSizeLikeMapper::Opset7() {
+  auto input_info = GetInput("Input");
+  auto out_info = GetOutput("Out");
+  float value = value_;
+  if (!str_value_.empty()) {
+    std::stringstream convert_stream(str_value_);
+    convert_stream >> value;
+  }
+  std::vector<int64_t> shape;
+  shape.assign(shape_.begin(), shape_.end());
+  shape[output_dim_idx_] = 1;
+
+  auto input_shape =
+      helper_->MakeNode("Shape", {input_info[0].name})->output(0);
+  auto batch =
+      helper_->Slice(input_shape, {0}, {input_dim_idx_}, {input_dim_idx_ + 1});
+
+  if (output_dim_idx_ == 0) {
+    auto constant =
+        helper_->Constant(shape, GetOnnxDtype(out_info[0].dtype), value);
+    auto repeat = batch;
+    if (shape.size() > 1) {
+      auto tmp = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                   std::vector<int64_t>(shape.size() - 1, 1));
+      repeat = helper_->Concat({batch, tmp}, 0);
+    }
+    helper_->MakeNode("Tile", {constant, repeat}, {out_info[0].name});
+  } else if (output_dim_idx_ == shape.size() - 1) {
+    auto constant =
+        helper_->Constant(shape, GetOnnxDtype(out_info[0].dtype), value);
+    auto repeat = batch;
+    if (shape.size() > 1) {
+      auto tmp = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                   std::vector<int64_t>(shape.size() - 1, 1));
+      repeat = helper_->Concat({tmp, batch}, 0);
+    }
+    helper_->MakeNode("Tile", {constant, repeat}, {out_info[0].name});
+  } else {
+    shape.erase(shape.begin() + output_dim_idx_);
+    shape.insert(shape.begin(), int64_t(1));
+    auto constant =
+        helper_->Constant(shape, GetOnnxDtype(out_info[0].dtype), value);
+    auto repeat = batch;
+    if (shape.size() > 1) {
+      auto tmp = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                   std::vector<int64_t>(shape.size() - 1, 1));
+      repeat = helper_->Concat({batch, tmp}, 0);
+    }
+    auto out = helper_->MakeNode("Tile", {constant, repeat})->output(0);
+    auto perm = Arange(1, shape.size());
+    perm.insert(perm.begin() + output_dim_idx_, int64_t(0));
+    helper_->Transpose(out, out_info[0].name, perm);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/fill_constant_batch_size_like.h b/paddle2onnx/mapper/tensor/fill_constant_batch_size_like.h
new file mode 100644
index 0000000000..c58ecf870d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/fill_constant_batch_size_like.h
@@ -0,0 +1,48 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class FillConstantBatchSizeLikeMapper : public Mapper {
+ public:
+  FillConstantBatchSizeLikeMapper(const PaddleParser& p, OnnxHelper* helper,
+                                  int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("dtype", &dtype_);
+    GetAttr("value", &value_);
+    GetAttr("shape", &shape_);
+    GetAttr("str_value", &str_value_);
+    GetAttr("input_dim_idx", &input_dim_idx_);
+    GetAttr("output_dim_idx", &output_dim_idx_);
+  }
+
+  int32_t GetMinOpset(bool verbose = true);
+  void Opset7();
+
+ private:
+  int64_t dtype_;
+  float value_;
+  std::string str_value_;
+  int64_t input_dim_idx_;
+  int64_t output_dim_idx_;
+  std::vector<int64_t> shape_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/fill_like.cc b/paddle2onnx/mapper/tensor/fill_like.cc
new file mode 100644
index 0000000000..5759502432
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/fill_like.cc
@@ -0,0 +1,48 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/tensor/fill_like.h"
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(fill_any_like, FillLikeMapper)
+REGISTER_MAPPER(fill_zeros_like, FillLikeMapper)
+
+void FillLikeMapper::Opset9() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  bool is_fixed_shape = true;
+  for (size_t i = 0; i < input_info[0].shape.size(); ++i) {
+    if (input_info[0].shape[i] < 0) {
+      is_fixed_shape = false;
+    }
+  }
+  if (is_fixed_shape) {
+    helper_->Constant(output_info[0].name, input_info[0].shape,
+                      GetOnnxDtype(output_info[0].dtype), value_);
+    return;
+  }
+  auto shape_node = helper_->MakeNode("Shape", {input_info[0].name});
+  int64_t dtype = output_info[0].dtype;
+  // There's some problem with tensorrt with `ConstantOfShape`
+  // Maybe we should use a graph pass to solve this problem
+  // but now we just to avoid using `ConstantOfShape`
+  auto const_node = helper_->Constant({1}, GetOnnxDtype(dtype), value_);
+  helper_->MakeNode("Expand", {const_node, shape_node->output(0)},
+                    {output_info[0].name});
+  //    helper_->ConstOfShape(shape_node->output(0), output_info[0].name,
+  //                         GetOnnxDtype(dtype), value_);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/fill_like.h b/paddle2onnx/mapper/tensor/fill_like.h
new file mode 100644
index 0000000000..b0b0ad2397
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/fill_like.h
@@ -0,0 +1,46 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class FillLikeMapper : public Mapper {
+ public:
+  FillLikeMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    if (OpType() == "fill_zeros_like") {
+      value_ = 0.0;
+    } else {
+      GetAttr("value", &value_);
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  void Opset9();
+
+ private:
+  float value_;
+  std::vector<std::string> op_mapper_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/flatten.cc b/paddle2onnx/mapper/tensor/flatten.cc
new file mode 100644
index 0000000000..5407138f49
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/flatten.cc
@@ -0,0 +1,75 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/flatten.h"
+
+#include <vector>
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(flatten_contiguous_range, FlattenMapper)
+
+void FlattenMapper::Opset7() {
+  auto input_info = GetInput("X");
+  if (start_axis_ < 0) {
+    start_axis_ += input_info[0].Rank();
+  }
+  if (stop_axis_ < 0) {
+    stop_axis_ += input_info[0].Rank();
+  }
+  auto output_info = GetOutput("Out");
+
+  auto unknown_dim_node =
+      helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, -1);
+  if (start_axis_ == 0 && stop_axis_ == input_info[0].Rank() - 1) {
+    helper_->MakeNode("Reshape", {input_info[0].name, unknown_dim_node},
+                      {output_info[0].name});
+  } else {
+    auto input_shape_node = helper_->MakeNode("Shape", {input_info[0].name});
+    if (start_axis_ == 0) {
+      auto second_part_shape =
+          helper_->Slice(input_shape_node->output(0), {0}, {stop_axis_ + 1},
+                         {input_info[0].Rank()});
+      auto new_shape_node =
+          helper_->MakeNode("Concat", {unknown_dim_node, second_part_shape});
+      AddAttribute(new_shape_node, "axis", int64_t(0));
+      helper_->MakeNode("Reshape",
+                        {input_info[0].name, new_shape_node->output(0)},
+                        {output_info[0].name});
+    } else if (stop_axis_ == input_info[0].Rank() - 1) {
+      auto first_part_shape =
+          helper_->Slice(input_shape_node->output(0), {0}, {0}, {start_axis_});
+      auto new_shape_node =
+          helper_->MakeNode("Concat", {first_part_shape, unknown_dim_node});
+      AddAttribute(new_shape_node, "axis", int64_t(0));
+      helper_->MakeNode("Reshape",
+                        {input_info[0].name, new_shape_node->output(0)},
+                        {output_info[0].name});
+    } else {
+      auto first_part_shape =
+          helper_->Slice(input_shape_node->output(0), {0}, {0}, {start_axis_});
+      auto second_part_shape =
+          helper_->Slice(input_shape_node->output(0), {0}, {stop_axis_ + 1},
+                         {input_info[0].Rank()});
+      auto new_shape_node = helper_->MakeNode(
+          "Concat", {first_part_shape, unknown_dim_node, second_part_shape});
+      AddAttribute(new_shape_node, "axis", int64_t(0));
+      helper_->MakeNode("Reshape",
+                        {input_info[0].name, new_shape_node->output(0)},
+                        {output_info[0].name});
+    }
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/flatten.h b/paddle2onnx/mapper/tensor/flatten.h
new file mode 100644
index 0000000000..0789e49bf7
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/flatten.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class FlattenMapper : public Mapper {
+ public:
+  FlattenMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("start_axis", &start_axis_);
+    GetAttr("stop_axis", &stop_axis_);
+  }
+
+  void Opset7();
+
+ private:
+  int64_t start_axis_;
+  int64_t stop_axis_;
+};
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/flatten2.cc b/paddle2onnx/mapper/tensor/flatten2.cc
new file mode 100644
index 0000000000..2615efa1e3
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/flatten2.cc
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/flatten2.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(flatten2, Flatten2Mapper)
+
+int32_t Flatten2Mapper::GetMinOpset(bool verbose) {
+  if (GetInput("X")[0].dtype != P2ODataType::FP32 || GetInput("X")[0].dtype != P2ODataType::FP64) {
+    Logger(verbose, 9) << "While data type of input is not float32/float64, "<< RequireOpset(9) << std::endl;
+    return 9;
+  }
+  return 7;
+}
+
+void Flatten2Mapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  int64_t axis = axis_;
+  auto node = helper_->MakeNode("Flatten", {input_info[0].name}, {output_info[0].name});
+  AddAttribute(node, "axis", axis);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/flatten2.h b/paddle2onnx/mapper/tensor/flatten2.h
new file mode 100644
index 0000000000..05656335d3
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/flatten2.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Flatten2Mapper : public Mapper {
+ public:
+  Flatten2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/flip.cc b/paddle2onnx/mapper/tensor/flip.cc
new file mode 100644
index 0000000000..6c423e9884
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/flip.cc
@@ -0,0 +1,89 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/flip.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(flip, FlipMapper)
+
+int32_t FlipMapper::GetMinOpset(bool verbose) {
+  auto input_info = parser_->GetOpInput(block_idx_, op_idx_, "X");
+  for (auto i = 0; i < axes_.size(); i++) {
+    if (input_info[0].shape[axes_[i]] <= 0) {
+      Error() << "The dimension in axis of input must be fixed for flip "
+                 "operator, but now the input shape in axis is unkown."
+              << std::endl;
+      return -1;
+    }
+  }
+  return 7;
+}
+
+void FlipMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string input_name = input_info[0].name;
+  bool need_convert = false;
+  if (input_info[0].dtype == P2ODataType::BOOL ||
+      input_info[0].dtype == P2ODataType::FP64) {
+    need_convert = true;
+    input_name = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+  }
+
+  std::string temp_input = input_name;
+  for (auto i = 0; i < axes_.size(); ++i) {
+    int64_t axis = axes_[i];
+    if (input_info[0].shape[axis] == 1) {
+      if (i != axes_.size() - 1) {
+        continue;
+      }
+      if (need_convert) {
+        input_name = helper_->AutoCast(temp_input, output_info[0].name,
+                                       P2ODataType::FP32, output_info[0].dtype);
+      } else {
+        auto out_node =
+            helper_->MakeNode("Identity", {temp_input}, {output_info[0].name});
+      }
+    } else {
+      std::vector<int64_t> split;
+      split.resize(input_info[0].shape[axis], 1);
+      std::vector<std::string> splits_outputs =
+          helper_->Split(temp_input, split, axis);
+      std::vector<std::string> reversed_splits;
+      for (int64_t index = splits_outputs.size() - 1; index >= 0; --index) {
+        reversed_splits.push_back(splits_outputs[index]);
+      }
+      if (i != axes_.size() - 1) {
+        auto concat_node = helper_->MakeNode("Concat", reversed_splits);
+        AddAttribute(concat_node, "axis", axis);
+        temp_input = concat_node->output(0);
+      } else {
+        if (need_convert) {
+          auto concat_node = helper_->MakeNode("Concat", reversed_splits);
+          AddAttribute(concat_node, "axis", axis);
+          helper_->AutoCast(concat_node->output(0), output_info[0].name,
+                            P2ODataType::FP32, output_info[0].dtype);
+        } else {
+          auto concat_node = helper_->MakeNode("Concat", reversed_splits,
+                                               {output_info[0].name});
+          AddAttribute(concat_node, "axis", axis);
+        }
+      }
+    }
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/flip.h b/paddle2onnx/mapper/tensor/flip.h
new file mode 100755
index 0000000000..5e40d3e1aa
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/flip.h
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class FlipMapper : public Mapper {
+ public:
+  FlipMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axes_);
+    auto input_info = GetInput("X");
+    for (auto i = 0; i < axes_.size(); i++) {
+      if (axes_[i] < 0) {
+        axes_[i] += input_info[0].Rank();
+      }
+    }
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<int64_t> axes_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/gather.cc b/paddle2onnx/mapper/tensor/gather.cc
new file mode 100644
index 0000000000..4cfb6f371a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/gather.cc
@@ -0,0 +1,85 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/gather.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(gather, GatherMapper)
+
+int32_t GatherMapper::GetMinOpset(bool verbose) {
+  if (HasInput("Axis")) {
+    if (!IsConstantInput("Axis")) {
+      Error() << "Parameter axis as input tensor is not supported."
+              << std::endl;
+      return -1;
+    }
+  }
+  auto index_info = GetInput("Index");
+  if (index_info[0].shape.size() > 1) {
+    Logger(verbose, 11) << "While rank of index > 1, " << RequireOpset(11)
+                        << std::endl;
+    return 11;
+  }
+  return 7;
+}
+
+void GatherMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto index_info = GetInput("Index");
+  auto out_info = GetOutput("Out");
+
+  bool has_input_axis = HasInput("Axis");
+  auto axis = axis_;
+  if (has_input_axis) {
+    std::vector<int64_t> axes;
+    Assert(TryGetInputValue("Axis", &axes),
+           "Paddle2ONNX does not support axis as input tensor for operator: "
+           "gather.");
+    axis = axes[0];
+  }
+  Assert(index_info[0].shape.size() == 1,
+         "Paddle2ONNX: While rank of index > 1, opset must >= 11 for operator: "
+         "gather.");
+  auto node = helper_->MakeNode("Gather", {x_info[0].name, index_info[0].name},
+                                {out_info[0].name});
+  AddAttribute(node, "axis", axis);
+}
+
+void GatherMapper::Opset11() {
+  auto x_info = GetInput("X");
+  auto index_info = GetInput("Index");
+  auto out_info = GetOutput("Out");
+
+  bool has_input_axis = HasInput("Axis");
+  auto axis = axis_;
+  if (has_input_axis) {
+    std::vector<int64_t> axes;
+    Assert(TryGetInputValue("Axis", &axes),
+           "Paddle2ONNX does not support axis as input tensor for operator: "
+           "gather.");
+    axis = axes[0];
+  }
+  if (index_info[0].shape.size() == 1) {
+    auto node = helper_->MakeNode(
+        "Gather", {x_info[0].name, index_info[0].name}, {out_info[0].name});
+    AddAttribute(node, "axis", axis);
+  } else {
+    auto index = helper_->AutoCast(index_info[0].name, index_info[0].dtype,
+                                   P2ODataType::INT64);
+    helper_->MakeNode("GatherND", {x_info[0].name, index_info[0].name},
+                      {out_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/gather.h b/paddle2onnx/mapper/tensor/gather.h
new file mode 100644
index 0000000000..b12c7f5b2c
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/gather.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GatherMapper : public Mapper {
+ public:
+  GatherMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    if (HasAttr("axis")) {
+      GetAttr("axis", &axis_);
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset11();
+
+ private:
+  int64_t axis_ = 0;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/gather_nd.cc b/paddle2onnx/mapper/tensor/gather_nd.cc
new file mode 100755
index 0000000000..d7fcb875df
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/gather_nd.cc
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/gather_nd.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(gather_nd, GatherNdMapper)
+
+int32_t GatherNdMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void GatherNdMapper::Opset11() {
+  auto input_x_info = GetInput("X");
+  auto input_index_info = GetInput("Index");
+  auto output_info = GetOutput("Out");
+
+  std::string index_node = helper_->AutoCast(
+      input_index_info[0].name, input_index_info[0].dtype, P2ODataType::INT64);
+
+  helper_->MakeNode("GatherND", {input_x_info[0].name, index_node},
+                    {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/gather_nd.h b/paddle2onnx/mapper/tensor/gather_nd.h
new file mode 100755
index 0000000000..ae7de4f5c4
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/gather_nd.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GatherNdMapper : public Mapper {
+ public:
+  GatherNdMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/gaussian_random.cc b/paddle2onnx/mapper/tensor/gaussian_random.cc
new file mode 100644
index 0000000000..511a11d6d8
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/gaussian_random.cc
@@ -0,0 +1,64 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/gaussian_random.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(gaussian_random, GaussianRandomMapper)
+
+int32_t GaussianRandomMapper::GetMinOpset(bool verbose) {
+  if (HasInput("ShapeTensor") && !IsConstantInput("ShapeTensor")) {
+    Logger(verbose, 9) << "While ShapeTensor as input and it's not a constant tensor, " << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  if (HasInput("ShapeTensorList")) {
+    Logger(verbose, 9) << "While ShapeTensorList as input, " << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  return 7;
+}
+
+void GaussianRandomMapper::Opset7() {
+  auto out_info = GetOutput("Out");
+  std::string shape_tensor_name = "";
+  std::vector<int64_t> shape;
+  if (HasInput("ShapeTensor")) {
+    if (!TryGetInputValue("ShapeTensor", &shape)) {
+      auto shape_info = GetInput("ShapeTensor");
+      shape_tensor_name = helper_->AutoCast(shape_info[0].name, shape_info[0].dtype, P2ODataType::INT64);
+    }
+  } else if (HasInput("ShapeTensorList")) {
+    auto shape_info = GetInput("ShapeTensorList");
+    shape_tensor_name = helper_->ConcatIndices(shape_info);
+  } else {
+    shape.assign(shape_.begin(), shape_.end()); 
+  }
+  if (shape.size() > 0) {
+    auto node = helper_->MakeNode("RandomNormal", {}, {out_info[0].name});
+    AddAttribute(node, "dtype", GetOnnxDtype(out_info[0].dtype));
+    AddAttribute(node, "mean", mean_);
+    AddAttribute(node, "scale", std_);
+    AddAttribute(node, "shape", shape_);
+    AddAttribute(node, "seed", static_cast<float>(seed_));
+  } else {
+    auto tensor = helper_->ConstOfShape(shape_tensor_name, GetOnnxDtype(out_info[0].dtype), float(0));
+    auto node = helper_->MakeNode("RandomNormalLike", {tensor}, {out_info[0].name});
+    AddAttribute(node, "dtype", GetOnnxDtype(out_info[0].dtype));
+    AddAttribute(node, "mean", mean_);
+    AddAttribute(node, "scale", std_);
+    AddAttribute(node, "seed", static_cast<float>(seed_));
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/gaussian_random.h b/paddle2onnx/mapper/tensor/gaussian_random.h
new file mode 100644
index 0000000000..0fde072ed0
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/gaussian_random.h
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GaussianRandomMapper : public Mapper {
+ public:
+  GaussianRandomMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("mean", &mean_);
+    GetAttr("std", &std_);
+    GetAttr("shape", &shape_);
+    GetAttr("seed", &seed_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+ private:
+  std::vector<int64_t> shape_;
+  float mean_;
+  float std_;
+  int64_t seed_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/greater_equal.cc b/paddle2onnx/mapper/tensor/greater_equal.cc
new file mode 100644
index 0000000000..ac386755b0
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/greater_equal.cc
@@ -0,0 +1,51 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/greater_equal.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(greater_equal, GreaterEqualMapper)
+
+void GreaterEqualMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+  if (out_dtype != P2ODataType::FP32 && out_dtype != P2ODataType::FP64 &&
+      helper_->GetOpsetVersion() < 11) {
+    aligned_inputs[0] =
+        helper_->AutoCast(aligned_inputs[0], out_dtype, P2ODataType::FP32);
+    aligned_inputs[1] =
+        helper_->AutoCast(aligned_inputs[1], out_dtype, P2ODataType::FP32);
+  }
+
+  auto out = helper_->MakeNode("Less", aligned_inputs)->output(0);
+  helper_->MakeNode("Not", {out}, {out_info[0].name});
+}
+
+void GreaterEqualMapper::Opset12() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+
+  helper_->MakeNode("GreaterOrEqual", aligned_inputs, {out_info[0].name});
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/greater_equal.h b/paddle2onnx/mapper/tensor/greater_equal.h
new file mode 100644
index 0000000000..200ea6bd8c
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/greater_equal.h
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GreaterEqualMapper : public Mapper {
+ public:
+  GreaterEqualMapper(const PaddleParser& p, OnnxHelper* helper,
+                     int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+  void Opset12();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/greater_than.cc b/paddle2onnx/mapper/tensor/greater_than.cc
new file mode 100644
index 0000000000..36c4220a64
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/greater_than.cc
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/greater_than.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(greater_than, GreaterThanMapper)
+
+void GreaterThanMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+  if (out_dtype != P2ODataType::FP32 && out_dtype != P2ODataType::FP64 &&
+      helper_->GetOpsetVersion() < 11) {
+    aligned_inputs[0] =
+        helper_->AutoCast(aligned_inputs[0], out_dtype, P2ODataType::FP32);
+    aligned_inputs[1] =
+        helper_->AutoCast(aligned_inputs[1], out_dtype, P2ODataType::FP32);
+  }
+
+  helper_->MakeNode("Greater", aligned_inputs, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/greater_than.h b/paddle2onnx/mapper/tensor/greater_than.h
new file mode 100644
index 0000000000..c9867d864c
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/greater_than.h
@@ -0,0 +1,28 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GreaterThanMapper : public Mapper {
+ public:
+  GreaterThanMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/grid_sampler.cc b/paddle2onnx/mapper/tensor/grid_sampler.cc
new file mode 100644
index 0000000000..0a08e4cdac
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/grid_sampler.cc
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/grid_sampler.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(grid_sampler, GridSamplerMapper)
+
+int32_t GridSamplerMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 16) << RequireOpset(16) << std::endl;
+  return 16;
+};
+
+void GridSamplerMapper::Opset16() {
+  auto x_info = GetInput("X");
+  auto grid_info = GetInput("Grid");
+  auto out_info = GetOutput("Output");
+  std::string cast_input =
+      helper_->AutoCast(x_info[0].name, x_info[0].dtype, P2ODataType::FP32);
+  std::string cast_grid = helper_->AutoCast(
+      grid_info[0].name, grid_info[0].dtype, P2ODataType::FP32);
+  auto node = helper_->MakeNode("GridSample", {cast_input, cast_grid});
+  AddAttribute(node, "padding_mode", padding_mode_);
+  AddAttribute(node, "mode", mode_);
+  AddAttribute(node, "align_corners", static_cast<int64_t>(align_corners_));
+  helper_->AutoCast(node->output(0), out_info[0].name, P2ODataType::FP32,
+                    out_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/grid_sampler.h b/paddle2onnx/mapper/tensor/grid_sampler.h
new file mode 100644
index 0000000000..28da730ca3
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/grid_sampler.h
@@ -0,0 +1,43 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class GridSamplerMapper : public Mapper {
+ public:
+  GridSamplerMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("padding_mode", &padding_mode_);
+    GetAttr("mode", &mode_);
+    GetAttr("align_corners", &align_corners_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+
+  void Opset16();
+
+ private:
+  std::string padding_mode_ = "zeros";
+  std::string mode_ = "bilinear";
+  bool align_corners_ = false;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/index_sample.cc b/paddle2onnx/mapper/tensor/index_sample.cc
new file mode 100644
index 0000000000..1522dcbefe
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/index_sample.cc
@@ -0,0 +1,44 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/index_sample.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(index_sample, IndexSampleMapper)
+
+int32_t IndexSampleMapper::GetMinOpset(bool verbose) {
+  auto x_info = GetInput("X");
+  auto index_info = GetInput("Index");
+  if (x_info[0].Rank() != 2 || index_info[0].Rank() != 2) {
+    Error() << "The rank of X and Index must be 2, but the rank of X is: "
+            << x_info[0].Rank()
+            << " , and the rank of Index is: " << index_info[0].Rank() << "."
+            << std::endl;
+    return -1;
+  }
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void IndexSampleMapper::Opset11() {
+  auto x_info = GetInput("X");
+  auto index_info = GetInput("Index");
+  auto out_info = GetOutput("Out");
+  auto node =
+      helper_->MakeNode("GatherElements", {x_info[0].name, index_info[0].name},
+                        {out_info[0].name});
+  AddAttribute(node, "axis", static_cast<int64_t>(1));
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/index_sample.h b/paddle2onnx/mapper/tensor/index_sample.h
new file mode 100755
index 0000000000..f6bcaabd5a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/index_sample.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class IndexSampleMapper : public Mapper {
+ public:
+  IndexSampleMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/index_select.cc b/paddle2onnx/mapper/tensor/index_select.cc
new file mode 100644
index 0000000000..3ff40cd2ab
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/index_select.cc
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/index_select.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(index_select, IndexSelectMapper)
+
+void IndexSelectMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto index_info = GetInput("Index");
+  auto out_info = GetOutput("Out");
+  auto node = helper_->MakeNode("Gather", {x_info[0].name, index_info[0].name},
+                                {out_info[0].name});
+  AddAttribute(node, "axis", axis_);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/index_select.h b/paddle2onnx/mapper/tensor/index_select.h
new file mode 100644
index 0000000000..463718a736
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/index_select.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class IndexSelectMapper : public Mapper {
+ public:
+  IndexSelectMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("dim", &axis_);
+  }
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/less_equal.cc b/paddle2onnx/mapper/tensor/less_equal.cc
new file mode 100644
index 0000000000..43abd8f8b5
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/less_equal.cc
@@ -0,0 +1,51 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/less_equal.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(less_equal, LessEqualMapper)
+
+void LessEqualMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+  if (out_dtype != P2ODataType::FP32 && out_dtype != P2ODataType::FP64 &&
+      helper_->GetOpsetVersion() < 11) {
+    aligned_inputs[0] =
+        helper_->AutoCast(aligned_inputs[0], out_dtype, P2ODataType::FP32);
+    aligned_inputs[1] =
+        helper_->AutoCast(aligned_inputs[1], out_dtype, P2ODataType::FP32);
+  }
+
+  auto out = helper_->MakeNode("Greater", aligned_inputs)->output(0);
+  helper_->MakeNode("Not", {out}, {out_info[0].name});
+}
+
+void LessEqualMapper::Opset12() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+
+  helper_->MakeNode("LessOrEqual", aligned_inputs, {out_info[0].name});
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/less_equal.h b/paddle2onnx/mapper/tensor/less_equal.h
new file mode 100644
index 0000000000..c1a2654256
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/less_equal.h
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LessEqualMapper : public Mapper {
+ public:
+  LessEqualMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+  void Opset12();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/less_than.cc b/paddle2onnx/mapper/tensor/less_than.cc
new file mode 100644
index 0000000000..f0db74dcac
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/less_than.cc
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/less_than.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(less_than, LessThanMapper)
+
+void LessThanMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+  if (out_dtype != P2ODataType::FP32 && out_dtype != P2ODataType::FP64 &&
+      helper_->GetOpsetVersion() < 11) {
+    aligned_inputs[0] =
+        helper_->AutoCast(aligned_inputs[0], out_dtype, P2ODataType::FP32);
+    aligned_inputs[1] =
+        helper_->AutoCast(aligned_inputs[1], out_dtype, P2ODataType::FP32);
+  }
+
+  helper_->MakeNode("Less", aligned_inputs, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/less_than.h b/paddle2onnx/mapper/tensor/less_than.h
new file mode 100644
index 0000000000..a25432d1b1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/less_than.h
@@ -0,0 +1,28 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LessThanMapper : public Mapper {
+ public:
+  LessThanMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/linspace.cc b/paddle2onnx/mapper/tensor/linspace.cc
new file mode 100644
index 0000000000..9d62fb058f
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/linspace.cc
@@ -0,0 +1,75 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/linspace.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(linspace, LinspaceMapper)
+
+int32_t LinspaceMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 9) << RequireOpset(9) << std::endl;
+  return 9;
+};
+
+void LinspaceMapper::Opset9() {
+  auto start_info = GetInput("Start");
+  auto stop_info = GetInput("Stop");
+  auto num_info = GetInput("Num");
+  auto output_info = GetOutput("Out");
+
+  std::string cast_start = helper_->AutoCast(
+      start_info[0].name, start_info[0].dtype, P2ODataType::FP32);
+  std::string cast_stop = helper_->AutoCast(
+      stop_info[0].name, stop_info[0].dtype, P2ODataType::FP32);
+
+  auto sub_a_node = helper_->MakeNode("Sub", {cast_stop, cast_start});
+
+  std::string one_node = helper_->Constant(GetOnnxDtype(num_info[0].dtype),
+                                           std::vector<int32_t>(1, 1));
+
+  auto sub_b_node = helper_->MakeNode("Sub", {num_info[0].name, one_node});
+
+  std::string sub_b_float_node = helper_->AutoCast(
+      sub_b_node->output(0), num_info[0].dtype, P2ODataType::FP32);
+
+  auto step =
+      helper_->MakeNode("Div", {sub_a_node->output(0), sub_b_float_node});
+
+  std::string range_tensor = helper_->AutoCast(
+      num_info[0].name, num_info[0].dtype, P2ODataType::INT64);
+
+  std::string one_like_node = helper_->ConstOfShape(
+      range_tensor, GetOnnxDtype(P2ODataType::FP32), static_cast<float>(1));
+
+  auto none_zero_node = helper_->MakeNode("NonZero", {one_like_node});
+
+  std::string trans_squeeze =
+      helper_->Squeeze(none_zero_node->output(0), std::vector<int64_t>(1, 0));
+
+  std::string cast_trans_squeeze =
+      helper_->AutoCast(trans_squeeze, P2ODataType::INT64, P2ODataType::FP32);
+
+  auto mul_node =
+      helper_->MakeNode("Mul", {cast_trans_squeeze, step->output(0)});
+
+  auto add_node = helper_->MakeNode("Add", {mul_node->output(0), cast_start});
+
+  helper_->AutoCast(add_node->output(0), output_info[0].name, P2ODataType::FP32,
+                    output_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/linspace.h b/paddle2onnx/mapper/tensor/linspace.h
new file mode 100644
index 0000000000..b43c97d472
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/linspace.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LinspaceMapper : public Mapper {
+ public:
+  LinspaceMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("dtype", &dtype_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset9();
+
+ private:
+  int64_t dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/logical_not.cc b/paddle2onnx/mapper/tensor/logical_not.cc
new file mode 100644
index 0000000000..c1b79aba01
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/logical_not.cc
@@ -0,0 +1,27 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/logical_not.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(logical_not, LogicalNotMapper)
+
+void LogicalNotMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  helper_->MakeNode("Not", {input_info[0].name}, {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/logical_not.h b/paddle2onnx/mapper/tensor/logical_not.h
new file mode 100644
index 0000000000..f667628fb5
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/logical_not.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LogicalNotMapper : public Mapper {
+ public:
+  LogicalNotMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/logical_op.cc b/paddle2onnx/mapper/tensor/logical_op.cc
new file mode 100644
index 0000000000..5f0646228a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/logical_op.cc
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/logical_op.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(logical_and, LogicalOpMapper)
+REGISTER_MAPPER(logical_or, LogicalOpMapper)
+REGISTER_MAPPER(logical_xor, LogicalOpMapper)
+
+void LogicalOpMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  std::map<std::string, std::string> op_mapper;
+  op_mapper["logical_and"] = "And";
+  op_mapper["logical_or"] = "Or";
+  op_mapper["logical_xor"] = "Xor";
+
+  helper_->MakeNode(op_mapper[OpType()], {x_info[0].name, y_info[0].name},
+                    {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/logical_op.h b/paddle2onnx/mapper/tensor/logical_op.h
new file mode 100644
index 0000000000..163b668514
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/logical_op.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LogicalOpMapper : public Mapper {
+ public:
+  LogicalOpMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                  int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/lookup_table.cc b/paddle2onnx/mapper/tensor/lookup_table.cc
new file mode 100644
index 0000000000..5342ba2ef1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/lookup_table.cc
@@ -0,0 +1,132 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/lookup_table.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(lookup_table, LookupTableMapper)
+REGISTER_MAPPER(lookup_table_v2, LookupTableMapper)
+
+int32_t LookupTableMapper::GetMinOpset(bool verbose) {
+  auto input_w_info = GetInput("W");
+  bool has_minus = false;
+  for (auto i : input_w_info[0].shape) {
+    has_minus = (i == -1);
+    if (has_minus) {
+      break;
+    }
+  }
+  if (padding_idx_ != -1 && has_minus) {
+    Logger(verbose, 11)
+        << "While the input W has dynamic shape and padding_idx != -1, "
+        << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  return 7;
+}
+
+void LookupTableMapper::Opset7() {
+  auto input_ids_info = GetInput("Ids");
+  auto input_w_info = GetInput("W");
+  auto output_info = GetOutput("Out");
+
+  std::string ids_node = input_ids_info[0].name;
+  auto ids_shape = input_ids_info[0].shape;
+  if (OpType() == "lookup_table" && ids_shape[ids_shape.size() - 1] == 1) {
+    ids_node = helper_->Squeeze(input_ids_info[0].name, {-1});
+  }
+
+  auto input_shape = input_w_info[0].shape;
+  int64_t sum_val = 1;
+  for (auto i : input_shape) {
+    sum_val *= i;
+  }
+  int interval = sum_val / input_shape[0];
+
+  if (padding_idx_ != -1) {
+    std::vector<int64_t> data(sum_val, 1);
+    for (auto i = 0; i < interval; i++) {
+      data[padding_idx_ * interval + i] = 0;
+    }
+    std::string constant = helper_->Constant(
+        input_shape, GetOnnxDtype(input_w_info[0].dtype), data);
+    auto weight_node =
+        helper_->MakeNode("Mul", {input_w_info[0].name, constant});
+    helper_->MakeNode("Gather", {weight_node->output(0), ids_node},
+                      {output_info[0].name});
+  } else {
+    helper_->MakeNode("Gather", {input_w_info[0].name, ids_node},
+                      {output_info[0].name});
+  }
+}
+
+void LookupTableMapper::Opset11() {
+  auto input_ids_info = GetInput("Ids");
+  auto input_w_info = GetInput("W");
+  auto output_info = GetOutput("Out");
+
+  std::string ids_node = input_ids_info[0].name;
+  auto ids_shape = input_ids_info[0].shape;
+  if (OpType() == "lookup_table" && ids_shape[ids_shape.size() - 1] == 1) {
+    ids_node = helper_->Squeeze(input_ids_info[0].name, {-1});
+  }
+
+  auto input_shape = input_w_info[0].shape;
+  int64_t sum_val = 1;
+  for (auto i : input_shape) {
+    sum_val *= i;
+  }
+  int interval = sum_val / input_shape[0];
+
+  if (padding_idx_ != -1) {
+    bool has_minus = false;
+    for (auto i : input_w_info[0].shape) {
+      has_minus = (i == -1);
+      if (has_minus) {
+        break;
+      }
+    }
+    if (has_minus) {
+      std::vector<int64_t> shape = {interval};
+      std::string replace_data =
+          helper_->Constant(shape, GetOnnxDtype(input_w_info[0].dtype), 0.0);
+      std::string index = helper_->Constant(
+          {1}, ONNX_NAMESPACE::TensorProto::INT64, padding_idx_);
+      auto scatter_node = helper_->MakeNode(
+          "ScatterND", {input_w_info[0].name, index, replace_data});
+      helper_->MakeNode("Gather", {scatter_node->output(0), ids_node},
+                        {output_info[0].name});
+    } else {
+      std::vector<int64_t> data(sum_val, 1);
+      for (auto i = 0; i < interval; i++) {
+        data[padding_idx_ * interval + i] = 0;
+      }
+      std::string constant = helper_->Constant(
+          input_shape, GetOnnxDtype(input_w_info[0].dtype), data);
+      auto weight_node =
+          helper_->MakeNode("Mul", {input_w_info[0].name, constant});
+      helper_->MakeNode("Gather", {weight_node->output(0), ids_node},
+                        {output_info[0].name});
+    }
+  } else {
+    helper_->MakeNode("Gather", {input_w_info[0].name, ids_node},
+                      {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/lookup_table.h b/paddle2onnx/mapper/tensor/lookup_table.h
new file mode 100644
index 0000000000..df537a981e
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/lookup_table.h
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class LookupTableMapper : public Mapper {
+ public:
+  LookupTableMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                    int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("padding_idx", &padding_idx_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset11();
+
+ private:
+  int64_t padding_idx_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/matmul.cc b/paddle2onnx/mapper/tensor/matmul.cc
new file mode 100644
index 0000000000..fd8bb12644
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/matmul.cc
@@ -0,0 +1,61 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/matmul.h"
+#include <cmath>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(matmul, MatmulMapper)
+
+std::string MatmulMapper::GetTrans(std::vector<TensorInfo>& input_info) {
+  std::string castd_name = input_info[0].name;
+  if (input_info[0].dtype == P2ODataType::FP64) {
+    castd_name = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+  }
+  std::vector<int64_t> perm = Arange(0, input_info[0].Rank());
+  std::swap(perm[perm.size() - 1], perm[perm.size() - 2]);
+  auto transpose_node = helper_->MakeNode("Transpose", {castd_name});
+  AddAttribute(transpose_node, "perm", perm);
+  return transpose_node->output(0);
+}
+
+void MatmulMapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+  std::string input_x = input_x_info[0].name;
+  if (transpose_X_) {
+    input_x = GetTrans(input_x_info);
+  }
+  std::string input_y = input_y_info[0].name;
+  if (transpose_Y_) {
+    input_y = GetTrans(input_y_info);
+  }
+  if (fabs(alpha_ - 1.0) < 1e-6) {
+    auto node = helper_->MakeNode("MatMul", {input_x, input_y});
+    helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                      input_y_info[0].dtype);
+  } else {
+    auto mutmul_node = helper_->MakeNode("MatMul", {input_x, input_y});
+    std::string scale_node =
+        helper_->Constant({1}, GetOnnxDtype(input_x_info[0].dtype), alpha_);
+    auto mul_node =
+        helper_->MakeNode("Mul", {mutmul_node->output(0), scale_node});
+    helper_->AutoCast(mul_node->output(0), output_info[0].name,
+                      P2ODataType::FP32, input_y_info[0].dtype);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/matmul.h b/paddle2onnx/mapper/tensor/matmul.h
new file mode 100644
index 0000000000..16957701fb
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/matmul.h
@@ -0,0 +1,42 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class MatmulMapper : public Mapper {
+ public:
+  MatmulMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("transpose_X", &transpose_X_);
+    GetAttr("transpose_Y", &transpose_Y_);
+    GetAttr("alpha", &alpha_);
+  }
+
+  void Opset7();
+
+ private:
+  std::string GetTrans(std::vector<TensorInfo>& input_info);
+  bool transpose_X_ = false;
+  bool transpose_Y_ = false;
+  float alpha_ = 1.0;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/matmul_v2.cc b/paddle2onnx/mapper/tensor/matmul_v2.cc
new file mode 100644
index 0000000000..f78a26f424
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/matmul_v2.cc
@@ -0,0 +1,51 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/matmul_v2.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(matmul_v2, MatmulV2Mapper)
+
+std::string MatmulV2Mapper::GetTrans(std::vector<TensorInfo>& input_info) {
+  std::string castd_name = helper_->AutoCast(
+      input_info[0].name, input_info[0].dtype, P2ODataType::FP32);
+  std::vector<int64_t> perm = Arange(0, input_info[0].Rank());
+  std::swap(perm[perm.size() - 1], perm[perm.size() - 2]);
+  auto transpose_node = helper_->MakeNode("Transpose", {castd_name});
+  AddAttribute(transpose_node, "perm", perm);
+  return transpose_node->output(0);
+}
+
+void MatmulV2Mapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Y");
+  auto output_info = GetOutput("Out");
+  std::string input_x = input_x_info[0].name;
+  if (trans_x_) {
+    input_x = GetTrans(input_x_info);
+  }
+  std::string input_y = input_y_info[0].name;
+  if (trans_y_) {
+    input_y = GetTrans(input_y_info);
+  }
+  auto node = helper_->MakeNode("MatMul", {input_x, input_y});
+  helper_->AutoCast(node->output(0), output_info[0].name, P2ODataType::FP32,
+                    input_y_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/matmul_v2.h b/paddle2onnx/mapper/tensor/matmul_v2.h
new file mode 100644
index 0000000000..bb3762a34d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/matmul_v2.h
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class MatmulV2Mapper : public Mapper {
+ public:
+  MatmulV2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("trans_x", &trans_x_);
+    GetAttr("trans_y", &trans_y_);
+  }
+
+  void Opset7();
+
+ private:
+  std::string GetTrans(std::vector<TensorInfo>& input_info);
+  bool trans_x_ = false;
+  bool trans_y_ = false;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/mean.cc b/paddle2onnx/mapper/tensor/mean.cc
new file mode 100644
index 0000000000..de1fae3cbe
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/mean.cc
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/mean.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(mean, MeanMapper)
+
+void MeanMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  auto input = helper_->Reshape(x_info[0].name, {-1});
+  auto node = helper_->MakeNode("ReduceMean", {input}, {out_info[0].name});
+  AddAttribute(node, "axes", std::vector<int64_t>(1, 0));
+  AddAttribute(node, "keepdims", int64_t(1));
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/mean.h b/paddle2onnx/mapper/tensor/mean.h
new file mode 100644
index 0000000000..bbb6a2bd8d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/mean.h
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class MeanMapper : public Mapper {
+ public:
+  MeanMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/meshgrid.cc b/paddle2onnx/mapper/tensor/meshgrid.cc
new file mode 100644
index 0000000000..b855d1544b
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/meshgrid.cc
@@ -0,0 +1,46 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/meshgrid.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(meshgrid, MeshgridMapper)
+
+void MeshgridMapper::Opset8() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+
+  std::vector<std::string> x_shapes(x_info.size());
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    x_shapes[i] = helper_->MakeNode("Shape", {x_info[i].name})->output(0);
+  }
+  auto out_shape = helper_->Concat(x_shapes, 0);
+  for (size_t i = 0; i < x_info.size(); ++i) {
+    std::vector<std::string> intermediate_shape(x_info.size());
+    for (size_t j = 0; j < x_info.size(); ++j) {
+      if (j == i) {
+        intermediate_shape[j] = x_shapes[i];
+      } else {
+        intermediate_shape[j] = helper_->Constant(
+            ONNX_NAMESPACE::TensorProto::INT64, std::vector<int64_t>(1, 1));
+      }
+    }
+    auto t_reshaped = helper_->Concat(intermediate_shape, 0);
+    t_reshaped =
+        helper_->MakeNode("Reshape", {x_info[i].name, t_reshaped})->output(0);
+    helper_->MakeNode("Expand", {t_reshaped, out_shape}, {out_info[i].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/meshgrid.h b/paddle2onnx/mapper/tensor/meshgrid.h
new file mode 100644
index 0000000000..d83d677b00
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/meshgrid.h
@@ -0,0 +1,35 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class MeshgridMapper : public Mapper {
+ public:
+  MeshgridMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    MarkAsExperimentalOp();
+  }
+
+  int32_t GetMinOpset(bool verbose = false) { return 8; }
+  void Opset8();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/mul.cc b/paddle2onnx/mapper/tensor/mul.cc
new file mode 100644
index 0000000000..3ba9e1212e
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/mul.cc
@@ -0,0 +1,50 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/mul.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(mul, MulMapper)
+
+void MulMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  auto x_input = x_info[0].name;
+  auto y_input = y_info[0].name;
+  if (x_info[0].Rank() > 2) {
+    auto node = helper_->MakeNode("Flatten", {x_input});
+    AddAttribute(node, "axis", x_num_col_dims_);
+    x_input = node->output(0);
+  }
+  if (y_info[0].Rank() > 2) {
+    auto node = helper_->MakeNode("Flatten", {y_input});
+    AddAttribute(node, "axis", y_num_col_dims_);
+    y_input = node->output(0);
+  }
+  auto out = helper_->MakeNode("MatMul", {x_input, y_input})->output(0);
+
+  if (x_info[0].Rank() != 2 || y_info[0].Rank() != 2) {
+    auto x_shape = helper_->MakeNode("Shape", {x_info[0].name})->output(0);
+    auto y_shape = helper_->MakeNode("Shape", {y_info[0].name})->output(0);
+    auto out_shape_0 = helper_->Slice(x_shape, {0}, {0}, {x_num_col_dims_});
+    auto out_shape_1 = helper_->Slice(y_shape, {0}, {y_num_col_dims_}, {y_info[0].Rank()});
+    auto out_shape = helper_->Concat({out_shape_0, out_shape_1}, 0);
+    out = helper_->MakeNode("Reshape", {out, out_shape})->output(0);
+  }
+  helper_->MakeNode("Identity", {out}, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/mul.h b/paddle2onnx/mapper/tensor/mul.h
new file mode 100644
index 0000000000..ad3171948a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/mul.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class MulMapper : public Mapper {
+ public:
+  MulMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+            int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("x_num_col_dims", &x_num_col_dims_);
+    GetAttr("y_num_col_dims", &y_num_col_dims_);
+  }
+  void Opset7();
+ private:
+  int64_t x_num_col_dims_ = 1;
+  int64_t y_num_col_dims_ = 1;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/mv.cc b/paddle2onnx/mapper/tensor/mv.cc
new file mode 100644
index 0000000000..dcbbdb8e30
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/mv.cc
@@ -0,0 +1,33 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/mv.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(mv, MVMapper)
+
+void MVMapper::Opset7() {
+  auto input_x_info = GetInput("X");
+  auto input_y_info = GetInput("Vec");
+  auto output_info = GetOutput("Out");
+  auto node =
+      helper_->MakeNode("MatMul", {input_x_info[0].name, input_y_info[0].name},
+                        {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/mv.h b/paddle2onnx/mapper/tensor/mv.h
new file mode 100644
index 0000000000..20acbf3c3b
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/mv.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class MVMapper : public Mapper {
+ public:
+  MVMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+           int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/nonzero.cc b/paddle2onnx/mapper/tensor/nonzero.cc
new file mode 100644
index 0000000000..fd4c0abed1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/nonzero.cc
@@ -0,0 +1,28 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/nonzero.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(where_index, NonZeroMapper)
+
+void NonZeroMapper::Opset9() {
+  auto input_info = GetInput("Condition");
+  auto output_info = GetOutput("Out");
+  auto non_zero_indices =
+      helper_->MakeNode("NonZero", {input_info[0].name})->output(0);
+  helper_->Transpose(non_zero_indices, output_info[0].name, {1, 0});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/nonzero.h b/paddle2onnx/mapper/tensor/nonzero.h
new file mode 100644
index 0000000000..a5c26dba24
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/nonzero.h
@@ -0,0 +1,33 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class NonZeroMapper : public Mapper {
+ public:
+  NonZeroMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  void Opset9();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/not_equal.cc b/paddle2onnx/mapper/tensor/not_equal.cc
new file mode 100644
index 0000000000..66b987f1b9
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/not_equal.cc
@@ -0,0 +1,45 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/not_equal.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(not_equal, NotEqualMapper)
+
+int32_t NotEqualMapper::GetMinOpset(bool verbose) {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  if (x_info[0].dtype == P2ODataType::FP32 ||
+      x_info[0].dtype == P2ODataType::FP64) {
+    Logger(verbose, 11) << "While input is dtype of float32/float64, "
+                        << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  return 7;
+}
+
+void NotEqualMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto out_info = GetOutput("Out");
+
+  int out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment({x_info[0], y_info[0]}, &out_dtype);
+
+  auto output = helper_->MakeNode("Equal", aligned_inputs)->output(0);
+  helper_->MakeNode("Not", {output}, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/not_equal.h b/paddle2onnx/mapper/tensor/not_equal.h
new file mode 100644
index 0000000000..7c9f9c5f3d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/not_equal.h
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class NotEqualMapper : public Mapper {
+ public:
+  NotEqualMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/one_hot_v2.cc b/paddle2onnx/mapper/tensor/one_hot_v2.cc
new file mode 100644
index 0000000000..6b877bb9b2
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/one_hot_v2.cc
@@ -0,0 +1,58 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/one_hot_v2.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(one_hot_v2, OneHotV2Mapper)
+
+int32_t OneHotV2Mapper::GetMinOpset(bool verbose) {
+  if (allow_out_of_range_) {
+    Error() << "allow_out_of_range is not supported in one_hot_v2."
+            << std::endl;
+    return -1;
+  }
+  auto output_info = GetOutput("Out");
+  if (output_info[0].dtype != dtype_) {
+    Error() << "dtype attribute and output dtype do not match." << std::endl;
+    return -1;
+  }
+  Logger(verbose, 9) << RequireOpset(9) << std::endl;
+  return 9;
+}
+
+void OneHotV2Mapper::Opset9() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string casted_input = helper_->AutoCast(
+      input_info[0].name, input_info[0].dtype, P2ODataType::INT64);
+
+  std::vector<int64_t> vals = {0, 1};
+  std::string value_node =
+      helper_->Constant(GetOnnxDtype(output_info[0].dtype), vals);
+
+  std::string depth_node = "";
+  if (HasInput("depth_tensor")) {
+    auto input_depth_info = GetInput("depth_tensor");
+    depth_node = input_depth_info[0].name;
+  } else {
+    depth_node =
+        helper_->Constant({1}, GetOnnxDtype(input_info[0].dtype), depth_);
+  }
+  auto one_hot_node = helper_->MakeNode(
+      "OneHot", {casted_input, depth_node, value_node}, {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/one_hot_v2.h b/paddle2onnx/mapper/tensor/one_hot_v2.h
new file mode 100644
index 0000000000..bf56021051
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/one_hot_v2.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class OneHotV2Mapper : public Mapper {
+ public:
+  OneHotV2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("allow_out_of_range", &allow_out_of_range_);
+    GetAttr("depth", &depth_);
+    GetAttr("dtype", &dtype_);
+  }
+  int32_t GetMinOpset(bool verbose);
+  void Opset9();
+
+ private:
+  bool allow_out_of_range_;
+  int64_t depth_;
+  int64_t dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/p_norm.cc b/paddle2onnx/mapper/tensor/p_norm.cc
new file mode 100755
index 0000000000..84bc84476f
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/p_norm.cc
@@ -0,0 +1,50 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/p_norm.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(p_norm, PNormMapper)
+
+void PNormMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string pnode =
+      helper_->Constant({1}, GetOnnxDtype(input_info[0].dtype), porder_);
+
+  auto abs_node = helper_->MakeNode("Abs", {input_info[0].name});
+  auto pow_node = helper_->MakeNode("Pow", {abs_node->output(0), pnode});
+  std::string reducesum_node = "";
+  std::vector<int64_t> axes_val = {axis_};
+  if (helper_->GetOpsetVersion() < 13) {
+    auto node = helper_->MakeNode("ReduceSum", {pow_node->output(0)});
+    AddAttribute(node, "axes", axes_val);
+    AddAttribute(node, "keepdims", static_cast<int64_t>(keepdim_));
+    reducesum_node = node->output(0);
+  } else {
+    std::string axes_node =
+        helper_->Constant(GetOnnxDtype(P2ODataType::INT64), axes_val);
+    auto node =
+        helper_->MakeNode("ReduceSum", {pow_node->output(0), axes_node});
+    AddAttribute(node, "keepdims", static_cast<int64_t>(keepdim_));
+    reducesum_node = node->output(0);
+  }
+
+  std::string pnode1 =
+      helper_->Constant({1}, GetOnnxDtype(input_info[0].dtype), 1.0 / porder_);
+  helper_->MakeNode("Pow", {reducesum_node, pnode1}, {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/p_norm.h b/paddle2onnx/mapper/tensor/p_norm.h
new file mode 100755
index 0000000000..e7417369c3
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/p_norm.h
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class PNormMapper : public Mapper {
+ public:
+  PNormMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("keepdim", &keepdim_);
+    GetAttr("axis", &axis_);
+    GetAttr("porder", &porder_);
+  }
+  void Opset7();
+
+ private:
+  bool keepdim_;
+  int64_t axis_;
+  float porder_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/partial_ops.cc b/paddle2onnx/mapper/tensor/partial_ops.cc
new file mode 100755
index 0000000000..d22f3e73db
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/partial_ops.cc
@@ -0,0 +1,88 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/partial_ops.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(partial_sum, PartialOpsMapper)
+REGISTER_MAPPER(partial_concat, PartialOpsMapper)
+
+int32_t PartialOpsMapper::GetMinOpset(bool verbose) {
+  auto input_info = GetInput("X");
+  for (auto &in : input_info) {
+    if (in.Rank() != 2) {
+      Error() << "The input dim of partial_sum OP must be 2." << std::endl;
+      return -1;
+    }
+  }
+  if (start_index_ < 0) {
+    start_index_ = start_index_ + input_info[0].shape[1];
+  }
+  int64_t batch_size = input_info[0].shape[0];
+  int64_t max_length = input_info[0].shape[1];
+  for (auto &in : input_info) {
+    if (in.shape[0] != batch_size || in.shape[1] != max_length) {
+      Error()
+          << "The batch_size and max_length of all inputs must be same in " +
+                 OpType() + " OP."
+          << std::endl;
+      return -1;
+    }
+  }
+  if (max_length < start_index_) {
+    Error() << "start_index must be less than input len in " + OpType() + " OP."
+            << std::endl;
+    return -1;
+  }
+  if (length_ > 0 && start_index_ + length_ > max_length) {
+    Error() << "start_index + length is larger than input length in " +
+                   OpType() + " OP."
+            << std::endl;
+    return -1;
+  }
+  auto iter = op_mapper_.find(OpType());
+  if (op_mapper_.end() == iter) {
+    Error() << "Cannot find " + OpType() + " in partial op_mapper."
+            << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void PartialOpsMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  int64_t end;
+  if (length_ < 0) {
+    end = input_info[0].shape[1];
+  } else {
+    end = start_index_ + length_;
+  }
+  std::vector<std::string> slice_outputs;
+  for (auto &in : input_info) {
+    auto out = helper_->Slice(in.name, {1}, {start_index_}, {end});
+    std::string casted_node =
+        helper_->AutoCast(out, in.dtype, P2ODataType::FP32);
+    slice_outputs.push_back(casted_node);
+  }
+  auto iter = op_mapper_.find(OpType());
+  auto node = helper_->MakeNode(iter->second, slice_outputs);
+  if (iter->second == "Concat") {
+    AddAttribute(node, "axis", static_cast<int64_t>(1));
+  }
+  helper_->AutoCast(node->output(0), {output_info[0].name}, P2ODataType::FP32,
+                    output_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/partial_ops.h b/paddle2onnx/mapper/tensor/partial_ops.h
new file mode 100755
index 0000000000..54eb7ecc3a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/partial_ops.h
@@ -0,0 +1,42 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class PartialOpsMapper : public Mapper {
+ public:
+  PartialOpsMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("start_index", &start_index_);
+    GetAttr("length", &length_);
+    op_mapper_["partial_sum"] = "Sum";
+    op_mapper_["partial_concat"] = "Concat";
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::map<std::string, std::string> op_mapper_;
+  int64_t start_index_;
+  int64_t length_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/pixel_shuffle.cc b/paddle2onnx/mapper/tensor/pixel_shuffle.cc
new file mode 100644
index 0000000000..9f10087d11
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/pixel_shuffle.cc
@@ -0,0 +1,30 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/pixel_shuffle.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(pixel_shuffle, PixelShuffleMapper)
+
+void PixelShuffleMapper::Opset11() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  auto node = helper_->MakeNode("DepthToSpace", {input_info[0].name},
+                                {output_info[0].name});
+  AddAttribute(node, "blocksize", upscale_factor_);
+  AddAttribute(node, "mode", static_cast<std::string>("CRD"));
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/pixel_shuffle.h b/paddle2onnx/mapper/tensor/pixel_shuffle.h
new file mode 100644
index 0000000000..9158810604
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/pixel_shuffle.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class PixelShuffleMapper : public Mapper {
+ public:
+  PixelShuffleMapper(const PaddleParser& p, OnnxHelper* helper,
+                     int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("upscale_factor", &upscale_factor_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 11) << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  void Opset11();
+
+ private:
+  int64_t upscale_factor_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/pow.cc b/paddle2onnx/mapper/tensor/pow.cc
new file mode 100644
index 0000000000..57dc18801b
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/pow.cc
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/pow.h"
+#include <unordered_set>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(pow, PowMapper)
+
+void PowMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  auto factor_node = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                                           std::vector<float>(1, factor_));
+  if (input_info[0].dtype != P2ODataType::FP32) {
+       std::string x_cast_name = helper_->AutoCast(
+        {input_info[0].name}, input_info[0].dtype, P2ODataType::FP32);
+     auto node = helper_->MakeNode("Pow", {x_cast_name, factor_node});
+     helper_->AutoCast(node->output(0), {output_info[0].name}, P2ODataType::FP32,
+                      input_info[0].dtype);
+  } else {
+      helper_->MakeNode("Pow", {input_info[0].name, factor_node}, {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/pow.h b/paddle2onnx/mapper/tensor/pow.h
new file mode 100644
index 0000000000..60b4450946
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/pow.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class PowMapper : public Mapper {
+ public:
+  PowMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("factor", &factor_);
+  }
+  void Opset7();
+
+ private:
+  float factor_ = 0.0;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/range.cc b/paddle2onnx/mapper/tensor/range.cc
new file mode 100644
index 0000000000..0254bf6a05
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/range.cc
@@ -0,0 +1,76 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/range.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(range, RangeMapper)
+
+void RangeMapper::Opset11() {
+  auto start_info = GetInput("Start");
+  auto end_info = GetInput("End");
+  auto step_info = GetInput("Step");
+  auto out_info = GetOutput("Out");
+  int32_t out_dtype = -1;
+  // TODO(jiangjiajun) cast for constant is an eleminable operation
+  std::vector<std::string> aligned_inputs = helper_->DtypeAlignment(
+      {start_info[0], end_info[0], step_info[0]}, &out_dtype);
+  std::vector<int64_t> empty_axes;
+
+  //  // Trick for tensorrt
+  //  if (out_dtype == P2ODataType::INT32 || out_dtype == P2ODataType::INT64 ||
+  //  true) {
+  //    if (start_info[0].Rank() != 1) {
+  //      aligned_inputs[0] = helper_->Reshape(aligned_inputs[0], {-1});
+  //    }
+  //    if (end_info[0].Rank() != 1) {
+  //      aligned_inputs[1] = helper_->Reshape(aligned_inputs[1], {-1});
+  //    }
+  //    if (step_info[0].Rank() != 1) {
+  //      aligned_inputs[2] = helper_->Reshape(aligned_inputs[2], {-1});
+  //    }
+  //    auto length = helper_->MakeNode("Sub", {aligned_inputs[1],
+  //    aligned_inputs[0]})->output(0);
+  //    length = helper_->AutoCast(length, out_dtype, P2ODataType::INT64);
+  //    auto one = helper_->Constant({1}, GetOnnxDtype(out_dtype), int64_t(1));
+  //    auto expaned_one = helper_->MakeNode("Expand", {one,
+  //    length})->output(0); auto axis = helper_->Constant({},
+  //    ONNX_NAMESPACE::TensorProto::INT64, int64_t(0)); auto cumsumed_data =
+  //    helper_->MakeNode("CumSum", {expaned_one, axis})->output(0);
+  //    cumsumed_data = helper_->MakeNode("Sub", {cumsumed_data,
+  //    one})->output(0);
+  //
+  //    auto zero = helper_->Constant({1},  ONNX_NAMESPACE::TensorProto::INT64,
+  //    int64_t(0));
+  //    auto new_step = helper_->AutoCast(aligned_inputs[2], step_info[0].dtype,
+  //    P2ODataType::INT64);
+  //    helper_->MakeNode("Slice", {cumsumed_data, zero, length, zero,
+  //    new_step}, {out_info[0].name}); return;
+  //  }
+
+  // TODO(jiangjiajun) squeeze for constant is an eleminable operation
+  if (start_info[0].shape.size() > 0) {
+    aligned_inputs[0] = helper_->Squeeze(aligned_inputs[0], empty_axes);
+  }
+  if (end_info[0].shape.size() > 0) {
+    aligned_inputs[1] = helper_->Squeeze(aligned_inputs[1], empty_axes);
+  }
+  if (step_info[0].shape.size() > 0) {
+    aligned_inputs[2] = helper_->Squeeze(aligned_inputs[2], empty_axes);
+  }
+  auto out = helper_->MakeNode("Range", aligned_inputs)->output(0);
+  helper_->AutoCast(out, out_info[0].name, out_dtype, out_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/range.h b/paddle2onnx/mapper/tensor/range.h
new file mode 100644
index 0000000000..df5c2dfe32
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/range.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class RangeMapper : public Mapper {
+ public:
+  RangeMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 11) << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  void Opset11();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/reduce.cc b/paddle2onnx/mapper/tensor/reduce.cc
new file mode 100644
index 0000000000..82b98619c1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/reduce.cc
@@ -0,0 +1,144 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/reduce.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(reduce_mean, ReduceMapper)
+REGISTER_MAPPER(reduce_sum, ReduceMapper)
+REGISTER_MAPPER(reduce_min, ReduceMapper)
+REGISTER_MAPPER(reduce_max, ReduceMapper)
+REGISTER_MAPPER(reduce_prod, ReduceMapper)
+REGISTER_MAPPER(logsumexp, ReduceMapper)
+REGISTER_MAPPER(reduce_all, ReduceMapper)
+REGISTER_MAPPER(reduce_any, ReduceMapper)
+
+int32_t ReduceMapper::GetMinOpset(bool verbose) {
+  std::string axis_name;
+  if (OpType() == "logsumexp") {
+    axis_name = "axis";
+  } else {
+    axis_name = "dim";
+  }
+  if (IsAttrVar(axis_name) && !IsConstant(GetAttrVar(axis_name)[0])) {
+    if (OpType() == "reduce_sum") {
+      return 13;
+    }
+    Error() << "While Attribute(" << axis_name
+            << ")'s type is Tensor, it's not supported "
+               "unless it's a constant tensor."
+            << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void ReduceMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+  std::map<std::string, std::string> op_map;
+  op_map["reduce_mean"] = "ReduceMean";
+  op_map["reduce_sum"] = "ReduceSum";
+  op_map["reduce_min"] = "ReduceMin";
+  op_map["reduce_max"] = "ReduceMax";
+  op_map["reduce_prod"] = "ReduceProd";
+  op_map["logsumexp"] = "ReduceLogSumExp";
+  std::string out = "";
+
+  std::string axis_name;
+  if (OpType() == "logsumexp") {
+    axis_name = "axis";
+  } else {
+    axis_name = "dim";
+  }
+
+  if (IsAttrVar(axis_name)) {
+    auto info = GetAttrVar(axis_name);
+    TryGetValue(info[0], &dim_);
+  } else {
+    GetAttr(axis_name, &dim_);
+  }
+
+  bool reduce_all_axes = dim_.size() == x_info[0].Rank();
+  if (reduce_all_) {
+    reduce_all_axes = true;
+  }
+
+  if (helper_->GetOpsetVersion() >= 13 && OpType() == "reduce_sum") {
+    std::string dims = "";
+    if (IsAttrVar(axis_name)) {
+      auto info = GetAttrVar(axis_name);
+      dims = helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
+    } else {
+      if (!reduce_all_) {
+        dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, dim_);
+      } else {
+        dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                 Arange(0, x_info[0].Rank()));
+      }
+    }
+    auto reduce_node =
+        helper_->MakeNode(op_map[OpType()], {x_info[0].name, dims});
+    AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
+    out = reduce_node->output(0);
+  } else if (OpType() == "reduce_all") {
+    auto int32_x =
+        helper_->AutoCast(x_info[0].name, x_info[0].dtype, P2ODataType::INT32);
+    auto reduce_node = helper_->MakeNode("ReduceMin", {int32_x});
+    if (!reduce_all_) {
+      AddAttribute(reduce_node, "axes", dim_);
+    } else {
+      AddAttribute(reduce_node, "axes", Arange(0, x_info[0].Rank()));
+    }
+    AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
+    out = helper_->AutoCast(reduce_node->output(0), P2ODataType::INT32,
+                            P2ODataType::BOOL);
+  } else if (OpType() == "reduce_any") {
+    auto int32_x =
+        helper_->AutoCast(x_info[0].name, x_info[0].dtype, P2ODataType::INT32);
+    auto reduce_node = helper_->MakeNode("ReduceMax", {int32_x});
+    if (!reduce_all_) {
+      AddAttribute(reduce_node, "axes", dim_);
+    } else {
+      AddAttribute(reduce_node, "axes", Arange(0, x_info[0].Rank()));
+    }
+    AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
+    out = helper_->AutoCast(reduce_node->output(0), P2ODataType::INT32,
+                            P2ODataType::BOOL);
+  } else {
+    std::string input_name = x_info[0].name;
+    if (OpType() == "reduce_prod" && x_info[0].dtype == P2ODataType::FP64) {
+      input_name = helper_->AutoCast(x_info[0].name, P2ODataType::FP64,
+                                     P2ODataType::FP32);
+    }
+    auto reduce_node = helper_->MakeNode(op_map[OpType()], {input_name});
+    if (!reduce_all_) {
+      AddAttribute(reduce_node, "axes", dim_);
+    } else {
+      AddAttribute(reduce_node, "axes", Arange(0, x_info[0].Rank()));
+    }
+    AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
+    out = reduce_node->output(0);
+    if (OpType() == "reduce_prod" && x_info[0].dtype == P2ODataType::FP64) {
+      out = helper_->AutoCast(reduce_node->output(0), P2ODataType::FP32,
+                              P2ODataType::FP64);
+    }
+  }
+  if (!keep_dim_ && reduce_all_axes) {
+    out = helper_->Reshape(out, {-1});
+  }
+  helper_->AutoCast(out, out_info[0].name, x_info[0].dtype, out_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/reduce.h b/paddle2onnx/mapper/tensor/reduce.h
new file mode 100755
index 0000000000..901a3f48c6
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/reduce.h
@@ -0,0 +1,50 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ReduceMapper : public Mapper {
+ public:
+  ReduceMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    if (OpType() == "logsumexp") {
+      GetAttr("keepdim", &keep_dim_);
+      GetAttr("reduce_all", &reduce_all_);
+    } else {
+      GetAttr("keep_dim", &keep_dim_);
+      GetAttr("reduce_all", &reduce_all_);
+      GetAttr("in_dtype", &in_dtype_);
+      GetAttr("out_dtype", &out_dtype_);
+    }
+  }
+  void Opset7();
+
+  int32_t GetMinOpset(bool verbose = false);
+
+ private:
+  bool keep_dim_;
+  bool reduce_all_;
+  int64_t in_dtype_;
+  int64_t out_dtype_;
+  std::vector<int64_t> dim_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/reshape2.cc b/paddle2onnx/mapper/tensor/reshape2.cc
new file mode 100644
index 0000000000..ccdbc5a86e
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/reshape2.cc
@@ -0,0 +1,54 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/reshape2.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(reshape2, Reshape2Mapper)
+
+void Reshape2Mapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::string shape_name = "ShapeTensor";
+  if (!HasInput(shape_name)) {
+    shape_name = "Shape";
+  }
+
+  std::string new_shape = "";
+  if (HasInput(shape_name)) {
+    auto shape_info = GetInput(shape_name);
+    if (shape_info.size() > 1) {
+      new_shape = helper_->ConcatIndices(shape_info);
+    } else {
+      new_shape = helper_->AutoCast(shape_info[0].name, shape_info[0].dtype,
+                                    P2ODataType::INT64);
+    }
+  } else {
+    std::vector<int64_t> value;
+    GetAttr("shape", &value);
+    new_shape = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, value);
+  }
+  auto node = helper_->MakeNode("Reshape", {input_info[0].name, new_shape},
+                    {output_info[0].name});
+  if (helper_->GetOpsetVersion()>= 14) {
+    AddAttribute(node, "allowzero", int64_t(0));
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/reshape2.h b/paddle2onnx/mapper/tensor/reshape2.h
new file mode 100644
index 0000000000..65ce420619
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/reshape2.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Reshape2Mapper : public Mapper {
+ public:
+  Reshape2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/scale.cc b/paddle2onnx/mapper/tensor/scale.cc
new file mode 100644
index 0000000000..a2afb0cfaa
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/scale.cc
@@ -0,0 +1,76 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "paddle2onnx/mapper/tensor/scale.h"
+
+#include <vector>
+
+namespace paddle2onnx {
+
+REGISTER_MAPPER(scale, ScaleMapper)
+
+void ScaleMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  bool has_scale_tensor = HasInput("ScaleTensor");
+  bool is_scale_1 = ((scale_ - 1.0) < 1e-06 && (scale_ - 1.0) > -1e-06);
+  bool is_bias_0 = (bias_ < 1e-06 && bias_ > -1e-06);
+
+  if (!has_scale_tensor && is_scale_1 && is_bias_0) {
+    helper_->MakeNode("Identity", {input_info[0].name}, {output_info[0].name});
+  } else {
+    auto input = helper_->AutoCast(input_info[0].name, input_info[0].dtype,
+                                   P2ODataType::FP32);
+    std::string out = input;
+    if (bias_after_scale_) {
+      if (!is_scale_1 || HasInput("ScaleTensor")) {
+        if (HasInput("ScaleTensor")) {
+          auto scale_info = GetInput("ScaleTensor");
+          auto scale = helper_->AutoCast(
+              scale_info[0].name, scale_info[0].dtype, P2ODataType::FP32);
+          out = helper_->MakeNode("Mul", {out, scale})->output(0);
+        } else {
+          auto scale = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                                         std::vector<float>(1, scale_));
+          out = helper_->MakeNode("Mul", {out, scale})->output(0);
+        }
+      }
+      if (!is_bias_0) {
+        auto bias = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                                      std::vector<float>(1, bias_));
+        out = helper_->MakeNode("Add", {out, bias})->output(0);
+      }
+    } else {
+      if (!is_bias_0) {
+        auto bias = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                                      std::vector<float>(1, bias_));
+        out = helper_->MakeNode("Add", {out, bias})->output(0);
+      }
+      if (!is_scale_1 || HasInput("ScaleTensor")) {
+        if (HasInput("ScaleTensor")) {
+          auto scale_info = GetInput("ScaleTensor");
+          auto scale = helper_->AutoCast(
+              scale_info[0].name, scale_info[0].dtype, P2ODataType::FP32);
+          out = helper_->MakeNode("Mul", {out, scale})->output(0);
+        } else {
+          auto scale = helper_->Constant(ONNX_NAMESPACE::TensorProto::FLOAT,
+                                         std::vector<float>(1, scale_));
+          out = helper_->MakeNode("Mul", {out, scale})->output(0);
+        }
+      }
+    }
+    helper_->AutoCast(out, output_info[0].name, P2ODataType::FP32,
+                      output_info[0].dtype);
+  }
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/scale.h b/paddle2onnx/mapper/tensor/scale.h
new file mode 100644
index 0000000000..3970516c3d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/scale.h
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ScaleMapper : public Mapper {
+ public:
+  ScaleMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("scale", &scale_);
+    GetAttr("bias", &bias_);
+    GetAttr("bias_after_scale", &bias_after_scale_);
+  }
+
+  void Opset7();
+
+ private:
+  float scale_ = 1.0;
+  float bias_ = 0.0;
+  bool bias_after_scale_ = true;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/scatter.cc b/paddle2onnx/mapper/tensor/scatter.cc
new file mode 100644
index 0000000000..73bb7dd6a1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/scatter.cc
@@ -0,0 +1,77 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/scatter.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(scatter, ScatterMapper)
+
+int32_t ScatterMapper::GetMinOpset(bool verbose) {
+  if (!overwrite_) {
+    Logger(verbose, 16) << "When overwrite is False, " << RequireOpset(16)
+                        << std::endl;
+    return 16;
+  }
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void ScatterMapper::Opset11() {
+  auto input_x_info = GetInput("X");
+  auto input_ids_info = GetInput("Ids");
+  auto input_updates_info = GetInput("Updates");
+  auto output_info = GetOutput("Out");
+
+  std::string ids_node = helper_->AutoCast(
+      input_ids_info[0].name, input_ids_info[0].dtype, P2ODataType::INT64);
+
+  std::vector<int64_t> shape_val = {input_ids_info[0].shape[0], 1};
+  std::string shape_node =
+      helper_->Constant(GetOnnxDtype(P2ODataType::INT64), shape_val);
+
+  auto reshape_index_node =
+      helper_->MakeNode("Reshape", {ids_node, shape_node});
+
+  if (!overwrite_) {
+    auto shape_node = helper_->MakeNode("Shape", {input_x_info[0].name});
+    std::string zeros_like_node = helper_->ConstOfShape(
+        shape_node->output(0), GetOnnxDtype(input_x_info[0].dtype),
+        static_cast<float>(0));
+    auto scatter_nd_node = helper_->MakeNode(
+        "ScatterND", {zeros_like_node, reshape_index_node->output(0),
+                      input_updates_info[0].name});
+    AddAttribute(scatter_nd_node, "reduction", "add");
+
+    std::string zero_node = helper_->Constant(
+        {1}, GetOnnxDtype(input_x_info[0].dtype), static_cast<float>(0));
+
+    auto equal_node =
+        helper_->MakeNode("Equal", {scatter_nd_node->output(0), zero_node});
+
+    std::string condition_node = helper_->AutoCast(
+        equal_node->output(0), P2ODataType::INT64, P2ODataType::BOOL);
+
+    helper_->MakeNode("Where", {condition_node, input_x_info[0].name,
+                                scatter_nd_node->output(0)},
+                      {output_info[0].name});
+
+  } else {
+    auto node = helper_->MakeNode(
+        "ScatterND", {input_x_info[0].name, reshape_index_node->output(0),
+                      input_updates_info[0].name},
+        {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/scatter.h b/paddle2onnx/mapper/tensor/scatter.h
new file mode 100644
index 0000000000..cefa80faeb
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/scatter.h
@@ -0,0 +1,37 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ScatterMapper : public Mapper {
+ public:
+  ScatterMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("overwrite", &overwrite_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+
+ private:
+  bool overwrite_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/scatter_nd_add.cc b/paddle2onnx/mapper/tensor/scatter_nd_add.cc
new file mode 100644
index 0000000000..7bbe5c9282
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/scatter_nd_add.cc
@@ -0,0 +1,48 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/scatter_nd_add.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(scatter_nd_add, ScatterNdAddMapper)
+
+int32_t ScatterNdAddMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 16) << RequireOpset(16) << std::endl;
+  return 16;
+}
+
+void ScatterNdAddMapper::Opset16() {
+  auto input_x_info = GetInput("X");
+  auto input_ids_info = GetInput("Index");
+  auto input_updates_info = GetInput("Updates");
+  auto output_info = GetOutput("Out");
+
+  auto shape_node = helper_->MakeNode("Shape", {input_x_info[0].name});
+
+  std::string zeros_like_node = helper_->ConstOfShape(
+      shape_node->output(0), GetOnnxDtype(input_x_info[0].dtype),
+      static_cast<float>(0));
+
+  std::string input_ids_node = helper_->AutoCast(
+      input_ids_info[0].name, input_ids_info[0].dtype, P2ODataType::INT64);
+
+  auto scatter_nd_node = helper_->MakeNode(
+      "ScatterND",
+      {zeros_like_node, input_ids_node, input_updates_info[0].name});
+  AddAttribute(scatter_nd_node, "reduction", "add");
+  helper_->MakeNode("Add", {input_x_info[0].name, scatter_nd_node->output(0)},
+                    {output_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/scatter_nd_add.h b/paddle2onnx/mapper/tensor/scatter_nd_add.h
new file mode 100644
index 0000000000..053e5076d1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/scatter_nd_add.h
@@ -0,0 +1,34 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class ScatterNdAddMapper : public Mapper {
+ public:
+  ScatterNdAddMapper(const PaddleParser& p, OnnxHelper* helper,
+                     int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset16();
+
+ private:
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/set_value.cc b/paddle2onnx/mapper/tensor/set_value.cc
new file mode 100644
index 0000000000..7b630b1f95
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/set_value.cc
@@ -0,0 +1,143 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/set_value.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(set_value, SetValueMapper)
+
+int32_t SetValueMapper::GetMinOpset(bool verbose) {
+  if (none_axes_.size() > 0) {
+    Error() << "Attribute none_axes is not supported." << std::endl;
+    return -1;
+  }
+  if (axes_.size() > 1) {
+    Error() << "Attribute axes is supported while it only contains 1 element."
+            << std::endl;
+    return -1;
+  }
+  if (steps_.size() > 1) {
+    Error() << "ttribute steps is supported while it only contains 1 element."
+            << std::endl;
+    return -1;
+  }
+  if (GetInput("Input")[0].dtype == P2ODataType::BOOL) {
+    Error() << "Input X with data type of boolean is not supported."
+            << std::endl;
+    return -1;
+  }
+  Logger(verbose, 12) << RequireOpset(12) << std::endl;
+  return 12;
+}
+
+void SetValueMapper::Opset12() {
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Out");
+  std::string starts = "";
+  if (HasInput("StartsTensorList")) {
+    // if negtive value exists, not supported
+    starts = helper_->ConcatIndices(GetInput("StartsTensorList"));
+  } else {
+    starts = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, starts_);
+  }
+  std::string ends = "";
+  if (HasInput("EndsTensorList")) {
+    ends = helper_->ConcatIndices(GetInput("EndsTensorList"));
+  } else {
+    // if out of range value in end exists, not supported
+    ends = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, ends_);
+  }
+
+  auto input_tensor = input_info[0].name;
+  std::string axes = helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64,
+                                       int64_t(axes_[0]));
+  // process out of range ends
+  auto input_shape = helper_->MakeNode("Shape", {input_tensor})->output(0);
+  auto gather_end_bound = helper_->MakeNode("Gather", {input_shape, axes});
+  AddAttribute(gather_end_bound, "axis", int64_t(0));
+  ends =
+      helper_->MakeNode("Min", {gather_end_bound->output(0), ends})->output(0);
+
+  std::string steps = "";
+  if (HasInput("StepsTensorList")) {
+    steps = helper_->ConcatIndices(GetInput("StepsTensorList"));
+  } else {
+    steps = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, steps_);
+  }
+  std::string value = "";
+  int64_t value_rank = input_info[0].Rank();
+  if (HasInput("ValueTensor")) {
+    auto value_info = GetInput("ValueTensor");
+    value = value_info[0].name;
+    value_rank = value_info[0].Rank();
+  } else {
+    value_rank = shape_.size();
+    int in_dtype = input_info[0].dtype;
+    if (in_dtype == P2ODataType::INT32 || in_dtype == P2ODataType::INT64) {
+      value = helper_->Assign(GetOnnxDtype(output_info[0].dtype), shape_,
+                              int_values_);
+    } else if (in_dtype == P2ODataType::FP32) {
+      value = helper_->Assign(GetOnnxDtype(output_info[0].dtype), shape_,
+                              fp32_values_);
+    } else if (in_dtype == P2ODataType::FP64) {
+      value = helper_->Assign(GetOnnxDtype(output_info[0].dtype), shape_,
+                              fp64_values_);
+    }
+  }
+
+  auto sliced_data =
+      helper_->MakeNode("Slice", {input_tensor, starts, ends, axes, steps})
+          ->output(0);
+
+  auto sliced_shape = helper_->MakeNode("Shape", {sliced_data})->output(0);
+  if (decrease_axes_.size() > 0 && value_rank != input_info[0].Rank()) {
+    value = helper_->Unsqueeze(value, decrease_axes_);
+  }
+  auto expand_value =
+      helper_->MakeNode("Expand", {value, sliced_shape})->output(0);
+
+  auto indices = helper_
+                     ->MakeNode("Range", {helper_->Squeeze(starts, {}),
+                                          helper_->Squeeze(ends, {}),
+                                          helper_->Squeeze(steps, {})})
+                     ->output(0);
+  if (axes_[0] == 0) {
+    indices = helper_->Unsqueeze(indices, {1});
+    helper_->MakeNode("ScatterND", {input_tensor, indices, expand_value},
+                      {output_info[0].name});
+  } else {
+    std::vector<int64_t> indices_shape(input_info[0].Rank(), 1);
+    indices_shape[axes_[0]] = -1;
+    indices = helper_->Reshape(indices, indices_shape);
+    auto one =
+        helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, int64_t(1));
+    if (axes_[0] == input_info[0].Rank() - 1) {
+      auto part_shape = helper_->Slice(sliced_shape, {0}, {0}, {axes_[0]});
+      auto tiled_shape = helper_->Concat({part_shape, one}, 0);
+      indices = helper_->MakeNode("Tile", {indices, tiled_shape})->output(0);
+    } else {
+      auto part_0_shape = helper_->Slice(sliced_shape, {0}, {0}, {axes_[0]});
+      auto part_1_shape = helper_->Slice(sliced_shape, {0}, {axes_[0] + 1},
+                                         {input_info[0].Rank()});
+      auto tiled_shape = helper_->Concat({part_0_shape, one, part_1_shape}, 0);
+      indices = helper_->MakeNode("Tile", {indices, tiled_shape})->output(0);
+    }
+    auto scatter_node = helper_->MakeNode("ScatterElements",
+                                          {input_tensor, indices, expand_value},
+                                          {output_info[0].name});
+    AddAttribute(scatter_node, "axis", axes_[0]);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/set_value.h b/paddle2onnx/mapper/tensor/set_value.h
new file mode 100644
index 0000000000..c5ddb9a97a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/set_value.h
@@ -0,0 +1,65 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class SetValueMapper : public Mapper {
+ public:
+  SetValueMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    MarkAsExperimentalOp();
+    GetAttr("axes", &axes_);
+    GetAttr("starts", &starts_);
+    GetAttr("ends", &ends_);
+    GetAttr("steps", &steps_);
+    GetAttr("shape", &shape_);
+    GetAttr("decrease_axes", &decrease_axes_);
+    GetAttr("none_axes", &none_axes_);
+    if (!HasInput("ValueTensor")) {
+      auto dtype = GetInput("Input")[0].dtype;
+      if (dtype == P2ODataType::INT32) {
+        GetAttr("int32_values", &int_values_);
+      } else if (dtype == P2ODataType::INT64) {
+        GetAttr("int64_values", &int_values_);
+      } else if (dtype == P2ODataType::FP32) {
+        GetAttr("fp32_values", &fp32_values_);
+      } else if (dtype == P2ODataType::FP64) {
+        GetAttr("fp64_values", &fp64_values_);
+      }
+    }
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset12();
+
+ private:
+  std::vector<int64_t> axes_;
+  std::vector<int64_t> starts_;
+  std::vector<int64_t> ends_;
+  std::vector<int64_t> steps_;
+  std::vector<int64_t> shape_;
+  std::vector<int64_t> decrease_axes_;
+  std::vector<int64_t> none_axes_;
+  std::vector<int64_t> int_values_;
+  std::vector<float> fp32_values_;
+  std::vector<double> fp64_values_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/slice.cc b/paddle2onnx/mapper/tensor/slice.cc
new file mode 100644
index 0000000000..317f3b9d5a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/slice.cc
@@ -0,0 +1,175 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/slice.h"
+
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(slice, SliceMapper)
+REGISTER_MAPPER(strided_slice, SliceMapper)
+
+int32_t SliceMapper::GetMinOpset(bool verbose) {
+  if (HasInput("StartsTensorList") || HasInput("EndsTensorList") ||
+      HasInput("StridesTensorList")) {
+    Logger(verbose, 10)
+        << "While has input StartsTensorList/EndsTensorListStridesTensorList, "
+        << RequireOpset(10) << std::endl;
+    return 10;
+  }
+  if (HasInput("StartsTensor")) {
+    auto info = GetInput("StartsTensor");
+    if (!IsConstantInput("StartsTensor")) {
+      Logger(verbose, 10)
+          << "While has input StartsTensor, and it's not a constant tensor, "
+          << RequireOpset(10) << std::endl;
+      return 10;
+    }
+  }
+  if (HasInput("EndsTensor")) {
+    auto info = GetInput("EndsTensor");
+    if (!IsConstantInput("EndsTensor")) {
+      Logger(verbose, 10)
+          << "While has input EndsTensor, and it's not a constant tensor, "
+          << RequireOpset(10) << std::endl;
+      return 10;
+    }
+  }
+  if (HasInput("StridesTensor") || strides_.size() > 0) {
+    Logger(verbose, 10) << "While has strides, " << RequireOpset(10)
+                        << std::endl;
+    return 10;
+  }
+  return 7;
+}
+
+std::vector<int64_t> SliceMapper::DecreaseAxis() {
+  std::vector<int64_t> decrease_axis;
+  bool has_attr = HasAttr("decrease_axis");
+  if (has_attr) {
+    GetAttr("decrease_axis", &decrease_axis);
+    auto input_info = GetInput("Input");
+    auto output_info = GetOutput("Out");
+    if (output_info[0].shape.size() == 1 && output_info[0].shape[0] == 0) {
+      return decrease_axis;
+    }
+    if (input_info[0].shape.size() > output_info[0].shape.size()) {
+      return decrease_axis;
+    }
+    return {};
+  }
+  return decrease_axis;
+}
+
+void SliceMapper::Opset7() {
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Out");
+
+  Assert(!HasInput("StartsTensorList"),
+         "While slice/strided_slice has input StartsTensorList, requires "
+         "opset_version >= 10");
+
+  std::vector<int64_t> starts;
+  if (HasInput("StartsTensor")) {
+    Assert(TryGetInputValue("StartsTensor", &starts),
+           "While slice/strided_slice has input StartsTensor, and it's not a "
+           "constant tensor, then requires opset_version >= 10");
+  } else {
+    starts = starts_;
+  }
+
+  Assert(!HasInput("EndsTensorList"),
+         "While slice/strided_slice has input EndsTensorList, requires "
+         "opset_version >= 10");
+  std::vector<int64_t> ends;
+  if (HasInput("EndsTensor")) {
+    auto info = GetInput("EndsTensor");
+    Assert(TryGetInputValue("EndsTensor", &ends),
+           "While slice/strided_slice has input EndsTensor, and it's not a "
+           "constant tensor, then requires opset_version >= 10");
+  } else {
+    ends = ends_;
+  }
+
+  std::vector<int64_t> decrease_axis = DecreaseAxis();
+  if (decrease_axis.empty()) {
+    helper_->Slice(input_info[0].name, output_info[0].name, axes_, starts,
+                   ends);
+  } else {
+    std::string node = helper_->Slice(input_info[0].name, axes_, starts, ends);
+    helper_->Squeeze(node, output_info[0].name, decrease_axis);
+  }
+}
+
+void SliceMapper::Opset10() {
+  auto input_info = GetInput("Input");
+  auto output_info = GetOutput("Out");
+
+  std::string starts = "";
+  if (HasInput("StartsTensorList")) {
+    auto info = GetInput("StartsTensorList");
+    starts = helper_->ConcatIndices(info);
+  } else if (HasInput("StartsTensor")) {
+    auto info = GetInput("StartsTensor");
+    starts = helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
+  } else {
+    starts = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, starts_);
+  }
+
+  std::string ends = "";
+  if (HasInput("EndsTensorList")) {
+    auto info = GetInput("EndsTensorList");
+    ends = helper_->ConcatIndices(info);
+  } else if (HasInput("EndsTensor")) {
+    auto info = GetInput("EndsTensor");
+    ends = helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
+  } else {
+    ends = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, ends_);
+  }
+
+  std::string strides = "";
+  if (HasInput("StridesTensorList")) {
+    auto info = GetInput("StridesTensorList");
+    strides = helper_->ConcatIndices(info);
+  } else if (HasInput("StridesTensor")) {
+    auto info = GetInput("StridesTensor");
+    strides =
+        helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
+  } else {
+    if (strides_.size() == 0) {
+      strides = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64,
+                                  std::vector<int64_t>(axes_.size(), 1));
+    } else {
+      strides = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, strides_);
+    }
+  }
+
+  auto axes = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, axes_);
+  std::vector<int64_t> decrease_axis = DecreaseAxis();
+  if (decrease_axis.empty()) {
+    helper_->MakeNode("Slice",
+                      {input_info[0].name, starts, ends, axes, strides},
+                      {output_info[0].name});
+  } else {
+    auto out = helper_
+                   ->MakeNode("Slice",
+                              {input_info[0].name, starts, ends, axes, strides})
+                   ->output(0);
+    helper_->Squeeze(out, output_info[0].name, decrease_axis);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/slice.h b/paddle2onnx/mapper/tensor/slice.h
new file mode 100644
index 0000000000..38e5dc2f73
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/slice.h
@@ -0,0 +1,52 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class SliceMapper : public Mapper {
+ public:
+  SliceMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axes", &axes_);
+    GetAttr("starts", &starts_);
+    GetAttr("ends", &ends_);
+    if (HasAttr("strides")) {
+      GetAttr("strides", &strides_);
+    }
+    if (HasAttr("decrease_axis_")) {
+      GetAttr("decrease_axis", &decrease_axis_);
+    }
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset10();
+
+ private:
+  std::vector<int64_t> DecreaseAxis();
+  std::vector<int64_t> axes_;
+  std::vector<int64_t> starts_;
+  std::vector<int64_t> ends_;
+  std::vector<int64_t> strides_;
+  std::vector<int64_t> decrease_axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/split.cc b/paddle2onnx/mapper/tensor/split.cc
new file mode 100644
index 0000000000..2642c85b66
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/split.cc
@@ -0,0 +1,145 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/split.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(split, SplitMapper)
+
+int32_t SplitMapper::GetMinOpset(bool verbose) {
+  int64_t axis = axis_;
+  if (HasInput("AxisTensor")) {
+    std::vector<int64_t> value;
+    if (!TryGetInputValue("AxisTensor", &value)) {
+      Error() << "While AxisTensor as the input and it's not a constant "
+                 "tensor, the conversion is not supported yet."
+              << std::endl;
+      return -1;
+    }
+    axis = value[0];
+  }
+
+  if (HasInput("SectionsTensorList")) {
+    Logger(verbose, 13) << "While has input SectionsTensorList, "
+                        << RequireOpset(13) << std::endl;
+    return 13;
+  }
+
+  for (size_t i = 0; i < sections_.size(); ++i) {
+    if (sections_[i] < 0) {
+      auto info = GetInput("X");
+      if (info[0].shape[axis] < 0) {
+        Error() << "Cannot convert split op, while there's -1 in sections and "
+                   "cannot be infered by input shape."
+                << std::endl;
+        return -1;
+      }
+    }
+  }
+  return 7;
+}
+
+void SplitMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  int64_t axis = axis_;
+  if (HasInput("AxisTensor")) {
+    std::vector<int64_t> value;
+    Assert(TryGetInputValue("AxisTensor", &value),
+           "[Paddle2ONNX](split) Cannot get constant value from AxisTensor.");
+    axis = value[0];
+  }
+  if (axis < 0) {
+    axis += input_info[0].Rank();
+  }
+  Assert(!HasInput("SectionsTensorList"),
+         "[Paddle2ONNX](split) While SectionTensorList as input, requires "
+         "opset_version >= 13.");
+
+  int sum_of_kown_dim = 0;
+  for (size_t i = 0; i < sections_.size(); ++i) {
+    if (sections_[i] > 0) {
+      sum_of_kown_dim += sections_[i];
+    }
+  }
+  for (size_t i = 0; i < sections_.size(); ++i) {
+    if (sections_[i] < 0) {
+      Assert(input_info[0].shape[axis] > 0,
+             "Cannot convert split op, while there's -1 in sections and cannot "
+             "be infered by input shape.");
+      sections_[i] = input_info[0].shape[axis] - sum_of_kown_dim;
+    }
+  }
+
+  std::vector<std::string> output_names(output_info.size());
+  for (size_t i = 0; i < output_info.size(); ++i) {
+    output_names[i] = output_info[i].name;
+  }
+
+  helper_->Split(input_info[0].name, output_names, sections_, axis);
+}
+
+void SplitMapper::Opset13() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  int64_t axis = axis_;
+  if (HasInput("AxisTensor")) {
+    std::vector<int64_t> value;
+    Assert(TryGetInputValue("AxisTensor", &value),
+           "[Paddle2ONNX](split) Cannot get constant value from AxisTensor.");
+    axis = value[0];
+  }
+  if (axis < 0) {
+    axis += input_info[0].Rank();
+  }
+
+  std::string splits = "";
+  if (HasInput("SectionsTensorList")) {
+    auto info = GetInput("SectionsTensorList");
+    splits = helper_->ConcatIndices(info);
+  } else if (sections_.size() > 0) {
+    int sum_of_kown_dim = 0;
+    for (size_t i = 0; i < sections_.size(); ++i) {
+      if (sections_[i] > 0) {
+        sum_of_kown_dim += sections_[i];
+      }
+    }
+    for (size_t i = 0; i < sections_.size(); ++i) {
+      if (sections_[i] < 0) {
+        Assert(input_info[0].shape[axis] > 0,
+               "Cannot convert split op, while there's -1 in sections and "
+               "cannot be infered by input shape.");
+        sections_[i] = input_info[0].shape[axis] - sum_of_kown_dim;
+      }
+    }
+    splits = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, sections_);
+  }
+
+  std::vector<std::string> output_names(output_info.size());
+  for (size_t i = 0; i < output_info.size(); ++i) {
+    output_names[i] = output_info[i].name;
+  }
+  if (splits != "") {
+    auto node =
+        helper_->MakeNode("Split", {input_info[0].name, splits}, output_names);
+    AddAttribute(node, "axis", axis);
+  } else {
+    auto node = helper_->MakeNode("Split", {input_info[0].name}, output_names);
+    AddAttribute(node, "axis", axis);
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/split.h b/paddle2onnx/mapper/tensor/split.h
new file mode 100644
index 0000000000..2665cfcdb4
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/split.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class SplitMapper : public Mapper {
+ public:
+  SplitMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+    GetAttr("sections", &sections_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset13();
+
+ private:
+  int64_t axis_;
+  std::vector<int64_t> sections_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/squeeze2.cc b/paddle2onnx/mapper/tensor/squeeze2.cc
new file mode 100644
index 0000000000..953995875a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/squeeze2.cc
@@ -0,0 +1,84 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/squeeze2.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(squeeze2, Squeeze2Mapper)
+
+int32_t Squeeze2Mapper::GetMinOpset(bool verbose) {
+  if (IsAttrVar("axes")) {
+    auto infos = GetAttrVar("axes");
+    for (auto &info : infos) {
+      if (!IsConstant(info)) {
+        return 13;
+      }
+    }
+  }
+  return 7;
+}
+
+void Squeeze2Mapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::vector<int64_t> ret;
+  ret.reserve(input_info[0].shape.size());
+  for (auto i : input_info[0].shape) {
+    if (i > 1) ret.push_back(i);
+  }
+  if (ret.size() == input_info[0].Rank()) {
+    helper_->MakeNode("Identity", {input_info[0].name}, {output_info[0].name});
+  } else {
+    if (helper_->GetOpsetVersion() >= 13 && IsAttrVar("axes")) {
+      auto axes_info = GetAttrVar("axes");
+      std::string axes_name;
+      if (axes_info.size() == 1U) {
+        axes_name = helper_->AutoCast(axes_info[0].name, axes_info[0].dtype,
+                                      P2ODataType::INT64);
+      } else {
+        axes_name = helper_->ConcatIndices(axes_info);
+      }
+      helper_->MakeNode("Squeeze", {input_info[0].name, axes_name},
+                        {output_info[0].name});
+    } else {
+      if (IsAttrVar("axes")) {
+        auto axes_info = GetAttrVar("axes");
+        for (int64_t index = 0; index < axes_info.size(); index++) {
+          std::vector<int64_t> temp;
+          TryGetValue(axes_info[index], &temp);
+          for (auto &data : temp) {
+            axes_.push_back(data);
+          }
+        }
+      } else {
+        GetAttr("axes", &axes_);
+      }
+      std::vector<int64_t> axes(axes_.begin(), axes_.end());
+      for (size_t i = 0; i < axes.size(); ++i) {
+        if (axes[i] < 0) {
+          axes[i] += input_info[0].Rank();
+        }
+      }
+      if (axes.size() > 0) {
+        std::sort(axes.begin(), axes.end());
+        helper_->Squeeze(input_info[0].name, output_info[0].name, axes);
+      } else {
+        helper_->Squeeze(input_info[0].name, output_info[0].name, {});
+      }
+    }
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/squeeze2.h b/paddle2onnx/mapper/tensor/squeeze2.h
new file mode 100644
index 0000000000..ffa31c8854
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/squeeze2.h
@@ -0,0 +1,35 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Squeeze2Mapper : public Mapper {
+ public:
+  Squeeze2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                 int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  std::vector<int64_t> axes_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/stack.cc b/paddle2onnx/mapper/tensor/stack.cc
new file mode 100644
index 0000000000..4c6f249db1
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/stack.cc
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/stack.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(stack, StackMapper)
+
+void StackMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetOutput("Y");
+
+  int32_t out_dtype = 0;
+  std::vector<std::string> aligned_inputs =
+      helper_->DtypeAlignment(x_info, &out_dtype);
+  auto axis = axis_;
+  if (axis < 0) {
+    axis = axis + x_info[0].Rank() + 1;
+  }
+  for (size_t i = 0; i < aligned_inputs.size(); ++i) {
+    aligned_inputs[i] =
+        helper_->Unsqueeze(aligned_inputs[i], std::vector<int64_t>(1, axis));
+  }
+  auto out = helper_->Concat(aligned_inputs, axis_);
+  helper_->AutoCast(out, y_info[0].name, out_dtype, y_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/stack.h b/paddle2onnx/mapper/tensor/stack.h
new file mode 100644
index 0000000000..36d01d54a5
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/stack.h
@@ -0,0 +1,33 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class StackMapper : public Mapper {
+ public:
+  StackMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/take_along_axis.cc b/paddle2onnx/mapper/tensor/take_along_axis.cc
new file mode 100755
index 0000000000..7262ffb3f7
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/take_along_axis.cc
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/take_along_axis.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(take_along_axis, TakeAlongAxisMapper)
+
+int32_t TakeAlongAxisMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void TakeAlongAxisMapper::Opset11() {
+  auto x_info = GetInput("Input");
+  auto index_info = GetInput("Index");
+  auto out_info = GetOutput("Result");
+
+  auto axis = axis_;
+
+  auto node =
+      helper_->MakeNode("GatherElements", {x_info[0].name, index_info[0].name},
+                        {out_info[0].name});
+  AddAttribute(node, "axis", axis);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/take_along_axis.h b/paddle2onnx/mapper/tensor/take_along_axis.h
new file mode 100755
index 0000000000..8299d3de92
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/take_along_axis.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class TakeAlongAxisMapper : public Mapper {
+ public:
+  TakeAlongAxisMapper(const PaddleParser& p, OnnxHelper* helper,
+                      int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("Axis", &axis_);
+  }
+
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/temporal_shift.cc b/paddle2onnx/mapper/tensor/temporal_shift.cc
new file mode 100644
index 0000000000..2a2aac5106
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/temporal_shift.cc
@@ -0,0 +1,79 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/temporal_shift.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(temporal_shift, TemporalShiftMapper)
+
+int32_t TemporalShiftMapper::GetMinOpset(bool verbose) {
+  if (data_format_ == "NHWC") {
+    Error() << "Only support data_format of NCHW, but now the data format is "
+            << data_format_ << "." << std::endl;
+    return -1;
+  }
+  auto input_info = GetOutput("Out");
+  if (input_info[0].Rank() != 4) {
+    Error() << "The input dims must be 4, but now the input dims is "
+            << std::to_string(input_info[0].Rank()) << "." << std::endl;
+    return -1;
+  }
+  return 7;
+}
+
+void TemporalShiftMapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  int64_t C = input_info[0].shape[1];
+  int64_t H = input_info[0].shape[2];
+  int64_t W = input_info[0].shape[3];
+  std::vector<int64_t> reshape_shape = {-1, seg_num_, C, H, W};
+
+  std::string reshape_input =
+      helper_->Reshape(input_info[0].name, reshape_shape);
+
+  std::vector<int64_t> paddings(10, 0);
+  paddings[1] = 1;
+  paddings[6] = 1;
+
+  std::string padding_constant_node =
+      helper_->Constant(GetOnnxDtype(P2ODataType::INT64), paddings);
+
+  std::string pad_node = "";
+  if (helper_->GetOpsetVersion() < 11) {
+    auto node = helper_->MakeNode("Pad", {reshape_input});
+    AddAttribute(node, "pads", paddings);
+    float val = 0.0;
+    AddAttribute(node, "value", val);
+    pad_node = node->output(0);
+  } else {
+    auto node =
+        helper_->MakeNode("Pad", {reshape_input, padding_constant_node});
+    pad_node = node->output(0);
+  }
+
+  int64_t C1 = C * shift_ratio_;
+  int64_t C2 = 2 * C * shift_ratio_;
+  std::string slice_1 =
+      helper_->Slice(pad_node, {1, 2}, {0, 0}, {seg_num_, C1});
+  std::string slice_2 =
+      helper_->Slice(pad_node, {1, 2}, {2, C1}, {2 + seg_num_, C2});
+  std::string slice_3 =
+      helper_->Slice(pad_node, {1, 2}, {1, C2}, {1 + seg_num_, C});
+  std::string concat_out = helper_->Concat({slice_1, slice_2, slice_3}, 2);
+  helper_->Reshape(concat_out, output_info[0].name, {-1, C, H, W});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/temporal_shift.h b/paddle2onnx/mapper/tensor/temporal_shift.h
new file mode 100644
index 0000000000..d01ffb2718
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/temporal_shift.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class TemporalShiftMapper : public Mapper {
+ public:
+  TemporalShiftMapper(const PaddleParser& p, OnnxHelper* helper,
+                      int64_t block_id, int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("data_format", &data_format_);
+    GetAttr("shift_ratio", &shift_ratio_);
+    GetAttr("seg_num", &seg_num_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+
+ private:
+  int64_t seg_num_;
+  float shift_ratio_;
+  std::string data_format_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/tile.cc b/paddle2onnx/mapper/tensor/tile.cc
new file mode 100644
index 0000000000..97d9c97e21
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/tile.cc
@@ -0,0 +1,55 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/tile.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(tile, TileMapper)
+
+void TileMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto out_info = GetOutput("Out");
+
+  bool has_repeats_tensor = HasInput("RepeatTimes");
+  bool has_repeats_tensor_list = HasInput("repeat_times_tensor");
+  std::string repeats = "";
+  // NOTE(Aurelius84): we need to deprecate this branch in the future.
+  if (has_repeats_tensor) {
+    auto repeats_info = GetInput("RepeatTimes");
+    repeats = helper_->AutoCast(repeats_info[0].name, repeats_info[0].dtype,
+                                P2ODataType::INT64);
+  } else if (has_repeats_tensor_list) {
+    auto repeats_info = GetInput("repeat_times_tensor");
+    repeats = helper_->ConcatIndices(repeats_info);
+  } else if (IsAttrVar("repeat_times")) {
+    auto repeats_info = GetAttrVar("repeat_times");
+    if (repeats_info.size() == 1U) {  // repeat_times is a whole Tensor
+      repeats = helper_->AutoCast(repeats_info[0].name, repeats_info[0].dtype,
+                                  P2ODataType::INT64);
+    } else {  // repeat_times is a tensor list
+      repeats = helper_->ConcatIndices(repeats_info);
+    }
+  } else {
+    std::vector<int64_t> values;
+    GetAttr("repeat_times", &values);
+    int64_t nums = values.size();
+    for (int64_t i = 0; i < x_info[0].Rank() - nums; i++) {
+      values.insert(values.begin(), 1);
+    }
+    repeats = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, values);
+  }
+  helper_->MakeNode("Tile", {x_info[0].name, repeats}, {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/tile.h b/paddle2onnx/mapper/tensor/tile.h
new file mode 100644
index 0000000000..f8a55494dd
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/tile.h
@@ -0,0 +1,28 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class TileMapper : public Mapper {
+ public:
+  TileMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  void Opset7();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/top_k.cc b/paddle2onnx/mapper/tensor/top_k.cc
new file mode 100755
index 0000000000..bff09ee608
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/top_k.cc
@@ -0,0 +1,44 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/top_k.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(top_k, TopKMapper)
+
+void TopKMapper::Opset11() {
+  auto x_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto indices_info = GetOutput("Indices");
+
+  std::string k = "";
+  if (HasInput("K")) {
+    auto k_info = GetInput("K");
+    k = helper_->AutoCast(k_info[0].name, k_info[0].dtype, P2ODataType::INT64);
+    if (k_info[0].Rank() == 0) {
+      k = helper_->Reshape(k, std::vector<int64_t>(1, -1));
+    }
+  } else {
+    int64_t k_value = 0;
+    GetAttr("k", &k_value);
+    k = helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, k_value);
+  }
+  auto out_node = helper_->MakeNode("TopK", {x_info[0].name, k}, 2);
+  helper_->AutoCast(out_node->output(0), output_info[0].name, x_info[0].dtype,
+                    output_info[0].dtype);
+  helper_->AutoCast(out_node->output(1), indices_info[0].name,
+                    P2ODataType::INT64, indices_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/top_k.h b/paddle2onnx/mapper/tensor/top_k.h
new file mode 100644
index 0000000000..868c6b3567
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/top_k.h
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class TopKMapper : public Mapper {
+ public:
+  TopKMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+             int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+  int32_t GetMinOpset(bool verbose) {
+    Logger(verbose, 11) << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  void Opset11();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/top_k_v2.cc b/paddle2onnx/mapper/tensor/top_k_v2.cc
new file mode 100755
index 0000000000..c41561b6c6
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/top_k_v2.cc
@@ -0,0 +1,47 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/top_k_v2.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(top_k_v2, TopKV2Mapper)
+
+void TopKV2Mapper::Opset11() {
+  auto x_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+  auto indices_info = GetOutput("Indices");
+
+  std::string k = "";
+  if (HasInput("K")) {
+    auto k_info = GetInput("K");
+    k = helper_->AutoCast(k_info[0].name, k_info[0].dtype, P2ODataType::INT64);
+    if (k_info[0].Rank() == 0) {
+      k = helper_->Reshape(k, std::vector<int64_t>(1, -1));
+    }
+  } else {
+    int64_t k_value = 0;
+    GetAttr("k", &k_value);
+    k = helper_->Constant({1}, ONNX_NAMESPACE::TensorProto::INT64, k_value);
+  }
+  auto out_node = helper_->MakeNode("TopK", {x_info[0].name, k}, 2);
+  AddAttribute(out_node, "largest", static_cast<int64_t>(largest_));
+  AddAttribute(out_node, "sorted", static_cast<int64_t>(sorted_));
+  AddAttribute(out_node, "axis", axis_);
+  helper_->AutoCast(out_node->output(0), output_info[0].name, x_info[0].dtype,
+                    output_info[0].dtype);
+  helper_->AutoCast(out_node->output(1), indices_info[0].name,
+                    P2ODataType::INT64, indices_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/top_k_v2.h b/paddle2onnx/mapper/tensor/top_k_v2.h
new file mode 100644
index 0000000000..1610e28e07
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/top_k_v2.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class TopKV2Mapper : public Mapper {
+ public:
+  TopKV2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("largest", &largest_);
+    GetAttr("sorted", &sorted_);
+    GetAttr("axis", &axis_);
+  }
+  int32_t GetMinOpset(bool verbose) {
+    Logger(verbose, 11) << RequireOpset(11) << std::endl;
+    return 11;
+  }
+  void Opset11();
+
+ private:
+  bool largest_;
+  bool sorted_;
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/transpose2.cc b/paddle2onnx/mapper/tensor/transpose2.cc
new file mode 100644
index 0000000000..aa0e9a2b7a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/transpose2.cc
@@ -0,0 +1,32 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/transpose2.h"
+
+#include <string>
+#include <vector>
+
+namespace paddle2onnx {
+REGISTER_MAPPER(transpose2, Transpose2Mapper)
+
+void Transpose2Mapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  auto node = helper_->MakeNode("Transpose", {input_info[0].name},
+                                {output_info[0].name});
+  AddAttribute(node, "perm", axis_);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/transpose2.h b/paddle2onnx/mapper/tensor/transpose2.h
new file mode 100644
index 0000000000..25c86fd50b
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/transpose2.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Transpose2Mapper : public Mapper {
+ public:
+  Transpose2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  void Opset7();
+
+ private:
+  std::vector<int64_t> axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/unique.cc b/paddle2onnx/mapper/tensor/unique.cc
new file mode 100644
index 0000000000..38e5d1e6ba
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/unique.cc
@@ -0,0 +1,49 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/unique.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(unique, UniqueMapper)
+
+int32_t UniqueMapper::GetMinOpset(bool verbose) {
+  Logger(verbose, 11) << RequireOpset(11) << std::endl;
+  return 11;
+}
+
+void UniqueMapper::Opset11() {
+  auto intput_info = GetInput("X");
+  auto output_out_info = GetOutput("Out");
+  auto output_indices_info = GetOutput("Indices");
+  auto output_index_info = GetOutput("Index");
+  auto output_counts_info = GetOutput("Counts");
+
+  std::string out_index_name = "out_index";
+  std::string out_indices_name = "out_indices";
+  std::string out_counts_name = "out_counts";
+  auto node = helper_->MakeNode("Unique", {intput_info[0].name},
+                                {output_out_info[0].name, out_indices_name,
+                                 out_index_name, out_counts_name});
+  if (axis_.size()) {
+    AddAttribute(node, "axis", axis_[0]);
+  }
+  helper_->AutoCast(out_index_name, output_index_info[0].name,
+                    P2ODataType::INT64, output_index_info[0].dtype);
+  helper_->AutoCast(out_indices_name, output_indices_info[0].name,
+                    P2ODataType::INT64, output_indices_info[0].dtype);
+  helper_->AutoCast(out_counts_name, output_counts_info[0].name,
+                    P2ODataType::INT64, output_counts_info[0].dtype);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/unique.h b/paddle2onnx/mapper/tensor/unique.h
new file mode 100644
index 0000000000..1049c5c97f
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/unique.h
@@ -0,0 +1,39 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class UniqueMapper : public Mapper {
+ public:
+  UniqueMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+               int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+    GetAttr("dtype", &dtype_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset11();
+
+ private:
+  std::vector<int64_t> axis_;
+  int64_t dtype_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/unsqueeze2.cc b/paddle2onnx/mapper/tensor/unsqueeze2.cc
new file mode 100644
index 0000000000..ddeba0a1df
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/unsqueeze2.cc
@@ -0,0 +1,94 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/unsqueeze2.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(unsqueeze2, Unsqueeze2Mapper)
+
+int32_t Unsqueeze2Mapper::GetMinOpset(bool verbose) {
+  if (axes_.size() == 0) {
+    if (HasInput("AxesTensorList")) {
+      Logger(verbose, 13) << "While AxisTensorList as input, "
+                          << RequireOpset(13) << std::endl;
+      return 13;
+    } else if (HasInput("AxesTensor")) {
+      auto info = GetInput("AxesTensor");
+      if (!IsConstantInput("AxesTensor")) {
+        Logger(verbose, 13)
+            << "While AxesTensor as input, and it's not a constant tensor, "
+            << RequireOpset(13) << std::endl;
+        return 13;
+      } else {
+        return 7;
+      }
+    }
+  }
+  return 7;
+}
+
+void Unsqueeze2Mapper::Opset7() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::vector<int64_t> axes;
+  if (axes_.empty()) {
+    Assert(TryGetInputValue("AxesTensor", &axes),
+           "While unsqueeze2 has input AxesTensor, it cannot be exported by "
+           "Paddle2ONNX");
+  } else {
+    axes.assign(axes_.begin(), axes_.end());
+  }
+  for (size_t i = 0; i < axes.size(); ++i) {
+    if (axes[i] < 0) {
+      axes[i] = axes[i] + input_info[0].Rank() + i + 1;
+    }
+  }
+  helper_->Unsqueeze(input_info[0].name, output_info[0].name, axes);
+}
+
+void Unsqueeze2Mapper::Opset13() {
+  auto input_info = GetInput("X");
+  auto output_info = GetOutput("Out");
+
+  std::vector<int64_t> axes;
+  if (axes_.empty()) {
+    TryGetInputValue("AxesTensor", &axes);
+  } else {
+    axes.assign(axes_.begin(), axes_.end());
+  }
+  for (size_t i = 0; i < axes.size(); ++i) {
+    if (axes[i] < 0) {
+      axes[i] = axes[i] + input_info[0].Rank() + i + 1;
+    }
+  }
+
+  if (axes.size() > 0) {
+    helper_->Unsqueeze(input_info[0].name, output_info[0].name, axes);
+  } else {
+    std::string axes_node = "";
+    if (HasInput("AxesTensorList")) {
+      auto info = GetInput("AxesTensorList");
+      axes_node = helper_->ConcatIndices(info);
+    } else {
+      auto info = GetInput("AxesTensor");
+      axes_node =
+          helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
+    }
+    helper_->MakeNode("Unsqueeze", {input_info[0].name, axes_node},
+                      {output_info[0].name});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/unsqueeze2.h b/paddle2onnx/mapper/tensor/unsqueeze2.h
new file mode 100644
index 0000000000..1d1fe2771d
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/unsqueeze2.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class Unsqueeze2Mapper : public Mapper {
+ public:
+  Unsqueeze2Mapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                   int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axes", &axes_);
+  }
+  int32_t GetMinOpset(bool verbose = false);
+  void Opset7();
+  void Opset13();
+
+ private:
+  std::vector<int64_t> axes_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/unstack.cc b/paddle2onnx/mapper/tensor/unstack.cc
new file mode 100644
index 0000000000..832dc94a3a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/unstack.cc
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/unstack.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(unstack, UnstackMapper)
+
+void UnstackMapper::Opset7() {
+  auto x_info = GetInput("X");
+  auto y_info = GetOutput("Y");
+
+  if (axis_ < 0) {
+    axis_ = axis_ + x_info[0].Rank();
+  }
+
+  auto split_nodes = helper_->Split(
+      x_info[0].name, std::vector<int64_t>(y_info.size(), 1), axis_);
+
+  for (size_t i = 0; i < split_nodes.size(); ++i) {
+    helper_->Squeeze(split_nodes[i], y_info[i].name, {axis_});
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/unstack.h b/paddle2onnx/mapper/tensor/unstack.h
new file mode 100644
index 0000000000..1ad3d6f0c5
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/unstack.h
@@ -0,0 +1,33 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class UnstackMapper : public Mapper {
+ public:
+  UnstackMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+                int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {
+    GetAttr("axis", &axis_);
+  }
+  void Opset7();
+
+ private:
+  int64_t axis_;
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/where.cc b/paddle2onnx/mapper/tensor/where.cc
new file mode 100644
index 0000000000..f8adf16149
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/where.cc
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/mapper/tensor/where.h"
+
+namespace paddle2onnx {
+REGISTER_MAPPER(where, WhereMapper)
+
+void WhereMapper::Opset9() {
+  auto x_info = GetInput("X");
+  auto y_info = GetInput("Y");
+  auto cond_info = GetInput("Condition");
+  auto out_info = GetOutput("Out");
+
+  helper_->MakeNode("Where",
+                    {cond_info[0].name, x_info[0].name, y_info[0].name},
+                    {out_info[0].name});
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mapper/tensor/where.h b/paddle2onnx/mapper/tensor/where.h
new file mode 100644
index 0000000000..5f64303f7a
--- /dev/null
+++ b/paddle2onnx/mapper/tensor/where.h
@@ -0,0 +1,36 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+
+#include "paddle2onnx/mapper/mapper.h"
+
+namespace paddle2onnx {
+
+class WhereMapper : public Mapper {
+ public:
+  WhereMapper(const PaddleParser& p, OnnxHelper* helper, int64_t block_id,
+              int64_t op_id)
+      : Mapper(p, helper, block_id, op_id) {}
+
+  int32_t GetMinOpset(bool verbose = false) {
+    Logger(verbose, 9) << RequireOpset(9) << std::endl;
+    return 9;
+  }
+  void Opset9();
+};
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/mappers_registry.h.in b/paddle2onnx/mappers_registry.h.in
new file mode 100755
index 0000000000..5387f8ab1a
--- /dev/null
+++ b/paddle2onnx/mappers_registry.h.in
@@ -0,0 +1,214 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+
+// If you want to use the paddle2onnx static library, add the
+// registration logic here synchronously each time you add a new op.
+#ifndef WITH_PADDLE2ONNX_STATIC_INTERNAL
+#cmakedefine WITH_PADDLE2ONNX_STATIC_INTERNAL
+#endif
+
+#if defined(WITH_PADDLE2ONNX_STATIC_INTERNAL)
+#if (!defined(WITH_PADDLE2ONNX_STATIC_INTERNAL_AT_COMPILING))
+
+#if defined(_WIN32)
+#define UNUSED
+#define __builtin_expect(EXP, C) (EXP)
+#else
+#define UNUSED __attribute__((unused))
+#endif
+
+#define USE_P2O_MAPPER(op_name, class_name)                           \
+  namespace paddle2onnx {                                             \
+    extern int Touch##op_name##class_name();                          \
+    int op_name##__registry__ UNUSED = Touch##op_name##class_name();  \
+  }
+
+USE_P2O_MAPPER(quantize_linear, QuantizeLinearMapper)
+USE_P2O_MAPPER(dequantize_linear, DequantizeLinearMapper)
+USE_P2O_MAPPER(batch_norm, BatchNormMapper)
+USE_P2O_MAPPER(dropout, DropoutMapper)
+USE_P2O_MAPPER(layer_norm, LayerNormMapper)
+USE_P2O_MAPPER(rnn, RnnMapper)
+USE_P2O_MAPPER(softmax_with_cross_entropy, SoftmaxCrossEntropyLossMapper)
+USE_P2O_MAPPER(affine_channel, AffineChannelMapper)
+USE_P2O_MAPPER(conv3d, Conv3dMapper)
+USE_P2O_MAPPER(conv2d_transpose, Conv2dTransposeMapper)
+USE_P2O_MAPPER(depthwise_conv2d_transpose, Conv2dTransposeMapper)
+USE_P2O_MAPPER(conv2d, Conv2dMapper)
+USE_P2O_MAPPER(depthwise_conv2d, Conv2dMapper)
+USE_P2O_MAPPER(pool3d, Pool3dMapper)
+USE_P2O_MAPPER(max_pool3d_with_index, Pool3dMapper)
+USE_P2O_MAPPER(instance_norm, InstanceNormMapper)
+USE_P2O_MAPPER(pool2d, Pool2dMapper)
+USE_P2O_MAPPER(max_pool2d_with_index, Pool2dMapper)
+USE_P2O_MAPPER(shape, ShapeMapper)
+USE_P2O_MAPPER(bilinear_interp, InterpolateMapper)
+USE_P2O_MAPPER(bilinear_interp_v2, InterpolateMapper)
+USE_P2O_MAPPER(nearest_interp_v2, InterpolateMapper)
+USE_P2O_MAPPER(bicubic_interp_v2, InterpolateMapper)
+USE_P2O_MAPPER(linear_interp_v2, InterpolateMapper)
+USE_P2O_MAPPER(trilinear_interp_v2, InterpolateMapper)
+USE_P2O_MAPPER(data_norm, DataNormMapper)
+USE_P2O_MAPPER(norm, NormMapper)
+USE_P2O_MAPPER(group_norm, GroupNormMapper)
+USE_P2O_MAPPER(pad3d, Pad3DMapper)
+USE_P2O_MAPPER(relu, ActivationMapper)
+USE_P2O_MAPPER(relu6, Relu6Mapper)
+USE_P2O_MAPPER(tanh, ActivationMapper)
+USE_P2O_MAPPER(log, ActivationMapper)
+USE_P2O_MAPPER(sigmoid, ActivationMapper)
+USE_P2O_MAPPER(sqrt, ActivationMapper)
+USE_P2O_MAPPER(softplus, ActivationMapper)
+USE_P2O_MAPPER(exp, ActivationMapper)
+USE_P2O_MAPPER(floor, ActivationMapper)
+USE_P2O_MAPPER(cos, ActivationMapper)
+USE_P2O_MAPPER(sin, ActivationMapper)
+USE_P2O_MAPPER(round, ActivationMapper)
+USE_P2O_MAPPER(abs, ActivationMapper)
+USE_P2O_MAPPER(acos, ActivationMapper)
+USE_P2O_MAPPER(asin, ActivationMapper)
+USE_P2O_MAPPER(atan, ActivationMapper)
+USE_P2O_MAPPER(sinh, ActivationMapper)
+USE_P2O_MAPPER(tan, ActivationMapper)
+USE_P2O_MAPPER(ceil, ActivationMapper)
+USE_P2O_MAPPER(cosh, ActivationMapper)
+USE_P2O_MAPPER(softsign, ActivationMapper)
+USE_P2O_MAPPER(sign, ActivationMapper)
+USE_P2O_MAPPER(erf, ActivationMapper)
+USE_P2O_MAPPER(reciprocal, ActivationMapper)
+USE_P2O_MAPPER(leaky_relu, LeakyReluMapper)
+USE_P2O_MAPPER(gelu, GeluMapper)
+USE_P2O_MAPPER(selu, SeluMapper)
+USE_P2O_MAPPER(prelu, PReluMapper)
+USE_P2O_MAPPER(hard_sigmoid, HardSigmoidMapper)
+USE_P2O_MAPPER(swish, SwishMapper)
+USE_P2O_MAPPER(hard_swish, HardSwishMapper)
+USE_P2O_MAPPER(softmax, SoftMaxMapper)
+USE_P2O_MAPPER(brelu, BReluMapper)
+USE_P2O_MAPPER(elu, EluMapper)
+USE_P2O_MAPPER(hard_shrink, HardShrinkMapper)
+USE_P2O_MAPPER(softshrink, SoftShrinkMapper)
+USE_P2O_MAPPER(mish, MishMapper)
+USE_P2O_MAPPER(square, SquareMapper)
+USE_P2O_MAPPER(size, SizeMapper)
+USE_P2O_MAPPER(rsqrt, RsqrtMapper)
+USE_P2O_MAPPER(logsigmoid, LogSigmoidMapper)
+USE_P2O_MAPPER(log_softmax, LogSoftmaxMapper)
+USE_P2O_MAPPER(tanh_shrink, TanhShrinkMapper)
+USE_P2O_MAPPER(thresholded_relu, ThresholdedReluMapper)
+USE_P2O_MAPPER(log1p, Log1PMapper)
+USE_P2O_MAPPER(log2, Log2Mapper)
+USE_P2O_MAPPER(log10, Log10Mapper)
+USE_P2O_MAPPER(silu, SiluMapper)
+USE_P2O_MAPPER(elementwise_add, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_sub, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_div, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_mul, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_min, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_max, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_pow, ElementwiseMapper)
+USE_P2O_MAPPER(elementwise_mod, ElementWiseModMapper)
+USE_P2O_MAPPER(elementwise_floordiv, ElementWiseFloordivMapper)
+USE_P2O_MAPPER(flatten2, Flatten2Mapper)
+USE_P2O_MAPPER(squeeze2, Squeeze2Mapper)
+USE_P2O_MAPPER(clip, ClipMapper)
+USE_P2O_MAPPER(pixel_shuffle, PixelShuffleMapper)
+USE_P2O_MAPPER(one_hot_v2, OneHotV2Mapper)
+USE_P2O_MAPPER(expand_v2, ExpandV2Mapper)
+USE_P2O_MAPPER(cumsum, CumsumMapper)
+USE_P2O_MAPPER(dot, DotMapper)
+USE_P2O_MAPPER(less_than, LessThanMapper)
+USE_P2O_MAPPER(reshape2, Reshape2Mapper)
+USE_P2O_MAPPER(not_equal, NotEqualMapper)
+USE_P2O_MAPPER(arg_min, ArgMinMapper)
+USE_P2O_MAPPER(matmul_v2, MatmulV2Mapper)
+USE_P2O_MAPPER(greater_equal, GreaterEqualMapper)
+USE_P2O_MAPPER(scale, ScaleMapper)
+USE_P2O_MAPPER(expand, ExpandMapper)
+USE_P2O_MAPPER(dist, DistMapper)
+USE_P2O_MAPPER(expand_as_v2, ExpandAsMapper)
+USE_P2O_MAPPER(p_norm, PNormMapper)
+USE_P2O_MAPPER(split, SplitMapper)
+USE_P2O_MAPPER(mean, MeanMapper)
+USE_P2O_MAPPER(set_value, SetValueMapper)
+USE_P2O_MAPPER(transpose2, Transpose2Mapper)
+USE_P2O_MAPPER(temporal_shift, TemporalShiftMapper)
+USE_P2O_MAPPER(mul, MulMapper)
+USE_P2O_MAPPER(flip, FlipMapper)
+USE_P2O_MAPPER(bmm, BmmMapper)
+USE_P2O_MAPPER(index_select, IndexSelectMapper)
+USE_P2O_MAPPER(stack, StackMapper)
+USE_P2O_MAPPER(gather_nd, GatherNdMapper)
+USE_P2O_MAPPER(lookup_table, LookupTableMapper)
+USE_P2O_MAPPER(lookup_table_v2, LookupTableMapper)
+USE_P2O_MAPPER(meshgrid, MeshgridMapper)
+USE_P2O_MAPPER(scatter, ScatterMapper)
+USE_P2O_MAPPER(assign, AssignMapper)
+USE_P2O_MAPPER(share_data, AssignMapper)
+USE_P2O_MAPPER(scatter_nd_add, ScatterNdAddMapper)
+USE_P2O_MAPPER(top_k_v2, TopKV2Mapper)
+USE_P2O_MAPPER(range, RangeMapper)
+USE_P2O_MAPPER(logical_not, LogicalNotMapper)
+USE_P2O_MAPPER(linspace, LinspaceMapper)
+USE_P2O_MAPPER(eye, EyeMapper)
+USE_P2O_MAPPER(where, WhereMapper)
+USE_P2O_MAPPER(unique, UniqueMapper)
+USE_P2O_MAPPER(assign_value, AssignValueMapper)
+USE_P2O_MAPPER(unstack, UnstackMapper)
+USE_P2O_MAPPER(fill_any_like, FillLikeMapper)
+USE_P2O_MAPPER(fill_zeros_like, FillLikeMapper)
+USE_P2O_MAPPER(partial_sum, PartialOpsMapper)
+USE_P2O_MAPPER(partial_concat, PartialOpsMapper)
+USE_P2O_MAPPER(top_k, TopKMapper)
+USE_P2O_MAPPER(grid_sampler, GridSamplerMapper)
+USE_P2O_MAPPER(sum, AddNMapper)
+USE_P2O_MAPPER(unsqueeze2, Unsqueeze2Mapper)
+USE_P2O_MAPPER(arg_max, ArgMaxMapper)
+USE_P2O_MAPPER(cast, CastMapper)
+USE_P2O_MAPPER(matmul, MatmulMapper)
+USE_P2O_MAPPER(gather, GatherMapper)
+USE_P2O_MAPPER(fill_constant, FillConstantMapper)
+USE_P2O_MAPPER(take_along_axis, TakeAlongAxisMapper)
+USE_P2O_MAPPER(greater_than, GreaterThanMapper)
+USE_P2O_MAPPER(argsort, ArgsortMapper)
+USE_P2O_MAPPER(pow, PowMapper)
+USE_P2O_MAPPER(tile, TileMapper)
+USE_P2O_MAPPER(logical_and, LogicalOpMapper)
+USE_P2O_MAPPER(logical_or, LogicalOpMapper)
+USE_P2O_MAPPER(logical_xor, LogicalOpMapper)
+USE_P2O_MAPPER(fill_constant_batch_size_like, FillConstantBatchSizeLikeMapper)
+USE_P2O_MAPPER(slice, SliceMapper)
+USE_P2O_MAPPER(strided_slice, SliceMapper)
+USE_P2O_MAPPER(where_index, NonZeroMapper)
+USE_P2O_MAPPER(reduce_mean, ReduceMapper)
+USE_P2O_MAPPER(reduce_sum, ReduceMapper)
+USE_P2O_MAPPER(reduce_min, ReduceMapper)
+USE_P2O_MAPPER(reduce_max, ReduceMapper)
+USE_P2O_MAPPER(reduce_prod, ReduceMapper)
+USE_P2O_MAPPER(logsumexp, ReduceMapper)
+USE_P2O_MAPPER(reduce_all, ReduceMapper)
+USE_P2O_MAPPER(reduce_any, ReduceMapper)
+USE_P2O_MAPPER(concat, ConcatMapper)
+USE_P2O_MAPPER(less_equal, LessEqualMapper)
+USE_P2O_MAPPER(gaussian_random, GaussianRandomMapper)
+USE_P2O_MAPPER(flatten_contiguous_range, FlattenMapper)
+USE_P2O_MAPPER(equal, EqualMapper)
+USE_P2O_MAPPER(yolo_box, YoloBoxMapper)
+USE_P2O_MAPPER(multiclass_nms3, NMSMapper)
+USE_P2O_MAPPER(roi_align, RoiAlignMapper)
+USE_P2O_MAPPER(index_sample, IndexSampleMapper)
+USE_P2O_MAPPER(mv, MvMapper)
+#endif // WITH_PADDLE2ONNX_STATIC_INTERNAL_AT_COMPILING
+#endif // WITH_PADDLE2ONNX_STATIC_INTERNAL
diff --git a/paddle2onnx/onnx_reader.cc b/paddle2onnx/onnx_reader.cc
new file mode 100644
index 0000000000..0cf1160b1d
--- /dev/null
+++ b/paddle2onnx/onnx_reader.cc
@@ -0,0 +1,124 @@
+#include <fstream>
+#include <iostream>
+#include <set>
+#include <string>
+#include <cstring>
+#include "paddle2onnx/converter.h"
+#include "paddle2onnx/mapper/exporter.h"
+#include "paddle2onnx/optimizer/paddle2onnx_optimizer.h"
+
+namespace paddle2onnx {
+
+int32_t GetDataTypeFromOnnx(int dtype) {
+  if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    return 0;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::DOUBLE) {
+    return 1;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::UINT8) {
+    return 2;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT8) {
+    return 3;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT32) {
+    return 4;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::INT64) {
+    return 5;
+  } else if (dtype == ONNX_NAMESPACE::TensorProto::FLOAT16) {
+    return 6;
+  }
+  Assert(false, "Only support float/double/uint8/int32/int64/float16 in OnnxReader.");
+  return -1;
+}
+
+OnnxReader::OnnxReader(const char* model_buffer, int buffer_size) {
+  ONNX_NAMESPACE::ModelProto model;
+  std::string content(model_buffer, model_buffer + buffer_size);
+  model.ParseFromString(content);
+
+  std::set<std::string> initializer_names;
+  for (auto i = 0; i < model.graph().initializer_size(); ++i) {
+    initializer_names.insert(model.graph().initializer(i).name());
+  }
+
+  num_outputs = model.graph().output_size();
+  Assert(num_outputs <= 100,
+         "The number of outputs is exceed 100, unexpected situation.");
+
+  num_inputs = 0;
+  for (int i = 0; i < model.graph().input_size(); ++i) {
+    if (initializer_names.find(model.graph().input(i).name()) !=
+        initializer_names.end()) {
+      continue;
+    }
+    num_inputs += 1;
+    Assert(num_inputs <= 100,
+           "The number of inputs is exceed 100, unexpected situation.");
+
+    inputs[i].dtype =
+        GetDataTypeFromOnnx(model.graph().input(i).type().tensor_type().elem_type());
+    std::strcpy(inputs[i].name, model.graph().input(i).name().c_str());
+    auto& shape = model.graph().input(i).type().tensor_type().shape();
+    int dim_size = shape.dim_size();
+    inputs[i].rank = dim_size;
+    inputs[i].shape = new int64_t[dim_size];
+    for (int j = 0; j < dim_size; ++j) {
+      inputs[i].shape[j] = static_cast<int64_t>(shape.dim(j).dim_value());
+      if (inputs[i].shape[j] <= 0) {
+        inputs[i].shape[j] = -1;
+      }
+    }
+  }
+
+  for (int i = 0; i < num_outputs; ++i) {
+    std::strcpy(outputs[i].name, model.graph().output(i).name().c_str());
+    outputs[i].dtype =
+        GetDataTypeFromOnnx(model.graph().output(i).type().tensor_type().elem_type());
+    auto& shape = model.graph().output(i).type().tensor_type().shape();
+    int dim_size = shape.dim_size();
+    outputs[i].rank = dim_size;
+    outputs[i].shape = new int64_t[dim_size];
+    for (int j = 0; j < dim_size; ++j) {
+      outputs[i].shape[j] = static_cast<int64_t>(shape.dim(j).dim_value());
+      if (outputs[i].shape[j] <= 0) {
+        outputs[i].shape[j] = -1;
+      }
+    }
+  }
+}
+
+bool RemoveMultiClassNMS(const char* model_buffer, int buffer_size,
+                         char** out_model, int* out_model_size) {
+  ONNX_NAMESPACE::ModelProto model;
+  std::string content(model_buffer, model_buffer + buffer_size);
+  model.ParseFromString(content);
+  auto* graph = model.mutable_graph();
+  int nms_index = -1;
+  std::vector<std::string> inputs;
+  for (int i = 0; i < graph->node_size(); ++i) {
+    if (graph->node(i).op_type() == "MultiClassNMS") {
+      nms_index = -1;
+      for (int j = 0; j < graph->node(i).input_size(); ++j) {
+        inputs.push_back(graph->node(i).input(j));
+      }
+      break;
+    }
+  }
+  graph->clear_output();
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    auto output = graph->add_output();
+    output->set_name(inputs[i]);
+    auto type_proto = output->mutable_type();
+    auto tensor_type_proto = type_proto->mutable_tensor_type();
+    tensor_type_proto->set_elem_type(ONNX_NAMESPACE::TensorProto::FLOAT);
+    auto shape = tensor_type_proto->mutable_shape();
+    shape->add_dim()->set_dim_value(-1);
+    shape->add_dim()->set_dim_value(-1);
+    shape->add_dim()->set_dim_value(-1);
+  }
+  auto optimized_model = ONNX_NAMESPACE::optimization::OptimizeOnnxModel(model);
+  *out_model_size = optimized_model.ByteSizeLong();
+  *out_model = new char[*out_model_size];
+  optimized_model.SerializeToArray(*out_model, *out_model_size);
+  return true;
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/optimize.py b/paddle2onnx/optimize.py
new file mode 100755
index 0000000000..a4bf35b6e1
--- /dev/null
+++ b/paddle2onnx/optimize.py
@@ -0,0 +1,46 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+
+import argparse
+import sys
+from paddle2onnx.utils import logging
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input_model',
+        required=True,
+        help='The path of input onnx model file.')
+    parser.add_argument(
+        '--output_model',
+        required=True,
+        help='The file path to write optimized onnx model file.')
+    parser.add_argument(
+        '--input_shape_dict',
+        default="",
+        help="The shape infos of inputs, e.g --input_shape_dict=\"{'image': [1, 3, 608, 608], 'scale_factor': [1, 2]}\""
+    )
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_arguments()
+    import paddle2onnx.paddle2onnx_cpp2py_export as c_p2o
+    shape_dict = {}
+    if args.input_shape_dict != "":
+        shape_dict = eval(args.input_shape_dict)
+    c_p2o.optimize(args.input_model, args.output_model, shape_dict)
+    logging.info("Model optmized, saved in {}.".format(args.output_model))
diff --git a/paddle2onnx/optimizer/convert_fp32_to_fp16.cc b/paddle2onnx/optimizer/convert_fp32_to_fp16.cc
new file mode 100644
index 0000000000..71a92cc7f5
--- /dev/null
+++ b/paddle2onnx/optimizer/convert_fp32_to_fp16.cc
@@ -0,0 +1,843 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/optimizer/convert_fp32_to_fp16.h"
+#include "paddle2onnx/utils/utils.h"
+
+namespace paddle2onnx {
+
+void ConvertFp32ToFp16::ConvertValToFloat16(float val, uint16_t* x) {
+  // Conversion routine adapted from
+  // http://stackoverflow.com/questions/1659440/32-bit-to-16-bit-floating-point-conversion
+  Bits v, s;
+  v.f = val;
+  uint32_t sign = v.si & sigN;
+  v.si ^= sign;
+  sign >>= shiftSign;  // logical shift
+  s.si = mulN;
+  s.si = s.f * v.f;  // correct subnormals
+  v.si ^= (s.si ^ v.si) & -(minN > v.si);
+  v.si ^= (infN ^ v.si) & -((infN > v.si) & (v.si > maxN));
+  v.si ^= (nanN ^ v.si) & -((nanN > v.si) & (v.si > infN));
+  v.ui >>= shift;  // logical shift
+  v.si ^= ((v.si - maxD) ^ v.si) & -(v.si > maxC);
+  v.si ^= ((v.si - minD) ^ v.si) & -(v.si > subC);
+  *x = v.ui | sign;
+}
+
+void ConvertFp32ToFp16::SortNodes(ONNX_NAMESPACE::ModelProto* model) {
+  // return the topo sort of nodes;
+  // 1. Get i2o_mapper and  constant_nodes, i2o_mapper means the node map to its
+  // all output nodes, constant_nodes save all constant nodes.
+  // 2. Nodes without output nodes are first saved to new_nodes, and then
+  // cyclically delete the records of the node in i2o_mapper items, and nodes
+  // whose output nodes are empty are also saved to new_nodes in turn.
+  // 3. Store constant nodes in new_nodes.
+  // 4. Reverse new_nodes, then assign to nodes.
+  auto graph = model->mutable_graph();
+
+  // means the node map to its all output nodes
+  std::map<std::string, std::vector<std::string>> i2o_mapper;
+  // constant_nodes save all constant nodes.
+  std::vector<ONNX_NAMESPACE::NodeProto> constant_nodes;
+  // name map to its node
+  std::map<std::string, ONNX_NAMESPACE::NodeProto> name2node_mapper;
+  for (int64_t i = 0; i < graph->node_size(); i++) {
+    auto node = graph->mutable_node(i);
+    if (node->op_type() == "Constant") {
+      constant_nodes.push_back(*node);
+      continue;
+    }
+    name2node_mapper[node->name()] = *node;
+    for (int64_t in_index = 0; in_index < node->input_size(); in_index++) {
+      std::string input = node->input(in_index);
+      for (int64_t j = 0; j < graph->node_size(); j++) {
+        if (i == j) {
+          continue;
+        }
+        auto input_node = graph->mutable_node(j);
+        if (input_node->op_type() == "Constant") {
+          continue;
+        }
+        for (int64_t out_index = 0; out_index < input_node->output_size();
+             out_index++) {
+          // find the pre node
+          if (input == input_node->output(out_index)) {
+            // does not find other input node before
+            if (i2o_mapper.find(input_node->name()) == i2o_mapper.end()) {
+              i2o_mapper[input_node->name()] = {node->name()};
+            } else {
+              auto iter =
+                  std::find(i2o_mapper[input_node->name()].begin(),
+                            i2o_mapper[input_node->name()].end(), node->name());
+              // not been found before
+              if (iter == i2o_mapper[input_node->name()].end()) {
+                i2o_mapper[input_node->name()].push_back(node->name());
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // Store topologically sorted nodes
+  std::vector<ONNX_NAMESPACE::NodeProto> new_nodes;
+
+  for (int64_t i = 0; i < graph->node_size(); i++) {
+    auto node = graph->mutable_node(i);
+    auto node_name = node->name();
+    if (node->op_type() == "Constant") {
+      continue;
+    }
+    // Store those nodes that have no output first.
+    if (i2o_mapper.find(node_name) == i2o_mapper.end()) {
+      new_nodes.push_back(*node);
+    }
+  }
+
+  int64_t index = 0;
+  while (index < new_nodes.size()) {
+    auto current_node = new_nodes[index];
+    std::string current_node_name = current_node.name();
+    for (auto iter = i2o_mapper.begin(); iter != i2o_mapper.end(); iter++) {
+      std::string input_node_name = iter->first;
+      std::vector<std::string>* output_nodes_name = &iter->second;
+      if (output_nodes_name->empty()) {
+        continue;
+      }
+      auto in_inter = std::find(output_nodes_name->begin(),
+                                output_nodes_name->end(), current_node_name);
+      // if find the pre node, erase current node name in i2o_mapper
+      if (in_inter != output_nodes_name->end()) {
+        output_nodes_name->erase(in_inter);
+      }
+      // if find on node that have no output, store it
+      if (output_nodes_name->empty()) {
+        new_nodes.push_back(name2node_mapper[input_node_name]);
+      }
+    }
+    index++;
+  }
+
+  // store all constant node finally
+  for (auto& node : constant_nodes) {
+    new_nodes.push_back(node);
+  }
+
+  // reverse the sorted nodes
+  std::reverse(new_nodes.begin(), new_nodes.end());
+
+  Assert(model->mutable_graph()->node_size() == new_nodes.size(),
+         "The number of nodes after topological sorting is not equal to the "
+         "number before sorting");
+  // copy all new_nodes to graph
+  for (int64_t i = 0; i < graph->node_size(); i++) {
+    auto node = graph->mutable_node(i);
+    node->CopyFrom(new_nodes[i]);
+  }
+}
+
+std::string ConvertFp32ToFp16::GenName(const std::string& prefix) {
+  int64_t name_index = 0;
+  auto iter = name_index_mapper.find(prefix);
+  if (iter != name_index_mapper.end()) {
+    name_index = iter->second;
+    name_index_mapper[prefix]++;
+  } else {
+    name_index_mapper[prefix] = 1;
+  }
+  return prefix + std::to_string(name_index);
+}
+
+ONNX_NAMESPACE::ValueInfoProto* ConvertFp32ToFp16::MakeValueInfoFromTensor(
+    const ONNX_NAMESPACE::TensorProto& tensor) {
+  ONNX_NAMESPACE::ValueInfoProto* value_info =
+      new ONNX_NAMESPACE::ValueInfoProto();
+  value_info->set_name(tensor.name());
+  auto type_proto = value_info->mutable_type();
+  auto tensor_type_proto = type_proto->mutable_tensor_type();
+  tensor_type_proto->set_elem_type(tensor.data_type());  // TODO
+  auto shape = tensor_type_proto->mutable_shape();
+  for (auto i = 0; i < tensor.dims_size(); i++) {
+    auto dim = tensor.dims(i);
+    if (dim < 0) {
+      auto dynamic_dim_name = GenName("DynamicDimension");
+      shape->add_dim()->set_dim_param(dynamic_dim_name);
+    } else {
+      shape->add_dim()->set_dim_value(dim);
+    }
+  }
+  return value_info;
+}
+
+ONNX_NAMESPACE::NodeProto* ConvertFp32ToFp16::MakeCastNode(
+    const std::string& op_name, const std::vector<std::string>& inputs,
+    const std::vector<std::string>& outputs, int32_t to_dtype) {
+  ONNX_NAMESPACE::NodeProto* node = new ONNX_NAMESPACE::NodeProto();
+  node->set_name(op_name);
+  node->set_op_type("Cast");
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    node->add_input(inputs[i]);
+  }
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    node->add_output(outputs[i]);
+  }
+  auto attr = node->add_attribute();
+  attr->set_name("to");
+  attr->set_i(static_cast<int>(to_dtype));
+  attr->set_type(ONNX_NAMESPACE::AttributeProto::INT);
+  return node;
+}
+
+bool ConvertFp32ToFp16::GetTensorValue(
+    const ONNX_NAMESPACE::TensorProto& tensor, std::vector<float>* value) {
+  auto dtype = tensor.data_type();
+  if (dtype != ONNX_NAMESPACE::TensorProto::FLOAT) {
+    return false;
+  }
+  std::vector<int64_t> shape;
+  for (int64_t i = 0; i < tensor.dims_size(); i++) {
+    shape.push_back(tensor.dims(i));
+  }
+  int64_t nums = 1;
+  for (auto& i : shape) nums *= i;
+  value->resize(nums);
+  memcpy(value->data(), tensor.raw_data().data(), nums * sizeof(float));
+  return value->size();
+}
+
+// When the value of a tensor is greater than 10000, it is reserved as FP32 and
+// not converted.
+bool ConvertFp32ToFp16::KeepNodeType(ONNX_NAMESPACE::NodeProto* node) {
+  auto KeepType = [=](const ONNX_NAMESPACE::TensorProto& tensor) {
+    std::vector<float> fp32_val;
+    GetTensorValue(tensor, &fp32_val);
+    for (auto i = 0; i < fp32_val.size(); i++) {
+      if (fp32_val[i] > 10000) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  for (auto attr_index = 0; attr_index < node->attribute_size(); attr_index++) {
+    auto attr = node->attribute(attr_index);
+    if (attr.has_t() && KeepType(attr.t())) {
+      return true;
+    }
+    for (auto t_index = 0; t_index < attr.tensors_size(); t_index++) {
+      if (KeepType(attr.tensors(t_index))) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+void ConvertFp32ToFp16::ConvertTensorFloatToFloat16(
+    ONNX_NAMESPACE::TensorProto* tensor) {
+  if (tensor->data_type() == ONNX_NAMESPACE::TensorProto::FLOAT) {
+    if (tensor->float_data_size()) {
+      Assert(false, "No implemented! Please raise an issue to us.");
+    }
+    if (tensor->has_raw_data()) {
+      std::vector<float> fp32_val;
+      GetTensorValue(*tensor, &fp32_val);
+      if (fp32_val.empty()) {
+        return;
+      }
+      converted_attr++;
+      tensor->set_data_type(ONNX_NAMESPACE::TensorProto::FLOAT16);
+
+      std::vector<uint16_t> fp16_val(fp32_val.size(), 0);
+
+      float pos_min_val = max_finite_val_;
+      float pos_max_val = min_positive_val_;
+      float neg_min_val = -1 * max_finite_val_;
+      float neg_max_val = -1 * min_positive_val_;
+
+      for (auto i = 0; i < fp32_val.size(); i++) {
+        if (0 < fp32_val[i] && fp32_val[i] < min_positive_val_) {
+          if (fp32_val[i] < pos_min_val) {
+            pos_min_val = fp32_val[i];
+          }
+          fp32_val[i] = min_positive_val_;
+        } else if (0 > fp32_val[i] && fp32_val[i] > -1 * min_positive_val_) {
+          if (fp32_val[i] > neg_min_val) {
+            neg_min_val = fp32_val[i];
+          }
+          fp32_val[i] = -1 * min_positive_val_;
+        } else if (fp32_val[i] > max_finite_val_) {
+          if (fp32_val[i] > pos_max_val) {
+            pos_max_val = fp32_val[i];
+          }
+          fp32_val[i] = max_finite_val_;
+        } else if (fp32_val[i] < -1 * max_finite_val_) {
+          if (fp32_val[i] < neg_max_val) {
+            neg_max_val = fp32_val[i];
+          }
+          fp32_val[i] = -1 * max_finite_val_;
+        }
+        ConvertValToFloat16(fp32_val[i], &fp16_val[i]);
+      }
+      if (pos_min_val < max_finite_val_ - 1) {
+        P2OLogger() << "[Info] the float32 number: " << pos_min_val
+                    << " will be truncated to: " << min_positive_val_
+                    << std::endl;
+      }
+      if (pos_max_val > min_positive_val_ + 1) {
+        P2OLogger() << "[Info] the float32 number: " << pos_max_val
+                    << " will be truncated to: " << max_finite_val_
+                    << std::endl;
+      }
+      if (neg_min_val > -1 * max_finite_val_ + 1) {
+        P2OLogger() << "[Info] the float32 number: " << neg_min_val
+                    << " will be truncated to: " << -1 * min_positive_val_
+                    << std::endl;
+      }
+      if (neg_max_val < -1 * min_positive_val_ - 1) {
+        P2OLogger() << "[Info] the float32 number: " << neg_max_val
+                    << " will be truncated to: " << -1 * max_finite_val_
+                    << std::endl;
+      }
+      tensor->set_raw_data(std::string((const char*)(fp16_val.data()),
+                                       fp16_val.size() * sizeof(uint16_t)));
+    }
+  }
+}
+
+// return if the next node of name is Cast and its attr type is dtype.
+bool ConvertFp32ToFp16::CastedTo(const std::string& name,
+                                 ONNX_NAMESPACE::ModelProto& model,
+                                 int64_t dtype) {
+  auto graph = model.mutable_graph();
+  std::vector<ONNX_NAMESPACE::NodeProto*> next_nodes;
+  for (auto i = 0; i < graph->node_size(); i++) {
+    auto n = graph->mutable_node(i);
+    for (auto i_index = 0; i_index < n->input_size(); i_index++) {
+      std::string input = n->input(i_index);
+      if (name == input) {
+        next_nodes.push_back(n);
+      }
+    }
+  }
+  bool casted = false;
+  for (auto node : next_nodes) {
+    if (node->op_type() == "Cast") {
+      for (auto attr_index = 0; attr_index < node->attribute_size();
+           attr_index++) {
+        if (node->attribute(attr_index).has_i() &&
+            node->attribute(attr_index).i() == dtype) {
+          casted = true;
+          break;
+        }
+      }
+    }
+  }
+  return casted;
+}
+
+// return if the pre node of name is Cast and its attr type is dtype.
+bool ConvertFp32ToFp16::CastedFrom(const std::string& name,
+                                   ONNX_NAMESPACE::ModelProto& model,
+                                   int64_t dtype) {
+  auto graph = model.mutable_graph();
+  std::vector<ONNX_NAMESPACE::NodeProto*> pre_nodes;
+  for (auto i = 0; i < graph->node_size(); i++) {
+    auto n = graph->mutable_node(i);
+    for (auto o_index = 0; o_index < n->output_size(); o_index++) {
+      std::string output = n->output(o_index);
+      if (name == output) {
+        pre_nodes.push_back(n);
+      }
+    }
+  }
+  bool casted = false;
+  for (auto node : pre_nodes) {
+    if (node->op_type() == "Cast") {
+      for (auto attr_index = 0; attr_index < node->attribute_size();
+           attr_index++) {
+        if (node->attribute(attr_index).has_i() &&
+            node->attribute(attr_index).i() == dtype) {
+          casted = true;
+          break;
+        }
+      }
+    }
+  }
+  return casted;
+}
+
+// return if the name is the input of DEFAULT_OP_BLOCK_LIST
+bool ConvertFp32ToFp16::IsInputOfOpBlock(const std::string& name,
+                                         ONNX_NAMESPACE::ModelProto& model) {
+  auto graph = model.mutable_graph();
+  for (auto i = 0; i < graph->node_size(); i++) {
+    auto n = graph->mutable_node(i);
+    if (std::find(op_block_list_.begin(), op_block_list_.end(), n->op_type()) ==
+        op_block_list_.end()) {
+      continue;
+    }
+
+    for (auto i_index = 0; i_index < n->input_size(); i_index++) {
+      std::string input = n->input(i_index);
+      if (name == input) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+bool ConvertFp32ToFp16::IsOutputOfOpBlockAndFP32Out(
+    const std::string& name, ONNX_NAMESPACE::ModelProto& model) {
+  auto graph = model.mutable_graph();
+  for (auto i = 0; i < graph->node_size(); i++) {
+    auto n = graph->mutable_node(i);
+    if (std::find(op_block_list_.begin(), op_block_list_.end(), n->op_type()) ==
+            op_block_list_.end() &&
+        std::find(fp32_output_op_list.begin(), fp32_output_op_list.end(),
+                  n->op_type()) == fp32_output_op_list.end()) {
+      continue;
+    }
+    for (auto o_index = 0; o_index < n->output_size(); o_index++) {
+      std::string output = n->output(o_index);
+      if (name == output) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+void ConvertFp32ToFp16::KeepIoType(ONNX_NAMESPACE::ModelProto* model) {
+  auto graph = model->mutable_graph();
+  for (auto i = 0; i < graph->input_size(); i++) {
+    auto input = graph->input(i);
+    if (input.type().tensor_type().elem_type() ==
+        ONNX_NAMESPACE::TensorProto::FLOAT) {
+      // if the pre node is cast, and it is cast to float16, we do not need add
+      // Cast OP any more
+      if (CastedTo(input.name(), *model, 10)) {
+        graph_io_to_skip.push_back(input.name());
+        continue;
+      }
+      std::string output_name = "graph_input_cast_" + std::to_string(i);
+      name_mapping[input.name()] = output_name;
+      graph_io_to_skip.push_back(input.name());
+      std::string node_name = "graph_input_cast" + std::to_string(i);
+      auto new_value_info = graph->add_value_info();
+      new_value_info->CopyFrom(input);
+      new_value_info->set_name(output_name);
+      new_value_info->mutable_type()->mutable_tensor_type()->set_elem_type(
+          ONNX_NAMESPACE::TensorProto::FLOAT16);
+      auto new_node =
+          MakeCastNode(node_name, {input.name()}, {output_name}, 10);
+      *(graph->add_node()) = (*new_node);
+      value_info_list.push_back(new_value_info);
+      io_casts.push_back(node_name);
+    }
+  }
+  for (auto i = 0; i < graph->output_size(); i++) {
+    auto output = graph->output(i);
+    if (output.type().tensor_type().elem_type() ==
+        ONNX_NAMESPACE::TensorProto::FLOAT) {
+      // if the next node is cast, and it is cast to float, we do not need add
+      // Cast OP any more
+      if (CastedFrom(output.name(), *model, 1)) {
+        graph_io_to_skip.push_back(output.name());
+        continue;
+      }
+      std::string output_name = "graph_output_cast_" + std::to_string(i);
+      name_mapping[output.name()] = output_name;
+      graph_io_to_skip.push_back(output.name());
+      std::string node_name = "graph_output_cast" + std::to_string(i);
+      auto new_value_info = graph->add_value_info();
+      new_value_info->CopyFrom(output);
+      new_value_info->set_name(output_name);
+      new_value_info->mutable_type()->mutable_tensor_type()->set_elem_type(
+          ONNX_NAMESPACE::TensorProto::FLOAT16);
+      auto new_node =
+          MakeCastNode(node_name, {output_name}, {output.name()}, 1);
+      *(graph->add_node()) = (*new_node);
+      value_info_list.push_back(new_value_info);
+      io_casts.push_back(node_name);
+    }
+  }
+}
+
+void ConvertFp32ToFp16::ConvertAttribute(ONNX_NAMESPACE::ModelProto* model) {
+  proto_node new_node(*model);
+  queue.push_back(new_node);
+
+  while (queue.size()) {
+    next_level.clear();
+    for (auto q : queue) {
+      // process model proto
+      if (q.node_type == "model" && model->has_graph()) {
+        proto_node new_node(model->mutable_graph());
+        next_level.push_back(new_node);
+      }
+      // process graph proto
+      if (q.node_type == "graph") {
+        for (auto i = 0; i < q.graph->node_size(); i++) {
+          auto n = q.graph->mutable_node(i);
+          if (std::find(io_casts.begin(), io_casts.end(), n->name()) !=
+              io_casts.end()) {
+            continue;
+          }
+          for (auto i_index = 0; i_index < n->input_size(); i_index++) {
+            std::string* input = n->mutable_input(i_index);
+            auto iter = name_mapping.find(*input);
+            if (iter != name_mapping.end()) {
+              *input = iter->second;
+            }
+          }
+          for (auto o_index = 0; o_index < n->output_size(); o_index++) {
+            std::string* output = n->mutable_output(o_index);
+            auto iter = name_mapping.find(*output);
+            if (iter != name_mapping.end()) {
+              *output = iter->second;
+            }
+          }
+          // If the op type is in op_block_list_ or fp32_output_op_list,
+          // or needs to be kept without conversion, then store the node in
+          // node_list,
+          // which is convenient for adding cast op in the front or back
+          if (KeepNodeType(n)) {
+            if (n->op_type() != "Constant" &&
+                std::find(node_list.begin(), node_list.end(), n) ==
+                    node_list.end()) {
+              node_list.push_back(n);
+            } else {
+              for (auto index = 0; index < q.graph->node_size(); index++) {
+                auto keep_type_node = q.graph->mutable_node(index);
+                bool is_pre_node =
+                    std::find(keep_type_node->input().begin(),
+                              keep_type_node->input().end(),
+                              n->output()[0]) != keep_type_node->input().end();
+                if (is_pre_node &&
+                    std::find(node_list.begin(), node_list.end(), n) ==
+                        node_list.end()) {
+                  node_list.push_back(keep_type_node);
+                  Assert(
+                      n->op_type() == "Constant",
+                      "The node type be Constant, but it is: " + n->op_type());
+                  keep_type_tensors.push_back(n->output()[0]);
+                }
+              }
+            }
+          } else if ((std::find(op_block_list_.begin(), op_block_list_.end(),
+                                n->op_type()) != op_block_list_.end() ||
+                      std::find(fp32_output_op_list.begin(),
+                                fp32_output_op_list.end(),
+                                n->op_type()) != fp32_output_op_list.end()) &&
+                     std::find(node_list.begin(), node_list.end(), n) ==
+                         node_list.end()) {
+            node_list.push_back(n);
+          } else {
+            std::string op_name = n->name();
+            if (n->op_type() == "Cast") {
+              for (auto attr_index = 0; attr_index < n->attribute_size();
+                   attr_index++) {
+                auto attr = n->mutable_attribute(attr_index);
+                if (attr->name() == "to" && attr->i() == 1) {
+                  attr->set_i(10);
+                  break;
+                }
+              }
+            }
+            for (auto attr_index = 0; attr_index < n->attribute_size();
+                 attr_index++) {
+              proto_node new_node(n->mutable_attribute(attr_index));
+              next_level.push_back(new_node);
+            }
+          }
+        }
+      }
+
+      // process attribute proto
+      if (q.node_type == "attribute") {
+        if (q.attr->has_g()) {
+          proto_node new_node(q.attr->mutable_g());
+          next_level.push_back(new_node);
+        }
+
+        for (auto g_index = 0; g_index < q.attr->graphs_size(); g_index++) {
+          proto_node new_node(q.attr->mutable_graphs(g_index));
+          next_level.push_back(new_node);
+        }
+        if (q.attr->has_t()) {
+          ConvertTensorFloatToFloat16(q.attr->mutable_t());
+        }
+
+        for (auto t_index = 0; t_index < q.attr->tensors_size(); t_index++) {
+          ConvertTensorFloatToFloat16(q.attr->mutable_tensors(t_index));
+        }
+      }
+
+      // process graph proto
+      if (q.node_type == "graph") {
+        for (auto init_index = 0; init_index < q.graph->initializer_size();
+             init_index++) {
+          auto init = q.graph->mutable_initializer(init_index);
+          if (init->data_type() == ONNX_NAMESPACE::TensorProto::FLOAT) {
+            ConvertTensorFloatToFloat16(init);
+            auto new_value = MakeValueInfoFromTensor(*init);
+            value_info_list.push_back(new_value);
+          }
+        }
+        for (auto i_index = 0; i_index < q.graph->input_size(); i_index++) {
+          auto input = q.graph->mutable_input(i_index);
+          bool skip =
+              std::find(graph_io_to_skip.begin(), graph_io_to_skip.end(),
+                        input->name()) != graph_io_to_skip.end();
+          if (!skip &&
+              input->type().tensor_type().elem_type() ==
+                  ONNX_NAMESPACE::TensorProto::FLOAT) {
+            input->mutable_type()->mutable_tensor_type()->set_elem_type(
+                ONNX_NAMESPACE::TensorProto::FLOAT16);
+            value_info_list.push_back(input);
+          }
+        }
+        for (auto o_index = 0; o_index < q.graph->output_size(); o_index++) {
+          auto output = q.graph->mutable_output(o_index);
+          bool skip =
+              std::find(graph_io_to_skip.begin(), graph_io_to_skip.end(),
+                        output->name()) != graph_io_to_skip.end();
+          if (!skip &&
+              output->type().tensor_type().elem_type() ==
+                  ONNX_NAMESPACE::TensorProto::FLOAT) {
+            output->mutable_type()->mutable_tensor_type()->set_elem_type(
+                ONNX_NAMESPACE::TensorProto::FLOAT16);
+            value_info_list.push_back(output);
+          }
+        }
+        for (auto value_index = 0; value_index < q.graph->value_info_size();
+             value_index++) {
+          auto value = q.graph->mutable_value_info(value_index);
+          bool skip =
+              std::find(graph_io_to_skip.begin(), graph_io_to_skip.end(),
+                        value->name()) != graph_io_to_skip.end();
+
+          // in Resize op, when the dims of input tensor is zero.
+          bool zero_shape_constant = false;
+          for (auto i = 0; i < q.graph->node_size(); i++) {
+            auto n = q.graph->mutable_node(i);
+            if (n->op_type() != "Constant" || n->output(0) != value->name()) {
+              continue;
+            }
+            for (auto attr_index = 0; attr_index < n->attribute_size();
+                 attr_index++) {
+              auto attr = n->mutable_attribute(attr_index);
+              if (attr->has_t() && attr->t().dims_size() == 1 &&
+                  attr->t().dims(0) == 0) {
+                zero_shape_constant = true;
+              }
+            }
+            if (zero_shape_constant) break;
+          }
+
+          // if it is a tensor that should keep type float
+          bool keep_type_tensor =
+              std::find(keep_type_tensors.begin(), keep_type_tensors.end(),
+                        value->name()) != keep_type_tensors.end();
+          if (!zero_shape_constant && !skip && !keep_type_tensor &&
+              value->type().tensor_type().elem_type() ==
+                  ONNX_NAMESPACE::TensorProto::FLOAT) {
+            value->mutable_type()->mutable_tensor_type()->set_elem_type(
+                ONNX_NAMESPACE::TensorProto::FLOAT16);
+            value_info_list.push_back(value);
+          }
+        }
+      }
+    }
+    queue.clear();
+    queue = next_level;
+  }
+  // the model is a FP16 model
+  if (!converted_attr) {
+    return;
+  }
+
+  auto graph = model->mutable_graph();
+  for (auto node : node_list) {
+    // Handle the case of fp32_output OPs
+    // Add cast op for node output
+    if (std::find(fp32_output_op_list.begin(), fp32_output_op_list.end(),
+                  node->op_type()) != fp32_output_op_list.end()) {
+      for (auto o_index = 0; o_index < node->output_size(); o_index++) {
+        std::string* output = node->mutable_output(o_index);
+        for (auto v_index = 0; v_index < graph->value_info().size();
+             v_index++) {
+          auto value_info = graph->mutable_value_info(v_index);
+          if (value_info->name() == *output) {
+            if (value_info->type().tensor_type().elem_type() ==
+                    ONNX_NAMESPACE::TensorProto::FLOAT16 &&
+                !CastedTo(*output, *model, 10)) {
+              std::string input_name = GenName(node->name() + "_output_cast_");
+              std::string node_name = GenName(node->name() + "_output_cast");
+              auto new_node =
+                  MakeCastNode(node_name, {input_name}, {*output}, 10);
+              *(graph->add_node()) = (*new_node);
+              *(node->mutable_output(o_index)) = input_name;
+            } else {
+              break;
+            }
+          }
+        }
+      }
+      continue;
+    }
+
+    // Handle the case of custom OPs
+    if (std::find(custom_ops_.begin(), custom_ops_.end(), node->op_type()) !=
+        custom_ops_.end()) {
+      // add cast op for node input
+      for (auto i_index = 0; i_index < node->input_size(); i_index++) {
+        std::string* input = node->mutable_input(i_index);
+        for (auto v_index = 0; v_index < graph->value_info().size();
+             v_index++) {
+          auto value_info = graph->mutable_value_info(v_index);
+          if (value_info->name() == *input) {
+            if (value_info->type().tensor_type().elem_type() ==
+                    ONNX_NAMESPACE::TensorProto::FLOAT16 &&
+                !CastedFrom(*input, *model, 1)) {
+              std::string output_name = GenName(node->name() + "_input_cast_");
+              std::string node_name = GenName(node->name() + "_input_cast");
+              auto new_node =
+                  MakeCastNode(node_name, {*input}, {output_name}, 1);
+              *(graph->add_node()) = (*new_node);
+              *(node->mutable_input(i_index)) = output_name;
+            } else {
+              break;
+            }
+          }
+        }
+      }
+      // add cast op for node output
+      for (auto o_index = 0; o_index < node->output_size(); o_index++) {
+        std::string* output = node->mutable_output(o_index);
+        for (auto v_index = 0; v_index < graph->value_info().size();
+             v_index++) {
+          auto value_info = graph->mutable_value_info(v_index);
+          if (value_info->name() == *output) {
+            if (value_info->type().tensor_type().elem_type() ==
+                    ONNX_NAMESPACE::TensorProto::FLOAT16 &&
+                !CastedTo(*output, *model, 10)) {
+              std::string input_name = GenName(node->name() + "_output_cast_");
+              std::string node_name = GenName(node->name() + "_output_cast");
+              auto new_node =
+                  MakeCastNode(node_name, {input_name}, {*output}, 10);
+              *(graph->add_node()) = (*new_node);
+              *(node->mutable_output(o_index)) = input_name;
+            } else if (value_info->type().tensor_type().elem_type() ==
+                       ONNX_NAMESPACE::TensorProto::FLOAT16) {
+              value_info->mutable_type()->mutable_tensor_type()->set_elem_type(
+                  ONNX_NAMESPACE::TensorProto::FLOAT);
+            } else {
+              break;
+            }
+          }
+        }
+      }
+    } else {
+      // Handle the case of DEFAULT_OP_BLOCK_LIST OPs
+      for (auto i_index = 0; i_index < node->input_size(); i_index++) {
+        std::string* input = node->mutable_input(i_index);
+        for (auto value_index = 0; value_index < value_info_list.size();
+             value_index++) {
+          auto value_info = value_info_list[value_index];
+          if (value_info->has_name() && *input == value_info->name() &&
+              !CastedFrom(*input, *model, 1)) {
+            auto new_value_info = model->mutable_graph()->add_value_info();
+            new_value_info->CopyFrom(*value_info);
+            std::string output_name = GenName(node->name() + "_input_cast_");
+            new_value_info->set_name(output_name);
+            new_value_info->mutable_type()
+                ->mutable_tensor_type()
+                ->set_elem_type(ONNX_NAMESPACE::TensorProto::FLOAT);
+            std::string node_name = GenName(node->name() + "_input_cast");
+            auto new_node = MakeCastNode(node_name, {*input}, {output_name}, 1);
+            *(graph->add_node()) = (*new_node);
+            *(node->mutable_input(i_index)) = output_name;
+            break;
+          }
+        }
+      }
+      for (auto o_index = 0; o_index < node->output_size(); o_index++) {
+        std::string* output = node->mutable_output(o_index);
+        for (auto value_index = 0; value_index < value_info_list.size();
+             value_index++) {
+          if (*output == value_info_list[value_index]->name() &&
+              !CastedTo(*output, *model, 10)) {
+            auto new_value_info = model->mutable_graph()->add_value_info();
+            new_value_info->CopyFrom(*value_info_list[value_index]);
+            std::string input_name = GenName(node->name() + "_output_cast_");
+            new_value_info->set_name(input_name);
+            new_value_info->mutable_type()
+                ->mutable_tensor_type()
+                ->set_elem_type(ONNX_NAMESPACE::TensorProto::FLOAT);
+            std::string node_name = GenName(node->name() + "_output_cast");
+            auto new_node =
+                MakeCastNode(node_name, {input_name}, {*output}, 10);
+            *(graph->add_node()) = (*new_node);
+            *(node->mutable_output(o_index)) = input_name;
+            break;
+          }
+        }
+      }
+    }
+  }
+}
+
+bool ConvertFp32ToFp16::IsFP16Model(const ONNX_NAMESPACE::ModelProto& model) {
+  for (auto node : model.graph().node()) {
+    if (node.op_type() == "Cast") {
+      auto name = node.name();
+      if (name.find("_output_cast") != name.npos ||
+          name.find("_input_cast") != name.npos ||
+          name.find("graph_output_cast") != name.npos ||
+          name.find("graph_input_cast") != name.npos) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+void ConvertFp32ToFp16::Convert(ONNX_NAMESPACE::ModelProto* model) {
+  if (op_block_list_.empty()) {
+    op_block_list_ = DEFAULT_OP_BLOCK_LIST;
+  }
+  if (custom_ops_.size()) {
+    op_block_list_.insert(op_block_list_.end(), custom_ops_.begin(),
+                          custom_ops_.end());
+  }
+  shape_inference::InferShapes(*model);
+  // 1 if it is a FP16 model, skip this
+  if (IsFP16Model(*model)) {
+    P2OLogger() << "[Info] The input ONNX Model is a FP16 model." << std::endl;
+    return;
+  }
+  // 2 keep IO types
+  KeepIoType(model);
+  // 3 ConvertAttribute
+  ConvertAttribute(model);
+  // 4 sortnodes
+  SortNodes(model);
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/optimizer/convert_fp32_to_fp16.h b/paddle2onnx/optimizer/convert_fp32_to_fp16.h
new file mode 100755
index 0000000000..157bee66c4
--- /dev/null
+++ b/paddle2onnx/optimizer/convert_fp32_to_fp16.h
@@ -0,0 +1,235 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <onnx/onnx_pb.h>
+
+#include <cmath>
+#include <fstream>
+#include <iomanip>
+
+#include <onnx/shape_inference/implementation.h>
+#include "paddle2onnx/mapper/mapper.h"
+#include "paddle2onnx/parser/parser.h"
+namespace paddle2onnx {
+
+struct proto_node {
+ public:
+  std::string node_type;  // model, graph, node, arribute
+  ONNX_NAMESPACE::ModelProto* model;
+  ONNX_NAMESPACE::GraphProto* graph;
+  ONNX_NAMESPACE::NodeProto* node;
+  ONNX_NAMESPACE::AttributeProto* attr;
+
+  explicit proto_node(ONNX_NAMESPACE::ModelProto new_model) {
+    node_type = "model";
+    model = &new_model;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::ModelProto* new_model) {
+    node_type = "model";
+    model = new_model;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::GraphProto new_graph) {
+    node_type = "graph";
+    graph = &new_graph;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::GraphProto* new_graph) {
+    node_type = "graph";
+    graph = new_graph;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::NodeProto new_node) {
+    node_type = "node";
+    node = &new_node;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::NodeProto* new_node) {
+    node_type = "node";
+    node = new_node;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::AttributeProto new_attribute) {
+    node_type = "attribute";
+    attr = &new_attribute;
+  }
+
+  explicit proto_node(ONNX_NAMESPACE::AttributeProto* new_attribute) {
+    node_type = "attribute";
+    attr = new_attribute;
+  }
+};
+
+struct ConvertFp32ToFp16 {
+ public:
+  ConvertFp32ToFp16(float min_positive_val = 1e-7, float max_finite_val = 1e4,
+                    bool keep_io_types = false,
+                    bool disable_shape_infer = false,
+                    const std::vector<std::string>& op_block_list = {},
+                    const std::vector<std::string>& node_block_list = {}) {
+    min_positive_val_ = min_positive_val;
+    max_finite_val_ = max_finite_val;
+    keep_io_types_ = keep_io_types;
+    disable_shape_infer_ = disable_shape_infer;
+    op_block_list_ = op_block_list;
+    node_block_list_ = node_block_list;
+  }
+
+  void Convert(ONNX_NAMESPACE::ModelProto* model);
+
+  ONNX_NAMESPACE::NodeProto* MakeCastNode(
+      const std::string& op_name, const std::vector<std::string>& inputs,
+      const std::vector<std::string>& outputs, int32_t to_dtype);
+
+  ONNX_NAMESPACE::ValueInfoProto* MakeValueInfoFromTensor(
+      const ONNX_NAMESPACE::TensorProto& tensor);
+
+  void KeepIoType(ONNX_NAMESPACE::ModelProto* model);
+
+  void ConvertAttribute(ONNX_NAMESPACE::ModelProto* model);
+
+  void ConvertTensorFloatToFloat16(ONNX_NAMESPACE::TensorProto* tensor);
+
+  // return if keep the type of node
+  bool KeepNodeType(ONNX_NAMESPACE::NodeProto* node);
+
+  bool GetTensorValue(const ONNX_NAMESPACE::TensorProto& tensor,
+                      std::vector<float>* value);
+
+  // topo sort
+  void SortNodes(ONNX_NAMESPACE::ModelProto* model);
+
+  void ConvertValToFloat16(float val, uint16_t* x);
+
+  // return if the next node of name is Cast and its attr type is dtype.
+  bool CastedTo(const std::string& name, ONNX_NAMESPACE::ModelProto& model,
+                int64_t dtype);
+  // return if the pre node of name is Cast and its attr type is dtype.
+  bool CastedFrom(const std::string& name, ONNX_NAMESPACE::ModelProto& model,
+                  int64_t dtype);
+  // return if the name is the input of DEFAULT_OP_BLOCK_LIST
+  bool IsInputOfOpBlock(const std::string& name,
+                        ONNX_NAMESPACE::ModelProto& model);
+
+  // return if the name is the input of DEFAULT_OP_BLOCK_LIST and
+  // fp32_output_op_list
+  bool IsOutputOfOpBlockAndFP32Out(const std::string& name,
+                                   ONNX_NAMESPACE::ModelProto& model);
+
+  void SetCustomOps(const std::map<std::string, std::string>& custom_ops) {
+    if (custom_ops.size()) {
+      custom_ops_.clear();
+      for (auto op : custom_ops) {
+        custom_ops_.push_back(op.second);
+      }
+    }
+  }
+  // If the input ONNX model is a FP16 model, return True
+  bool IsFP16Model(const ONNX_NAMESPACE::ModelProto& model);
+
+ private:
+  union Bits {
+    float f;
+    int32_t si;
+    uint32_t ui;
+  };
+  static const int shift = 13;
+  static const int shiftSign = 16;
+
+  static const int32_t infN = 0x7F800000;
+  static const int32_t maxN = 0x477FE000;  // max flt16 as flt32
+  static const int32_t minN = 0x38800000;  // min flt16 normal as flt32
+  static const int32_t sigN = 0x80000000;  // sign bit
+
+  static constexpr int32_t infC = infN >> shift;
+  static constexpr int32_t nanN = (infC + 1)
+                                  << shift;  // minimum flt16 nan as float32
+  static constexpr int32_t maxC = maxN >> shift;
+  static constexpr int32_t minC = minN >> shift;
+  static constexpr int32_t sigC = sigN >> shiftSign;
+
+  static const int32_t mulN = 0x52000000;  // (1 << 23) / minN
+  static const int32_t mulC = 0x33800000;  // minN / (1 << (23 - shift))
+  static const int32_t subC = 0x003FF;     // max flt32 subnormal downshifted
+  static const int32_t norC = 0x00400;     // min flt32 normal downshifted
+
+  static constexpr int32_t maxD = infC - maxC - 1;
+  static constexpr int32_t minD = minC - subC - 1;
+
+  float min_positive_val_ = 1e-7;
+  float max_finite_val_ = 1e4;
+  bool keep_io_types_ = false;
+  bool disable_shape_infer_ = false;
+  std::vector<std::string> op_block_list_ = {};
+  std::vector<std::string> node_block_list_ = {};
+
+  std::vector<std::string> custom_ops_ = {"AdaptivePool2d", "MultiClassNMS"};
+
+  int64_t converted_attr = 0;
+
+  std::map<std::string, std::string> name_mapping;
+  std::vector<std::string> graph_io_to_skip;
+  std::vector<ONNX_NAMESPACE::ValueInfoProto*> value_info_list;
+  std::vector<std::string> io_casts;
+
+  std::vector<ONNX_NAMESPACE::NodeProto*> node_list;
+
+  std::vector<proto_node> queue;
+  std::vector<proto_node> next_level;
+
+  std::map<std::string, int64_t> name_index_mapper;
+  // int64_t name_index = 0;
+  std::string GenName(const std::string& prefix);
+
+  // save the tensor names that should keep data type
+  std::vector<std::string> keep_type_tensors;
+
+  // The input can be FP16, but the output can only be fp32
+  std::vector<std::string> fp32_output_op_list = {"RandomNormalLike"};
+
+  std::vector<std::string> DEFAULT_OP_BLOCK_LIST = {
+      "ArrayFeatureExtractor",
+      "ReduceMean",  // this op may cause wrong results on FP16
+      "Binarizer",
+      "CastMap",
+      "CategoryMapper",
+      "DictVectorizer",
+      "FeatureVectorizer",
+      "Imputer",
+      "LabelEncoder",
+      "LinearClassifier",
+      "LinearRegressor",
+      "Normalizer",
+      "OneHotEncoder",
+      "RandomUniformLike",
+      "SVMClassifier",
+      "SVMRegressor",
+      "Scaler",
+      "TreeEnsembleClassifier",
+      "TreeEnsembleRegressor",
+      "ZipMap",
+      "NonMaxSuppression",
+      "TopK",
+      "RoiAlign",
+      "Resize",
+      "Range",
+      "CumSum",
+      "Min",
+      "Max",
+      "Upsample",  // The following OP is added by Paddle developer
+      "EyeLike"};
+};
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/optimizer/eliminate_non_transpose.h b/paddle2onnx/optimizer/eliminate_non_transpose.h
new file mode 100644
index 0000000000..3a1d28757f
--- /dev/null
+++ b/paddle2onnx/optimizer/eliminate_non_transpose.h
@@ -0,0 +1,60 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateNonTranspose final : public PredicateBasedPass {
+  explicit EliminateNonTranspose()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override { return "eliminate_non_transpose"; }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kTranspose;
+  }
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    if (node->hasAttribute(kperm)) {
+      auto perm = node->is(kperm);
+      for (size_t i = 0; i < perm.size(); ++i) {
+        if (perm[i] != i) {
+          return false;
+        }
+      }
+    }
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->input());
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/fuse_constant_cast.h b/paddle2onnx/optimizer/fuse_constant_cast.h
new file mode 100644
index 0000000000..0fbaa4d554
--- /dev/null
+++ b/paddle2onnx/optimizer/fuse_constant_cast.h
@@ -0,0 +1,67 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   B = Reshape(Constant)
+// After:
+//   B = Constant (Constant with new shape)
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConstantCast final : public PredicateBasedPass {
+  explicit FuseConstantCast()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override { return "fuse_constant_cast"; }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kCast &&
+           node->inputs()[0]->node()->kind() == kConstant;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+
+    if (n->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* cast = n;
+    Node* constant = n->inputs()[0]->node();
+    Tensor t = constant->t(kvalue);
+    auto dtype = cast->i(kto);
+    t.elem_type() = dtype;
+    constant->t_(kvalue, std::move(t));
+    if (!tryReplacingAllUsesWith(cast->output(), cast->inputs()[0])) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/fuse_constant_reshape.h b/paddle2onnx/optimizer/fuse_constant_reshape.h
new file mode 100644
index 0000000000..ba886f5c1c
--- /dev/null
+++ b/paddle2onnx/optimizer/fuse_constant_reshape.h
@@ -0,0 +1,138 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   B = Reshape(Constant)
+// After:
+//   B = Constant (Constant with new shape)
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConstantReshape final : public PredicateBasedPass {
+  explicit FuseConstantReshape()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override { return "fuse_constant_reshape"; }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kReshape &&
+           node->inputs()[0]->node()->kind() == kConstant;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+
+    // check if Constant is only used by Reshape
+    if (n->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* reshape = n;
+    Node* constant = n->inputs()[0]->node();
+
+    // Process 'reshape' data
+    std::vector<int64_t> shape;
+    if (reshape->hasAttribute(kshape)) {
+      // opset 5 and below
+      shape = reshape->is(kshape);
+    } else {
+      // opset 6 and above - first check if 'reshape' has 'shape' input
+      // constant
+      if (reshape->inputs()[1]->node()->kind() != kConstant) {
+        return false;
+      }
+      if (reshape->inputs()[1]->uses().size() > 1) {
+        return false;
+      }
+      Node* shape_const = reshape->inputs()[1]->node();
+      Tensor t = shape_const->t(kvalue);
+      shape = ParseData<int64_t>(&t);
+    }
+
+    int allow_zero = 0;
+    Symbol sym = Symbol("allowzero");
+    if (reshape->hasAttribute(sym)) {
+      allow_zero = reshape->i(sym);
+    }
+
+    Tensor t = constant->t(kvalue);
+    const auto& ori_size = t.sizes();
+
+    // process 0 in shape
+    if (allow_zero != 0) {
+      for (size_t i = 0; i < shape.size(); ++i) {
+        if (shape[i] == 0) {
+          // illegal situation
+          if (ori_size.size() <= i) {
+            return false;
+          }
+          shape[i] = ori_size[i];
+        }
+      }
+    }
+
+    // process -1 in shape
+    int count_of_unkown = 0;
+    int index_of_unkown = -1;
+    for (size_t i = 0; i < shape.size(); ++i) {
+      if (shape[i] == -1) {
+        count_of_unkown += 1;
+        index_of_unkown = i;
+      }
+    }
+    // illegal situtaion
+    if (count_of_unkown > 1) {
+      return false;
+    }
+    int64_t numel = std::accumulate(ori_size.begin(), ori_size.end(), 1,
+                                    std::multiplies<int>());
+    if (index_of_unkown >= 0) {
+      int64_t value_of_unkown = -1 * numel /
+                                std::accumulate(shape.begin(), shape.end(), 1,
+                                                std::multiplies<int>());
+      shape[index_of_unkown] = value_of_unkown;
+    }
+
+    t.sizes().clear();
+    t.sizes().insert(t.sizes().begin(), shape.begin(),
+                     shape.begin() + shape.size());
+    constant->t_(kvalue, std::move(t));
+
+    // update constant node
+    constant->output()->setSizes(reshape->output()->sizes());
+    constant->output()->setElemType(reshape->output()->elemType());
+    const bool replacing_success =
+        tryReplacingAllUsesWith(reshape->output(), reshape->inputs()[0]);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/fuse_constant_unsqueeze.h b/paddle2onnx/optimizer/fuse_constant_unsqueeze.h
new file mode 100644
index 0000000000..e4fa7cdf46
--- /dev/null
+++ b/paddle2onnx/optimizer/fuse_constant_unsqueeze.h
@@ -0,0 +1,107 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   B = Unsqueeze(Constant, axes)
+// After:
+//   B = Constant (Constant with new shape)
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConstantUnsqueeze final : public PredicateBasedPass {
+  explicit FuseConstantUnsqueeze()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override { return "fuse_constant_unsqueeze"; }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kUnsqueeze &&
+           node->inputs()[0]->node()->kind() == kConstant;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+
+    // check if Constant is only used by Reshape
+    if (n->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* unsqueeze = n;
+    Node* constant = n->inputs()[0]->node();
+
+    // Process 'axes' data
+    std::vector<int64_t> axes;
+    if (unsqueeze->hasAttribute(kaxes)) {
+      // opset 13 below
+      axes = unsqueeze->is(kaxes);
+    } else {
+      // opset 13 and above - first check if 'unsqueeze' has 'axes' input
+      // constant
+      if (unsqueeze->inputs()[1]->node()->kind() != kConstant) {
+        return false;
+      }
+      if (unsqueeze->inputs()[1]->uses().size() > 1) {
+        return false;
+      }
+      Node* axes_const = unsqueeze->inputs()[1]->node();
+      Tensor t = axes_const->t(kvalue);
+      axes = ParseData<int64_t>(&t);
+    }
+
+    Tensor t = constant->t(kvalue);
+    const auto& ori_size = t.sizes();
+    for (size_t i = 0; i < axes.size(); ++i) {
+      if (axes[i] < 0) {
+        axes[i] = axes[i] + ori_size.size() + i + 1;
+      }
+    }
+
+    std::vector<int64_t> new_size(ori_size.begin(), ori_size.end());
+    for (size_t i = 0; i < axes.size(); ++i) {
+      new_size.insert(new_size.begin() + axes[i], 1);
+    }
+
+    t.sizes().clear();
+    t.sizes().insert(t.sizes().begin(), new_size.begin(),
+                     new_size.begin() + new_size.size());
+    constant->t_(kvalue, std::move(t));
+
+    // update constant node
+    constant->output()->setSizes(unsqueeze->output()->sizes());
+    constant->output()->setElemType(unsqueeze->output()->elemType());
+    const bool replacing_success =
+        tryReplacingAllUsesWith(unsqueeze->output(), unsqueeze->inputs()[0]);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/fuse_paddle_conv_bias.h b/paddle2onnx/optimizer/fuse_paddle_conv_bias.h
new file mode 100644
index 0000000000..89ed8883a1
--- /dev/null
+++ b/paddle2onnx/optimizer/fuse_paddle_conv_bias.h
@@ -0,0 +1,96 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Only support conv2d + bias now
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FusePaddleConvBias final : public PredicateBasedPass {
+  explicit FusePaddleConvBias()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override { return "fuse_paddle_conv_bias"; }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kAdd && node->inputs()[0]->node()->kind() == kConv &&
+           node->inputs()[1]->node()->kind() == kConstant &&
+           node->inputs()[0]->node()->inputs()[1]->node()->kind() == kConstant;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+
+    // check if Conv is only used by Add
+    if (n->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+    // check if bias is only used by Add
+    if (n->inputs()[1]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* add = n;
+    Node* conv = n->inputs()[0]->node();
+    Node* bias = n->inputs()[1]->node();
+    Node* weight = conv->inputs()[1]->node();
+
+    if (conv->inputs().size() > 2) {
+      return false;
+    }
+
+    Tensor bias_tensor = bias->t(kvalue);
+    Tensor weight_tensor = weight->t(kvalue);
+    const auto& bias_shape = bias_tensor.sizes();
+    const auto& weight_shape = weight_tensor.sizes();
+    if (bias_shape.size() != 4 || bias_shape.size() != weight_shape.size()) {
+      return false;
+    }
+    if (bias_shape[0] != 1 || bias_shape[2] != 1 || bias_shape[3] != 1) {
+      return false;
+    }
+    if (bias_shape[1] != weight_shape[0]) {
+      return false;
+    }
+    // reshape bias node
+    bias_tensor.sizes().clear();
+    bias_tensor.sizes().push_back(weight_shape[0]);
+    bias->t_(kvalue, std::move(bias_tensor));
+
+    conv->addInput(bias->outputs()[0]);
+    conv->output()->setSizes(add->output()->sizes());
+    conv->output()->setElemType(add->output()->elemType());
+    const bool replacing_success =
+        tryReplacingAllUsesWith(add->output(), add->inputs()[0]);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/fuse_squeeze_act_unsqueeze.h.bak b/paddle2onnx/optimizer/fuse_squeeze_act_unsqueeze.h.bak
new file mode 100644
index 0000000000..dc5821b59e
--- /dev/null
+++ b/paddle2onnx/optimizer/fuse_squeeze_act_unsqueeze.h.bak
@@ -0,0 +1,102 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   axis = n
+//   A = Squeeze(x, axis)
+//   B = Act(A)
+//   C = Unsqueeze(x, axis)
+// After:
+//   C = act(x)
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseSqueezeActUnsqueeze final : public PredicateBasedPass {
+  explicit FuseSqueezeActUnsqueeze()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override { return "fuse_squeeze_act_unsqueeze"; }
+
+  bool patternMatchPredicate(Node* node) override {
+    // if you want more act supported
+    // just modify here
+    return node->kind() == kUnqueeze &&
+           node->inputs()[0]->node()->kind() == kLeakyRelu &&
+           node->inputs()[0]->node()->inputs()[0] == kSqueeze;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+
+    if (n->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+    if (n->inputs()[0]->node->inputs[0]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* unsqueeze = n;
+    Node* act = n->inputs()[0]->node();
+    Node* squeeze = act->inputs()[0]->node();
+
+    // Process 'axes' data
+    std::vector<int64_t> unsqueeze_axes;
+    if (unsqueeze->hasAttribute(kaxes)) {
+      // opset 13 below
+      unsqueeze_axes = unsqueeze->is(kaxes);
+    } else {
+      // opset 13 and above - first check if 'unsqueeze' has 'axes' input
+      // constant
+      if (unsqueeze->inputs()[1]->node()->kind() != kConstant) {
+        return false;
+      }
+      if (unsqueeze->inputs()[1]->uses().size() > 1) {
+        return false;
+      }
+      Node* axes_const = unsqueeze->inputs()[1]->node();
+      Tensor t = axes_const->t(kvalue);
+      unsqueeze_axes = ParseData<int64_t>(&t);
+    }
+
+    Tensor t = constant->t(kvalue);
+    const auto& ori_size = t.sizes();
+    for (size_t i = 0; i < axes.size(); ++i) {
+      if (axes[i] < 0) {
+        axes[i] = axes[i] +  ori_size.size() + i + 1;
+      }
+    }
+
+    std::vector<int64_t> new_size(ori_size.begin(), ori_size.end());
+    for (size_t i = 0; i < axes.size(); ++i) {
+      new_size.insert(new_size.begin() + axes[i], 1);
+    }
+
+    t.sizes().clear();
+    t.sizes().insert(t.sizes().begin(), new_size.begin(),
+                     new_size.begin() + new_size.size());
+    constant->t_(kvalue, std::move(t));
+
+    // update constant node
+    constant->output()->setSizes(unsqueeze->output()->sizes());
+    constant->output()->setElemType(unsqueeze->output()->elemType());
+    const bool replacing_success =
+        tryReplacingAllUsesWith(unsqueeze->output(), unsqueeze->inputs()[0]);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/fuse_unsqueeze_conv2d_squeeze.h b/paddle2onnx/optimizer/fuse_unsqueeze_conv2d_squeeze.h
new file mode 100644
index 0000000000..e97a15c985
--- /dev/null
+++ b/paddle2onnx/optimizer/fuse_unsqueeze_conv2d_squeeze.h
@@ -0,0 +1,170 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   X = Tensor(N, C, H)
+//   B = Unsqueeze(X, 2)
+//   C = Conv2d(B)
+//   D = Squeeze(C, 2)
+// After:
+//   D = Conv1d(X)
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseUnsqueezeConv2dSqueeze final : public PredicateBasedPass {
+  explicit FuseUnsqueezeConv2dSqueeze()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_unsqueeze_conv2d_squeeze";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kSqueeze &&
+           node->inputs()[0]->node()->kind() == kConv &&
+           node->inputs()[0]->node()->inputs()[0]->node()->kind() == kUnsqueeze;
+  }
+
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    Node* squeeze_node = n;
+    Node* conv_node = n->inputs()[0]->node();
+    Node* unsqueeze_node = conv_node->inputs()[0]->node();
+    if (squeeze_node->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+    if (conv_node->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* weight_node = conv_node->inputs()[1]->node();
+    if (weight_node->kind() != kConstant) {
+      return false;
+    }
+    Tensor weight = weight_node->t(kvalue);
+    if (weight.sizes().size() != 4) {
+      return false;
+    }
+    if (weight.sizes()[2] != 1) {
+      return false;
+    }
+    {
+      std::vector<int64_t> axes;
+      if (squeeze_node->hasAttribute(kaxes)) {
+        // opset 12 and below
+        axes = squeeze_node->is(kaxes);
+      } else {
+        // opset 13 and above
+        if (squeeze_node->inputs()[1]->node()->kind() != kConstant) {
+          return false;
+        }
+        if (squeeze_node->inputs()[1]->uses().size() > 1) {
+          return false;
+        }
+        Tensor t = squeeze_node->inputs()[1]->node()->t(kvalue);
+        axes = ParseData<int64_t>(&t);
+      }
+      if (axes.size() != 1 || axes[0] != 2) {
+        return false;
+      }
+    }
+    {
+      std::vector<int64_t> axes;
+      if (unsqueeze_node->hasAttribute(kaxes)) {
+        // opset 12 and below
+        axes = unsqueeze_node->is(kaxes);
+      } else {
+        // opset 13 and above
+        if (unsqueeze_node->inputs()[1]->node()->kind() != kConstant) {
+          return false;
+        }
+        if (unsqueeze_node->inputs()[1]->uses().size() > 1) {
+          return false;
+        }
+        Tensor t = unsqueeze_node->inputs()[1]->node()->t(kvalue);
+        axes = ParseData<int64_t>(&t);
+      }
+      if (axes.size() != 1 || axes[0] != 2) {
+        return false;
+      }
+    }
+    // update conv weight
+    weight.sizes().erase(weight.sizes().begin() + 2);
+    weight_node->t_(kvalue, std::move(weight));
+
+    if (conv_node->hasAttribute(kdilations)) {
+      std::vector<int64_t> dilations = conv_node->is(kdilations);
+      if (dilations.size() != 2 || dilations[0] != 1) {
+        return false;
+      }
+      dilations.erase(dilations.begin() + 0);
+      conv_node->is_(kdilations, std::move(dilations));
+    }
+    if (conv_node->hasAttribute(kkernel_shape)) {
+      std::vector<int64_t> kernel_shape = conv_node->is(kkernel_shape);
+      if (kernel_shape.size() != 2 || kernel_shape[0] != 1) {
+        return false;
+      }
+      kernel_shape.erase(kernel_shape.begin() + 0);
+      conv_node->is_(kkernel_shape, std::move(kernel_shape));
+    }
+    if (conv_node->hasAttribute(kpads)) {
+      std::vector<int64_t> pads = conv_node->is(kpads);
+      if (pads.size() != 4 || pads[0] != 0 || pads[2] != 0) {
+        return false;
+      }
+      pads.erase(pads.begin() + 2);
+      pads.erase(pads.begin() + 0);
+      conv_node->is_(kpads, std::move(pads));
+    }
+    if (conv_node->hasAttribute(kstrides)) {
+      std::vector<int64_t> strides = conv_node->is(kstrides);
+      if (strides.size() != 2 || strides[0] != 1) {
+        return false;
+      }
+      strides.erase(strides.begin() + 0);
+      conv_node->is_(kstrides, std::move(strides));
+    }
+
+    conv_node->replaceInput(0, unsqueeze_node->inputs()[0]);
+    if (!tryReplacingAllUsesWith(unsqueeze_node->output(),
+                                 unsqueeze_node->inputs()[0])) {
+      return false;
+    }
+    if (!tryReplacingAllUsesWith(squeeze_node->output(),
+                                 squeeze_node->inputs()[0])) {
+      return false;
+    }
+    //    unsqueeze_node->destroy();
+    //    squeeze_node->destroy();
+    //    destroy_current = NodeDestroyType::DestroyZero;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/paddle2onnx_optimizer.cc b/paddle2onnx/optimizer/paddle2onnx_optimizer.cc
new file mode 100644
index 0000000000..0900688b7f
--- /dev/null
+++ b/paddle2onnx/optimizer/paddle2onnx_optimizer.cc
@@ -0,0 +1,240 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/optimizer/paddle2onnx_optimizer.h"
+#include <onnx/shape_inference/implementation.h>
+#include <fstream>
+#include "onnxoptimizer/optimize.h"
+#include "paddle2onnx/optimizer/eliminate_non_transpose.h"
+#include "paddle2onnx/optimizer/fuse_constant_cast.h"
+#include "paddle2onnx/optimizer/fuse_constant_reshape.h"
+#include "paddle2onnx/optimizer/fuse_constant_unsqueeze.h"
+#include "paddle2onnx/optimizer/fuse_paddle_conv_bias.h"
+#include "paddle2onnx/optimizer/fuse_unsqueeze_conv2d_squeeze.h"
+#include "paddle2onnx/optimizer/replace_add_to_identity.h"
+#include "paddle2onnx/optimizer/replace_mul_to_identity.h"
+#include "paddle2onnx/utils/utils.h"
+
+#include "paddle2onnx/converter.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+ONNX_NAMESPACE::ModelProto OptimizeOnnxModel(
+    const ONNX_NAMESPACE::ModelProto& model_proto) {
+  OptimizerOption option;
+  option.passes.clear();
+  option.passes.push_back("eliminate_identity");
+  option.passes.push_back("eliminate_deadend");
+
+  auto optimized_model_proto =
+      ONNX_NAMESPACE::optimization::Optimize(model_proto, option.passes);
+
+  // reinfer shape for this onnx model
+  auto graph = optimized_model_proto.mutable_graph();
+  // clear all the type info of outputs
+  auto output_size = graph->output_size();
+  for (size_t i = 0; i < output_size; ++i) {
+    graph->mutable_output(i)->clear_type();
+  }
+
+  try {
+    shape_inference::InferShapes(optimized_model_proto);
+  } catch (const std::exception& e) {
+    P2OLogger(true) << "[ERROR] Failed to reinfer shape for this model."
+                    << std::endl;
+    P2OLogger(true) << e.what() << std::endl;
+  }
+  return optimized_model_proto;
+}
+
+std::shared_ptr<ONNX_NAMESPACE::ModelProto> LoadModelFromFile(
+    const std::string& file_path) {
+  auto model_proto = std::make_shared<ONNX_NAMESPACE::ModelProto>();
+  std::ifstream fin(file_path, std::ios::in | std::ios::binary);
+  if (!fin.is_open()) {
+    P2OLogger(true)
+        << "Failed to read model file: " << file_path
+        << ", please make sure your model file or file path is valid."
+        << std::endl;
+    return model_proto;
+  }
+  std::string contents;
+  fin.seekg(0, std::ios::end);
+  contents.clear();
+  contents.resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(contents.at(0)), contents.size());
+  fin.close();
+
+  if (!model_proto->ParseFromString(contents)) {
+    P2OLogger(true) << "Failed to load ONNX model from file." << std::endl;
+    return model_proto;
+  }
+  return model_proto;
+}
+
+bool OptimizePaddle2ONNX(const std::string& model_path,
+                         const std::string& optimized_model_path,
+                         const OptimizerOption& option) {
+  auto model_proto = LoadModelFromFile(model_path);
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantReshape>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantUnsqueeze>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FusePaddleConvBias>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseUnsqueezeConv2dSqueeze>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::EliminateNonTranspose>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantCast>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::ReplaceMulToIdentity>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::ReplaceAddToIdentity>();
+
+  auto optimized_model_proto = ONNX_NAMESPACE::optimization::Optimize(
+      *(model_proto.get()), option.passes);
+  std::string optimized_model_str;
+  if (!optimized_model_proto.SerializeToString(&optimized_model_str)) {
+    P2OLogger(true) << "Failed to serialize the optimized model protobuf."
+                    << std::endl;
+    return false;
+  }
+
+  std::fstream out(optimized_model_path, std::ios::out | std::ios::binary);
+  if (!out) {
+    P2OLogger(true) << "Failed to write the optimized model to disk at "
+                    << optimized_model_path << "." << std::endl;
+    return false;
+  }
+  out << optimized_model_str;
+  out.close();
+  return true;
+}
+
+bool OptimizePaddle2ONNX(
+    const std::string& model_path, const std::string& optimized_model_path,
+    const std::map<std::string, std::vector<int>>& shape_infos,
+    const OptimizerOption& option) {
+  auto model_proto = LoadModelFromFile(model_path);
+  if (shape_infos.size() > 0) {
+    // reinfer shape for this onnx model
+    auto graph = model_proto->mutable_graph();
+    // clear all the type info of outputs
+    auto output_size = graph->output_size();
+    for (size_t i = 0; i < output_size; ++i) {
+      graph->mutable_output(i)->clear_type();
+    }
+    // reset type info of inputs
+    auto input_size = graph->input_size();
+    for (size_t i = 0; i < input_size; ++i) {
+      auto input_name = graph->input(i).name();
+      auto iter = shape_infos.find(input_name);
+      if (iter != shape_infos.end()) {
+        auto tensor_type_proto =
+            graph->mutable_input(i)->mutable_type()->mutable_tensor_type();
+        tensor_type_proto->clear_shape();
+        auto shape = tensor_type_proto->mutable_shape();
+        for (auto& dim : iter->second) {
+          shape->add_dim()->set_dim_value(dim);
+        }
+      }
+    }
+
+    try {
+      shape_inference::InferShapes(*(model_proto.get()));
+    } catch (const std::exception& e) {
+      P2OLogger(true) << "[ERROR] Failed to reinfer shape for this model."
+                      << std::endl;
+      P2OLogger(true) << e.what() << std::endl;
+      return false;
+    }
+  }
+
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantReshape>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantUnsqueeze>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FusePaddleConvBias>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseUnsqueezeConv2dSqueeze>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::EliminateNonTranspose>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::FuseConstantCast>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::ReplaceMulToIdentity>();
+  ONNX_NAMESPACE::optimization::Optimizer::passes
+      .registerPass<ONNX_NAMESPACE::optimization::ReplaceAddToIdentity>();
+
+  auto optimized_model_proto = ONNX_NAMESPACE::optimization::Optimize(
+      *(model_proto.get()), option.passes);
+  std::string optimized_model_str;
+  if (!optimized_model_proto.SerializeToString(&optimized_model_str)) {
+    P2OLogger(true) << "Failed to serialize the optimized model protobuf."
+                    << std::endl;
+    return false;
+  }
+
+  std::fstream out(optimized_model_path, std::ios::out | std::ios::binary);
+  if (!out) {
+    P2OLogger(true) << "Failed to write the optimized model to disk at "
+                    << optimized_model_path << "." << std::endl;
+    return false;
+  }
+  out << optimized_model_str;
+  out.close();
+  return true;
+}
+
+bool Paddle2ONNXFP32ToFP16(const std::string& model_path,
+                           const std::string& converted_model_path) {
+  std::ifstream fin(model_path, std::ios::in | std::ios::binary);
+  if (!fin.is_open()) {
+    P2OLogger(true)
+        << "Failed to read model file: " << model_path
+        << ", please make sure your model file or file path is valid."
+        << std::endl;
+    return false;
+  }
+  std::string contents;
+  fin.seekg(0, std::ios::end);
+  contents.clear();
+  contents.resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(contents.at(0)), contents.size());
+  fin.close();
+
+  char* out_model_ptr = nullptr;
+  int size = 0;
+  ConvertFP32ToFP16(contents.c_str(), contents.size(), &out_model_ptr, &size);
+  std::string onnx_proto(out_model_ptr, out_model_ptr + size);
+
+  std::fstream out(converted_model_path, std::ios::out | std::ios::binary);
+  if (!out) {
+    P2OLogger(true) << "Failed to write the optimized model to disk at "
+                    << converted_model_path << "." << std::endl;
+    return false;
+  }
+  out << onnx_proto;
+  out.close();
+  return true;
+}
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/paddle2onnx_optimizer.h b/paddle2onnx/optimizer/paddle2onnx_optimizer.h
new file mode 100644
index 0000000000..743291996a
--- /dev/null
+++ b/paddle2onnx/optimizer/paddle2onnx_optimizer.h
@@ -0,0 +1,58 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <onnx/onnx_pb.h>
+#include <map>
+#include <string>
+#include <vector>
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct OptimizerOption {
+  std::vector<std::string> passes;
+  OptimizerOption() {
+    passes.push_back("eliminate_identity");
+    passes.push_back("eliminate_deadend");
+    passes.push_back("fuse_constant_reshape");
+    passes.push_back("fuse_constant_unsqueeze");
+    passes.push_back("fuse_paddle_conv_bias");
+    passes.push_back("fuse_consecutive_transposes");
+    passes.push_back("eliminate_non_transpose");
+    passes.push_back("replace_mul_to_identity");
+    passes.push_back("replace_add_to_identity");
+    passes.push_back("fuse_matmul_add_bias_into_gemm");
+    passes.push_back("eliminate_identity");
+    passes.push_back("eliminate_deadend");
+  }
+};
+
+ONNX_NAMESPACE::ModelProto OptimizeOnnxModel(
+    const ONNX_NAMESPACE::ModelProto& model);
+
+bool OptimizePaddle2ONNX(const std::string& model_path,
+                         const std::string& optimized_model_path,
+                         const OptimizerOption& option = OptimizerOption());
+
+bool OptimizePaddle2ONNX(
+    const std::string& model_path, const std::string& optimized_model_path,
+    const std::map<std::string, std::vector<int>>& shape_infos,
+    const OptimizerOption& option = OptimizerOption());
+
+bool Paddle2ONNXFP32ToFP16(const std::string& model_path,
+                           const std::string& optimized_model_path);
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/replace_add_to_identity.h b/paddle2onnx/optimizer/replace_add_to_identity.h
new file mode 100644
index 0000000000..cfff7fcde2
--- /dev/null
+++ b/paddle2onnx/optimizer/replace_add_to_identity.h
@@ -0,0 +1,122 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   X = Constant() all elements equal to 0, shape is () or (1,)
+//   Y = Tensor()
+//   C = X + Y
+// After:
+//   C = Identity(Y)
+
+#include <numeric>
+#include <cmath>
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct ReplaceAddToIdentity final : public PredicateBasedPass {
+  explicit ReplaceAddToIdentity()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "replace_add_to_identity";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kAdd &&
+           (node->inputs()[0]->node()->kind() == kConstant || node->inputs()[1]->node()->kind() == kConstant); 
+  }
+
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    Node* add_node = n;
+    Node* add_ipt_0 = n->inputs()[0]->node();
+    Node* add_ipt_1 = n->inputs()[1]->node();
+
+    if (add_ipt_0->kind() == kConstant) {
+      auto bias = add_ipt_0->t(kvalue);
+      if (bias.sizes().size() == 1 && bias.sizes()[0] != 1) {
+        return false;
+      }
+      if (bias.sizes().size() > 1) {
+        return false;
+      }
+      const auto& float_data = bias.floats();
+      if (float_data.size() > 0 && fabs(float_data[0]) > 1e-05) {
+        return false;
+      }
+      const auto& double_data = bias.doubles();
+      if (double_data.size() > 0 && fabs(double_data[0]) > 1e-05) {
+        return false;
+      }
+      const auto& int32_data = bias.int32s();
+      if (int32_data.size() > 0 && int32_data[0] != 0) {
+        return false;
+      }
+      const auto& int64_data = bias.int64s();
+      if (int64_data.size() > 0 && int64_data[0] != 0) {
+        return false;
+      }
+      if (float_data.size() == 0 && double_data.size() == 0 && int32_data.size() == 0 && int64_data.size() == 0) {
+        return false;
+      }
+      if (!tryReplacingAllUsesWith(add_node->output(), add_node->inputs()[1])) {
+        return false;
+      }
+    } else {
+      auto bias = add_ipt_1->t(kvalue);
+      if (bias.sizes().size() == 1 && bias.sizes()[0] != 1) {
+        return false;
+      }
+      if (bias.sizes().size() > 1) {
+        return false;
+      }
+      const auto& float_data = bias.floats();
+      if (float_data.size() > 0 && fabs(float_data[0]) > 1e-05) {
+        return false;
+      }
+      const auto& double_data = bias.doubles();
+      if (double_data.size() > 0 && fabs(double_data[0]) > 1e-05) {
+        return false;
+      }
+      const auto& int32_data = bias.int32s();
+      if (int32_data.size() > 0 && int32_data[0] != 0) {
+        return false;
+      }
+      const auto& int64_data = bias.int64s();
+      if (int64_data.size() > 0 && int64_data[0] != 0) {
+        return false;
+      }
+      if (float_data.size() == 0 && double_data.size() == 0 && int32_data.size() == 0 && int64_data.size() == 0) {
+        return false;
+      }
+      if (!tryReplacingAllUsesWith(add_node->output(), add_node->inputs()[0])) {
+        return false;
+      }
+    }
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/optimizer/replace_mul_to_identity.h b/paddle2onnx/optimizer/replace_mul_to_identity.h
new file mode 100644
index 0000000000..64516cbeb6
--- /dev/null
+++ b/paddle2onnx/optimizer/replace_mul_to_identity.h
@@ -0,0 +1,122 @@
+//   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// Before:
+//   X = Constant() all elements equal to 1, shape is () or (1,)
+//   Y = Tensor()
+//   C = X * Y
+// After:
+//   C = Identity(Y)
+
+#include <numeric>
+#include <cmath>
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct ReplaceMulToIdentity final : public PredicateBasedPass {
+  explicit ReplaceMulToIdentity()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "replace_mul_to_identity";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kMul &&
+           (node->inputs()[0]->node()->kind() == kConstant || node->inputs()[1]->node()->kind() == kConstant); 
+  }
+
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    Node* mul_node = n;
+    Node* mul_ipt_0 = n->inputs()[0]->node();
+    Node* mul_ipt_1 = n->inputs()[1]->node();
+
+    if (mul_ipt_0->kind() == kConstant) {
+      auto scale = mul_ipt_0->t(kvalue);
+      if (scale.sizes().size() == 1 && scale.sizes()[0] != 1) {
+        return false;
+      }
+      if (scale.sizes().size() > 1) {
+        return false;
+      }
+      const auto& float_data = scale.floats();
+      if (float_data.size() > 0 && fabs(float_data[0] - 1.0) > 1e-05) {
+        return false;
+      }
+      const auto& double_data = scale.doubles();
+      if (double_data.size() > 0 && fabs(double_data[0] - 1.0) > 1e-05) {
+        return false;
+      }
+      const auto& int32_data = scale.int32s();
+      if (int32_data.size() > 0 && int32_data[0] != 1) {
+        return false;
+      }
+      const auto& int64_data = scale.int64s();
+      if (int64_data.size() > 0 && int64_data[0] != 1) {
+        return false;
+      }
+      if (float_data.size() == 0 && double_data.size() == 0 && int32_data.size() == 0 && int64_data.size() == 0) {
+        return false;
+      }
+      if (!tryReplacingAllUsesWith(mul_node->output(), mul_node->inputs()[1])) {
+        return false;
+      }
+    } else {
+      auto scale = mul_ipt_1->t(kvalue);
+      if (scale.sizes().size() == 1 && scale.sizes()[0] != 1) {
+        return false;
+      }
+      if (scale.sizes().size() > 1) {
+        return false;
+      }
+      const auto& float_data = scale.floats();
+      if (float_data.size() > 0 && fabs(float_data[0] - 1.0) > 1e-05) {
+        return false;
+      }
+      const auto& double_data = scale.doubles();
+      if (double_data.size() > 0 && fabs(double_data[0] - 1.0) > 1e-05) {
+        return false;
+      }
+      const auto& int32_data = scale.int32s();
+      if (int32_data.size() > 0 && int32_data[0] != 1) {
+        return false;
+      }
+      const auto& int64_data = scale.int64s();
+      if (int64_data.size() > 0 && int64_data[0] != 1) {
+        return false;
+      }
+      if (float_data.size() == 0 && double_data.size() == 0 && int32_data.size() == 0 && int64_data.size() == 0) {
+        return false;
+      }
+      if (!tryReplacingAllUsesWith(mul_node->output(), mul_node->inputs()[0])) {
+        return false;
+      }
+    }
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/paddle2onnx/paddle_reader.cc b/paddle2onnx/paddle_reader.cc
new file mode 100755
index 0000000000..4d4aca513d
--- /dev/null
+++ b/paddle2onnx/paddle_reader.cc
@@ -0,0 +1,78 @@
+#include <fstream>
+#include <iostream>
+#include <set>
+#include <string>
+#include <cstring>
+#include "paddle2onnx/converter.h"
+#include "paddle2onnx/mapper/exporter.h"
+#include "paddle2onnx/parser/parser.h"
+
+namespace paddle2onnx {
+
+int32_t GetDataTypeFromPaddle(int dtype) {
+  if (dtype == P2ODataType::FP32) {
+    return 0;
+  } else if (dtype == P2ODataType::FP64) {
+    return 1;
+  } else if (dtype == P2ODataType::UINT8) {
+    return 2;
+  } else if (dtype == P2ODataType::INT8) {
+    return 3;
+  } else if (dtype == P2ODataType::INT32) {
+    return 4;
+  } else if (dtype == P2ODataType::INT64) {
+    return 5;
+  }
+  Assert(false, "Only support float/double/uint8/int32/int64 in PaddleReader.");
+  return -1;
+}
+
+PaddleReader::PaddleReader(const char* model_buffer, int buffer_size) {
+  PaddleParser parser;
+  Assert(parser.Init(model_buffer, buffer_size),
+         "Failed to parse PaddlePaddle model.");
+
+  num_inputs = parser.inputs.size();
+  num_outputs = parser.outputs.size();
+  for (int i = 0; i < num_inputs; ++i) {
+    std::strcpy(inputs[i].name, parser.inputs[i].name.c_str());
+    inputs[i].rank = parser.inputs[i].Rank();
+    inputs[i].shape = new int64_t[inputs[i].rank];
+    for (int j = 0; j < inputs[i].rank; ++j) {
+      inputs[i].shape[j] = parser.inputs[i].shape[j];
+    }
+    inputs[i].dtype = GetDataTypeFromPaddle(parser.inputs[i].dtype);
+  }
+
+  for (int i = 0; i < num_outputs; ++i) {
+    std::strcpy(outputs[i].name, parser.outputs[i].name.c_str());
+    outputs[i].rank = parser.outputs[i].Rank();
+    outputs[i].shape = new int64_t[outputs[i].rank];
+    for (int j = 0; j < outputs[i].rank; ++j) {
+      outputs[i].shape[j] = parser.outputs[i].shape[j];
+    }
+    outputs[i].dtype = GetDataTypeFromPaddle(parser.outputs[i].dtype);
+  }
+  for (size_t i = 0; i < parser.NumOfOps(0); ++i) {
+    if (parser.GetOpDesc(0, i).type().find("quantize") != std::string::npos) {
+      is_quantize_model = true;
+      break;
+    }
+  }
+  for (size_t i = 0; i < parser.NumOfOps(0); ++i) {
+    if (parser.GetOpDesc(0, i).type().find("multiclass_nms3") != std::string::npos) {
+      has_nms = true;
+      auto& op = parser.GetOpDesc(0, i);
+      parser.GetOpAttr(op, "background_label", &nms_params.background_label);
+      parser.GetOpAttr(op, "keep_top_k", &nms_params.keep_top_k);
+      parser.GetOpAttr(op, "nms_eta", &nms_params.nms_eta);
+      parser.GetOpAttr(op, "nms_threshold", &nms_params.nms_threshold);
+      parser.GetOpAttr(op, "score_threshold", &nms_params.score_threshold);
+      parser.GetOpAttr(op, "nms_top_k", &nms_params.nms_top_k);
+      parser.GetOpAttr(op, "normalized", &nms_params.normalized);
+      break;
+    }
+  }
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/parser/parser.cc b/paddle2onnx/parser/parser.cc
new file mode 100755
index 0000000000..d2c743a2d4
--- /dev/null
+++ b/paddle2onnx/parser/parser.cc
@@ -0,0 +1,862 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle2onnx/parser/parser.h"
+
+#include <cstring>
+#include <fstream>
+#include <sstream>
+#include <string>
+
+#include "paddle2onnx/utils/utils.h"
+
+namespace paddle2onnx {
+bool PaddleParser::LoadProgram(const std::string& model) {
+  prog = std::make_shared<paddle2onnx::framework::proto::ProgramDesc>();
+  std::ifstream fin(model, std::ios::in | std::ios::binary);
+  if (!fin.is_open()) {
+    P2OLogger() << "Failed to read model file: " << model
+                << ", please make sure your model file or file path is valid."
+                << std::endl;
+    return false;
+  }
+
+  std::string contents;
+  fin.seekg(0, std::ios::end);
+  contents.clear();
+  contents.resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(contents.at(0)), contents.size());
+  fin.close();
+
+  if (!prog->ParseFromString(contents)) {
+    P2OLogger() << "Failed to parse paddlepaddle model from read content."
+                << std::endl;
+    return false;
+  }
+  return true;
+}
+
+bool PaddleParser::LoadProgram(const void* model_buffer, int64_t model_size) {
+  prog = std::make_shared<paddle2onnx::framework::proto::ProgramDesc>();
+  if (!prog->ParseFromArray(model_buffer, model_size)) {
+    P2OLogger() << "Failed to parse PaddlePaddle model from memory buffer."
+                << std::endl;
+    return false;
+  }
+  return true;
+}
+
+bool PaddleParser::GetParamNames(std::vector<std::string>* var_names) {
+  var_names->clear();
+  int block_size = prog->blocks_size();
+  for (auto i = 0; i < block_size; ++i) {
+    auto block = prog->blocks(i);
+    int vars_size = block.vars_size();
+    for (auto j = 0; j < vars_size; ++j) {
+      auto type = block.vars(j).type().type();
+      if (type == framework::proto::VarType_Type::VarType_Type_SELECTED_ROWS) {
+        P2OLogger()
+            << "VarType of SELECTED_ROWS is not supported by Paddle2ONNX."
+            << std::endl;
+        return false;
+      }
+      if (type == framework::proto::VarType_Type::VarType_Type_FEED_MINIBATCH) {
+        continue;
+      }
+      if (type == paddle2onnx::framework::proto::VarType_Type::
+                      VarType_Type_FETCH_LIST) {
+        continue;
+      }
+      if (type ==
+          paddle2onnx::framework::proto::VarType_Type::VarType_Type_READER) {
+        continue;
+      }
+      if (type ==
+          paddle2onnx::framework::proto::VarType_Type::VarType_Type_RAW) {
+        continue;
+      }
+      if (!block.vars(j).persistable()) {
+        continue;
+      }
+      var_names->push_back(block.vars(j).name());
+    }
+  }
+  std::sort(var_names->begin(), var_names->end());
+  return true;
+}
+
+bool PaddleParser::LoadParamsFromMemoryBuffer(
+    const std::string& params_buffer) {
+  params.clear();
+  int64_t total_size = params_buffer.size();
+
+  std::vector<std::string> var_names;
+  GetParamNames(&var_names);
+
+  int read_size = 0;
+  while (read_size < total_size) {
+    auto index = params.size();
+    if (index >= var_names.size()) {
+      P2OLogger() << "Unexcepted situation happend, while reading the "
+                     "parameters of PaddlePaddle model."
+                  << std::endl;
+      return false;
+    }
+
+    {
+      // read version, we don't need this
+      uint32_t version;
+      params_buffer.copy(reinterpret_cast<char*>(&version), sizeof(version),
+                         read_size);
+      read_size += sizeof(version);
+    }
+    {
+      // read lod_level, we don't use it
+      // this has to be zero, otherwise not support
+      uint64_t lod_level;
+      params_buffer.copy(reinterpret_cast<char*>(&lod_level), sizeof(lod_level),
+                         read_size);
+      read_size += sizeof(lod_level);
+      if (lod_level != 0) {
+        P2OLogger() << "Only supports weight with lod_level = 0." << std::endl;
+        return false;
+      }
+    }
+    {
+      // Another version, we don't use it
+      uint32_t version;
+      params_buffer.copy(reinterpret_cast<char*>(&version), sizeof(version),
+                         read_size);
+      read_size += sizeof(version);
+    }
+    {
+      // read size of TensorDesc
+      int32_t size;
+      params_buffer.copy(reinterpret_cast<char*>(&size), sizeof(size),
+                         read_size);
+      read_size += sizeof(size);
+      // read TensorDesc
+      std::unique_ptr<char[]> buf(new char[size]);
+      params_buffer.copy(reinterpret_cast<char*>(buf.get()), size, read_size);
+      read_size += size;
+
+      std::unique_ptr<paddle2onnx::framework::proto::VarType_TensorDesc>
+          tensor_desc(new paddle2onnx::framework::proto::VarType_TensorDesc());
+      tensor_desc->ParseFromArray(buf.get(), size);
+
+      Weight weight;
+
+      int32_t numel = 1;
+      int32_t data_type = tensor_desc->data_type();
+      weight.dtype = data_type;
+      for (auto i = 0; i < tensor_desc->dims().size(); ++i) {
+        numel *= tensor_desc->dims()[i];
+        weight.shape.push_back(tensor_desc->dims()[i]);
+      }
+
+      // read weight data
+      weight.buffer.resize(numel * PaddleDataTypeSize(data_type));
+      params_buffer.copy(weight.buffer.data(),
+                         numel * PaddleDataTypeSize(data_type), read_size);
+      read_size += numel * PaddleDataTypeSize(data_type);
+      params[var_names[index]] = weight;
+    }
+  }
+  return true;
+}
+
+bool PaddleParser::LoadParamsFromMemoryBuffer(const void* params_buffer,
+                                              int64_t params_size) {
+  params.clear();
+
+  const char* read_pointer = reinterpret_cast<const char*>(params_buffer);
+  std::vector<std::string> var_names;
+  GetParamNames(&var_names);
+
+  while (params_size > 0) {
+    auto index = params.size();
+    if (index >= var_names.size()) {
+      P2OLogger() << "Unexcepted situation happend, while reading the "
+                     "parameters of PaddlePaddle model."
+                  << std::endl;
+      return false;
+    }
+
+    {
+      // read version, we don't need this
+      uint32_t version;
+      std::memcpy(&version, read_pointer, sizeof(version));
+      read_pointer += sizeof(version);
+      params_size -= sizeof(version);
+    }
+    {
+      // read lod_level, we don't use it
+      // this has to be zero, otherwise not support
+      uint64_t lod_level;
+      std::memcpy(&lod_level, read_pointer, sizeof(lod_level));
+      read_pointer += sizeof(lod_level);
+      params_size -= sizeof(lod_level);
+      if (lod_level != 0) {
+        P2OLogger() << "Only supports weight with lod_level = 0." << std::endl;
+        return false;
+      }
+    }
+    {
+      // Another version, we don't use it
+      uint32_t version;
+      std::memcpy(&version, read_pointer, sizeof(version));
+      read_pointer += sizeof(version);
+      params_size -= sizeof(version);
+    }
+    {
+      // read size of TensorDesc
+      int32_t size;
+      std::memcpy(&size, read_pointer, sizeof(size));
+      read_pointer += sizeof(size);
+      params_size -= sizeof(size);
+      // read TensorDesc
+      std::unique_ptr<char[]> buf(new char[size]);
+      std::memcpy(buf.get(), read_pointer, size);
+      read_pointer += size;
+      params_size -= size;
+
+      std::unique_ptr<paddle2onnx::framework::proto::VarType_TensorDesc>
+          tensor_desc(new paddle2onnx::framework::proto::VarType_TensorDesc());
+      tensor_desc->ParseFromArray(buf.get(), size);
+
+      Weight weight;
+
+      int32_t numel = 1;
+      int32_t data_type = tensor_desc->data_type();
+      weight.dtype = data_type;
+      for (auto i = 0; i < tensor_desc->dims().size(); ++i) {
+        numel *= tensor_desc->dims()[i];
+        weight.shape.push_back(tensor_desc->dims()[i]);
+      }
+
+      // read weight data
+      weight.buffer.resize(numel * PaddleDataTypeSize(data_type));
+      std::memcpy(weight.buffer.data(), read_pointer,
+                  numel * PaddleDataTypeSize(data_type));
+      read_pointer += numel * PaddleDataTypeSize(data_type);
+      params_size -= numel * PaddleDataTypeSize(data_type);
+      params[var_names[index]] = weight;
+    }
+  }
+  return true;
+}
+
+bool PaddleParser::LoadParams(const std::string& path) {
+  params.clear();
+  std::ifstream is(path, std::ios::in | std::ios::binary);
+  if (!is.is_open()) {
+    P2OLogger() << "Cannot open file " << path << " to read." << std::endl;
+    return false;
+  }
+  is.seekg(0, std::ios::end);
+  int64_t total_size = is.tellg();
+  is.seekg(0, std::ios::beg);
+  std::vector<std::string> var_names;
+  GetParamNames(&var_names);
+
+  int64_t read_size = 0;
+  while (read_size < total_size) {
+    {
+      // read version, we don't need this
+      uint32_t version;
+      read_size += sizeof(version);
+      is.read(reinterpret_cast<char*>(&version), sizeof(version));
+    }
+    {
+      // read lod_level, we don't use it
+      // this has to be zero, otherwise not support
+      uint64_t lod_level;
+      read_size += sizeof(lod_level);
+      is.read(reinterpret_cast<char*>(&lod_level), sizeof(lod_level));
+      Assert(lod_level == 0,
+             "Paddle2ONNX: Only support weight with lod_level = 0.");
+    }
+    {
+      // Another version, we don't use it
+      uint32_t version;
+      read_size += sizeof(version);
+      is.read(reinterpret_cast<char*>(&version), sizeof(version));
+    }
+    {
+      // read size of TensorDesc
+      int32_t size;
+      read_size += sizeof(size);
+      is.read(reinterpret_cast<char*>(&size), sizeof(size));
+      // read TensorDesc
+      std::unique_ptr<char[]> buf(new char[size]);
+      read_size += size;
+      is.read(reinterpret_cast<char*>(buf.get()), size);
+
+      std::unique_ptr<paddle2onnx::framework::proto::VarType_TensorDesc>
+          tensor_desc(new paddle2onnx::framework::proto::VarType_TensorDesc());
+      tensor_desc->ParseFromArray(buf.get(), size);
+
+      Weight weight;
+
+      int32_t numel = 1;
+      int32_t data_type = tensor_desc->data_type();
+      weight.dtype = data_type;
+      for (auto i = 0; i < tensor_desc->dims().size(); ++i) {
+        numel *= tensor_desc->dims()[i];
+        weight.shape.push_back(tensor_desc->dims()[i]);
+      }
+
+      // read weight data
+      weight.buffer.resize(numel * PaddleDataTypeSize(data_type));
+      read_size += numel * PaddleDataTypeSize(data_type);
+      is.read(weight.buffer.data(), numel * PaddleDataTypeSize(data_type));
+      auto index = params.size();
+      if (index >= var_names.size()) {
+        P2OLogger() << "Unexcepted situation happend while reading parameters "
+                       "of PaddlePaddle model."
+                    << std::endl;
+        return false;
+      }
+      params[var_names[index]] = weight;
+    }
+  }
+  is.close();
+  return true;
+}
+
+int PaddleParser::NumOfBlocks() const { return prog->blocks_size(); }
+
+int PaddleParser::NumOfOps(int block_idx) const {
+  Assert(block_idx < NumOfBlocks(),
+         "block_idx is greater than number of blocks.");
+  return prog->blocks(block_idx).ops_size();
+}
+
+const framework::proto::OpDesc& PaddleParser::GetOpDesc(int32_t block_idx,
+                                                        int32_t op_idx) const {
+  Assert(block_idx < NumOfBlocks(),
+         "block_idx is greater than number of blocks.");
+  Assert(op_idx < NumOfOps(block_idx),
+         "op_idx is greater than number of operators.");
+  return prog->blocks(block_idx).ops(op_idx);
+}
+
+void PaddleParser::InitBlock() {
+  //  if (ExistsDumplicateTensorName()) {
+  //    return false;
+  //  }
+  GetBlocksVarName2Id();
+  GetBlocksOps();
+  GetGlobalBlockInputOutputInfo();
+}
+
+bool PaddleParser::Init(const std::string& _model, const std::string& _params) {
+  std::vector<Weight> weights;
+  if (!LoadProgram(_model)) {
+    P2OLogger() << "Failed to load program of PaddlePaddle model." << std::endl;
+    return false;
+  }
+  if (_params != "") {
+    if (!LoadParams(_params)) {
+      P2OLogger() << "Failed to load parameters of PaddlePaddle model."
+                  << std::endl;
+      return false;
+    }
+  }
+  InitBlock();
+  return true;
+}
+
+bool PaddleParser::Init(const void* model_buffer, int64_t model_size,
+                        const void* params_buffer, int64_t params_size) {
+  std::vector<Weight> weights;
+  if (!LoadProgram(model_buffer, model_size)) {
+    P2OLogger() << "Failed to load program of PaddlePaddle model from memory."
+                << std::endl;
+    return false;
+  }
+  if (params_buffer != nullptr && params_size > 0) {
+    if (!LoadParamsFromMemoryBuffer(params_buffer, params_size)) {
+      P2OLogger()
+          << "Failed to load parameters of PaddlePaddle model from memory."
+          << std::endl;
+      return false;
+    }
+  }
+  InitBlock();
+  return true;
+}
+
+bool PaddleParser::IsConstantTensor(const int64_t& block_id,
+                                    const std::string& tensor_name) const {
+  Assert(block_id < _constant_ops.size(),
+         "block_id is out of range while calling IsConstantTensor.");
+  bool is_constant = false;
+  {
+    auto iter = _constant_ops[block_id].find(tensor_name);
+    is_constant = (iter != _constant_ops[block_id].end());
+  }
+  if (!is_constant) {
+    auto iter = params.find(tensor_name);
+    is_constant = (iter != params.end());
+  }
+  return is_constant;
+}
+
+void PaddleParser::GetBlocksVarName2Id() {
+  _blocks_var_name2id.clear();
+  _blocks_var_name2id.resize(prog->blocks_size());
+  for (auto i = 0; i < prog->blocks_size(); ++i) {
+    for (auto j = 0; j < prog->blocks(i).vars_size(); ++j) {
+      _blocks_var_name2id[i][prog->blocks(i).vars(j).name()] = j;
+    }
+  }
+}
+
+void PaddleParser::GetBlocksOps() {
+  is_quantized_model = false;
+  _blocks_ops.clear();
+  _constant_ops.clear();
+  _blocks_ops.resize(prog->blocks_size());
+  _constant_ops.resize(prog->blocks_size());
+  for (auto i = 0; i < prog->blocks_size(); ++i) {
+    _blocks_ops[i].reserve(prog->blocks(i).ops_size());
+    for (auto j = 0; j < prog->blocks(i).ops_size(); ++j) {
+      _blocks_ops[i].push_back(&prog->blocks(i).ops(j));
+      if (prog->blocks(i).ops(j).type() == "assign_value") {
+        _constant_ops[i][prog->blocks(i).ops(j).outputs(0).arguments(0)] = j;
+      }
+      // Determine whether the model is a quantized model, if it is a quantized
+      // model, set is_quantized_model to be true
+      if (!is_quantized_model &&
+          prog->blocks(i).ops(j).type().find("quantize") != std::string::npos) {
+        is_quantized_model = true;
+        P2OLogger() << "[Info] The Paddle model is a quantized model. "
+                    << std::endl;
+      }
+    }
+  }
+}
+
+TensorInfo PaddleParser::GetTensorInfo(
+    const std::string& name,
+    const paddle2onnx::framework::proto::BlockDesc& block) const {
+  auto block_idx = block.idx();
+  auto iter = _blocks_var_name2id[block_idx].find(name);
+  if (iter == _blocks_var_name2id[block_idx].end()) {
+    if (block_idx == 0) {
+      Assert(false,
+             "Cannot find " + name + " in _blocks_var_name2id(global block).");
+    } else {
+      block_idx = block.parent_idx();
+      iter = _blocks_var_name2id[block_idx].find(name);
+      Assert(iter != _blocks_var_name2id[block_idx].end(),
+             "Cannot find " + name + " in _blocks_var_name2id(parent block).");
+    }
+  }
+  auto var_idx = iter->second;
+
+  // Dangerous conversion, lod tensor array is under limited supporting
+  // Only works in some control flow situation
+  if (prog->blocks(block_idx).vars(var_idx).type().has_tensor_array()) {
+    auto tensor_array =
+        prog->blocks(block_idx).vars(var_idx).type().tensor_array();
+    TensorInfo info;
+    info.is_tensor_array = true;
+    info.name = name;
+    info.dtype = tensor_array.tensor().data_type();
+    for (auto i = 0; i < tensor_array.tensor().dims_size(); ++i) {
+      info.shape.push_back(tensor_array.tensor().dims(i));
+    }
+    return info;
+  }
+
+  auto tensor = prog->blocks(block_idx).vars(var_idx).type().lod_tensor();
+  TensorInfo info;
+  info.name = name;
+  info.dtype = tensor.tensor().data_type();
+  for (auto i = 0; i < tensor.tensor().dims_size(); ++i) {
+    info.shape.push_back(tensor.tensor().dims(i));
+  }
+
+  return info;
+}
+
+bool PaddleParser::OpHasInput(int64_t block_id, int64_t op_id,
+                              const std::string& name) const {
+  auto& block = prog->blocks(block_id);
+  auto& op = block.ops(op_id);
+  for (auto i = 0; i < op.inputs_size(); ++i) {
+    if (op.inputs(i).parameter() == name) {
+      if (op.inputs(i).arguments_size() > 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+std::vector<TensorInfo> PaddleParser::GetOpInput(
+    int64_t block_id, int64_t op_id, const std::string& name) const {
+  auto& block = prog->blocks(block_id);
+  auto& op = block.ops(op_id);
+  std::vector<TensorInfo> inputs;
+  bool found = false;
+  for (auto i = 0; i < op.inputs_size(); ++i) {
+    if (op.inputs(i).parameter() == name) {
+      for (auto j = 0; j < op.inputs(i).arguments_size(); ++j) {
+        inputs.push_back(GetTensorInfo(op.inputs(i).arguments(j), block));
+        found = true;
+      }
+      break;
+    }
+  }
+  Assert(found, "Cannot find input: " + name + " in operator: " + op.type());
+  return inputs;
+}
+
+bool PaddleParser::OpHasOutput(int64_t block_id, int64_t op_id,
+                               const std::string& name) const {
+  auto& block = prog->blocks(block_id);
+  auto& op = block.ops(op_id);
+  for (auto i = 0; i < op.outputs_size(); ++i) {
+    if (op.outputs(i).parameter() == name) {
+      if (op.outputs(i).arguments_size() > 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+std::vector<TensorInfo> PaddleParser::GetOpOutput(
+    int64_t block_id, int64_t op_id, const std::string& name) const {
+  auto& block = prog->blocks(block_id);
+  auto& op = block.ops(op_id);
+  std::vector<TensorInfo> outputs;
+  bool found = false;
+  for (auto i = 0; i < op.outputs_size(); ++i) {
+    if (op.outputs(i).parameter() == name) {
+      for (auto j = 0; j < op.outputs(i).arguments_size(); ++j) {
+        outputs.push_back(GetTensorInfo(op.outputs(i).arguments(j), block));
+        found = true;
+      }
+      break;
+    }
+  }
+  Assert(found, "Cannot find output: " + name + " in operator: " + op.type());
+  return outputs;
+}
+
+bool PaddleParser::OpIsAttrVar(int64_t block_id, int64_t op_id,
+                               const std::string& name) const {
+  bool is_attr_var = false;
+  auto& op = GetOpDesc(block_id, op_id);
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name && IsAttrVar(op, i)) {
+      is_attr_var = true;
+      break;
+    }
+  }
+  return is_attr_var;
+}
+
+std::vector<TensorInfo> PaddleParser::GetOpAttrVar(
+    int64_t block_id, int64_t op_id, const std::string& name) const {
+  auto& block = prog->blocks(block_id);
+  auto& op = block.ops(op_id);
+
+  bool found = false;
+  std::vector<TensorInfo> inputs;
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      Assert(IsAttrVar(op, i), "Required AttrVar: " + name +
+                                   " type is Variable in operator: " +
+                                   op.type());
+      // Case 1: Attribute is a single Var
+      if (op.attrs(i).has_var_name()) {
+        inputs.push_back(GetTensorInfo(op.attrs(i).var_name(), block));
+      } else {  // Case 2: Attribute is a List[Var]
+        for (int idx = 0; idx < op.attrs(i).vars_name_size(); ++idx) {
+          auto& var_name = op.attrs(i).vars_name(idx);
+          inputs.push_back(GetTensorInfo(var_name, block));
+        }
+      }
+      found = true;
+      break;
+    }
+  }
+  Assert(found, "Cannot find AttrVar: " + name + " in operator: " + op.type());
+  return inputs;
+}
+
+bool PaddleParser::IsAttrVar(const paddle2onnx::framework::proto::OpDesc& op,
+                             const int64_t& attr_id) const {
+  return op.attrs(attr_id).has_var_name() ||
+         op.attrs(attr_id).vars_name_size() > 0;
+}
+
+bool PaddleParser::OpHasAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name) const {
+  bool found = false;
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    // set found to true when name is in op attrs and can use GetOpAttr to get
+    // value
+    if (op.attrs(i).name() == name) {
+      found = true;
+      break;
+    }
+  }
+  return found;
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name, int64_t* res) const {
+  bool found = false;
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).has_i() || op.attrs(i).has_l(),
+             "Cannot find int32/int64 data from attr: " + name + " in op:" +
+                 op.type());
+      if (op.attrs(i).has_i()) {
+        *res = (int64_t)(op.attrs(i).i());
+      } else {
+        *res = op.attrs(i).l();
+      }
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name, float* res) const {
+  bool found = false;
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).has_f(), "Cannot find float data from attr: " + name +
+                                      " in op: " + op.type());
+      *res = op.attrs(i).f();
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name, bool* res) const {
+  bool found = false;
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).has_b(), "Cannot find bool data from attr: " + name +
+                                      " in op: " + op.type());
+      *res = op.attrs(i).b();
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name, std::string* res) const {
+  bool found = false;
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).has_s(), "Cannot find string data from attr: " + name +
+                                      " in op: " + op.type());
+      *res = op.attrs(i).s();
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name,
+                             std::vector<int64_t>* res) const {
+  bool found = false;
+  res->clear();
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).ints_size() >= 0 || op.attrs(i).longs_size() >= 0,
+             "Cannot find list of int32/int64 data from attr: " + name +
+                 " in op: " + op.type());
+      if (op.attrs(i).ints_size() > 0) {
+        for (auto j = 0; j < op.attrs(i).ints_size(); ++j) {
+          res->push_back(static_cast<int64_t>(op.attrs(i).ints(j)));
+        }
+      } else {
+        for (auto j = 0; j < op.attrs(i).longs_size(); ++j) {
+          res->push_back(op.attrs(i).longs(j));
+        }
+      }
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name,
+                             std::vector<float>* res) const {
+  bool found = false;
+  res->clear();
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).floats_size() >= 0,
+             "Cannot find list of float data from attr: " + name + " in op: " +
+                 op.type());
+      for (auto j = 0; j < op.attrs(i).floats_size(); ++j) {
+        res->push_back(static_cast<float>(op.attrs(i).floats(j)));
+      }
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                             const std::string& name,
+                             std::vector<double>* res) const {
+  bool found = false;
+  res->clear();
+  for (auto i = 0; i < op.attrs_size(); ++i) {
+    if (op.attrs(i).name() == name) {
+      found = true;
+      if (IsAttrVar(op, i)) break;
+      Assert(op.attrs(i).float64s_size() >= 0,
+             "Cannot find list of double data from attr: " + name + " in op: " +
+                 op.type());
+      for (auto j = 0; j < op.attrs(i).float64s_size(); ++j) {
+        res->push_back(static_cast<double>(op.attrs(i).float64s(j)));
+      }
+      break;
+    }
+  }
+  Assert(found, "Cannot found attribute " + name + " in op: " + op.type());
+}
+
+void PaddleParser::GetGlobalBlockInputOutputInfo() {
+  inputs.clear();
+  outputs.clear();
+  // record the origin order of Paddle model
+  std::vector<TensorInfo> inputs_with_no_order;
+  std::vector<TensorInfo> outputs_with_no_order;
+  std::vector<int64_t> input_order;
+  std::vector<int64_t> output_order;
+
+  for (auto i = 0; i < prog->blocks(0).ops_size(); ++i) {
+    if (prog->blocks(0).ops(i).type() == "fetch") {
+      std::string name = prog->blocks(0).ops(i).inputs(0).arguments(0);
+      outputs_with_no_order.push_back(GetTensorInfo(name, prog->blocks(0)));
+      int64_t order = -1;
+      GetOpAttr(prog->blocks(0).ops(i), "col", &order);
+      output_order.push_back(order);
+    } else if (prog->blocks(0).ops(i).type() == "feed") {
+      std::string name = prog->blocks(0).ops(i).outputs(0).arguments(0);
+      inputs_with_no_order.push_back(GetTensorInfo(name, prog->blocks(0)));
+      int64_t order = -1;
+      GetOpAttr(prog->blocks(0).ops(i), "col", &order);
+      input_order.push_back(order);
+    }
+
+    // This is a trick check, due to the uncorrect shape inference of Paddle
+    // model
+    // Remove this after shape inference fixed
+    if (prog->blocks(0).ops(i).type() == "multiclass_nms3") {
+      _has_nms = true;
+    }
+  }
+
+  // Reorder the inputs and outputs to keep same with the original Paddle model
+  inputs.resize(input_order.size());
+  for (size_t i = 0; i < input_order.size(); ++i) {
+    inputs[input_order[i]] = inputs_with_no_order[i];
+  }
+  outputs.resize(output_order.size());
+  for (size_t i = 0; i < output_order.size(); ++i) {
+    outputs[output_order[i]] = outputs_with_no_order[i];
+  }
+
+  // Trick setting for nms, remove this after shape inference fixed
+  if (_has_nms) {
+    for (size_t i = 0; i < outputs.size(); ++i) {
+      if (outputs[i].shape.size() == 2) {
+        if (outputs[i].shape[1] == 6) {
+          outputs[i].shape[0] = -1;
+        }
+      }
+    }
+  }
+}
+
+int32_t PaddleDataTypeSize(int32_t paddle_dtype) {
+  Assert(paddle_dtype != FP16, "Float16 is not supported.");
+  if (paddle_dtype == P2ODataType::BOOL) {
+    return sizeof(bool);
+  } else if (paddle_dtype == P2ODataType::INT8) {
+    return sizeof(int8_t);
+  } else if (paddle_dtype == P2ODataType::INT16) {
+    return sizeof(int16_t);
+  } else if (paddle_dtype == P2ODataType::INT32) {
+    return sizeof(int32_t);
+  } else if (paddle_dtype == P2ODataType::INT64) {
+    return sizeof(int64_t);
+  } else if (paddle_dtype == P2ODataType::FP32) {
+    return sizeof(float);
+  } else if (paddle_dtype == P2ODataType::FP64) {
+    return sizeof(double);
+  } else if (paddle_dtype == P2ODataType::UINT8) {
+    return sizeof(uint8_t);
+  } else {
+    Assert(false, "Unexpected data type: " + std::to_string(paddle_dtype));
+  }
+  return -1;
+}
+
+bool PaddleParser::ExistsDumplicateTensorName() const {
+  std::set<std::string> names;
+  for (auto i = 0; i < prog->blocks(0).ops_size(); ++i) {
+    auto& op = prog->blocks(0).ops(i);
+    for (auto j = 0; j < op.outputs_size(); ++j) {
+      for (auto k = 0; k < op.outputs(j).arguments_size(); ++k) {
+        if (op.type() == "fetch") {
+          continue;
+        }
+        if (names.find(op.outputs(j).arguments(k)) != names.end()) {
+          P2OLogger() << "There's dumplicate output name: "
+                      << op.outputs(j).arguments(k)
+                      << " in this model, not supported yet." << std::endl;
+          return true;
+        }
+        names.insert(op.outputs(j).arguments(k));
+      }
+    }
+  }
+  return false;
+}
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/parser/parser.h b/paddle2onnx/parser/parser.h
new file mode 100644
index 0000000000..0d15a72bc5
--- /dev/null
+++ b/paddle2onnx/parser/parser.h
@@ -0,0 +1,258 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <numeric>
+#include <type_traits>
+
+#include "paddle2onnx/proto/p2o_paddle.pb.h"
+#include "paddle2onnx/utils/utils.h"
+
+namespace paddle2onnx {
+
+enum P2ODataType {
+  BOOL,
+  INT16,
+  INT32,
+  INT64,
+  FP16,
+  FP32,
+  FP64,
+  X7,
+  X8,
+  X9,
+  X10,
+  X11,
+  X12,
+  X13,
+  X14,
+  X15,
+  X16,
+  X17,
+  X18,
+  X19,
+  UINT8,
+  INT8,
+};
+int32_t PaddleDataTypeSize(int32_t paddle_dtype);
+
+struct TensorInfo {
+  std::string name;
+  std::vector<int64_t> shape;
+  int64_t Rank() const { return static_cast<int64_t>(shape.size()); }
+  int32_t dtype;
+  bool is_tensor_array = false;
+
+  TensorInfo() {}
+  TensorInfo(const std::string& _name, const std::vector<int64_t>& _shape,
+             const int32_t& _dtype) {
+    name = _name;
+    shape.assign(_shape.begin(), _shape.end());
+    dtype = _dtype;
+  }
+
+  TensorInfo(const TensorInfo& info) {
+    name = info.name;
+    shape.assign(info.shape.begin(), info.shape.end());
+    dtype = info.dtype;
+    is_tensor_array = info.is_tensor_array;
+  }
+};
+
+struct Weight {
+  std::vector<char> buffer;
+  std::vector<int32_t> shape;
+  int32_t dtype;
+
+  template <typename T>
+  void set(int32_t data_type, const std::vector<int64_t>& dims,
+           const std::vector<T>& data) {
+    buffer.clear();
+    shape.clear();
+    dtype = data_type;
+    buffer.resize(data.size() * PaddleDataTypeSize(dtype));
+    memcpy(buffer.data(), data.data(), data.size() * PaddleDataTypeSize(dtype));
+    for (auto& d : dims) {
+      shape.push_back(d);
+    }
+  }
+  template <typename T>
+  void get(std::vector<T>* data) const {
+    int64_t nums = std::accumulate(std::begin(shape), std::end(shape), 1,
+                                   std::multiplies<int64_t>());
+    data->resize(nums);
+    if (dtype == P2ODataType::INT64) {
+      std::vector<int64_t> value(nums);
+      memcpy(value.data(), buffer.data(), nums * sizeof(int64_t));
+      data->assign(value.begin(), value.end());
+    } else if (dtype == P2ODataType::INT32) {
+      std::vector<int32_t> value(nums);
+      memcpy(value.data(), buffer.data(), nums * sizeof(int32_t));
+      data->assign(value.begin(), value.end());
+    } else if (dtype == P2ODataType::INT8) {
+      std::vector<int8_t> value(nums);
+      memcpy(value.data(), buffer.data(), nums * sizeof(int8_t));
+      data->assign(value.begin(), value.end());
+    } else if (dtype == P2ODataType::FP32) {
+      std::vector<float> value(nums);
+      memcpy(value.data(), buffer.data(), nums * sizeof(float));
+      data->assign(value.begin(), value.end());
+    } else if (dtype == P2ODataType::FP64) {
+      std::vector<double> value(nums);
+      memcpy(value.data(), buffer.data(), nums * sizeof(double));
+      data->assign(value.begin(), value.end());
+    } else {
+      Assert(false,
+             "Weight::get() only support int64/int32/int8/float32/float64.");
+    }
+  }
+};
+
+class PaddleParser {
+ public:
+  // recording variable name:id for each block of a program
+  std::vector<std::map<std::string, int32_t>> _blocks_var_name2id;
+  // recoring set of operators for each block
+  std::vector<std::vector<const paddle2onnx::framework::proto::OpDesc*>>
+      _blocks_ops;
+  std::shared_ptr<paddle2onnx::framework::proto::ProgramDesc> prog;
+  std::map<std::string, Weight> params;
+  std::vector<TensorInfo> inputs;
+  std::vector<TensorInfo> outputs;
+  bool is_quantized_model = false;  // If the Paddle model is a quantized model,
+                                    // set is_quantized_model to be true
+
+  bool Init(const std::string& _model, const std::string& _params = "");
+  bool Init(const void* model_buffer, int64_t model_size,
+            const void* params_buffer = nullptr, int64_t params_size = 0);
+  void InitBlock();
+
+  int NumOfBlocks() const;
+  int NumOfOps(int block_idx) const;
+  bool HasNms() const { return _has_nms; }
+  const framework::proto::OpDesc& GetOpDesc(int32_t block_idx,
+                                            int32_t op_idx) const;
+
+  bool OpHasInput(int64_t block_id, int64_t op_id,
+                  const std::string& name) const;
+  bool OpHasOutput(int64_t block_id, int64_t op_id,
+                   const std::string& name) const;
+
+  std::vector<TensorInfo> GetOpInput(int64_t block_id, int64_t op_id,
+                                     const std::string& name) const;
+  std::vector<TensorInfo> GetOpOutput(int64_t block_id, int64_t op_id,
+                                      const std::string& name) const;
+
+  bool OpIsAttrVar(int64_t block_id, int64_t op_id,
+                   const std::string& name) const;
+
+  std::vector<TensorInfo> GetOpAttrVar(int64_t block_id, int64_t op_id,
+                                       const std::string& name) const;
+
+  bool OpHasAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name) const;
+
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, int64_t* res) const;
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, float* res) const;
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, bool* res) const;
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, std::string* res) const;
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, std::vector<int64_t>* res) const;
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, std::vector<float>* res) const;
+  void GetOpAttr(const paddle2onnx::framework::proto::OpDesc& op,
+                 const std::string& name, std::vector<double>* res) const;
+
+  bool IsConstantTensor(const int64_t& block_idx,
+                        const std::string& tensor_name) const;
+  template <typename T>
+  bool TryGetTensorValue(const int64_t& block_id,
+                         const std::string& tensor_name,
+                         std::vector<T>* data) const;
+
+ private:
+  // If the model has same output name in difference operators
+  // will fail to convert
+  bool IsAttrVar(const paddle2onnx::framework::proto::OpDesc& op,
+                 const int64_t& attr_id) const;
+  bool ExistsDumplicateTensorName() const;
+  void GetBlocksVarName2Id();
+  void GetBlocksOps();
+  TensorInfo GetTensorInfo(
+      const std::string& name,
+      const paddle2onnx::framework::proto::BlockDesc& block) const;
+  void GetGlobalBlockInputOutputInfo();
+  bool GetParamNames(std::vector<std::string>* var_names);
+  bool LoadProgram(const std::string& model);
+  bool LoadProgram(const void* model_buffer, int64_t model_size);
+  bool LoadParams(const std::string& path);
+  bool LoadParamsFromMemoryBuffer(const std::string& buffer);
+  bool LoadParamsFromMemoryBuffer(const void* params_buffer,
+                                  int64_t params_size);
+  // This is a trick flag
+  // While there's a nms operator in paddle model,
+  // the shape inference of paddle is not correct
+  bool _has_nms = false;
+  std::vector<std::map<std::string, int64_t>> _constant_ops;
+};
+
+template <typename T>
+bool PaddleParser::TryGetTensorValue(const int64_t& block_id,
+                                     const std::string& tensor_name,
+                                     std::vector<T>* data) const {
+  {
+    auto iter = params.find(tensor_name);
+    if (iter != params.end()) {
+      (iter->second).get(data);
+      return true;
+    }
+  }
+  Assert(block_id < _constant_ops.size(),
+         "block_id is out of range while calling TryGetTensorValue.");
+  auto iter = _constant_ops[block_id].find(tensor_name);
+  if (iter == _constant_ops[block_id].end()) {
+    return false;
+  }
+  Assert(iter->second < _blocks_ops[block_id].size(),
+         "op_idx is out of range while calling TryGetTensorValue.");
+  auto op = _blocks_ops[block_id][iter->second];
+  int64_t dtype;
+  GetOpAttr(*op, "dtype", &dtype);
+  if (dtype == P2ODataType::INT64) {
+    std::vector<int64_t> value;
+    GetOpAttr(*op, "int64_values", &value);
+    data->assign(value.begin(), value.end());
+  } else if (dtype == P2ODataType::INT32) {
+    std::vector<int64_t> value;
+    GetOpAttr(*op, "int32_values", &value);
+    data->assign(value.begin(), value.end());
+  } else if (dtype == P2ODataType::FP32) {
+    std::vector<float> value;
+    GetOpAttr(*op, "fp32_values", &value);
+    data->assign(value.begin(), value.end());
+  } else {
+    Assert(
+        false,
+        "Only support int32/int64/float32 data type in assign_value operator.");
+  }
+  return true;
+}
+
+}  // namespace paddle2onnx
diff --git a/paddle2onnx/proto/CMakeLists.txt b/paddle2onnx/proto/CMakeLists.txt
new file mode 100644
index 0000000000..41c2ba81e8
--- /dev/null
+++ b/paddle2onnx/proto/CMakeLists.txt
@@ -0,0 +1,11 @@
+if(NOT TARGET protobuf)
+    include(FindProtobuf)
+    find_package(Protobuf REQUIRED)
+    set(Protobuf_USE_STATIC_LIBS ON)
+    include_directories(${PROTOBUF_INCLUDE_DIR})
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR})
+    set(PROTOBUF_LIBRARIES ${PROTOBUF_LIBRARIES} CACHE INTERNAL "" FORCE)
+endif()
+
+PROTOBUF_GENERATE_CPP(PROTO_SRC PROTO_HEADER p2o_paddle.proto)
+add_library(p2o_paddle_proto ${PROTO_HEADER} ${PROTO_SRC})
diff --git a/paddle2onnx/proto/p2o_paddle.proto b/paddle2onnx/proto/p2o_paddle.proto
new file mode 100644
index 0000000000..6398981f63
--- /dev/null
+++ b/paddle2onnx/proto/p2o_paddle.proto
@@ -0,0 +1,249 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+syntax = "proto2";
+package paddle2onnx.framework.proto;
+
+// Any incompatible changes to ProgramDesc and its dependencies should
+// raise the version defined version.h.
+//
+// Serailization and Deserialization codes should be modified in a way
+// that supports old versions following the version and compatibility policy.
+message Version {
+  optional int64 version = 1 [default = 0];
+}
+
+enum AttrType {
+  INT = 0;
+  FLOAT = 1;
+  STRING = 2;
+  INTS = 3;
+  FLOATS = 4;
+  STRINGS = 5;
+  BOOLEAN = 6;
+  BOOLEANS = 7;
+  BLOCK = 8;
+  LONG = 9;
+  BLOCKS = 10;
+  LONGS = 11;
+  FLOAT64S = 12;
+  VAR = 13;
+  VARS = 14;
+}
+
+message ProcessMeshDesc {
+  required int32 id = 1;
+  required int32 parent_id = 2;
+  repeated int32 topology = 3;
+  repeated int32 process_group = 4;
+};
+
+// OpDesc describes an instance of a C++ framework::OperatorBase
+// derived class type.
+message OpDesc {
+  message Attr {
+    required string name = 1;
+    required AttrType type = 2;
+    optional int32 i = 3;
+    optional float f = 4;
+    optional string s = 5;
+    repeated int32 ints = 6;
+    repeated float floats = 7;
+    repeated string strings = 8;
+    optional bool b = 10;
+    repeated bool bools = 11;
+    optional int32 block_idx = 12;
+    optional int64 l = 13;
+    repeated int32 blocks_idx = 14;
+    repeated int64 longs = 15;
+    repeated double float64s = 16;
+    optional string var_name = 17;
+    repeated string vars_name = 18;
+  };
+
+  message Var {
+    required string parameter = 1;
+    repeated string arguments = 2;
+  };
+
+  required string type = 3;
+  repeated Var inputs = 1;
+  repeated Var outputs = 2;
+  repeated Attr attrs = 4;
+  optional bool is_target = 5 [default = false];
+};
+
+// OpProto describes a C++ framework::OperatorBase derived class.
+message OpProto {
+  // VarProto describes the C++ type framework::Variable.
+  message Var {
+    required string name = 1;
+    required string comment = 2;
+
+    optional bool duplicable = 3 [default = false];
+    optional bool intermediate = 4 [default = false];
+    optional bool dispensable = 5 [default = false];
+    optional bool extra = 6 [default = false];
+    optional bool quant = 7 [default = false];
+  }
+
+  // AttrProto describes the C++ type Attribute.
+  message Attr {
+    required string name = 1;
+    required AttrType type = 2;
+    required string comment = 3;
+    // If that attribute is generated, it means the Paddle third
+    // language binding has responsibility to fill that
+    // attribute. End-User should not set that attribute.
+    optional bool generated = 4 [default = false];
+    optional bool extra = 5 [default = false];
+    optional bool quant = 6 [default = false];
+    optional bool support_tensor = 7 [default = false];
+  }
+
+  required string type = 1;
+  repeated Var inputs = 2;
+  repeated Var outputs = 3;
+  repeated Attr attrs = 4;
+  required string comment = 5;
+}
+
+message VarType {
+  enum Type {
+    // Pod Types
+    BOOL = 0;
+    INT16 = 1;
+    INT32 = 2;
+    INT64 = 3;
+    FP16 = 4;
+    FP32 = 5;
+    FP64 = 6;
+    // Tensor<size_t> is used in C++.
+    SIZE_T = 19;
+    UINT8 = 20;
+    INT8 = 21;
+    BF16 = 22;
+    COMPLEX64 = 23;
+    COMPLEX128 = 24;
+
+    // Other types that may need additional descriptions
+    LOD_TENSOR = 7;
+    SELECTED_ROWS = 8;
+    FEED_MINIBATCH = 9;
+    FETCH_LIST = 10;
+    STEP_SCOPES = 11;
+    LOD_RANK_TABLE = 12;
+    LOD_TENSOR_ARRAY = 13;
+    PLACE_LIST = 14;
+    READER = 15;
+    // Any runtime decided variable type is raw
+    // raw variables should manage their own allocations
+    // in operators like nccl_op
+    RAW = 17;
+    TUPLE = 18;
+
+    STRING = 25;
+    STRINGS = 26;
+    VOCAB = 27;
+    FEED_LIST = 28;
+  }
+
+  required Type type = 1;
+
+  message TensorDesc {
+    // Should only be PODType. Is enforced in C++
+    required Type data_type = 1;
+    repeated int64 dims = 2;  // [UNK, 640, 480] is saved as [-1, 640, 480]
+  }
+  optional TensorDesc selected_rows = 2;
+
+  message LoDTensorDesc {
+    required TensorDesc tensor = 1;
+    optional int32 lod_level = 2 [default = 0];
+  }
+  optional LoDTensorDesc lod_tensor = 3;
+
+  message LoDTensorArrayDesc {
+    required TensorDesc tensor = 1;
+    optional int32 lod_level = 2 [default = 0];
+  }
+  optional LoDTensorArrayDesc tensor_array = 4;
+
+  message ReaderDesc {
+    repeated LoDTensorDesc lod_tensor = 1;
+  }
+  optional ReaderDesc reader = 5;
+
+  message Tuple {
+    repeated Type element_type = 1;
+  }
+  optional Tuple tuple = 7;
+
+  optional TensorDesc string = 8;
+  optional TensorDesc strings = 9;
+  optional TensorDesc vocab = 10;
+}
+
+message VarDesc {
+  message Attr {
+    required string name = 1;
+    required AttrType type = 2;
+    optional int32 i = 3;
+    optional string s = 4;
+    repeated int32 ints = 5;
+  };
+
+  required string name = 1;
+  required VarType type = 2;
+  optional bool persistable = 3 [default = false];
+  // True if the variable is an input data and
+  // have to check the feed data shape and dtype
+  optional bool need_check_feed = 4 [default = false];
+  optional bool is_parameter = 5 [default = false];
+  optional bool stop_gradient = 6 [default = false];
+  repeated Attr attrs = 7;
+}
+
+message BlockDesc {
+  required int32 idx = 1;
+  required int32 parent_idx = 2;
+  repeated VarDesc vars = 3;
+  repeated OpDesc ops = 4;
+  optional int32 forward_block_idx = 5 [default = -1];
+}
+
+// In some cases, Paddle may perform operator definition iterations,
+// and the operator uses OpVersionMap for compatibility testing.
+message OpVersion {
+  required int32 version = 1;
+}
+message OpVersionMap {
+  message OpVersionPair {
+    required string op_name = 1;
+    required OpVersion op_version = 2;
+  }
+  repeated OpVersionPair pair = 1;
+}
+
+// Please refer to
+// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md
+// for more details.
+// TODO(panyx0718): A model can have multiple programs. Need a
+// way to distinguish them. Maybe ID or name?
+message ProgramDesc {
+  reserved 2, 3;  // For backward compatibility.
+  repeated BlockDesc blocks = 1;
+  optional Version version = 4;
+  optional OpVersionMap op_version_map = 5;
+}
diff --git a/paddle2onnx/utils.py b/paddle2onnx/utils.py
new file mode 100644
index 0000000000..a65ca483b7
--- /dev/null
+++ b/paddle2onnx/utils.py
@@ -0,0 +1,131 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import importlib
+import collections
+import time
+import os
+import sys
+
+
+def try_import(module_name):
+    """Try importing a module, with an informative error message on failure."""
+    install_name = module_name
+    try:
+        mod = importlib.import_module(module_name)
+        return mod
+    except ImportError:
+        err_msg = (
+            "Failed importing {}. This likely means that some modules "
+            "requires additional dependencies that have to be "
+            "manually installed (usually with `pip install {}`). ").format(
+                module_name, install_name)
+        raise ImportError(err_msg)
+
+
+def check_model(onnx_model):
+    onnx = try_import('onnx')
+    try:
+        onnx.checker.check_model(onnx_model)
+    except Exception:
+        raise Exception('ONNX model is not valid.')
+    finally:
+        logging.info('ONNX model generated is valid.')
+
+
+levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
+
+
+class logging():
+    log_level = 2
+
+    @staticmethod
+    def log(level=2, message="", use_color=False):
+        current_time = time.time()
+        time_array = time.localtime(current_time)
+        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
+        if logging.log_level >= level:
+            if use_color:
+                print("\033[1;31;40m{} [{}]\t{}\033[0m".format(
+                    current_time, levels[level], message).encode("utf-8")
+                      .decode("latin1"))
+            else:
+                print("{} [{}]\t{}".format(current_time, levels[level], message)
+                      .encode("utf-8").decode("latin1"))
+            sys.stdout.flush()
+
+    @staticmethod
+    def debug(message="", use_color=False):
+        logging.log(level=3, message=message, use_color=use_color)
+
+    @staticmethod
+    def info(message="", use_color=False):
+        logging.log(level=2, message=message, use_color=use_color)
+
+    @staticmethod
+    def warning(message="", use_color=True):
+        logging.log(level=1, message=message, use_color=use_color)
+
+    @staticmethod
+    def error(message="", use_color=True, exit=True):
+        logging.log(level=0, message=message, use_color=use_color)
+        if exit:
+            sys.exit(-1)
+
+
+def compare_value(a, b, cond):
+    if cond == 'equal':
+        if a != b:
+            return False
+        return True
+    if cond == 'greater_than':
+        if a <= b:
+            return False
+        return True
+    if cond == 'greater_equal':
+        if a < b:
+            return False
+        return True
+    if cond == 'less_equal':
+        if a > b:
+            return False
+        return True
+    if cond == 'less_than':
+        if a >= b:
+            return False
+        return True
+
+
+def compare_attr(actual_value, target_value, attr_name, cond='equal'):
+    if not compare_value(actual_value, target_value, cond):
+        raise ValueError('Support {} {} {}, actually got {}=={}.'.format(
+            attr_name, cond, target_value, attr_name, actual_value))
+
+
+def compare_attr_between_dims(attr, dims, attr_name, cond='equal'):
+    if not compare_value(attr[dims[0]], attr[dims[1]], cond):
+        expect_info = 'Support {}[{}] {} {}[{}], '.format(
+            attr_name, dims[0], cond, attr_name, dims[1])
+        actual_info = 'actually got {}[{}]=={}, not {} {}[{}]=={}.'.format(
+            attr_name, dims[0], attr[dims[0]], cond, attr_name, dims[1],
+            attr[dims[1]])
+        raise ValueError(expect_info + actual_info)
+
+
+def require_fixed_shape(op_name=None):
+    logging.error(
+        "[{}]Fixed shape is required, refer this doc for more information: https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/FAQ.md".
+        format(op_name))
diff --git a/paddle2onnx/utils/utils.h b/paddle2onnx/utils/utils.h
new file mode 100644
index 0000000000..463760703b
--- /dev/null
+++ b/paddle2onnx/utils/utils.h
@@ -0,0 +1,80 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <stdlib.h>
+
+#include <iostream>
+#include <sstream>
+#include <string>
+
+namespace paddle2onnx {
+
+inline void Assert(bool condition, const std::string& message) {
+  if (!condition) {
+    fprintf(stderr, "[ERROR] %s\n", message.c_str());
+    std::abort();
+  }
+}
+
+inline const std::string RequireOpset(const int32_t& opset_version) {
+  return "Requires the minimal opset version of " +
+         std::to_string(opset_version) + ".";
+}
+
+class P2OLogger {
+ public:
+  P2OLogger() {
+    line_ = "";
+    prefix_ = "[Paddle2ONNX]";
+    verbose_ = true;
+  }
+  explicit P2OLogger(bool verbose,
+                     const std::string& prefix = "[Paddle2ONNX]") {
+    verbose_ = verbose;
+    line_ = "";
+    prefix_ = prefix;
+  }
+
+  template <typename T>
+  P2OLogger& operator<<(const T& val) {
+    if (!verbose_) {
+      return *this;
+    }
+    std::stringstream ss;
+    ss << val;
+    line_ += ss.str();
+    return *this;
+  }
+  P2OLogger& operator<<(std::ostream& (*os)(std::ostream&)) {
+    if (!verbose_) {
+      return *this;
+    }
+    std::cout << prefix_ << " " << line_ << std::endl;
+    line_ = "";
+    return *this;
+  }
+  ~P2OLogger() {
+    if (!verbose_ && line_ != "") {
+      std::cout << line_ << std::endl;
+    }
+  }
+
+ private:
+  std::string line_;
+  std::string prefix_;
+  bool verbose_ = true;
+};
+
+}  // namespace paddle2onnx
diff --git a/scripts/clean_sdk.sh b/scripts/clean_sdk.sh
new file mode 100644
index 0000000000..bb90b7a1e9
--- /dev/null
+++ b/scripts/clean_sdk.sh
@@ -0,0 +1,7 @@
+origin_install_dir=$1
+
+rm -rf $origin_install_dir/include/onnx $origin_install_dir/include/paddle2onnx
+mv $origin_install_dir/lib $origin_install_dir/lib_bak
+mkdir $origin_install_dir/lib
+cp $origin_install_dir/lib_bak/*fastdeploy* $origin_install_dir/lib
+rm -rf $origin_install_dir/lib_bak
diff --git a/third_party/onnx/.azure-pipelines/Linux-CI.yml b/third_party/onnx/.azure-pipelines/Linux-CI.yml
new file mode 100644
index 0000000000..dd200ba335
--- /dev/null
+++ b/third_party/onnx/.azure-pipelines/Linux-CI.yml
@@ -0,0 +1,140 @@
+trigger:
+- main
+
+jobs:
+- job: 'Test'
+  pool:
+    vmImage: 'Ubuntu-18.04'
+  strategy:
+    matrix:
+      py39-ml-debug:
+        python.version: '3.9'
+        onnx_ml: 1
+        onnx_debug: 1
+        onnx_lite: 0
+      py38:
+        python.version: '3.8'
+        onnx_ml: 0
+        onnx_debug: 0
+        onnx_lite: 1
+      py38-ml:
+        python.version: '3.8'
+        onnx_ml: 1
+        onnx_debug: 0
+        onnx_lite: 1
+      py37:
+        python.version: '3.7'
+        onnx_ml: 0
+        onnx_debug: 0
+        onnx_lite: 1
+      py37-ml:
+        python.version: '3.7'
+        onnx_ml: 1
+        onnx_debug: 0
+        onnx_lite: 1
+    maxParallel: 6
+
+  steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: '$(python.version)'
+      addToPath: true
+
+  - script: |
+      python -m pip -q install virtualenv
+      python -m virtualenv py$(python.version)
+      source py$(python.version)/bin/activate
+
+      source workflow_scripts/protobuf/build_protobuf_unix.sh $(nproc)
+
+      python -m pip install -q --upgrade pip
+      python -m pip install -q -r requirements-release.txt
+
+      sudo apt-get install -qq -o=Dpkg::Use-Pty=0 -y --no-install-recommends dos2unix
+      git submodule update --init --recursive
+      export ONNX_BUILD_TESTS=1
+      if [ '$(onnx_debug)' == '1' ]; then
+        export DEBUG=1
+      fi
+      if [ '$(onnx_ml)' == '1' ]; then
+        export ONNX_ML=1
+      fi
+      export CMAKE_ARGS="-DONNXIFI_DUMMY_BACKEND=ON -DONNX_WERROR=ON"
+      if [ '$(onnx_lite)' == '1' ]; then
+        export CMAKE_ARGS="${CMAKE_ARGS} -DONNX_USE_LITE_PROTO=ON"
+      fi
+      export ONNX_NAMESPACE=ONNX_NAMESPACE_FOO_BAR_FOR_CI
+      python setup.py --quiet install
+    displayName: 'Install ONNX and dependencies'
+
+  - script: |
+      source py$(python.version)/bin/activate
+
+      # style check (flake8 and mypy)
+      pip install .[lint]
+      bash tools/style.sh
+      if [ $? -ne 0 ]; then
+        echo "style check failed"
+        exit 1
+      fi
+
+      # check line endings to be UNIX
+      find . -type f -regextype posix-extended -regex '.*\.(py|cpp|md|h|cc|proto|proto3|in)' | xargs dos2unix --quiet
+      git status
+      git diff --exit-code
+
+      # check auto-gen files up-to-date
+      python onnx/defs/gen_doc.py
+      python onnx/gen_proto.py -l
+      python onnx/gen_proto.py -l --ml
+      python onnx/backend/test/stat_coverage.py
+
+      git status
+      git diff --exit-code -- . ':(exclude)onnx/onnx-data.proto' ':(exclude)onnx/onnx-data.proto3'
+      if [ $? -ne 0 ]; then
+        echo "git diff returned failures"
+        exit 1
+      fi
+
+      # Do not hardcode onnx's namespace in the c++ source code, so that
+      # other libraries who statically link with onnx can hide onnx symbols
+      # in a private namespace.
+      ! grep -R --include='*.cc' --include='*.h' 'namespace onnx' .
+      ! grep -R --include='*.cc' --include='*.h' 'onnx::' .
+
+      pytest
+      if [ $? -ne 0 ]; then
+        echo "pytest failed"
+        exit 1
+      fi
+
+      # onnx c++ API tests
+      export LD_LIBRARY_PATH="./.setuptools-cmake-build/:$LD_LIBRARY_PATH"
+      ./.setuptools-cmake-build/onnx_gtests
+      if [ $? -ne 0 ]; then
+        echo "onnx_gtests failed"
+        exit 1
+      fi
+
+      ./.setuptools-cmake-build/onnxifi_test_driver_gtests onnx/backend/test/data/node
+      if [ $? -ne 0 ]; then
+        echo "onnxifi_test_driver_gtests failed"
+        exit 1
+      fi
+
+      # Mypy only works with our generated _pb.py files when we install in develop mode, so let's do that
+      python -m pip uninstall -y onnx
+      rm -rf .setuptools-cmake-build
+      python -m pip install -q .
+
+    displayName: 'Run ONNX tests'
+
+  - script: |
+      source py$(python.version)/bin/activate
+      # onnx.checker all existing backend data
+      python workflow_scripts/test_generated_backend.py
+      # onnx.checker all generated backend data
+      python onnx/backend/test/cmd_tools.py generate-data
+      python workflow_scripts/test_generated_backend.py
+
+    displayName: Test backend test data
diff --git a/third_party/onnx/.azure-pipelines/MacOS-CI.yml b/third_party/onnx/.azure-pipelines/MacOS-CI.yml
new file mode 100644
index 0000000000..e5ec5e930d
--- /dev/null
+++ b/third_party/onnx/.azure-pipelines/MacOS-CI.yml
@@ -0,0 +1,105 @@
+trigger:
+- main
+
+jobs:
+- job: 'Test'
+  pool:
+    vmImage: 'macOS-10.15'
+  strategy:
+    matrix:
+      py39:
+        python.version: '3.9'
+        onnx_ml: 0
+        onnx_debug: 0
+        onnx_lite: 0
+      py38:
+        python.version: '3.8'
+        onnx_ml: 0
+        onnx_debug: 0
+        onnx_lite: 1
+      py38-ml:
+        python.version: '3.8'
+        onnx_ml: 1
+        onnx_debug: 0
+        onnx_lite: 1
+      py38-ml-debug:
+        python.version: '3.8'
+        onnx_ml: 1
+        onnx_debug: 1
+        onnx_lite: 0
+      py37-ml-debug:
+        python.version: '3.7'
+        onnx_ml: 1
+        onnx_debug: 1
+        onnx_lite: 0
+    maxParallel: 6
+
+  steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: '$(python.version)'
+
+  - script: |
+      # Install protobuf 3.16.0 from source
+      export NUM_CORES=`sysctl -n hw.logicalcpu`
+      source workflow_scripts/protobuf/build_protobuf_unix.sh $NUM_CORES $(pwd)/protobuf/protobuf_install
+
+      git submodule update --init --recursive
+      python -m pip install -q --upgrade pip
+      python -m pip install -q -r requirements-release.txt
+      export ONNX_BUILD_TESTS=1
+      if [ '$(onnx_debug)' == '1' ]; then
+        export DEBUG=1
+      fi
+      if [ '$(onnx_ml)' == '1' ]; then
+        export ONNX_ML=1
+      fi
+      export CMAKE_ARGS="-DONNXIFI_DUMMY_BACKEND=ON -DONNX_WERROR=ON"
+      if [ '$(onnx_lite)' == '1' ]; then
+        export CMAKE_ARGS="${CMAKE_ARGS} -DONNX_USE_LITE_PROTO=ON"
+      fi
+      export ONNX_NAMESPACE=ONNX_NAMESPACE_FOO_BAR_FOR_CI
+      python setup.py -q install
+    displayName: 'Install dependencies and ONNX'
+
+  - script: |
+      # check auto-gen files up-to-date
+      python onnx/defs/gen_doc.py
+      python onnx/gen_proto.py -l
+      python onnx/gen_proto.py -l --ml
+      python onnx/backend/test/stat_coverage.py
+
+      git status
+      git diff --exit-code  -- . ':(exclude)onnx/onnx-data.proto' ':(exclude)onnx/onnx-data.proto3'
+      if [ $? -ne 0 ]; then
+        echo "git diff returned failures"
+        exit 1
+      fi
+
+      # Do not hardcode onnx's namespace in the c++ source code, so that
+      # other libraries who statically link with onnx can hide onnx symbols
+      # in a private namespace.
+      ! grep -R --include='*.cc' --include='*.h' 'namespace onnx' .
+      ! grep -R --include='*.cc' --include='*.h' 'onnx::' .
+
+      pytest
+      if [ $? -ne 0 ]; then
+        echo "pytest failed"
+        exit 1
+      fi
+
+      # onnx c++ API tests
+      export LD_LIBRARY_PATH="./.setuptools-cmake-build/:$LD_LIBRARY_PATH"
+      ./.setuptools-cmake-build/onnx_gtests
+      if [ $? -ne 0 ]; then
+        echo "onnx_gtests failed"
+        exit 1
+      fi
+
+      ./.setuptools-cmake-build/onnxifi_test_driver_gtests onnx/backend/test/data/node
+      if [ $? -ne 0 ]; then
+        echo "onnxifi_test_driver_gtests failed"
+        exit 1
+      fi
+
+    displayName: 'Run ONNX Tests'
diff --git a/third_party/onnx/.azure-pipelines/Windows-CI.yml b/third_party/onnx/.azure-pipelines/Windows-CI.yml
new file mode 100644
index 0000000000..38a2e6f176
--- /dev/null
+++ b/third_party/onnx/.azure-pipelines/Windows-CI.yml
@@ -0,0 +1,77 @@
+trigger:
+- main
+
+jobs:
+
+- job: 'Test'
+  pool:
+    vmImage: 'windows-2019'
+  strategy:
+    matrix:
+      py39:
+        python.version: '3.9'
+        onnx_ml: 0
+        onnx_verify_proto: 0
+      py39_verify_proto:
+        python.version: '3.9'
+        onnx_ml: 0
+        onnx_verify_proto: 1
+      py38:
+        python.version: '3.8'
+        onnx_ml: 0
+        onnx_verify_proto: 0
+      py37:
+        python.version: '3.7'
+        onnx_ml: 0
+        onnx_verify_proto: 0
+      py37_onnx_ml:
+        python.version: '3.7'
+        onnx_ml: 1
+        onnx_verify_proto: 0
+    maxParallel: 4
+
+  steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: '$(python.version)'
+      architecture: 'x64'
+
+  - powershell: Write-Host "##vso[task.prependpath]$env:CONDA\Scripts"
+    displayName: Add conda to PATH
+
+  - script: |
+      conda create --yes --quiet --name py$(python.version) python=$(python.version)
+      conda install -n py$(python.version) -y -c conda-forge numpy libprotobuf=3.16.0
+    displayName: Create Anaconda environment
+
+  - script: |
+      call activate py$(python.version)
+      python -m pip install -q --upgrade pip
+      python -m pip install -q -r requirements-release.txt
+
+      git submodule update --init --recursive
+      set ONNX_BUILD_TESTS=1
+      set ONNX_ML=$(onnx_ml)
+      set CMAKE_ARGS=-DONNX_USE_PROTOBUF_SHARED_LIBS=ON -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON
+
+      python setup.py -q install
+      pytest
+      IF NOT %ERRORLEVEL% EQU 0 (
+        @echo "pytest failed"
+        EXIT 1
+      )
+
+      python onnx/defs/gen_doc.py
+      python onnx/gen_proto.py -l
+      python onnx/gen_proto.py -l --ml
+      
+      git diff --exit-code  -- . :(exclude)onnx/onnx-data.proto :(exclude)onnx/onnx-data.proto3
+      IF NOT %ERRORLEVEL% EQU 0 (
+        @echo "git diff returned failures"
+        EXIT 1
+      )
+
+      rm -rf .setuptools-cmake-build
+
+
+    displayName: Install and test ONNX
diff --git a/third_party/onnx/.clang-format b/third_party/onnx/.clang-format
new file mode 100644
index 0000000000..f5f354e5fd
--- /dev/null
+++ b/third_party/onnx/.clang-format
@@ -0,0 +1,101 @@
+---
+Language:        Cpp
+AccessModifierOffset: -1
+AlignAfterOpenBracket: AlwaysBreak
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: Left
+AlignOperands:   false
+AlignTrailingComments: false
+AllowAllParametersOfDeclarationOnNextLine: false
+AllowShortBlocksOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine: false
+AllowShortFunctionsOnASingleLine: Empty
+AllowShortIfStatementsOnASingleLine: false
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterDefinitionReturnType: None
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: false
+BinPackParameters: false
+BraceWrapping:
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Attach
+BreakBeforeInheritanceComma: false
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializersBeforeComma: false
+BreakConstructorInitializers: BeforeColon
+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: false
+ColumnLimit:     120
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - FOR_EACH_RANGE
+  - FOR_EACH
+  - BOOST_FOREACH
+IncludeCategories:
+  - Regex:           '^<.*\.h(pp)?>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        2
+  - Regex:           '.*'
+    Priority:        3
+IncludeIsMainRegex: '(Test)?$'
+IndentCaseLabels: true
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: false
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Left
+ReflowComments:  true
+SortIncludes:    true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: true
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles:  false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        8
+UseTab:          Never
\ No newline at end of file
diff --git a/third_party/onnx/.gitattributes b/third_party/onnx/.gitattributes
new file mode 100644
index 0000000000..46239f68ad
--- /dev/null
+++ b/third_party/onnx/.gitattributes
@@ -0,0 +1 @@
+*.pb binary linguist-detectable=false
diff --git a/third_party/onnx/.gitignore b/third_party/onnx/.gitignore
new file mode 100644
index 0000000000..cf345e7055
--- /dev/null
+++ b/third_party/onnx/.gitignore
@@ -0,0 +1,104 @@
+## General
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.cuo
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.pyd
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+
+# Compiled protocol buffers
+*.pb.h
+*.pb.cc
+onnx/*_pb2.py
+onnx/*_pb.py
+onnx/*_pb2.pyi
+onnx/*_pb.pyi
+
+# Compiled python
+*.pyc
+
+# Compiled MATLAB
+*.mex*
+
+# IPython notebook checkpoints
+.ipynb_checkpoints
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# Sublime Text settings
+*.sublime-workspace
+*.sublime-project
+
+# Eclipse Project settings
+*.*project
+.settings
+
+# QtCreator files
+*.user
+
+# PyCharm files
+.idea
+
+# Visual Studio Code files
+.vscode
+
+# OSX dir files
+.DS_Store
+
+## ONNX
+
+# build, distribute, and bins (+ python proto bindings)
+build
+build_*
+.build_debug/*
+.build_release/*
+.setuptools-cmake-build/*
+
+# setup.py intermediates
+.eggs
+dist
+onnx.egg-info
+*.ninja
+.ninja_deps
+.ninja_log
+compile_commands.json
+
+# generated files
+onnx/version.py
+compile_commands.json
+tools/protoc-gen-mypy.sh
+
+# test generated files
+.cache
+.coverage
+onnx/examples/.coverage.nbval
+.pytest_cache
+test_report
+
+# autocomplete
+.ycm_extra_conf.py
+
+# test coverage data files
+*.gcov
+
+.mypy_cache
+virtualenv
+venv
+
+# direnv, posh-direnv
+.envrc
+.psenvrc
diff --git a/third_party/onnx/.gitmodules b/third_party/onnx/.gitmodules
new file mode 100644
index 0000000000..2b647e1a87
--- /dev/null
+++ b/third_party/onnx/.gitmodules
@@ -0,0 +1,7 @@
+[submodule "third_party/pybind11"]
+	path = third_party/pybind11
+	url = https://github.com/pybind/pybind11.git
+	branch = master
+[submodule "third_party/benchmark"]
+	path = third_party/benchmark
+	url = https://github.com/google/benchmark.git
diff --git a/third_party/onnx/.lgtm.yml b/third_party/onnx/.lgtm.yml
new file mode 100644
index 0000000000..fa1cabd919
--- /dev/null
+++ b/third_party/onnx/.lgtm.yml
@@ -0,0 +1,12 @@
+# By default, C/C++ extraction from LGTM.com uses Python 2
+# Add this file to make it use Python 3
+# Build protobuf from source with -fPIC is required here to prevent protobuf symbol error
+extraction:
+  cpp:
+    before_index:
+      # Always use Python 3
+      - cp `which python3` $LGTM_WORKSPACE/python
+      - PATH=$LGTM_WORKSPACE:$PATH
+      # Build protobuf from source with -fPIC
+      - mkdir protobuf && cd protobuf
+      - source ../workflow_scripts/protobuf/build_protobuf_unix.sh $(nproc) $(pwd)/protobuf_install
diff --git a/third_party/onnx/CMakeLists.txt b/third_party/onnx/CMakeLists.txt
new file mode 100644
index 0000000000..f914e78b1c
--- /dev/null
+++ b/third_party/onnx/CMakeLists.txt
@@ -0,0 +1,714 @@
+# Minimum CMake required
+cmake_minimum_required(VERSION 3.1)
+include(cmake/Utils.cmake)
+# Set default build type
+if(NOT CMAKE_BUILD_TYPE)
+  message(STATUS "Build type not set - defaulting to Release")
+  set(
+    CMAKE_BUILD_TYPE "Release"
+    CACHE
+      STRING
+      "Choose the type of build from: Debug Release RelWithDebInfo MinSizeRel Coverage."
+    FORCE)
+endif()
+cmake_policy(SET CMP0063 NEW)
+
+# Project
+project(onnx C CXX)
+option(ONNX_BUILD_BENCHMARKS "Build ONNX micro-benchmarks" OFF)
+option(ONNX_USE_PROTOBUF_SHARED_LIBS "Build ONNX using protobuf shared library. Sets PROTOBUF_USE_DLLS CMAKE Flag and Protobuf_USE_STATIC_LIBS. " OFF)
+
+option(BUILD_ONNX_PYTHON "Build Python binaries" OFF)
+option(ONNX_GEN_PB_TYPE_STUBS "Generate protobuf python type stubs" ON)
+option(ONNX_WERROR "Build with Werror" OFF)
+option(ONNX_COVERAGE "Build with coverage instrumentation" OFF)
+option(ONNX_BUILD_TESTS "Build ONNX C++ APIs Tests" OFF)
+option(ONNX_USE_LITE_PROTO "Use lite protobuf instead of full." OFF)
+option(ONNXIFI_ENABLE_EXT "Enable onnxifi extensions." OFF)
+option(ONNX_DISABLE_EXCEPTIONS "Disable exception handling." OFF)
+option(ONNX_DISABLE_STATIC_REGISTRATION "Disable static registration for onnx operator schemas." OFF)
+
+if(NOT DEFINED ONNX_ML)
+  if(DEFINED ENV{ONNX_ML})
+    set(DEFAULT_ONNX_ML $ENV{ONNX_ML})
+  else()
+    set(DEFAULT_ONNX_ML ON)
+  endif()
+  option(ONNX_ML "Enable traditional ML API." ${DEFAULT_ONNX_ML})
+endif()
+
+option(ONNXIFI_DUMMY_BACKEND "Use dummy backend in onnxifi test driver." OFF)
+
+if(NOT DEFINED ONNX_VERIFY_PROTO3)
+  if(DEFINED ENV{ONNX_VERIFY_PROTO3})
+    set(PROTO3_ENABLED $ENV{ONNX_VERIFY_PROTO3})
+  else()
+    set(PROTO3_ENABLED OFF)
+  endif()
+  option(ONNX_VERIFY_PROTO3 "Generate code by proto3" ${PROTO3_ENABLED})
+endif()
+
+if(ONNX_USE_PROTOBUF_SHARED_LIBS)
+  if(MSVC)
+    #TODO: if ONNX_USE_MSVC_STATIC_RUNTIME is ON, it may not work
+    add_definitions(-DPROTOBUF_USE_DLLS)
+  endif()
+  set(Protobuf_USE_STATIC_LIBS OFF)
+else()
+  set(Protobuf_USE_STATIC_LIBS ON)
+endif()
+
+# Set C++11 as standard for the whole project
+if(NOT MSVC)
+  set(CMAKE_CXX_STANDARD 11)
+endif(NOT MSVC)
+
+set(ONNX_ROOT ${PROJECT_SOURCE_DIR})
+
+# Read ONNX version
+file(READ "${PROJECT_SOURCE_DIR}/VERSION_NUMBER" ONNX_VERSION)
+string(STRIP "${ONNX_VERSION}" ONNX_VERSION)
+
+if(NOT MSVC)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor")
+  set(CMAKE_C_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0")
+  set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0")
+  if(ONNX_COVERAGE)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
+  endif()
+endif()
+
+if(NOT ONNX_NAMESPACE)
+  set(ONNX_NAMESPACE "onnx")
+endif()
+
+if(MSVC)
+  if(NOT ONNX_DISABLE_EXCEPTIONS)
+    string(APPEND CMAKE_CXX_FLAGS " /EHsc /wd26812")
+    string(APPEND CMAKE_C_FLAGS " /EHsc /wd26812")
+  endif()
+endif()
+
+if(ONNX_DISABLE_EXCEPTIONS)
+  add_compile_definitions("ONNX_NO_EXCEPTIONS")
+  # Disable C++ exceptions.
+  if(MSVC)
+    string(REGEX REPLACE "/EHsc" "/EHs-c-" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+    add_definitions(-D_HAS_EXCEPTIONS=0)
+  else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables")
+  endif()
+endif()
+
+if(ONNX_WERROR)
+  if(MSVC)
+    string(APPEND CMAKE_CXX_FLAGS " /WX")
+    string(APPEND CMAKE_C_FLAGS " /WX")
+  else()
+    string(APPEND CMAKE_CXX_FLAGS " -Werror")
+    string(APPEND CMAKE_C_FLAGS " -Werror -Wno-error=incompatible-pointer-types")
+  endif()
+endif()
+
+# find_package Python has replaced PythonInterp and PythonLibs since cmake 3.12
+# Use the following command in the future; now this is only compatible with the latest pybind11
+# find_package(Python ${PY_VERSION} COMPONENTS Interpreter Development REQUIRED)
+find_package(PythonInterp ${PY_VERSION} REQUIRED)
+find_package(PythonLibs ${PY_VERSION})
+
+if(CMAKE_SYSTEM_NAME STREQUAL "AIX")
+  set(CMAKE_NO_SYSTEM_FROM_IMPORTED 1)
+endif()
+
+if(ONNX_BUILD_TESTS)
+  list(APPEND CMAKE_MODULE_PATH ${ONNX_ROOT}/cmake/external)
+  include(googletest)
+endif()
+
+if((ONNX_USE_LITE_PROTO AND TARGET protobuf::libprotobuf-lite) OR ((NOT ONNX_USE_LITE_PROTO) AND TARGET protobuf::libprotobuf))
+  # Sometimes we need to use protoc compiled for host architecture while linking
+  # libprotobuf against target architecture. See https://github.com/caffe2/caffe
+  # 2/blob/96f35ad75480b25c1a23d6e9e97bccae9f7a7f9c/cmake/ProtoBuf.cmake#L92-L99
+  if(EXISTS "${ONNX_CUSTOM_PROTOC_EXECUTABLE}")
+    message(STATUS "Using custom protoc executable")
+    set(ONNX_PROTOC_EXECUTABLE ${ONNX_CUSTOM_PROTOC_EXECUTABLE})
+  else()
+    set(ONNX_PROTOC_EXECUTABLE $<TARGET_FILE:protobuf::protoc>)
+  endif()
+else()
+  # Customized version of find Protobuf. We need to avoid situations mentioned
+  # in https://github.com/caffe2/caffe2/blob/b7d983f255ef5496474f1ea188edb5e0ac4
+  # 42761/cmake/ProtoBuf.cmake#L82-L92 The following section is stolen from
+  # cmake/ProtoBuf.cmake in Caffe2
+  find_program(Protobuf_PROTOC_EXECUTABLE
+               NAMES protoc
+               DOC "The Google Protocol Buffers Compiler")
+
+  # Only if protoc was found, seed the include directories and libraries. We
+  # assume that protoc is installed at PREFIX/bin. We use get_filename_component
+  # to resolve PREFIX.
+  if(Protobuf_PROTOC_EXECUTABLE)
+    set(ONNX_PROTOC_EXECUTABLE ${Protobuf_PROTOC_EXECUTABLE})
+    get_filename_component(_PROTOBUF_INSTALL_PREFIX
+                           ${Protobuf_PROTOC_EXECUTABLE} DIRECTORY)
+    get_filename_component(_PROTOBUF_INSTALL_PREFIX
+                           ${_PROTOBUF_INSTALL_PREFIX}/.. REALPATH)
+    find_library(Protobuf_PROTOC_LIBRARY
+                 NAMES protoc
+                 PATHS ${_PROTOBUF_INSTALL_PREFIX}/lib
+                 NO_DEFAULT_PATH)
+    if(ONNX_USE_LITE_PROTO)
+      find_library(Protobuf_LITE_LIBRARY
+        NAMES protobuf-lite
+        PATHS ${_PROTOBUF_INSTALL_PREFIX}/lib
+        NO_DEFAULT_PATH)
+    else()
+      find_library(Protobuf_LIBRARY
+        NAMES protobuf
+        PATHS ${_PROTOBUF_INSTALL_PREFIX}/lib
+        NO_DEFAULT_PATH)
+    endif(ONNX_USE_LITE_PROTO)
+    find_path(Protobuf_INCLUDE_DIR google/protobuf/service.h
+              PATHS ${_PROTOBUF_INSTALL_PREFIX}/include
+              NO_DEFAULT_PATH)
+    find_package(Protobuf REQUIRED)
+  endif()
+endif()
+
+# Build the libraries with -fPIC
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+
+# function(RELATIVE_PROTOBUF_GENERATE_CPP SRCS HDRS ROOT_DIR) from https://githu
+# b.com/tensorflow/tensorflow/blob/d2c3b873c6f8ff999a2e4ee707a84ff00d9c15a5/tens
+# orflow/contrib/cmake/tf_core_framework.cmake to solve the problem that
+# customized dir can't be specified when calling PROTOBUF_GENERATE_CPP.
+function(RELATIVE_PROTOBUF_GENERATE_CPP NAME SRCS HDRS ROOT_DIR DEPEND)
+  if(NOT ARGN)
+    message(
+      SEND_ERROR
+        "Error: RELATIVE_PROTOBUF_GENERATE_CPP() called without any proto files"
+      )
+    return()
+  endif()
+
+  # Add ONNX_API prefix to protobuf classes and methods in all cases
+  set(ONNX_DLLEXPORT_STR "dllexport_decl=ONNX_API:")
+
+  set(${SRCS})
+  set(${HDRS})
+
+  set(GEN_PROTO_PY ${ROOT_DIR}/onnx/gen_proto.py)
+  foreach(INFILE ${ARGN})
+    set(ABS_FILE ${ROOT_DIR}/${INFILE})
+    get_filename_component(FILE_DIR ${ABS_FILE} DIRECTORY)
+    get_filename_component(FILE_WE ${INFILE} NAME_WE)
+    # "onnx-data" check is because we do not want to create/compile an "onnx-data-ml.proto" file
+    if(ONNX_ML AND NOT(FILE_WE STREQUAL "onnx-data"))
+      if(ONNX_NAMESPACE STREQUAL "onnx")
+        set(GENERATED_FILE_WE "${FILE_WE}-ml")
+      else()
+        set(GENERATED_FILE_WE "${FILE_WE}_${ONNX_NAMESPACE}-ml")
+      endif()
+    else()
+      if(ONNX_NAMESPACE STREQUAL "onnx")
+        set(GENERATED_FILE_WE "${FILE_WE}")
+      else()
+        set(GENERATED_FILE_WE "${FILE_WE}_${ONNX_NAMESPACE}")
+      endif()
+    endif()
+    file(RELATIVE_PATH REL_DIR ${ROOT_DIR} ${FILE_DIR})
+    set(OUTPUT_PROTO_DIR "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}")
+
+    set(OUTPUT_PB_HEADER "${OUTPUT_PROTO_DIR}/${GENERATED_FILE_WE}.pb.h")
+    set(OUTPUT_PB_SRC "${OUTPUT_PROTO_DIR}/${GENERATED_FILE_WE}.pb.cc")
+    set(GENERATED_PROTO "${OUTPUT_PROTO_DIR}/${GENERATED_FILE_WE}.proto")
+    if(NOT (ONNX_NAMESPACE STREQUAL "onnx"))
+      # We need this dummy header generated by gen_proto.py when ONNX_NAMESPACE
+      # is not onnx
+      list(APPEND ${HDRS} "${OUTPUT_PROTO_DIR}/${GENERATED_FILE_WE}.pb.h")
+    endif()
+    list(APPEND ${SRCS} "${OUTPUT_PB_SRC}")
+    list(APPEND ${HDRS} "${OUTPUT_PB_HEADER}")
+
+    if(NOT EXISTS "${OUTPUT_PROTO_DIR}")
+      file(MAKE_DIRECTORY "${OUTPUT_PROTO_DIR}")
+    endif()
+
+    set(GEN_PROTO_ARGS
+        -p
+        "${ONNX_NAMESPACE}"
+        -o
+        "${OUTPUT_PROTO_DIR}"
+        "${FILE_WE}")
+    if(ONNX_ML)
+      list(APPEND GEN_PROTO_ARGS -m)
+    endif()
+    if(ONNX_USE_LITE_PROTO)
+      list(APPEND GEN_PROTO_ARGS -l)
+    endif()
+    if(ONNX_VERIFY_PROTO3)
+        if(NOT ONNX_PROTOC_EXECUTABLE)
+          message(FATAL_ERROR "Protobuf compiler not found")
+        endif()
+        list(APPEND GEN_PROTO_ARGS --protoc_path)
+        list(APPEND GEN_PROTO_ARGS "${ONNX_PROTOC_EXECUTABLE}")
+    endif()
+
+    add_custom_command(OUTPUT "${GENERATED_PROTO}"
+                       COMMAND "${PYTHON_EXECUTABLE}" "${GEN_PROTO_PY}"
+                               ARGS ${GEN_PROTO_ARGS}
+                       DEPENDS ${INFILE}
+                       COMMENT "Running gen_proto.py on ${INFILE}"
+                       VERBATIM)
+    message("Generated: ${GENERATED_PROTO}")
+    set(PROTOC_ARGS
+        ${GENERATED_PROTO}
+        -I
+        ${CMAKE_CURRENT_BINARY_DIR}
+        --cpp_out
+        ${ONNX_DLLEXPORT_STR}${CMAKE_CURRENT_BINARY_DIR})
+    if(BUILD_ONNX_PYTHON)
+      list(APPEND PROTOC_ARGS --python_out
+                  ${CMAKE_CURRENT_BINARY_DIR})
+      if(ONNX_GEN_PB_TYPE_STUBS)
+        # Haven't figured out how to generate mypy stubs on Windows yet
+        if(NOT WIN32)
+          # use PYTHON_EXECUTABLE to python protoc-gen-mypy.py
+          configure_file(
+            ${ROOT_DIR}/tools/protoc-gen-mypy.sh.in
+            ${PROJECT_BINARY_DIR}/tools/protoc-gen-mypy.sh
+            @ONLY)
+          execute_process(COMMAND chmod +x ${PROJECT_BINARY_DIR}/tools/protoc-gen-mypy.sh)
+          configure_file(
+            ${ROOT_DIR}/tools/protoc-gen-mypy.py
+            ${PROJECT_BINARY_DIR}/tools/protoc-gen-mypy.py
+            COPYONLY)
+          set(PROTOC_MYPY_PLUGIN_FILE ${PROJECT_BINARY_DIR}/tools/protoc-gen-mypy.sh)
+        else()
+          set(PROTOC_MYPY_PLUGIN_FILE ${ROOT_DIR}/tools/protoc-gen-mypy.bat)
+        endif()
+        list(APPEND PROTOC_ARGS
+                    --plugin
+                    protoc-gen-mypy=${PROTOC_MYPY_PLUGIN_FILE}
+                    --mypy_out
+                    ${ONNX_DLLEXPORT_STR}${CMAKE_CURRENT_BINARY_DIR})
+      endif()
+    endif()
+    if(NOT ONNX_PROTOC_EXECUTABLE)
+      message(FATAL_ERROR "Protobuf compiler not found")
+    endif()
+    if(ONNX_PROTO_POST_BUILD_SCRIPT)
+      add_custom_command(
+        OUTPUT "${OUTPUT_PB_SRC}" "${OUTPUT_PB_HEADER}"
+        COMMAND ${ONNX_PROTOC_EXECUTABLE} ARGS ${PROTOC_ARGS}
+        COMMAND "${CMAKE_COMMAND}" -DFILENAME=${OUTPUT_PB_HEADER}
+                -DNAMESPACES=${ONNX_NAMESPACE} -P
+                ${ONNX_PROTO_POST_BUILD_SCRIPT}
+        COMMAND "${CMAKE_COMMAND}" -DFILENAME=${OUTPUT_PB_SRC}
+                -DNAMESPACES=${ONNX_NAMESPACE} -P
+                ${ONNX_PROTO_POST_BUILD_SCRIPT}
+        DEPENDS ${GENERATED_PROTO} ${DEPEND}
+        COMMENT "Running C++ protocol buffer compiler on ${GENERATED_PROTO}"
+        VERBATIM)
+    else()
+      add_custom_command(
+        OUTPUT "${OUTPUT_PB_SRC}" "${OUTPUT_PB_HEADER}"
+        COMMAND ${ONNX_PROTOC_EXECUTABLE} ARGS ${PROTOC_ARGS}
+        DEPENDS ${GENERATED_PROTO} ${DEPEND}
+        COMMENT "Running C++ protocol buffer compiler on ${GENERATED_PROTO}"
+        VERBATIM)
+    endif()
+    add_custom_target(${NAME} DEPENDS ${OUTPUT_PB_SRC} ${OUTPUT_PB_HEADER})
+  endforeach()
+
+  set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE)
+  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
+  set(${HDRS} ${${HDRS}} PARENT_SCOPE)
+endfunction()
+
+relative_protobuf_generate_cpp(gen_onnx_proto
+                               __tmp_srcs
+                               __tmp_hdrs
+                               ${ONNX_ROOT}
+                               ""
+                               onnx/onnx.in.proto)
+list(APPEND ONNX_PROTO_SRCS ${__tmp_srcs})
+list(APPEND ONNX_PROTO_HDRS ${__tmp_hdrs})
+
+relative_protobuf_generate_cpp(gen_onnx_operators_proto
+                               __tmp_srcs
+                               __tmp_hdrs
+                               ${ONNX_ROOT}
+                               gen_onnx_proto
+                               onnx/onnx-operators.in.proto)
+list(APPEND ONNX_PROTO_SRCS ${__tmp_srcs})
+list(APPEND ONNX_PROTO_HDRS ${__tmp_hdrs})
+
+relative_protobuf_generate_cpp(gen_onnx_data_proto
+                               __tmp_srcs
+                               __tmp_hdrs
+                               ${ONNX_ROOT}
+                               gen_onnx_proto
+                               onnx/onnx-data.in.proto)
+list(APPEND ONNX_PROTO_SRCS ${__tmp_srcs})
+list(APPEND ONNX_PROTO_HDRS ${__tmp_hdrs})
+
+file(GLOB_RECURSE __tmp_srcs "${ONNX_ROOT}/onnx/*.h" "${ONNX_ROOT}/onnx/*.cc")
+file(GLOB_RECURSE onnx_gtests_src "${ONNX_ROOT}/onnx/test/cpp/*.h"
+    "${ONNX_ROOT}/onnx/test/cpp/*.cc"
+    "${ONNX_ROOT}/onnx/backend/test/cpp/*.cc"
+    "${ONNX_ROOT}/onnx/backend/test/cpp/*.h")
+list(REMOVE_ITEM __tmp_srcs "${ONNX_ROOT}/onnx/cpp2py_export.cc")
+list(REMOVE_ITEM __tmp_srcs ${onnx_gtests_src})
+list(APPEND ONNX_SRCS ${__tmp_srcs})
+
+add_library(onnx_proto ${ONNX_PROTO_SRCS} ${ONNX_PROTO_HDRS})
+add_dependencies(onnx_proto gen_onnx_operators_proto gen_onnx_data_proto)
+target_include_directories(onnx_proto PUBLIC
+  $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+  $<INSTALL_INTERFACE:include>
+  $<BUILD_INTERFACE:${PROTOBUF_INCLUDE_DIRS}>)
+
+if (MSVC)
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(EXTRA_FLAGS
+          "-Wno-implicit-function-declaration"
+          "-Wno-undefined-inline"
+          "-Wno-incompatible-pointer-types"
+          "-Wno-dllexport-explicit-instantiation-decl"
+          "-Wno-microsoft-unqualified-friend"
+          "-Wno-absolute-value"
+          "-Wno-unused-variable"
+          "-Wno-unknown-argument"
+          "-Wno-writable-strings"
+          "-Qunused-arguments")
+  else()
+    set(EXTRA_FLAGS "")
+  endif()
+  if (BUILD_SHARED_LIBS OR ONNX_BUILD_MAIN_LIB)
+    set(ONNX_API_DEFINE "-DONNX_API=__declspec(dllexport)")
+  else()
+    set(ONNX_API_DEFINE "-DONNX_API=")
+  endif()
+else()
+  # On non-Windows, hide all symbols we don't need
+  set(ONNX_API_DEFINE "-DONNX_API=__attribute__\(\(__visibility__\(\"default\"\)\)\)")
+  set_target_properties(onnx_proto PROPERTIES CXX_VISIBILITY_PRESET hidden)
+  set_target_properties(onnx_proto PROPERTIES VISIBILITY_INLINES_HIDDEN 1)
+endif()
+target_compile_definitions(onnx_proto PRIVATE ${ONNX_API_DEFINE})
+
+if(ONNX_USE_LITE_PROTO)
+  if(TARGET protobuf::libprotobuf-lite)
+    target_link_libraries(onnx_proto PUBLIC protobuf::libprotobuf-lite)
+  else()
+    target_link_libraries(onnx_proto PUBLIC ${PROTOBUF_LITE_LIBRARIES})
+  endif()
+else()
+  if(TARGET protobuf::libprotobuf)
+    target_link_libraries(onnx_proto PUBLIC protobuf::libprotobuf)
+  else()
+    target_link_libraries(onnx_proto PUBLIC ${PROTOBUF_LIBRARIES})
+  endif()
+endif()
+add_onnx_global_defines(onnx_proto)
+
+if(CMAKE_SYSTEM_NAME STREQUAL "AIX")
+  # whole-archive linker option not available on AIX.
+  # So, create a object library
+  add_library(onnx OBJECT ${ONNX_SRCS})
+else()
+  add_library(onnx ${ONNX_SRCS})
+endif()
+
+target_include_directories(onnx PUBLIC
+  $<BUILD_INTERFACE:${ONNX_ROOT}>
+  $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+  $<INSTALL_INTERFACE:include>)
+target_link_libraries(onnx PUBLIC onnx_proto)
+add_onnx_global_defines(onnx)
+
+if(BUILD_ONNX_PYTHON)
+  if("${PY_EXT_SUFFIX}" STREQUAL "")
+    if(MSVC)
+      set(PY_EXT_SUFFIX ".pyd")
+    else()
+      set(PY_EXT_SUFFIX ".so")
+    endif()
+  endif()
+
+  add_library(onnx_cpp2py_export MODULE "${ONNX_ROOT}/onnx/cpp2py_export.cc")
+  set_target_properties(onnx_cpp2py_export PROPERTIES PREFIX "")
+  set_target_properties(onnx_cpp2py_export
+                        PROPERTIES COMPILE_FLAGS "-fvisibility=hidden")
+  set_target_properties(onnx_cpp2py_export PROPERTIES SUFFIX ${PY_EXT_SUFFIX})
+  set_target_properties(onnx_cpp2py_export
+                        PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})
+  target_include_directories(onnx_cpp2py_export PRIVATE
+                             $<BUILD_INTERFACE:${ONNX_ROOT}>
+                             $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+                             $<INSTALL_INTERFACE:include>)
+
+  # pybind11 is a header only lib
+  find_package(pybind11 2.2 CONFIG)
+  if(NOT pybind11_FOUND)
+    if(EXISTS "${ONNX_ROOT}/third_party/pybind11/include/pybind11/pybind11.h")
+      add_subdirectory("${ONNX_ROOT}/third_party/pybind11")
+    else()
+      message(FATAL_ERROR "cannot find pybind")
+    endif()
+  endif()
+
+  target_include_directories(onnx_cpp2py_export PUBLIC
+    "${pybind11_INCLUDE_DIRS}"
+    "${PYTHON_INCLUDE_DIRS}")
+
+  if(APPLE)
+    set_target_properties(onnx_cpp2py_export
+                          PROPERTIES LINK_FLAGS "-undefined dynamic_lookup")
+    target_link_libraries(onnx_cpp2py_export
+                          PRIVATE -Wl,-force_load,$<TARGET_FILE:onnx>)
+  elseif(MSVC)
+    # In MSVC, we will add whole archive in default
+    target_link_libraries(onnx_cpp2py_export
+                          PRIVATE -WHOLEARCHIVE:$<TARGET_FILE:onnx>)
+  elseif(CMAKE_SYSTEM_NAME STREQUAL "AIX")
+    # whole-archive linker option not available on AIX
+    target_sources(onnx_cpp2py_export
+                          PRIVATE $<TARGET_OBJECTS:onnx>)
+  else()
+    # Assume everything else is like gcc
+    target_link_libraries(onnx_cpp2py_export
+                          PRIVATE "-Wl,--whole-archive" $<TARGET_FILE:onnx>
+                                  "-Wl,--no-whole-archive")
+    set_target_properties(onnx_cpp2py_export
+                          PROPERTIES LINK_FLAGS "-Wl,--exclude-libs,ALL")
+  endif()
+
+  target_link_libraries(onnx_cpp2py_export PRIVATE onnx)
+
+  if(MSVC)
+    target_link_libraries(onnx_cpp2py_export PRIVATE ${PYTHON_LIBRARIES})
+    target_compile_options(onnx_cpp2py_export
+                           PRIVATE /MP
+                                   /wd4244 # 'argument': conversion from 'google::
+                                           # protobuf::uint64' to 'int', possible
+                                           # loss of data
+                                   /wd4267 # Conversion from 'size_t' to 'int',
+                                           # possible loss of data
+                                   /wd4996 # The second parameter is ignored.
+                                   ${EXTRA_FLAGS})
+    if(ONNX_USE_PROTOBUF_SHARED_LIBS)
+      target_compile_options(onnx_cpp2py_export
+                             PRIVATE /wd4251 # 'identifier' : class 'type1' needs to
+                                             # have dll-interface to be used by
+                                             # clients of class 'type2'
+                            )
+    endif()
+    add_msvc_runtime_flag(onnx_cpp2py_export)
+    add_onnx_global_defines(onnx_cpp2py_export)
+  endif()
+endif()
+
+if(ONNX_BUILD_BENCHMARKS)
+  if(NOT TARGET benchmark)
+    # We will not need to test benchmark lib itself.
+    set(BENCHMARK_ENABLE_TESTING OFF
+        CACHE BOOL "Disable benchmark testing as we don't need it.")
+    # We will not need to install benchmark since we link it statically.
+    set(BENCHMARK_ENABLE_INSTALL OFF
+        CACHE BOOL
+              "Disable benchmark install to avoid overwriting vendor install.")
+    add_subdirectory(${PROJECT_SOURCE_DIR}/third_party/benchmark)
+  endif()
+
+  add_executable(protobuf-bench tools/protobuf-bench.cc)
+  target_include_directories(protobuf-bench PUBLIC
+    $<BUILD_INTERFACE:${ONNX_ROOT}>
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+    $<INSTALL_INTERFACE:include>
+    $<BUILD_INTERFACE:${PROTOBUF_INCLUDE_DIRS}>)
+  target_link_libraries(protobuf-bench onnx_proto benchmark)
+endif()
+
+# Export include directories
+set(ONNX_INCLUDE_DIRS "${ONNX_ROOT}" "${CMAKE_CURRENT_BINARY_DIR}")
+get_directory_property(hasParent PARENT_DIRECTORY)
+if(hasParent)
+  set(ONNX_INCLUDE_DIRS ${ONNX_INCLUDE_DIRS} PARENT_SCOPE)
+endif()
+
+if(MSVC)
+  target_compile_options(onnx_proto
+                         PRIVATE /MP
+                                 /wd4244 #'argument': conversion from 'google::
+                                         #protobuf::uint64' to 'int', possible
+                                         # loss of data
+                                 /wd4267 # Conversion from 'size_t' to 'int',
+                                         # possible loss of data
+                                 ${EXTRA_FLAGS})
+  target_compile_options(onnx
+                         PRIVATE /MP
+                                 /wd4244 # 'argument': conversion from 'google::
+                                         # protobuf::uint64' to 'int', possible
+                                         # loss of data
+                                 /wd4267 # Conversion from 'size_t' to 'int',
+                                         # possible loss of data
+                                 /wd4996 # The second parameter is ignored.
+                                 ${EXTRA_FLAGS})
+  if(ONNX_USE_PROTOBUF_SHARED_LIBS)
+      target_compile_options(onnx_proto
+                             PRIVATE /wd4251 # 'identifier' : class 'type1' needs to
+                                             # have dll-interface to be used by
+                                             # clients of class 'type2'
+                            )
+      target_compile_options(onnx
+                             PRIVATE /wd4251 # 'identifier' : class 'type1' needs to
+                                             # have dll-interface to be used by
+                                             # clients of class 'type2'
+                            )
+  endif()
+  add_msvc_runtime_flag(onnx_proto)
+  add_msvc_runtime_flag(onnx)
+  set(onnx_static_library_flags
+      -IGNORE:4221 # LNK4221: This object file does not define any previously
+                   # undefined public symbols, so it will not be used by any
+                   # link operation that consumes this library
+      )
+  set_target_properties(onnx
+                        PROPERTIES STATIC_LIBRARY_FLAGS
+                                   "${onnx_static_library_flags}")
+elseif(APPLE)
+
+else()
+  target_compile_options(onnx PRIVATE -Wall -Wextra)
+endif()
+
+if(APPLE)
+  set_target_properties(onnx PROPERTIES LINK_FLAGS "-undefined dynamic_lookup")
+endif()
+
+# ---[ ONNX Interface for Framework Integratin (ONNXIFI)
+add_library(onnxifi INTERFACE)
+target_include_directories(onnxifi INTERFACE
+  $<BUILD_INTERFACE:${ONNX_ROOT}>
+  $<INSTALL_INTERFACE:include>)
+
+# ---[ ONNXIFI loader
+add_library(onnxifi_loader STATIC onnx/onnxifi_loader.c)
+target_include_directories(onnxifi_loader PUBLIC
+  $<BUILD_INTERFACE:${ONNX_ROOT}>
+  $<INSTALL_INTERFACE:include>)
+# Users of ONNX backend API would compile it with their toolchain, so it is
+# implemented in standard C89 for maximum compatibility
+set_target_properties(onnxifi_loader
+                      PROPERTIES C_STANDARD
+                                 90
+                                 C_EXTENSIONS
+                                 NO)
+target_link_libraries(onnxifi_loader PUBLIC onnxifi ${CMAKE_DL_LIBS})
+if(MSVC)
+  add_msvc_runtime_flag(onnxifi_loader)
+endif()
+
+if (NOT ANDROID AND NOT IOS)
+  # ---[ ONNXIFI wrapper
+  add_library(onnxifi_wrapper MODULE onnx/onnxifi_wrapper.c)
+  if(MSVC)
+    add_msvc_runtime_flag(onnxifi_wrapper)
+  endif()
+  target_include_directories(onnxifi_wrapper PRIVATE
+    $<BUILD_INTERFACE:${ONNX_ROOT}>
+    $<INSTALL_INTERFACE:include>)
+  set_target_properties(onnxifi_wrapper PROPERTIES
+    C_STANDARD 99
+    C_EXTENSIONS NO
+    OUTPUT_NAME "onnxifi"
+    POSITION_INDEPENDENT_CODE YES)
+  target_link_libraries(onnxifi_wrapper PRIVATE onnxifi_loader onnxifi)
+  if(DEFINED ONNXIFI_SEARCH_DIR)
+    target_compile_definitions(onnxifi_wrapper PRIVATE "ONNXIFI_SEARCH_DIR=\"${ONNXIFI_SEARCH_DIR}\"")
+  endif()
+  if(WIN32)
+    if(MSVC)
+      target_compile_definitions(onnxifi_wrapper PRIVATE "ONNXIFI_PUBLIC=__declspec(dllexport)")
+    else()
+      target_compile_definitions(onnxifi_wrapper PRIVATE "ONNXIFI_PUBLIC=__attribute__((__dllexport__))")
+    endif()
+  endif()
+  if(APPLE)
+    # By default CMake would use .so suffix on Mac
+    set_target_properties(onnxifi_wrapper PROPERTIES SUFFIX ".dylib")
+  endif()
+endif()
+
+# ---[ ONNXIFI dummy backend
+add_library(onnxifi_dummy SHARED onnx/onnxifi_dummy.c)
+
+if(ONNXIFI_ENABLE_EXT)
+  add_definitions(-DONNXIFI_ENABLE_EXT=ON)
+endif()
+
+target_include_directories(onnxifi_dummy PRIVATE
+  $<BUILD_INTERFACE:${ONNX_ROOT}>
+  $<INSTALL_INTERFACE:include>)
+target_link_libraries(onnxifi_dummy PUBLIC onnxifi ${CMAKE_DL_LIBS})
+target_compile_definitions(onnxifi_dummy PRIVATE ONNXIFI_BUILD_LIBRARY=TRUE)
+if(MSVC)
+  add_msvc_runtime_flag(onnxifi_dummy)
+
+  target_compile_options(onnxifi_dummy
+                         PRIVATE /wd4113 # 'identifier1' differs in parameter
+                                         # lists from 'identifier2'
+                        )
+endif()
+
+include(GNUInstallDirs)
+
+install(DIRECTORY ${ONNX_ROOT}/onnx
+        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+        FILES_MATCHING
+        PATTERN "*.h")
+install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/onnx
+        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+        FILES_MATCHING
+        PATTERN "*.h")
+
+configure_file(
+  ${PROJECT_SOURCE_DIR}/cmake/ONNXConfigVersion.cmake.in
+  ${PROJECT_BINARY_DIR}/ONNXConfigVersion.cmake
+  @ONLY)
+configure_file(
+  ${PROJECT_SOURCE_DIR}/cmake/ONNXConfig.cmake.in
+  ${PROJECT_BINARY_DIR}/ONNXConfig.cmake
+  @ONLY)
+install(FILES
+  ${PROJECT_BINARY_DIR}/ONNXConfigVersion.cmake
+  ${PROJECT_BINARY_DIR}/ONNXConfig.cmake
+  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ONNX
+  COMPONENT dev)
+install(EXPORT ONNXTargets DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/ONNX")
+install(TARGETS
+  onnx onnx_proto
+  onnxifi onnxifi_dummy onnxifi_loader
+  EXPORT ONNXTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
+
+if(NOT ANDROID AND NOT IOS)
+  install(TARGETS onnxifi_wrapper
+    EXPORT ONNXTargets DESTINATION lib)
+endif()
+
+if(ONNXIFI_DUMMY_BACKEND)
+  add_definitions(-DONNXIFI_DUMMY_BACKEND=1)
+endif()
+
+if(ONNX_BUILD_TESTS)
+  include(${ONNX_ROOT}/cmake/unittest.cmake)
+endif()
+
+# Support older g++ to use PRId64
+add_definitions(-D__STDC_FORMAT_MACROS)
+
+include(cmake/summary.cmake)
+onnx_print_configuration_summary()
diff --git a/third_party/onnx/CODEOWNERS b/third_party/onnx/CODEOWNERS
new file mode 100644
index 0000000000..ea3d8d9a27
--- /dev/null
+++ b/third_party/onnx/CODEOWNERS
@@ -0,0 +1,10 @@
+* @onnx/sig-archinfra-approvers
+/community  @onnx/steering-committee
+/onnx/defs  @onnx/sig-operators-approvers
+/onnx/backend/test  @onnx/sig-operators-approvers
+/docs/AddNewOp.md @onnx/sig-operators-approvers
+/docs/TestCoverage*.md  @onnx/sig-operators-approvers
+/docs/Operators*.md @onnx/sig-operators-approvers
+/docs/OpConventions.md @onnx/sig-operators-approvers
+/docs/Broadcasting.md @onnx/sig-operators-approvers
+/docs/Changelog*.md @onnx/sig-operators-approvers
diff --git a/third_party/onnx/CODE_OF_CONDUCT.md b/third_party/onnx/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000..f282992e0b
--- /dev/null
+++ b/third_party/onnx/CODE_OF_CONDUCT.md
@@ -0,0 +1 @@
+The ONNX Code Of Conduct is described at https://onnx.ai/codeofconduct.html
diff --git a/third_party/onnx/CONTRIBUTING.md b/third_party/onnx/CONTRIBUTING.md
new file mode 100644
index 0000000000..4a214751c1
--- /dev/null
+++ b/third_party/onnx/CONTRIBUTING.md
@@ -0,0 +1,48 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Community Involvement and Contribution Guidelines
+
+ONNX is a community project and we welcome your contributions! In addition to contributing code, you can also contribute in many other ways:
+
+* Meetings and Discussions
+Join SIGS, Working Groups, Community meetings to learn about what is needed and then where there is a good fit to interest and areas of expertise, find ways to actively contribute.  Participate in [ONNX technical discussions](https://github.com/onnx/onnx/discussions) on GitHub.  Join the ONNX Slack channels at LF AI and Data, help answer questions and welcome new members.
+
+* Use Cases and Tools
+Develop use cases for ONNX and advocate for ONNX in developer conferences and meetups.  Develop tools that import and export using the ONNX spec, and help grow the community of ONNX users.  Become a champion for ONNX in your company or organization.
+
+* Roadmap and Features
+Understand the ONNX roadmap document, feature priorities, and help implement them.  Become an ONNX code and documentation contributor, and work towards committer status on important repos.
+
+* Releases and Model Zoo
+Help in achieving a release of ONNX, including increasing the number of models in the ONNX Model Zoo that exercise ONNX features.
+
+* Publications and Blogs
+Add to the growing number of arXiv papers that refer to ONNX.  Create blogs, presentations, books, articles and other materials that help increase the adoption of ONNX, and grow the community of users and contributors.
+
+* Steering Committee
+Attend ONNX Steering Committee meetings - they are open to all in the community. Help out where needed and appropriate on SC to-do items. Note that SIG and Working Groups leaders as well as others with demonstrated commitment and contributions to ONNX community may want to self-nominate during the annual SC election cycle.
+
+## Contributing code
+
+You can submit a pull request (PR) with your code. The [SIG](community/sigs.md) or [Working Group](community/working-groups.md) that is responsible for the area of the project your PR touches will review it and merge once any comments are addressed.
+
+### DCO
+ONNX has adopted the [DCO](https://en.wikipedia.org/wiki/Developer_Certificate_of_Origin). All code repositories under ONNX require a DCO. (ONNX previously used a CLA, which is being replaced with the DCO.)
+
+DCO is provided by including a sign-off-by line in commit messages. Using the `-s` flag for `git commit` will automatically append this line. For example, running `git commit -s -m 'commit info.'` it will produce a commit that has the message `commit info. Signed-off-by: My Name <my_email@my_company.com>`. The DCO bot will ensure commits are signed with an email address that matches the commit author before they are eligible to be merged.
+
+If you are using a GUI like the GitHub web site or GitHub Desktop, you'll need to append the `Signed-off-by: My Name <my_email@my_company.com>` manually to each commit message.
+
+NOTE: the sign-off is needed for each commit in the PR, not at the PR level.
+
+If you have old commits that are not signed, use the following commands to squash the old PR (original branch) into a single commit. This is an easier way to signoff old commits in old PR.
+
+```bash
+git checkout main
+git checkout -b temporary_patch              # create a new branch as temporary
+git merge --squash original_patch            # copy from old branch
+git branch -d original_patch                 # remove old branch
+git checkout -b original_patch               # create a new branch with the same name (override)
+git commit -m 'type your own commit msg' -s  # signoff that single commit
+git push origin original_patch -f            # forcibly override the old branch`
+```
diff --git a/third_party/onnx/LICENSE b/third_party/onnx/LICENSE
new file mode 100644
index 0000000000..d645695673
--- /dev/null
+++ b/third_party/onnx/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
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+
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
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+      the terms of any separate license agreement you may have executed
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+   6. Trademarks. This License does not grant permission to use the trade
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diff --git a/third_party/onnx/MANIFEST.in b/third_party/onnx/MANIFEST.in
new file mode 100644
index 0000000000..ee04daa2cc
--- /dev/null
+++ b/third_party/onnx/MANIFEST.in
@@ -0,0 +1,10 @@
+recursive-include onnx *.h *.c *.cc *.proto
+recursive-include third_party *
+include LICENSE
+recursive-include onnx/backend/test/data *
+recursive-include onnx/examples *
+recursive-include cmake *
+recursive-include tools *
+include VERSION_NUMBER
+include CMakeLists.txt
+include requirements.txt
diff --git a/third_party/onnx/README.md b/third_party/onnx/README.md
new file mode 100644
index 0000000000..9765cbaa32
--- /dev/null
+++ b/third_party/onnx/README.md
@@ -0,0 +1,287 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+<p align="center"><img width="40%" src="https://github.com/onnx/onnx/raw/main/docs/ONNX_logo_main.png" /></p>
+
+
+[![Build Status](https://dev.azure.com/onnx-pipelines/onnx/_apis/build/status/Windows-CI?branchName=main)](https://dev.azure.com/onnx-pipelines/onnx/_build/latest?definitionId=5&branchName=main)
+[![Build Status](https://dev.azure.com/onnx-pipelines/onnx/_apis/build/status/Linux-CI?branchName=main)](https://dev.azure.com/onnx-pipelines/onnx/_build/latest?definitionId=7&branchName=main)
+[![Build Status](https://dev.azure.com/onnx-pipelines/onnx/_apis/build/status/MacOS-CI?branchName=main)](https://dev.azure.com/onnx-pipelines/onnx/_build/latest?definitionId=6&branchName=main)
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/3313/badge)](https://bestpractices.coreinfrastructure.org/projects/3313)
+
+[Open Neural Network Exchange (ONNX)](https://onnx.ai) is an open ecosystem that empowers AI developers
+to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard
+data types. Currently we focus on the capabilities needed for inferencing (scoring).
+
+ONNX is [widely supported](http://onnx.ai/supported-tools) and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community. We invite the community to join us and further evolve ONNX.
+
+# Use ONNX
+* [Tutorials for creating ONNX models](https://github.com/onnx/tutorials).
+* [Pre-trained ONNX models](https://github.com/onnx/models)
+
+# Learn about the ONNX spec
+* [Overview](docs/Overview.md)
+* [ONNX intermediate representation spec](docs/IR.md)
+* [Versioning principles of the spec](docs/Versioning.md)
+* [Operators documentation](docs/Operators.md)
+* [Python API Overview](docs/PythonAPIOverview.md)
+
+# Programming utilities for working with ONNX Graphs
+* [Shape and Type Inference](docs/ShapeInference.md)
+* [Graph Optimization](https://github.com/onnx/optimizer)
+* [Opset Version Conversion](docs/VersionConverter.md)
+
+# NOTICE: ONNX now uses main branch as default branch
+Here are the [steps](https://github.com/onnx/onnx/wiki/How-to-migrate-to-main-branch-in-local-repo) from ONNX wiki for migrating to main branch in local repo.
+
+# Contribute
+ONNX is a [community project](community/readme.md). We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the [Special Interest Groups](community/sigs.md) and [Working Groups](community/working-groups.md) to shape the future of ONNX.
+
+Check out our [contribution guide](docs/CONTRIBUTING.md) to get started.
+
+If you think some operator should be added to ONNX specification, please read
+[this document](docs/AddNewOp.md).
+
+# Discuss
+We encourage you to open [Issues](https://github.com/onnx/onnx/issues), or use [Slack](https://lfaifoundation.slack.com/) (If you have not joined yet, please use this [link](https://join.slack.com/t/lfaifoundation/shared_invite/zt-o65errpw-gMTbwNr7FnNbVXNVFkmyNA) to join the group) for more real-time discussion.
+
+# Follow Us
+Stay up to date with the latest ONNX news. [[Facebook](https://www.facebook.com/onnxai/)] [[Twitter](https://twitter.com/onnxai)]
+
+
+
+
+
+
+# Installation
+
+## Prerequisites
+
+```
+numpy >= 1.16.6
+protobuf >= 3.12.2
+typing-extensions >= 3.6.2.1
+```
+
+## Official Python packages
+ONNX released packages are published in PyPi.
+```
+pip install numpy protobuf==3.16.0
+pip install onnx
+```
+
+[Weekly packages](https://test.pypi.org/project/onnx-weekly/) are published in test pypi to enable experimentation and early testing.
+
+
+## Conda packages
+A binary build of ONNX is available from [Conda](https://conda.io), in [conda-forge](https://conda-forge.org/):
+```
+conda install -c conda-forge numpy protobuf==3.16.0 libprotobuf=3.16.0
+conda install -c conda-forge onnx
+```
+
+You can also use the [onnx-dev docker image](https://hub.docker.com/r/onnx/onnx-dev) for a Linux-based installation without having to worry about dependency versioning.
+
+
+## Build ONNX from Source
+Before building from source uninstall any existing versions of onnx `pip uninstall onnx`.
+
+Generally speaking, you need to install [protobuf C/C++ libraries and tools](https://github.com/protocolbuffers/protobuf) before proceeding forward. Then depending on how you installed protobuf, you need to set environment variable CMAKE_ARGS to "-DONNX_USE_PROTOBUF_SHARED_LIBS=ON" or "-DONNX_USE_PROTOBUF_SHARED_LIBS=OFF".  For example, you may need to run the following command:
+
+Linux:
+```bash
+export CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON"
+```
+Windows:
+```bat
+set CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON"
+```
+
+The ON/OFF depends on what kind of protobuf library you have. Shared libraries are files ending with \*.dll/\*.so/\*.dylib. Static libraries are files ending with \*.a/\*.lib. This option depends on how you get your protobuf library and how it was built. And it is default OFF. You don't need to run the commands above if you'd prefer to use a static protobuf library.
+
+
+### Windows
+If you are building ONNX from source, it is recommended that you also build Protobuf locally as a static library. The version distributed with conda-forge is a DLL, but ONNX expects it to be a static library. Building protobuf locally also lets you control the version of protobuf. The tested and recommended version is 3.16.0.
+
+The instructions in this README assume you are using Visual Studio.  It is recommended that you run all the commands from a shell started from "x64 Native Tools Command Prompt for VS 2019" and keep the build system generator for cmake (e.g., cmake -G "Visual Studio 16 2019") consistent while building protobuf as well as ONNX.
+
+You can get protobuf by running the following commands:
+```bat
+git clone https://github.com/protocolbuffers/protobuf.git
+cd protobuf
+git checkout v3.16.0
+cd cmake
+cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_INSTALL_PREFIX=<protobuf_install_dir> -Dprotobuf_MSVC_STATIC_RUNTIME=OFF -Dprotobuf_BUILD_SHARED_LIBS=OFF -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_BUILD_EXAMPLES=OFF .
+msbuild protobuf.sln /m /p:Configuration=Release
+msbuild INSTALL.vcxproj /p:Configuration=Release
+```
+Then it will be built as a static library and installed to <protobuf_install_dir>. Please add the bin directory(which contains protoc.exe) to your PATH.
+
+```bat
+set PATH=<protobuf_install_dir>/bin;%PATH%
+```
+
+Please note: if your protobuf_install_dir contains spaces, **do not** add quotation marks around it.
+
+Alternative: if you don't want to change your PATH, you can set ONNX_PROTOC_EXECUTABLE instead.
+```bat
+set CMAKE_ARGS=-DONNX_PROTOC_EXECUTABLE=<full_path_to_protoc.exe>
+```
+
+Then you can build ONNX as:
+```
+git clone https://github.com/onnx/onnx.git
+cd onnx
+git submodule update --init --recursive
+# prefer lite proto
+set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
+pip install -e .
+```
+### Linux
+
+First, you need to install protobuf.
+
+Ubuntu users: the quickest way to install protobuf is to run
+
+```bash
+apt-get install python3-pip python3-dev libprotobuf-dev protobuf-compiler
+```
+
+Then you can build ONNX as:
+```
+export CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON"
+git clone --recursive https://github.com/onnx/onnx.git
+cd onnx
+# prefer lite proto
+set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
+pip install -e .
+```
+
+Otherwise, you may need to install it from source. You can use the following commands to do it:
+
+Debian/Ubuntu:
+```
+git clone https://github.com/protocolbuffers/protobuf.git
+cd protobuf
+git checkout v3.16.0
+git submodule update --init --recursive
+mkdir build_source && cd build_source
+cmake ../cmake -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_PREFIX=/usr -DCMAKE_INSTALL_SYSCONFDIR=/etc -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release
+make -j$(nproc)
+make install
+```
+
+CentOS/RHEL/Fedora:
+```
+git clone https://github.com/protocolbuffers/protobuf.git
+cd protobuf
+git checkout v3.16.0
+git submodule update --init --recursive
+mkdir build_source && cd build_source
+cmake ../cmake  -DCMAKE_INSTALL_LIBDIR=lib64 -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_PREFIX=/usr -DCMAKE_INSTALL_SYSCONFDIR=/etc -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release
+make -j$(nproc)
+make install
+```
+
+Here "-DCMAKE_POSITION_INDEPENDENT_CODE=ON" is crucial. By default static libraries are built without "-fPIC" flag, they are not position independent code. But shared libraries must be position independent code. Python C/C++ extensions(like ONNX) are shared libraries. So if a static library was not built with "-fPIC", it can't be linked to such a shared library.
+
+Once build is successful, update PATH to include protobuf paths.
+
+Then you can build ONNX as:
+```
+git clone https://github.com/onnx/onnx.git
+cd onnx
+git submodule update --init --recursive
+# prefer lite proto
+set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
+pip install -e .
+```
+
+* **Mac**
+```
+export NUM_CORES=`sysctl -n hw.ncpu`
+brew update
+brew install autoconf && brew install automake
+wget https://github.com/protocolbuffers/protobuf/releases/download/v3.16.0/protobuf-cpp-3.16.0.tar.gz
+tar -xvf protobuf-cpp-3.16.0.tar.gz
+cd protobuf-3.16.0
+mkdir build_source && cd build_source
+cmake ../cmake -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release
+make -j${NUM_CORES}
+make install
+```
+
+Once build is successful, update PATH to include protobuf paths.
+
+Then you can build ONNX as:
+```
+git clone --recursive https://github.com/onnx/onnx.git
+cd onnx
+# prefer lite proto
+set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
+pip install -e .
+```
+
+
+## Verify Installation
+After installation, run
+
+```
+python -c "import onnx"
+```
+
+to verify it works.
+
+
+## Common Build Options
+For full list refer to CMakeLists.txt
+**Environment variables**
+* `USE_MSVC_STATIC_RUNTIME` should be 1 or 0, not ON or OFF. When set to 1 onnx links statically to runtime library.
+**Default**: USE_MSVC_STATIC_RUNTIME=0
+
+* `DEBUG` should be 0 or 1. When set to 1 onnx is built in debug mode. or debug versions of the dependencies, you need to open the [CMakeLists file](CMakeLists.txt) and append a letter `d` at the end of the package name lines. For example, `NAMES protobuf-lite` would become `NAMES protobuf-lited`.
+**Default**: Debug=0
+
+**CMake variables**
+* `ONNX_USE_PROTOBUF_SHARED_LIBS` should be ON or OFF.
+**Default**: ONNX_USE_PROTOBUF_SHARED_LIBS=OFF USE_MSVC_STATIC_RUNTIME=0
+`ONNX_USE_PROTOBUF_SHARED_LIBS` determines how onnx links to protobuf libraries.
+    - When set to ON - onnx will dynamically link to protobuf shared libs, PROTOBUF_USE_DLLS will be defined as described [here](https://github.com/protocolbuffers/protobuf/blob/master/cmake/README.md#dlls-vs-static-linking), Protobuf_USE_STATIC_LIBS will be set to OFF and `USE_MSVC_STATIC_RUNTIME` must be 0.
+    - When set to OFF - onnx will link statically to protobuf, and Protobuf_USE_STATIC_LIBS will be set to ON (to force the use of the static libraries) and `USE_MSVC_STATIC_RUNTIME` can be 0 or 1.
+
+* `ONNX_USE_LITE_PROTO` should be ON or OFF. When set to ON onnx uses lite protobuf instead of full protobuf.
+**Default**: ONNX_USE_LITE_PROTO=OFF
+
+* `ONNX_WERROR` should be ON or OFF. When set to ON warnings are treated as errors.
+**Default**: ONNX_WERROR=OFF in local builds, ON in CI and release pipelines.
+
+
+## Common Errors
+* Note: the `import onnx` command does not work from the source checkout directory; in this case you'll see `ModuleNotFoundError: No module named 'onnx.onnx_cpp2py_export'`. Change into another directory to fix this error.
+
+* Building ONNX on Ubuntu works well, but on CentOS/RHEL and other ManyLinux systems, you might need to open the [CMakeLists file][CMakeLists] and replace all instances of `/lib` with `/lib64`.
+
+# Testing
+
+ONNX uses [pytest](https://docs.pytest.org) as test driver. In order to run tests, you will first need to install pytest:
+
+```
+pip install pytest nbval
+```
+
+After installing pytest, use the following command to run tests.
+
+```
+pytest
+```
+
+# Development
+
+Check out the [contributor guide](docs/CONTRIBUTING.md) for instructions.
+
+# License
+
+[Apache License v2.0](LICENSE)
+
+# Code of Conduct
+
+[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)
diff --git a/third_party/onnx/RELEASE-MANAGEMENT.md b/third_party/onnx/RELEASE-MANAGEMENT.md
new file mode 100644
index 0000000000..507cc8002e
--- /dev/null
+++ b/third_party/onnx/RELEASE-MANAGEMENT.md
@@ -0,0 +1,71 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX release management
+
+This describes the process by which versions of ONNX are officially released to the public.
+
+Releases
+--------
+
+Releases are versioned according to [ONNX Versioning](docs/Versioning.md). This describes IR and operator versioning policies, as well as propose how models themselves should be versioned.
+
+On a regular basis, new versions of ONNX are published, representing the aggregate of changes in the IR and operator sets. Such releases use semantic versioning to describe the progression of the standard.
+
+The GitHub repo for ONNX provides release branches where the project is stabilized as per the process described here. Release notes are used to communicate the stability and status of a release. The main branch will be used to continue work for subsequent releases.
+
+Major, minor and patch releases will have branch names and version numbers reflecting the nature of the change as per semantic versioning definitions.
+
+Workflow
+--------
+
+The following workflow describes the steps taken to release an update of ONNX,
+and can be undertaken regardless of whether a major, minor or patch release is
+to be produced.
+
+- The trigger for the workflow will typically be a time-based trigger based on
+  elapsed time (say every three months), but for the first release, we will
+  start the process in early November 2017.
+
+- The release manager will announce the intent of the process (to produce a
+  major, minor or patch update) and the overall timeline. A release branch is
+  created with the name rel-major#.minor#(.patch#), and any version
+  references in build scripts or version checks are updated.
+
+- The release manager announces the initial commit for testing. The first
+  period lasts a week; any regressions found should be fixed, typically via
+  the main branch. Incomplete features should be done or excised during this
+  period. A distribution can be made available with an -RC1 suffix.
+
+- The release manager announces a second round of testing (unless it's only a
+  patch update with no regressions found). Only critical bugs are fixed at
+  this point, or those introduced by patches from the first week. A third
+  weeek may be introduced at the release manager's discretion if significant
+  fixes need to be taken. Distributions with -RCn suffixes can be made
+  available if convenient.
+
+- Release notes are updated with final changes, and a file with sources is
+  provided along with a release on the GitHub project page.
+
+Testing
+-------
+
+The release process really consists of communicating, testing to establish a
+known state of the project, and distributing files. This section deals with
+the second task.
+
+At the very least, the tests that are part of the /test folder should be run
+under a variety of configurations. Issues fixed should ensure coverage in this
+suite to avoid regressions.
+
+Send a Pull Request for updates to this section to include a configuration you
+can help test if you care about one that's missing.
+
+The community is encouraged to perform additional testing during the test
+periods. Bugs and issues should be filed in the ONNX GitHub repo.
+
+Distribution
+------------
+
+The distribution of files follows the basic procedures described in
+[Creating Releases](https://help.github.com/articles/creating-releases/).
+
diff --git a/third_party/onnx/SECURITY.md b/third_party/onnx/SECURITY.md
new file mode 100644
index 0000000000..d9497cd94e
--- /dev/null
+++ b/third_party/onnx/SECURITY.md
@@ -0,0 +1,15 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Security Policy
+
+## Reporting a Vulnerability
+If you think you have found a security vulnerability, please send a report to onnx-security@lists.lfaidata.foundation. Please do not post security vulnerabilities on Slack.
+
+We don't currently have a PGP key, unfortunately.
+
+An ONNX committer will send you a response indicating the next steps in handling your report. After the initial reply to your report, the committer will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance.
+
+Important: Please don't disclose the vulnerability before it has been fixed and announced, to protect our users.
+
+## Security announcements
+Please subscribe to the [announcements mailing list](https://lists.lfaidata.foundation/g/onnx-announce), where we post notifications and remediation details for security vulnerabilities.
diff --git a/third_party/onnx/VERSION_NUMBER b/third_party/onnx/VERSION_NUMBER
new file mode 100644
index 0000000000..1cac385c6c
--- /dev/null
+++ b/third_party/onnx/VERSION_NUMBER
@@ -0,0 +1 @@
+1.11.0
diff --git a/third_party/onnx/cmake/ONNXConfig.cmake.in b/third_party/onnx/cmake/ONNXConfig.cmake.in
new file mode 100644
index 0000000000..27837e919b
--- /dev/null
+++ b/third_party/onnx/cmake/ONNXConfig.cmake.in
@@ -0,0 +1,23 @@
+# - Config file for the ONNX package
+# It defines the following variable(s)
+#   ONNX_INCLUDE_DIRS     - include directories for FooBar
+# as well as ONNX targets for other cmake libraries to use.
+
+# library version information
+set(ONNX_VERSION "@ONNX_VERSION@")
+
+# import targets
+include ("${CMAKE_CURRENT_LIST_DIR}/ONNXTargets.cmake")
+
+# include directory.
+#
+# Newer versions of CMake set the INTERFACE_INCLUDE_DIRECTORIES property
+# of the imported targets. It is hence not necessary to add this path
+# manually to the include search path for targets which link to gflags.
+# The following lines are here for backward compatibility, in case one
+# would like to use the old-style include path.
+get_filename_component(
+    CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(
+    _INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+set(ONNX_INCLUDE_DIRS "${_INSTALL_PREFIX}/include")
diff --git a/third_party/onnx/cmake/ONNXConfigVersion.cmake.in b/third_party/onnx/cmake/ONNXConfigVersion.cmake.in
new file mode 100644
index 0000000000..03783c3a54
--- /dev/null
+++ b/third_party/onnx/cmake/ONNXConfigVersion.cmake.in
@@ -0,0 +1,11 @@
+set(PACKAGE_VERSION "@ONNX_VERSION@")
+
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
diff --git a/third_party/onnx/cmake/Utils.cmake b/third_party/onnx/cmake/Utils.cmake
new file mode 100644
index 0000000000..c0c82475c3
--- /dev/null
+++ b/third_party/onnx/cmake/Utils.cmake
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+#
+# Add MSVC RunTime Flag
+function(add_msvc_runtime_flag lib)
+  if(${ONNX_USE_MSVC_STATIC_RUNTIME})
+    target_compile_options(${lib} PRIVATE $<$<NOT:$<CONFIG:Debug>>:/MT> $<$<CONFIG:Debug>:/MTd>)    
+  else()
+    target_compile_options(${lib} PRIVATE $<$<NOT:$<CONFIG:Debug>>:/MD> $<$<CONFIG:Debug>:/MDd>)
+  endif()
+endfunction()
+
+function(add_onnx_global_defines target)
+  target_compile_definitions(${target} PUBLIC "ONNX_NAMESPACE=${ONNX_NAMESPACE}")
+
+  if(ONNX_ML)
+    target_compile_definitions(${target} PUBLIC "ONNX_ML=1")
+  endif()
+
+  if(ONNX_USE_LITE_PROTO)
+    target_compile_definitions(${target} PUBLIC "ONNX_USE_LITE_PROTO=1")
+  endif()
+
+  if(ONNX_DISABLE_STATIC_REGISTRATION)
+    target_compile_definitions(${target} PUBLIC "__ONNX_DISABLE_STATIC_REGISTRATION")
+  endif()  
+endfunction()
+
+function(add_whole_archive_flag lib output_var)
+  if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
+    set(${output_var} -Wl,-force_load,$<TARGET_FILE:${lib}> PARENT_SCOPE)
+  elseif(MSVC)
+    # In MSVC, we will add whole archive in default.
+    set(${output_var} -WHOLEARCHIVE:$<SHELL_PATH:$<TARGET_FILE:${lib}>>
+        PARENT_SCOPE)
+  else()
+    # Assume everything else is like gcc
+    set(${output_var}
+        "-Wl,--whole-archive $<TARGET_FILE:${lib}> -Wl,--no-whole-archive"
+        PARENT_SCOPE)
+  endif()
+endfunction()
diff --git a/third_party/onnx/cmake/external/googletest.cmake b/third_party/onnx/cmake/external/googletest.cmake
new file mode 100644
index 0000000000..d8131fb398
--- /dev/null
+++ b/third_party/onnx/cmake/external/googletest.cmake
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+
+include (ExternalProject)
+
+set(googletest_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/include)
+set(googletest_URL https://github.com/google/googletest.git)
+set(googletest_BUILD ${CMAKE_CURRENT_BINARY_DIR}/googletest/)
+set(googletest_TAG e93da23920e5b6887d6a6a291c3a59f83f5b579e)
+#0fe96607d85cf3a25ac40da369db62bbee2939a5
+
+if(WIN32 AND CMAKE_BUILD_TYPE MATCHES Debug)
+  set(googletest_STATIC_LIBRARIES
+      ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/Debug/gtestd.lib)
+elseif(WIN32 AND NOT CMAKE_BUILD_TYPE MATCHES Debug)
+  set(googletest_STATIC_LIBRARIES
+      ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/Release/gtest.lib)
+elseif(CMAKE_BUILD_TYPE MATCHES Debug)
+  set(googletest_STATIC_LIBRARIES
+      ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/libgtestd.a)
+else()
+  set(googletest_STATIC_LIBRARIES
+      ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/libgtest.a)
+endif()
+
+if(ONNX_USE_MSVC_STATIC_RUNTIME)
+  set(ONNX_USE_MSVC_SHARED_RUNTIME OFF)
+else()
+  set(ONNX_USE_MSVC_SHARED_RUNTIME ON)
+endif()
+ExternalProject_Add(googletest
+    PREFIX googletest
+    GIT_REPOSITORY ${googletest_URL}
+    GIT_TAG ${googletest_TAG}
+    DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
+    BUILD_IN_SOURCE 1
+    BUILD_COMMAND ${CMAKE_COMMAND} --build . --config ${CMAKE_BUILD_TYPE} --target gtest
+    INSTALL_COMMAND ""
+    CMAKE_CACHE_ARGS
+        -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+        -DBUILD_GMOCK:BOOL=OFF
+        -DBUILD_GTEST:BOOL=ON
+        -Dgtest_force_shared_crt:BOOL=${ONNX_USE_MSVC_SHARED_RUNTIME}
+    BUILD_BYPRODUCTS ${googletest_STATIC_LIBRARIES}
+)
diff --git a/third_party/onnx/cmake/summary.cmake b/third_party/onnx/cmake/summary.cmake
new file mode 100644
index 0000000000..74c7230c5a
--- /dev/null
+++ b/third_party/onnx/cmake/summary.cmake
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Prints accumulated ONNX configuration summary
+function (onnx_print_configuration_summary)
+  message(STATUS "")
+  message(STATUS "******** Summary ********")
+  message(STATUS "  CMake version             : ${CMAKE_VERSION}")
+  message(STATUS "  CMake command             : ${CMAKE_COMMAND}")
+  message(STATUS "  System                    : ${CMAKE_SYSTEM_NAME}")
+  message(STATUS "  C++ compiler              : ${CMAKE_CXX_COMPILER}")
+  message(STATUS "  C++ compiler version      : ${CMAKE_CXX_COMPILER_VERSION}")
+  message(STATUS "  CXX flags                 : ${CMAKE_CXX_FLAGS}")
+  message(STATUS "  Build type                : ${CMAKE_BUILD_TYPE}")
+  get_directory_property(tmp DIRECTORY ${PROJECT_SOURCE_DIR} COMPILE_DEFINITIONS)
+  message(STATUS "  Compile definitions       : ${tmp}")
+  message(STATUS "  CMAKE_PREFIX_PATH         : ${CMAKE_PREFIX_PATH}")
+  message(STATUS "  CMAKE_INSTALL_PREFIX      : ${CMAKE_INSTALL_PREFIX}")
+  message(STATUS "  CMAKE_MODULE_PATH         : ${CMAKE_MODULE_PATH}")
+  message(STATUS "")
+  message(STATUS "  ONNX version              : ${ONNX_VERSION}")
+  message(STATUS "  ONNX NAMESPACE            : ${ONNX_NAMESPACE}")
+  message(STATUS "  ONNX_USE_LITE_PROTO       : ${ONNX_USE_LITE_PROTO}")
+  message(STATUS "  USE_PROTOBUF_SHARED_LIBS  : ${ONNX_USE_PROTOBUF_SHARED_LIBS}")
+  message(STATUS "  Protobuf_USE_STATIC_LIBS  : ${Protobuf_USE_STATIC_LIBS}")
+  message(STATUS "  ONNX_DISABLE_EXCEPTIONS   : ${ONNX_DISABLE_EXCEPTIONS}")
+  message(STATUS "  ONNX_WERROR               : ${ONNX_WERROR}")
+  message(STATUS "  ONNX_BUILD_TESTS          : ${ONNX_BUILD_TESTS}")
+  message(STATUS "  ONNX_BUILD_BENCHMARKS     : ${ONNX_BUILD_BENCHMARKS}")
+  message(STATUS "  ONNXIFI_DUMMY_BACKEND     : ${ONNXIFI_DUMMY_BACKEND}")
+  message(STATUS "  ONNXIFI_ENABLE_EXT        : ${ONNXIFI_ENABLE_EXT}")
+  message(STATUS "")
+  message(STATUS "  Protobuf compiler         : ${PROTOBUF_PROTOC_EXECUTABLE}")
+  message(STATUS "  Protobuf includes         : ${PROTOBUF_INCLUDE_DIRS}")
+  message(STATUS "  Protobuf libraries        : ${PROTOBUF_LIBRARIES}")
+  message(STATUS "  BUILD_ONNX_PYTHON         : ${BUILD_ONNX_PYTHON}")
+  if (${BUILD_ONNX_PYTHON})
+    message(STATUS "    Python version        : ${PY_VERSION}")
+    message(STATUS "    Python executable     : ${PYTHON_EXECUTABLE}")
+    message(STATUS "    Python includes       : ${PYTHON_INCLUDE_DIR}")
+  endif()
+endfunction()
diff --git a/third_party/onnx/cmake/unittest.cmake b/third_party/onnx/cmake/unittest.cmake
new file mode 100644
index 0000000000..83c6f96703
--- /dev/null
+++ b/third_party/onnx/cmake/unittest.cmake
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+
+set(UT_NAME ${PROJECT_NAME}_gtests)
+set(ONNX_ROOT ${PROJECT_SOURCE_DIR})
+set(ONNXIFI_TEST_DRIVER onnxifi_test_driver_gtests)
+
+include(${ONNX_ROOT}/cmake/Utils.cmake)
+
+find_package(Threads)
+
+set(${UT_NAME}_libs ${googletest_STATIC_LIBRARIES})
+set(${ONNXIFI_TEST_DRIVER}_libs ${googletest_STATIC_LIBRARIES})
+
+list(APPEND ${UT_NAME}_libs onnx)
+list(APPEND ${UT_NAME}_libs onnx_proto)
+list(APPEND ${UT_NAME}_libs onnxifi_loader)
+list(APPEND ${UT_NAME}_libs onnxifi)
+list(APPEND ${UT_NAME}_libs ${PROTOBUF_LIBRARIES})
+
+list(APPEND ${ONNXIFI_TEST_DRIVER}_libs onnx)
+list(APPEND ${ONNXIFI_TEST_DRIVER}_libs onnx_proto)
+list(APPEND ${ONNXIFI_TEST_DRIVER}_libs onnxifi_loader)
+list(APPEND ${ONNXIFI_TEST_DRIVER}_libs ${PROTOBUF_LIBRARIES})
+list(APPEND ${ONNXIFI_TEST_DRIVER}_libs onnxifi)
+
+file(GLOB_RECURSE ${UT_NAME}_src "${ONNX_ROOT}/onnx/test/cpp/*.cc")
+file(GLOB_RECURSE ${ONNXIFI_TEST_DRIVER}_src "${ONNX_ROOT}/onnx/backend/test/cpp/*.h" "${ONNX_ROOT}/onnx/backend/test/cpp/*.cc")
+
+function(AddTest)
+  cmake_parse_arguments(_UT "" "TARGET" "LIBS;SOURCES" ${ARGN})
+
+  list(REMOVE_DUPLICATES _UT_LIBS)
+  list(REMOVE_DUPLICATES _UT_SOURCES)
+
+  add_executable(${_UT_TARGET} ${_UT_SOURCES})
+  add_dependencies(${_UT_TARGET} onnx onnx_proto googletest)
+
+  target_include_directories(${_UT_TARGET}
+                             PUBLIC ${googletest_INCLUDE_DIRS}
+                                    ${ONNX_INCLUDE_DIRS}
+                                    ${PROTOBUF_INCLUDE_DIRS}
+                                    ${ONNX_ROOT}
+                                    ${CMAKE_CURRENT_BINARY_DIR})
+  target_link_libraries(${_UT_TARGET} ${_UT_LIBS} ${CMAKE_THREAD_LIBS_INIT})
+  if(TARGET protobuf::libprotobuf)
+    target_link_libraries(${_UT_TARGET} protobuf::libprotobuf)
+  else()
+    target_link_libraries(${_UT_TARGET} ${PROTOBUF_LIBRARIES})
+  endif()
+
+  if(WIN32)
+    target_compile_options(${_UT_TARGET}
+                           PRIVATE /EHsc # exception handling - C++ may throw,
+                                         # extern "C" will not
+                           )
+    add_msvc_runtime_flag(${_UT_TARGET})
+  endif()
+
+  if(MSVC)
+    target_compile_options(${_UT_TARGET}
+                           PRIVATE /wd4146 # unary minus operator applied to
+                                           # unsigned type, result still
+                                           # unsigned from include\google\protob
+                                           # uf\wire_format_lite.h
+                                 /wd4244 # 'argument': conversion from 'google::
+                                         # protobuf::uint64' to 'int', possible 
+                                         # loss of data
+                                 /wd4267 # Conversion from 'size_t' to 'int', 
+                                         # possible loss of data
+                                 /wd4996 # The second parameter is ignored.
+                           )
+      if(ONNX_USE_PROTOBUF_SHARED_LIBS)
+        target_compile_options(${_UT_TARGET}
+                               PRIVATE /wd4251 # 'identifier' : class 'type1' needs to
+                                               # have dll-interface to be used by
+                                               # clients of class 'type2'
+                              )
+      endif()
+  endif()
+
+  set(TEST_ARGS)
+  if(ONNX_GENERATE_TEST_REPORTS)
+    # generate a report file next to the test program
+    list(
+      APPEND
+        TEST_ARGS
+        "--gtest_output=xml:$<SHELL_PATH:$<TARGET_FILE:${_UT_TARGET}>.$<CONFIG>.results.xml>"
+      )
+  endif()
+
+  add_test(NAME ${_UT_TARGET}
+           COMMAND ${_UT_TARGET} ${TEST_ARGS}
+           WORKING_DIRECTORY $<TARGET_FILE_DIR:${_UT_TARGET}>)
+
+endfunction(AddTest)
+
+addtest(TARGET ${UT_NAME} SOURCES ${${UT_NAME}_src} LIBS ${${UT_NAME}_libs})
+addtest(TARGET ${ONNXIFI_TEST_DRIVER} SOURCES ${${ONNXIFI_TEST_DRIVER}_src} LIBS ${${ONNXIFI_TEST_DRIVER}_libs})
diff --git a/third_party/onnx/community/logo_request.md b/third_party/onnx/community/logo_request.md
new file mode 100644
index 0000000000..5cee33d97d
--- /dev/null
+++ b/third_party/onnx/community/logo_request.md
@@ -0,0 +1,12 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+## Member Company logos
+
+Member Companies as defined [here](readme.md#community-roles) can request their logo be displayed on https://onnx.ai and other materials.
+
+To have your logo displayed, submit a PR to https://github.com/onnx/onnx.github.io with the following:
+1. Text of the PR must include written permission indicating the logo can be used on the onnx.ai website as well as in presentations showing ONNX Member Companies
+2. A high quality logo file with transparent background needs to be committed in the "assets" directory. The image file should be 300dpi (best if physical printing is ever required) or a vector file which can be saved in any resolution.
+3. The URL of your company or product web page. Ideally the page mentions ONNX.
+
+Member Companies may ask for their logo to be removed at any time and their status as Member Company rescinded. The Steering Committee also can vote to remove a Member Company of their status.
diff --git a/third_party/onnx/community/readme.md b/third_party/onnx/community/readme.md
new file mode 100644
index 0000000000..ec7b7a8cb4
--- /dev/null
+++ b/third_party/onnx/community/readme.md
@@ -0,0 +1,177 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Open Governance
+
+## TL;DR
+
+ONNX is rolling out open governance to encourage broader participation beyond the founding companies. We hope this will make the decision making process more transparent, enable better technical decisions with consideration of more viewpoints, and share the work of maintenance. We want ONNX to be the standard the whole community rallies to without reservations.
+
+ONNX open governance creates 3 roles: Member, Contributor, Approver. 3 structures are also created: Steering Committee, Special Interest Groups (SIGs), Working Groups. Contributors and Approvers can vote for the Steering Committee members. The Steering Committee charters SIGs and appoints SIG chairs. Every piece of ONNX belongs to some SIG. Contributors and Approvers participate in one or more SIGs. Our governance structured is based on the successful model of Kubernetes.
+
+The effort is bootstrapped with an initial Steering Committee and set of SIGs with the first elections to occur after 1 year.
+
+## Principles
+
+The ONNX community adheres to the following principles:
+
+* __Open__: ONNX is open source. See repository guidelines and CLA, below.
+* __Welcoming and respectful__: See Code of Conduct, below.
+* __Transparent and accessible__: Work and collaboration should be done in public. See SIG governance, below.
+* __Merit__: Ideas and contributions are accepted according to their technical merit and alignment with project objectives, scope and design principles. Engineering investment >> corporate sponsorship
+* __Speed__: Contributing the time and effort to ensure fast decision-making is key to ensuring that the specifications produced is aligned to the fast iteration of machine learning technologies.
+
+## Community Roles
+
+### Members
+Members are individuals who are interested in or participate in the ONNX community. Members are able to follow and participate in all public modes of communication used by the ONNX community including but not limited to GitHub, Slack, Stack Overflow, email announcements and discussion aliases. Members are expected to adhere to the Code of Conduct but do not have any specific responsibilities.
+
+### Contributors
+Contributors are Members who are active contributors in the community. They can have issues and PRs assigned to them. They also have voting privileges. Contributors can be active in many ways including but not limited to:
+
+* Authoring or reviewing PRs on GitHub
+* Filing or commenting on issues on GitHub
+* Contributing to SIG, subproject, or community discussions (e.g. Slack, meetings, email discussion forums, Stack Overflow, etc)
+* Creator of content, promoting and advocating the ONNX specification
+
+A Member can become a Contributor by being sponsored by 2 existing Approvers from different companies. Contributors who are not active in the last 12 months will be removed.
+
+### Approvers
+Approvers are Contributors who are experienced with some aspect of the project and with general software engineering principles. Approvers are responsible for reviewing contributions for acceptance by considering not just code quality but also holistic impact of the contribution including compatibility, performance, and interactions with other areas.
+
+Approvers need to be active Contributors for at least 3 months and be sponsored by a SIG chair with no objections from other SIG chairs.
+
+### Member Companies
+Member Companies are organizations that support ONNX in one or more of the following ways:
+* Having employees participate in SIGs, Working Groups, or the Steering Committee
+* Hosting a workshop or meetup for ONNX
+* Providing resources for building or hosting ONNX assets
+* Doing media or PR activities to promote ONNX
+* Shipping a product that supports ONNX
+
+Member Companies do not have any voting rights, except via their employees who are Contributors. Affiliates and subsidiaries are considered part of the Member Company and not as separate organizations. Being a Member Company does not by itself confer any compliance or certification to the Member Company's products.
+
+Member Companies can request their logo be displayed on the website and other materials by following these [instructions](logo_request.md).
+
+## Organizational Structure
+
+The ONNX community is organized in the following manner, with all governance and execution being planned and coordinated as follows:
+
+* **Steering Committee** is made up of a set number of people whose charter it is to define and iterate on the vision, goals, and governance process of the ONNX community.
+* **Special  Interest Groups (SIGs)** are persistent groups that are responsible for specific parts of the project. SIGs must have open and transparent proceedings. Anyone is welcome to participate and contribute provided they follow the Code of Conduct. The purpose of a SIG is to develop a set of goals to be achieved over a set period of time, and then to gather input, drive consensus and closure, implement code contributions, and other related activities to achieve the goal. SIGs are also responsible for ongoing maintenance of the code in their areas.
+* **Working Groups** are temporary groups that are formed to address issues that cross SIG boundaries. Working groups do not own any code ownership or other long term artifacts. Working groups can report back and act through involved SIGs.
+
+### Steering Committee
+
+#### Role
+
+The Steering Committee has a set of rights and responsibilities including the following:
+
+* Define, evolve, and defend the vision, values, mission, and scope of the project.
+* Define, evolve, and defend a Code of Conduct,  which must include a neutral, unbiased process for resolving conflicts.
+* Define and evolve project governance structures and policies, including how members become contributors, approvers, SIG chairs, etc.
+* Charter and refine policy for defining new community groups (Special Interest Groups, Working Groups, and any future possible defined structure), and establish transparency and accountability policies for such groups.
+* Decide, for the purpose of elections, who is a  member of standing of the ONNX project, and what privileges that entails.
+* Decide which functional areas and scope are part of the ONNX project, including accepting new or pruning old SIGs and Working Groups.
+* Decide how and when official releases of ONNX artifacts are made and what they include.
+* Declare releases when quality/feature/other requirements are met.
+* Control access to, establish processes regarding, and provide a final escalation path for any ONNX repository, which currently includes all repositories under the ONNX GitHub organizations
+* Control and delegate access to and establish  processes regarding other project resources/assets, including artifact repositories, build and test infrastructure, web sites and their domains, blogs, social-media accounts, etc.
+* Define any certification process.
+* Manage the ONNX brand and any outbound marketing.
+* Make decisions by majority vote if consensus cannot be reached.
+
+#### Structure
+
+The Steering Committee consists of 5 individuals. No single Member Company may have more than 1 representative. Members serve 1 year terms.
+
+The starting composition will be individuals from Microsoft, Facebook, Amazon, and 2 other Member Companies, who have been picked by the three founding members based on contributions and experience.
+
+After the initial term of each Steering Committee representative is completed, their seat will be open for any contributor in the community to be elected into the seat via a community vote. Only contributors may vote, but would be restricted to one vote per Member Company. Therefore, if a Member Company had three contributors in good standing, the three contributors would have to select who would vote on their behalf.
+
+If a member of the Steering Committee changes companies, by default they retain and may continue on with the role. However, should their company/organization ask this person to resign from the role, new elections will be held. If the employment change results in a single Member Company having more than one representative, then one of them must resign, and new elections are held.
+
+The Steering Committee will decide on and publish an election process within 3 months of formalizing this organizational structure. This will cover voting eligibility, eligibility for candidacy, election process and schedule. During this time period, the Steering Committee will also establish SIGs and Working Groups.
+
+A Steering Committee member can be removed due to Code of Conduct violations.
+
+### SIG - Special Interest Groups
+
+#### Role
+
+The ONNX project is organized primarily into Special Interest Groups, or SIGs. Each SIG is comprised of individuals from multiple companies and organizations, with a common purpose of advancing the project with respect to a specific topic.
+
+Our goal is to enable a distributed decision structure and code ownership, as well as providing focused forums for getting work done, making decisions, and on-boarding new contributors. Every identifiable part of the project (e.g., repository, subdirectory, API, test, issue, PR, Slack channel) is intended to be owned by some SIG. At the time of inception of this organizational structure, the following SIGs will be present:
+
+* Architecture & Infra
+    * This SIG is responsible for defining and maintaining the core ONNX format, the build and CI/CD systems for ONNX repositories, publishing release packages for ONNX, the onnx-docker repository, and creating tools to help integrate with and test against the ONNX standard. This SIG is also the defacto owner of files in the main ONNX repository unless explicitly owned by another SIG.
+* Operator Standardization
+    * This SIG is responsible for determining the operators that are part of the ONNX spec (ONNX and ONNX-ML domains), ensuring high quality operator definitions and documentation, establishing criteria for adding new operators, managing ops domains and compliance tiers, and enforcing versioning mechanisms.
+* Converters
+    * This SIG is responsible for developing and maintaining the various converter repositories under ONNX.
+* Model zoo and tutorials
+    * This SIG is responsible for the respective repositories with the charter of providing a comprehensive collection of state of the art ONNX models from a variety of sources and making it easy for users to get started with ONNX and the ecosystem around it.
+
+#### Structure
+
+SIGs must have at least one, and may have up to two SIG chairs at any given time. SIG chairs are intended to be organizers and facilitators, responsible for the operation of the SIG and for communication and coordination with the other SIGs, the Steering Committee, and the broader community. All SIG chairs are appointed by the Steering Committee. If there are more than two contributors being considered for a particular SIG, the Steering Committee will vote on and resolve who the chairs would be. Candidates need to be Approvers.
+
+Each SIG must have a charter that specifies its scope (topics, subsystems, code repos and directories), responsibilities, and areas of authority. Charters are submitted to the ONNX GitHub via PR for review and approval by the Steering Committee who will be looking to ensure the scope of the SIG as represented in the charter is reasonable. All SIGs are expected to follow the standards established by the Steering Committee for how Contributors are roles of authority/leadership are selected/granted, how decisions are made, and how conflicts are resolved.
+
+A primary reason that SIGs exist is as forums for collaboration. Much work in a SIG should stay local within that SIG. However, SIGs must communicate in the open, ensure other SIGs and community members can find meeting notes, discussions, designs, and decisions, and periodically communicate a high-level summary of the SIG's work to the community. SIGs are also responsible to:
+
+* Meet regularly, at least monthly
+* Keep up-to-date meeting notes, linked from the SIG's page in the community repo
+* Announce meeting agenda and minutes after each meeting, on their SIG mailing list and/or Slack channel
+* Ensure the SIG's mailing list is archived (i.e on GitHub)
+* Report activity in overall ONNX community meetings
+* Participate in release planning meetings, retrospective, etc (if relevant)
+* Actively triage issues, PRs, test failures, etc. related to code and tests owned by the SIG
+* Use the above forums as the primary means of working, communicating, and collaborating, as opposed to private emails and meetings
+
+#### Decision making
+
+When it is time to formalize the work-product from a SIG, votes are taken from every contributor who participates in the SIG. The list of active contributors is determined by the one (or two) SIG leads to ensure that only those who have actively participated in the SIG can vote. At this time there is no restrictions on how many contributors from any one Member Company can participate (and hence vote). The Steering Committee will monitor how the community behaves and apply constraints if needed in the future.
+
+While most work shouldn’t require expensive coordination with other SIGs, there will be efforts (features, refactoring, etc.) that cross SIG boundaries. In this case, it is expected that the SIGs coordinate with each other and come to mutually agreed solutions. In some cases, it may make sense to form a Working Group for joint work. Cross-SIG coordination will naturally require more time and implies a certain amount of overhead. This is intentional to encourage changes to be well encapsulated whenever possible.
+
+### WG - Working Groups
+
+Working Groups (WGs) are primarily used to facilitate topics of discussion that cross SIG lines, or are topics which are short-lived and require a limited set of decisions to be agreed upon. Working groups:
+
+* do not own code
+* have a clear goal measured through specific deliverables
+* will be disbanded after the goal is achieved
+
+Working Groups can create specifications, recommendations, or implementations for submission to the relevant SIGs for approval and acceptance. At time of inception of this organizational structure, the following WGs will be present:
+
+   * Edge / Mobile
+   * Training
+   * Quantization
+   * Data Processing
+
+Working Groups are formed by submitting a proposal via PR to the Steering Committee. The proposal should cover:
+
+* what is the exact problem being worked on
+* what is the exit criteria
+* who are the chairs (up to 2)
+* what are the meeting and discussion mechanics
+
+Working Groups are disbanded when there is no activity for more than *3 months* or when the chair informs the Steering Committee.
+
+## Repository Guidelines
+
+All repositories under the ONNX github org:
+
+* Must adopt the ONNX Code of Conduct
+* All code projects use the Apache 2.0 license. Documentation repositories must use the Creative Commons License version 4.0.
+* Must adopt the DCO bot
+* All OWNERS must be members of standing as defined by ability to vote in ONNX steering committee elections
+* Repository must be approved by the Steering Committee
+
+Repositories can be removed when they are inactive by archiving them.
+
+(Code repositories previously used the MIT license and moved to Apache 2.0 in October 2020.)
+
+## CLA / DCO
+
+As of October 2020, the CLA (https://cla-assistant.io/onnx/onnx) has been retired. All commits are subject to the DCO (https://www.developercertificate.com/) and need to be signed.
diff --git a/third_party/onnx/community/repo_guidelines.md b/third_party/onnx/community/repo_guidelines.md
new file mode 100644
index 0000000000..15f0a02f0d
--- /dev/null
+++ b/third_party/onnx/community/repo_guidelines.md
@@ -0,0 +1,35 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Repositories under ONNX GitHub organization
+
+The ONNX GitHub organization contains a number of repositories. Every repository is owned by a SIG and the Steering Committee is responsible for managing these repos. Requests for creating, transferring, modifying, or archiving repositories can be made by filing an issue a request against https://github.com/onnx/steering-committee.
+
+## Rules for all repos
+
+* Must be owned and managed by one of the ONNX SIGs
+* Must be actively maintained
+* Must adopt the ONNX Code of Conduct
+* Must adopt the standard ONNX license(s)
+* Must adopt the ONNX CLA bot
+* Must adopt all ONNX automation (like LGTM)
+* Must have CI or other automation in place for repos containing code to ensure quality
+* All OWNERS must be members of standing as defined by ability to vote in Steering Committee elections.
+
+## Requirements for new, contributed repos
+
+We are happy to accept contributions as repos under the ONNX organization of new projects that meet the following requirements:
+
+* Project is closely related to ONNX
+* Adds value to the ONNX ecosystem
+* Determined to need a new repo rather than a folder in an existing repo
+* All contributors must have signed the ONNX CLA
+* Licenses of dependencies must be acceptable
+* Committment to maintain the repo
+* Approval of the SIG that will own the repo
+* Approval of the Steering Committee
+
+If you want to contribute a repository, you should first work with the SIG that will own it. Then the SIG can work with the Steering Committee to finalize.
+
+## Archiving repos
+
+Repositories that are inactive or unneeded will be archived. The SIG that owns the repo is responsible for deciding when it should be archived. SIGs should regularly validate the repos they own are still active and necessary.
diff --git a/third_party/onnx/community/sc-election-guidelines.md b/third_party/onnx/community/sc-election-guidelines.md
new file mode 100644
index 0000000000..99adce30c7
--- /dev/null
+++ b/third_party/onnx/community/sc-election-guidelines.md
@@ -0,0 +1,67 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Steering Commitee election guideline
+
+## Introduction
+
+To encourage community participation and wider adoption in the industry, ONNX has introduced [open governance](https://github.com/onnx/onnx/wiki/Expanded-ONNX-Steering-Committee-Announced!) in March 2018. The governance has three defined structures to propel the development of ONNX project forward: [Steering Committee](/community/readme.md#steering-committee), [Special Interest Groups (SIGs)](/community/readme.md#sig---special-interest-groups), and [Working Groups (WGs)](/community/readme.md#wg---working-groups). While SIGs and WGs primarily focus on the techinical roadmap of ONNX, the Steering Committe is responsible for setting the vision and governance process of the ONNX community.
+
+For the first year of its ONNX open governance, representatives from Facebook, Microsoft, AWS, Intel and Nvidia are chosen to serve as the ONNX Steering Committee to help guide the project. The Steering Committee will be elected by the [Contributors](/community/readme.md#community-roles) in its second year and will be re-elected every year.
+
+This document is created to provide guidelines for the election process to ensure maximum transparency and fairness.
+
+
+## Timeline
+
+Candidate applications will be accepted in April, and the election will be held in May. The new term for Steering Committee begins on June 1st of the corresponding year. The following table outlines the schedule for the election process.
+
+| Schedule     | Event               |
+|:-------------|:--------------------|
+| 1st Monday of April| Application for Steering Committee candidates open. |
+| 3rd Monday of April| Candidates and their applications posted on github. Campaigning session held over video conference with an option to pre-record.|
+| 1st Monday of May| Election begins.     |
+| 2nd Monday of May| Election closes, and votes counted. Election results announced in the same week.|
+| 3rd Monday of May| Previous Steering Committee to meet the newly elected Committee for official transition.|
+| June 1 | New term begins with elected Steering Committee. Steering Committee Emeritus members help with the transition for the month of June.      |
+
+
+## Eligibility
+
+### Eligibility for Steering Committee candidacy
+Candidates will be self-nominated, and they do not necessarly need to be a [Contributor](/community/readme.md#community-roles) to the ONNX project. The duties of the Steering Committee extend beyond simply contributing code to the ONNX project.
+
+
+### Eligibility for voting
+
+To participate in the Steering commitee election, you must be a Contributor to the ONNX project. As defined in the community guideline, Contributor is sponsored by 2 approvers from different companies.
+
+Contributors are further required to submit their github handle, email address, and affiliated company name to be eligible for voting. Any Contributor who has not submitted their information by before April 31st will not be able to participate in the election. The Steering Committee is currently reviewing options for collecting contributor information, and the best option will be notified to the Contributors shortly.
+
+## Candidacy process
+
+## Voting process
+
+### General election procedure
+In order to promote fairness, the Steering Committee has decided to limit 1 vote per Member Company. Contributors will be able to vote individually, but their votes will be rolled up to represent the vote of associated Member Company. This procedure will prevent large companies with lots of Contributors from dominating the election results.
+
+### Voting mechanics and algorithm
+
+The election will use [Condorcet ranking](https://en.wikipedia.org/wiki/Condorcet_method) with [Schulze method](https://en.wikipedia.org/wiki/Schulze_method). Condorcet ranking allows voters to indicate ranked preference for candidates, and Schultz method provides an algorithm to tally the overall preference.
+
+For ONNX Steering Committee election, the Condorcet ranking with Schulze method will be performed twice. The individual Contributor votes gets tallied first to Member Companies, and the results of the Member Company votes are ranked again using the same method.
+
+### Voting platform
+We will use Condorcet Internet Voting Service ([civs.cs.cornell.edu](https://civs.cs.cornell.edu/)) to collect votes from Contributors.
+
+After votes are casted, the results of individual votes will be uploaded to ONNX Github election directory to ensure transparency.
+
+## Election officers and Steering Committee emeritus members
+
+### Election officers
+Two election officers will be chosen from the current Steering committee to oversee the election process. They are responsible for overseeing the progress of the election and ensure the process is correctly implemented. Their duties include coordinating election as shown in the timeline above, tallying votes and announcing results for the ONNX community.
+
+### Steering Committee emeritus members
+Two Steering Committee members will remain as emeritus members for the newly elected Committee to help with transition process for 1 month. If previous Steering Committee members are relected, then they will guide the transition for the new members, and there will not be a separate Steering Committe emeritus members.
+
+
+
diff --git a/third_party/onnx/community/sigs.md b/third_party/onnx/community/sigs.md
new file mode 100644
index 0000000000..c294d388f9
--- /dev/null
+++ b/third_party/onnx/community/sigs.md
@@ -0,0 +1,20 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# SIGs - Special Interest Groups
+
+As described in the ONNX [governance](/community/readme.md#sig---special-interest-groups), Special Interest Groups (SIGs) are persistent groups responsible for specific parts of the project. SIGs have open and transparent proceedings to develop goals and implement code contributions. SIGs are also responsible for ongoing maintenance of the code in their areas.
+
+## Joining a SIG
+
+If you are interested in participating, please [join the discussion](https://slack.lfai.foundation/) in the respective Slack channels. Details about any upcoming meetings will also be shared in the Slack channels. SIG artifacts can be found in the [sigs repository](https://github.com/onnx/sigs).
+
+You can find the schedule of SIG meetings on the [LF AI calendar](https://wiki.lfai.foundation/pages/viewpage.action?pageId=18481196)
+
+## Current SIGs
+
+| Name | Responsibilities |
+| ---- | ---------------- |
+| [Architecture & Infra](https://lfaifoundation.slack.com/archives/C018Y2QG7V2) | Defining and maintaining the core ONNX format, the build and CI/CD systems for ONNX repositories, publishing release packages for ONNX, the onnx-docker repository, and creating tools to help integrate with and test against the ONNX standard. This SIG is also the defacto owner of files in the main ONNX repository unless explicitly owned by another SIG. |
+| [Operators](https://lfaifoundation.slack.com/archives/C018Y2RAY4C) | Determining the operators that are part of the ONNX spec (ONNX and ONNX-ML domains), ensuring high quality operator definitions and documentation, establishing criteria for adding new operators, managing ops domains and compliance tiers, and enforcing versioning mechanisms. |
+| [Converters](https://lfaifoundation.slack.com/archives/C0171FSKZBN) | Developing and maintaining the various converter repositories under ONNX. |
+| [Models and tutorials](https://lfaifoundation.slack.com/archives/C018RE2BRBL) | Providing a comprehensive collection of state of the art ONNX models from a variety of sources and making it easy for users to get started with ONNX and the ecosystem around it. |
diff --git a/third_party/onnx/community/working-groups.md b/third_party/onnx/community/working-groups.md
new file mode 100644
index 0000000000..4d03b35997
--- /dev/null
+++ b/third_party/onnx/community/working-groups.md
@@ -0,0 +1,37 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Working Groups
+
+As described in the ONNX [governance](/community/readme.md#wg---working-groups), Working Groups (WGs) are temporary groups formed to address issues that cross SIG boundaries. Working Groups have a have a clear goal measured through specific deliverables and disband after the goal is achieved. Working groups do not own artifacts long term; they create specifications, recommendations, and/or code implementations for submission to the relevant SIGs for approval and acceptance.
+
+## Proposing a new working group
+New Working Groups are created when there is sufficient interest in a topic area and someone volunteers to be the chair for the group and submits a proposal to the steering committee. The chair facilitates the discussion and helps synthesize proposals and decisions.
+
+## Joining a working group
+Working Groups have most of their discussions on Slack. If you are interested in participating, please join the discussion in the respective Slack channels. Details about any upcoming meetings will also be shared in the Slack channel. Working Group artifacts can be found in the [working-groups repository](https://github.com/onnx/working-groups).
+
+You can find the schedule of meetings on the [LF AI wiki](https://wiki.lfai.foundation/pages/viewpage.action?pageId=18481196)
+
+## Active working groups
+
+| Working Group      | Objectives    |
+| ------------------ | ------------- |
+| [Release](https://lfaifoundation.slack.com/archives/C018VGGJUGK) | Improve the release process for ONNX |
+| [Training](https://lfaifoundation.slack.com/archives/C018K560U14) | Expand ONNX to support training as well as inference |
+| [Preprocessing](https://lfaifoundation.slack.com/archives/C02AANGFBJB) | Make data preprocessing part of ONNX |
+
+## Completed working groups
+
+| Working Group      | Objectives    | Status |
+| ------------------ | ------------- | ------ |
+| [Control Flow and Loops](https://gitter.im/onnx/ControlFlowWG) | Enable dynamic control structures to enable advanced models for NLP, speech, and video/image processing | Completed - If, Loop, and Scan operators were added in ONNX 1.3 release. |
+| [Quantization](https://gitter.im/onnx/quantization) | Enhance ONNX to support quantized data types and operators on a variety of runtimes and hardware devices | Completed - added in ONNX 1.5 release. |
+| [Foundation](https://gitter.im/onnx/foundation) | Identify and evaluate non-profit foundation options for the ONNX consortium.  Execute on best option. | Completed - ONNX joined LF AI in November 2019|
+
+## Inactive working groups
+
+| Working Group      | Objectives    | Status |
+| ------------------ | ------------- | ------ |
+| [Testing/Compliance](https://gitter.im/onnx/test_compliance) | Create tools, tests and APIs to ensure models and backends comply with the ONNX specification | became part of the ArchInfra SIG for now |
+| [Edge/Mobile](https://gitter.im/onnx/edge) | Enable deployment of ONNX models to edge platforms by identifying the ONNX operators that must be supported by mobile and IoT runtimes, defining tool chain requirements, and contributing to ONNX compatibility tests | Stopped as of January 2020 |
+| [Data Pipelines](https://gitter.im/onnx/pipelines) | Define new operators for processing the data that goes in and comes out | No recent activity |
diff --git a/third_party/onnx/docs/AddNewOp.md b/third_party/onnx/docs/AddNewOp.md
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+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Table of Contents
+1. [New Operator or Function](#new_operator_or_function)
+    1. [Step 1: Proposing a new operator/function](#step1_new_operator_or_function)
+    2. [Step 2: Submit PR](#step2_new_operator_or_function)
+    3. [Step 3: PR Review by Operators SIG](#step3_new_operator_or_function)
+    4. [Step 4: ONNX release](#step4_new_operator_or_function)
+
+2. [Removing Operator or Function](#removing_operator_or_function)
+    1. [Removing operator](#removing_operator)
+    2. [Removing function](#removing_function)
+    3. [Document removing operator or function](#document_removing_operator_or_function)
+
+# Proposing and submitting a new operator or function to ONNX <a name="new_operator_or_function"></a>
+
+Operators are the basic building blocks that define ONNX model. With a rich set of operators, ONNX can describe most DNN and ML models from various frameworks. Functions allow for composing complex operators from more primitive operators. The ONNX specification includes a core set of operators that enable many models. It is a non-goal to add all possible operators, however more operators are added as needed to cover evolving needs.
+
+In this document, we describe the process of accepting a new proposed operator and how to properly submit a new operator as part of ONNX standard. The goal is to improve on what we currently have based on our experience, learning and feedbacks we gathered from the community.
+
+## 4 steps to add an operator <a name="steps_to_add_an_operator"></a>
+1. Decide what to propose
+2. Submit PR for new operator/function
+3. Review of PR by Operators SIG
+4. Merging of PR and inclusion in next ONNX release
+
+## Step 1: Proposing a new operator/function <a name="step1_new_operator_or_function"></a>
+In order to propose a new operator/function, the following is needed:
+1. If the operator can be composed by other ONNX operators, then it should be a function and not an operator (we have a function in ONNX : MeanVarianceNormalization).
+2. If the operators can be split to new primitives, propose those primitives instead and make the operator a function.
+3. Based on a model. This will help us understand the usage and that it solves an actual problem. For the case of the model being private or IP and can't be shared, the operator doesn't belong to the standard and should be implemented as custom OP.
+4. The operator needs to be implemented by at-least one (well-known) framework. This help us to understand the actual behavior of the operator and its usage.
+5. Operator signature and behavior:
+    1. If the operator is available in numpy, prefer numpy semantics.
+    2. If the operator is available in more than one frameworks, make sure that your design is general and cover those frameworks.
+6. Prefer attributes over inputs.
+
+## Step 2: Submit PR <a name="step2_new_operator_or_function"></a>
+Once the criteria of proposing new operator/function has been satisfied, you will need to submit a PR for the new operator/function. Here the expectation of what the PR should include. The reviewer is expected to verify the completeness of the PR before signoff.
+1. Description:
+    1. Write a detailed description about the operator, and its expected behavior. Pretty much, the description should be clear enough to avoid confusion between implementors.
+    2. Add an example in the description to illustrate the usage.
+    3. Add reference to the source of the operator in the corresponding framework in the description (if possible).
+    4. Write the mathematic formula or a pseudocode in the description. The core algorithm needs to be very clear.
+2. Write a reference implementation in Python, this reference implementation should cover all the expected behavior of the operator. Only in extremely rare case, we will waive this requirement.
+3. Operator version: check out our
+[versioning doc](/docs/Versioning.md#operator-versioning)
+4. Write unit test, that cover main usage and corner cases.
+    1. The testing examples will be extracted to the doc.
+    2. We also generate binary data for it.
+    3. Example: [onnx/backend/test/case/node/abs.py](/onnx/backend/test/case/node/abs.py)
+5. Add at least one automatic upgrade test for your operator in [onnx/test/automatic_upgrade_test.py](/onnx/test/automatic_upgrade_test.py) using `_test_op_upgrade`. These tests create a given operator at a given opset version (usually the version the operator was introduced in) and test that the version converter is able to convert them to the highest available version. So for a new operator `_test_op_upgrade` will not test anything, but as soon as the operator gets updated in a future opset the test will autoamtically become nontrivial.
+6. Update the documentation and generate the test data.
+    1. Running [the script](/tools/update_doc.sh)
+to update the doc and generate the test data.
+7. Shape Inference function
+    1. Please provide a shape inference function in cases where it is meaningful and applicable.
+    2. In cases where shape inference is not possible, it must have logic to perform
+rank inference at the very least (adding right amount of dimensions to the output shape)
+    3. Shape inference functions must be accompanied by unit tests ([onnx/test/shape_inference_test.py](/onnx/test/shape_inference_test.py)).
+    4. You can refer to the shape inference function for the `TopK` operator while implementing your own function ([onnx/defs/math/defs.cc](/onnx/defs/math/defs.cc))
+
+### Example to Follow
+[PR 1959](https://github.com/onnx/onnx/pull/1959) is a good example to follow.
+
+## Step 3: PR Review by Operators SIG <a name="step3_new_operator_or_function"></a>
+The [Operators SIG](https://github.com/onnx/sigs/tree/main/operators) is responsible for the operators/functions in the ONNX specification. The SIG regularly meets and reviews PRs.
+
+### Sign-off
+At least two sign-off from the Operators SIG [contributors](https://github.com/onnx/onnx/tree/main/community#community-roles).
+
+## Step 4: ONNX release <a name="step4_new_operator_or_function"></a>
+Once the PR is reviewed and signed off by the Operators SIG, it will be merged. Your new operator/function will be part of the main branch and available to anyone building from source. These are not official releases. ONNX periodically releases official new versions that are a snapshot of the main branch. Your new operator/function will be part of that release.
+
+# Removing operator or function <a name="removing_operator_or_function"></a>
+There are a lot of reasons for removing existing ONNX operator or function, such us being replaced with different operator or can be decomposed by a set of other operators. This document describes the criteria of removing an existing ONNX operator from the standard.
+
+## Removing operator <a name="removing_operator"></a>
+Any operator in ONNX was added because it was required by a model and/or framework. In order to deprecate such an operator we need to do the following.
+* Operator can’t be deprecated unless there is a replacement.
+    * Replacement can be a more general operator that supersedes the old one.
+    * Or a set of primitive operators that together can implement the same functionality and behavior of the deprecated operator (Function).
+* If the deprecated operator can be decomposed by existing operators then it must be converted to a function.
+* If replacement isn’t in ONNX standard yet, then add the replacement operator or set of operators first.
+* Add a version adapter which turns the operator into its replacement for the version converter. Example: [onnx/version_converter/adapters/upsample_9_10.h](/onnx/version_converter/adapters/upsample_9_10.h)
+* No grace period is needed for deprecated operators.
+
+## Removing function <a name="removing_function"></a>
+Function, by definition, is composed of ONNX primitives; however, function could have been accelerated by framework or runtime that support ONNX. So, removing function is not recommended, with the exception of adding another single function which supersede its functionality.
+
+## Document removing operator or function <a name="document_removing_operator_or_function"></a>
+To make sure everyone is aware of the deprecation, the following need to happen:
+* Any removed operator or function from ONNX need to be mentioned in the release note.
+* Their old documentation needs to be updated to show the new replacement and the mapping between the old to the new.
+    * Only `def.cc` need to be remove, `old.cc` will remain.
+    * `old.cc` need to be updated with the mapping to the replacement.
+* ONNX checker need to be updated to error with a proper message.
+* All removed operators need to be appended at the end of the `operator.md` file.
diff --git a/third_party/onnx/docs/Broadcasting.md b/third_party/onnx/docs/Broadcasting.md
new file mode 100644
index 0000000000..02be7251f6
--- /dev/null
+++ b/third_party/onnx/docs/Broadcasting.md
@@ -0,0 +1,72 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Broadcasting in ONNX
+
+In ONNX, element-wise operators can take inputs with different shape,
+as long as the input tensors are broadcastable to the same shape.
+ONNX supports two types of broadcasting: multidirectional broadcasting and
+unidirectional broadcasting. We will introduce these two types of broadcasting
+respectively in the following sections.
+
+
+## Multidirectional Broadcasting
+
+In ONNX, a set of tensors are multidirectional broadcastable to the same shape
+if one of the following is true:
+- The tensors all have exactly the same shape.
+- The tensors all have the same number of dimensions and the length of
+each dimensions is either a common length or 1.
+- The tensors that have too few dimensions can have their shapes prepended
+with a dimension of length 1 to satisfy property 2.
+
+For example, the following tensor shapes are supported by multidirectional broadcasting:
+
+- shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (2, 3, 4, 5), shape(B) = (5,), ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (4, 5), shape(B) = (2, 3, 4, 5), ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (1, 4, 5), shape(B) = (2, 3, 1, 1), ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (3, 4, 5), shape(B) = (2, 1, 1, 1), ==> shape(result) = (2, 3, 4, 5)
+
+Multidirectional broadcasting is the same as [Numpy's broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#general-broadcasting-rules).
+
+Multidirectional broadcasting is supported by the following operators in ONNX:
+- [Add](Operators.md#Add)
+- [And](Operators.md#And)
+- [Div](Operators.md#Div)
+- [Equal](Operators.md#Equal)
+- [Greater](Operators.md#Greater)
+- [Less](Operators.md#Less)
+- [Max](Operators.md#Max)
+- [Mean](Operators.md#Mean)
+- [Min](Operators.md#Min)
+- [Mul](Operators.md#Mul)
+- [Or](Operators.md#Or)
+- [Pow](Operators.md#Pow)
+- [Sub](Operators.md#Sub)
+- [Sum](Operators.md#Sum)
+- [Where](Operators.md#Where)
+- [Xor](Operators.md#Xor)
+
+## Unidirectional Broadcasting
+
+In ONNX, tensor B is unidirectional broadcastable to tensor A
+if one of the following is true:
+- Tensor A and B both have exactly the same shape.
+- Tensor A and B all have the same number of dimensions and the length of
+each dimensions is either a common length or B's length is 1.
+- Tensor B has too few dimensions, and B can have its shapes prepended
+with a dimension of length 1 to satisfy property 2.
+
+When unidirectional broadcasting happens, the output's shape is the same as
+the shape of A (i.e., the larger shape of two input tensors).
+
+In the following examples, tensor B is unidirectional broadcastable to tensor A:
+
+- shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (2, 3, 4, 5), shape(B) = (5,), ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (2, 3, 4, 5), shape(B) = (2, 1, 1, 5), ==> shape(result) = (2, 3, 4, 5)
+- shape(A) = (2, 3, 4, 5), shape(B) = (1, 3, 1, 5), ==> shape(result) = (2, 3, 4, 5)
+
+Unidirectional broadcasting is supported by the following operators in ONNX:
+- [Gemm](Operators.md#Gemm)
+- [PRelu](Operators.md#PRelu)
diff --git a/third_party/onnx/docs/CIPipelines.md b/third_party/onnx/docs/CIPipelines.md
new file mode 100644
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--- /dev/null
+++ b/third_party/onnx/docs/CIPipelines.md
@@ -0,0 +1,32 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+## ONNX CI Pipelines
+
+* CI pipelines matrix:
+
+  |   | When it runs | Config | Test |
+  -- | -- | -- | -- | 
+  [Linux-CI](/.azure-pipelines/Linux-CI.yml) | Every PR | <ul><li>Ubuntu-18.04</li><li>DEBUG=1 or 0</li><li>ONNX_USE_LITE_PROTO=ON or OFF</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_BUILD_TESTS=1</li><li>ONNX_WERROR=ON</li><li>ONNX_ML=1 or 0</li></ul>| <ul><li>ONNX C++ tests</li><li>Style check (flake8 and mypy)</li><li>Test doc generation</li><li>Test proto generation</li><li>Verify backend node data</li><li>Verify node test generation</li></ul> | 
+  [Windows-CI](/.azure-pipelines/Windows-CI.yml) | Every PR  | <ul><li>vs2017-win2016</li><li>ONNX_USE_LITE_PROTO=ON</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=ON</li><li>ONNX_BUILD_TESTS=1</li><li>ONNX_WERROR=ON</li><li>ONNX_ML=1 or 0</li></ul>| <ul><li>Test building ONNX in conda environment</li><li>Test doc generation</li><li>Test proto generation</li></ul> |
+  [Mac-CI](/.azure-pipelines/MacOS-CI.yml) | Every PR  | <ul><li>macOS-10.14</li><li>DEBUG=1</li><li>ONNX_USE_LITE_PROTO=ON or OFF</li><li>ONNX_ML=1 or 0</li><li>ONNX_BUILD_TESTS=1</li><li>ONNX_WERROR=ON</li></ul>| <ul><li>ONNX C++ tests</li><li>Test doc generation</li><li>Test proto generation</li></ul>|
+  [Windows_No_Exception CI](/.github/workflows/win_no_exception_ci.yml) | Every PR  | <ul><li>vs2019-winlatest</li><li>ONNX_DISABLE_EXCEPTIONS=ON</li><li>ONNX_USE_LITE_PROTO=ON</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_ML=1</li><li>ONNX_USE_MSVC_STATIC_RUNTIME=ON</li><li>ONNX_DISABLE_STATIC_REGISTRATION=ON or OFF</li></ul>| <ul><li>Only ONNX C++ tests</li><li>Test selective schema loading</li></ul> |
+  [WindowsRelease](/.github/workflows/release_win.yml) | <ul><li>Main branch</li><li>Release branch</li><li>Weekly(1)</li></ul> | <ul><li>Latest Windows</li><li>x86 and x64</li><li>ONNX_USE_LITE_PROTO=ON</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_ML=1</li><li>ONNX_USE_MSVC_STATIC_RUNTIME=OFF</li></ul>| <ul><li> Release Windows wheel</li><li>Release onnx-weekly package</li><li>Verify backend node data</li><li>Verify node test generation</li><li>Verify with different dependency versions - latest numpy version, latest and min supported protobuf version(2)</li><li>Verify ONNX with the latest [ort-nightly](https://test.pypi.org/project/ort-nightly/)(3).</li></ul> |
+  [LinuxRelease_aarch64](/.github/workflows/release_linux_aarch64.yml) | <ul><li>Main branch</li><li>Release branch</li><li>Weekly</li></ul>  | <ul><li>Latest manylinux2014_aarch64</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_ML=1</li></ul>| <ul><li> Release Linux aarch64 wheel</li><li>Release onnx-weekly package</li><li>Verify backend node data</li><li>Verify node test generation</li><li>Verify with different dependency versions - latest numpy version, latest and min supported protobuf version</li><li>Verify ONNX with the latest ort-nightly.</li></ul> |
+  [LinuxRelease_x86_64](/.github/workflows/release_linux_x86_64.yml) | <ul><li>Main branch</li><li>Release branch</li><li>Weekly</li></ul> | <ul><li>Latest LinuxRelease_x86_64</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_ML=1</li></ul>| <ul><li> Release Linux x86_64 wheel</li><li>Release onnx-weekly package</li><li>Test TEST_HUB=1(4)</li><li>Verify backend node data</li><li>Verify node test generation</li></li><li>Verify with different dependency versions - latest numpy version, latest and min supported protobuf version</li><li>Verify ONNX with the latest ort-nightly.</li></ul> |
+  [MacRelease](/.github/workflows/release_win.yml) | <ul><li>Main branch</li><li>Release branch</li><li>Weekly</li></ul> | <ul><li>macos-10.15</li><li> MACOSX_DEPLOYMENT_TARGET=10.12(5) </li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_ML=1</li></ul>| <ul><li>Release Mac wheel</li><li>Release onnx-weekly package</li><li>Verify backend node data</li><li>Verify node test generation</li><li>Verify with different dependency versions - latest numpy version, latest and min supported protobuf version</li><li>Verify ONNX with the latest ort-nightly.</li><li>Test source distribution generation</li><li>Test build with source distribution</li><li>Release onnx-weekly source distribution</li></ul>  
+  [Weekly CI with latest onnx.checker](/.github/workflows/weekly_mac_ci.yml) | weekly(6) |<ul><li>macos-latest</li><li>MACOSX_DEPLOYMENT_TARGET=10.15</li><li>ONNX_USE_PROTOBUF_SHARED_LIBS=OFF</li><li>ONNX_ML=1</li></ul>| <ul><li>Test latest ONNX checker</li><li>Test latest ONNX shape inference</li><li>With all models from [onnx/models](https://github.com/onnx/models)(7)</li></ul> |
+  
+  * (1) When the release CIs will run:
+    * After a PR has been merged into main/rel-* branch
+    * Run weekly (Sunday midnight) and release Python wheel to [onnx-weekly](https://test.pypi.org/project/onnx-weekly/) package on TestPyPI.
+    * Any PR targeting rel-* branch
+    * To manually run them, add a PR label "run release CIs" (only maintainers have permission).
+  * (2) Minimum supported versions are listed [here](/requirements.txt).  
+  * (3) [Test](/onnx/test/test_with_ort.py) ONNX Python wheel with `onnxruntime.InferenceSession` from latest ONNXRuntime. Please note that ort-nightly does not support Windows-x86 thus its verification is skipped.  
+  * (4) TEST_HUB=1 will test [onnx.hub](/onnx/test/hub_test.py) by using this API to download an ONNX model from onnx/models. This test is restricted to only 1 pipeline for saving quota usage.
+  * (5) Although the build environment is macos-10.15, use MACOSX_DEPLOYMENT_TARGET=10.12 and -p [macosx_10_12_x86_64](https://github.com/onnx/onnx/blob/2e048660ffa8243596aaf3338e60c7c0575458f2/.github/workflows/release_mac.yml#L74) to force the wheel to support 10.12+.  
+  
+  * (6):
+    * The ONNX Model Zoo test will run weekly (Sunday midnight)
+    * To manually trigger it, add a PR label "test ONNX Model Zoo" (only maintainers have permission). Please note that it will need a lot of download bandwidth from [onnx/models](https://github.com/onnx/models) so use it with caution.
+  * (7) Some old deprecated models (opset-1) are [skipped](/workflow_scripts/config.py).
diff --git a/third_party/onnx/docs/CONTRIBUTING.md b/third_party/onnx/docs/CONTRIBUTING.md
new file mode 100644
index 0000000000..9b0c2aeac9
--- /dev/null
+++ b/third_party/onnx/docs/CONTRIBUTING.md
@@ -0,0 +1,118 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Development
+
+To build ONNX from source please follow the instructions listed [here](https://github.com/onnx/onnx#build-onnx-from-source).
+
+Then, after you have made changes to Python and C++ files:
+
+- `Python files`: the changes are effective immediately in your installation. You don't need to install these again.
+- `C++ files`: you need to install these again to trigger the native extension build.
+
+Assuming build succeed in the initial step, simply running
+```
+pip install -e .
+```
+from onnx root dir should work.
+
+## Folder structure
+
+- `onnx/`: the main folder that all code lies under
+  - `onnx.proto`: the protobuf that contains all the structures
+  - `checker.py`: a utility to check whether a serialized ONNX proto is legal
+  - `shape_inference.py`: a utility to infer types and shapes for ONNX models
+  - `version_converter.py`: a utility to upgrade or downgrade version for ONNX models
+  - `parser.py`: a utility to create an ONNX model or graph from a textual representation
+  - `hub.py`: a utility for downloading models from [ONNX Model Zoo](https://github.com/onnx/models)
+  - `compose.py`: a utility to merge ONNX models
+  - `helper.py`: tools for graph operation
+  - `defs/`: a subfolder that defines the ONNX operators
+  - `test/`: test files
+
+## Generated operator documentation
+
+[Operator docs in Operators.md](Operators.md) are automatically generated based on C++ operator definitions and backend Python snippets. To refresh these docs, run the following commands from the repo root and commit the results. Note `ONNX_ML=0` updates Operators.md whereas `ONNX_ML=1` updates Operators-ml.md:
+
+```
+set ONNX_ML=0
+pip install setup.py
+python onnx/defs/gen_doc.py
+```
+
+## Adding a new operator
+
+ONNX is an open standard, and we encourage developers to contribute high
+quality operators to ONNX specification.
+Before proposing a new operator, please read [the tutorial](AddNewOp.md).
+
+# Code style
+
+We use flake8, mypy, and clang-format for checking code format.
+*Note: You'll find the versions of these tools in `setup.py`.*
+You can run these checks by:
+
+```
+pip install -e .[lint]
+
+./tools/style.sh
+```
+
+# Testing
+
+ONNX uses [pytest](https://docs.pytest.org) as a test driver. To run tests, you'll first need to install pytest:
+
+```
+pip install pytest nbval
+```
+
+After installing pytest, run from the root of the repo:
+
+```
+pytest
+```
+
+to begin the tests.
+
+You'll need to regenerate test coverage too, by running this command from the root of the repo:
+
+```
+python onnx\backend\test\stat_coverage.py
+```
+
+## Cpp tests (googletest)
+
+Some functionalities are tested with googletest. Those tests are listed in `test/cpp`, and include tests for shape inference, data propagation, parser, and others.
+
+To run them, first build ONNX with `-DONNX_BUILD_TESTS=1` or `ONNX_BUILD_TESTS=1 pip install -e .` and then run `.setuptools-cmake-build/onnx_gtests`.
+
+# Static typing (mypy)
+
+We use [mypy](http://mypy-lang.org/) to run static type checks on the onnx code base. To check that your code passes, you'll first need to install the mypy type checker. If you're using python 3, call from your onnx source folder:
+
+```
+pip install -e .[lint]
+```
+
+*Note: You'll find the version we're currently using in `setup.py`.*
+
+After having installed mypy, you can run the type checks:
+
+```
+python setup.py typecheck
+```
+# CI Pipelines
+
+Every PR needs to pass CIs before merge. CI pipelines details are [here](CIPipelines.md).
+
+# Other developer documentation
+
+* [How to implement ONNX backend (ONNX to something converter)](ImplementingAnOnnxBackend.md)
+* [Backend test infrastructure and how to add tests](OnnxBackendTest.md)
+
+# License
+
+[Apache License v2.0](/LICENSE)
+
+# Code of Conduct
+
+[ONNX Open Source Code of Conduct](http://onnx.ai/codeofconduct.html)
diff --git a/third_party/onnx/docs/Changelog-ml.md b/third_party/onnx/docs/Changelog-ml.md
new file mode 100644
index 0000000000..30f5beca97
--- /dev/null
+++ b/third_party/onnx/docs/Changelog-ml.md
@@ -0,0 +1,1149 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+## Operator Changelog
+*This file is automatically generated from the
+            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).
+            Do not modify directly and instead edit operator definitions.*
+
+For an operator input/output's differentiability, it can be differentiable,
+            non-differentiable, or undefined. If a variable's differentiability
+            is not specified, that variable has undefined differentiability.
+
+# ai.onnx.ml
+## Version 1 of the 'ai.onnx.ml' operator set
+### <a name="ai.onnx.ml.ArrayFeatureExtractor-1"></a>**ai.onnx.ml.ArrayFeatureExtractor-1**</a>
+
+  Select elements of the input tensor based on the indices passed.<br>
+      The indices are applied to the last axes of the tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be selected</dd>
+<dt><tt>Y</tt> : tensor(int64)</dt>
+<dd>The indices, based on 0 as the first index of any dimension.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>Selected output data as an array</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(string)</dt>
+<dd>The input must be a tensor of a numeric type or string. The output will be of the same tensor type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.Binarizer-1"></a>**ai.onnx.ml.Binarizer-1**</a>
+
+  Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>threshold</tt> : float (default is 0.0)</dt>
+<dd>Values greater than this are mapped to 1, others to 0.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be binarized</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Binarized output data</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type. The output will be of the same tensor type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.CastMap-1"></a>**ai.onnx.ml.CastMap-1**</a>
+
+  Converts a map to a tensor.<br>The map key must be an int64 and the values will be ordered
+      in ascending order based on this key.<br>The operator supports dense packing or sparse packing.
+      If using sparse packing, the key cannot exceed the max_map-1 value.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>cast_to</tt> : string (default is TO_FLOAT)</dt>
+<dd>A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.</dd>
+<dt><tt>map_form</tt> : string (default is DENSE)</dt>
+<dd>Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'.</dd>
+<dt><tt>max_map</tt> : int (default is 1)</dt>
+<dd>If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>The input map that is to be cast to a tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>A tensor representing the same data as the input map, ordered by their keys</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : map(int64, string), map(int64, float)</dt>
+<dd>The input must be an integer map to either string or float.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(float), tensor(int64)</dt>
+<dd>The output is a 1-D tensor of string, float, or integer.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.CategoryMapper-1"></a>**ai.onnx.ml.CategoryMapper-1**</a>
+
+  Converts strings to integers and vice versa.<br>
+      Two sequences of equal length are used to map between integers and strings,
+      with strings and integers at the same index detailing the mapping.<br>
+      Each operator converts either integers to strings or strings to integers, depending
+      on which default value attribute is provided. Only one default value attribute
+      should be defined.<br>
+      If the string default value is set, it will convert integers to strings.
+      If the int default value is set, it will convert strings to integers.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>cats_int64s</tt> : list of ints</dt>
+<dd>The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.</dd>
+<dt><tt>cats_strings</tt> : list of strings</dt>
+<dd>The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence</dd>
+<dt><tt>default_int64</tt> : int (default is -1)</dt>
+<dd>An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.</dd>
+<dt><tt>default_string</tt> : string (default is _Unused)</dt>
+<dd>A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Output data. If strings are input, the output values are integers, and vice versa.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(string), tensor(int64)</dt>
+<dd>The input must be a tensor of strings or integers, either [N,C] or [C].</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output is a tensor of strings or integers. Its shape will be the same as the input shape.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.DictVectorizer-1"></a>**ai.onnx.ml.DictVectorizer-1**</a>
+
+  Uses an index mapping to convert a dictionary to an array.<br>
+      Given a dictionary, each key is looked up in the vocabulary attribute corresponding to
+      the key type. The index into the vocabulary array at which the key is found is then
+      used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.<br>
+      The key type of the input map must correspond to the element type of the defined vocabulary attribute.
+      Therefore, the output array will be equal in length to the index mapping vector parameter.
+      All keys in the input dictionary must be present in the index mapping vector.
+      For each item in the input dictionary, insert its value in the output array.
+      Any keys not present in the input dictionary, will be zero in the output array.<br>
+      For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``,
+      then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>int64_vocabulary</tt> : list of ints</dt>
+<dd>An integer vocabulary array.<br>One and only one of the vocabularies must be defined.</dd>
+<dt><tt>string_vocabulary</tt> : list of strings</dt>
+<dd>A string vocabulary array.<br>One and only one of the vocabularies must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>A dictionary.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>A 1-D tensor holding values from the input dictionary.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : map(string, int64), map(int64, string), map(int64, float), map(int64, double), map(string, float), map(string, double)</dt>
+<dd>The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.</dd>
+<dt><tt>T2</tt> : tensor(int64), tensor(float), tensor(double), tensor(string)</dt>
+<dd>The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.FeatureVectorizer-1"></a>**ai.onnx.ml.FeatureVectorizer-1**</a>
+
+  Concatenates input tensors into one continuous output.<br>
+      All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C].
+      Inputs are copied to the output maintaining the order of the input arguments.<br>
+      All inputs must be integers or floats, while the output will be all floating point values.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>inputdimensions</tt> : list of ints</dt>
+<dd>The size of each input in the input list</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>X</tt> (variadic) : T1</dt>
+<dd>An ordered collection of tensors, all with the same element type.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>The output array, elements ordered as the inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int32), tensor(int64), tensor(float), tensor(double)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.Imputer-1"></a>**ai.onnx.ml.Imputer-1**</a>
+
+  Replaces inputs that equal one value with another, leaving all other elements alone.<br>
+      This operator is typically used to replace missing values in situations where they have a canonical
+      representation, such as -1, 0, NaN, or some extreme value.<br>
+      One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor
+      holds floats, integers if the input tensor holds integers. The imputed values must all fit within the
+      width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,
+      which one depends on whether floats or integers are being processed.<br>
+      The imputed_value attribute length can be 1 element, or it can have one element per input feature.<br>In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>imputed_value_floats</tt> : list of floats</dt>
+<dd>Value(s) to change to</dd>
+<dt><tt>imputed_value_int64s</tt> : list of ints</dt>
+<dd>Value(s) to change to.</dd>
+<dt><tt>replaced_value_float</tt> : float (default is 0.0)</dt>
+<dd>A value that needs replacing.</dd>
+<dt><tt>replaced_value_int64</tt> : int (default is 0)</dt>
+<dd>A value that needs replacing.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be processed.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Imputed output data</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.LabelEncoder-1"></a>**ai.onnx.ml.LabelEncoder-1**</a>
+
+  Converts strings to integers and vice versa.<br>
+      If the string default value is set, it will convert integers to strings.
+      If the int default value is set, it will convert strings to integers.<br>
+      Each operator converts either integers to strings or strings to integers, depending
+      on which default value attribute is provided. Only one default value attribute
+      should be defined.<br>
+      When converting from integers to strings, the string is fetched from the
+      'classes_strings' list, by simple indexing.<br>
+      When converting from strings to integers, the string is looked up in the list
+      and the index at which it is found is used as the converted value.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classes_strings</tt> : list of strings</dt>
+<dd>A list of labels.</dd>
+<dt><tt>default_int64</tt> : int (default is -1)</dt>
+<dd>An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.</dd>
+<dt><tt>default_string</tt> : string (default is _Unused)</dt>
+<dd>A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Output data. If strings are input, the output values are integers, and vice versa.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(string), tensor(int64)</dt>
+<dd>The input type must be a tensor of integers or strings, of any shape.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output type will be a tensor of strings or integers, and will have the same shape as the input.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.LinearClassifier-1"></a>**ai.onnx.ml.LinearClassifier-1**</a>
+
+  Linear classifier
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classlabels_ints</tt> : list of ints</dt>
+<dd>Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels when using string labels. One and only one 'classlabels' attribute must be defined.</dd>
+<dt><tt>coefficients</tt> : list of floats (required)</dt>
+<dd>A collection of weights of the model(s).</dd>
+<dt><tt>intercepts</tt> : list of floats</dt>
+<dd>A collection of intercepts.</dd>
+<dt><tt>multi_class</tt> : int (default is 0)</dt>
+<dd>Indicates whether to do OvR or multinomial (0=OvR is the default).</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Data to be classified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Classification outputs (one class per example).</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>Classification scores ([N,E] - one score for each class and example</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output will be a tensor of strings or integers.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.LinearRegressor-1"></a>**ai.onnx.ml.LinearRegressor-1**</a>
+
+  Generalized linear regression evaluation.<br>
+      If targets is set to 1 (default) then univariate regression is performed.<br>
+      If targets is set to M then M sets of coefficients must be passed in as a sequence
+      and M results will be output for each input n in N.<br>
+      The coefficients array is of length n, and the coefficients for each target are contiguous.
+      Intercepts are optional but if provided must match the number of targets.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coefficients</tt> : list of floats</dt>
+<dd>Weights of the model(s).</dd>
+<dt><tt>intercepts</tt> : list of floats</dt>
+<dd>Weights of the intercepts, if used.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>targets</tt> : int (default is 1)</dt>
+<dd>The total number of regression targets, 1 if not defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be regressed.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Regression outputs (one per target, per example).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.Normalizer-1"></a>**ai.onnx.ml.Normalizer-1**</a>
+
+  Normalize the input.  There are three normalization modes, which have the corresponding formulas,
+      defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':<br>
+  <br>
+      Max: Y = X / max(X)<br>
+      L1:  Y = X / sum(X)<br>
+      L2:  Y = sqrt(X^2 / sum(X^2)}<br>
+      In all modes, if the divisor is zero, Y == X.
+  <br>
+      For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row
+      of the batch is normalized independently.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>norm</tt> : string (default is MAX)</dt>
+<dd>One of 'MAX,' 'L1,' 'L2'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be encoded, a tensor of shape [N,C] or [C]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Encoded output data</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.OneHotEncoder-1"></a>**ai.onnx.ml.OneHotEncoder-1**</a>
+
+  Replace each input element with an array of ones and zeros, where a single
+      one is placed at the index of the category that was passed in. The total category count
+      will determine the size of the extra dimension of the output array Y.<br>
+      For example, if we pass a tensor with a single value of 4, and a category count of 8,
+      the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.<br>
+      This operator assumes every input feature is from the same set of categories.<br>
+      If the input is a tensor of float, int32, or double, the data will be cast
+      to integers and the cats_int64s category list will be used for the lookups.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>cats_int64s</tt> : list of ints</dt>
+<dd>List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.</dd>
+<dt><tt>cats_strings</tt> : list of strings</dt>
+<dd>List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined.</dd>
+<dt><tt>zeros</tt> : int (default is 1)</dt>
+<dd>If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be encoded.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Encoded output data, having one more dimension than X.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(string), tensor(int64), tensor(int32), tensor(float), tensor(double)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.SVMClassifier-1"></a>**ai.onnx.ml.SVMClassifier-1**</a>
+
+  Support Vector Machine classifier
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classlabels_ints</tt> : list of ints</dt>
+<dd>Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>coefficients</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>kernel_params</tt> : list of floats</dt>
+<dd>List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.</dd>
+<dt><tt>kernel_type</tt> : string (default is LINEAR)</dt>
+<dd>The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>prob_a</tt> : list of floats</dt>
+<dd>First set of probability coefficients.</dd>
+<dt><tt>prob_b</tt> : list of floats</dt>
+<dd>Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.</dd>
+<dt><tt>rho</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>support_vectors</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>vectors_per_class</tt> : list of ints</dt>
+<dd></dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Data to be classified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Classification outputs (one class per example).</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type, either [C] or [N,C].</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.SVMRegressor-1"></a>**ai.onnx.ml.SVMRegressor-1**</a>
+
+  Support Vector Machine regression prediction and one-class SVM anomaly detection.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coefficients</tt> : list of floats</dt>
+<dd>Support vector coefficients.</dd>
+<dt><tt>kernel_params</tt> : list of floats</dt>
+<dd>List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.</dd>
+<dt><tt>kernel_type</tt> : string (default is LINEAR)</dt>
+<dd>The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.</dd>
+<dt><tt>n_supports</tt> : int (default is 0)</dt>
+<dd>The number of support vectors.</dd>
+<dt><tt>one_class</tt> : int (default is 0)</dt>
+<dd>Flag indicating whether the regression is a one-class SVM or not.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'</dd>
+<dt><tt>rho</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>support_vectors</tt> : list of floats</dt>
+<dd>Chosen support vectors</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be regressed.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Regression outputs (one score per target per example).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type, either [C] or [N,C].</dd>
+</dl>
+
+### <a name="ai.onnx.ml.Scaler-1"></a>**ai.onnx.ml.Scaler-1**</a>
+
+  Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>offset</tt> : list of floats</dt>
+<dd>First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.</dd>
+<dt><tt>scale</tt> : list of floats</dt>
+<dd>Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be scaled.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Scaled output data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.TreeEnsembleClassifier-1"></a>**ai.onnx.ml.TreeEnsembleClassifier-1**</a>
+
+  Tree Ensemble classifier.  Returns the top class for each of N inputs.<br>
+      The attributes named 'nodes_X' form a sequence of tuples, associated by
+      index into the sequences, which must all be of equal length. These tuples
+      define the nodes.<br>
+      Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.
+      A leaf may have multiple votes, where each vote is weighted by
+      the associated class_weights index.<br>
+      One and only one of classlabels_strings or classlabels_int64s
+      will be defined. The class_ids are indices into this list.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>base_values</tt> : list of floats</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>class_ids</tt> : list of ints</dt>
+<dd>The index of the class list that each weight is for.</dd>
+<dt><tt>class_nodeids</tt> : list of ints</dt>
+<dd>node id that this weight is for.</dd>
+<dt><tt>class_treeids</tt> : list of ints</dt>
+<dd>The id of the tree that this node is in.</dd>
+<dt><tt>class_weights</tt> : list of floats</dt>
+<dd>The weight for the class in class_id.</dd>
+<dt><tt>classlabels_int64s</tt> : list of ints</dt>
+<dd>Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>nodes_falsenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is false.</dd>
+<dt><tt>nodes_featureids</tt> : list of ints</dt>
+<dd>Feature id for each node.</dd>
+<dt><tt>nodes_hitrates</tt> : list of floats</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_missing_value_tracks_true</tt> : list of ints</dt>
+<dd>For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.</dd>
+<dt><tt>nodes_modes</tt> : list of strings</dt>
+<dd>The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'</dd>
+<dt><tt>nodes_nodeids</tt> : list of ints</dt>
+<dd>Node id for each node. Ids may restart at zero for each tree, but it not required to.</dd>
+<dt><tt>nodes_treeids</tt> : list of ints</dt>
+<dd>Tree id for each node.</dd>
+<dt><tt>nodes_truenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is true.</dd>
+<dt><tt>nodes_values</tt> : list of floats</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input of shape [N,F]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>N, Top class for each point</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>The class score for each class, for each point, a tensor of shape [N,E].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.TreeEnsembleRegressor-1"></a>**ai.onnx.ml.TreeEnsembleRegressor-1**</a>
+
+  Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
+      All args with nodes_ are fields of a tuple of tree nodes, and
+      it is assumed they are the same length, and an index i will decode the
+      tuple across these inputs.  Each node id can appear only once
+      for each tree id.<br>
+      All fields prefixed with target_ are tuples of votes at the leaves.<br>
+      A leaf may have multiple votes, where each vote is weighted by
+      the associated target_weights index.<br>
+      All trees must have their node ids start at 0 and increment by 1.<br>
+      Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>aggregate_function</tt> : string (default is SUM)</dt>
+<dd>Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'</dd>
+<dt><tt>base_values</tt> : list of floats</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>n_targets</tt> : int</dt>
+<dd>The total number of targets.</dd>
+<dt><tt>nodes_falsenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is false</dd>
+<dt><tt>nodes_featureids</tt> : list of ints</dt>
+<dd>Feature id for each node.</dd>
+<dt><tt>nodes_hitrates</tt> : list of floats</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_missing_value_tracks_true</tt> : list of ints</dt>
+<dd>For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.</dd>
+<dt><tt>nodes_modes</tt> : list of strings</dt>
+<dd>The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'</dd>
+<dt><tt>nodes_nodeids</tt> : list of ints</dt>
+<dd>Node id for each node. Node ids must restart at zero for each tree and increase sequentially.</dd>
+<dt><tt>nodes_treeids</tt> : list of ints</dt>
+<dd>Tree id for each node.</dd>
+<dt><tt>nodes_truenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is true</dd>
+<dt><tt>nodes_values</tt> : list of floats</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>target_ids</tt> : list of ints</dt>
+<dd>The index of the target that each weight is for</dd>
+<dt><tt>target_nodeids</tt> : list of ints</dt>
+<dd>The node id of each weight</dd>
+<dt><tt>target_treeids</tt> : list of ints</dt>
+<dd>The id of the tree that each node is in.</dd>
+<dt><tt>target_weights</tt> : list of floats</dt>
+<dd>The weight for each target</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input of shape [N,F]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>N classes</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.ZipMap-1"></a>**ai.onnx.ml.ZipMap-1**</a>
+
+  Creates a map from the input and the attributes.<br>
+      The values are provided by the input tensor, while the keys are specified by the attributes.
+      Must provide keys in either classlabels_strings or classlabels_int64s (but not both).<br>
+      The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.<br>
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classlabels_int64s</tt> : list of ints</dt>
+<dd>The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : tensor(float)</dt>
+<dd>The input values</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>The output map</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : seq(map(string, float)), seq(map(int64, float))</dt>
+<dd>The output will be a sequence of string or integer maps to float.</dd>
+</dl>
+
+## Version 2 of the 'ai.onnx.ml' operator set
+### <a name="ai.onnx.ml.LabelEncoder-2"></a>**ai.onnx.ml.LabelEncoder-2**</a>
+
+  Maps each element in the input tensor to another value.<br>
+      The mapping is determined by the two parallel attributes, 'keys_*' and
+      'values_*' attribute. The i-th value in the specified 'keys_*' attribute
+      would be mapped to the i-th value in the specified 'values_*' attribute. It
+      implies that input's element type and the element type of the specified
+      'keys_*' should be identical while the output type is identical to the
+      specified 'values_*' attribute. If an input element can not be found in the
+      specified 'keys_*' attribute, the 'default_*' that matches the specified
+      'values_*' attribute may be used as its output value.<br>
+      Let's consider an example which maps a string tensor to an integer tensor.
+      Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6],
+      and 'default_int64' is '-1'.  The input ["Dori", "Amy", "Amy", "Sally",
+      "Sally"] would be mapped to [-1, 5, 5, 6, 6].<br>
+      Since this operator is an one-to-one mapping, its input and output shapes
+      are the same. Notice that only one of 'keys_*'/'values_*' can be set.<br>
+      For key look-up, bit-wise comparison is used so even a float NaN can be
+      mapped to a value in 'values_*' attribute.<br>
+
+#### Version
+
+This version of the operator has been available since version 2 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>default_float</tt> : float (default is -0.0)</dt>
+<dd>A float.</dd>
+<dt><tt>default_int64</tt> : int (default is -1)</dt>
+<dd>An integer.</dd>
+<dt><tt>default_string</tt> : string (default is _Unused)</dt>
+<dd>A string.</dd>
+<dt><tt>keys_floats</tt> : list of floats</dt>
+<dd>A list of floats.</dd>
+<dt><tt>keys_int64s</tt> : list of ints</dt>
+<dd>A list of ints.</dd>
+<dt><tt>keys_strings</tt> : list of strings</dt>
+<dd>A list of strings. One and only one of 'keys_*'s should be set.</dd>
+<dt><tt>values_floats</tt> : list of floats</dt>
+<dd>A list of floats.</dd>
+<dt><tt>values_int64s</tt> : list of ints</dt>
+<dd>A list of ints.</dd>
+<dt><tt>values_strings</tt> : list of strings</dt>
+<dd>A list of strings. One and only one of 'value_*'s should be set.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data. It can be either tensor or scalar.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Output data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(string), tensor(int64), tensor(float)</dt>
+<dd>The input type is a tensor of any shape.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64), tensor(float)</dt>
+<dd>Output type is determined by the specified 'values_*' attribute.</dd>
+</dl>
+
+## Version 3 of the 'ai.onnx.ml' operator set
+### <a name="ai.onnx.ml.TreeEnsembleClassifier-3"></a>**ai.onnx.ml.TreeEnsembleClassifier-3**</a>
+
+  Tree Ensemble classifier. Returns the top class for each of N inputs.<br>
+      The attributes named 'nodes_X' form a sequence of tuples, associated by
+      index into the sequences, which must all be of equal length. These tuples
+      define the nodes.<br>
+      Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.
+      A leaf may have multiple votes, where each vote is weighted by
+      the associated class_weights index.<br>
+      One and only one of classlabels_strings or classlabels_int64s
+      will be defined. The class_ids are indices into this list.
+      All fields ending with <i>_as_tensor</i> can be used instead of the
+      same parameter without the suffix if the element type is double and not float.
+
+#### Version
+
+This version of the operator has been available since version 3 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>base_values</tt> : list of floats</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>base_values_as_tensor</tt> : tensor</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>class_ids</tt> : list of ints</dt>
+<dd>The index of the class list that each weight is for.</dd>
+<dt><tt>class_nodeids</tt> : list of ints</dt>
+<dd>node id that this weight is for.</dd>
+<dt><tt>class_treeids</tt> : list of ints</dt>
+<dd>The id of the tree that this node is in.</dd>
+<dt><tt>class_weights</tt> : list of floats</dt>
+<dd>The weight for the class in class_id.</dd>
+<dt><tt>class_weights_as_tensor</tt> : tensor</dt>
+<dd>The weight for the class in class_id.</dd>
+<dt><tt>classlabels_int64s</tt> : list of ints</dt>
+<dd>Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>nodes_falsenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is false.</dd>
+<dt><tt>nodes_featureids</tt> : list of ints</dt>
+<dd>Feature id for each node.</dd>
+<dt><tt>nodes_hitrates</tt> : list of floats</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_hitrates_as_tensor</tt> : tensor</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_missing_value_tracks_true</tt> : list of ints</dt>
+<dd>For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.</dd>
+<dt><tt>nodes_modes</tt> : list of strings</dt>
+<dd>The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'</dd>
+<dt><tt>nodes_nodeids</tt> : list of ints</dt>
+<dd>Node id for each node. Ids may restart at zero for each tree, but it not required to.</dd>
+<dt><tt>nodes_treeids</tt> : list of ints</dt>
+<dd>Tree id for each node.</dd>
+<dt><tt>nodes_truenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is true.</dd>
+<dt><tt>nodes_values</tt> : list of floats</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>nodes_values_as_tensor</tt> : tensor</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input of shape [N,F]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>N, Top class for each point</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>The class score for each class, for each point, a tensor of shape [N,E].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.</dd>
+</dl>
+
+### <a name="ai.onnx.ml.TreeEnsembleRegressor-3"></a>**ai.onnx.ml.TreeEnsembleRegressor-3**</a>
+
+  Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
+      All args with nodes_ are fields of a tuple of tree nodes, and
+      it is assumed they are the same length, and an index i will decode the
+      tuple across these inputs.  Each node id can appear only once
+      for each tree id.<br>
+      All fields prefixed with target_ are tuples of votes at the leaves.<br>
+      A leaf may have multiple votes, where each vote is weighted by
+      the associated target_weights index.<br>
+      All fields ending with <i>_as_tensor</i> can be used instead of the
+      same parameter without the suffix if the element type is double and not float.
+      All trees must have their node ids start at 0 and increment by 1.<br>
+      Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
+
+#### Version
+
+This version of the operator has been available since version 3 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>aggregate_function</tt> : string (default is SUM)</dt>
+<dd>Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'</dd>
+<dt><tt>base_values</tt> : list of floats</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>base_values_as_tensor</tt> : tensor</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>n_targets</tt> : int</dt>
+<dd>The total number of targets.</dd>
+<dt><tt>nodes_falsenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is false</dd>
+<dt><tt>nodes_featureids</tt> : list of ints</dt>
+<dd>Feature id for each node.</dd>
+<dt><tt>nodes_hitrates</tt> : list of floats</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_hitrates_as_tensor</tt> : tensor</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_missing_value_tracks_true</tt> : list of ints</dt>
+<dd>For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.</dd>
+<dt><tt>nodes_modes</tt> : list of strings</dt>
+<dd>The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'</dd>
+<dt><tt>nodes_nodeids</tt> : list of ints</dt>
+<dd>Node id for each node. Node ids must restart at zero for each tree and increase sequentially.</dd>
+<dt><tt>nodes_treeids</tt> : list of ints</dt>
+<dd>Tree id for each node.</dd>
+<dt><tt>nodes_truenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is true</dd>
+<dt><tt>nodes_values</tt> : list of floats</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>nodes_values_as_tensor</tt> : tensor</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>target_ids</tt> : list of ints</dt>
+<dd>The index of the target that each weight is for</dd>
+<dt><tt>target_nodeids</tt> : list of ints</dt>
+<dd>The node id of each weight</dd>
+<dt><tt>target_treeids</tt> : list of ints</dt>
+<dd>The id of the tree that each node is in.</dd>
+<dt><tt>target_weights</tt> : list of floats</dt>
+<dd>The weight for each target</dd>
+<dt><tt>target_weights_as_tensor</tt> : tensor</dt>
+<dd>The weight for each target</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input of shape [N,F]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>N classes</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+</dl>
+
diff --git a/third_party/onnx/docs/Changelog.md b/third_party/onnx/docs/Changelog.md
new file mode 100644
index 0000000000..51fa7cf23d
--- /dev/null
+++ b/third_party/onnx/docs/Changelog.md
@@ -0,0 +1,21442 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+## Operator Changelog
+*This file is automatically generated from the
+            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).
+            Do not modify directly and instead edit operator definitions.*
+
+For an operator input/output's differentiability, it can be differentiable,
+            non-differentiable, or undefined. If a variable's differentiability
+            is not specified, that variable has undefined differentiability.
+
+# ai.onnx (default)
+## Version 1 of the default ONNX operator set
+### <a name="Abs-1"></a>**Abs-1**</a>
+
+  Absolute takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the absolute is, y = abs(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Add-1"></a>**Add-1**</a>
+
+  Performs element-wise binary addition (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="And-1"></a>**And-1**</a>
+
+  Returns the tensor resulted from performing the `and` logical operation
+  elementwise on the input tensors `A` and `B`.
+
+  If broadcasting is enabled, the right-hand-side argument will be broadcasted
+  to match the shape of left-hand-side argument. See the doc of `Add` for a
+  detailed description of the broadcasting rules.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Left input tensor for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Right input tensor for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="ArgMax-1"></a>**ArgMax-1**</a>
+
+  Computes the indices of the max elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="ArgMin-1"></a>**ArgMin-1**</a>
+
+  Computes the indices of the min elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="AveragePool-1"></a>**AveragePool-1**</a>
+
+  AveragePool consumes an input tensor X and applies average pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   average pooling consisting of computing the average on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is divided by the number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="BatchNormalization-1"></a>**BatchNormalization-1**</a>
+
+  Carries out batch normalization as described in the paper
+  https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+  there are multiple cases for the number of outputs, which we list below:
+
+  Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+  Output case #2: Y (test mode)
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints (required)</dt>
+<dd>legacy optimization attribute.</dd>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero, default is 1e-5f.</dd>
+<dt><tt>is_test</tt> : int (default is 0)</dt>
+<dd>If set to nonzero, run spatial batch normalization in test mode, default is 0.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.</dd>
+<dt><tt>spatial</tt> : int (default is 1)</dt>
+<dd>If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input 4-dimensional tensor of shape NCHW.</dd>
+<dt><tt>scale</tt> : T</dt>
+<dd>The scale as a 1-dimensional tensor of size C to be applied to the output.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>The bias as a 1-dimensional tensor of size C to be applied to the output.</dd>
+<dt><tt>mean</tt> : T</dt>
+<dd>The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.</dd>
+<dt><tt>var</tt> : T</dt>
+<dd>The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.</dd>
+</dl>
+
+#### Outputs (1 - 5)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>The output 4-dimensional tensor of the same shape as X.</dd>
+<dt><tt>mean</tt> (optional) : T</dt>
+<dd>The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.</dd>
+<dt><tt>var</tt> (optional) : T</dt>
+<dd>The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.</dd>
+<dt><tt>saved_mean</tt> (optional) : T</dt>
+<dd>Saved mean used during training to speed up gradient computation. Should not be used for testing.</dd>
+<dt><tt>saved_var</tt> (optional) : T</dt>
+<dd>Saved variance used during training to speed up gradient computation. Should not be used for testing.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Cast-1"></a>**Cast-1**</a>
+
+  The operator casts the elements of a given input tensor to a data type
+  specified by the 'to' argument and returns an output tensor of the same size in
+  the converted type. The 'to' argument must be one of the data types specified
+  in the 'DataType' enum field in the TensorProto message.
+  NOTE: Casting to and from strings is not supported yet.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>to</tt> : string (required)</dt>
+<dd>The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to be cast.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor with the same shape as input with type specified by the 'to' argument</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain input types. Casting from strings and complex are not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain output types. Casting to strings and complex are not supported.</dd>
+</dl>
+
+### <a name="Ceil-1"></a>**Ceil-1**</a>
+
+  Ceil takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the ceil is, y = ceil(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Clip-1"></a>**Clip-1**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified with arguments 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max() respectively.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+<dt><tt>max</tt> : float</dt>
+<dd>Maximum value, above which element is replaced by max</dd>
+<dt><tt>min</tt> : float</dt>
+<dd>Minimum value, under which element is replaced by min</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Concat-1"></a>**Concat-1**</a>
+
+  Concatenate a list of tensors into a single tensor
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>Which axis to concat on.  Default value is 1.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic) : T</dt>
+<dd>List of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+### <a name="Constant-1"></a>**Constant-1**</a>
+
+  A constant tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>value</tt> : tensor (required)</dt>
+<dd>The value for the elements of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor containing the same value of the provided tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Conv-1"></a>**Conv-1**</a>
+
+  The convolution operator consumes an input tensor and a filter, and
+  computes the output.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. </dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ConvTranspose-1"></a>**ConvTranspose-1**</a>
+
+  The convolution transpose operator consumes an input tensor and a filter,
+  and computes the output.
+
+  If the pads parameter is provided the shape of the output is calculated via the following equation:
+
+    output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]
+
+  output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
+
+    total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
+    If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
+    Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
+
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
+<dt><tt>output_padding</tt> : list of ints</dt>
+<dd>The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.</dd>
+<dt><tt>output_shape</tt> : list of ints</dt>
+<dd>The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="DepthToSpace-1"></a>**DepthToSpace-1**</a>
+
+  DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+  This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+  the input tensor where values from the depth dimension are moved in spatial blocks to the height
+  and width dimensions.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Div-1"></a>**Div-1**</a>
+
+  Performs element-wise binary division (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Dropout-1"></a>**Dropout-1**</a>
+
+  Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
+  output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
+  test mode or not, the output Y will either be a random dropout, or a simple
+  copy of the input. Note that our implementation of Dropout does scaling in
+  the training phase, so during testing nothing needs to be done.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+<dt><tt>is_test</tt> : int (default is 0)</dt>
+<dd>(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.</dd>
+<dt><tt>ratio</tt> : float (default is 0.5)</dt>
+<dd>(float, default 0.5) the ratio of random dropout</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>The input data as Tensor.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional) : T</dt>
+<dd>The output mask. If is_test is nonzero, this output is not filled.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Elu-1"></a>**Elu-1**</a>
+
+  Elu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
+  0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Coefficient of ELU default to 1.0.</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Equal-1"></a>**Equal-1**</a>
+
+  Returns the tensor resulted from performing the `equal` logical operation
+  elementwise on the input tensors `A` and `B`.
+
+  If broadcasting is enabled, the right-hand-side argument will be broadcasted
+  to match the shape of left-hand-side argument. See the doc of `Add` for a
+  detailed description of the broadcasting rules.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Left input tensor for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Right input tensor for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input to integral tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Exp-1"></a>**Exp-1**</a>
+
+  Calculates the exponential of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The exponential of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Flatten-1"></a>**Flatten-1**</a>
+
+  Flattens the input tensor into a 2D matrix. If input tensor has shape
+  (d_0, d_1, ... d_n) then the output will have shape
+  (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>A tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Floor-1"></a>**Floor-1**</a>
+
+  Floor takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the floor is, y = floor(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="GRU-1"></a>**GRU-1**</a>
+
+  Computes an one-layer GRU. This operator is usually supported via some custom
+  implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `z` - update gate
+
+  `r` - reset gate
+
+  `h` - hidden gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+  `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+  `Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+  `Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+  `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+  `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+  `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+  `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh):
+
+    - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz)
+
+    - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr)
+
+    - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0
+
+    - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0
+
+    - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is foward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>output_sequence</tt> : int (default is 0)</dt>
+<dd>The sequence output for the hidden is optional if 0. Default 0.</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.</dd>
+<dt><tt>Y_h</tt> : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="Gather-1"></a>**Gather-1**</a>
+
+  Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+  entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+  them in an output tensor of rank q + (r - 1).
+  Example 1:
+  ```
+    data = [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+    indices = [
+        [0, 1],
+        [1, 2],
+    ]
+    output = [
+        [
+            [1.0, 1.2],
+            [2.3, 3.4],
+        ],
+        [
+            [2.3, 3.4],
+            [4.5, 5.7],
+        ],
+    ]
+  ```
+  Example 2:
+  ```
+    data = [
+        [1.0, 1.2, 1.9],
+        [2.3, 3.4, 3.9],
+        [4.5, 5.7, 5.9],
+    ]
+    indices = [
+        [0, 2],
+    ]
+    axis = 1,
+    output = [
+        [
+            [1.0, 1.9],
+            [2.3, 3.9],
+            [4.5, 5.9],
+        ],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : Tind</dt>
+<dd>Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank q + (r - 1).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="Gemm-1"></a>**Gemm-1**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+  Compute Y = alpha * A * B + beta * C, where input tensor A has
+  dimension (M X K), input tensor B has dimension (K X N), input tensor C and
+  output tensor Y have dimension (M X N).
+  If attribute broadcast is non-zero, input tensor C will be broadcasted to match
+  the dimension requirement. A will be transposed before doing the computation
+  if attribute transA is non-zero, same for B and transB.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B, the default value is 1.0.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C, the default value is 1.0.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Whether C should be broadcasted</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Input tensor A</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Input tensor B</dd>
+<dt><tt>C</tt> : T</dt>
+<dd>Input tensor C, can be inplace.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="GlobalAveragePool-1"></a>**GlobalAveragePool-1**</a>
+
+  GlobalAveragePool consumes an input tensor X and applies average pooling across
+   the values in the same channel. This is equivalent to AveragePool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="GlobalLpPool-1"></a>**GlobalLpPool-1**</a>
+
+  GlobalLpPool consumes an input tensor X and applies lp pool pooling across the
+   the values in the same channel. This is equivalent to LpPool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>p</tt> : float (default is 2.0)</dt>
+<dd>p value of the Lp norm used to pool over the input data, default is 2.0.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="GlobalMaxPool-1"></a>**GlobalMaxPool-1**</a>
+
+  GlobalMaxPool consumes an input tensor X and applies max pooling across
+   the values in the same channel. This is equivalent to MaxPool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Greater-1"></a>**Greater-1**</a>
+
+  Returns the tensor resulted from performing the `greater` logical operation
+  elementwise on the input tensors `A` and `B`.
+
+  If broadcasting is enabled, the right-hand-side argument will be broadcasted
+  to match the shape of left-hand-side argument. See the doc of `Add` for a
+  detailed description of the broadcasting rules.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Left input tensor for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Right input tensor for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="HardSigmoid-1"></a>**HardSigmoid-1**</a>
+
+  HardSigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.2)</dt>
+<dd>Value of alpha default to 0.2</dd>
+<dt><tt>beta</tt> : float (default is 0.5)</dt>
+<dd>Value of beta default to 0.5</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Hardmax-1"></a>**Hardmax-1**</a>
+
+  The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch
+   of the given input. The input is a 2-D tensor (Tensor<float>) of size
+  (batch_size x input_feature_dimensions). The output tensor has the same shape
+  and contains the hardmax values of the corresponding input.
+
+  Input does not need to explicitly be a 2D vector; rather, it will be
+  coerced into one. For an arbitrary n-dimensional tensor
+  input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+  the axis provided, then input will be coerced into a 2-dimensional tensor with
+  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+  case where axis=1, this means the input tensor will be coerced into a 2D tensor
+  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+  Each of these dimensions must be matched correctly, or else the operator
+  will throw errors.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input tensor that's coerced into a 2D matrix of size (NxD) as described above.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output values with the same shape as input tensor (the original size without coercion).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Identity-1"></a>**Identity-1**</a>
+
+  Identity operator
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor to copy input into.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="If-1"></a>**If-1**</a>
+
+  If conditional
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>else_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.</dd>
+<dt><tt>then_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>cond</tt> : B</dt>
+<dd>Condition for the if</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Only bool</dd>
+</dl>
+
+### <a name="InstanceNormalization-1"></a>**InstanceNormalization-1**</a>
+
+  Carries out instance normalization as described in the paper
+  https://arxiv.org/abs/1607.08022.
+
+  y = scale * (x - mean) / sqrt(variance + epsilon) + B,
+  where mean and variance are computed per instance per channel.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero, default is 1e-5f.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input 4-dimensional tensor of shape NCHW.</dd>
+<dt><tt>scale</tt> : T</dt>
+<dd>The input 1-dimensional scale tensor of size C.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>The input 1-dimensional bias tensor of size C.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output 4-dimensional tensor of the same shape as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LRN-1"></a>**LRN-1**</a>
+
+  Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+  It normalizes over local input regions.
+  The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+  of shape (N x C x D1 x D2, ..., Dk), its region is
+  {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
+
+  square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+  where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
+
+  Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.0001)</dt>
+<dd>Scaling parameter.</dd>
+<dt><tt>beta</tt> : float (default is 0.75)</dt>
+<dd>The exponent.</dd>
+<dt><tt>bias</tt> : float (default is 1.0)</dt>
+<dd></dd>
+<dt><tt>size</tt> : int (required)</dt>
+<dd>The number of channels to sum over</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor, which has the shape and type as input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output  types to float tensors.</dd>
+</dl>
+
+### <a name="LSTM-1"></a>**LSTM-1**</a>
+
+  Computes an one-layer LSTM. This operator is usually supported via some
+  custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `o` - output gate
+
+  `f` - forget gate
+
+  `c` - cell gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+  `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+  `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+  `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+  `P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+  `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+  `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+  `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+  `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+  `PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+    - it = f(Xt*(Wi^T) + Ht-1*Ri + Pi (.) Ct-1 + Wbi + Rbi)
+
+    - ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf)
+
+    - ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc)
+
+    - Ct = ft (.) Ct-1 + it (.) ct
+
+    - ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo)
+
+    - Ht = ot (.) h(Ct)
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>input_forget</tt> : int (default is 0)</dt>
+<dd>Couple the input and forget gates if 1, default 0.</dd>
+<dt><tt>output_sequence</tt> : int (default is 0)</dt>
+<dd>The sequence output for the hidden is optional if 0. Default 0.</dd>
+</dl>
+
+#### Inputs (3 - 8)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>initial_c</tt> (optional) : T</dt>
+<dd>Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>P</tt> (optional) : T</dt>
+<dd>The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.</dd>
+</dl>
+
+#### Outputs (0 - 3)
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.</dd>
+<dt><tt>Y_h</tt> (optional) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>Y_c</tt> (optional) : T</dt>
+<dd>The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="LeakyRelu-1"></a>**LeakyRelu-1**</a>
+
+  LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+  output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+  `f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.01)</dt>
+<dd>Coefficient of leakage default to 0.01.</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Less-1"></a>**Less-1**</a>
+
+  Returns the tensor resulted from performing the `less` logical operation
+  elementwise on the input tensors `A` and `B`.
+
+  If broadcasting is enabled, the right-hand-side argument will be broadcasted
+  to match the shape of left-hand-side argument. See the doc of `Add` for a
+  detailed description of the broadcasting rules.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Left input tensor for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Right input tensor for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Log-1"></a>**Log-1**</a>
+
+  Calculates the natural log of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The natural log of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LogSoftmax-1"></a>**LogSoftmax-1**</a>
+
+  The operator computes the logsoftmax (log of softmax) values for each layer in the batch
+   of the given input. The input is a 2-D tensor (Tensor<float>) of size
+  (batch_size x input_feature_dimensions). The output tensor has the same shape
+  and contains the logsoftmax values of the corresponding input.
+
+  Input does not need to explicitly be a 2D vector; rather, it will be
+  coerced into one. For an arbitrary n-dimensional tensor
+  input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+  the axis provided, then input will be coerced into a 2-dimensional tensor with
+  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+  case where axis=1, this means the input tensor will be coerced into a 2D tensor
+  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+  Each of these dimensions must be matched correctly, or else the operator
+  will throw errors.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input tensor that's coerced into a 2D matrix of size (NxD) as described above.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output values with the same shape as input tensor (the original size without coercion).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Loop-1"></a>**Loop-1**</a>
+
+  Generic Looping construct. This loop has multiple termination conditions:
+
+  1) Trip count. Iteration count specified at runtime. Set by
+     specifying the input M. Optional. Set to empty string to omit.
+     Note that a static trip count (specified at graph construction time) can be
+     specified by passing in a constant node for input M.
+  2) Loop termination condition. This is an input to the op that determines
+     whether to run the first iteration and also a loop-carried dependency for
+     the body graph. The body graph must yield a value for the condition variable,
+     whether this input is provided or not.
+
+  This table summarizes the operating modes of this operator with equivalent
+  C-style code:
+
+      Operator inputs defined as (max_trip_count, condition_var).
+
+      input ("", ""):
+          for (int i=0; ; ++i) {
+            cond = ... // Note this value is ignored, but is required in the body
+          }
+
+      input ("", cond) // Note this is analogous to a while loop
+          bool cond = ...;
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input ("", 1) // Note this is analogous to a do-while loop
+          bool cond = true
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input (trip_count, "") // Note this is analogous to a for loop
+          int trip_count = ...
+          for (int i=0; i < trip_count; ++i) {
+            cond = ...; // ignored
+          }
+
+      input (trip_count, cond)
+          int trip_count = ...;
+          bool cond = ...;
+          for (int i=0; i < trip_count && cond; ++i) {
+            cond = ...;
+          }
+
+
+  *Sample usage - cond as well as trip count*
+
+      graph predict-net {
+        %a = Constant[value = <Scalar Tensor [3]>]()
+        %b = Constant[value = <Scalar Tensor [6]>]()
+        %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+        %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+        %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+        return
+      }
+
+      graph body-net (
+        %i[INT32, scalar]
+        %keepgoing[BOOL, scalar]
+        %b[INT32, scalar]
+      ) {
+        %my_local = Add(%a, %b)
+        %b_out = Sub(%a, %b)
+        %keepgoing_out = Greater(%my_local, %b_out)
+        %user_defined_vals = Add(%b, %b)
+        return %keepgoing_out, %b_out, %user_defined_vals
+      }
+
+  *Sample equivalent C code*
+
+      {
+        /* User-defined code (enclosing scope) */
+        int a = 3, b = 6;
+        bool keepgoing = true; // Analogous to input cond
+        /* End user-defined code */
+
+        /* Implicitly-defined code */
+        const int max_trip_count = 10; // Analogous to input M
+        int user_defined_vals[]; // Imagine this is resizable
+        /* End implicitly-defined code */
+        for (int i=0; i < max_trip_count && keepgoing; ++i) {
+          /* User-defined code (loop body) */
+          int my_local = a + b; // Reading values in the enclosing scope is fine
+          b = a - b; // writes fine if we specify b as a loop-carried dependency
+          keepgoing = my_local > b; // keepgoing is a loop-carried dependency
+          user_defined_vals[i] = b + b;
+          /* End user-defined code */
+        }
+        // my_local = 123; // Can't do this. my_local was defined in the the body
+
+        // These below values are live-out from the loop and therefore accessible
+        b_out; user_defined_vals; keepgoing_out;
+      }
+
+  There are several things of note in this code snippet:
+
+  1) Values from the enclosing scope (i.e. variable a here) are in scope and can
+     be referenced in the inputs of the loop.
+  2) Any variables which you wish to make available in the enclosing scope (i.e.
+     the variables b and keepgoing) must be declared as either loop-carried
+     dependencies (both at the op inputs and output and at the body net input and
+     output) or scan_outputs.
+  3) Values created in the body cannot be accessed in the enclosing scope.
+
+  Note that the semantics of this op support "diagonal" or "wavefront" execution.
+  (See Step 3 here for an example:
+  https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+  Frontends should emit multi-layer RNNs as a series of While operators (with
+  time being the inner looping dimension), with each successive layer consuming
+  the scan_outputs from the previous layer, possibly going through several
+  point-wise operators (e.g. dropout, residual connections, linear layer).
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>M</tt> (optional) : I</dt>
+<dd>A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.</dd>
+<dt><tt>cond</tt> (optional) : B</dt>
+<dd>A boolean termination condition. Optional. Pass empty string to skip.</dd>
+<dt><tt>v_initial</tt> (variadic, heterogeneous) : V</dt>
+<dd>The initial values of any loop-carried dependencies (values that change across loop iterations)</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>v_final_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final N loop carried dependency values then K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>tensor of int64, which should be a scalar.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>tensor of bool, which should be a scalar.</dd>
+</dl>
+
+### <a name="LpNormalization-1"></a>**LpNormalization-1**</a>
+
+  Given a matrix, apply Lp-normalization along the provided axis.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>The axis on which to apply normalization, -1 mean last axis.</dd>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>The order of the normalization, only 1 or 2 are supported.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input matrix</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Matrix after normalization</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LpPool-1"></a>**LpPool-1**</a>
+
+  LpPool consumes an input tensor X and applies Lp pooling across the
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   Lp pooling consisting of computing the Lp norm on all values of a subset
+   of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>p</tt> : float (default is 2.0)</dt>
+<dd>p value of the Lp norm used to pool over the input data, default is 2.0.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="MatMul-1"></a>**MatMul-1**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>N-dimensional matrix A</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>N-dimensional matrix B</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Matrix multiply results from A * B</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Max-1"></a>**Max-1**</a>
+
+  Element-wise max of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Max.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>max</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="MaxPool-1"></a>**MaxPool-1**</a>
+
+  MaxPool consumes an input tensor X and applies max pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   max pooling consisting of computing the max on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is maximum number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="MaxRoiPool-1"></a>**MaxRoiPool-1**</a>
+
+  ROI max pool consumes an input tensor X and region of interests (RoIs) to
+   apply max pooling across each RoI, to produce output 4-D tensor of shape
+   (num_rois, channels, pooled_shape[0], pooled_shape[1]).
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>pooled_shape</tt> : list of ints (required)</dt>
+<dd>ROI pool output shape (height, width).</dd>
+<dt><tt>spatial_scale</tt> : float (default is 1.0)</dt>
+<dd>Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.</dd>
+<dt><tt>rois</tt> (non-differentiable) : T</dt>
+<dd>RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Mean-1"></a>**Mean-1**</a>
+
+  Element-wise mean of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Mean.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>mean</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Min-1"></a>**Min-1**</a>
+
+  Element-wise min of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Min</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>min</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Mul-1"></a>**Mul-1**</a>
+
+  Performs element-wise binary multiplication (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Neg-1"></a>**Neg-1**</a>
+
+  Neg takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where each element flipped sign, y = -x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Not-1"></a>**Not-1**</a>
+
+  Returns the negation of the input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input/output to boolean tensors.</dd>
+</dl>
+
+### <a name="Or-1"></a>**Or-1**</a>
+
+  Returns the tensor resulted from performing the `or` logical operation
+  elementwise on the input tensors `A` and `B`.
+
+  If broadcasting is enabled, the right-hand-side argument will be broadcasted
+  to match the shape of left-hand-side argument. See the doc of `Add` for a
+  detailed description of the broadcasting rules.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Left input tensor for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Right input tensor for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="PRelu-1"></a>**PRelu-1**</a>
+
+  PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+  output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+  `f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>slope</tt> : T</dt>
+<dd>Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Pad-1"></a>**Pad-1**</a>
+
+  Given `data` tensor, paddings, mode, and value.
+  Example:
+    Insert 0 paddings to the beginning of the second dimension.
+    data = [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+    paddings = [0, 0, 2, 0]
+    output = [
+        [
+            [0.0, 0.0, 1.0, 1.2],
+            [0.0, 0.0, 2.3, 3.4],
+            [0.0, 0.0, 4.5, 5.7],
+        ],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is constant)</dt>
+<dd>Three modes: constant(default), reflect, edge</dd>
+<dt><tt>paddings</tt> : list of ints (required)</dt>
+<dd>List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.</dd>
+<dt><tt>value</tt> : float (default is 0.0)</dt>
+<dd>One float, indicates the value to be filled, default is 0</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor after padding.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Pow-1"></a>**Pow-1**</a>
+
+  Pow takes input data (Tensor<T>) and exponent Tensor, and
+  produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+  is applied to the data tensor elementwise.
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor of any shape, base of the exponent.</dd>
+<dt><tt>Y</tt> : T</dt>
+<dd>Input tensor of any shape broadcastable to X shape, the exponent component.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>Output tensor (same size as X)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="RNN-1"></a>**RNN-1**</a>
+
+  Computes an one-layer simple RNN. This operator is usually supported
+  via some custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `Wi` - W parameter weight matrix for input gate
+
+  `Ri` - R recurrence weight matrix for input gate
+
+  `Wbi` - W parameter bias vector for input gate
+
+  `Rbi` - R parameter bias vector for input gate
+
+  `WBi` - W parameter weight matrix for backward input gate
+
+  `RBi` - R recurrence weight matrix for backward input gate
+
+  `WBbi` - WR bias vectors for backward input gate
+
+  `RBbi` - RR bias vectors for backward input gate
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Tanh):
+
+    - Ht = f(Xt*(Wi^T) + Ht-1*Ri + Wbi + Rbi)
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings (default is ['Tanh', 'Tanh'])</dt>
+<dd>One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>output_sequence</tt> : int (default is 0)</dt>
+<dd>The sequence output for the hidden is optional if 0. Default 0.</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.</dd>
+<dt><tt>Y_h</tt> (optional) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="RandomNormal-1"></a>**RandomNormal-1**</a>
+
+  Generate a tensor with random values drawn from a normal distribution. The shape
+  of the tensor is specified by the `shape` argument and the parameter of the normal distribution
+  specified by `mean` and `scale`.
+
+  The data type is specified by the 'dtype' argument. The 'dtype' argument must
+  be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int (default is 1)</dt>
+<dd>The data type for the elements of the output tensor. Default is TensorProto::FLOAT.</dd>
+<dt><tt>mean</tt> : float (default is 0.0)</dt>
+<dd>The mean of the normal distribution.</dd>
+<dt><tt>scale</tt> : float (default is 1.0)</dt>
+<dd>The standard deviation of the normal distribution.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+<dt><tt>shape</tt> : list of ints (required)</dt>
+<dd>The shape of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of random values drawn from normal distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+### <a name="RandomNormalLike-1"></a>**RandomNormalLike-1**</a>
+
+  Generate a tensor with random values drawn from a normal distribution.
+  The shape of the output tensor is copied from the shape of the input tensor,
+  and the parameters of the normal distribution are specified by `mean` and `scale`.
+
+  The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
+  The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message, and be valid as an output type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.</dd>
+<dt><tt>mean</tt> : float (default is 0.0)</dt>
+<dd>The mean of the normal distribution.</dd>
+<dt><tt>scale</tt> : float (default is 1.0)</dt>
+<dd>The standard deviation of the normal distribution.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to copy shape and optionally type information from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor of random values drawn from normal distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+### <a name="RandomUniform-1"></a>**RandomUniform-1**</a>
+
+  Generate a tensor with random values drawn from a uniform distribution. The shape
+  of the tensor is specified by the `shape` argument and the range by `low` and `high`.
+
+  The data type is specified by the 'dtype' argument. The 'dtype' argument must
+  be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int (default is 1)</dt>
+<dd>The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.</dd>
+<dt><tt>high</tt> : float (default is 1.0)</dt>
+<dd>Upper boundary of the output values.</dd>
+<dt><tt>low</tt> : float (default is 0.0)</dt>
+<dd>Lower boundary of the output values.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+<dt><tt>shape</tt> : list of ints (required)</dt>
+<dd>The shape of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of random values drawn from uniform distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+### <a name="RandomUniformLike-1"></a>**RandomUniformLike-1**</a>
+
+  Generate a tensor with random values drawn from a uniform distribution.
+  The shape of the output tensor is copied from the shape of the input tensor,
+  and the parameters of the uniform distribution are specified by `low` and `high`.
+
+  The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
+  The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message and be valid as an output type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.</dd>
+<dt><tt>high</tt> : float (default is 1.0)</dt>
+<dd>Upper boundary of the output values.</dd>
+<dt><tt>low</tt> : float (default is 0.0)</dt>
+<dd>Lower boundary of the output values.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to copy shape and optionally type information from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor of random values drawn from uniform distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+### <a name="Reciprocal-1"></a>**Reciprocal-1**</a>
+
+  Reciprocal takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ReduceL1-1"></a>**ReduceL1-1**</a>
+
+  Computes the L1 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceL2-1"></a>**ReduceL2-1**</a>
+
+  Computes the L2 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceLogSum-1"></a>**ReduceLogSum-1**</a>
+
+  Computes the log sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceLogSumExp-1"></a>**ReduceLogSumExp-1**</a>
+
+  Computes the log sum exponent of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMax-1"></a>**ReduceMax-1**</a>
+
+  Computes the max of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMean-1"></a>**ReduceMean-1**</a>
+
+  Computes the mean of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMin-1"></a>**ReduceMin-1**</a>
+
+  Computes the min of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceProd-1"></a>**ReduceProd-1**</a>
+
+  Computes the product of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceSum-1"></a>**ReduceSum-1**</a>
+
+  Computes the sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceSumSquare-1"></a>**ReduceSumSquare-1**</a>
+
+  Computes the sum square of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Relu-1"></a>**Relu-1**</a>
+
+  Relu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Reshape-1"></a>**Reshape-1**</a>
+
+  Reshape the input tensor similar to numpy.reshape.
+  It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor.
+  At most one dimension of the new shape can be -1. In this case, the value is
+  inferred from the size of the tensor and the remaining dimensions. A dimension
+  could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+  from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
+  The input tensor's shape and the output tensor's shape are required to have the same number of elements.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+<dt><tt>shape</tt> : list of ints</dt>
+<dd>New shape</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reshaped</tt> : T</dt>
+<dd>Reshaped data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Selu-1"></a>**Selu-1**</a>
+
+  Selu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the scaled exponential linear unit function,
+  `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.6732)</dt>
+<dd>Coefficient of SELU default to 1.6732.</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+<dt><tt>gamma</tt> : float (default is 1.0507)</dt>
+<dd>Coefficient of SELU default to 1.0507.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Shape-1"></a>**Shape-1**</a>
+
+  Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>shape</tt> : T1</dt>
+<dd>Shape of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor.</dd>
+</dl>
+
+### <a name="Sigmoid-1"></a>**Sigmoid-1**</a>
+
+  Sigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+  tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Size-1"></a>**Size-1**</a>
+
+  Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>size</tt> : T1</dt>
+<dd>Total number of elements of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor, which should be a scalar though.</dd>
+</dl>
+
+### <a name="Slice-1"></a>**Slice-1**</a>
+
+  Produces a slice of the input tensor along multiple axes. Similar to numpy:
+  https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+  Slices uses `axes`, `starts` and `ends` attributes to specify the start and end
+  dimension for each axis in the list of axes, it uses this information to
+  slice the input `data` tensor. If a negative value is passed for any of the
+  start or end indices, it represent number of elements before the end of that
+  dimension. If the value passed to start or end is larger than the `n` (the
+  number of elements in this dimension), it represents `n`. For slicing to the
+  end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
+  If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+  Example 1:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    axes = [0, 1]
+    starts = [1, 0]
+    ends = [2, 3]
+    result = [
+        [5, 6, 7],
+    ]
+  Example 2:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    starts = [0, 1]
+    ends = [-1, 1000]
+    result = [
+        [2, 3, 4],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].</dd>
+<dt><tt>ends</tt> : list of ints (required)</dt>
+<dd>Ending indices (exclusive) of corresponding axis in axes`</dd>
+<dt><tt>starts</tt> : list of ints (required)</dt>
+<dd>Starting indices of corresponding axis in `axes`</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of data to extract slices from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Sliced data tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Softmax-1"></a>**Softmax-1**</a>
+
+  The operator computes the softmax (normalized exponential) values for each layer in the batch
+   of the given input. The input is a 2-D tensor (Tensor<float>) of size
+  (batch_size x input_feature_dimensions). The output tensor has the same shape
+  and contains the softmax values of the corresponding input.
+
+  Input does not need to explicitly be a 2D vector; rather, it will be
+  coerced into one. For an arbitrary n-dimensional tensor
+  input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+  the axis provided, then input will be coerced into a 2-dimensional tensor with
+  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+  case where axis=1, this means the input tensor will be coerced into a 2D tensor
+  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+  Each of these dimensions must be matched correctly, or else the operator
+  will throw errors.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input tensor that's coerced into a 2D matrix of size (NxD) as described above.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output values with the same shape as input tensor (the original size without coercion).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Softplus-1"></a>**Softplus-1**</a>
+
+  Softplus takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Softsign-1"></a>**Softsign-1**</a>
+
+  Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The softsign (x/(1+|x|)) values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="SpaceToDepth-1"></a>**SpaceToDepth-1**</a>
+
+  SpaceToDepth rearranges blocks of spatial data into depth. More specifically,
+  this op outputs a copy of the input tensor where values from the height and width dimensions
+  are moved to the depth dimension.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Split-1"></a>**Split-1**</a>
+
+  Split a tensor into a list of tensors, along the specified
+  'axis'. The lengths of the split can be specified using argument 'axis' or
+  optional second input blob to the operator. Otherwise, the tensor is split
+  to equal sized parts.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>Which axis to split on</dd>
+<dt><tt>split</tt> : list of ints</dt>
+<dd>length of each output</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The tensor to split</dd>
+<dt><tt>split</tt> (optional) : T</dt>
+<dd>Optional list of output lengths (see also arg 'split')</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs...</tt> (variadic) : T</dt>
+<dd>One or more outputs forming list of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+</dl>
+
+### <a name="Sqrt-1"></a>**Sqrt-1**</a>
+
+  Square root takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the square root is, y = x^0.5, is applied to
+  the tensor elementwise. If x is negative, then it will return NaN.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Squeeze-1"></a>**Squeeze-1**</a>
+
+  Remove single-dimensional entries from the shape of a tensor.
+  Takes a  parameter `axes` with a list of axes to squeeze.
+  If `axes` is not provided, all the single dimensions will be removed from
+  the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>List of non-negative integers, indicate the dimensions to squeeze.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensors with at least max(dims) dimensions.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>squeezed</tt> : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Sub-1"></a>**Sub-1**</a>
+
+  Performs element-wise binary subtraction (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Sum-1"></a>**Sum-1**</a>
+
+  Element-wise sum of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Sum.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>sum</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Tanh-1"></a>**Tanh-1**</a>
+
+  Calculates the hyperbolic tangent of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>consumed_inputs</tt> : list of ints</dt>
+<dd>legacy optimization attribute.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>1-D input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The hyperbolic tangent values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Tile-1"></a>**Tile-1**</a>
+
+  Repeat the elements of a tensor along an axis.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor of any shape.</dd>
+<dt><tt>tiles</tt> : T</dt>
+<dd>Number of repeated copies to make of the input tensor.</dd>
+<dt><tt>axis</tt> : T</dt>
+<dd>Axis along which to repeat.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of same shape and type as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain tiles and axis's type to int64 tensors.</dd>
+</dl>
+
+### <a name="TopK-1"></a>**TopK-1**</a>
+
+  Retrieve the top-K elements along a specified axis. Given an input tensor of
+  shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+    -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+      which contains the values of the top k elements along the specified axis
+    -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+     contains the indices of the top k elements (original indices from the input
+     tensor).
+  Given two equivalent values, this operator uses the indices along the axis  as
+   a tiebreaker. That is, the element with the lower index will appear first.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>Dimension on which to do the sort.</dd>
+<dt><tt>k</tt> : int (required)</dt>
+<dd>Number of top elements to retrieve</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_n, r]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Values</tt> : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor</dd>
+<dt><tt>Indices</tt> : I</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="Transpose-1"></a>**Transpose-1**</a>
+
+  Transpose the input tensor similar to numpy.transpose. For example, when
+  perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
+  will be (2, 1, 3).
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>perm</tt> : list of ints</dt>
+<dd>A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>transposed</tt> : T</dt>
+<dd>Transposed output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Unsqueeze-1"></a>**Unsqueeze-1**</a>
+
+  Insert single-dimensional entries to the shape of a tensor.
+  Takes one required argument `axes`, a list of dimensions that will be inserted.
+  Dimension indices in `axes` are as seen in the output tensor. For example:
+    Given a tensor such that tensor with shape [3, 4, 5], then
+    Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints (required)</dt>
+<dd>List of non-negative integers, indicate the dimensions to be inserted</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Original tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>expanded</tt> : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Upsample-1"></a>**Upsample-1**</a>
+
+  Upsample the input tensor.
+  The width and height of the output tensor are:
+    output_width = floor(input_width * width_scale),
+    output_height = floor(input_height * height_scale).
+  Example:
+    Given `data` tensor, width_scale, height_scale, mode,
+    Upsample the input 4-D tensor in nearest mode:
+    data = [[[
+        [1, 2],
+        [3, 4]
+    ]]]
+    width_scale = 2
+    height_scale = 2
+    mode = "nearest"
+    output = [[[
+        [1, 1, 2, 2],
+        [1, 1, 2, 2],
+        [3, 3, 4, 4],
+        [3, 3, 4, 4]
+    ]]]
+
+#### Version
+
+No versioning maintained for experimental ops.
+#### Attributes
+
+<dl>
+<dt><tt>height_scale</tt> : float (required)</dt>
+<dd>The scale along height dimension. It takes value greater than or equal to 1.</dd>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Two interpolation modes: nearest(default), bilinear</dd>
+<dt><tt>width_scale</tt> : float (required)</dt>
+<dd>The scale along width dimension. It takes value greater than or equal to 1.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>4-D tensor, [N,C,H,W]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>4-D tensor after resizing, [N,C,H,W]</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to bool, int32, int64, float16, float, double tensors.</dd>
+</dl>
+
+### <a name="Xor-1"></a>**Xor-1**</a>
+
+  Returns the tensor resulted from performing the `xor` logical operation
+  elementwise on the input tensors `A` and `B`.
+
+  If broadcasting is enabled, the right-hand-side argument will be broadcasted
+  to match the shape of left-hand-side argument. See the doc of `Add` for a
+  detailed description of the broadcasting rules.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Left input tensor for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Right input tensor for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+## Version 2 of the default ONNX operator set
+### <a name="GlobalLpPool-2"></a>**GlobalLpPool-2**</a>
+
+  GlobalLpPool consumes an input tensor X and applies lp pool pooling across
+   the values in the same channel. This is equivalent to LpPool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 2 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>p value of the Lp norm used to pool over the input data.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LpPool-2"></a>**LpPool-2**</a>
+
+  LpPool consumes an input tensor X and applies Lp pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   Lp pooling consisting of computing the Lp norm on all values of a subset
+   of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing.
+
+#### Version
+
+This version of the operator has been available since version 2 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>p value of the Lp norm used to pool over the input data.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Pad-2"></a>**Pad-2**</a>
+
+  Given `data` tensor, pads, mode, and value.
+  Example:
+    Insert 0 pads to the beginning of the second dimension.
+    data = [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+    pads = [0, 2, 0, 0]
+    output = [
+        [
+            [0.0, 0.0, 1.0, 1.2],
+            [0.0, 0.0, 2.3, 3.4],
+            [0.0, 0.0, 4.5, 5.7],
+        ],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 2 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is constant)</dt>
+<dd>Three modes: constant(default), reflect, edge</dd>
+<dt><tt>pads</tt> : list of ints (required)</dt>
+<dd>List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.</dd>
+<dt><tt>value</tt> : float (default is 0.0)</dt>
+<dd>One float, indicates the value to be filled.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor after padding.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Split-2"></a>**Split-2**</a>
+
+  Split a tensor into a list of tensors, along the specified
+  'axis'. Lengths of the parts can be specified using argument 'split'.
+  Otherwise, the tensor is split to equal sized parts.
+
+#### Version
+
+This version of the operator has been available since version 2 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to split on. </dd>
+<dt><tt>split</tt> : list of ints</dt>
+<dd>length of each output</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The tensor to split</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic) : T</dt>
+<dd>One or more outputs forming list of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+## Version 3 of the default ONNX operator set
+### <a name="GRU-3"></a>**GRU-3**</a>
+
+  Computes an one-layer GRU. This operator is usually supported via some custom
+  implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `z` - update gate
+
+  `r` - reset gate
+
+  `h` - hidden gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+  `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+  `Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+  `Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+  `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+  `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+  `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+  `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh):
+
+    - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz)
+
+    - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr)
+
+    - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0
+
+    - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0
+
+    - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+
+#### Version
+
+This version of the operator has been available since version 3 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>linear_before_reset</tt> : int (default is 0)</dt>
+<dd>When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.</dd>
+<dt><tt>output_sequence</tt> : int (default is 0)</dt>
+<dd>The sequence output for the hidden is optional if 0. Default 0.</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.</dd>
+<dt><tt>Y_h</tt> (optional) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+## Version 4 of the default ONNX operator set
+### <a name="Concat-4"></a>**Concat-4**</a>
+
+  Concatenate a list of tensors into a single tensor
+
+#### Version
+
+This version of the operator has been available since version 4 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (required)</dt>
+<dd>Which axis to concat on</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic) : T</dt>
+<dd>List of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+## Version 5 of the default ONNX operator set
+### <a name="Reshape-5"></a>**Reshape-5**</a>
+
+  Reshape the input tensor similar to numpy.reshape.
+  First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+  At most one dimension of the new shape can be -1. In this case, the value is
+  inferred from the size of the tensor and the remaining dimensions. A dimension
+  could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+  from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
+  The input tensor's shape and the output tensor's shape are required to have the same number of elements.
+
+#### Version
+
+This version of the operator has been available since version 5 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+<dt><tt>shape</tt> : tensor(int64)</dt>
+<dd>Specified shape for output.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reshaped</tt> : T</dt>
+<dd>Reshaped data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+## Version 6 of the default ONNX operator set
+### <a name="Abs-6"></a>**Abs-6**</a>
+
+  Absolute takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the absolute is, y = abs(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Add-6"></a>**Add-6**</a>
+
+  Performs element-wise binary addition (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="BatchNormalization-6"></a>**BatchNormalization-6**</a>
+
+  Carries out batch normalization as described in the paper
+  https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+  there are multiple cases for the number of outputs, which we list below:
+
+  Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+  Output case #2: Y (test mode)
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero, default is 1e-5f.</dd>
+<dt><tt>is_test</tt> : int (default is 0)</dt>
+<dd>If set to nonzero, run spatial batch normalization in test mode, default is 0.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.</dd>
+<dt><tt>spatial</tt> : int (default is 1)</dt>
+<dd>If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+<dt><tt>scale</tt> : T</dt>
+<dd>The scale as a 1-dimensional tensor of size C to be applied to the output.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>The bias as a 1-dimensional tensor of size C to be applied to the output.</dd>
+<dt><tt>mean</tt> : T</dt>
+<dd>The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.</dd>
+<dt><tt>var</tt> : T</dt>
+<dd>The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.</dd>
+</dl>
+
+#### Outputs (1 - 5)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>The output tensor of the same shape as X.</dd>
+<dt><tt>mean</tt> (optional) : T</dt>
+<dd>The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.</dd>
+<dt><tt>var</tt> (optional) : T</dt>
+<dd>The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.</dd>
+<dt><tt>saved_mean</tt> (optional) : T</dt>
+<dd>Saved mean used during training to speed up gradient computation. Should not be used for testing.</dd>
+<dt><tt>saved_var</tt> (optional) : T</dt>
+<dd>Saved variance used during training to speed up gradient computation. Should not be used for testing.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Cast-6"></a>**Cast-6**</a>
+
+  The operator casts the elements of a given input tensor to a data type
+  specified by the 'to' argument and returns an output tensor of the same size in
+  the converted type. The 'to' argument must be one of the data types specified
+  in the 'DataType' enum field in the TensorProto message.
+  NOTE: Casting to and from strings is not supported yet.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>to</tt> : int (required)</dt>
+<dd>The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to be cast.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor with the same shape as input with type specified by the 'to' argument</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain input types. Casting from strings and complex are not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain output types. Casting to strings and complex are not supported.</dd>
+</dl>
+
+### <a name="Ceil-6"></a>**Ceil-6**</a>
+
+  Ceil takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the ceil is, y = ceil(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Clip-6"></a>**Clip-6**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified with arguments 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max() respectively.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>max</tt> : float (default is (3.402823e+38))</dt>
+<dd>Maximum value, above which element is replaced by max</dd>
+<dt><tt>min</tt> : float (default is (-3.402823e+38))</dt>
+<dd>Minimum value, under which element is replaced by min</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Div-6"></a>**Div-6**</a>
+
+  Performs element-wise binary division (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Dropout-6"></a>**Dropout-6**</a>
+
+  Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
+  output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
+  test mode or not, the output Y will either be a random dropout, or a simple
+  copy of the input. Note that our implementation of Dropout does scaling in
+  the training phase, so during testing nothing needs to be done.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>is_test</tt> : int (default is 0)</dt>
+<dd>(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.</dd>
+<dt><tt>ratio</tt> : float (default is 0.5)</dt>
+<dd>(float, default 0.5) the ratio of random dropout</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>The input data as Tensor.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional) : T</dt>
+<dd>The output mask. If is_test is nonzero, this output is not filled.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Elu-6"></a>**Elu-6**</a>
+
+  Elu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
+  0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.
+
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Coefficient of ELU.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>1D output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Exp-6"></a>**Exp-6**</a>
+
+  Calculates the exponential of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The exponential of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Floor-6"></a>**Floor-6**</a>
+
+  Floor takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the floor is, y = floor(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Gemm-6"></a>**Gemm-6**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+  Compute Y = alpha * A * B + beta * C, where input tensor A has
+  dimension (M X K), input tensor B has dimension (K X N), input tensor C and
+  output tensor Y have dimension (M X N).
+  If attribute broadcast is non-zero, input tensor C will be broadcasted to match
+  the dimension requirement. A will be transposed before doing the computation
+  if attribute transA is non-zero, same for B and transB.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B, the default value is 1.0.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C, the default value is 1.0.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Whether C should be broadcasted</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Input tensor A</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Input tensor B</dd>
+<dt><tt>C</tt> : T</dt>
+<dd>Input tensor C</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="HardSigmoid-6"></a>**HardSigmoid-6**</a>
+
+  HardSigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.2)</dt>
+<dd>Value of alpha.</dd>
+<dt><tt>beta</tt> : float (default is 0.5)</dt>
+<dd>Value of beta.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="InstanceNormalization-6"></a>**InstanceNormalization-6**</a>
+
+  Carries out instance normalization as described in the paper
+  https://arxiv.org/abs/1607.08022.
+
+  y = scale * (x - mean) / sqrt(variance + epsilon) + B,
+  where mean and variance are computed per instance per channel.
+
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+<dt><tt>scale</tt> (differentiable) : T</dt>
+<dd>The input 1-dimensional scale tensor of size C.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>The input 1-dimensional bias tensor of size C.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output tensor of the same shape as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LeakyRelu-6"></a>**LeakyRelu-6**</a>
+
+  LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+  output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+  `f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.01)</dt>
+<dd>Coefficient of leakage.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Log-6"></a>**Log-6**</a>
+
+  Calculates the natural log of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The natural log of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Max-6"></a>**Max-6**</a>
+
+  Element-wise max of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Max.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>max</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Mean-6"></a>**Mean-6**</a>
+
+  Element-wise mean of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Mean.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>mean</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Min-6"></a>**Min-6**</a>
+
+  Element-wise min of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Min</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>min</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Mul-6"></a>**Mul-6**</a>
+
+  Performs element-wise binary multiplication (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Neg-6"></a>**Neg-6**</a>
+
+  Neg takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where each element flipped sign, y = -x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double)</dt>
+<dd>Constrain input and output types to signed numeric tensors.</dd>
+</dl>
+
+### <a name="PRelu-6"></a>**PRelu-6**</a>
+
+  PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+  output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+  `f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>slope</tt> : T</dt>
+<dd>Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Reciprocal-6"></a>**Reciprocal-6**</a>
+
+  Reciprocal takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Relu-6"></a>**Relu-6**</a>
+
+  Relu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Selu-6"></a>**Selu-6**</a>
+
+  Selu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the scaled exponential linear unit function,
+  `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.67326)</dt>
+<dd>Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).</dd>
+<dt><tt>gamma</tt> : float (default is 1.0507)</dt>
+<dd>Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Sigmoid-6"></a>**Sigmoid-6**</a>
+
+  Sigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+  tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Sqrt-6"></a>**Sqrt-6**</a>
+
+  Square root takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the square root is, y = x^0.5, is applied to
+  the tensor elementwise. If x is negative, then it will return NaN.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Sub-6"></a>**Sub-6**</a>
+
+  Performs element-wise binary subtraction (with limited broadcast support).
+
+  If necessary the right-hand-side argument will be broadcasted to match the
+  shape of left-hand-side argument. When broadcasting is specified, the second
+  tensor can either be of element size 1 (including a scalar tensor and any
+  tensor with rank equal to or smaller than the first tensor), or having its
+  shape as a contiguous subset of the first tensor's shape. The starting of the
+  mutually equal shape is specified by the argument "axis", and if it is not set,
+  suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+  For example, the following tensor shapes are supported (with broadcast=1):
+
+    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+  Attribute `broadcast=1` needs to be passed to enable broadcasting.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>If set, defines the broadcast dimensions. See doc for details.</dd>
+<dt><tt>broadcast</tt> : int (default is 0)</dt>
+<dd>Pass 1 to enable broadcasting</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand, should share the type with the second operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same dimensions and type as A</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Sum-6"></a>**Sum-6**</a>
+
+  Element-wise sum of each of the input tensors. All inputs and outputs must
+  have the same shape and data type.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for Sum.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>sum</tt> : T</dt>
+<dd>Output tensor. Same dimension as inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Tanh-6"></a>**Tanh-6**</a>
+
+  Calculates the hyperbolic tangent of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The hyperbolic tangent values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Tile-6"></a>**Tile-6**</a>
+
+  Constructs a tensor by tiling a given tensor.
+  This is the same as function `tile` in Numpy, but no broadcast.
+  For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor of any shape.</dd>
+<dt><tt>repeats</tt> : T1</dt>
+<dd>1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain repeat's type to int64 tensors.</dd>
+</dl>
+
+## Version 7 of the default ONNX operator set
+### <a name="Acos-7"></a>**Acos-7**</a>
+
+  Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The arccosine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Add-7"></a>**Add-7**</a>
+
+  Performs element-wise binary addition (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="And-7"></a>**And-7**</a>
+
+  Returns the tensor resulted from performing the `and` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Asin-7"></a>**Asin-7**</a>
+
+  Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The arcsine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Atan-7"></a>**Atan-7**</a>
+
+  Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The arctangent of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="AveragePool-7"></a>**AveragePool-7**</a>
+
+  AveragePool consumes an input tensor X and applies average pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   average pooling consisting of computing the average on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).
+
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>count_include_pad</tt> : int (default is 0)</dt>
+<dd>Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="BatchNormalization-7"></a>**BatchNormalization-7**</a>
+
+  Carries out batch normalization as described in the paper
+      https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+      there are multiple cases for the number of outputs, which we list below:
+
+      Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+      Output case #2: Y (test mode)
+          This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).</dd>
+<dt><tt>spatial</tt> : int (default is 1)</dt>
+<dd>If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+<dt><tt>scale</tt> : T</dt>
+<dd>If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)</dd>
+<dt><tt>mean</tt> : T</dt>
+<dd>If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).</dd>
+<dt><tt>var</tt> : T</dt>
+<dd>If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).</dd>
+</dl>
+
+#### Outputs (1 - 5)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>The output tensor of the same shape as X</dd>
+<dt><tt>mean</tt> (optional) : T</dt>
+<dd>The running mean after the BatchNormalization operator.</dd>
+<dt><tt>var</tt> (optional) : T</dt>
+<dd>The running variance after the BatchNormalization operator.</dd>
+<dt><tt>saved_mean</tt> (optional) : T</dt>
+<dd>Saved mean used during training to speed up gradient computation.</dd>
+<dt><tt>saved_var</tt> (optional) : T</dt>
+<dd>Saved variance used during training to speed up gradient computation.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Cos-7"></a>**Cos-7**</a>
+
+  Calculates the cosine of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The cosine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Div-7"></a>**Div-7**</a>
+
+  Performs element-wise binary division (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Dropout-7"></a>**Dropout-7**</a>
+
+  Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
+  output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
+  test mode or not, the output Y will either be a random dropout, or a simple
+  copy of the input. Note that our implementation of Dropout does scaling in
+  the training phase, so during testing nothing needs to be done.
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>ratio</tt> : float (default is 0.5)</dt>
+<dd>The ratio of random dropout</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>The input data as Tensor.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional) : T</dt>
+<dd>The output mask.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Equal-7"></a>**Equal-7**</a>
+
+  Returns the tensor resulted from performing the `equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input to integral tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="GRU-7"></a>**GRU-7**</a>
+
+  Computes an one-layer GRU. This operator is usually supported via some custom
+  implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `z` - update gate
+
+  `r` - reset gate
+
+  `h` - hidden gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+  `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+  `Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+  `Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+  `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+  `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+  `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+  `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh):
+
+    - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
+
+    - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
+
+    - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
+
+    - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
+
+    - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>linear_before_reset</tt> : int (default is 0)</dt>
+<dd>When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="Gemm-7"></a>**Gemm-7**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+  A' = transpose(A) if transA else A
+
+  B' = transpose(B) if transB else B
+
+  Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+  input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+  and output tensor Y has shape (M, N). A will be transposed before doing the
+  computation if attribute transA is non-zero, same for B and transB.
+  This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C.</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.</dd>
+<dt><tt>C</tt> : T</dt>
+<dd>Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor of shape (M, N).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Greater-7"></a>**Greater-7**</a>
+
+  Returns the tensor resulted from performing the `greater` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="LSTM-7"></a>**LSTM-7**</a>
+
+  Computes an one-layer LSTM. This operator is usually supported via some
+  custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `o` - output gate
+
+  `f` - forget gate
+
+  `c` - cell gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+  `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+  `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+  `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+  `P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+  `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+  `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+  `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+  `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+  `PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+    - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
+
+    - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
+
+    - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+
+    - Ct = ft (.) Ct-1 + it (.) ct
+
+    - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
+
+    - Ht = ot (.) h(Ct)
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>input_forget</tt> : int (default is 0)</dt>
+<dd>Couple the input and forget gates if 1.</dd>
+</dl>
+
+#### Inputs (3 - 8)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>initial_c</tt> (optional) : T</dt>
+<dd>Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>P</tt> (optional) : T</dt>
+<dd>The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.</dd>
+</dl>
+
+#### Outputs (0 - 3)
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>Y_c</tt> (optional) : T</dt>
+<dd>The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="Less-7"></a>**Less-7**</a>
+
+  Returns the tensor resulted from performing the `less` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Mul-7"></a>**Mul-7**</a>
+
+  Performs element-wise binary multiplication (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Multinomial-7"></a>**Multinomial-7**</a>
+
+  Generate a tensor of samples from a multinomial distribution according to the probabilities
+  of each of the possible outcomes.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int (default is 6)</dt>
+<dd>(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.</dd>
+<dt><tt>sample_size</tt> : int (default is 1)</dt>
+<dd>Number of times to sample.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain output types to integral tensors.</dd>
+</dl>
+
+### <a name="Or-7"></a>**Or-7**</a>
+
+  Returns the tensor resulted from performing the `or` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="PRelu-7"></a>**PRelu-7**</a>
+
+  PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+  output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+  `f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+  This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>slope</tt> : T</dt>
+<dd>Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor (same size as X)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Pow-7"></a>**Pow-7**</a>
+
+  Pow takes input data (Tensor<T>) and exponent Tensor, and
+  produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+  is applied to the data tensor elementwise.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>First operand, base of the exponent.</dd>
+<dt><tt>Y</tt> : T</dt>
+<dd>Second operand, power of the exponent.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="RNN-7"></a>**RNN-7**</a>
+
+  Computes an one-layer simple RNN. This operator is usually supported
+  via some custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `Wi` - W parameter weight matrix for input gate
+
+  `Ri` - R recurrence weight matrix for input gate
+
+  `Wbi` - W parameter bias vector for input gate
+
+  `Rbi` - R parameter bias vector for input gate
+
+  `WBi` - W parameter weight matrix for backward input gate
+
+  `RBi` - R recurrence weight matrix for backward input gate
+
+  `WBbi` - WR bias vectors for backward input gate
+
+  `RBbi` - RR bias vectors for backward input gate
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Tanh):
+
+    - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings (default is ['Tanh', 'Tanh'])</dt>
+<dd>One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> : T</dt>
+<dd>The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="Sin-7"></a>**Sin-7**</a>
+
+  Calculates the sine of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The sine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Sub-7"></a>**Sub-7**</a>
+
+  Performs element-wise binary subtraction (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Tan-7"></a>**Tan-7**</a>
+
+  Calculates the tangent of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The tangent of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Upsample-7"></a>**Upsample-7**</a>
+
+  Upsample the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * scale).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)</dd>
+<dt><tt>scales</tt> : list of floats (required)</dt>
+<dd>The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>N-D tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>N-D tensor after resizing</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Xor-7"></a>**Xor-7**</a>
+
+  Returns the tensor resulted from performing the `xor` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+## Version 8 of the default ONNX operator set
+### <a name="Expand-8"></a>**Expand-8**</a>
+
+  Broadcast the input tensor following the given shape and the broadcast rule.
+  The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):
+  Dimensions are right alignment;
+  Two corresponding dimensions must have the same value, or one of them is equal to 1.
+  Also, this operator is similar to numpy.broadcast_to(input, shape),
+  but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size().
+  It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1,
+  or the shape.ndim < input.shape.ndim.
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>shape</tt> : tensor(int64)</dt>
+<dd>A 1-D tensor indicates the shape you want to expand to, following the broadcast rule</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensors.</dd>
+</dl>
+
+### <a name="Max-8"></a>**Max-8**</a>
+
+  Element-wise max of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for max.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>max</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="MaxPool-8"></a>**MaxPool-8**</a>
+
+  MaxPool consumes an input tensor X and applies max pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   max pooling consisting of computing the max on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is maximum number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>storage_order</tt> : int (default is 0)</dt>
+<dd>The storage order of the tensor. 0 is row major, and 1 is column major.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+<dt><tt>Indices</tt> (optional) : I</dt>
+<dd>Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="Mean-8"></a>**Mean-8**</a>
+
+  Element-wise mean of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for mean.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>mean</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Min-8"></a>**Min-8**</a>
+
+  Element-wise min of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for min.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>min</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Scan-8"></a>**Scan-8**</a>
+
+  Scan can be used to iterate over one or more scan_input tensors,
+  constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+  functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+  generalizations of RNN-like constructs for sequence-to-sequence processing.
+  Other tensors (referred to as state_variables here) can be used to carry a state
+  when iterating from one element to another (similar to hidden-state in RNNs, also referred
+  to as loop-carried dependences in the context of loops). All these tensors are required to
+  have the same shape in each iteration of the loop (a restriction imposed to enable efficient
+  memory allocation). Many common usages involve a single scan_input tensor (where functionality
+  similar to scan, fold and map can be obtained). When more than one scan_input is used,
+  a behavior similar to zip is obtained.
+
+  The attribute body must be a graph, specifying the computation to be performed in
+  every iteration. It takes as input the current values of the state_variables and
+  the current iterated element of the scan_inputs. It must return the (updated) values
+  of the state_variables and zero or more scan_output_element tensors. The values of the
+  scan_output_element tensors are concatenated over all the iterations to produce the
+  scan_output values of the scan construct (similar to the concatenated intermediate
+  hidden-state values of RNN-like constructs).
+
+  The scan operation returns the final values of the state_variables as well as the
+  scan_outputs.
+
+  The operation supports batching, and the batch-axis is required to be 0.
+  When multiple scan_input tensors are used, they must all have the same batch-size,
+  and they must all have the same maximum-sequence-length (the dimensionality of the
+  sequence axis or scan axis). The sequence axis or scan axis is required to be 1.
+
+  The operation has an optional sequence_lens input (of shape [BATCH_SIZE]) to
+  allow variable length sequences of length <= the maximum-sequence-length. If this
+  input is not specified, all sequences are assumed to be of length equal to
+  maximum-sequence-length. For variable length input sequences, the scan_outputs
+  will consist of a sequence of same length as the input, padded to the
+  maximum-sequence-length.
+
+  The optional attribute directions can be used to scan a sequence in the reverse direction.
+  If this attribute is omitted, all sequences are scanned in the forward direction.
+  A bidirectional scan be performed by specifying the same tensor input twice in the
+  scan_inputs, once with a forward direction, and once with a backward direction.
+
+  Note that because of the ONNX restriction that only the last parameter of an operator can
+  be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+  Similarly, the final-states and scan-outputs are listed together as one output parameter.
+  The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+  The behavior of
+
+      Scan <
+          num_scan_inputs = m,
+          body = loop-body
+      > (sequence_lengths, init_1, ..., init_n, scan_1, ..., scan_m)
+
+  is equivalent to the following pseudo-code:
+
+      // T.shape[0] denotes the batch-size of T
+      // The batch-size of scan_1, ..., scan_m are all required to be equal
+      batch_size = scan_1.shape[0];
+
+      // scan_i.shape[1] denotes the (max) sequence-length of scan_i
+      // scan_i.shape[1] is required to be equal to scan_j.shape[1] for all i,j.
+      max_sequence_length = scan_1.shape[1];
+
+      for (int batch = 0; batch < batch_size; ++batch) {
+          // initialize state-variables
+          st_1 = init_1; ... st_n = init_n;
+          // initialize scan-output variables: [] denotes an empty tensor
+          scan_out_1 = []; ...; scan_out_k = [];
+          // identify number of iterations:
+          N = (sequence_lengths specified) ? sequence_lengths[batch] : max_sequence_length;
+
+          // execute loop
+          for (int t = 0; t < N; ++t) {
+              // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+              // of rank one less than T obtained by indexing T at position t along axis k.
+              si_1 = (scan_1<axis=0>[batch])<axis=1>[t];
+              ... ;
+              si_m = (scan_m<axis=0>[batch])<axis=1>[t];
+              // execute loop-body
+              st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+              // accumulate the scan-output elements
+              scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+          }
+          // accumulate the outputs for this batch:
+          bst_1[batch] = st_1; ..., bst_n[batch] = st_n;
+          // Note scan-outputs will have size max_sequence_length, but only first N values will be meaningful.
+          // The remaining values have an undefined value.
+          b_scan_out_1[batch] = scan_out_1; ...; b_scan_out_k[batch] = scan_out_k;
+      }
+      return bst_1, ..., bst_n, b_scan_out_1, ..., b_scan_out_k;
+
+
+
+  *Sample usage: Encoding RNN using a Scan*
+
+  The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+  recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+  be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+  %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+  values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+      graph rnn-encoding {
+        %H_0 = ...
+        %X = ...
+        %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1]("", %H_0, %X)
+        return %Y, %Y_h
+      }
+
+      graph rnn-cell-1 (
+        %H_tminus1[FLOAT, tensor]
+        %X_t[FLOAT, tensor]
+      ) {
+        %Wi = ...
+        %Ri = ...
+        %Wbi = ...
+        %Rbi = ...
+        %t1 = X_t * (Wi^T)
+        %t2 = H_tminus1*(Ri^T)
+        %t3 = Add(%t1, %t2)
+        %t4 = Add(%t3, %Wbi)
+        %t5 = Add(%t4, %Rbi)
+        %Ht = Tanh(%t5)
+        %Accumulate = Identity(%Ht)
+        return %Ht, %Accumulate
+      }
+
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.</dd>
+<dt><tt>directions</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.</dd>
+<dt><tt>num_scan_inputs</tt> : int (required)</dt>
+<dd>An attribute specifying the number of scan_inputs M. </dd>
+</dl>
+
+#### Inputs (2 - &#8734;)
+
+<dl>
+<dt><tt>sequence_lens</tt> (optional) : I</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).</dd>
+<dt><tt>initial_state_and_scan_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Initial values of the loop's N state variables followed by M scan_inputs</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>final_state_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final values of the loop's N state variables followed by K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Int64 tensor</dd>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+</dl>
+
+### <a name="Sum-8"></a>**Sum-8**</a>
+
+  Element-wise sum of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 8 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for sum.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>sum</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+## Version 9 of the default ONNX operator set
+### <a name="Acosh-9"></a>**Acosh-9**</a>
+
+  Calculates the hyperbolic arccosine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic arccosine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Asinh-9"></a>**Asinh-9**</a>
+
+  Calculates the hyperbolic arcsine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic arcsine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Atanh-9"></a>**Atanh-9**</a>
+
+  Calculates the hyperbolic arctangent of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic arctangent values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="BatchNormalization-9"></a>**BatchNormalization-9**</a>
+
+  Carries out batch normalization as described in the paper
+  https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+  there are multiple cases for the number of outputs, which we list below:
+
+  Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+  Output case #2: Y (test mode)
+
+  For previous (depreciated) non-spatial cases, implementors are suggested
+  to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op.
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1</dd>
+<dt><tt>scale</tt> (differentiable) : T</dt>
+<dd>Scale tensor of shape (C).</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Bias tensor of shape (C).</dd>
+<dt><tt>mean</tt> (differentiable) : T</dt>
+<dd>running (training) or estimated (testing) mean tensor of shape (C).</dd>
+<dt><tt>var</tt> (differentiable) : T</dt>
+<dd>running (training) or estimated (testing) variance tensor of shape (C).</dd>
+</dl>
+
+#### Outputs (1 - 5)
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>The output tensor of the same shape as X</dd>
+<dt><tt>mean</tt> (optional, non-differentiable) : T</dt>
+<dd>The running mean after the BatchNormalization operator.</dd>
+<dt><tt>var</tt> (optional, non-differentiable) : T</dt>
+<dd>The running variance after the BatchNormalization operator.</dd>
+<dt><tt>saved_mean</tt> (optional, non-differentiable) : T</dt>
+<dd>Saved mean used during training to speed up gradient computation.</dd>
+<dt><tt>saved_var</tt> (optional, non-differentiable) : T</dt>
+<dd>Saved variance used during training to speed up gradient computation.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Cast-9"></a>**Cast-9**</a>
+
+  The operator casts the elements of a given input tensor to a data type
+  specified by the 'to' argument and returns an output tensor of the same size in
+  the converted type. The 'to' argument must be one of the data types specified
+  in the 'DataType' enum field in the TensorProto message.
+
+  Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
+  (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
+  result 100. There are some string literals reserved for special floating-point values;
+  "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
+  Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
+  this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
+  to string tensors, plain floating-point representation (such as "314.15926") would be used.
+  Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
+  of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
+
+  Conversion from a numerical type to any numerical type is always allowed.
+  User must be aware of precision loss and value change caused by range difference between two types.
+  For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
+  an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>to</tt> : int (required)</dt>
+<dd>The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to be cast.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor with the same shape as input with type specified by the 'to' argument</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string)</dt>
+<dd>Constrain input types. Casting from complex is not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string)</dt>
+<dd>Constrain output types. Casting to complex is not supported.</dd>
+</dl>
+
+### <a name="Compress-9"></a>**Compress-9**</a>
+
+  Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index.
+      In case axis is not provided, input is flattened before elements are selected.
+      Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html
+
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>condition</tt> : T1</dt>
+<dd>Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain to boolean tensors.</dd>
+</dl>
+
+### <a name="Constant-9"></a>**Constant-9**</a>
+
+  A constant tensor.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>value</tt> : tensor (required)</dt>
+<dd>The value for the elements of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor containing the same value of the provided tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="ConstantOfShape-9"></a>**ConstantOfShape-9**</a>
+
+  Generate a tensor with given value and shape.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>value</tt> : tensor</dt>
+<dd>(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain input types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain output types to be numerics.</dd>
+</dl>
+
+### <a name="Cosh-9"></a>**Cosh-9**</a>
+
+  Calculates the hyperbolic cosine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic cosine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Erf-9"></a>**Erf-9**</a>
+
+  Computes the error function of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The error function of the input tensor computed element-wise. It has the same shape and type of the input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="EyeLike-9"></a>**EyeLike-9**</a>
+
+  Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D
+  tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the
+  same as the input tensor. The data type can be specified by the 'dtype' argument. If
+  'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal
+  is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals.
+  The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message and be valid as an output type.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.</dd>
+<dt><tt>k</tt> : int (default is 0)</dt>
+<dd>(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal,  and k < 0 populates a lower diagonal.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>2D input tensor to copy shape, and optionally, type information from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor, same shape as input tensor T1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain input types. Strings and complex are not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain output types. Strings and complex are not supported.</dd>
+</dl>
+
+### <a name="Flatten-9"></a>**Flatten-9**</a>
+
+  Flattens the input tensor into a 2D matrix. If input tensor has shape
+  (d_0, d_1, ... d_n) then the output will have shape
+  (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>A tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output to all tensor types.</dd>
+</dl>
+
+### <a name="Gemm-9"></a>**Gemm-9**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+  A' = transpose(A) if transA else A
+
+  B' = transpose(B) if transB else B
+
+  Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+  input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+  and output tensor Y has shape (M, N). A will be transposed before doing the
+  computation if attribute transA is non-zero, same for B and transB.
+  This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C.</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.</dd>
+<dt><tt>C</tt> : T</dt>
+<dd>Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor of shape (M, N).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="Greater-9"></a>**Greater-9**</a>
+
+  Returns the tensor resulted from performing the `greater` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="IsNaN-9"></a>**IsNaN-9**</a>
+
+  Returns which elements of the input are NaN.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output types to boolean tensors.</dd>
+</dl>
+
+### <a name="Less-9"></a>**Less-9**</a>
+
+  Returns the tensor resulted from performing the `less` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="MatMul-9"></a>**MatMul-9**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>N-dimensional matrix A</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>N-dimensional matrix B</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Matrix multiply results from A * B</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="MaxUnpool-9"></a>**MaxUnpool-9**</a>
+
+  MaxUnpool essentially computes the partial inverse of the MaxPool op.
+   The input information to this op is typically the the output information from a MaxPool op. The first
+   input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
+   from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
+   to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
+   The third (optional) input is a tensor that specifies the output size of the unpooling operation.
+
+  MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal
+   values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling
+   the result of an unpooling operation should give back the original input to the unpooling op.
+
+  MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.
+   The third input argument, output_size, is meant to disambiguate the op and produce output tensor of
+   known/predictable size.
+
+  In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,
+   which define the exact unpooling op. The attributes typically have the same values as the corrsponding
+   pooling op that the unpooling op is trying to invert.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>I</tt> : T2</dt>
+<dd>Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).</dd>
+<dt><tt>output_shape</tt> (optional) : T2</dt>
+<dd>The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T1</dt>
+<dd>Output data tensor that contains the result of the unpooling.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="MeanVarianceNormalization-9"></a>**MeanVarianceNormalization-9**</a>
+
+  A MeanVarianceNormalization Function: Perform mean variance normalization
+        on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints (default is ['0', '2', '3'])</dt>
+<dd>A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="NonZero-9"></a>**NonZero-9**</a>
+
+  Returns the indices of the elements that are non-zero
+      (in row-major order - by dimension).
+      NonZero behaves similar to numpy.nonzero:
+      https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(int64)</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to all tensor types.</dd>
+</dl>
+
+### <a name="OneHot-9"></a>**OneHot-9**</a>
+
+  Produces a one-hot tensor based on inputs.
+      The locations represented by the index values in the 'indices' input tensor will have 'on_value'
+      and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'
+      are specified as part of required input argument 'values', which is a two-element tensor of format
+      [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the
+      input tensor. The additional dimension is for one-hot representation. The additional dimension will
+      be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional
+      dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional
+      dimension is specified by required scalar input 'depth'. The type of the output tensor is the same
+      as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside
+      the range [0, depth) will result in one-hot representation with all 'off_value' values in the
+      output tensor.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>indices</tt> : T1</dt>
+<dd>Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.</dd>
+<dt><tt>depth</tt> : T2</dt>
+<dd>Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.</dd>
+<dt><tt>values</tt> : T3</dt>
+<dd>Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T3</dt>
+<dd>Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+<dt><tt>T2</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+<dt><tt>T3</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+</dl>
+
+### <a name="PRelu-9"></a>**PRelu-9**</a>
+
+  PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+  output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+  `f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+  This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>slope</tt> (differentiable) : T</dt>
+<dd>Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor (same size as X)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="Scan-9"></a>**Scan-9**</a>
+
+  Scan can be used to iterate over one or more scan_input tensors,
+  constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+  functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+  generalizations of RNN-like constructs for sequence-to-sequence processing.
+  Other tensors (referred to as state_variables here) can be used to carry a state
+  when iterating from one element to another (similar to hidden-state in RNNs, also referred
+  to as loop-carried dependences in the context of loops).
+  Many common usages involve a single scan_input tensor (where functionality
+  similar to scan, fold and map can be obtained). When more than one scan_input is used,
+  a behavior similar to zip is obtained.
+
+  The attribute body must be a graph, specifying the computation to be performed in
+  every iteration. It takes as input the current values of the state_variables and
+  the current iterated element of the scan_inputs. It must return the (updated) values
+  of the state_variables and zero or more scan_output_element tensors. The values of the
+  scan_output_element tensors are concatenated over all the iterations to produce the
+  scan_output values of the scan construct (similar to the concatenated intermediate
+  hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+  well as scan_output_element tensors) are required to have the same shape in each iteration
+  of the loop (a restriction imposed to enable efficient memory allocation).
+
+  Note that the iterated element passed to the body subgraph does not have a sequence
+  axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+  The scan operation returns the final values of the state_variables as well as the
+  scan_outputs.
+
+  The optional attribute scan_input_directions specifies the direction (forward or backward)
+  for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+  direction. A bidirectional scan may be performed by specifying the same tensor input twice
+  in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+  The scan_output of the operation is produced by concatenating the scan_output_element
+  values produced by the body in each iteration.  The optional attribute scan_output_directions
+  specifies the direction in which scan_output is constructed (by appending or prepending the
+  scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+  is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+  The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+  If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+  batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+  Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+  The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+  are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+  scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+  value of 1.
+
+  Note that because of the ONNX restriction that only the last parameter of an operator can
+  be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+  Similarly, the final-states and scan-outputs are listed together as one output parameter.
+  The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+  The behavior of
+
+      Scan <
+          num_scan_inputs = m,
+          body = loop-body,
+          scan_input_axes = [axis_1, ..., axis_m]
+      > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+  is equivalent to the following pseudo-code:
+
+      // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+      // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+      sequence_length = scan_1.shape[axis_1];
+
+      // initialize state-variables
+      st_1 = init_1; ... st_n = init_n;
+      // initialize scan-output variables: [] denotes an empty tensor
+      scan_out_1 = []; ...; scan_out_k = [];
+      // identify number of iterations:
+
+      // execute loop
+      for (int t = 0; t < sequence_length; ++t) {
+          // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+          // of rank one less than T obtained by indexing T at position t along axis k.
+          si_1 = scan_1<axis=axis_1>[t];
+          ... ;
+          si_m = scan_m<axis=axis_m>[t];
+          // execute loop-body
+          st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+          // accumulate the scan-output elements
+          scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+      }
+
+      return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+  *Sample usage: Encoding RNN using a Scan*
+
+  The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+  recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+  be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+  %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+  values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+      graph rnn-encoding {
+        %H_0 = ...
+        %X = ...
+        %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+        return %Y, %Y_h
+      }
+
+      graph rnn-cell-1 (
+        %H_tminus1[FLOAT, tensor]
+        %X_t[FLOAT, tensor]
+      ) {
+        %Wi = ...
+        %Ri = ...
+        %Wbi = ...
+        %Rbi = ...
+        %t1 = X_t * (Wi^T)
+        %t2 = H_tminus1*(Ri^T)
+        %t3 = Add(%t1, %t2)
+        %t4 = Add(%t3, %Wbi)
+        %t5 = Add(%t4, %Rbi)
+        %Ht = Tanh(%t5)
+        %Accumulate = Identity(%Ht)
+        return %Ht, %Accumulate
+      }
+
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.</dd>
+<dt><tt>num_scan_inputs</tt> : int (required)</dt>
+<dd>An attribute specifying the number of scan_inputs M. </dd>
+<dt><tt>scan_input_axes</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.</dd>
+<dt><tt>scan_input_directions</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.</dd>
+<dt><tt>scan_output_axes</tt> : list of ints</dt>
+<dd>An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.</dd>
+<dt><tt>scan_output_directions</tt> : list of ints</dt>
+<dd>An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>initial_state_and_scan_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Initial values of the loop's N state variables followed by M scan_inputs</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>final_state_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final values of the loop's N state variables followed by K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+</dl>
+
+### <a name="Scatter-9"></a>**Scatter-9**</a>
+
+  Given `data`, `updates` and `indices` input tensors of rank r >= 1, write the values provided by `updates`
+  into the first input, `data`, along `axis` dimension of `data` (by default outer-most one as axis=0) at corresponding `indices`.
+  For each entry in `updates`, the target index in `data` is specified by corresponding entry in `indices`
+  for dimension = axis, and index in source for dimension != axis. For instance, in a 2-D tensor case,
+  data[indices[i][j]][j] = updates[i][j] if axis = 0, or data[i][indices[i][j]] = updates[i][j] if axis = 1,
+  where i and j are loop counters from 0 up to the respective size in `updates` - 1.
+  Example 1:
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  Example 2:
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : Tind</dt>
+<dd>Tensor of int32/int64 indices, of r >= 1 (same rank as input).</dd>
+<dt><tt>updates</tt> : T</dt>
+<dd>Tensor of rank r >=1 (same rank and shape as indices)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank r >= 1 (same rank as input).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="Shrink-9"></a>**Shrink-9**</a>
+
+  Shrink takes one input data (Tensor<numeric>) and produces one Tensor output,
+  having same datatype and shape with input. It has two attributes, lambd and
+  bias. The formula of this operator is: If x < -lambd, y = x + bias;
+  If x > lambd, y = x - bias; Otherwise, y = 0.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>bias</tt> : float (default is 0.0)</dt>
+<dd>The bias value added to output. Default is 0.</dd>
+<dt><tt>lambd</tt> : float (default is 0.5)</dt>
+<dd>The lambd value for the Shrink formulation. Default is 0.5.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input data as Tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+</dl>
+
+### <a name="Sign-9"></a>**Sign-9**</a>
+
+  Calculate the sign of the given input tensor element-wise.
+  If input > 0, output 1. if input < 0, output -1. if input == 0, output 0.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The sign of the input tensor computed element-wise. It has the same shape and type of the input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Sinh-9"></a>**Sinh-9**</a>
+
+  Calculates the hyperbolic sine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic sine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="TfIdfVectorizer-9"></a>**TfIdfVectorizer-9**</a>
+
+  This transform extracts n-grams from the input sequence and save them as a vector. Input can
+  be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input.
+  For 2-D input, the output is also a  2-D tensor whose i-th row is the n-gram representation of the i-th input row.
+  More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1].
+  If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor.
+
+  In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original
+  sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips.
+  If the number of skips is 2, we should skip two tokens when scanning through the original sequence.
+  Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2.
+  The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4].
+  If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]
+  indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively.
+
+  The output vector (denoted by Y) stores the count of each n-gram;
+  Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping
+  between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0],
+  ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17],
+  respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output.
+  Note that we may consider all skips up to S when generating the n-grams.
+
+  The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and
+  the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF",
+  this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute.
+
+  Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor.
+  If pool_strings is set, the input must be a string tensor.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>max_gram_length</tt> : int (required)</dt>
+<dd>Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.</dd>
+<dt><tt>max_skip_count</tt> : int (required)</dt>
+<dd>Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1</dd>
+<dt><tt>min_gram_length</tt> : int (required)</dt>
+<dd>Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.</dd>
+<dt><tt>mode</tt> : string (required)</dt>
+<dd>The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)</dd>
+<dt><tt>ngram_counts</tt> : list of ints (required)</dt>
+<dd>The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.</dd>
+<dt><tt>ngram_indexes</tt> : list of ints (required)</dt>
+<dd>list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.</dd>
+<dt><tt>pool_int64s</tt> : list of ints</dt>
+<dd>List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.</dd>
+<dt><tt>pool_strings</tt> : list of strings</dt>
+<dd>List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.</dd>
+<dt><tt>weights</tt> : list of floats</dt>
+<dd>list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input for n-gram extraction</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T1</dt>
+<dd>Ngram results</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(string), tensor(int32), tensor(int64)</dt>
+<dd>Input is ether string UTF-8 or int32/int64</dd>
+<dt><tt>T1</tt> : tensor(float)</dt>
+<dd>1-D tensor of floats</dd>
+</dl>
+
+### <a name="Upsample-9"></a>**Upsample-9**</a>
+
+  Upsample the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * scale).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>N-D tensor</dd>
+<dt><tt>scales</tt> : tensor(float)</dt>
+<dd>The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>N-D tensor after resizing</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input 'X' and output 'Y' to all tensor types.</dd>
+</dl>
+
+### <a name="Where-9"></a>**Where-9**</a>
+
+  Return elements, either from X or Y, depending on condition.
+  Where behaves like
+  [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
+  with three parameters.
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>condition</tt> (non-differentiable) : B</dt>
+<dd>When True (nonzero), yield X, otherwise yield Y</dd>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>values selected at indices where condition is True</dd>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>values selected at indices where condition is False</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of shape equal to the broadcasted shape of condition, X, and Y.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Constrain to boolean tensors.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+## Version 10 of the default ONNX operator set
+### <a name="AveragePool-10"></a>**AveragePool-10**</a>
+
+  AveragePool consumes an input tensor X and applies average pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   average pooling consisting of computing the average on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).
+
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>count_include_pad</tt> : int (default is 0)</dt>
+<dd>Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ConvInteger-10"></a>**ConvInteger-10**</a>
+
+  The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point,
+  and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into. default is 1.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input 'w'.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.</dd>
+</dl>
+
+#### Inputs (2 - 4)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>w</tt> : T2</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. </dd>
+<dt><tt>x_zero_point</tt> (optional) : T1</dt>
+<dd>Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>w_zero_point</tt> (optional) : T2</dt>
+<dd>Zero point tensor for input 'w'. It's optional and default value is 0.  It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T3</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input x and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input w and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int32)</dt>
+<dd>Constrain output y data type to 32-bit integer tensor.</dd>
+</dl>
+
+### <a name="DequantizeLinear-10"></a>**DequantizeLinear-10**</a>
+
+  The linear dequantization operator. It consumes a quantized tensor, a scale, a zero point to compute the full precision tensor.
+  The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' are both scalars.
+  'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
+  there's no zero point (zero point is supposed to be 0).
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>x</tt> : T</dt>
+<dd>N-D quantized input tensor to be de-quantized.</dd>
+<dt><tt>x_scale</tt> : tensor(float)</dt>
+<dd>Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>x_zero_point</tt> (optional) : T</dt>
+<dd>Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : tensor(float)</dt>
+<dd>N-D full precision output tensor. It has same shape as input 'x'.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int8), tensor(uint8), tensor(int32)</dt>
+<dd>Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.</dd>
+</dl>
+
+### <a name="Dropout-10"></a>**Dropout-10**</a>
+
+  Dropout takes one input floating tensor and produces two tensor outputs,
+  output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is
+  in test mode or not, the output Y will either be a random dropout, or a simple
+  copy of the input. Note that our implementation of Dropout does scaling in
+  the training phase, so during testing nothing needs to be done.
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>ratio</tt> : float (default is 0.5)</dt>
+<dd>The ratio of random dropout</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>The input data as Tensor.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional) : T1</dt>
+<dd>The output mask.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output mask types to boolean tensors.</dd>
+</dl>
+
+### <a name="IsInf-10"></a>**IsInf-10**</a>
+
+  Map infinity to true and other values to false.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>detect_negative</tt> : int (default is 1)</dt>
+<dd>(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.</dd>
+<dt><tt>detect_positive</tt> : int (default is 1)</dt>
+<dd>(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T1</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T2</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output types to boolean tensors.</dd>
+</dl>
+
+### <a name="MatMulInteger-10"></a>**MatMulInteger-10**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+  The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs (2 - 4)
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T1</dt>
+<dd>N-dimensional matrix A</dd>
+<dt><tt>B</tt> (non-differentiable) : T2</dt>
+<dd>N-dimensional matrix B</dd>
+<dt><tt>a_zero_point</tt> (optional, non-differentiable) : T1</dt>
+<dd>Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. </dd>
+<dt><tt>b_zero_point</tt> (optional, non-differentiable) : T2</dt>
+<dd>Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T3</dt>
+<dd>Matrix multiply results from A * B</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input A data type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input B data type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int32)</dt>
+<dd>Constrain output Y data type as 32-bit integer tensor.</dd>
+</dl>
+
+### <a name="MaxPool-10"></a>**MaxPool-10**</a>
+
+  MaxPool consumes an input tensor X and applies max pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   max pooling consisting of computing the max on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
+   ```
+   The output of each pooling window is maximum number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>Dilation value along each spatial axis of filter.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>storage_order</tt> : int (default is 0)</dt>
+<dd>The storage order of the tensor. 0 is row major, and 1 is column major.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+<dt><tt>Indices</tt> (optional) : I</dt>
+<dd>Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="Mod-10"></a>**Mod-10**</a>
+
+  Performs element-wise binary modulus (with Numpy-style broadcasting support).
+      The sign of the remainder is the same as that of the Divisor.
+
+      Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
+      (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
+      This attribute is set to 0 by default causing the behavior to be like integer mod.
+      Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
+
+      If the input type is floating point, then `fmod` attribute must be set to 1.
+
+      In case of dividend being zero, the results will be platform dependent.
+
+    This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>fmod</tt> : int (default is 0)</dt>
+<dd>Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Dividend tensor</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Divisor tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T</dt>
+<dd>Remainder tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="NonMaxSuppression-10"></a>**NonMaxSuppression-10**</a>
+
+  Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
+  Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box.
+  Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to
+  orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system
+  result in the same boxes being selected by the algorithm.
+  The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes.
+  The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>center_point_box</tt> : int (default is 0)</dt>
+<dd>Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.</dd>
+</dl>
+
+#### Inputs (2 - 5)
+
+<dl>
+<dt><tt>boxes</tt> : tensor(float)</dt>
+<dd>An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.</dd>
+<dt><tt>scores</tt> : tensor(float)</dt>
+<dd>An input tensor with shape [num_batches, num_classes, spatial_dimension]</dd>
+<dt><tt>max_output_boxes_per_class</tt> (optional) : tensor(int64)</dt>
+<dd>Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.</dd>
+<dt><tt>iou_threshold</tt> (optional) : tensor(float)</dt>
+<dd>Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.</dd>
+<dt><tt>score_threshold</tt> (optional) : tensor(float)</dt>
+<dd>Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>selected_indices</tt> : tensor(int64)</dt>
+<dd>selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].</dd>
+</dl>
+
+#### Type Constraints
+
+
+### <a name="QLinearConv-10"></a>**QLinearConv-10**</a>
+
+  The convolution operator consumes a quantized input tensor, its scale and zero point,
+  a quantized filter, its scale and zero point, and output's scale and zero point,
+  and computes the quantized output. Each scale and zero-point pair must have same shape.
+  It means they must be either scalars (per tensor) or 1-D tensors (per output channel).
+  Each input or output and its related zero point must have same type.
+  When bias is present it must be quantized using scale = input scale * weight scale and
+  zero point as 0.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into. default is 1.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input 'w'.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (8 - 9)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>x_scale</tt> : tensor(float)</dt>
+<dd>Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>x_zero_point</tt> : T1</dt>
+<dd>Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>w</tt> : T2</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. </dd>
+<dt><tt>w_scale</tt> : tensor(float)</dt>
+<dd>Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).</dd>
+<dt><tt>w_zero_point</tt> : T2</dt>
+<dd>Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>y_zero_point</tt> : T3</dt>
+<dd>Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>B</tt> (optional) : T4</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T3</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain filter type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain output type to 8-bit integer tensor.</dd>
+<dt><tt>T4</tt> : tensor(int32)</dt>
+<dd>Constrain bias type to 32-bit integer tensor.</dd>
+</dl>
+
+### <a name="QLinearMatMul-10"></a>**QLinearMatMul-10**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+  It consumes two quantized input tensors, their scales and zero points, scale and zero point of output,
+  and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).
+  For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.
+  Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor
+  (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row
+  or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be
+  an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]
+  for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may
+  have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.
+  Production must never overflow, and accumulation may overflow if and only if in 32 bits.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>a</tt> (non-differentiable) : T1</dt>
+<dd>N-dimensional quantized matrix a</dd>
+<dt><tt>a_scale</tt> (non-differentiable) : tensor(float)</dt>
+<dd>scale of quantized input a</dd>
+<dt><tt>a_zero_point</tt> (non-differentiable) : T1</dt>
+<dd>zero point of quantized input a</dd>
+<dt><tt>b</tt> (non-differentiable) : T2</dt>
+<dd>N-dimensional quantized matrix b</dd>
+<dt><tt>b_scale</tt> (non-differentiable) : tensor(float)</dt>
+<dd>scale of quantized input b</dd>
+<dt><tt>b_zero_point</tt> (non-differentiable) : T2</dt>
+<dd>zero point of quantized input b</dd>
+<dt><tt>y_scale</tt> (non-differentiable) : tensor(float)</dt>
+<dd>scale of quantized output y</dd>
+<dt><tt>y_zero_point</tt> (non-differentiable) : T3</dt>
+<dd>zero point of quantized output y</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> (non-differentiable) : T3</dt>
+<dd>Quantized matrix multiply results from a * b</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input a and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input b and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain output y and its zero point data type to 8-bit integer tensor.</dd>
+</dl>
+
+### <a name="QuantizeLinear-10"></a>**QuantizeLinear-10**</a>
+
+  The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor.
+  The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
+  For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>N-D full precision Input tensor to be quantized.</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>y_zero_point</tt> (optional) : T2</dt>
+<dd>Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T2</dt>
+<dd>N-D quantized output tensor. It has same shape as input 'x'.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(int32)</dt>
+<dd>Constrain 'x' to float or int32 tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.</dd>
+</dl>
+
+### <a name="Resize-10"></a>**Resize-10**</a>
+
+  Resize the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * scale).
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>N-D tensor</dd>
+<dt><tt>scales</tt> : tensor(float)</dt>
+<dd>The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>N-D tensor after resizing</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input 'X' and output 'Y' to all tensor types.</dd>
+</dl>
+
+### <a name="ReverseSequence-10"></a>**ReverseSequence-10**</a>
+
+  Reverse batch of sequences having different lengths specified by `sequence_lens`.
+
+  For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis,
+  and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed
+  sequences on the first sequence_lens[i] elements, then have original values copied for the other elements.
+
+  Example 1:
+    input = [[0.0, 4.0, 8.0,  12.0],
+             [1.0, 5.0, 9.0,  13.0],
+             [2.0, 6.0, 10.0, 14.0],
+             [3.0, 7.0, 11.0, 15.0]]
+    sequence_lens = [4, 3, 2, 1]
+    time_axis = 0
+    batch_axis = 1
+
+    output = [[3.0, 6.0, 9.0,  12.0],
+              [2.0, 5.0, 8.0,  13.0],
+              [1.0, 4.0, 10.0, 14.0],
+              [0.0, 7.0, 11.0, 15.0]]
+
+  Example 2:
+    input = [[0.0,  1.0,  2.0,  3.0 ],
+             [4.0,  5.0,  6.0,  7.0 ],
+             [8.0,  9.0,  10.0, 11.0],
+             [12.0, 13.0, 14.0, 15.0]]
+    sequence_lens = [1, 2, 3, 4]
+    time_axis = 1
+    batch_axis = 0
+
+    output = [[0.0,  1.0,  2.0,  3.0 ],
+              [5.0,  4.0,  6.0,  7.0 ],
+              [10.0, 9.0,  8.0,  11.0],
+              [15.0, 14.0, 13.0, 12.0]]
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>batch_axis</tt> : int (default is 1)</dt>
+<dd>(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.</dd>
+<dt><tt>time_axis</tt> : int (default is 0)</dt>
+<dd>(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Tensor of rank r >= 2.</dd>
+<dt><tt>sequence_lens</tt> : tensor(int64)</dt>
+<dd>Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Tensor with same shape of input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+</dl>
+
+### <a name="RoiAlign-10"></a>**RoiAlign-10**</a>
+
+  Region of Interest (RoI) align operation described in the
+  [Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
+  RoiAlign consumes an input tensor X and region of interests (rois)
+  to apply pooling across each RoI; it produces a 4-D tensor of shape
+  (num_rois, C, output_height, output_width).
+
+  RoiAlign is proposed to avoid the misalignment by removing
+  quantizations while converting from original image into feature
+  map and from feature map into RoI feature; in each ROI bin,
+  the value of the sampled locations are computed directly
+  through bilinear interpolation.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is avg)</dt>
+<dd>The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.</dd>
+<dt><tt>output_height</tt> : int (default is 1)</dt>
+<dd>default 1; Pooled output Y's height.</dd>
+<dt><tt>output_width</tt> : int (default is 1)</dt>
+<dd>default 1; Pooled output Y's width.</dd>
+<dt><tt>sampling_ratio</tt> : int (default is 0)</dt>
+<dd>Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.</dd>
+<dt><tt>spatial_scale</tt> : float (default is 1.0)</dt>
+<dd>Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.</dd>
+<dt><tt>rois</tt> : T1</dt>
+<dd>RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.</dd>
+<dt><tt>batch_indices</tt> : T2</dt>
+<dd>1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T1</dt>
+<dd>RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain types to int tensors.</dd>
+</dl>
+
+### <a name="Slice-10"></a>**Slice-10**</a>
+
+  Produces a slice of the input tensor along multiple axes. Similar to numpy:
+  https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+  Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
+  dimension and step for each axis in the list of axes, it uses this information to
+  slice the input `data` tensor. If a negative value is passed for any of the
+  start or end indices, it represent number of elements before the end of that
+  dimension. If the value passed to start or end is larger than the `n` (the
+  number of elements in this dimension), it represents `n`. For slicing to the
+  end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
+  If a negative value is passed for step, it represents slicing backward.
+  If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+  If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+  Example 1:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    axes = [0, 1]
+    starts = [1, 0]
+    ends = [2, 3]
+    steps = [1, 2]
+    result = [
+        [5, 7],
+    ]
+  Example 2:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    starts = [0, 1]
+    ends = [-1, 1000]
+    result = [
+        [2, 3, 4],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs (3 - 5)
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of data to extract slices from.</dd>
+<dt><tt>starts</tt> : Tind</dt>
+<dd>1-D tensor of starting indices of corresponding axis in `axes`</dd>
+<dt><tt>ends</tt> : Tind</dt>
+<dd>1-D tensor of ending indices (exclusive) of corresponding axis in `axes`</dd>
+<dt><tt>axes</tt> (optional) : Tind</dt>
+<dd>1-D tensor of axes that `starts` and `ends` apply to.</dd>
+<dt><tt>steps</tt> (optional) : Tind</dt>
+<dd>1-D tensor of slice step of corresponding axis in `axes`. Default to 1. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Sliced data tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="StringNormalizer-10"></a>**StringNormalizer-10**</a>
+
+  StringNormalization performs string operations for basic cleaning.
+  This operator has only one input (denoted by X) and only one output
+  (denoted by Y). This operator first examines the elements in the X,
+  and removes elements specified in "stopwords" attribute.
+  After removing stop words, the intermediate result can be further lowercased,
+  uppercased, or just returned depending the "case_change_action" attribute.
+  This operator only accepts [C]- and [1, C]-tensor.
+  If all elements in X are dropped, the output will be the empty value of string tensor with shape [1]
+  if input shape is [C] and shape [1, 1] if input shape is [1, C].
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>case_change_action</tt> : string (default is NONE)</dt>
+<dd>string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"</dd>
+<dt><tt>is_case_sensitive</tt> : int (default is 0)</dt>
+<dd>Boolean. Whether the identification of stop words in X is case-sensitive. Default is false</dd>
+<dt><tt>locale</tt> : string</dt>
+<dd>Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.</dd>
+<dt><tt>stopwords</tt> : list of strings</dt>
+<dd>List of stop words. If not set, no word would be removed from X.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : tensor(string)</dt>
+<dd>UTF-8 strings to normalize</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(string)</dt>
+<dd>UTF-8 Normalized strings</dd>
+</dl>
+
+#### Type Constraints
+
+
+### <a name="ThresholdedRelu-10"></a>**ThresholdedRelu-10**</a>
+
+  ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Threshold value</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="TopK-10"></a>**TopK-10**</a>
+
+  Retrieve the top-K elements along a specified axis. Given an input tensor of
+  shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+    -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+      which contains the values of the top k elements along the specified axis
+    -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+     contains the indices of the top k elements (original indices from the input
+     tensor).
+
+  Given two equivalent values, this operator uses the indices along the axis  as
+   a tiebreaker. That is, the element with the lower index will appear first.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>Dimension on which to do the sort.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_n, r]</dd>
+<dt><tt>K</tt> : tensor(int64)</dt>
+<dd>A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Values</tt> : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor</dd>
+<dt><tt>Indices</tt> : I</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="Upsample-10"></a>**Upsample-10** (deprecated)</a>
+
+  Upsample the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * scale).
+
+#### Version
+
+This version of the operator has been deprecated since version 10 of the default ONNX operator set.
+
+## Version 11 of the default ONNX operator set
+### <a name="ArgMax-11"></a>**ArgMax-11**</a>
+
+  Computes the indices of the max elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="ArgMin-11"></a>**ArgMin-11**</a>
+
+  Computes the indices of the min elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="AveragePool-11"></a>**AveragePool-11**</a>
+
+  AveragePool consumes an input tensor X and applies average pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   average pooling consisting of computing the average on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>count_include_pad</tt> : int (default is 0)</dt>
+<dd>Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="BitShift-11"></a>**BitShift-11**</a>
+
+  Bitwise shift operator performs element-wise operation. For each input element, if the
+   attribute "direction" is "RIGHT", this operator moves its binary representation toward
+   the right side so that the input value is effectively decreased. If the attribute "direction"
+   is "LEFT", bits of binary representation moves toward the left side, which results the
+   increase of its actual value. The input X is the tensor to be shifted and another input
+   Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4],
+   and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with
+   X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8].
+
+   Because this operator supports Numpy-style broadcasting, X's and Y's shapes are
+   not necessarily identical.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>direction</tt> : string (required)</dt>
+<dd>Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>First operand, input to be shifted.</dd>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Second operand, amounts of shift.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64)</dt>
+<dd>Constrain input and output types to integer tensors.</dd>
+</dl>
+
+### <a name="Clip-11"></a>**Clip-11**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified by the inputs 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max(), respectively.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+<dt><tt>min</tt> (optional) : T</dt>
+<dd>Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).</dd>
+<dt><tt>max</tt> (optional) : T</dt>
+<dd>Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Compress-11"></a>**Compress-11**</a>
+
+  Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index.
+      In case axis is not provided, input is flattened before elements are selected.
+      Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>condition</tt> (non-differentiable) : T1</dt>
+<dd>Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain to boolean tensors.</dd>
+</dl>
+
+### <a name="Concat-11"></a>**Concat-11**</a>
+
+  Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (required)</dt>
+<dd>Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic) : T</dt>
+<dd>List of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+### <a name="ConcatFromSequence-11"></a>**ConcatFromSequence-11**</a>
+
+  Concatenate a sequence of tensors into a single tensor.
+  All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
+  By default 'new_axis' is 0, the behavior is similar to numpy.concatenate.
+  When 'new_axis' is 1, the behavior is similar to numpy.stack.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (required)</dt>
+<dd>Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. </dd>
+<dt><tt>new_axis</tt> : int (default is 0)</dt>
+<dd>Insert and concatenate on a new axis or not, default 0 means do not insert new axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Sequence of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input types to any tensor type.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+### <a name="Constant-11"></a>**Constant-11**</a>
+
+  A constant tensor. Exactly one of the two attributes, either value or sparse_value,
+  must be specified.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>sparse_value</tt> : sparse_tensor</dt>
+<dd>The value for the elements of the output tensor in sparse format.</dd>
+<dt><tt>value</tt> : tensor</dt>
+<dd>The value for the elements of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor containing the same value of the provided tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Conv-11"></a>**Conv-11**</a>
+
+  The convolution operator consumes an input tensor and a filter, and
+  computes the output.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ConvTranspose-11"></a>**ConvTranspose-11**</a>
+
+  The convolution transpose operator consumes an input tensor and a filter,
+  and computes the output.
+
+  If the pads parameter is provided the shape of the output is calculated via the following equation:
+
+    output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]
+
+  output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
+
+    total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
+    If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
+    Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
+
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
+<dt><tt>output_padding</tt> : list of ints</dt>
+<dd>Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.</dd>
+<dt><tt>output_shape</tt> : list of ints</dt>
+<dd>The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="CumSum-11"></a>**CumSum-11**</a>
+
+  Performs cumulative sum of the input elements along the given axis.
+  By default, it will do the sum inclusively meaning the first element is copied as is.
+  Through an `exclusive` attribute, this behavior can change to exclude the first element.
+  It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.
+
+  Example:
+  ```
+  input_x = [1, 2, 3]
+  axis=0
+  output = [1, 3, 6]
+  exclusive=1
+  output = [0, 1, 3]
+  exclusive=0
+  reverse=1
+  output = [6, 5, 3]
+  exclusive=1
+  reverse=1
+  output = [5, 3, 0]
+  ```
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>exclusive</tt> : int (default is 0)</dt>
+<dd>If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.</dd>
+<dt><tt>reverse</tt> : int (default is 0)</dt>
+<dd>If set to 1 will perform the sums in reverse direction.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>x</tt> (differentiable) : T</dt>
+<dd>An input tensor that is to be processed.</dd>
+<dt><tt>axis</tt> (non-differentiable) : T2</dt>
+<dd>A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same type as 'x' with cumulative sums of the x's elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float), tensor(double)</dt>
+<dd>Input can be of any tensor type.</dd>
+<dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
+<dd>axis tensor can be int32 or int64 only</dd>
+</dl>
+
+### <a name="DepthToSpace-11"></a>**DepthToSpace-11**</a>
+
+  DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+  This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+  the input tensor where values from the depth dimension are moved in spatial blocks to the height
+  and width dimensions. By default, `mode` = `DCR`.
+  In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
+  following order: depth, column, and then row. The output y is computed from the input x as below:
+
+  b, c, h, w = x.shape
+
+  tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+
+  tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+
+  y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
+
+
+  In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
+  following order: column, row, and the depth. The output y is computed from the input x as below:
+
+  b, c, h, w = x.shape
+
+  tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
+
+  tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+
+  y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+<dt><tt>mode</tt> : string (default is DCR)</dt>
+<dd>DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Det-11"></a>**Det-11**</a>
+
+  Det calculates determinant of a square matrix or batches of square matrices.
+  Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions,
+  and the inner-most 2 dimensions form square matrices.
+  The output is a tensor of shape `[*]`, containing the determinants of all input submatrices.
+  e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to floating-point tensors.</dd>
+</dl>
+
+### <a name="DynamicQuantizeLinear-11"></a>**DynamicQuantizeLinear-11**</a>
+
+  A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data.
+  Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.
+  Scale is calculated as:
+  ```
+   y_scale = (max(x) - min(x))/(qmax - qmin)
+   * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
+   * data range is adjusted to include 0.
+  ```
+  Zero point is calculated as:
+  ```
+  intermediate_zero_point = qmin - min(x)/y_scale
+  y_zero_point = cast(round(saturate(itermediate_zero_point)))
+  * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
+  * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
+  * rounding to nearest ties to even.
+  ```
+  Data quantization formula is:
+  ```
+  y = saturate (round (x / y_scale) + y_zero_point)
+  * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
+  * rounding to nearest ties to even.
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T2</dt>
+<dd>Quantized output tensor</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Output scale. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>y_zero_point</tt> : T2</dt>
+<dd>Output zero point. It's a scalar, which means a per-tensor/layer quantization.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float)</dt>
+<dd>Constrain 'x' to float tensor.</dd>
+<dt><tt>T2</tt> : tensor(uint8)</dt>
+<dd>Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.</dd>
+</dl>
+
+### <a name="Equal-11"></a>**Equal-11**</a>
+
+  Returns the tensor resulted from performing the `equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Flatten-11"></a>**Flatten-11**</a>
+
+  Flattens the input tensor into a 2D matrix. If input tensor has shape
+  (d_0, d_1, ... d_n) then the output will have shape
+  (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>A tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output to all tensor types.</dd>
+</dl>
+
+### <a name="Gather-11"></a>**Gather-11**</a>
+
+  Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+  entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+  them in an output tensor of rank q + (r - 1).
+
+  axis = 0 :
+
+  Let
+  k = indices[i_{0}, ..., i_{q-1}]
+  Then
+  output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]
+
+  ```
+    data = [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+    indices = [
+        [0, 1],
+        [1, 2],
+    ]
+    output = [
+        [
+            [1.0, 1.2],
+            [2.3, 3.4],
+        ],
+        [
+            [2.3, 3.4],
+            [4.5, 5.7],
+        ],
+    ]
+  ```
+  axis = 1 :
+
+  Let
+  k = indices[i_{0}, ..., i_{q-1}]
+  Then
+  output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]
+
+  ```
+    data = [
+        [1.0, 1.2, 1.9],
+        [2.3, 3.4, 3.9],
+        [4.5, 5.7, 5.9],
+    ]
+    indices = [
+        [0, 2],
+    ]
+    axis = 1,
+    output = [
+        [
+            [1.0, 1.9],
+            [2.3, 3.9],
+            [4.5, 5.9],
+        ],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : Tind</dt>
+<dd>Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank q + (r - 1).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="GatherElements-11"></a>**GatherElements-11**</a>
+
+  GatherElements takes two inputs `data` and `indices` of the same rank r >= 1
+  and an optional attribute `axis` that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). It is an indexing operation
+  that produces its output by indexing into the input data tensor at index
+  positions determined by elements of the `indices` tensor.
+  Its output shape is the same as the shape of `indices` and consists of one value
+  (gathered from the `data`) for each element in `indices`.
+
+  For instance, in the 3-D case (r = 3), the output produced is determined
+  by the following equations:
+  ```
+    out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
+    out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
+    out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
+  ```
+
+  This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation.
+
+  Example 1:
+  ```
+    data = [
+        [1, 2],
+        [3, 4],
+    ]
+    indices = [
+        [0, 0],
+        [1, 0],
+    ]
+    axis = 1
+    output = [
+        [
+          [1, 1],
+          [4, 3],
+        ],
+    ]
+  ```
+  Example 2:
+  ```
+    data = [
+        [1, 2, 3],
+        [4, 5, 6],
+        [7, 8, 9],
+    ]
+    indices = [
+        [1, 2, 0],
+        [2, 0, 0],
+    ]
+    axis = 0
+    output = [
+        [
+          [4, 8, 3],
+          [7, 2, 3],
+        ],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : Tind</dt>
+<dd>Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of the same shape as indices.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="GatherND-11"></a>**GatherND-11**</a>
+
+  Given `data` tensor of rank `r` >= 1, and `indices` tensor of rank `q` >= 1, this operator gathers
+  slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1`.
+
+  `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+  where each element defines a slice of `data`
+
+  Some salient points about the inputs' rank and shape:
+
+  1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+  2) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r` (inclusive)
+
+  3) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+     It is an error if any of the index values are out of bounds.
+
+  The output is computed as follows:
+
+  The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+  1) If `indices_shape[-1] > r` => error condition
+
+  2) If `indices_shape[-1] == r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor
+     containing 1-D tensors of dimension `r`. Let us think of each such `r` ranked tensor as `indices_slice`.
+     Each *scalar value* corresponding to `data[indices_slice]` is filled into the corresponding location of the `(q-1)`-dimensional tensor
+     to form the `output` tensor (Example 1 below)
+
+  3) If `indices_shape[-1] < r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor
+     containing 1-D tensors of dimension `< r`. Let us think of each such tensors as `indices_slice`.
+     Each *tensor slice* corresponding to `data[indices_slice , :]` is filled into the corresponding location of the `(q-1)`-dimensional tensor
+     to form the `output` tensor (Examples 2, 3, and 4 below)
+
+  This operator is the inverse of `ScatterND`.
+
+  `Example 1`
+
+    data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+    indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+    output  = [0,3]           # output_shape = [2]
+
+  `Example 2`
+
+    data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+    indices = [[1],[0]]      # indices_shape = [2, 1]
+
+    output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+  `Example 3`
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+    output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+  `Example 4`
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+    output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+### <a name="Gemm-11"></a>**Gemm-11**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+  A' = transpose(A) if transA else A
+
+  B' = transpose(B) if transB else B
+
+  Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+  input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+  and output tensor Y has shape (M, N). A will be transposed before doing the
+  computation if attribute transA is non-zero, same for B and transB.
+  This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md).
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C.</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>A</tt> : T</dt>
+<dd>Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.</dd>
+<dt><tt>B</tt> : T</dt>
+<dd>Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.</dd>
+<dt><tt>C</tt> (optional) : T</dt>
+<dd>Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output tensor of shape (M, N).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="Hardmax-11"></a>**Hardmax-11**</a>
+
+  The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch
+   of the given input.
+
+  The input does not need to explicitly be a 2D vector; rather, it will be
+  coerced into one. For an arbitrary n-dimensional tensor
+  input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+  the axis provided, then input will be coerced into a 2-dimensional tensor with
+  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+  case where axis=1, this means the input tensor will be coerced into a 2D tensor
+  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+  Each of these dimensions must be matched correctly, or else the operator
+  will throw errors. The output tensor has the same shape
+  and contains the hardmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input tensor that's coerced into a 2D matrix of size (NxD) as described above.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output values with the same shape as input tensor (the original size without coercion).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="If-11"></a>**If-11**</a>
+
+  If conditional
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>else_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.</dd>
+<dt><tt>then_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>cond</tt> : B</dt>
+<dd>Condition for the if</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Only bool</dd>
+</dl>
+
+### <a name="LogSoftmax-11"></a>**LogSoftmax-11**</a>
+
+  The operator computes the logsoftmax (log of softmax) values for each layer in the batch
+   of the given input.
+
+  The input does not need to explicitly be a 2D vector; rather, it will be
+  coerced into one. For an arbitrary n-dimensional tensor
+  input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+  the axis provided, then input will be coerced into a 2-dimensional tensor with
+  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+  case where axis=1, this means the input tensor will be coerced into a 2D tensor
+  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+  Each of these dimensions must be matched correctly, or else the operator
+  will throw errors. The output tensor has the same shape
+  and contains the logsoftmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input tensor that's coerced into a 2D matrix of size (NxD) as described above.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output values with the same shape as input tensor (the original size without coercion).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Loop-11"></a>**Loop-11**</a>
+
+  Generic Looping construct. This loop has multiple termination conditions:
+
+  1) Trip count. Iteration count specified at runtime. Set by
+     specifying the input M. Optional. Set to empty string to omit.
+     Note that a static trip count (specified at graph construction time) can be
+     specified by passing in a constant node for input M.
+  2) Loop termination condition. This is an input to the op that determines
+     whether to run the first iteration and also a loop-carried dependency for
+     the body graph. The body graph must yield a value for the condition variable,
+     whether this input is provided or not.
+
+  This table summarizes the operating modes of this operator with equivalent
+  C-style code:
+
+      Operator inputs defined as (max_trip_count, condition_var).
+
+      input ("", ""):
+          for (int i=0; ; ++i) {
+            cond = ... // Note this value is ignored, but is required in the body
+          }
+
+      input ("", cond) // Note this is analogous to a while loop
+          bool cond = ...;
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input ("", 1) // Note this is analogous to a do-while loop
+          bool cond = true
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input (trip_count, "") // Note this is analogous to a for loop
+          int trip_count = ...
+          for (int i=0; i < trip_count; ++i) {
+            cond = ...; // ignored
+          }
+
+      input (trip_count, cond)
+          int trip_count = ...;
+          bool cond = ...;
+          for (int i=0; i < trip_count && cond; ++i) {
+            cond = ...;
+          }
+
+
+  *Sample usage - cond as well as trip count*
+
+      graph predict-net {
+        %a = Constant[value = <Scalar Tensor [3]>]()
+        %b = Constant[value = <Scalar Tensor [6]>]()
+        %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+        %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+        %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+        return
+      }
+
+      graph body-net (
+        %i[INT32, scalar]           // iteration number
+        %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+        %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+      ) {
+        %my_local = Add(%a, %b_in)
+        %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+        %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+        %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+        return %keepgoing_out, %b_out, %user_defined_val
+      }
+
+  *Sample equivalent C code*
+
+      {
+        /* User-defined code (enclosing scope) */
+        int a = 3, b = 6;
+        bool keepgoing = true; // Analogous to input cond
+        /* End user-defined code */
+
+        /* Implicitly-defined code */
+        const int max_trip_count = 10; // Analogous to input M
+        int user_defined_vals[]; // Imagine this is resizable
+        /* End implicitly-defined code */
+        /* initialize loop-carried variables and scan-output variables */
+        bool keepgoing_out = keepgoing
+        int b_out = b
+
+        for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+          /* Implicitly-defined code: bind actual parameter values
+             to formal parameter variables of loop-body */
+          bool keepgoing_in = keepgoing_out;
+          bool b_in = b_out;
+
+          /* User-defined code (loop body) */
+          int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+          b_out = a - b_in;
+          keepgoing_out = my_local > b_out;
+          user_defined_val = b_in + b_in; // b_in and b_out are different variables
+          /* End user-defined code */
+
+          /* Implicitly defined-code */
+          user_defined_vals[i] = user_defined_val // accumulate scan-output values
+        }
+        // int t = my_local; // Can't do this. my_local is not accessible here.
+
+        // The values below are bound to the output variables of the loop and therefore accessible
+        // b_out; user_defined_vals; keepgoing_out;
+      }
+
+  There are several things of note in this code snippet:
+
+  1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+     be referenced in the inputs of the loop.
+  2) Any values computed in the loop body that needs to be used in a subsequent
+     iteration or after the loop are modelled using a pair of variables in the loop-body,
+     consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+     These are referred to as loop-carried dependences. The loop operation node
+     supplies the input value of the input variable for the first iteration, and
+     returns the output value of the output variable produced by the final
+     iteration.
+  3) Scan_output variables are used to implicitly concatenate values computed across
+     all the iterations. In the above example, the value of user_defined_val computed
+     over all iterations are concatenated and returned as the value of user_defined_vals
+     after the loop.
+  4) Values created in the body cannot be accessed in the enclosing scope,
+     except using the mechanism described above.
+
+  Note that the semantics of this op support "diagonal" or "wavefront" execution.
+  (See Step 3 here for an example:
+  https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+  Frontends should emit multi-layer RNNs as a series of While operators (with
+  time being the inner looping dimension), with each successive layer consuming
+  the scan_outputs from the previous layer, possibly going through several
+  point-wise operators (e.g. dropout, residual connections, linear layer).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.</dd>
+</dl>
+
+#### Inputs (2 - &#8734;)
+
+<dl>
+<dt><tt>M</tt> (optional) : I</dt>
+<dd>A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.</dd>
+<dt><tt>cond</tt> (optional) : B</dt>
+<dd>A boolean termination condition. Optional. Pass empty string to skip.</dd>
+<dt><tt>v_initial</tt> (variadic, heterogeneous) : V</dt>
+<dd>The initial values of any loop-carried dependencies (values that change across loop iterations)</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>v_final_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final N loop carried dependency values then K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>tensor of int64, which should be a scalar.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>tensor of bool, which should be a scalar.</dd>
+</dl>
+
+### <a name="LpPool-11"></a>**LpPool-11**</a>
+
+  LpPool consumes an input tensor X and applies Lp pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   Lp pooling consisting of computing the Lp norm on all values of a subset
+   of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>p value of the Lp norm used to pool over the input data.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="MaxPool-11"></a>**MaxPool-11**</a>
+
+  MaxPool consumes an input tensor X and applies max pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   max pooling consisting of computing the max on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
+   ```
+   The output of each pooling window is maximum number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>storage_order</tt> : int (default is 0)</dt>
+<dd>The storage order of the tensor. 0 is row major, and 1 is column major.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+<dt><tt>Indices</tt> (optional) : I</dt>
+<dd>Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="MaxUnpool-11"></a>**MaxUnpool-11**</a>
+
+  MaxUnpool essentially computes the partial inverse of the MaxPool op.
+   The input information to this op is typically the the output information from a MaxPool op. The first
+   input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
+   from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
+   to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
+   The third (optional) input is a tensor that specifies the output size of the unpooling operation.
+
+  MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal
+   values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling
+   the result of an unpooling operation should give back the original input to the unpooling op.
+
+  MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.
+   The third input argument, output_size, is meant to disambiguate the op and produce output tensor of
+   known/predictable size.
+
+  In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,
+   which define the exact unpooling op. The attributes typically have the same values as the corrsponding
+   pooling op that the unpooling op is trying to invert.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T1</dt>
+<dd>Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>I</tt> (non-differentiable) : T2</dt>
+<dd>Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).</dd>
+<dt><tt>output_shape</tt> (optional, non-differentiable) : T2</dt>
+<dd>The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T1</dt>
+<dd>Output data tensor that contains the result of the unpooling.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="NonMaxSuppression-11"></a>**NonMaxSuppression-11**</a>
+
+  Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
+  Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box.
+  Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to
+  orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system
+  result in the same boxes being selected by the algorithm.
+  The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes.
+  The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>center_point_box</tt> : int (default is 0)</dt>
+<dd>Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.</dd>
+</dl>
+
+#### Inputs (2 - 5)
+
+<dl>
+<dt><tt>boxes</tt> : tensor(float)</dt>
+<dd>An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.</dd>
+<dt><tt>scores</tt> : tensor(float)</dt>
+<dd>An input tensor with shape [num_batches, num_classes, spatial_dimension]</dd>
+<dt><tt>max_output_boxes_per_class</tt> (optional) : tensor(int64)</dt>
+<dd>Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.</dd>
+<dt><tt>iou_threshold</tt> (optional) : tensor(float)</dt>
+<dd>Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.</dd>
+<dt><tt>score_threshold</tt> (optional) : tensor(float)</dt>
+<dd>Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>selected_indices</tt> : tensor(int64)</dt>
+<dd>selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].</dd>
+</dl>
+
+#### Type Constraints
+
+
+### <a name="OneHot-11"></a>**OneHot-11**</a>
+
+  Produces a one-hot tensor based on inputs.
+      The locations represented by the index values in the 'indices' input tensor will have 'on_value'
+      and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'
+      are specified as part of required input argument 'values', which is a two-element tensor of format
+      [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the
+      input tensor. The additional dimension is for one-hot representation. The additional dimension will
+      be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional
+      dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional
+      dimension is specified by required scalar input 'depth'. The type of the output tensor is the same
+      as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside
+      the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the
+      output tensor.
+
+      when axis = 0:
+      output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise.
+
+      when axis = -1:
+      output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise.
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>indices</tt> (non-differentiable) : T1</dt>
+<dd>Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.</dd>
+<dt><tt>depth</tt> (non-differentiable) : T2</dt>
+<dd>Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.</dd>
+<dt><tt>values</tt> (non-differentiable) : T3</dt>
+<dd>Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (non-differentiable) : T3</dt>
+<dd>Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+<dt><tt>T2</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+<dt><tt>T3</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+</dl>
+
+### <a name="Pad-11"></a>**Pad-11**</a>
+
+  Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`,
+  a padded tensor (`output`) is generated.
+
+  The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):
+
+  1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0)
+
+  2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
+
+  3) `edge` - pads with the edge values of array
+
+
+  Example 1 (`constant` mode):
+    Insert 0 pads to the beginning of the second dimension.
+
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'constant'
+
+    constant_value = 0.0
+
+    output =
+    [
+        [0.0, 0.0, 1.0, 1.2],
+        [0.0, 0.0, 2.3, 3.4],
+        [0.0, 0.0, 4.5, 5.7],
+    ]
+
+
+  Example 2 (`reflect` mode):
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'reflect'
+
+    output =
+    [
+        [1.0, 1.2, 1.0, 1.2],
+        [2.3, 3.4, 2.3, 3.4],
+        [4.5, 5.7, 4.5, 5.7],
+    ]
+
+
+  Example 3 (`edge` mode):
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'edge'
+
+    output =
+    [
+        [1.0, 1.0, 1.0, 1.2],
+        [2.3, 2.3, 2.3, 3.4],
+        [4.5, 4.5, 4.5, 5.7],
+    ]
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is constant)</dt>
+<dd>Supported modes: `constant`(default), `reflect`, `edge`</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Input tensor.</dd>
+<dt><tt>pads</tt> : tensor(int64)</dt>
+<dd>Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.</dd>
+<dt><tt>constant_value</tt> (optional) : T</dt>
+<dd>(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor after padding.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output to only numeric types.</dd>
+</dl>
+
+### <a name="Range-11"></a>**Range-11**</a>
+
+  Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta`
+  up to `limit` (exclusive).
+
+  The number of elements in the output of range is computed as below-
+
+  `number_of_elements = max( ceil( (limit - start) / delta ) , 0 )`
+
+  The pseudocode determining the contents of the output is shown below-
+
+  `for(int i=0; i<number_of_elements; ++i)`
+
+  `{`
+
+  `    output[i] =  start + (i * delta);  `
+
+  `}`
+
+  `Example 1`
+  Inputs: start = 3, limit = 9, delta = 3
+  Output: [3, 6]
+
+  `Example 2`
+  Inputs: start = 10, limit = 4, delta = -2
+  Output: [10, 8, 6]
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>start</tt> : T</dt>
+<dd>Scalar. First entry for the range of output values.</dd>
+<dt><tt>limit</tt> : T</dt>
+<dd>Scalar. Exclusive upper limit for the range of output values.</dd>
+<dt><tt>delta</tt> : T</dt>
+<dd>Scalar. Value to step by.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>A 1-D tensor with same type as the inputs containing generated range of values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input types to common numeric type tensors.</dd>
+</dl>
+
+### <a name="ReduceL1-11"></a>**ReduceL1-11**</a>
+
+  Computes the L1 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceL2-11"></a>**ReduceL2-11**</a>
+
+  Computes the L2 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceLogSum-11"></a>**ReduceLogSum-11**</a>
+
+  Computes the log sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceLogSumExp-11"></a>**ReduceLogSumExp-11**</a>
+
+  Computes the log sum exponent of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMax-11"></a>**ReduceMax-11**</a>
+
+  Computes the max of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMean-11"></a>**ReduceMean-11**</a>
+
+  Computes the mean of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMin-11"></a>**ReduceMin-11**</a>
+
+  Computes the min of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceProd-11"></a>**ReduceProd-11**</a>
+
+  Computes the product of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceSum-11"></a>**ReduceSum-11**</a>
+
+  Computes the sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceSumSquare-11"></a>**ReduceSumSquare-11**</a>
+
+  Computes the sum square of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Resize-11"></a>**Resize-11**</a>
+
+  Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coordinate_transformation_mode</tt> : string (default is half_pixel)</dt>
+<dd>
+This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
+
+The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example.
+Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/>
+
+if coordinate_transformation_mode is "half_pixel", <br/>
+x_original = (x_resized + 0.5) / scale - 0.5, <br/>
+
+if coordinate_transformation_mode is "pytorch_half_pixel", <br/>
+x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/>
+
+if coordinate_transformation_mode is "align_corners", <br/>
+x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
+
+if coordinate_transformation_mode is "asymmetric", <br/>
+x_original = x_resized / scale, <br/>
+
+if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/>
+x_original = (x_resized + 0.5) / scale, <br/>
+
+if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).</dd>
+<dt><tt>cubic_coeff_a</tt> : float (default is -0.75)</dt>
+<dd>The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".</dd>
+<dt><tt>exclude_outside</tt> : int (default is 0)</dt>
+<dd>If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.</dd>
+<dt><tt>extrapolation_value</tt> : float (default is 0.0)</dt>
+<dd>When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.</dd>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).</dd>
+<dt><tt>nearest_mode</tt> : string (default is round_prefer_floor)</dt>
+<dd>Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".</dd>
+</dl>
+
+#### Inputs (3 - 4)
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>N-D tensor</dd>
+<dt><tt>roi</tt> : T2</dt>
+<dd>1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"</dd>
+<dt><tt>scales</tt> : tensor(float)</dt>
+<dd>The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. If 'size' is needed, the user must set 'scales' to an empty tensor.</dd>
+<dt><tt>sizes</tt> (optional) : tensor(int64)</dt>
+<dd>The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. May only be set if 'scales' is set to an empty tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T1</dt>
+<dd>N-D tensor after resizing</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input 'X' and output 'Y' to all tensor types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain roi type to float or double.</dd>
+</dl>
+
+### <a name="Round-11"></a>**Round-11**</a>
+
+  Round takes one input Tensor and rounds the values, element-wise, meaning
+  it finds the nearest integer for each value.
+  In case of halfs, the rule is to round them to the nearest even integer.
+  The output tensor has the same shape and type as the input.
+
+  Examples:
+  ```
+  round([0.9]) = [1.0]
+  round([2.5]) = [2.0]
+  round([2.3]) = [2.0]
+  round([1.5]) = [2.0]
+  round([-4.5]) = [-4.0]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Scan-11"></a>**Scan-11**</a>
+
+  Scan can be used to iterate over one or more scan_input tensors,
+  constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+  functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+  generalizations of RNN-like constructs for sequence-to-sequence processing.
+  Other tensors (referred to as state_variables here) can be used to carry a state
+  when iterating from one element to another (similar to hidden-state in RNNs, also referred
+  to as loop-carried dependences in the context of loops).
+  Many common usages involve a single scan_input tensor (where functionality
+  similar to scan, fold and map can be obtained). When more than one scan_input is used,
+  a behavior similar to zip is obtained.
+
+  The attribute body must be a graph, specifying the computation to be performed in
+  every iteration. It takes as input the current values of the state_variables and
+  the current iterated element of the scan_inputs. It must return the (updated) values
+  of the state_variables and zero or more scan_output_element tensors. The values of the
+  scan_output_element tensors are concatenated over all the iterations to produce the
+  scan_output values of the scan construct (similar to the concatenated intermediate
+  hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+  well as scan_output_element tensors) are required to have the same shape in each iteration
+  of the loop (a restriction imposed to enable efficient memory allocation).
+
+  Note that the iterated element passed to the body subgraph does not have a sequence
+  axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+  The scan operation returns the final values of the state_variables as well as the
+  scan_outputs.
+
+  The optional attribute scan_input_directions specifies the direction (forward or backward)
+  for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+  direction. A bidirectional scan may be performed by specifying the same tensor input twice
+  in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+  The scan_output of the operation is produced by concatenating the scan_output_element
+  values produced by the body in each iteration.  The optional attribute scan_output_directions
+  specifies the direction in which scan_output is constructed (by appending or prepending the
+  scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+  is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+  The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+  If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+  batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+  Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+  The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+  are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+  scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+  value of 1.
+
+  Note that because of the ONNX restriction that only the last parameter of an operator can
+  be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+  Similarly, the final-states and scan-outputs are listed together as one output parameter.
+  The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+  The behavior of
+
+      Scan <
+          num_scan_inputs = m,
+          body = loop-body,
+          scan_input_axes = [axis_1, ..., axis_m]
+      > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+  is equivalent to the following pseudo-code:
+
+      // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+      // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+      sequence_length = scan_1.shape[axis_1];
+
+      // initialize state-variables
+      st_1 = init_1; ... st_n = init_n;
+      // initialize scan-output variables: [] denotes an empty tensor
+      scan_out_1 = []; ...; scan_out_k = [];
+      // identify number of iterations:
+
+      // execute loop
+      for (int t = 0; t < sequence_length; ++t) {
+          // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+          // of rank one less than T obtained by indexing T at position t along axis k.
+          si_1 = scan_1<axis=axis_1>[t];
+          ... ;
+          si_m = scan_m<axis=axis_m>[t];
+          // execute loop-body
+          st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+          // accumulate the scan-output elements
+          scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+      }
+
+      return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+  *Sample usage: Encoding RNN using a Scan*
+
+  The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+  recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+  be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+  %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+  values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+      graph rnn-encoding {
+        %H_0 = ...
+        %X = ...
+        %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+        return %Y, %Y_h
+      }
+
+      graph rnn-cell-1 (
+        %H_tminus1[FLOAT, tensor]
+        %X_t[FLOAT, tensor]
+      ) {
+        %Wi = ...
+        %Ri = ...
+        %Wbi = ...
+        %Rbi = ...
+        %t1 = X_t * (Wi^T)
+        %t2 = H_tminus1*(Ri^T)
+        %t3 = Add(%t1, %t2)
+        %t4 = Add(%t3, %Wbi)
+        %t5 = Add(%t4, %Rbi)
+        %Ht = Tanh(%t5)
+        %Accumulate = Identity(%Ht)
+        return %Ht, %Accumulate
+      }
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.</dd>
+<dt><tt>num_scan_inputs</tt> : int (required)</dt>
+<dd>An attribute specifying the number of scan_inputs M. </dd>
+<dt><tt>scan_input_axes</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>scan_input_directions</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.</dd>
+<dt><tt>scan_output_axes</tt> : list of ints</dt>
+<dd>An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].</dd>
+<dt><tt>scan_output_directions</tt> : list of ints</dt>
+<dd>An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>initial_state_and_scan_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Initial values of the loop's N state variables followed by M scan_inputs</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>final_state_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final values of the loop's N state variables followed by K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+</dl>
+
+### <a name="Scatter-11"></a>**Scatter-11** (deprecated)</a>
+
+  This operator is deprecated. Please use ScatterElements, which provides the same functionality.
+
+  Scatter takes three inputs `data`, `updates`, and `indices` of the same
+  rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value
+  to values specified by `updates` at specific index positions specified by
+  `indices`. Its output shape is the same as the shape of `data`.
+
+  For each entry in `updates`, the target index in `data` is obtained by combining
+  the corresponding entry in `indices` with the index of the entry itself: the
+  index-value for dimension = axis is obtained from the value of the corresponding
+  entry in `indices` and the index-value for dimension != axis is obtained from the
+  index of the entry itself.
+
+  For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+  is performed as below:
+  ```
+    output[indices[i][j]][j] = updates[i][j] if axis = 0,
+    output[i][indices[i][j]] = updates[i][j] if axis = 1,
+  ```
+
+  This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+  Example 1:
+  ```
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  ```
+  Example 2:
+  ```
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+  ```
+
+#### Version
+
+This version of the operator has been deprecated since version 11 of the default ONNX operator set.
+
+### <a name="ScatterElements-11"></a>**ScatterElements-11**</a>
+
+  ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+  rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value
+  to values specified by `updates` at specific index positions specified by
+  `indices`. Its output shape is the same as the shape of `data`.
+
+  For each entry in `updates`, the target index in `data` is obtained by combining
+  the corresponding entry in `indices` with the index of the entry itself: the
+  index-value for dimension = axis is obtained from the value of the corresponding
+  entry in `indices` and the index-value for dimension != axis is obtained from the
+  index of the entry itself.
+
+  For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+  is performed as below:
+  ```
+    output[indices[i][j]][j] = updates[i][j] if axis = 0,
+    output[i][indices[i][j]] = updates[i][j] if axis = 1,
+  ```
+
+  This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+  Example 1:
+  ```
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  ```
+  Example 2:
+  ```
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : Tind</dt>
+<dd>Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+<dt><tt>updates</tt> : T</dt>
+<dd>Tensor of rank r >=1 (same rank and shape as indices)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank r >= 1 (same rank as input).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="ScatterND-11"></a>**ScatterND-11**</a>
+
+  ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+  and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value to values
+  specified by `updates` at specific index positions specified by `indices`. Its output shape
+  is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+  That is, two or more `updates` for the same index-location is not supported.
+
+  `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+   `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+  Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+  update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+  update to a slice of the tensor.
+
+  `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+  first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+  The remaining dimensions of `updates` correspond to the dimensions of the
+  replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+  corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+  must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+  of shapes.
+
+  The `output` is calculated via the following equation:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] = updates[idx]
+
+  The order of iteration in the above loop is not specified.
+  In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+  This ensures that the output value does not depend on the iteration order.
+
+  This operator is the inverse of GatherND.
+
+  Example 1:
+  ```
+    data    = [1, 2, 3, 4, 5, 6, 7, 8]
+    indices = [[4], [3], [1], [7]]
+    updates = [9, 10, 11, 12]
+    output  = [1, 11, 3, 10, 9, 6, 7, 12]
+  ```
+
+  Example 2:
+  ```
+    data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+    indices = [[0], [2]]
+    updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+    output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1.</dd>
+<dt><tt>updates</tt> : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+### <a name="SequenceAt-11"></a>**SequenceAt-11**</a>
+
+  Outputs a tensor copy from the tensor at 'position' in 'input_sequence'.
+  Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
+  Negative value means counting positions from the back.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>position</tt> : I</dt>
+<dd>Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>tensor</tt> : T</dt>
+<dd>Output tensor at the specified position in the input sequence.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain position to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+### <a name="SequenceConstruct-11"></a>**SequenceConstruct-11**</a>
+
+  Construct a tensor sequence containing 'inputs' tensors.
+  All tensors in 'inputs' must have the same data type.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic) : T</dt>
+<dd>Tensors.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>Sequence enclosing the input tensors.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input types to any tensor type.</dd>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+### <a name="SequenceEmpty-11"></a>**SequenceEmpty-11**</a>
+
+  Construct an empty tensor sequence, with given data type.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type of the tensors in the output sequence. The default type is 'float'.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : S</dt>
+<dd>Empty sequence.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+### <a name="SequenceErase-11"></a>**SequenceErase-11**</a>
+
+  Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'.
+  Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
+  Negative value means counting positions from the back.
+  'position' is optional, by default it erases the last tensor from 'input_sequence'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>position</tt> (optional) : I</dt>
+<dd>Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>Output sequence that has the tensor at the specified position removed.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain position to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+### <a name="SequenceInsert-11"></a>**SequenceInsert-11**</a>
+
+  Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'.
+  'tensor' must have the same data type as 'input_sequence'.
+  Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'.
+  Negative value means counting positions from the back.
+  'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>tensor</tt> : T</dt>
+<dd>Input tensor to be inserted into the input sequence.</dd>
+<dt><tt>position</tt> (optional) : I</dt>
+<dd>Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>Output sequence that contains the inserted tensor at given position.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain position to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+### <a name="SequenceLength-11"></a>**SequenceLength-11**</a>
+
+  Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>length</tt> : I</dt>
+<dd>Length of input sequence. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain output to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+### <a name="Slice-11"></a>**Slice-11**</a>
+
+  Produces a slice of the input tensor along multiple axes. Similar to numpy:
+  https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+  Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
+  dimension and step for each axis in the list of axes, it uses this information to
+  slice the input `data` tensor. If a negative value is passed for any of the
+  start or end indices, it represents number of elements before the end of that
+  dimension. If the value passed to start or end is larger than the `n` (the
+  number of elements in this dimension), it represents `n`. For slicing to the
+  end of a dimension with unknown size, it is recommended to pass in `INT_MAX`
+  when sclicing forward and 'INT_MIN' when slicing backward.
+  If a negative value is passed for step, it represents slicing backward.
+  However step value cannot be 0.
+  If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+  If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+  Example 1:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    axes = [0, 1]
+    starts = [1, 0]
+    ends = [2, 3]
+    steps = [1, 2]
+    result = [
+        [5, 7],
+    ]
+  Example 2:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    starts = [0, 1]
+    ends = [-1, 1000]
+    result = [
+        [2, 3, 4],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (3 - 5)
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of data to extract slices from.</dd>
+<dt><tt>starts</tt> : Tind</dt>
+<dd>1-D tensor of starting indices of corresponding axis in `axes`</dd>
+<dt><tt>ends</tt> : Tind</dt>
+<dd>1-D tensor of ending indices (exclusive) of corresponding axis in `axes`</dd>
+<dt><tt>axes</tt> (optional) : Tind</dt>
+<dd>1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>steps</tt> (optional) : Tind</dt>
+<dd>1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Sliced data tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="Softmax-11"></a>**Softmax-11**</a>
+
+  The operator computes the softmax (normalized exponential) values for each layer in the batch
+   of the given input.
+
+  The input does not need to explicitly be a 2D vector; rather, it will be
+  coerced into one. For an arbitrary n-dimensional tensor
+  input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+  the axis provided, then input will be coerced into a 2-dimensional tensor with
+  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+  case where axis=1, this means the input tensor will be coerced into a 2D tensor
+  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+  Each of these dimensions must be matched correctly, or else the operator
+  will throw errors. The output tensor has the same shape
+  and contains the softmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The input tensor that's coerced into a 2D matrix of size (NxD) as described above.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output values with the same shape as input tensor (the original size without coercion).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Split-11"></a>**Split-11**</a>
+
+  Split a tensor into a list of tensors, along the specified
+  'axis'. Lengths of the parts can be specified using argument 'split'.
+  Otherwise, the tensor is split to equal sized parts.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).</dd>
+<dt><tt>split</tt> : list of ints</dt>
+<dd>length of each output. Values should be >= 0.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The tensor to split</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic) : T</dt>
+<dd>One or more outputs forming list of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="SplitToSequence-11"></a>**SplitToSequence-11**</a>
+
+  Split a tensor into a sequence of tensors, along the specified
+  'axis'. Lengths of the parts can be specified using argument 'split'.
+  'split' must contain only positive numbers.
+  'split' is either a scalar (tensor of empty shape), or a 1-D tensor.
+  If 'split' is a scalar, then 'input' will be split into equally sized chunks(if possible).
+  Last chunk will be smaller if the 'input' size along the given axis 'axis' is not divisible
+  by 'split'.
+  Otherwise, the tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis'
+  specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the
+  dimension size of input tensor on 'axis'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The tensor to split</dd>
+<dt><tt>split</tt> (optional) : I</dt>
+<dd>Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>One or more outputs forming a sequence of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input types to all tensor types.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain split size to integral tensor.</dd>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output types to all tensor types.</dd>
+</dl>
+
+### <a name="Squeeze-11"></a>**Squeeze-11**</a>
+
+  Remove single-dimensional entries from the shape of a tensor.
+  Takes a  parameter `axes` with a list of axes to squeeze.
+  If `axes` is not provided, all the single dimensions will be removed from
+  the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensors with at least max(dims) dimensions.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>squeezed</tt> : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="TopK-11"></a>**TopK-11**</a>
+
+  Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of
+  shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+    -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+      which contains the values of the top k elements along the specified axis
+    -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+     contains the indices of the top k elements (original indices from the input
+     tensor).
+
+  If "largest" is 1 (the default value) then the k largest elements are returned.
+  If "sorted" is 1 (the default value) then the resulting k elements will be sorted.
+  If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined.
+
+  Given two equivalent values, this operator uses the indices along the axis as
+   a tiebreaker. That is, the element with the lower index will appear first.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>largest</tt> : int (default is 1)</dt>
+<dd>Whether to return the top-K largest or smallest elements.</dd>
+<dt><tt>sorted</tt> : int (default is 1)</dt>
+<dd>Whether to return the elements in sorted order.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_n, r]</dd>
+<dt><tt>K</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Values</tt> (differentiable) : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor</dd>
+<dt><tt>Indices</tt> (non-differentiable) : I</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="Unique-11"></a>**Unique-11**</a>
+
+  Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned.
+  Otherwise the input tensor is flattened and unique values of the flattened tensor are returned.
+
+  This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs.
+  The first output tensor 'Y' contains all unique values or subtensors of the input.
+  The second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'..
+  The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. ".
+  The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input.
+
+  Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.
+
+  https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html
+
+  Example 1:
+    input_X = [2, 1, 1, 3, 4, 3]
+    attribute_sorted = 0
+    attribute_axis = None
+    output_Y = [2, 1, 3, 4]
+    output_indices = [0, 1, 3, 4]
+    output_inverse_indices = [0, 1, 1, 2, 3, 2]
+    output_counts = [1, 2, 2, 1]
+
+  Example 2:
+    input_X = [[1, 3], [2, 3]]
+    attribute_sorted = 1
+    attribute_axis = None
+    output_Y = [1, 2, 3]
+    output_indices = [0, 2, 1]
+    output_inverse_indices = [0, 2, 1, 2]
+    output_counts = [1, 1, 2]
+
+  Example 3:
+    input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]
+    attribute_sorted = 1
+    attribute_axis = 0
+    output_Y = [[1, 0, 0], [2, 3, 4]]
+    output_indices = [0, 2]
+    output_inverse_indices = [0, 0, 1]
+    output_counts = [2, 1]
+
+  Example 4:
+    input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+               [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]
+    attribute_sorted = 1
+    attribute_axis = 1
+
+    intermediate data are presented below for better understanding:
+
+    there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):
+    A: [[1, 1], [1, 1]],
+       [[0, 1], [0, 1]],
+       [[2, 1], [2, 1]],
+       [[0, 1], [0, 1]].
+
+    there are 3 unique subtensors:
+    [[1, 1], [1, 1]],
+    [[0, 1], [0, 1]],
+    [[2, 1], [2, 1]].
+
+    sorted unique subtensors:
+    B: [[0, 1], [0, 1]],
+       [[1, 1], [1, 1]],
+       [[2, 1], [2, 1]].
+
+    output_Y is constructed from B:
+    [[[0. 1.], [1. 1.], [2. 1.]],
+     [[0. 1.], [1. 1.], [2. 1.]]]
+
+    output_indices is to map from B to A:
+    [1, 0, 2]
+
+    output_inverse_indices is to map from A to B:
+    [1, 0, 2, 0]
+
+    output_counts = [2 1 1]
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>sorted</tt> : int (default is 1)</dt>
+<dd>(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>A N-D input tensor that is to be processed.</dd>
+</dl>
+
+#### Outputs (1 - 4)
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'</dd>
+<dt><tt>indices</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. </dd>
+<dt><tt>inverse_indices</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. </dd>
+<dt><tt>counts</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input can be of any tensor type.</dd>
+</dl>
+
+### <a name="Unsqueeze-11"></a>**Unsqueeze-11**</a>
+
+  Insert single-dimensional entries to the shape of an input tensor (`data`).
+  Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).
+
+  For example:
+    Given an input tensor (`data`) of shape [3, 4, 5], then
+    Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].
+
+  The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
+  The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
+  Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
+  The order of values in `axes` does not matter and can come in any order.
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints (required)</dt>
+<dd>List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Original tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>expanded</tt> : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+## Version 12 of the default ONNX operator set
+### <a name="ArgMax-12"></a>**ArgMax-12**</a>
+
+  Computes the indices of the max elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
+  If select_last_index is True (default False), the index of the last occurrence of the max
+  is selected if the max appears more than once in the input. Otherwise the index of the
+  first occurrence is selected.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>select_last_index</tt> : int (default is 0)</dt>
+<dd>Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="ArgMin-12"></a>**ArgMin-12**</a>
+
+  Computes the indices of the min elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
+  If select_last_index is True (default False), the index of the last occurrence of the min
+  is selected if the min appears more than once in the input. Otherwise the index of the
+  first occurrence is selected.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>select_last_index</tt> : int (default is 0)</dt>
+<dd>Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Celu-12"></a>**Celu-12**</a>
+
+  Continuously Differentiable Exponential Linear Units:
+  Perform the linear unit element-wise on the input tensor X
+  using formula:
+
+  ```
+  max(0,x) + min(0,alpha*(exp(x/alpha)-1))
+  ```
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float)</dt>
+<dd>Constrain input and output types to float32 tensors.</dd>
+</dl>
+
+### <a name="Clip-12"></a>**Clip-12**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified by the inputs 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max(), respectively.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+<dt><tt>min</tt> (optional) : T</dt>
+<dd>Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).</dd>
+<dt><tt>max</tt> (optional) : T</dt>
+<dd>Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Constant-12"></a>**Constant-12**</a>
+
+  This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
+  or value_* must be specified.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>sparse_value</tt> : sparse_tensor</dt>
+<dd>The value for the elements of the output tensor in sparse format.</dd>
+<dt><tt>value</tt> : tensor</dt>
+<dd>The value for the elements of the output tensor.</dd>
+<dt><tt>value_float</tt> : float</dt>
+<dd>The value for the sole element for the scalar, float32, output tensor.</dd>
+<dt><tt>value_floats</tt> : list of floats</dt>
+<dd>The values for the elements for the 1D, float32, output tensor.</dd>
+<dt><tt>value_int</tt> : int</dt>
+<dd>The value for the sole element for the scalar, int64, output tensor.</dd>
+<dt><tt>value_ints</tt> : list of ints</dt>
+<dd>The values for the elements for the 1D, int64, output tensor.</dd>
+<dt><tt>value_string</tt> : string</dt>
+<dd>The value for the sole element for the scalar, UTF-8 string, output tensor.</dd>
+<dt><tt>value_strings</tt> : list of strings</dt>
+<dd>The values for the elements for the 1D, UTF-8 string, output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor containing the same value of the provided tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Dropout-12"></a>**Dropout-12**</a>
+
+  Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
+  output (floating-point tensor) and mask (optional `Tensor<bool>`). If `training_mode` is true then the output Y will be a random dropout;
+  Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
+  the user can simply not pass `training_mode` input or set it to false.
+  ```
+  output = scale * data * mask,
+  ```
+  where
+  ```
+  scale = 1. / (1. - ratio).
+  ```
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>seed</tt> : int</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>The input data as Tensor.</dd>
+<dt><tt>ratio</tt> (optional) : T1</dt>
+<dd>The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.</dd>
+<dt><tt>training_mode</tt> (optional) : T2</dt>
+<dd>If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional) : T2</dt>
+<dd>The output mask.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input 'ratio' types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output 'mask' types to boolean tensors.</dd>
+</dl>
+
+### <a name="Einsum-12"></a>**Einsum-12**</a>
+
+  An einsum of the form ```term1, term2 -> output-term``` produces an output tensor using the following equation
+
+  ```output[output-term] = reduce-sum( input1[term1] * input2[term] )```
+
+  where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2)
+  that do not occur in the output-term.
+
+  The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation
+  convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to
+  an operand tensor, and the characters within the terms correspond to operands dimensions.
+
+  This sequence may be followed by "->" to separate the left and right hand side of the equation.
+  If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein
+  summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases,
+  output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the
+  equation.
+
+  When a dimension character is repeated in the left-hand side, it represents summation along the dimension.
+
+  The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions.
+  Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions.
+  The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the
+  beginning of the output. The equation string may contain space (U+0020) character.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>equation</tt> : string (required)</dt>
+<dd>Einsum expression string.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>Inputs</tt> (variadic, differentiable) : T</dt>
+<dd>Operands</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Output</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numerical tensor types.</dd>
+</dl>
+
+### <a name="GatherND-12"></a>**GatherND-12**</a>
+
+  Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers
+  slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`.
+
+  `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+  where each element defines a slice of `data`
+
+  `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of
+  `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension.
+
+  Some salient points about the inputs' rank and shape:
+
+  1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+  2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal.
+
+  3) b < min(q, r) is to be honored.
+
+  4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive)
+
+  5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+     It is an error if any of the index values are out of bounds.
+
+  The output is computed as follows:
+
+  The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+  1) If `indices_shape[-1] > r-b` => error condition
+
+  2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors
+     containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions
+     of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]`
+     is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below)
+
+  3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor
+     containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding
+     to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor
+     to form the `output` tensor (Examples 2, 3, 4 and 5 below)
+
+  This operator is the inverse of `ScatterND`.
+
+  `Example 1`
+
+    batch_dims = 0
+
+    data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+    indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+    output  = [0,3]           # output_shape = [2]
+
+  `Example 2`
+
+    batch_dims = 0
+
+    data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+    indices = [[1],[0]]      # indices_shape = [2, 1]
+
+    output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+  `Example 3`
+
+    batch_dims = 0
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+    output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+  `Example 4`
+
+    batch_dims = 0
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+    output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+  `Example 5`
+
+    batch_dims = 1
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[1],[0]]             # indices_shape = [2, 1]
+
+    output  = [[2,3],[4,5]]             # output_shape = [2, 2]
+
+
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>batch_dims</tt> : int (default is 0)</dt>
+<dd>The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+### <a name="GreaterOrEqual-12"></a>**GreaterOrEqual-12**</a>
+
+  Returns the tensor resulted from performing the `greater_equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="LessOrEqual-12"></a>**LessOrEqual-12**</a>
+
+  Returns the tensor resulted from performing the `less_equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Max-12"></a>**Max-12**</a>
+
+  Element-wise max of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for max.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>max</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+</dl>
+
+### <a name="MaxPool-12"></a>**MaxPool-12**</a>
+
+  MaxPool consumes an input tensor X and applies max pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   max pooling consisting of computing the max on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
+   ```
+   The output of each pooling window is maximum number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>storage_order</tt> : int (default is 0)</dt>
+<dd>The storage order of the tensor. 0 is row major, and 1 is column major.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+<dt><tt>Indices</tt> (optional, non-differentiable) : I</dt>
+<dd>Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input and output types to float and 8 bit tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+### <a name="Min-12"></a>**Min-12**</a>
+
+  Element-wise min of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic) : T</dt>
+<dd>List of tensors for min.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>min</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+</dl>
+
+### <a name="NegativeLogLikelihoodLoss-12"></a>**NegativeLogLikelihoodLoss-12**</a>
+
+  A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss.
+  Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0.
+  The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C).
+  The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes)
+  or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples.
+  The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as:
+      loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].
+  When an optional "weight" is provided, the sample loss is calculated as:
+      loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].
+  loss is zero for the case when target-value equals ignore_index.
+
+      loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index
+  If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk).
+  If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged:
+      mean(loss), if "weight" is not provided,
+  or if weight is provided,
+      sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
+  If "reduction" attribute is set to "sum", the output is a scalar:
+      sum(loss).
+  See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
+  Example 1:
+      // negative log likelihood loss, "none" reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+               [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      loss = np.zeros((N, d1))
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1]
+      // print(loss)
+      // [[-3. -2.]
+      //  [-0. -2.]]
+  Example 2:
+      // weighted negative log likelihood loss, sum reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+              [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      weight = [0.2, 0.3, 0.1]
+      loss = np.zeros((N, d1))
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1] * weight[c]
+      loss = np.sum(loss)
+      // print(loss)
+      // -1.1
+  Example 3:
+      // weighted negative log likelihood loss, mean reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+              [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      weight = [0.2, 0.3, 0.1]
+      loss = np.zeros((N, d1))
+      weight_total = 0
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1] * weight[c]
+              weight_total = weight_total + weight[c]
+      loss = np.sum(loss) / weight_total
+      // print(loss)
+      // -1.57
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>ignore_index</tt> : int</dt>
+<dd>Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.</dd>
+<dt><tt>reduction</tt> : string (default is mean)</dt>
+<dd>Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).</dd>
+<dt><tt>target</tt> : Tind</dt>
+<dd>Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.</dd>
+<dt><tt>weight</tt> (optional) : T</dt>
+<dd>Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>loss</tt> : T</dt>
+<dd>The negative log likelihood loss</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input, weight, and output types to floating-point tensors.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain target to integer types</dd>
+</dl>
+
+### <a name="Pow-12"></a>**Pow-12**</a>
+
+  Pow takes input data (Tensor<T>) and exponent Tensor, and
+  produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+  is applied to the data tensor elementwise.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>First operand, base of the exponent.</dd>
+<dt><tt>Y</tt> : T1</dt>
+<dd>Second operand, power of the exponent.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input X and output types to float/int tensors.</dd>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input Y types to float/int tensors.</dd>
+</dl>
+
+### <a name="ReduceMax-12"></a>**ReduceMax-12**</a>
+
+  Computes the max of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(uint8), tensor(int8)</dt>
+<dd>Constrain input and output types to high-precision and 8 bit numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMin-12"></a>**ReduceMin-12**</a>
+
+  Computes the min of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+  the resulted tensor have the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(uint8), tensor(int8)</dt>
+<dd>Constrain input and output types to high-precision and 8 bit numeric tensors.</dd>
+</dl>
+
+### <a name="SoftmaxCrossEntropyLoss-12"></a>**SoftmaxCrossEntropyLoss-12**</a>
+
+  Loss function that measures the softmax cross entropy
+  between 'scores' and 'labels'.
+  This operator first computes a loss tensor whose shape is identical to the labels input.
+  If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).
+  If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),
+  the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.
+  After L is available, this operator can optionally do a reduction operator.
+
+  shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),
+          with K >= 1 in case of K-dimensional loss.
+  shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),
+          with K >= 1 in case of K-dimensional loss.
+
+  The loss for one sample, l_i, can caculated as follows:
+      l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.
+  or
+      l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.
+
+  loss is zero for the case when label-value equals ignore_index.
+      l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index
+
+  where:
+      p = Softmax(scores)
+      y = Log(p)
+      c = labels[i][d1][d2]...[dk]
+
+  Finally, L is optionally reduced:
+  If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).
+  If reduction = 'sum', the output is scalar: Sum(L).
+  If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),
+  where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>ignore_index</tt> : int</dt>
+<dd>Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.</dd>
+<dt><tt>reduction</tt> : string (default is mean)</dt>
+<dd>Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>scores</tt> : T</dt>
+<dd>The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.</dd>
+<dt><tt>labels</tt> : Tind</dt>
+<dd>The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.</dd>
+<dt><tt>weights</tt> (optional) : T</dt>
+<dd>A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.</dd>
+<dt><tt>log_prob</tt> (optional) : T</dt>
+<dd>Log probability tensor. If the output of softmax is prob, its value is log(prob).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain target to integer types</dd>
+</dl>
+
+## Version 13 of the default ONNX operator set
+### <a name="Abs-13"></a>**Abs-13**</a>
+
+  Absolute takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the absolute is, y = abs(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Add-13"></a>**Add-13**</a>
+
+  Performs element-wise binary addition (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ArgMax-13"></a>**ArgMax-13**</a>
+
+  Computes the indices of the max elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
+  If select_last_index is True (default False), the index of the last occurrence of the max
+  is selected if the max appears more than once in the input. Otherwise the index of the
+  first occurrence is selected.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>select_last_index</tt> : int (default is 0)</dt>
+<dd>Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="ArgMin-13"></a>**ArgMin-13**</a>
+
+  Computes the indices of the min elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
+  If select_last_index is True (default False), the index of the last occurrence of the min
+  is selected if the min appears more than once in the input. Otherwise the index of the
+  first occurrence is selected.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>select_last_index</tt> : int (default is 0)</dt>
+<dd>Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Cast-13"></a>**Cast-13**</a>
+
+  The operator casts the elements of a given input tensor to a data type
+  specified by the 'to' argument and returns an output tensor of the same size in
+  the converted type. The 'to' argument must be one of the data types specified
+  in the 'DataType' enum field in the TensorProto message.
+
+  Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
+  (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
+  result 100. There are some string literals reserved for special floating-point values;
+  "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
+  Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
+  this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
+  to string tensors, plain floating-point representation (such as "314.15926") would be used.
+  Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
+  of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
+
+  Conversion from a numerical type to any numerical type is always allowed.
+  User must be aware of precision loss and value change caused by range difference between two types.
+  For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
+  an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>to</tt> : int (required)</dt>
+<dd>The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T1</dt>
+<dd>Input tensor to be cast.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T2</dt>
+<dd>Output tensor with the same shape as input with type specified by the 'to' argument</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain input types. Casting from complex is not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain output types. Casting to complex is not supported.</dd>
+</dl>
+
+### <a name="Ceil-13"></a>**Ceil-13**</a>
+
+  Ceil takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the ceil is, y = ceil(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Clip-13"></a>**Clip-13**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified by the inputs 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max(), respectively.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+<dt><tt>min</tt> (optional, non-differentiable) : T</dt>
+<dd>Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).</dd>
+<dt><tt>max</tt> (optional, non-differentiable) : T</dt>
+<dd>Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Concat-13"></a>**Concat-13**</a>
+
+  Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (required)</dt>
+<dd>Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> (differentiable) : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+### <a name="Constant-13"></a>**Constant-13**</a>
+
+  This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
+  or value_* must be specified.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>sparse_value</tt> : sparse_tensor</dt>
+<dd>The value for the elements of the output tensor in sparse format.</dd>
+<dt><tt>value</tt> : tensor</dt>
+<dd>The value for the elements of the output tensor.</dd>
+<dt><tt>value_float</tt> : float</dt>
+<dd>The value for the sole element for the scalar, float32, output tensor.</dd>
+<dt><tt>value_floats</tt> : list of floats</dt>
+<dd>The values for the elements for the 1D, float32, output tensor.</dd>
+<dt><tt>value_int</tt> : int</dt>
+<dd>The value for the sole element for the scalar, int64, output tensor.</dd>
+<dt><tt>value_ints</tt> : list of ints</dt>
+<dd>The values for the elements for the 1D, int64, output tensor.</dd>
+<dt><tt>value_string</tt> : string</dt>
+<dd>The value for the sole element for the scalar, UTF-8 string, output tensor.</dd>
+<dt><tt>value_strings</tt> : list of strings</dt>
+<dd>The values for the elements for the 1D, UTF-8 string, output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor containing the same value of the provided tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="DepthToSpace-13"></a>**DepthToSpace-13**</a>
+
+  DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+  This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+  the input tensor where values from the depth dimension are moved in spatial blocks to the height
+  and width dimensions. By default, `mode` = `DCR`.
+  In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
+  following order: depth, column, and then row. The output y is computed from the input x as below:
+
+  b, c, h, w = x.shape
+
+  tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+
+  tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+
+  y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
+
+
+  In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
+  following order: column, row, and the depth. The output y is computed from the input x as below:
+
+  b, c, h, w = x.shape
+
+  tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
+
+  tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+
+  y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+<dt><tt>mode</tt> : string (default is DCR)</dt>
+<dd>DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="DequantizeLinear-13"></a>**DequantizeLinear-13**</a>
+
+  The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor.
+  The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' must have same shape, and can be either a scalar
+  for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
+  'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
+  there's no zero point (zero point is supposed to be 0).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>x</tt> : T</dt>
+<dd>N-D quantized input tensor to be de-quantized.</dd>
+<dt><tt>x_scale</tt> : tensor(float)</dt>
+<dd>Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.</dd>
+<dt><tt>x_zero_point</tt> (optional) : T</dt>
+<dd>Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : tensor(float)</dt>
+<dd>N-D full precision output tensor. It has same shape as input 'x'.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int8), tensor(uint8), tensor(int32)</dt>
+<dd>Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.</dd>
+</dl>
+
+### <a name="Div-13"></a>**Div-13**</a>
+
+  Performs element-wise binary division (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Dropout-13"></a>**Dropout-13**</a>
+
+  Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
+  output (floating-point tensor) and mask (optional `Tensor<bool>`). If `training_mode` is true then the output Y will be a random dropout;
+  Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
+  the user can simply not pass `training_mode` input or set it to false.
+  ```
+  output = scale * data * mask,
+  ```
+  where
+  ```
+  scale = 1. / (1. - ratio).
+  ```
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>seed</tt> : int</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>The input data as Tensor.</dd>
+<dt><tt>ratio</tt> (optional, non-differentiable) : T1</dt>
+<dd>The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.</dd>
+<dt><tt>training_mode</tt> (optional, non-differentiable) : T2</dt>
+<dd>If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional, non-differentiable) : T2</dt>
+<dd>The output mask.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input 'ratio' types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output 'mask' types to boolean tensors.</dd>
+</dl>
+
+### <a name="Equal-13"></a>**Equal-13**</a>
+
+  Returns the tensor resulted from performing the `equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Erf-13"></a>**Erf-13**</a>
+
+  Computes the error function of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The error function of the input tensor computed element-wise. It has the same shape and type of the input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Exp-13"></a>**Exp-13**</a>
+
+  Calculates the exponential of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The exponential of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Expand-13"></a>**Expand-13**</a>
+
+  Broadcast the input tensor following the given shape and the broadcast rule.
+  The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):
+  Dimensions are right alignment;
+  Two corresponding dimensions must have the same value, or one of them is equal to 1.
+  Also, this operator is similar to numpy.broadcast_to(input, shape),
+  but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size().
+  It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1,
+  or the shape.ndim < input.shape.ndim.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>shape</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D tensor indicates the shape you want to expand to, following the broadcast rule</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensors.</dd>
+</dl>
+
+### <a name="Flatten-13"></a>**Flatten-13**</a>
+
+  Flattens the input tensor into a 2D matrix. If input tensor has shape
+  (d_0, d_1, ... d_n) then the output will have shape
+  (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>A tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output to all tensor types.</dd>
+</dl>
+
+### <a name="Floor-13"></a>**Floor-13**</a>
+
+  Floor takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the floor is, y = floor(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Gather-13"></a>**Gather-13**</a>
+
+  Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+  entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+  them in an output tensor of rank q + (r - 1).
+
+  axis = 0 :
+
+  Let
+  k = indices[i_{0}, ..., i_{q-1}]
+  Then
+  output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]
+
+  ```
+    data = [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+    indices = [
+        [0, 1],
+        [1, 2],
+    ]
+    output = [
+        [
+            [1.0, 1.2],
+            [2.3, 3.4],
+        ],
+        [
+            [2.3, 3.4],
+            [4.5, 5.7],
+        ],
+    ]
+  ```
+  axis = 1 :
+
+  Let
+  k = indices[i_{0}, ..., i_{q-1}]
+  Then
+  output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]
+
+  ```
+    data = [
+        [1.0, 1.2, 1.9],
+        [2.3, 3.4, 3.9],
+        [4.5, 5.7, 5.9],
+    ]
+    indices = [
+        [0, 2],
+    ]
+    axis = 1,
+    output = [
+            [[1.0, 1.9]],
+            [[2.3, 3.9]],
+            [[4.5, 5.9]],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + (r - 1).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="GatherElements-13"></a>**GatherElements-13**</a>
+
+  GatherElements takes two inputs `data` and `indices` of the same rank r >= 1
+  and an optional attribute `axis` that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). It is an indexing operation
+  that produces its output by indexing into the input data tensor at index
+  positions determined by elements of the `indices` tensor.
+  Its output shape is the same as the shape of `indices` and consists of one value
+  (gathered from the `data`) for each element in `indices`.
+
+  For instance, in the 3-D case (r = 3), the output produced is determined
+  by the following equations:
+  ```
+    out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
+    out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
+    out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
+  ```
+
+  This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation.
+
+  Example 1:
+  ```
+    data = [
+        [1, 2],
+        [3, 4],
+    ]
+    indices = [
+        [0, 0],
+        [1, 0],
+    ]
+    axis = 1
+    output = [
+        [1, 1],
+        [4, 3],
+    ]
+  ```
+  Example 2:
+  ```
+    data = [
+        [1, 2, 3],
+        [4, 5, 6],
+        [7, 8, 9],
+    ]
+    indices = [
+        [1, 2, 0],
+        [2, 0, 0],
+    ]
+    axis = 0
+    output = [
+        [4, 8, 3],
+        [7, 2, 3],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of the same shape as indices.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="GatherND-13"></a>**GatherND-13**</a>
+
+  Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers
+  slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`.
+
+  `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+  where each element defines a slice of `data`
+
+  `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of
+  `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension.
+
+  Some salient points about the inputs' rank and shape:
+
+  1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+  2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal.
+
+  3) b < min(q, r) is to be honored.
+
+  4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive)
+
+  5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+     It is an error if any of the index values are out of bounds.
+
+  The output is computed as follows:
+
+  The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+  1) If `indices_shape[-1] > r-b` => error condition
+
+  2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors
+     containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions
+     of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]`
+     is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below)
+
+  3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor
+     containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding
+     to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor
+     to form the `output` tensor (Examples 2, 3, 4 and 5 below)
+
+  This operator is the inverse of `ScatterND`.
+
+  `Example 1`
+
+    batch_dims = 0
+
+    data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+    indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+    output  = [0,3]           # output_shape = [2]
+
+  `Example 2`
+
+    batch_dims = 0
+
+    data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+    indices = [[1],[0]]      # indices_shape = [2, 1]
+
+    output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+  `Example 3`
+
+    batch_dims = 0
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+    output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+  `Example 4`
+
+    batch_dims = 0
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+    output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+  `Example 5`
+
+    batch_dims = 1
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[1],[0]]             # indices_shape = [2, 1]
+
+    output  = [[2,3],[4,5]]             # output_shape = [2, 2]
+
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>batch_dims</tt> : int (default is 0)</dt>
+<dd>The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+### <a name="Gemm-13"></a>**Gemm-13**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+  A' = transpose(A) if transA else A
+
+  B' = transpose(B) if transB else B
+
+  Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+  input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+  and output tensor Y has shape (M, N). A will be transposed before doing the
+  computation if attribute transA is non-zero, same for B and transB.
+  This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md).
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C.</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.</dd>
+<dt><tt>C</tt> (optional, differentiable) : T</dt>
+<dd>Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor of shape (M, N).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="Greater-13"></a>**Greater-13**</a>
+
+  Returns the tensor resulted from performing the `greater` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Hardmax-13"></a>**Hardmax-13**</a>
+
+  The operator computes the hardmax values for the given input:
+
+   Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise
+
+  The "axis" attribute indicates the dimension along which Hardmax
+  will be performed. The output tensor has the same shape
+  and contains the Hardmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>
+Describes the dimension Hardmax will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output values with the same shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Identity-13"></a>**Identity-13**</a>
+
+  Identity operator
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor to copy input into.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="If-13"></a>**If-13**</a>
+
+  If conditional
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>else_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.</dd>
+<dt><tt>then_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>cond</tt> : B</dt>
+<dd>Condition for the if</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>All Tensor and Sequence types</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Only bool</dd>
+</dl>
+
+### <a name="IsNaN-13"></a>**IsNaN-13**</a>
+
+  Returns which elements of the input are NaN.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T1</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T2</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output types to boolean tensors.</dd>
+</dl>
+
+### <a name="LRN-13"></a>**LRN-13**</a>
+
+  Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+  It normalizes over local input regions.
+  The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+  of shape (N x C x D1 x D2, ..., Dk), its region is
+  {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
+
+  square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+  where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
+
+  Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.0001)</dt>
+<dd>Scaling parameter.</dd>
+<dt><tt>beta</tt> : float (default is 0.75)</dt>
+<dd>The exponent.</dd>
+<dt><tt>bias</tt> : float (default is 1.0)</dt>
+<dd></dd>
+<dt><tt>size</tt> : int (required)</dt>
+<dd>The number of channels to sum over</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor, which has the shape and type as input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output  types to float tensors.</dd>
+</dl>
+
+### <a name="Less-13"></a>**Less-13**</a>
+
+  Returns the tensor resulted from performing the `less` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Log-13"></a>**Log-13**</a>
+
+  Calculates the natural log of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The natural log of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LogSoftmax-13"></a>**LogSoftmax-13**</a>
+
+  The operator computes the log of softmax values for the given input:
+
+   LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))
+
+  The "axis" attribute indicates the dimension along which LogSoftmax
+  will be performed. The output tensor has the same shape
+  and contains the LogSoftmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>
+Describes the dimension LogSoftmax will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output values with the same shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Loop-13"></a>**Loop-13**</a>
+
+  Generic Looping construct. This loop has multiple termination conditions:
+
+  1) Trip count. Iteration count specified at runtime. Set by
+     specifying the input M. Optional. Set to empty string to omit.
+     Note that a static trip count (specified at graph construction time) can be
+     specified by passing in a constant node for input M.
+  2) Loop termination condition. This is an input to the op that determines
+     whether to run the first iteration and also a loop-carried dependency for
+     the body graph. The body graph must yield a value for the condition variable,
+     whether this input is provided or not.
+
+  This table summarizes the operating modes of this operator with equivalent
+  C-style code:
+
+      Operator inputs defined as (max_trip_count, condition_var).
+
+      input ("", ""):
+          for (int i=0; ; ++i) {
+            cond = ... // Note this value is ignored, but is required in the body
+          }
+
+      input ("", cond) // Note this is analogous to a while loop
+          bool cond = ...;
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input ("", 1) // Note this is analogous to a do-while loop
+          bool cond = true
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input (trip_count, "") // Note this is analogous to a for loop
+          int trip_count = ...
+          for (int i=0; i < trip_count; ++i) {
+            cond = ...; // ignored
+          }
+
+      input (trip_count, cond)
+          int trip_count = ...;
+          bool cond = ...;
+          for (int i=0; i < trip_count && cond; ++i) {
+            cond = ...;
+          }
+
+
+  *Sample usage - cond as well as trip count*
+
+      graph predict-net {
+        %a = Constant[value = <Scalar Tensor [3]>]()
+        %b = Constant[value = <Scalar Tensor [6]>]()
+        %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+        %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+        %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+        return
+      }
+
+      graph body-net (
+        %i[INT32, scalar]           // iteration number
+        %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+        %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+      ) {
+        %my_local = Add(%a, %b_in)
+        %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+        %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+        %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+        return %keepgoing_out, %b_out, %user_defined_val
+      }
+
+  *Sample equivalent C code*
+
+      {
+        /* User-defined code (enclosing scope) */
+        int a = 3, b = 6;
+        bool keepgoing = true; // Analogous to input cond
+        /* End user-defined code */
+
+        /* Implicitly-defined code */
+        const int max_trip_count = 10; // Analogous to input M
+        int user_defined_vals[]; // Imagine this is resizable
+        /* End implicitly-defined code */
+        /* initialize loop-carried variables and scan-output variables */
+        bool keepgoing_out = keepgoing
+        int b_out = b
+
+        for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+          /* Implicitly-defined code: bind actual parameter values
+             to formal parameter variables of loop-body */
+          bool keepgoing_in = keepgoing_out;
+          bool b_in = b_out;
+
+          /* User-defined code (loop body) */
+          int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+          b_out = a - b_in;
+          keepgoing_out = my_local > b_out;
+          user_defined_val = b_in + b_in; // b_in and b_out are different variables
+          /* End user-defined code */
+
+          /* Implicitly defined-code */
+          user_defined_vals[i] = user_defined_val // accumulate scan-output values
+        }
+        // int t = my_local; // Can't do this. my_local is not accessible here.
+
+        // The values below are bound to the output variables of the loop and therefore accessible
+        // b_out; user_defined_vals; keepgoing_out;
+      }
+
+  There are several things of note in this code snippet:
+
+  1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+     be referenced in the inputs of the loop.
+  2) Any values computed in the loop body that needs to be used in a subsequent
+     iteration or after the loop are modelled using a pair of variables in the loop-body,
+     consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+     These are referred to as loop-carried dependences. The loop operation node
+     supplies the input value of the input variable for the first iteration, and
+     returns the output value of the output variable produced by the final
+     iteration.
+  3) Scan_output variables are used to implicitly concatenate values computed across
+     all the iterations. In the above example, the value of user_defined_val computed
+     over all iterations are concatenated and returned as the value of user_defined_vals
+     after the loop.
+  4) Values created in the body cannot be accessed in the enclosing scope,
+     except using the mechanism described above.
+
+  Note that the semantics of this op support "diagonal" or "wavefront" execution.
+  (See Step 3 here for an example:
+  https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+  Frontends should emit multi-layer RNNs as a series of While operators (with
+  time being the inner looping dimension), with each successive layer consuming
+  the scan_outputs from the previous layer, possibly going through several
+  point-wise operators (e.g. dropout, residual connections, linear layer).
+
+  The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.</dd>
+</dl>
+
+#### Inputs (2 - &#8734;)
+
+<dl>
+<dt><tt>M</tt> (optional) : I</dt>
+<dd>A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.</dd>
+<dt><tt>cond</tt> (optional) : B</dt>
+<dd>A boolean termination condition. Optional. Pass empty string to skip.</dd>
+<dt><tt>v_initial</tt> (variadic, heterogeneous) : V</dt>
+<dd>The initial values of any loop-carried dependencies (values that change across loop iterations)</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>v_final_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>All Tensor and Sequence types</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>tensor of int64, which should be a scalar.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>tensor of bool, which should be a scalar.</dd>
+</dl>
+
+### <a name="MatMul-13"></a>**MatMul-13**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>N-dimensional matrix A</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>N-dimensional matrix B</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Matrix multiply results from A * B</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="Max-13"></a>**Max-13**</a>
+
+  Element-wise max of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for max.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>max</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+</dl>
+
+### <a name="Mean-13"></a>**Mean-13**</a>
+
+  Element-wise mean of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for mean.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>mean</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="MeanVarianceNormalization-13"></a>**MeanVarianceNormalization-13**</a>
+
+  A MeanVarianceNormalization Function: Perform mean variance normalization
+        on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints (default is ['0', '2', '3'])</dt>
+<dd>A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Min-13"></a>**Min-13**</a>
+
+  Element-wise min of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for min.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>min</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+</dl>
+
+### <a name="Mod-13"></a>**Mod-13**</a>
+
+  Performs element-wise binary modulus (with Numpy-style broadcasting support).
+      The sign of the remainder is the same as that of the Divisor.
+
+      Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
+      (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
+      This attribute is set to 0 by default causing the behavior to be like integer mod.
+      Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
+
+      If the input type is floating point, then `fmod` attribute must be set to 1.
+
+      In case of dividend being zero, the results will be platform dependent.
+
+    This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>fmod</tt> : int (default is 0)</dt>
+<dd>Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>Dividend tensor</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Divisor tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Remainder tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Mul-13"></a>**Mul-13**</a>
+
+  Performs element-wise binary multiplication (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Neg-13"></a>**Neg-13**</a>
+
+  Neg takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where each element flipped sign, y = -x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to signed numeric tensors.</dd>
+</dl>
+
+### <a name="NegativeLogLikelihoodLoss-13"></a>**NegativeLogLikelihoodLoss-13**</a>
+
+  A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss.
+  Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0.
+  The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C).
+  The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes)
+  or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples.
+  The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as:
+
+      loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].
+
+  When an optional "weight" is provided, the sample loss is calculated as:
+
+      loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].
+
+  loss is zero for the case when target-value equals ignore_index.
+
+      loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index
+
+  If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk).
+  If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged:
+
+      mean(loss), if "weight" is not provided,
+
+  or if weight is provided,
+
+      sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
+
+  If "reduction" attribute is set to "sum", the output is a scalar:
+      sum(loss).
+
+  See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
+
+  Example 1:
+
+      // negative log likelihood loss, "none" reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+               [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+
+      loss = np.zeros((N, d1))
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1]
+
+      // print(loss)
+      // [[-3. -2.]
+      //  [-0. -2.]]
+
+  Example 2:
+
+      // weighted negative log likelihood loss, sum reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+              [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      weight = [0.2, 0.3, 0.1]
+      loss = np.zeros((N, d1))
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1] * weight[c]
+
+      loss = np.sum(loss)
+      // print(loss)
+      // -1.1
+
+  Example 3:
+
+      // weighted negative log likelihood loss, mean reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+              [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      weight = [0.2, 0.3, 0.1]
+      loss = np.zeros((N, d1))
+      weight_total = 0
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1] * weight[c]
+              weight_total = weight_total + weight[c]
+
+      loss = np.sum(loss) / weight_total
+      // print(loss)
+      // -1.57
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>ignore_index</tt> : int</dt>
+<dd>Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.</dd>
+<dt><tt>reduction</tt> : string (default is mean)</dt>
+<dd>Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).</dd>
+<dt><tt>target</tt> (non-differentiable) : Tind</dt>
+<dd>Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.</dd>
+<dt><tt>weight</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>loss</tt> (differentiable) : T</dt>
+<dd>The negative log likelihood loss</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input, weight, and output types to floating-point tensors.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain target to integer types</dd>
+</dl>
+
+### <a name="NonZero-13"></a>**NonZero-13**</a>
+
+  Returns the indices of the elements that are non-zero
+      (in row-major order - by dimension).
+      NonZero behaves similar to numpy.nonzero:
+      https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to all tensor types.</dd>
+</dl>
+
+### <a name="Pad-13"></a>**Pad-13**</a>
+
+  Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`,
+  a padded tensor (`output`) is generated.
+
+  The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):
+
+  1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False)
+
+  2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
+
+  3) `edge` - pads with the edge values of array
+
+
+  Example 1 (`constant` mode):
+    Insert 0 pads to the beginning of the second dimension.
+
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'constant'
+
+    constant_value = 0.0
+
+    output =
+    [
+        [0.0, 0.0, 1.0, 1.2],
+        [0.0, 0.0, 2.3, 3.4],
+        [0.0, 0.0, 4.5, 5.7],
+    ]
+
+
+  Example 2 (`reflect` mode):
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'reflect'
+
+    output =
+    [
+        [1.0, 1.2, 1.0, 1.2],
+        [2.3, 3.4, 2.3, 3.4],
+        [4.5, 5.7, 4.5, 5.7],
+    ]
+
+
+  Example 3 (`edge` mode):
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'edge'
+
+    output =
+    [
+        [1.0, 1.0, 1.0, 1.2],
+        [2.3, 2.3, 2.3, 3.4],
+        [4.5, 4.5, 4.5, 5.7],
+    ]
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is constant)</dt>
+<dd>Supported modes: `constant`(default), `reflect`, `edge`</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Input tensor.</dd>
+<dt><tt>pads</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.</dd>
+<dt><tt>constant_value</tt> (optional, non-differentiable) : T</dt>
+<dd>(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor after padding.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Pow-13"></a>**Pow-13**</a>
+
+  Pow takes input data (Tensor<T>) and exponent Tensor, and
+  produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+  is applied to the data tensor elementwise.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>First operand, base of the exponent.</dd>
+<dt><tt>Y</tt> (differentiable) : T1</dt>
+<dd>Second operand, power of the exponent.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input X and output types to float/int tensors.</dd>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input Y types to float/int tensors.</dd>
+</dl>
+
+### <a name="QuantizeLinear-13"></a>**QuantizeLinear-13**</a>
+
+  The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
+  The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
+  The quantization formula is y = saturate ((x / y_scale) + y_zero_point).
+  For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
+  For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>N-D full precision Input tensor to be quantized.</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.</dd>
+<dt><tt>y_zero_point</tt> (optional) : T2</dt>
+<dd>Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T2</dt>
+<dd>N-D quantized output tensor. It has same shape as input 'x'.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(int32)</dt>
+<dd>Constrain 'x' to float or int32 tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.</dd>
+</dl>
+
+### <a name="Reciprocal-13"></a>**Reciprocal-13**</a>
+
+  Reciprocal takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ReduceL1-13"></a>**ReduceL1-13**</a>
+
+  Computes the L1 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceL2-13"></a>**ReduceL2-13**</a>
+
+  Computes the L2 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceLogSum-13"></a>**ReduceLogSum-13**</a>
+
+  Computes the log sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceLogSumExp-13"></a>**ReduceLogSumExp-13**</a>
+
+  Computes the log sum exponent of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMax-13"></a>**ReduceMax-13**</a>
+
+  Computes the max of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)</dt>
+<dd>Constrain input and output types to high-precision and 8 bit numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMean-13"></a>**ReduceMean-13**</a>
+
+  Computes the mean of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceMin-13"></a>**ReduceMin-13**</a>
+
+  Computes the min of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)</dt>
+<dd>Constrain input and output types to high-precision and 8 bit numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceProd-13"></a>**ReduceProd-13**</a>
+
+  Computes the product of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceSum-13"></a>**ReduceSum-13**</a>
+
+  Computes the sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>noop_with_empty_axes</tt> : int (default is 0)</dt>
+<dd>Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+<dt><tt>axes</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="ReduceSumSquare-13"></a>**ReduceSumSquare-13**</a>
+
+  Computes the sum square of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Relu-13"></a>**Relu-13**</a>
+
+  Relu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Reshape-13"></a>**Reshape-13**</a>
+
+  Reshape the input tensor similar to numpy.reshape.
+  First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+  At most one dimension of the new shape can be -1. In this case, the value is
+  inferred from the size of the tensor and the remaining dimensions. A dimension
+  could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+  from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
+  The input tensor's shape and the output tensor's shape are required to have the same number of elements.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+<dt><tt>shape</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Specified shape for output.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reshaped</tt> (differentiable) : T</dt>
+<dd>Reshaped data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Resize-13"></a>**Resize-13**</a>
+
+  Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coordinate_transformation_mode</tt> : string (default is half_pixel)</dt>
+<dd>
+This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
+
+The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example.
+Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/>
+
+if coordinate_transformation_mode is "half_pixel", <br/>
+x_original = (x_resized + 0.5) / scale - 0.5, <br/>
+
+if coordinate_transformation_mode is "pytorch_half_pixel", <br/>
+x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/>
+
+if coordinate_transformation_mode is "align_corners", <br/>
+x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
+
+if coordinate_transformation_mode is "asymmetric", <br/>
+x_original = x_resized / scale, <br/>
+
+if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).</dd>
+<dt><tt>cubic_coeff_a</tt> : float (default is -0.75)</dt>
+<dd>The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".</dd>
+<dt><tt>exclude_outside</tt> : int (default is 0)</dt>
+<dd>If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.</dd>
+<dt><tt>extrapolation_value</tt> : float (default is 0.0)</dt>
+<dd>When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.</dd>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).</dd>
+<dt><tt>nearest_mode</tt> : string (default is round_prefer_floor)</dt>
+<dd>Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".</dd>
+</dl>
+
+#### Inputs (1 - 4)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T1</dt>
+<dd>N-D tensor</dd>
+<dt><tt>roi</tt> (optional, non-differentiable) : T2</dt>
+<dd>1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"</dd>
+<dt><tt>scales</tt> (optional, non-differentiable) : tensor(float)</dt>
+<dd>The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.</dd>
+<dt><tt>sizes</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T1</dt>
+<dd>N-D tensor after resizing</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input 'X' and output 'Y' to all tensor types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain roi type to float or double.</dd>
+</dl>
+
+### <a name="ScatterElements-13"></a>**ScatterElements-13**</a>
+
+  ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+  rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value
+  to values specified by `updates` at specific index positions specified by
+  `indices`. Its output shape is the same as the shape of `data`.
+
+  For each entry in `updates`, the target index in `data` is obtained by combining
+  the corresponding entry in `indices` with the index of the entry itself: the
+  index-value for dimension = axis is obtained from the value of the corresponding
+  entry in `indices` and the index-value for dimension != axis is obtained from the
+  index of the entry itself.
+
+  For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+  is performed as below:
+  ```
+    output[indices[i][j]][j] = updates[i][j] if axis = 0,
+    output[i][indices[i][j]] = updates[i][j] if axis = 1,
+  ```
+
+  This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+  Example 1:
+  ```
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  ```
+  Example 2:
+  ```
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+<dt><tt>updates</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >=1 (same rank and shape as indices)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1 (same rank as input).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="ScatterND-13"></a>**ScatterND-13**</a>
+
+  ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+  and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value to values
+  specified by `updates` at specific index positions specified by `indices`. Its output shape
+  is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+  That is, two or more `updates` for the same index-location is not supported.
+
+  `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+   `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+  Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+  update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+  update to a slice of the tensor.
+
+  `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+  first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+  The remaining dimensions of `updates` correspond to the dimensions of the
+  replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+  corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+  must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+  of shapes.
+
+  The `output` is calculated via the following equation:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] = updates[idx]
+
+  The order of iteration in the above loop is not specified.
+  In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+  This ensures that the output value does not depend on the iteration order.
+
+  This operator is the inverse of GatherND.
+
+  Example 1:
+  ```
+    data    = [1, 2, 3, 4, 5, 6, 7, 8]
+    indices = [[4], [3], [1], [7]]
+    updates = [9, 10, 11, 12]
+    output  = [1, 11, 3, 10, 9, 6, 7, 12]
+  ```
+
+  Example 2:
+  ```
+    data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+    indices = [[0], [2]]
+    updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+    output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1.</dd>
+<dt><tt>updates</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+### <a name="Shape-13"></a>**Shape-13**</a>
+
+  Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>shape</tt> (non-differentiable) : T1</dt>
+<dd>Shape of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor.</dd>
+</dl>
+
+### <a name="Sigmoid-13"></a>**Sigmoid-13**</a>
+
+  Sigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+  tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Sign-13"></a>**Sign-13**</a>
+
+  Calculate the sign of the given input tensor element-wise.
+  If input > 0, output 1. if input < 0, output -1. if input == 0, output 0.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (non-differentiable) : T</dt>
+<dd>The sign of the input tensor computed element-wise. It has the same shape and type of the input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Size-13"></a>**Size-13**</a>
+
+  Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>size</tt> (non-differentiable) : T1</dt>
+<dd>Total number of elements of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor, which should be a scalar though.</dd>
+</dl>
+
+### <a name="Slice-13"></a>**Slice-13**</a>
+
+  Produces a slice of the input tensor along multiple axes. Similar to numpy:
+  https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding
+
+  Slice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor
+  of its input `data` tensor.
+
+  An effective `start[i]`, `end[i]`, and `step[i]` must be computed for each `i`
+  in `[0, ... r-1]` where `r = rank(input)` as follows:
+
+  If `axes` are omitted, they are set to `[0, ..., r-1]`.
+  If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+
+  The effective values are initialized as `start[i] = 0`, `end[i] = dims[i]` where
+  `dims` are the dimensions of `input` and `step[i] = `1.
+
+  All negative elements of `axes` are made non-negatve by adding `r` to them, where
+  `r =rank(input)`.
+
+  All negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them,
+  where `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted
+  `starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping
+  and `[0, dims[axes[i]]-1]` for negative stepping.
+
+  The clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must
+  accommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping
+  `end[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it
+  is clamped to `[-1, dims[axes[i]]-1]`.
+
+  Finally, `step[axes[i]] = steps[i]`.
+
+  For slicing to the end of a dimension with unknown size, it is recommended to pass
+  in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward.
+
+  Example 1:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    axes = [0, 1]
+    starts = [1, 0]
+    ends = [2, 3]
+    steps = [1, 2]
+    result = [
+        [5, 7],
+    ]
+  Example 2:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    starts = [0, 1]
+    ends = [-1, 1000]
+    result = [
+        [2, 3, 4],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (3 - 5)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of data to extract slices from.</dd>
+<dt><tt>starts</tt> (non-differentiable) : Tind</dt>
+<dd>1-D tensor of starting indices of corresponding axis in `axes`</dd>
+<dt><tt>ends</tt> (non-differentiable) : Tind</dt>
+<dd>1-D tensor of ending indices (exclusive) of corresponding axis in `axes`</dd>
+<dt><tt>axes</tt> (optional, non-differentiable) : Tind</dt>
+<dd>1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.</dd>
+<dt><tt>steps</tt> (optional, non-differentiable) : Tind</dt>
+<dd>1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Sliced data tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="Softmax-13"></a>**Softmax-13**</a>
+
+  The operator computes the normalized exponential values for the given input:
+
+   Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1)
+
+  The "axis" attribute indicates the dimension along which Softmax
+  will be performed. The output tensor has the same shape
+  and contains the Softmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>
+Describes the dimension Softmax will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output values with the same shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="SoftmaxCrossEntropyLoss-13"></a>**SoftmaxCrossEntropyLoss-13**</a>
+
+  Loss function that measures the softmax cross entropy
+  between 'scores' and 'labels'.
+  This operator first computes a loss tensor whose shape is identical to the labels input.
+  If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).
+  If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),
+  the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.
+  After L is available, this operator can optionally do a reduction operator.
+
+  shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),
+          with K >= 1 in case of K-dimensional loss.
+  shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),
+          with K >= 1 in case of K-dimensional loss.
+
+  The loss for one sample, l_i, can caculated as follows:
+      l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.
+  or
+      l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.
+
+  loss is zero for the case when label-value equals ignore_index.
+      l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index
+
+  where:
+      p = Softmax(scores)
+      y = Log(p)
+      c = labels[i][d1][d2]...[dk]
+
+  Finally, L is optionally reduced:
+  If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).
+  If reduction = 'sum', the output is scalar: Sum(L).
+  If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),
+  where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>ignore_index</tt> : int</dt>
+<dd>Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.</dd>
+<dt><tt>reduction</tt> : string (default is mean)</dt>
+<dd>Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>scores</tt> (differentiable) : T</dt>
+<dd>The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.</dd>
+<dt><tt>labels</tt> (non-differentiable) : Tind</dt>
+<dd>The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.</dd>
+<dt><tt>weights</tt> (optional, non-differentiable) : T</dt>
+<dd>A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.</dd>
+<dt><tt>log_prob</tt> (optional, differentiable) : T</dt>
+<dd>Log probability tensor. If the output of softmax is prob, its value is log(prob).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain target to integer types</dd>
+</dl>
+
+### <a name="SpaceToDepth-13"></a>**SpaceToDepth-13**</a>
+
+  SpaceToDepth rearranges blocks of spatial data into depth. More specifically,
+  this op outputs a copy of the input tensor where values from the height and width dimensions
+  are moved to the depth dimension.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Split-13"></a>**Split-13**</a>
+
+  Split a tensor into a list of tensors, along the specified
+  'axis'. Lengths of the parts can be specified using input 'split'.
+  Otherwise, the tensor is split to equal sized parts.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The tensor to split</dd>
+<dt><tt>split</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, differentiable) : T</dt>
+<dd>One or more outputs forming list of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Sqrt-13"></a>**Sqrt-13**</a>
+
+  Square root takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the square root is, y = x^0.5, is applied to
+  the tensor elementwise. If x is negative, then it will return NaN.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Squeeze-13"></a>**Squeeze-13**</a>
+
+  Remove single-dimensional entries from the shape of a tensor.
+  Takes an input `axes` with a list of axes to squeeze.
+  If `axes` is not provided, all the single dimensions will be removed from
+  the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensors with at least max(dims) dimensions.</dd>
+<dt><tt>axes</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>squeezed</tt> (differentiable) : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Sub-13"></a>**Sub-13**</a>
+
+  Performs element-wise binary subtraction (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+### <a name="Sum-13"></a>**Sum-13**</a>
+
+  Element-wise sum of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for sum.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>sum</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Tanh-13"></a>**Tanh-13**</a>
+
+  Calculates the hyperbolic tangent of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic tangent values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Tile-13"></a>**Tile-13**</a>
+
+  Constructs a tensor by tiling a given tensor.
+  This is the same as function `tile` in Numpy, but no broadcast.
+  For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of any shape.</dd>
+<dt><tt>repeats</tt> (non-differentiable) : T1</dt>
+<dd>1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain repeat's type to int64 tensors.</dd>
+</dl>
+
+### <a name="Transpose-13"></a>**Transpose-13**</a>
+
+  Transpose the input tensor similar to numpy.transpose. For example, when
+  perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
+  will be (2, 1, 3).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>perm</tt> : list of ints</dt>
+<dd>A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>transposed</tt> (differentiable) : T</dt>
+<dd>Transposed output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Unsqueeze-13"></a>**Unsqueeze-13**</a>
+
+  Insert single-dimensional entries to the shape of an input tensor (`data`).
+  Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).
+
+  For example:
+    Given an input tensor (`data`) of shape [3, 4, 5], then
+    Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].
+
+  The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
+  The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
+  Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
+  The order of values in `axes` does not matter and can come in any order.
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Original tensor</dd>
+<dt><tt>axes</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>expanded</tt> (differentiable) : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+## Version 14 of the default ONNX operator set
+### <a name="Add-14"></a>**Add-14**</a>
+
+  Performs element-wise binary addition (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="BatchNormalization-14"></a>**BatchNormalization-14**</a>
+
+  Carries out batch normalization as described in the paper
+  https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+  There are five required inputs 'X', 'scale', 'B', 'input_mean' and
+  'input_var'.
+  Note that 'input_mean' and 'input_var' are expected to be the estimated
+  statistics in inference mode (training_mode=False, default),
+  and the running statistics in training mode (training_mode=True).
+  There are multiple cases for the number of outputs, which we list below:
+
+  Output case #1: Y, running_mean, running_var (training_mode=True)
+  Output case #2: Y (training_mode=False)
+
+  When training_mode=False, extra outputs are invalid.
+  The outputs are updated as follows when training_mode=True:
+  ```
+  running_mean = input_mean * momentum + current_mean * (1 - momentum)
+  running_var = input_var * momentum + current_var * (1 - momentum)
+
+  Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
+
+  where:
+
+  current_mean = ReduceMean(X, axis=all_except_channel_index)
+  current_var =  ReduceVar(X, axis=all_except_channel_index)
+
+  Notice that ReduceVar refers to the population variance, and it equals to
+  sum(sqrd(x_i - x_avg)) / N
+  where N is the population size (this formula does not use sample size N - 1).
+
+  ```
+
+  When training_mode=False:
+  ```
+  Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
+  ```
+
+  For previous (depreciated) non-spatial cases, implementors are suggested
+  to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op.
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).</dd>
+<dt><tt>training_mode</tt> : int (default is 0)</dt>
+<dd>If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1</dd>
+<dt><tt>scale</tt> (differentiable) : T</dt>
+<dd>Scale tensor of shape (C).</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Bias tensor of shape (C).</dd>
+<dt><tt>input_mean</tt> (differentiable) : U</dt>
+<dd>running (training) or estimated (testing) mean tensor of shape (C).</dd>
+<dt><tt>input_var</tt> (differentiable) : U</dt>
+<dd>running (training) or estimated (testing) variance tensor of shape (C).</dd>
+</dl>
+
+#### Outputs (1 - 3)
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>The output tensor of the same shape as X</dd>
+<dt><tt>running_mean</tt> (optional, non-differentiable) : U</dt>
+<dd>The running mean after the BatchNormalization operator.</dd>
+<dt><tt>running_var</tt> (optional, non-differentiable) : U</dt>
+<dd>The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>U</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain mean and variance types to float tensors. It allows all float type for U.</dd>
+</dl>
+
+### <a name="CumSum-14"></a>**CumSum-14**</a>
+
+  Performs cumulative sum of the input elements along the given axis.
+  By default, it will do the sum inclusively meaning the first element is copied as is.
+  Through an `exclusive` attribute, this behavior can change to exclude the first element.
+  It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.
+
+  Example:
+  ```
+  input_x = [1, 2, 3]
+  axis=0
+  output = [1, 3, 6]
+  exclusive=1
+  output = [0, 1, 3]
+  exclusive=0
+  reverse=1
+  output = [6, 5, 3]
+  exclusive=1
+  reverse=1
+  output = [5, 3, 0]
+  ```
+
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>exclusive</tt> : int (default is 0)</dt>
+<dd>If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.</dd>
+<dt><tt>reverse</tt> : int (default is 0)</dt>
+<dd>If set to 1 will perform the sums in reverse direction.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>x</tt> (differentiable) : T</dt>
+<dd>An input tensor that is to be processed.</dd>
+<dt><tt>axis</tt> (non-differentiable) : T2</dt>
+<dd>A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same type as 'x' with cumulative sums of the x's elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+<dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
+<dd>axis tensor can be int32 or int64 only</dd>
+</dl>
+
+### <a name="Div-14"></a>**Div-14**</a>
+
+  Performs element-wise binary division (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="GRU-14"></a>**GRU-14**</a>
+
+  Computes an one-layer GRU. This operator is usually supported via some custom
+  implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `z` - update gate
+
+  `r` - reset gate
+
+  `h` - hidden gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+  `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+  `Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+  `Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+  `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+  `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+  `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+  `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh):
+
+    - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
+
+    - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
+
+    - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
+
+    - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
+
+    - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>layout</tt> : int (default is 0)</dt>
+<dd>The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].</dd>
+<dt><tt>linear_before_reset</tt> : int (default is 0)</dt>
+<dd>When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> (differentiable) : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0</dd>
+<dt><tt>sequence_lens</tt> (optional, non-differentiable) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional, differentiable) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="HardSwish-14"></a>**HardSwish-14**</a>
+
+  HardSwish takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where
+  the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid<alpha, beta>(x),
+  where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="Identity-14"></a>**Identity-14**</a>
+
+  Identity operator
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : V</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : V</dt>
+<dd>Tensor to copy input into.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input and output types to all tensor and sequence types.</dd>
+</dl>
+
+### <a name="LSTM-14"></a>**LSTM-14**</a>
+
+  Computes an one-layer LSTM. This operator is usually supported via some
+  custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `o` - output gate
+
+  `f` - forget gate
+
+  `c` - cell gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+  `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+  `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+  `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+  `P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+  `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+  `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+  `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+  `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+  `PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+    - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
+
+    - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
+
+    - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+
+    - Ct = ft (.) Ct-1 + it (.) ct
+
+    - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
+
+    - Ht = ot (.) h(Ct)
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>input_forget</tt> : int (default is 0)</dt>
+<dd>Couple the input and forget gates if 1.</dd>
+<dt><tt>layout</tt> : int (default is 0)</dt>
+<dd>The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].</dd>
+</dl>
+
+#### Inputs (3 - 8)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> (differentiable) : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional, non-differentiable) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>initial_c</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>P</tt> (optional, differentiable) : T</dt>
+<dd>The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.</dd>
+</dl>
+
+#### Outputs (0 - 3)
+
+<dl>
+<dt><tt>Y</tt> (optional, differentiable) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>Y_c</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="Mul-14"></a>**Mul-14**</a>
+
+  Performs element-wise binary multiplication (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="RNN-14"></a>**RNN-14**</a>
+
+  Computes an one-layer simple RNN. This operator is usually supported
+  via some custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `Wi` - W parameter weight matrix for input gate
+
+  `Ri` - R recurrence weight matrix for input gate
+
+  `Wbi` - W parameter bias vector for input gate
+
+  `Rbi` - R parameter bias vector for input gate
+
+  `WBi` - W parameter weight matrix for backward input gate
+
+  `RBi` - R recurrence weight matrix for backward input gate
+
+  `WBbi` - WR bias vectors for backward input gate
+
+  `RBbi` - RR bias vectors for backward input gate
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Tanh):
+
+    - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings (default is ['Tanh', 'Tanh'])</dt>
+<dd>One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>layout</tt> : int (default is 0)</dt>
+<dd>The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> (differentiable) : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional, non-differentiable) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional, differentiable) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+### <a name="Relu-14"></a>**Relu-14**</a>
+
+  Relu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to signed numeric tensors.</dd>
+</dl>
+
+### <a name="Reshape-14"></a>**Reshape-14**</a>
+
+  Reshape the input tensor similar to numpy.reshape.
+  First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+  At most one dimension of the new shape can be -1. In this case, the value is
+  inferred from the size of the tensor and the remaining dimensions. A dimension
+  could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+  from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
+  dimension will be set explicitly to zero (i.e. not taken from input tensor).
+  Shape (second input) could be an empty shape, which means converting to a scalar.
+  The input tensor's shape and the output tensor's shape are required to have the same number of elements.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>allowzero</tt> : int (default is 0)</dt>
+<dd>(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+<dt><tt>shape</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Specified shape for output.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reshaped</tt> (differentiable) : T</dt>
+<dd>Reshaped data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+### <a name="Sub-14"></a>**Sub-14**</a>
+
+  Performs element-wise binary subtraction (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+### <a name="Trilu-14"></a>**Trilu-14**</a>
+
+  Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s).
+  The attribute "upper" determines whether the upper or lower part is retained. If set to true,
+  the upper triangular matrix is retained. Lower triangular matrix is retained otherwise.
+  Default value for the "upper" attribute is true.
+  Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists
+  of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal.
+  All other elements in the matrix are set to zero.
+  If k = 0, the triangular part on and above/below the main diagonal is retained.
+  If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it.
+  A negative k value retains the main diagonal and |k| diagonals below it.
+  If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it.
+  A negative k value excludes the main diagonal and (|k|-1) diagonals below it.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>upper</tt> : int (default is 1)</dt>
+<dd>Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of rank 2 or higher.</dd>
+<dt><tt>k</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same type and shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+## Version 15 of the default ONNX operator set
+### <a name="BatchNormalization-15"></a>**BatchNormalization-15**</a>
+
+  Carries out batch normalization as described in the paper
+  https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+  There are five required inputs 'X', 'scale', 'B', 'input_mean' and
+  'input_var'.
+  Note that 'input_mean' and 'input_var' are expected to be the estimated
+  statistics in inference mode (training_mode=False, default),
+  and the running statistics in training mode (training_mode=True).
+  There are multiple cases for the number of outputs, which we list below:
+
+  Output case #1: Y, running_mean, running_var (training_mode=True)
+  Output case #2: Y (training_mode=False)
+
+  When training_mode=False, extra outputs are invalid.
+  The outputs are updated as follows when training_mode=True:
+  ```
+  running_mean = input_mean * momentum + current_mean * (1 - momentum)
+  running_var = input_var * momentum + current_var * (1 - momentum)
+
+  Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
+
+  where:
+
+  current_mean = ReduceMean(X, axis=all_except_channel_index)
+  current_var =  ReduceVar(X, axis=all_except_channel_index)
+
+  Notice that ReduceVar refers to the population variance, and it equals to
+  sum(sqrd(x_i - x_avg)) / N
+  where N is the population size (this formula does not use sample size N - 1).
+
+  ```
+
+  The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs.
+
+  When training_mode=False:
+  ```
+  Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
+  ```
+
+  For previous (depreciated) non-spatial cases, implementors are suggested
+  to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op.
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).</dd>
+<dt><tt>training_mode</tt> : int (default is 0)</dt>
+<dd>If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1</dd>
+<dt><tt>scale</tt> (differentiable) : T1</dt>
+<dd>Scale tensor of shape (C).</dd>
+<dt><tt>B</tt> (differentiable) : T1</dt>
+<dd>Bias tensor of shape (C).</dd>
+<dt><tt>input_mean</tt> (differentiable) : T2</dt>
+<dd>running (training) or estimated (testing) mean tensor of shape (C).</dd>
+<dt><tt>input_var</tt> (differentiable) : T2</dt>
+<dd>running (training) or estimated (testing) variance tensor of shape (C).</dd>
+</dl>
+
+#### Outputs (1 - 3)
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>The output tensor of the same shape as X</dd>
+<dt><tt>running_mean</tt> (optional, non-differentiable) : T2</dt>
+<dd>The running mean after the BatchNormalization operator.</dd>
+<dt><tt>running_var</tt> (optional, non-differentiable) : T2</dt>
+<dd>The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain scale and bias types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain mean and variance types to float tensors.</dd>
+</dl>
+
+### <a name="Bernoulli-15"></a>**Bernoulli-15**</a>
+
+  Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor
+  containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number,
+  where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p).
+
+  This operator is non-deterministic and may not produce the same values in different
+  implementations (even if a seed is specified).
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>All values in input have to be in the range:[0, 1].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>The returned output tensor only has values 0 or 1, same shape as input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bool)</dt>
+<dd>Constrain output types to all numeric tensors and bool tensors.</dd>
+</dl>
+
+### <a name="CastLike-15"></a>**CastLike-15**</a>
+
+  The operator casts the elements of a given input tensor (the first input) to
+  the same data type as the elements of the second input tensor.
+  See documentation of the Cast operator for further details.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T1</dt>
+<dd>Input tensor to be cast.</dd>
+<dt><tt>target_type</tt> (non-differentiable) : T2</dt>
+<dd>The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T2</dt>
+<dd>Output tensor produced by casting the first input tensor to have the same type as the second input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain input types. Casting from complex is not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain output types. Casting to complex is not supported.</dd>
+</dl>
+
+### <a name="Optional-15"></a>**Optional-15**</a>
+
+  Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute,
+  or a non-empty value containing the input element.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>type</tt> : type_proto</dt>
+<dd>Type of the element in the optional output</dd>
+</dl>
+
+#### Inputs (0 - 1)
+
+<dl>
+<dt><tt>input</tt> (optional) : V</dt>
+<dd>The input element.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : O</dt>
+<dd>The optional output enclosing the input element.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input type to all tensor and sequence types.</dd>
+<dt><tt>O</tt> : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain output type to all optional tensor or optional sequence types.</dd>
+</dl>
+
+### <a name="OptionalGetElement-15"></a>**OptionalGetElement-15**</a>
+
+  Outputs the element in the optional-type input. It is an error if the input value does not have an element
+  and the behavior is undefined in this case.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : O</dt>
+<dd>The optional input.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : V</dt>
+<dd>Output element in the optional input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>O</tt> : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain input type to optional tensor and optional sequence types.</dd>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output type to all tensor or sequence types.</dd>
+</dl>
+
+### <a name="OptionalHasElement-15"></a>**OptionalHasElement-15**</a>
+
+  Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns false.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : O</dt>
+<dd>The optional input.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : B</dt>
+<dd>A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>O</tt> : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain input type to optional tensor and optional sequence types.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Constrain output to a boolean tensor.</dd>
+</dl>
+
+### <a name="Pow-15"></a>**Pow-15**</a>
+
+  Pow takes input data (Tensor<T>) and exponent Tensor, and
+  produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+  is applied to the data tensor elementwise.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>First operand, base of the exponent.</dd>
+<dt><tt>Y</tt> (differentiable) : T1</dt>
+<dd>Second operand, power of the exponent.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input X and output types to float/int tensors.</dd>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input Y types to float/int tensors.</dd>
+</dl>
+
+### <a name="Shape-15"></a>**Shape-15**</a>
+
+  Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+  Optional attributes start and end can be used to compute a slice of the input tensor's shape.
+  If start axis is omitted, the slice starts from axis 0.
+  The end axis, if specified, is exclusive (and the returned value will not include the size of that axis).
+  If the end axis is omitted, the axes upto the last one will be included.
+  Negative axes indicate counting back from the last axis.
+  Note that axes will be clamped to the range [0, r-1], where r is the
+  rank of the input tensor if they are out-of-range (after adding r in the case of
+  negative axis). Thus, specifying any end value > r is equivalent to specifying an end
+  value of r, and specifying any start value < -r is equivalent to specifying a start
+  value of 0.
+
+  For example:
+  Input tensor with shape: [2, 3, 4]
+  No attributes specified.
+  Output: [2, 3, 4]
+
+  Input tensor with shape: [2, 3, 4]
+  start: -1
+  Output: [4]
+
+  Input tensor with shape: [2, 3, 4]
+  end: -1
+  Output: [2, 3]
+
+  Input tensor with shape: [2, 3, 4]
+  start: 1
+  end: 2
+  Output: [3]
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>end</tt> : int</dt>
+<dd>(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.</dd>
+<dt><tt>start</tt> : int (default is 0)</dt>
+<dd>(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>shape</tt> (non-differentiable) : T1</dt>
+<dd>Shape of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor.</dd>
+</dl>
+
+## Version 16 of the default ONNX operator set
+### <a name="GreaterOrEqual-16"></a>**GreaterOrEqual-16**</a>
+
+  Returns the tensor resulted from performing the `greater_equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="GridSample-16"></a>**GridSample-16**</a>
+
+  Given an `input` and a flow-field `grid`, computes the `output` using `input` values and pixel locations from `grid`.
+  Currently, only spatial (4-D) inputs are supported. For `input` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2),
+  the `output` will have shape (N, C, H_out, W_out).
+  For each output location `output[N, C, H_out, W_out]`, the size-2 vector `grid[N, H_out, W_out]` specifies `input` pixel locations `x` and `y`,
+  which are used to interpolate the output value `output[N, C, H_out, W_out]`.
+
+  The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025).
+  See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>align_corners</tt> : int (default is 0)</dt>
+<dd>If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.</dd>
+<dt><tt>mode</tt> : string (default is bilinear)</dt>
+<dd>Three interpolation modes: bilinear (default), nearest and bicubic.</dd>
+<dt><tt>padding_mode</tt> : string (default is zeros)</dt>
+<dd>Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T1</dt>
+<dd>4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.</dd>
+<dt><tt>grid</tt> (non-differentiable) : T1</dt>
+<dd>Input offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T2</dt>
+<dd>4-D tensor of shape (N, C, H_out, W_out).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input types to all tensor types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+### <a name="Identity-16"></a>**Identity-16**</a>
+
+  Identity operator
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : V</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : V</dt>
+<dd>Tensor to copy input into.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain input and output types to all tensor, sequence, and optional types.</dd>
+</dl>
+
+### <a name="If-16"></a>**If-16**</a>
+
+  If conditional
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>else_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.</dd>
+<dt><tt>then_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>cond</tt> : B</dt>
+<dd>Condition for the if</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Only bool</dd>
+</dl>
+
+### <a name="LeakyRelu-16"></a>**LeakyRelu-16**</a>
+
+  LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+  output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+  `f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+
+  **History**
+  - Version 16 adds bfloat16 to the types allowed.
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.01)</dt>
+<dd>Coefficient of leakage.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="LessOrEqual-16"></a>**LessOrEqual-16**</a>
+
+  Returns the tensor resulted from performing the `less_equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+### <a name="Loop-16"></a>**Loop-16**</a>
+
+  Generic Looping construct. This loop has multiple termination conditions:
+
+  1) Trip count. Iteration count specified at runtime. Set by
+     specifying the input M. Optional. Set to empty string to omit.
+     Note that a static trip count (specified at graph construction time) can be
+     specified by passing in a constant node for input M.
+  2) Loop termination condition. This is an input to the op that determines
+     whether to run the first iteration and also a loop-carried dependency for
+     the body graph. The body graph must yield a value for the condition variable,
+     whether this input is provided or not.
+
+  This table summarizes the operating modes of this operator with equivalent
+  C-style code:
+
+      Operator inputs defined as (max_trip_count, condition_var).
+
+      input ("", ""):
+          for (int i=0; ; ++i) {
+            cond = ... // Note this value is ignored, but is required in the body
+          }
+
+      input ("", cond) // Note this is analogous to a while loop
+          bool cond = ...;
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input ("", 1) // Note this is analogous to a do-while loop
+          bool cond = true
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input (trip_count, "") // Note this is analogous to a for loop
+          int trip_count = ...
+          for (int i=0; i < trip_count; ++i) {
+            cond = ...; // ignored
+          }
+
+      input (trip_count, cond)
+          int trip_count = ...;
+          bool cond = ...;
+          for (int i=0; i < trip_count && cond; ++i) {
+            cond = ...;
+          }
+
+
+  *Sample usage - cond as well as trip count*
+
+      graph predict-net {
+        %a = Constant[value = <Scalar Tensor [3]>]()
+        %b = Constant[value = <Scalar Tensor [6]>]()
+        %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+        %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+        %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+        return
+      }
+
+      graph body-net (
+        %i[INT32, scalar]           // iteration number
+        %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+        %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+      ) {
+        %my_local = Add(%a, %b_in)
+        %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+        %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+        %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+        return %keepgoing_out, %b_out, %user_defined_val
+      }
+
+  *Sample equivalent C code*
+
+      {
+        /* User-defined code (enclosing scope) */
+        int a = 3, b = 6;
+        bool keepgoing = true; // Analogous to input cond
+        /* End user-defined code */
+
+        /* Implicitly-defined code */
+        const int max_trip_count = 10; // Analogous to input M
+        int user_defined_vals[]; // Imagine this is resizable
+        /* End implicitly-defined code */
+        /* initialize loop-carried variables and scan-output variables */
+        bool keepgoing_out = keepgoing
+        int b_out = b
+
+        for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+          /* Implicitly-defined code: bind actual parameter values
+             to formal parameter variables of loop-body */
+          bool keepgoing_in = keepgoing_out;
+          bool b_in = b_out;
+
+          /* User-defined code (loop body) */
+          int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+          b_out = a - b_in;
+          keepgoing_out = my_local > b_out;
+          user_defined_val = b_in + b_in; // b_in and b_out are different variables
+          /* End user-defined code */
+
+          /* Implicitly defined-code */
+          user_defined_vals[i] = user_defined_val // accumulate scan-output values
+        }
+        // int t = my_local; // Can't do this. my_local is not accessible here.
+
+        // The values below are bound to the output variables of the loop and therefore accessible
+        // b_out; user_defined_vals; keepgoing_out;
+      }
+
+  There are several things of note in this code snippet:
+
+  1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+     be referenced in the inputs of the loop.
+  2) Any values computed in the loop body that needs to be used in a subsequent
+     iteration or after the loop are modelled using a pair of variables in the loop-body,
+     consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+     These are referred to as loop-carried dependences. The loop operation node
+     supplies the input value of the input variable for the first iteration, and
+     returns the output value of the output variable produced by the final
+     iteration.
+  3) Scan_output variables are used to implicitly concatenate values computed across
+     all the iterations. In the above example, the value of user_defined_val computed
+     over all iterations are concatenated and returned as the value of user_defined_vals
+     after the loop.
+  4) Values created in the body cannot be accessed in the enclosing scope,
+     except using the mechanism described above.
+
+  Note that the semantics of this op support "diagonal" or "wavefront" execution.
+  (See Step 3 here for an example:
+  https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+  Frontends should emit multi-layer RNNs as a series of While operators (with
+  time being the inner looping dimension), with each successive layer consuming
+  the scan_outputs from the previous layer, possibly going through several
+  point-wise operators (e.g. dropout, residual connections, linear layer).
+
+  The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.</dd>
+</dl>
+
+#### Inputs (2 - &#8734;)
+
+<dl>
+<dt><tt>M</tt> (optional) : I</dt>
+<dd>A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.</dd>
+<dt><tt>cond</tt> (optional) : B</dt>
+<dd>A boolean termination condition. Optional. Pass empty string to skip.</dd>
+<dt><tt>v_initial</tt> (variadic, heterogeneous) : V</dt>
+<dd>The initial values of any loop-carried dependencies (values that change across loop iterations)</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>v_final_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>tensor of int64, which should be a scalar.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>tensor of bool, which should be a scalar.</dd>
+</dl>
+
+### <a name="PRelu-16"></a>**PRelu-16**</a>
+
+  PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+  output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+  `f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+
+  **History**
+  - Version 16 adds bfloat16 to the types allowed.
+  This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>slope</tt> (differentiable) : T</dt>
+<dd>Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor (same size as X)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+### <a name="RoiAlign-16"></a>**RoiAlign-16**</a>
+
+  Region of Interest (RoI) align operation described in the
+  [Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
+  RoiAlign consumes an input tensor X and region of interests (rois)
+  to apply pooling across each RoI; it produces a 4-D tensor of shape
+  (num_rois, C, output_height, output_width).
+
+  RoiAlign is proposed to avoid the misalignment by removing
+  quantizations while converting from original image into feature
+  map and from feature map into RoI feature; in each ROI bin,
+  the value of the sampled locations are computed directly
+  through bilinear interpolation.
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coordinate_transformation_mode</tt> : string (default is half_pixel)</dt>
+<dd>Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).</dd>
+<dt><tt>mode</tt> : string (default is avg)</dt>
+<dd>The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.</dd>
+<dt><tt>output_height</tt> : int (default is 1)</dt>
+<dd>default 1; Pooled output Y's height.</dd>
+<dt><tt>output_width</tt> : int (default is 1)</dt>
+<dd>default 1; Pooled output Y's width.</dd>
+<dt><tt>sampling_ratio</tt> : int (default is 0)</dt>
+<dd>Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.</dd>
+<dt><tt>spatial_scale</tt> : float (default is 1.0)</dt>
+<dd>Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.</dd>
+<dt><tt>rois</tt> : T1</dt>
+<dd>RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.</dd>
+<dt><tt>batch_indices</tt> : T2</dt>
+<dd>1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T1</dt>
+<dd>RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain types to int tensors.</dd>
+</dl>
+
+### <a name="Scan-16"></a>**Scan-16**</a>
+
+  Scan can be used to iterate over one or more scan_input tensors,
+  constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+  functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+  generalizations of RNN-like constructs for sequence-to-sequence processing.
+  Other tensors (referred to as state_variables here) can be used to carry a state
+  when iterating from one element to another (similar to hidden-state in RNNs, also referred
+  to as loop-carried dependences in the context of loops).
+  Many common usages involve a single scan_input tensor (where functionality
+  similar to scan, fold and map can be obtained). When more than one scan_input is used,
+  a behavior similar to zip is obtained.
+
+  The attribute body must be a graph, specifying the computation to be performed in
+  every iteration. It takes as input the current values of the state_variables and
+  the current iterated element of the scan_inputs. It must return the (updated) values
+  of the state_variables and zero or more scan_output_element tensors. The values of the
+  scan_output_element tensors are concatenated over all the iterations to produce the
+  scan_output values of the scan construct (similar to the concatenated intermediate
+  hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+  well as scan_output_element tensors) are required to have the same shape in each iteration
+  of the loop (a restriction imposed to enable efficient memory allocation).
+
+  Note that the iterated element passed to the body subgraph does not have a sequence
+  axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+  The scan operation returns the final values of the state_variables as well as the
+  scan_outputs.
+
+  The optional attribute scan_input_directions specifies the direction (forward or backward)
+  for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+  direction. A bidirectional scan may be performed by specifying the same tensor input twice
+  in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+  The scan_output of the operation is produced by concatenating the scan_output_element
+  values produced by the body in each iteration.  The optional attribute scan_output_directions
+  specifies the direction in which scan_output is constructed (by appending or prepending the
+  scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+  is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+  The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+  If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+  batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+  Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+  The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+  are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+  scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+  value of 1.
+
+  Note that because of the ONNX restriction that only the last parameter of an operator can
+  be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+  Similarly, the final-states and scan-outputs are listed together as one output parameter.
+  The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+  The behavior of
+
+      Scan <
+          num_scan_inputs = m,
+          body = loop-body,
+          scan_input_axes = [axis_1, ..., axis_m]
+      > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+  is equivalent to the following pseudo-code:
+
+      // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+      // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+      sequence_length = scan_1.shape[axis_1];
+
+      // initialize state-variables
+      st_1 = init_1; ... st_n = init_n;
+      // initialize scan-output variables: [] denotes an empty tensor
+      scan_out_1 = []; ...; scan_out_k = [];
+      // identify number of iterations:
+
+      // execute loop
+      for (int t = 0; t < sequence_length; ++t) {
+          // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+          // of rank one less than T obtained by indexing T at position t along axis k.
+          si_1 = scan_1<axis=axis_1>[t];
+          ... ;
+          si_m = scan_m<axis=axis_m>[t];
+          // execute loop-body
+          st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+          // accumulate the scan-output elements
+          scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+      }
+
+      return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+  *Sample usage: Encoding RNN using a Scan*
+
+  The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+  recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+  be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+  %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+  values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+      graph rnn-encoding {
+        %H_0 = ...
+        %X = ...
+        %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+        return %Y, %Y_h
+      }
+
+      graph rnn-cell-1 (
+        %H_tminus1[FLOAT, tensor]
+        %X_t[FLOAT, tensor]
+      ) {
+        %Wi = ...
+        %Ri = ...
+        %Wbi = ...
+        %Rbi = ...
+        %t1 = X_t * (Wi^T)
+        %t2 = H_tminus1*(Ri^T)
+        %t3 = Add(%t1, %t2)
+        %t4 = Add(%t3, %Wbi)
+        %t5 = Add(%t4, %Rbi)
+        %Ht = Tanh(%t5)
+        %Accumulate = Identity(%Ht)
+        return %Ht, %Accumulate
+      }
+
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.</dd>
+<dt><tt>num_scan_inputs</tt> : int (required)</dt>
+<dd>An attribute specifying the number of scan_inputs M. </dd>
+<dt><tt>scan_input_axes</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>scan_input_directions</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.</dd>
+<dt><tt>scan_output_axes</tt> : list of ints</dt>
+<dd>An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].</dd>
+<dt><tt>scan_output_directions</tt> : list of ints</dt>
+<dd>An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>initial_state_and_scan_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Initial values of the loop's N state variables followed by M scan_inputs</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>final_state_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final values of the loop's N state variables followed by K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+</dl>
+
+### <a name="ScatterElements-16"></a>**ScatterElements-16**</a>
+
+  ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+  rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value
+  to values specified by `updates` at specific index positions specified by
+  `indices`. Its output shape is the same as the shape of `data`.
+
+  For each entry in `updates`, the target index in `data` is obtained by combining
+  the corresponding entry in `indices` with the index of the entry itself: the
+  index-value for dimension = axis is obtained from the value of the corresponding
+  entry in `indices` and the index-value for dimension != axis is obtained from the
+  index of the entry itself.
+
+  `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates`
+  tensor into `output` at the specified `indices`.
+  In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2,
+  then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update
+  corresponding to the [i][j] entry is performed as below:
+  ```
+    output[indices[i][j]][j] = updates[i][j] if axis = 0,
+    output[i][indices[i][j]] = updates[i][j] if axis = 1,
+  ```
+  When `reduction` is set to "add", the update corresponding to the [i][j] entry is performed as below:
+  ```
+    output[indices[i][j]][j] += updates[i][j] if axis = 0,
+    output[i][indices[i][j]] += updates[i][j] if axis = 1,
+  ```
+  When `reduction` is set to "mul", the update corresponding to the [i][j] entry is performed as below:
+  ```
+    output[indices[i][j]][j] *= updates[i][j] if axis = 0,
+    output[i][indices[i][j]] *= updates[i][j] if axis = 1,
+  ```
+
+  This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+  Example 1:
+  ```
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  ```
+  Example 2:
+  ```
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>reduction</tt> : string (default is none)</dt>
+<dd>Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add':  reduction using the addition operation. 'mul': reduction using the multiplication operation.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+<dt><tt>updates</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >=1 (same rank and shape as indices)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1 (same rank as input).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+### <a name="ScatterND-16"></a>**ScatterND-16**</a>
+
+  ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+  and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value to values
+  specified by `updates` at specific index positions specified by `indices`. Its output shape
+  is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+  That is, two or more `updates` for the same index-location is not supported.
+
+  `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+   `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+  Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+  update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+  update to a slice of the tensor.
+
+  `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+  first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+  The remaining dimensions of `updates` correspond to the dimensions of the
+  replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+  corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+  must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+  of shapes.
+
+  The `output` is calculated via the following equation:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] = updates[idx]
+
+  The order of iteration in the above loop is not specified.
+  In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+  This ensures that the output value does not depend on the iteration order.
+
+  `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates`
+  tensor into `output` at the specified `indices`.
+  In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2,
+  then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order.
+  When `reduction` is set to "add", `output` is calculated as follows:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] += updates[idx]
+
+  When `reduction` is set to "mul", `output` is calculated as follows:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] *= updates[idx]
+
+  This operator is the inverse of GatherND.
+
+  Example 1:
+  ```
+    data    = [1, 2, 3, 4, 5, 6, 7, 8]
+    indices = [[4], [3], [1], [7]]
+    updates = [9, 10, 11, 12]
+    output  = [1, 11, 3, 10, 9, 6, 7, 12]
+  ```
+
+  Example 2:
+  ```
+    data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+    indices = [[0], [2]]
+    updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+    output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>reduction</tt> : string (default is none)</dt>
+<dd>Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add':  reduction using the addition operation. 'mul': reduction using the multiplication operation.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1.</dd>
+<dt><tt>updates</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+### <a name="Where-16"></a>**Where-16**</a>
+
+  Return elements, either from X or Y, depending on condition.
+  Where behaves like
+  [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
+  with three parameters.
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  **History**
+  - Version 16 adds bfloat16 to the types allowed (for the second and third parameter).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>condition</tt> (non-differentiable) : B</dt>
+<dd>When True (nonzero), yield X, otherwise yield Y</dd>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>values selected at indices where condition is True</dd>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>values selected at indices where condition is False</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of shape equal to the broadcasted shape of condition, X, and Y.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Constrain to boolean tensors.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types (including bfloat).</dd>
+</dl>
+
+## Version 17 of the default ONNX operator set
+### <a name="LayerNormalization-17"></a>**LayerNormalization-17**</a>
+
+  This is layer normalization defined in ONNX as function.
+        The overall computation can be split into two stages.
+        The first stage is standardization, which makes the
+        normalized elements have zero mean and unit variances.
+        The computation required by standardization can be
+        described by the following equations.
+        ```
+        Mean = ReduceMean<axes=normalized_axes>(X)
+        D = Sub(X, Mean)
+        DD = Mul(Diff, Diff)
+        Var = ReduceMean<axes=normalized_axes>(DD)
+        VarEps = Add(Var, epsilon)
+        StdDev = Sqrt(VarEps)
+        InvStdDev = Reciprocal(StdDev)
+        Normalized = Mul(D, InvStdDev)
+        ```
+        where `normalized_axes` is `[axis, ..., rank of X - 1]`.
+        The variables `Var` and `StdDev` stand for variance and
+        standard deviation, respectively. The second output is
+        `Mean` and the last one is `InvStdDev`.
+        Depending on `stash_type` attribute, the actual computation
+        must happen in different floating-point precision.
+        For example, if `stash_type` is 1, this operator casts
+        all input variables to 32-bit float, perform the computation, and
+        finally cast `Normalized` back to the original type of `X`.
+        The second stage then scales and shifts the outcome of the
+        first stage using
+        ```
+        NormalizedScaled = Mul(Normalized, Scale)
+        Y = Add(NormalizedScaled, B)
+        ```
+        The second stage doesn't depends on `stash_type`.
+        All equations are in [this syntax](https://github.com/onnx/onnx/blob/main/docs/Syntax.md).
+        The same variable (i.e., input, output, and attribute) uses
+        the same name in the equations above and this operator's definition.
+        Let `d[i]` indicate the i-th dimension of `X`.
+        If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`,
+        the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`.
+        `Y` and `X` have the same shape.
+
+#### Version
+
+This version of the operator has been available since version 17 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.</dd>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>stash_type</tt> : int (default is 1)</dt>
+<dd>Type of Mean and InvStdDev. This also specifies stage one's computation precision.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Tensor to be normalized.</dd>
+<dt><tt>Scale</tt> : T</dt>
+<dd>Scale tensor.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>Bias tensor.</dd>
+</dl>
+
+#### Outputs (1 - 3)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Normalized tensor.</dd>
+<dt><tt>Mean</tt> (optional) : U</dt>
+<dd>Saved mean used during training to speed up gradient computation</dd>
+<dt><tt>InvStdDev</tt> (optional) : U</dt>
+<dd>Saved inverse standard deviation used during training to speed up gradient computation.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types and output Y type to float tensors.</dd>
+<dt><tt>U</tt> : tensor(float), tensor(bfloat16)</dt>
+<dd>Type of Mean and InvStdDev tensors.</dd>
+</dl>
+
+### <a name="SequenceMap-17"></a>**SequenceMap-17**</a>
+
+  Applies a sub-graph to each sample in the input sequence(s).
+
+  Inputs can be either tensors or sequences, with the exception of the first input which must
+  be a sequence. The length of the first input sequence will determine the number of samples in the
+  outputs. Any other sequence inputs should have the same number of samples. The number of inputs
+  and outputs, should match the one of the subgraph.
+
+  For each i-th element in the output, a sample will be extracted from the input sequence(s) at
+  the i-th position and the sub-graph will be applied to it.
+  The outputs will contain the outputs of the sub-graph for each sample, in the same order as in
+  the input.
+
+  This operator assumes that processing each sample is independent and could executed in parallel
+  or in any order. Users cannot expect any specific ordering in which each subgraph is computed.
+
+#### Version
+
+This version of the operator has been available since version 17 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>additional_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Additional inputs to the graph</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>out_sequence</tt> (variadic, heterogeneous) : S</dt>
+<dd>Output sequence(s)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input types to any sequence type.</dd>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor or sequence type.</dd>
+</dl>
+
+# ai.onnx.preview.training
+## Version 1 of the 'ai.onnx.preview.training' operator set
+### <a name="ai.onnx.preview.training.Adagrad-1"></a>**ai.onnx.preview.training.Adagrad-1**</a>
+
+  Compute one iteration of ADAGRAD, a stochastic gradient based optimization
+      algorithm. This operator can conduct the optimization of multiple tensor variables.
+
+      Let's define the behavior of this operator. As you can imagine, ADAGRAD requires
+      some parameters:
+
+       - The initial learning-rate "R".
+       - The update count "T". That is, the number of training iterations conducted.
+       - A L2-norm regularization coefficient "norm_coefficient".
+       - A learning-rate decay factor "decay_factor".
+       - A small constant "epsilon" to avoid dividing-by-zero.
+
+      At each ADAGRAD iteration, the optimized tensors are moved along a direction
+      computed based on their estimated gradient and accumulated squared gradient. Assume
+      that only a single tensor "X" is updated by this operator. We need the value of "X",
+      its gradient "G", and its accumulated squared gradient "H". Therefore, variables in
+      this operator's input list are sequentially "R", "T", "X", "G", and "H". Other
+      parameters are given as attributes because they are usually constants. Also, the
+      corresponding output tensors are the new value of "X" (called "X_new"), and then
+      the new accumulated squared gradient (called "H_new"). Those outputs are computed
+      from the given inputs following the pseudo code below.
+
+      Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
+      numpy-style broadcasting support. The pseudo code to compute those outputs is:
+
+        // Compute a scalar learning-rate factor. At the first update of X, T is generally
+        // 0 (0-based update index) or 1 (1-based update index).
+        r = R / (1 + T * decay_factor);
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
+        G_regularized = norm_coefficient * X + G;
+
+        // Compute new accumulated squared gradient.
+        H_new = H + G_regularized * G_regularized;
+
+        // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...)
+        // computes element-wise square-root.
+        H_adaptive = Sqrt(H_new) + epsilon
+
+        // Compute the new value of "X".
+        X_new = X - r * G_regularized / H_adaptive;
+
+      If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same
+      pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a
+      concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
+      be concatenated too) and then just reuse the entire pseudo code.
+
+      Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
+      In that reference paper, this operator is a special case of the Figure 1's composite mirror
+      descent update.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>decay_factor</tt> : float (default is 0.0)</dt>
+<dd>The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.</dd>
+<dt><tt>epsilon</tt> : float (default is 0.0)</dt>
+<dd>Small scalar to avoid dividing by zero.</dd>
+<dt><tt>norm_coefficient</tt> : float (default is 0.0)</dt>
+<dd>Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>R</tt> : T1</dt>
+<dd>The initial learning rate.</dd>
+<dt><tt>T</tt> : T2</dt>
+<dd>The update count of "X". It should be a scalar.</dd>
+<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float scalars.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain input types to 64-bit integer scalars.</dd>
+<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ai.onnx.preview.training.Adam-1"></a>**ai.onnx.preview.training.Adam-1**</a>
+
+  Compute one iteration of Adam, a stochastic gradient based optimization
+      algorithm. This operator can conduct the optimization of multiple tensor variables.
+
+      Let's define the behavior of this operator. First of all, Adam requires
+      some parameters:
+
+       - The learning-rate "R".
+       - The update count "T". That is, the number of training iterations conducted.
+       - A L2-norm regularization coefficient "norm_coefficient".
+       - A small constant "epsilon" to avoid dividing-by-zero.
+       - Two coefficients, "alpha" and "beta".
+
+      At each Adam iteration, the optimized tensors are moved along a direction
+      computed based on their exponentially-averaged historical gradient and
+      exponentially-averaged historical squared gradient. Assume that only a tensor
+      "X" is being optimized. The rest of required information is
+
+       - the value of "X",
+       - "X"'s gradient (denoted by "G"),
+       - "X"'s exponentially-averaged historical gradient (denoted by "V"), and
+       - "X"'s exponentially-averaged historical squared gradient (denoted by "H").
+
+      Some of those parameters are passed into this operator as input tensors and others
+      are stored as this operator's attributes. Specifically, this operator's input tensor
+      list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is
+      the second input, and so on. Other parameters are given as attributes because they
+      are constants. Moreover, the corresponding output tensors are
+
+       - the new value of "X" (called "X_new"),
+       - the new exponentially-averaged historical gradient (denoted by "V_new"), and
+       - the new exponentially-averaged historical squared gradient (denoted by "H_new").
+
+      Those outputs are computed following the pseudo code below.
+
+      Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
+      numpy-style broadcasting support. The pseudo code to compute those outputs is:
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
+        G_regularized = norm_coefficient * X + G
+
+        // Update exponentially-averaged historical gradient.
+        V_new = alpha * V + (1 - alpha) * G_regularized
+
+        // Update exponentially-averaged historical squared gradient.
+        H_new = beta * H + (1 - beta) * G_regularized * G_regularized
+
+        // Compute the element-wise square-root of H_new. V_new will be element-wisely
+        // divided by H_sqrt for a better update direction.
+        H_sqrt = Sqrt(H_new) + epsilon
+
+        // Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power.
+        R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R
+
+        // Compute new value of "X".
+        X_new = X - R_adjusted * V_new / H_sqrt
+
+        // Post-update regularization.
+        X_final = (1 - norm_coefficient_post) * X_new
+
+      If there are multiple inputs to be optimized, the pseudo code will be applied
+      independently to each of them.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.9)</dt>
+<dd>Coefficient of previously accumulated gradient in running average. Default to 0.9.</dd>
+<dt><tt>beta</tt> : float (default is 0.999)</dt>
+<dd>Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.</dd>
+<dt><tt>epsilon</tt> : float (default is 0.0)</dt>
+<dd>Small scalar to avoid dividing by zero.</dd>
+<dt><tt>norm_coefficient</tt> : float (default is 0.0)</dt>
+<dd>Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
+<dt><tt>norm_coefficient_post</tt> : float (default is 0.0)</dt>
+<dd>Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>R</tt> : T1</dt>
+<dd>The initial learning rate.</dd>
+<dt><tt>T</tt> : T2</dt>
+<dd>The update count of "X". It should be a scalar.</dd>
+<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>The tensors to be optimized, followed by their respective gradients, followed by their respective accumulated gradients (aka momentum), followed by their respective accumulated squared gradients. For example, to optimize tensors "X_1" and "X_2,", the input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated gradient of "X_1", accumulated gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>New values of optimized tensors, followed by their respective new accumulated gradients, followed by their respective new accumulated squared gradients. For example, if two tensors "X_1" and "X_2" are optimized, the outputs list would be [new value of "X_1", new value of "X_2", new accumulated gradient of "X_1", new accumulated gradient of "X_2", new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float scalars.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain input types to 64-bit integer scalars.</dd>
+<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+### <a name="ai.onnx.preview.training.Gradient-1"></a>**ai.onnx.preview.training.Gradient-1**</a>
+
+  Gradient operator computes the partial derivatives of a specific tensor w.r.t.
+  some other tensors. This operator is widely used in gradient-based training
+  algorithms. To illustrate its use, let's consider a computation graph,
+
+  ```
+  X -----.
+         |
+         v
+  W --> Conv --> H --> Gemm --> Y
+                        ^
+                        |
+                        Z
+  ```
+
+  , where W and Z are trainable tensors. Note that operators' attributes are
+  omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of
+  Y with respect to W (Z). The user can compute gradient by inserting Gradient
+  operator to form another graph shown below.
+
+  ```
+  W --> Conv --> H --> Gemm --> Y
+  |      ^              ^
+  |      |              |
+  |      X              Z
+  |      |              |
+  |      |   .----------'
+  |      |   |  (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in
+  |      |   |   "xs" followed by "zs")
+  |      v   v
+  '---> Gradient(xs=["W", "Z"], zs=["X"], y="Y")
+         |   |
+         |   '-----------------------------------> dY/dW (1st output of Gradient)
+         |
+         '---------------------------------------> dY/dZ (2nd output of Gradient)
+  ```
+
+  By definition, the tensor "y" is a function of independent variables in "xs"
+  and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable
+  variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H"
+  cannot appear in "xs" and "zs". The reason is that "H" can be determined by
+  tensors "W" and "X" and therefore "H" is not an independent variable.
+
+  All outputs are optional. If needed, for example, user can assign an empty
+  string to the 1st output name of that Gradient to skip the generation of dY/dW.
+  Note that the concept of optional outputs can also be found in ONNX's RNN, GRU,
+  and LSTM.
+
+  Gradient operator can compute derivative against intermediate tensors. For
+  example, the gradient of Y with respect to H can be done via
+
+  ```
+  W --> Conv --> H --> Gemm --> Y
+         ^       |      ^
+         |       |      |
+         X       |      Z
+         .-------'      |
+         |   .----------'
+         |   | (H/Z is the 1st/2nd input of Gradient as shown in "xs")
+         v   v
+        Gradient(xs=["H", "Z"], y="Y")
+         |   |
+         |   '-----------------------------------> dY/dH (1st output of Gradient)
+         |
+         '---------------------------------------> dY/dZ (2nd output of Gradient)
+  ```
+
+  It is possible to represent high-order differentiation using Gradient operators.
+  For example, given the following linear model:
+
+  ```
+  W --> Gemm --> Y --> Loss --> O
+         ^              ^
+         |              |
+         X              L
+  ```
+
+  To compute the 2nd order derivative of O with respect to W (denoted by
+  d^2O/dW^2), one can do
+
+  ```
+  W --> Gemm --> Y --> Loss --> O
+  |      ^              ^
+  |      |              |
+  |      X .------------L
+  |      | |            |
+  |      | |            v
+  +------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient)
+  |      | |    |
+  |      | |    '---> dO/dW (2nd output of Gradient)
+  |      v v
+  '---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of
+         |                                                  Gradient)
+         |
+         |
+         '---> d^2O/dW^2 (2nd output of Gradient)
+  ```
+
+  The tensors named in attributes "xs", "zs", and "y" define the differentiated
+  computation graph, and the inputs to Gradient node define the values at
+  which the gradient is computed. We can feed different tensors to the identified
+  graph. For example, one can compute the gradient of Y with respect to H at
+  a specific value of H, H_1, by providing that value as an input to the Gradient
+  node.
+
+  ```
+  W --> Conv --> H --> Gemm --> Y
+         ^              ^
+         |              |
+         X              Z
+
+            Z_1 (2nd input of Gradient)
+             |
+             v
+  H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1.
+             |
+             '------------------------------> dY/dZ (2nd output of Gradient)
+  ```
+
+  When the inputs of Gradient are the tensors named in "xs" and "zs", the
+  computation can be optimized. More specifically, intermediate variables in
+  forward pass can be reused if the gradient is computed via reverse-mode
+  auto-differentiation.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>xs</tt> : list of strings (required)</dt>
+<dd>Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.</dd>
+<dt><tt>y</tt> : string (required)</dt>
+<dd>The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".</dd>
+<dt><tt>zs</tt> : list of strings</dt>
+<dd>Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>Inputs</tt> (variadic, heterogeneous) : T1</dt>
+<dd>The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute  "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>Outputs</tt> (variadic, heterogeneous) : T2</dt>
+<dd>The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Allow outputs to be any kind of tensor.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Allow inputs to be any kind of floating-point tensor.</dd>
+</dl>
+
+### <a name="ai.onnx.preview.training.Momentum-1"></a>**ai.onnx.preview.training.Momentum-1**</a>
+
+  Compute one iteration of stochastic gradient update with momentum.
+      This operator can conduct the optimization of multiple tensor variables.
+
+      Let's define the behavior of this operator. As you can imagine, SG with momentum requires
+      several parameters:
+
+       - The learning-rate "R".
+       - The update count "T". That is, the number of conducted training iterations. It should
+         be zero in the first training iteration.
+       - A L2-norm regularization coefficient "norm_coefficient".
+       - A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha".
+       - The scaling coefficient of current gradient "beta".
+       - An attribute to choose either standard momentum or Nesterov's momentum "mode" should
+         be used.
+
+      For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized.
+      Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This
+      Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new
+      momentum (called "V_new").
+
+      This operator supports two different momentum algorithms. Set the attribute "mode" to
+      "nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to
+      "standard" to use standard momentum. Computation details are described subsequently.
+
+      Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting.
+
+      Pseudo code for SG with standard momentum:
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
+        // values of all elements in X.
+        G_regularized = norm_coefficient * X + G
+
+        // In the first training iteration, beta should always be 1.
+        beta_adjusted = T > 0 ? beta : 1
+
+        // Compute the current momentum based on previous momentum and the current gradient.
+        V_new = alpha * V + beta_adjusted * G_regularized
+
+        // Update X.
+        X_new = X - R * V_new
+
+      Pseudo code for SG with Nesterov's momentum:
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
+        // values of all elements in X.
+        G_regularized = norm_coefficient * X + G;
+
+        // In the first training iteration, beta should always be 1.
+        beta_adjusted = T > 0 ? beta : 1
+
+        // Compute the current momentum based on previous momentum and the current gradient.
+        V_new = alpha * V + beta_adjusted * G_regularized;
+
+        // Compute final update direction and then update X.
+        X_new = X - R * (G_regularized + alpha * V_new)
+
+      If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same
+      pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a
+      concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
+      be concatenated too) and then our pseudo code becomes applicable.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (required)</dt>
+<dd>The decay factor of momentum. It should be a scalar.</dd>
+<dt><tt>beta</tt> : float (required)</dt>
+<dd>The coefficient of gradient in computing new momentum. It should be a scalar.</dd>
+<dt><tt>mode</tt> : string (required)</dt>
+<dd>Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentum</dd>
+<dt><tt>norm_coefficient</tt> : float (required)</dt>
+<dd>Coefficient of 0.5 * norm_coefficient * ||X||^2.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>R</tt> : T1</dt>
+<dd>The learning rate.</dd>
+<dt><tt>T</tt> : T2</dt>
+<dd>Update count of "X". It should be a scalar.</dd>
+<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float scalars.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain input types to 64-bit integer scalars.</dd>
+<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+</dl>
+
diff --git a/third_party/onnx/docs/DefineDifferentiability.md b/third_party/onnx/docs/DefineDifferentiability.md
new file mode 100644
index 0000000000..783ebd3b5a
--- /dev/null
+++ b/third_party/onnx/docs/DefineDifferentiability.md
@@ -0,0 +1,108 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# A Short Guide on the Differentiability Tag for ONNX Operators
+
+## Differentiability Tag
+The ONNX operator schema for each operator includes a differentiability tag for each input and output.
+In this document, we explain the meaning of this tag and how to ensure the correctness of the tags.
+Briefly, the tag identifies the set of differentiable inputs and differentiable outputs of an operator.
+The meaning of the tag is that the partial derivative of each differentiable output is defined with respect to each differentiable output.
+
+## Ways to Define Differentiability Tag
+The differentiability definition of an operator consists of several aspects.
+
+- Differentiable inputs, which can be referenced in Gradient's `xs` attribute.
+- Differentiable outputs, which can be referenced in Gradient's `y` attribute.
+- The math equation to compute the Jacobian matrix (or tensor). If a variable (input or output) is differentiable or not is judged by math. If the Jacobian matrix (or tensor) exists, then the considered operator has some differentiable inputs and outputs.
+
+There are several strategies to implement auto-differentiation such as forward accumulation, backward accumulation, and dual variable.
+Because most deep learning frameworks are backward-based, the reviewers should ensure the PR authors of tags provide enough details on that.
+We present a couple of methods below to verify the differentiability for ONNX operator.
+
+### Method 1: Reuse Existing Deep Learning Frameworks
+The first way is to show that the considered operator's backward operation exists in an existing framework such as Pytorch or Tensorflow. In this case, the author should provide a runnable python script which computes the backward pass of the considered operator. The author should also point out how to map the Pytorch or Tensor code to ONNX format (for example, the author can call `torch.onnx.export` to save an ONNX model). The following script shows the differentiability of ONNX Reshape using  Pytorch.
+
+```python
+import torch
+import torch.nn as nn
+
+# A single-operator model. It's literally a Pytorch Reshape.
+# Note that Pytorch Reshape can be directly mapped to ONNX Reshape.
+class MyModel(nn.Module):
+  def __init__(self):
+    super(MyModel, self).__init__()
+
+  def forward(self, x):
+    y = torch.reshape(x, (x.numel(),))
+    y.retain_grad()
+    return y
+
+model = MyModel()
+
+x = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True)
+y = model(x)
+dy = torch.tensor([1., 2., 3., 4.])
+
+torch.autograd.backward([y],
+  grad_tensors=[dy],
+  retain_graph=True,
+  create_graph=True,
+  grad_variables=None)
+
+# This example shows the input and the output in Pytorch are differentiable.
+# From the exported ONNX model below, we also see that "x" is the first input
+# of ONNX Reshape and "y" the output of ONNX Reshape. Therefore, we can say
+# the first input and the output of ONNX Reshape are differentiable.
+print(x.grad)
+print(y.grad)
+
+with open('model.onnx', 'wb') as f:
+  torch.onnx.export(model, x, f)
+```
+
+### Method 2: Manually Do the Math
+The second way is formally proving the existence of the Jacobian matrix (or tensor) from outputs to inputs with at least two numerical examples. In this case, the reviewer should go through the math and confirm if the numerical result is correct. The author should add enough details so that any STEM graduated student can easily review it.
+
+For example, to show the differentiability of Add, the author may first write down its equation:
+```
+C = A + B
+```
+For the sake of simplicity, assume `A` and `B` are same-shape vector.
+```
+A = [a1, a2]^T
+B = [b1, b2]^T
+C = [c1, c2]^T
+```
+Here we use the symbol `^T` to denote transpose of the attached matrix or vector.
+Let `X = [a1, a2, b1, b2]^T` and `Y = [c1, c2]^T` and consider Add as a function which maps `X` to `Y`.
+Then, this function's Jacobian matrix is a 4-by-2 matrix,
+```
+J = [[dc1/da1, dc2/da1],
+     [dc1/da2, dc2/da2],
+     [dc1/db1, dc2/db1],
+     [dc1/db2, dc2/db2]]
+  = [[1, 0],
+     [0, 1],
+     [1, 0],
+     [0, 1]]
+```
+If
+```
+dL/dC = [dL/dc1, dL/dc2]^T,
+```
+then `dL/dA = [dL/da1, dL/da2]^T` and `dL/dB = [dL/db1, dL/db2]^T` can be computed from elements in
+```
+  [[dL/da1], [dL/da2], [dL/db1], [dL/db2]]
+= J * dL/dC
+= [[dL/dc1], [dL/dc2], [dL/dc1], [dL/dc2]]
+```
+where `*` is standard matrix multiplication.
+If `dL/dC = [0.2, 0.8]^T`, then `dL/dA = [0.2, 0.8]^T` and `dL/dB = [0.2, 0.8]^T`.
+Notice that the procedure to compute `dL/dA` and `dL/dB` from `dL/dC` is usually called backward of an operator.
+We can see backward operator of Add takes `dL/dC` as an input and produces two outputs `dL/dA` and `dL/dB`.
+Consequently, all of `A`, `B`, and `C` are differentiable.
+By flattening tensor into 1-D vector, this example can be extended to cover all tensors when shape broadcasting is not needed.
+If broadcasting happens, the broadcasted element's gradient is the sum of all associated elements' gradient in its **non-broadcasting** case.
+Let's consider the above example again.
+If `B = [b]^T` becomes an 1-element vector, `B` may be broadcasted to `[b1, b2]^T` and `dL/dB = [dL/ db]^T = [dL/db1 + dL/db2]^T`.
+For high-dimensional tensors, this is in fact a ReduceSum operation along all expanded axes.
diff --git a/third_party/onnx/docs/DimensionDenotation.md b/third_party/onnx/docs/DimensionDenotation.md
new file mode 100644
index 0000000000..2c59b653f4
--- /dev/null
+++ b/third_party/onnx/docs/DimensionDenotation.md
@@ -0,0 +1,51 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Dimension Denotation
+
+Dimension Denotation is an experimental attempt to give tensor axis semantic descriptions and thus types and perform verification steps based on them subsequently.
+
+## Motivation
+
+The motivation of such a mechanism can be illustrated via a simple example. In the linear neural network specification below, we assume a NCHW model input:
+
+```
+input_in_NCHW -> Transpose(input, perm=[0, 2, 1, 3]) -> AveragePool(input, ...)
+```
+
+In this neural network, a user mistakenly constructed a neural network that transposes an NCHW input to a weird NHCW format and pass through spatial pooling that assumes a NCHW input format. As clearly a mistake as it is, no existing infrastructure will report an error to the user. This is should be deeply unnerving to programmers who rely heavily on type checking as an integral part of program correctness guarantee. This proposal seeks to resolve this vacuum of proper type-checking inherent in the current paradigm of neural network specification.
+
+This proposal consists of three key components: Denotation Definition, Denotation Propagation and Denotation Verification, each of which will be discussed in detail.
+
+## Denotation Definition
+
+To begin with, we define a set of types for tensor types. Such types are defined based on the following principles:
+1. Be fine grain enough to eliminate potential pitfalls. For instance, the above example illustrated in the motivation section mandates that we distinguish between a channel dimension and a spatial feature dimension to ensure the correctness of execution of the AveragePool op.
+2. Be coarse grain enough to alleviate the mental burden of users. For instance, in the above example, there is significantly less need to distinguish between a width dimension and a height dimension because operations like pooling and convolution often do not draw a distinction between various spatial dimensions. Thus, we summarize all the spatial dimensions as feature dimensions.
+3. As an important corollary of 2, be model agnostic. For instance, the semantics of feature dimensions in recurrent neural networks (RNN) and the semantics of spatial dimensions in convolutional neural network (CNN) are almost indistinguishable and therefore we permit users and developers to describe either as a feature dimension.
+
+Specifically, in our first proposal, we define the following set of standard denotations:
+
+1. `DATA_BATCH` describes a batch dimension of the training data. This corresponds to the `N` dimension in the more commonly used tensor format notation `NCHW`.
+2. `DATA_CHANNEL` describes a channel dimension of the training data. This corresponds to the `C` dimension.
+3. `DATA_TIME` describes a time dimension.
+4. `DATA_FEATURE` describes a feature dimension. This corresponds to the `H`, `W` dimension or the feature dimension in RNN.
+5. `FILTER_IN_CHANNEL` describes a filter in-channel dimension. This is the dimension that is identical (in size) to the channel dimension of the input image feature maps.
+6. `FILTER_OUT_CHANNEL` describes a filter out-channel dimension. This is the dimension that is identical (in size) to the channel dimension of the output image feature maps.
+7. `FILTER_SPATIAL` describes a filter spatial dimension.
+
+## Denotation Propagation
+
+Denotation Propagation happens when an operation permutes, destroys or creates dimensions with respect to its input tensor. In such scenarios, we will implement customized, operation-specific functions to infer the output tensor dimension denotation based on the input tensor dimension denotation. An example operation where denotation propagation happens is Transpose operation where the pseudocode for output dimension denotation inference can be formulated as a function of the input dimension denotation:
+
+```
+for i, j in enumerate(perm):
+    out_dim_denotaion[i] = in_dim_denotation[j]
+```
+
+## Denotation Verification
+
+Denotation Verification happens when an operation expects its input to arrive in a particular format. An example operation where denotation verification happens is AveragePool operation where the input, if annotated with dimension denotation, in the 2D case should have the denotation [`DATA_BATCH`, `DATA_CHANNEL`, `DATA_FEATURE`, `DATA_FEATURE`]. If there is a mismatch between the expected dimension denotation and the actual dimension denotation, an error should be reported.
+
+## Type Denotation
+
+See the [type denotation documentation](TypeDenotation.md) for more details on how to describe images and other types.
diff --git a/third_party/onnx/docs/ExternalData.md b/third_party/onnx/docs/ExternalData.md
new file mode 100644
index 0000000000..c1e53d7bef
--- /dev/null
+++ b/third_party/onnx/docs/ExternalData.md
@@ -0,0 +1,89 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# External Data
+
+## Loading an ONNX Model with External Data
+
+* [Default] If the external data is under the same directory of the model, simply use `onnx.load()`
+```python
+import onnx
+
+onnx_model = onnx.load('path/to/the/model.onnx')
+```
+
+* If the external data is under another directory, use `load_external_data_for_model()` to specify the directory path and load after using `onnx.load()`
+
+```python
+import onnx
+from onnx.external_data_helper import load_external_data_for_model
+
+onnx_model = onnx.load('path/to/the/model.onnx', load_external_data=False)
+load_external_data_for_model(onnx_model, 'data/directory/path/')
+# Then the onnx_model has loaded the external data from the specific directory
+```
+
+## Converting an ONNX Model to External Data
+```python
+import onnx
+from onnx.external_data_helper import convert_model_to_external_data
+
+onnx_model = ... # Your model in memory as ModelProto
+convert_model_to_external_data(onnx_model, all_tensors_to_one_file=True, location='filename', size_threshold=1024, convert_attribute=False)
+# Must be followed by save_model to save the converted model to a specific path
+onnx.save_model(onnx_model, 'path/to/save/the/model.onnx')
+# Then the onnx_model has converted raw data as external data and saved to specific directory
+```
+
+## Converting and Saving an ONNX Model to External Data
+```python
+import onnx
+
+onnx_model = ... # Your model in memory as ModelProto
+onnx.save_model(onnx_model, 'path/to/save/the/model.onnx', save_as_external_data=True, all_tensors_to_one_file=True, location='filename', size_threshold=1024, convert_attribute=False)
+# Then the onnx_model has converted raw data as external data and saved to specific directory
+```
+
+## onnx.checker for Models with External Data
+
+### Models with External Data (<2GB)
+
+Current checker supports checking models with external data. Specify either loaded onnx model or model path to the checker.
+
+### Large models >2GB
+However, for those models larger than 2GB, please use the model path for onnx.checker and the external data needs to be under the same directory.
+
+```python
+import onnx
+
+onnx.checker.check_model('path/to/the/model.onnx')
+# onnx.checker.check_model(loaded_onnx_model) will fail if given >2GB model
+```
+
+## TensorProto: data_location and external_data fields
+
+There are two fields related to the external data in TensorProto message type.
+
+### data_location field
+`data_location` field stores the location of data for this tensor. Value MUST be one of:
+* `MESSAGE` - data stored in type-specific fields inside the protobuf message.
+* `RAW` - data stored in raw_data field.
+* `EXTERNAL` - data stored in an external location as described by external_data field.
+* `value` not set - legacy value. Assume data is stored in raw_data (if set) otherwise in message.
+
+### external_data field
+`external_data` field stores key-value pairs of strings describing data location
+
+Recognized keys are:
+
+* `"location"` (required) - file path relative to the filesystem directory where the ONNX protobuf model was stored. Up-directory path components such as .. are disallowed and should be stripped when parsing.
+* `"offset"` (optional) - position of byte at which stored data begins. Integer stored as string. Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+* `"length"` (optional) - number of bytes containing data. Integer stored as string.
+* `"checksum"` (optional) - SHA1 digest of file specified in under 'location' key.
+
+After an ONNX file is loaded, all `external_data` fields may be updated with an additional key `("basepath")`, which stores the path to the directory from which he ONNX model file was loaded.
+
+### External data files
+Data stored in external data files will be in the same binary bytes string format as is used by the `raw_data` field in current ONNX implementations.
+
+Reference
+https://github.com/onnx/onnx/pull/678
diff --git a/third_party/onnx/docs/Hub.md b/third_party/onnx/docs/Hub.md
new file mode 100644
index 0000000000..9bd9c40e49
--- /dev/null
+++ b/third_party/onnx/docs/Hub.md
@@ -0,0 +1,235 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Model Hub
+
+The ONNX Model Hub is a simple and fast way to get started with state of the art pre-trained 
+ONNX models from the [ONNX Model Zoo](https://github.com/onnx/models). Furthermore, this allows researchers and model 
+developers the opportunity to share their pre-trained models with the broader community.
+
+## Install
+ The ONNX Model hub will be included in the `onnx` package from version 1.11 onwards. 
+ To use the hub before the 1.11 release please install from the weekly build:
+ 
+```shell script
+pip install -i https://test.pypi.org/simple/ onnx-weekly 
+```
+
+## Basic usage
+The ONNX Model Hub is capable of downloading, listing, and querying trained models from any git repository,
+ and defaults to the official [ONNX Model Zoo](https://github.com/onnx/models). In this section we demonstrate some of the basic functionality. 
+
+First please import the hub using:
+```python
+from onnx import hub
+```
+
+#### Downloading a model by name:
+
+The `load` function will default to searching the model zoo for the latest model with a matching name,
+ download this model to a local cache, and load the model into a `ModelProto` 
+ object for use with the ONNX runtime.
+
+```python
+model = hub.load("resnet50")
+```
+
+
+#### Downloading from custom repositories:
+
+Any repository with the proper structure can be a ONNX model hub. To download from other hubs,
+ or to specify a particular branch or commit on the main model hub one can provide the `repo` parameter:
+
+```python
+model = hub.load("resnet50", repo='onnx/models:771185265efbdc049fb223bd68ab1aeb1aecde76')
+```
+
+#### Listing and inspecting Models:
+
+The model hub provides APIs for querying the model zoo to learn more about available models.
+ This does not download the models, but rather just returns information about models matching the given arguments
+ 
+ ```python
+# List all models in the onnx/models:master repo
+all_models = hub.list_models()
+
+# List all versions/opsets of a specific model
+mnist_models = hub.list_models(model="mnist")
+
+# List all models matching a given "tag"
+vision_models = hub.list_models(tags=["vision"])
+```
+
+One can also inspect the metadata of a model prior to download with the `get_model_info` function:
+
+```python
+print(hub.get_model_info(model="mnist", opset=8))
+```
+This will print something like:
+```
+ModelInfo(
+    model=MNIST,
+    opset=8,
+    path=vision/classification/mnist/model/mnist-8.onnx,
+    metadata={
+     'model_sha': '2f06e72de813a8635c9bc0397ac447a601bdbfa7df4bebc278723b958831c9bf',
+     'model_bytes': 26454,
+     'tags': ['vision', 'classification', 'mnist'],
+     'io_ports': {
+        'inputs': [{'name': 'Input3', 'shape': [1, 1, 28, 28], 'type': 'tensor(float)'}],
+        'outputs': [{'name': 'Plus214_Output_0', 'shape': [1, 10], 'type': 'tensor(float)'}]},
+     'model_with_data_path': 'vision/classification/mnist/model/mnist-8.tar.gz',
+     'model_with_data_sha': '1dd098b0fe8bc750585eefc02013c37be1a1cae2bdba0191ccdb8e8518b3a882',
+     'model_with_data_bytes': 25962}
+)
+```
+
+
+## Local Caching
+
+The ONNX Model hub locally caches downloaded models in a configurable location 
+so that subsequent calls to `hub.load` do not require network connection.
+
+#### Default cache location
+The hub client looks for the following default cache locations in this order:
+
+1) `$ONNX_HOME/hub` if the `ONNX_HOME` environment variable is defined
+2) `$XDG_CACHE_HOME/hub` if the `XDG_CACHE_HOME` environment variable is defined
+3) `~/.cache/onnx/hub` where `~` is the user home directory
+
+#### Setting the cache location
+To manually set the cache location use:
+
+```python
+hub.set_dir("my/cache/directory")
+```
+
+Additionally one can inspect the cache location with:
+
+```python
+print(hub.get_dir())
+```
+
+#### Additional cache details
+
+To clear the model cache one can simply delete the cache directory using a python utility like `shutil` or `os`. 
+Furthermore one can choose to override the cached model using the `force_reload` option:
+
+```python
+model = hub.load("resnet50", force_reload=True)
+```
+
+We include this flag for completeness but note that models in the cache are disambiguated with sha256 hashes so 
+ the force_reload flag is not necessary for normal use.
+Finally we note that the model cache directory structure will mirror the directory structure 
+specified by the `model_path` field of the manifest, but with file names disambiguated with model SHA256 Hashes. 
+
+This way, the model cache is human readable, can disambiguate between multiple versions of models,
+ and can re-use cached models across different hubs if they have the same name and hash. 
+
+## Architecture
+
+![ONNX Hub Architecture](images/onnx_hub_arch.svg)
+
+The ONNX Hub consists of two main components, the client and the server.
+ The client code currently is included in the `onnx` package and can be pointed at a
+  server in the form of a hosted `ONNX_HUB_MANIFEST.json` within a github repository 
+  such as [the one in the ONNX Model Zoo](https://github.com/onnx/models/blob/master/ONNX_HUB_MANIFEST.json).
+  This manifest file is a JSON document which lists all models and their metadata
+   and is designed to be programming language agnostic. An example of a well formed model manifest entry is as follows:
+   
+   ```json
+{
+    "model": "BERT-Squad",
+    "model_path": "text/machine_comprehension/bert-squad/model/bertsquad-8.onnx",
+    "onnx_version": "1.3",
+    "opset_version": 8,
+    "metadata": {
+        "model_sha": "cad65b9807a5e0393e4f84331f9a0c5c844d9cc736e39781a80f9c48ca39447c",
+        "model_bytes": 435882893,
+        "tags": ["text", "machine comprehension", "bert-squad"],
+        "io_ports": {
+            "inputs": [
+                {
+                    "name": "unique_ids_raw_output___9:0",
+                    "shape": ["unk__475"],
+                    "type": "tensor(int64)"
+                },
+                {
+                    "name": "segment_ids:0",
+                    "shape": ["unk__476", 256],
+                    "type": "tensor(int64)"
+                },
+                {
+                    "name": "input_mask:0",
+                    "shape": ["unk__477", 256],
+                    "type": "tensor(int64)"
+                },
+                {
+                    "name": "input_ids:0",
+                    "shape": ["unk__478", 256],
+                    "type": "tensor(int64)"
+                }
+            ],
+            "outputs": [
+                {
+                    "name": "unstack:1",
+                    "shape": ["unk__479", 256],
+                    "type": "tensor(float)"
+                },
+                {
+                    "name": "unstack:0",
+                    "shape": ["unk__480", 256],
+                    "type": "tensor(float)"
+                },
+                {
+                    "name": "unique_ids:0",
+                    "shape": ["unk__481"],
+                    "type": "tensor(int64)"
+                }
+            ]
+        },
+        "model_with_data_path": "text/machine_comprehension/bert-squad/model/bertsquad-8.tar.gz",
+        "model_with_data_sha": "c8c6c7e0ab9e1333b86e8415a9d990b2570f9374f80be1c1cb72f182d266f666",
+        "model_with_data_bytes": 403400046
+    }
+}
+```
+These important fields are:
+
+- `model`: The name of the model used for querying
+- `model_path`: The relative path of the model stored in Git LFS. 
+- `onnx_version`: The ONNX version of the model
+- `opset_version`: The version of the opset. The client downloads the latest opset if left unspecified.
+- `metadata/model_sha`: Optional model sha specification for increased download security
+- `metadata/tags`: Optional high level tags to help users find models by a given type
+
+All other fields in the `metadata` field are optional for the client but provide important details for users.
+
+## Adding to the ONNX Model Hub
+
+
+#### Contributing an official model
+
+The simplest way to add a model to the official `onnx/models` version model hub is to follow 
+[these guidelines](https://github.com/onnx/models/blob/master/contribute.md) to contribute your model. Once contributed,
+ensure that your model has a markdown table in its `README.md` 
+([Example](https://github.com/onnx/models/tree/master/vision/classification/mobilenet)). The model hub
+ manifest generator will pull information from these markdown tables. To run the generator:
+ 
+ ```shell script
+git clone https://github.com/onnx/models.git
+git lfs pull --include="*" --exclude=""
+cd models/workflow_scripts
+python generate_onnx_hub_manifest.py
+```
+
+Once a new manifest is generated add, submit it in a pull request to ``onnx/models``
+
+#### Hosting your own ONNX Model Hub
+
+To host your own model hub, add an `ONNX_HUB_MANIFEST.json` to the top level of your github repository
+ ([Example](https://github.com/onnx/models/blob/master/ONNX_HUB_MANIFEST.json)). At a minimum your 
+ manifest entries should include the fields mentioned in 
+ the [Architecture Section](Hub.md#Architecture) of this document.
+ Once committed, check that you can download models 
+ using the "Downloading from custom repositories" section of this doc.
\ No newline at end of file
diff --git a/third_party/onnx/docs/IR.md b/third_party/onnx/docs/IR.md
new file mode 100644
index 0000000000..3729b3bb88
--- /dev/null
+++ b/third_party/onnx/docs/IR.md
@@ -0,0 +1,474 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+Open Neural Network Exchange Intermediate Representation (ONNX IR) Specification
+=========
+
+__Purpose__
+
+This document contains the normative specification of the semantics of ONNX.
+
+The `.proto` and `.proto3` files found under the [onnx folder](/onnx/) form the normative specification of its syntax authored in the [Protocol Buffers](https://developers.google.com/protocol-buffers) definition language. Commentary found in the `.proto` and `.proto3` files are intended to improve readability of those files, but are not normative if they conflict with this document. Such conflicts should be reported as documentation bugs.
+
+__Notes on model validation__
+
+A [tool](../onnx/checker.py) is available to perform general validation of models against this specification. It is implemented in C++ with a Python command-line wrapper.
+
+__Notes on language in this and all related documents__:
+
+1. The use of SHOULD, MUST, MAY and so on in this document is consistent with [RFC 2119](https://www.ietf.org/rfc/rfc2119.txt).
+
+2. The use of 'list' shall denote an ordered collection of items, 'set' shall denote an unordered collection of unique elements, and 'bag' an unordered collection of possibly non-unique elements.
+
+## Components
+
+ONNX is an open specification that consists of the following components:
+
+1)  A definition of an extensible computation graph model.
+
+2)  Definitions of standard data types.
+
+3)  Definitions of built-in operators.
+
+#1 and #2 together make up the ONNX Intermediate Representation, or 'IR', specification which is covered herein; the built-in operators are covered in documents listed at the end. Specifically, built-in operators are divided into a set of primitive operators and functions. A function is an operator whose semantics is formally expressed via expansion into a sub-graph (called the function body) using other operators (and functions). Functionality-wise, an ONNX compatible framework or runtime may inline a function body to execute it if it does not have corresponding implementation of the function.
+
+There are two official ONNX variants; the main distinction between the two is found in the default operator sets. __ONNX-ML__ extends the __ONNX__ operator set with ML algorithms that are not based on neural networks.
+
+Up to IR version 6, the ONNX specification and model format addressed only inference (also known as scoring). Starting from IR version 7, the ONNX specification and model format also support training. An ONNX training model is an extension of the inference-model. An inference-only runtime can consume a training model ignoring the training-related extensions. However, an inference-only model may enable a representation that is more optimal for inference purposes than a training model.
+
+## Runtime Agnostic
+
+ONNX does not pre-suppose or imply any particular method of runtime implementation.
+
+For example, an implementation may consist of a rich runtime which interprets the model; it may be a code generator that translates the model in its entirety to executable code for some target programming language; it may be a hardware implementation; it may be a combination of two or three of those.
+
+Nothing in this specification should be construed as advocating one implementation approach over any other; any comments on the inner workings of concrete implementations are to be interpreted as examples.
+
+## ONNX Versioning
+
+The IR specification, individual models, and operator sets are all versioned. Furthermore, each individual operator indicates which version of its containing operator set it was introduced or stabilized in.
+
+Version numbers can be used as a simple number, or used to encode [semantic versions](https://semver.org/)(AKA SemVer). If using semantic versions, the convention is to use the two most significant bytes for the major number, the next two bytes for the minor number, and the least significant four bytes for the patch/build/bugfix number. When using semantic versioning, at least one of the major/minor numbers MUST be non-zero.
+
+The IR specification uses simple monotonically increasing numbers for its versions. The valid IR versions are defined by the `onnx.Version` enumeration in [onnx.proto](/onnx/onnx.proto).
+
+Operator sets use a simple version number. Each operator set version represents a snapshot of the set of operators, and their semantics at a particular point in time.
+
+This specification does not provide guidance on what versioning scheme model producers should be using.
+
+More details on conventions and best practices for versioning of IR, operator sets, and models can be found in [Versioning](Versioning.md).
+
+## Extensible computation graph model
+
+ONNX specifies the portable, serialized format of a computation graph. It does not have to be the form a framework chooses to use internally. For example, an implementation may represent the model differently in memory if it is more efficient to manipulate during optimization passes.
+
+An implementation MAY extend ONNX by adding operators expressing semantics beyond the standard set of operators that all implementations MUST support. The mechanism for this is adding operator sets to the `opset_import` property in a model that depends on the extension operators.
+
+### Models
+
+The top-level ONNX construct is a ‘Model.’, and is represented in protocol buffers as the type `onnx.ModelProto`
+
+The main purpose of the model structure is to associate metadata with a graph which contains all the executable elements. The metadata is used when first reading the model file, giving an implementation the information it needs in order to determine whether it will be able to execute the model, generate logging messages, error reports, etc. Further, the metadata is useful to tools, such as IDEs and model galleries, which need it for informing humans about a given model’s purpose and characteristics.
+
+Each model has the following components:
+
+|Name|Type|Description|
+|---|---|---|
+|ir_version|int64|The ONNX version assumed by the model.|
+|opset_import|OperatorSetId|A collection of operator set identifiers made available to the model. An implementation must support all operators in the set or reject the model.|
+|producer_name|string|The name of the tool used to generate the model.|
+|producer_version|string|The version of the generating tool.|
+|domain|string|A reverse-DNS name to indicate the model namespace or domain, for example, 'org.onnx'|
+|model_version|int64|The version of the model itself, encoded in an integer.|
+|doc_string|string|Human-readable documentation for this model. Markdown is allowed.|
+|graph|Graph|The parameterized graph that is evaluated to execute the model.|
+|metadata_props|map<string,string>|Named metadata values; keys should be distinct.|
+|training_info|TrainingInfoProto[]|An optional extension that contains information for training.|
+|functions|FunctionProto[]|An optional list of functions local to the model.|
+
+ Models MUST specify a domain and use reverse domain names based on the responsible organization's identity, the same convention that is used for [naming Java packages](https://docs.oracle.com/javase/tutorial/java/package/namingpkgs.html).
+
+__Note: Exploring an ONNX file__
+
+You can use the `protoc` tool that is part of the Protocol Buffers distribution to examine the contents of an ONNX file, you do so like this:
+
+```
+$ protoc --decode=onnx.ModelProto onnx.proto < yourfile.onnx
+```
+
+Where [onnx.proto](/onnx/onnx.proto) is the file that is part of this repository.
+
+Alternatively, you can use a tool like [Netron](https://github.com/lutzroeder/netron) to explore the ONNX file.
+
+### Model Semantics
+
+The semantics of an inference-model is a _stateless function_ (except possibly for the state used for random-number generation). Thus, whenever an inference-model (without random-generator operations) is used to perform inference on the same input, it is expected to produce the same output.
+
+The semantics of a training model is that of a _stateful object_, with the state consisting of the current values of trained-weights (and any other auxiliary state required, such as momentum, for example, used by the learning algorithm). Specifically, its semantics is captured via three methods: an initialization method (which is used to initialize or reset the values of state variables), a training step method (to train using a batch of input-output pairs), and an inference method to perform inference using the current values of the learned weights. The first two methods update the state of the object, while the third method is side-effect-free.
+
+### Optional Metadata
+
+The 'metadata_props' field in the model is available for any kind of optional metadata that a tool or model developer chooses to place there. The following are the defined “standard� optional metadata properties of a model.
+
+Name|Type|Format|Description
+|---|---|---|---|
+model_author|string|A comma-separated list of names.|The personal name of the author(s) of the model, and/or their organizations.
+model_license|string|Name or URL.|The well-known name or URL of the license under which the model is made available.
+
+### Operator Set Identifiers
+
+Each operator set is uniquely identified by a (domain, version) pair.
+
+Name|Type|Description
+|---|---|---|
+domain|string|The domain of the operator set being identified.
+version|int64|The version of the operator set being identified. Same as 'opset_version' in the operator set.
+
+The operator set version is a simple integer value that is monotonically increased as new versions of the operator set are published.
+
+Operator sets other than the default operator set MUST specify a domain and SHOULD use reverse domain names based on the responsible organization's identity, the same convention that is used for [naming Java packages](https://docs.oracle.com/javase/tutorial/java/package/namingpkgs.html).
+
+### Operator Sets
+
+Each model MUST explicitly name the operator sets that it relies on for its functionality. Operator sets define the available operators and their version. Each model defines the imported operator sets by their domains. All models implicitly import the default ONNX operator set.
+
+Each operator set SHALL be defined in a separate document, also using protobuf as the serialization format. How operator set documents are found at runtime is implementation-dependent.
+
+__Note: As of the publication of this document, no ONNX implementation is known to process operator set documents.__
+
+The properties of an operator set are:
+
+Name|Type|Description
+|---|---|---|
+magic|string|The value ‘ONNXOPSET’
+ir_version|int32|The ONNX version corresponding to the operators.
+ir_version_prerelease|string|The prerelease component of the SemVer of the IR.
+ir_build_metadata|string|The build metadata of this version of the operator set.
+domain|string|The domain of the operator set. Must be unique among all sets.
+opset_version|int64|The version of the operator set.
+doc_string|string|Human-readable documentation for this operator set. Markdown is allowed.
+operator|Operator[]|The operators contained in this operator set.
+
+The operator set version is a simple integer value that is monotonically increased as new versions of the operator set are published.
+
+Operator sets other than the default operator set MUST specify a domain and SHOULD use reverse domain names based on the responsible organization's identity, the same convention that is used for [naming Java packages](https://docs.oracle.com/javase/tutorial/java/package/namingpkgs.html).
+
+### Operators
+
+Each operator used within a graph MUST be explicitly declared by one of the operator sets imported by the model.
+
+The properties of an operator definition are:
+
+Name|Type|Description
+|---|---|---|
+op_type|string|The name of the operator (case sensitive), as used in graph nodes. MUST be unique within the operator set’s domain.
+since_version|int64|The version of the operator set when this operator was introduced.
+status|OperatorStatus|One of ‘EXPERIMENTAL’ or ‘STABLE.’
+doc_string|string|A human-readable documentation string for this operator. Markdown is allowed.
+
+The version value MUST be the same value as the operator set version when the operator was first published. Subsequent versions of the operator set MUST NOT alter the signature or semantics of the operator once published as STABLE.
+
+The ‘status’ property indicates whether the syntax, semantics, or presence of the operator is in an experimental or stable stage. Once an operator is published as STABLE, it’s syntax and semantics MUST NOT change in subsequent versions of the operator set.
+
+There are two distinct ways to pass information to operators – inputs and attributes. Inputs represent graph inputs or values computed elsewhere in the graph, while attributes are used for values that are constants in the graph. This distinction may be highly relevant to achieving good performance for some implementations, while completely irrelevant to others.
+
+### Graphs
+
+A graph is used to describe a side-effect-free computation (function).
+A serialized graph is comprised of a set of metadata fields, a list of model parameters, and a list of computation nodes.
+
+Each computation dataflow graph is structured as a topologically sorted list of nodes that form a graph, which MUST be free of cycles. Each node represents a call to an operator or a model local function. Each node has zero or more inputs and one or more outputs.
+
+Graphs have the following properties:
+
+|Name|Type|Description|
+|---|---|---|
+name|string|The name of the model graph.
+node|Node[]|A list of nodes, forming a partially ordered computation graph based on input/output data dependencies. It is in topological order.
+initializer|Tensor[]|A list of named tensor values. When an initializer has the same name as a graph input, it specifies a default value for that input. When an initializer has a name different from all graph inputs, it specifies a constant value. The order of the list is unspecified.
+doc_string|string|Human-readable documentation for this model. Markdown is allowed.
+input|ValueInfo[]|The input parameters of the graph, possibly initialized by a default value found in ‘initializer.’
+output|ValueInfo[]|The output parameters of the graph. Once all output parameters have been written to by a graph execution, the execution is complete.
+value_info|ValueInfo[]|Used to store the type and shape information of values that are not inputs or outputs.
+
+ValueInfo has the following properties:
+
+Name|Type|Description
+|---|---|---|
+name|string|The name of the value/parameter.
+type|Type|The type of the value **including shape information**.
+doc_string|string|Human-readable documentation for this value. Markdown is allowed.
+
+Each main (top-level) graph MUST define the names, types and shapes of its inputs and outputs, which are specified as ‘value info’ structures. The main graph inputs and outputs are required to have a shape, indicating the rank, even though the exact dimensions need not be specified.
+
+Nested subgraphs (specified as attribute values) MUST define the names of its inputs and outputs
+and MAY define the types of its inputs and outputs.
+
+Each graph MUST specify a name.
+
+The graph MUST adhere to single static assignment (SSA) for all node outputs; this means that all node output names MUST be unique within a graph.
+
+Graphs SHOULD be populated with documentation strings, which MAY be interpreted using GitHub-style markdown syntax. HTML and other text-markup languages MAY NOT be used in documentation strings.
+
+### Names Within a Graph
+
+All names MUST adhere to [C90 identifier syntax rules](https://en.cppreference.com/w/c/language/identifier).
+
+Names of nodes, inputs, outputs, initializers, and attributes are organized into several namespaces. Within a namespace, each name MUST be unique for each given graph. Please see below for further clarification in the case where a graph contains nested subgraphs (as attribute values).
+
+The namespaces are:
+
+Namespace|Description
+|---|---|
+Attribute|The names of attributes of an operator. Unique for each operator.
+Value|The names of values – node inputs & outputs, tensor values (if named), graph inputs, outputs.
+Node|The names of graph nodes.
+Graph|The names of graphs within a domain, unique within the model domain.
+Operator|The names of operators within a domain.
+Shape|The names of tensor shape variables – scoped to the value information records of a graph, which is where shape variables occur.
+
+
+### Nodes
+
+Computation nodes are comprised of a name, the name of an operator that it invokes, a list of named inputs, a list of named outputs, and a list of attributes.
+
+Input and outputs are positionally associated with operator inputs and outputs. Attributes are associated with operator attributes by name.
+
+They have the following properties:
+
+Name|Type|Description
+|---|---|---|
+name|string|An optional name of the node, used for diagnostic purposes only.
+input|string[]|Names of the values used by the node to propagate input values to the node operator. It must refer to either a graph input, a graph initializer or a node output.
+output|string[]|Names of the outputs used by the node to capture data from the operator invoked by the node. It either introduces a  value in the graph or refers to a graph output.
+op_type|string|The symbolic identifier of the operator to invoke.
+domain|string|The domain of the operator set that contains the operator named by the op_type.
+attribute|Attribute[]|Named attributes, another form of operator parameterization, used for constant values rather than propagated values.
+doc_string|string|Human-readable documentation for this value. Markdown is allowed.
+
+A name belonging to the Value namespace may appear in multiple places, namely as a graph input, a graph initializer, a graph output, a node input, or a node output. The occurrence of a name as a graph input, a graph initializer, or as a node output is said to be a definition and the occurrence of a name as a node input or as a graph output is said to be a use.
+
+A value name used in a graph must have a unique definition, with the exception that the same name MAY appear in both the graph input list and graph initializer list. (Further exceptions apply in the presence of nested subgraphs, as described later.)
+
+When a name appears in both the initializer list and the graph input list, a runtime MAY allow a caller to specify a value for this (input) name overriding the value specified in the initializer and a runtime MAY allow users to omit specifying a value for this (input) name, choosing the value specified in the initializer. Names of constants that are not meant to be overridden by the caller should appear only in the initializer list and not in the graph input list. In models with IR version >= 4, in nested subgraphs used as attribute values, users MUST NOT use the same name as both a subgraph initializer and subgraph input unless the corresponding op's specification explicitly allows it. In models with IR version <= 3, users MAY use the same name as both a subgraph initializer and subgraph input, but this is restricted to support constants via initializers that are not intended to correspond to any actual inputs passed from the node into the subgraph. In particular, the control-flow operator semantics determines the set of inputs supplied to the execution of the subgraph, and these input names MUST NOT appear as subgraph initializers. Subgraph initializer names must appear in the graph input list _after_ the actual inputs. This allows the actual inputs and formal inputs to be matched positionally.
+
+Edges in the computation graph are established by outputs of one node being referenced by name in the inputs of a subsequent node.
+
+The outputs of a given node introduce new names into the graph. The values of node outputs are computed by the node's operator. Node inputs MAY refer to node outputs, graph inputs, and graph initializers. When the name of a node output coincides with the name of a graph output, the graph output's value is the corresponding output value computed by that node. A node input in a nested subgraph MAY refer to names introduced in outer graphs (as node outputs, graph inputs, or graph initializers).
+
+The graph MUST use single static assignment for all node outputs, which means that all node output names MUST be unique within a graph. In the case of a nested subgraph, a node output name MUST be distinct from the names from the outer scopes that are visible in the nested subgraph.
+
+Node dependencies MUST NOT create cycles in the computation graph.
+
+The number of inputs and outputs in a node, their types, the set of attributes specified in a node and their types MUST satisfy the constraints imposed by the signature of the node’s operator.
+
+The list of nodes defining the top-level computation graph MUST be ordered topologically; that is, if node K follows node N in the graph, none of the data inputs of N may refer to outputs of K.
+
+Node attributes are used to pass literal (static) values to operators.
+
+#### Input and Output Values
+
+The representation distinguishes between two kinds of values: attribute values, which are statically known, and input/output values. The types of values permitted in the two cases are different.
+
+Input and output values are found as graph inputs, outputs, and initializers, and as node inputs and outputs. Their values are determined at runtime, either by the code that initiates model execution, or by operators computing output values.
+
+#### Attributes
+
+Attribute values are only found in nodes, passed to operators by name association. Attribute values are runtime constants, in that their values are determined when a model graph is constructed and therefore not computed at runtime. A common use for attributes is to represent coefficients established during model training.
+
+Attributes have the following properties:
+
+Name|Type|Description
+|---|---|---|
+name|string|The name of the attribute. Must be unique among attributes, inputs, and outputs for any given operator and node.
+doc_string|string|Human-readable documentation for this value. Markdown is allowed.
+type|AttributeType|The type of the attribute, determining which of the remaining fields is used to hold the value of the attribute.
+f|float|A 32-bit floating-point value.
+i|int64|A 64-bit integer value.
+s|byte[]|UTF-8 string.
+t|Tensor|A tensor value.
+g|Graph|A graph.
+floats|float[]|A list of 32-bit floating-point values.
+ints|int64[]|A list of 64-bit integer values.
+strings|byte[][]|A list of UTF-8 strings.
+tensors|Tensor[]|A list of tensor values.
+graphs|Graph[]|A list of graphs.
+
+The properties ‘name’ and ‘type’ are required on all attributes, and ‘doc_string’ SHOULD be used on all attributes. An attribute MUST have only one of the value-carrying properties.
+
+
+#### Variadic Inputs and Outputs
+
+The last input or output of an operator MAY be marked as variadic. For example, the operator 'Max()' can be used to compute the maximum of a varying number of input values. A variadic operator has a minimum arity, which specifies the minimum number of operands that must be specified.
+
+For each variadic operator input, N or more node inputs must be specified where N is the minimum arity of the operator. For each variadic operator output, N or more node outputs must be specified where N is the minimum arity of the operator.
+
+#### Optional Inputs and Outputs
+
+Some operators have inputs that are marked as optional, which means that a referring node MAY forgo providing values for such inputs.
+
+Some operators have outputs that are optional. When an actual output parameter of an operator is not specified, the operator implementation MAY forgo computing values for such outputs.
+
+There are two ways to leave an optional input or output unspecified: the first, available only for trailing inputs and outputs, is to simply not provide that input; the second method is to use an empty string in place of an input or output name.
+
+Each node referring to an operator with optional outputs MUST provide a name for each output that is computed and MUST NOT provide names for outputs that are not computed.
+
+#### External Tensor Data
+
+The raw data for large constant tensors, such as initializers, MAY be serialised in a separate file. In such a case, the tensor MUST provide the filename relative to the model file and MUST NOT use the value fields. It MAY provide a byte offset and length within that file. It MAY also specify a SHA1 digest of the file. One file MAY contain the data for multiple tensors.
+
+More details can be found in [External Data](ExternalData.md).
+
+## Standard data types
+
+There are two official ONNX variants; the main distinction between the two is found in the supported types and the supported operators.
+
+With respect to supported types, both __ONNX__ and __ONNX-ML__ definition recognize tensors, sparse tensors, sequences, maps, and optionals as input and output types. Sequences and maps were supported from the IR version 6 (ONNX 1.6.0 release). Optional type was supported from IR vesion 8 (ONNX 1.10.0 release).
+
+The following data types are supported by ONNX for inputs and outputs of graphs and nodes as well as the initializers of a graph.
+
+Primitive numeric, string, and Boolean types MUST be used as elements of tensors.
+
+### Tensor Definition
+
+Tensors are a generalization of vectors and matrices; whereas vectors have one dimension, and matrices two, tensors can have any number of dimensions, including zero. A zero-dimensional tensor is logically equivalent to a scalar value.
+
+Mathematically, a tensor can be defined as a pair of sequences/lists (V, S) where S is the shape of the tensor (a list of non-negative integers) and V is a list of values with length equal to the product of the dimensions in S. Two tensors (V, S) and (V', S') are equal if and only if V = V' and S = S'. The length of S is referred to as the rank.
+
+ - If S has length 0, V must have length 1, since the empty product is defined to be 1. In this case, the tensor represents a scalar.
+ - S can contain dimensions of value 0. If any dimensions are 0, V must have length 0.
+ - If S has length 1, V has length equal to the single dimension in S. In this case, the tensor represents a vector.
+ - A tensor representing a vector of length 1 has shape [1], while a tensor representing a scalar has shape []. They both have a single element, but scalars are _not_ vectors of length 1.
+
+A tensor's shape S is a list but can be represented as a tensor with values S and shape [R] where R is the rank of the tensor.
+
+ - For a tensor (V, S), the tensor representing its shape is (S, [R]).
+ - The shape of a scalar is []. Represented as a tensor, [] has shape [0].
+
+#### Representation
+
+It is common to represent a tensor as a nested list. This generally works fine, but is problematic when zero dimensions are involved. A tensor of shape (5, 0) can be represented as [[], [], [], [], []], but (0, 5) is represented as [] which loses the information that the second dimension is 5.
+
+ - A nested list is not a complete representation of a tensor with dimensions of value zero.
+
+### Tensor Element Types
+
+|Group|Types|Description|
+|---|---|---|
+Floating Point Types|float16, float32, float64|Values adhering to the IEEE 754-2008 standard representation of floating-point data.
+Signed Integer Types|int8, int16, int32, int64|Signed integers are supported for 8-64 bit widths.
+Unsigned Integer Types|uint8, uint16|Unsigned integers of 8 or 16 bits are supported.
+Complex Types|complex64, complex128|A complex number with either 32- or 64-bit real and imaginary parts.
+Other|string|Strings represent textual data. All strings are encoded using UTF-8.
+Other|bool|Boolean values represent data with only two values, typically true and false.
+
+### Input / Output Data Types
+
+The following types are used to define the types of graph and node inputs and outputs.
+
+|Variant | Type | Description |
+|---|---|---|
+ONNX|dense tensors|Represents a Tensor. See definition above. 
+ONNX|sequence|Sequences are dense, ordered, collections of elements that are of homogeneous types.
+ONNX|map|Maps are associative tables, defined by a key type and a value type.
+ONNX|optional|Optionals are wrappers that may contain an element of tensor, sequence, or map type, or may be empty (containing none). [Details](ONNXTypes.md)
+
+#### Static tensor shapes
+
+In addition to element type, tensor types have a **static** shape. The static shape of a tensor variable is related to, but different from, the runtime (dynamic) shape of a tensor value. A static tensor shape is a list of records that indicates whether the tensor is a vector, a matrix, or a higher-dimensional value. For example, a 100x100 matrix has the shape [100,100].
+
+The static shape is defined by 'TensorShapeProto':
+
+```
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;
+    };
+  };
+  repeated Dimension dim = 1;
+}
+```
+Which is referenced by the Tensor type message:
+
+```
+  message Tensor {
+    optional TensorProto.DataType elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+```
+
+The empty list of dimension sizes, [], is a valid tensor shape, denoting a zero-dimension (scalar) value. A zero-dimension tensor is distinct from a tensor of unknown dimensionality, which is indicated by an absent 'shape' property in the Tensor message. When the shape property is absent in the type of a value (including node input),
+it indicates that the corresponding runtime value may have any shape. This sub-section describes how to interpret a missing-shape or a shape with missing dimensions etc. However, specific usage contexts may impose further constraints on a type and shape.
+For example, the inputs and outputs of a model (top-level graph) are required to *have* a shape, indicating the rank of inputs and outputs,
+even though the exact dimensions need not be specified.
+
+Each size in the list MAY be expressed as an integral value or as a "dimension variable," a string denoting that the actual size of the dimension is not statically constrained to a particular number. This is useful for declaring interfaces that care about the number of dimensions, but not the exact size of each dimension. A dimension MAY have neither dim_value nor dim_param set. Such a dimension represents an unknown dimension unrelated to other unknown dimensions.
+
+For example, a NxM matrix would have the shape list [N,M].
+
+The name of each dimension variable MUST adhere to [C90 identifier syntax rules](https://en.cppreference.com/w/c/language/identifier).
+
+Currently, dimension variables are not scoped. A dimension variable "N" represents the same value across the entire graph in a model. For example, if the graph has two inputs X and Y each with shape ["N"], then at runtime the values passed in for X and Y MUST be tensors of rank 1 with the same dimension. Nested sub-graphs currently share the same scope for dimension variables as the main-graph. This allows a model to relate the dimensions of tensors inside the subgraph to the dimensions of tensors in the outer graph.
+
+ONNX supports types such as Sequences of Tensors. The global scoping of dimension variables means that a variable with type "Sequence<Tensor<float, [M,N]>" represents a sequence of tensors that *all have the same shape*. The dimension variables M or N must be omitted from the above type if that dimension does not have a fixed size across all tensors in the sequence. The entire shape must be omitted from the type if different tensors in the sequence may have different ranks.
+
+For example, a graph that performs matrix cross-product may be defined as taking two inputs of shape [K,M] and [M,N], and producing an output of shape [K,N].
+
+Shapes MAY be defined using a combination of integers and variables.
+
+_Historical Notes_: The following extensions were considered early on, but were never implemented or supported.
+* The use of an empty string (as a dimension variable) to denote an unknown dimension not related to any other dimension. This was discarded in favor of using a Dimension with neither dim_value nor dim_param set.
+* The use of the string "\*" (as a dimension variable) to denote a sequence of zero or more dimensions of unknown cardinality. This is not supported. In the current implementation, the number of dimensions in a shape MUST represent the rank of the tensor. A tensor of unknown rank is represented using a TypeProto::Tensor object with no shape, which is legal.
+* A scoping mechanism to allow dimension variables that are local to a sub-graph (such as the body of a loop) may be useful, but is not currently supported.
+* ONNX supports types such as Sequences of Tensors. A scoping mechanism for the dimension variables local to a type may be useful to distinguish between the following two types: a sequence of square matrices (of differing sizes) vs a sequence of square matrices (all of same size). This is not currently supported.
+
+### Attribute Types
+
+The type system used for attributes is related to but slightly different from that used for of inputs and outputs. Attribute values may be a dense tensor, sparse tensor, a scalar numerical value, a string, a graph, or repeated values of one of the above mentioned types.
+
+## Training Related Information
+
+Training related information is described by one or more instances of _TrainingInfoProto_ contained in a model. Each TrainingInfoProto contains information describing both an initialization step and a training step.
+
+The initialization step is described using a Graph (TrainingInfoProto.initialization) and an initialization-binding map (TrainingInfoProto.initialization_binding). The initialization step is performed by evaluating the Graph, and assigning the outputs produced by the Graph to the _state variables_ of the training model as specified in the initialization-binding. The initialization-binding is conceptually a map, specified as a list of key-value pairs, where each key is the name of a state variable, and the value is the name of an output of the (initialization) Graph. Each name specified as a key in the binding MUST be the name of an initializer that appears in the main inference graph (i.e., in ModelProto.graph.initializer) or the name of an initializer that appears in TrainingInfoProto.algorithm.initializer. Each name specified as a value in the binding MUST be the name of an output of the TrainingInfoProto.initialization graph. Key values specified in the repeated initialization_binding field MUST be unique.
+
+The training step is similarly described using a Graph (TrainingInfoProto.algorithm) and an update-binding map (TrainingInfoProto.update_binding). The training step is performed by evaluating the Graph and assigning the outputs produced by the Graph to the state variables as specified in the update-binding. The constraints and description presented above for the initialization apply to the training step as well.
+
+Thus, the state variables of the training model consist of a subset of the initializers of the main inference graph (i.e., ModelProto.graph.initializer) and the training-algorithm graph (TrainingInfoProto.algorithm.initializer) as identified by the keys of the bindings (in TrainingInfoProto.initialization_binding and TrainingInfoProto.update_binding). Note that the state variables are not constant values in the context of training. They represent mutable variables shared by multiple graphs (implicitly declared in the top-level training model scope). This implicit declaration of shared mutable variables is used instead of an explicit declaration for purposes of backward compatibility with the inference graph representation.
+
+All state variables are pre-initialized to the value specified in the corresponding initializer. A subsequent call to perform the initialization step (using the appropriate API exposed by a runtime) updates the values of the state variables as described above. If the training model has more than one instance of TrainingInfoProto, the initialization step corresponding to each is performed in order. A TrainingInfoProto.initialization MAY be omitted (only if there are no initialization_bindings). For the training step, a runtime MAY allow users to invoke any one of the TrainingInfoProto.algorithm, allowing the training process to interleave the different algorithms as desired. The order in which the different TrainingProto.algorithms are called affects the training result, and it is the callers responsibility to call them in the correct order.
+
+## Other Specification Documents
+
+The ONNX specification is comprised of this document, which defines the semantics of the IR and the standard data types, and the following documents defining standard operator semantics and the IR syntax. The latter is specified as Protobuf v2 and v3 schema files.
+
+See the [metadata category documentation](MetadataProps.md) for more details.
+
+### Operators
+
+[Neural Network Operators](Operators.md)
+
+[Classical Machine Learning operators](Operators-ml.md)
+
+### Syntax
+
+[ONNX Models and Graphs - protobuf v2](../onnx/onnx.proto)
+
+[ONNX Models and Graphs - protobuf v3](../onnx/onnx.proto3)
+
+[ONNX-ML Models and Graphs - protobuf v2](../onnx/onnx-ml.proto)
+
+[ONNX-ML Models and Graphs - protobuf v3](../onnx/onnx-ml.proto3)
+
+[ONNX Operator Sets - protobuf v2](../onnx/onnx-operators.proto)
+
+[ONNX Operator Sets - protobuf v3](../onnx/onnx-operators.proto3)
+
+[ONNX-ML Operator Sets - protobuf v2](../onnx/onnx-operators-ml.proto)
+
+[ONNX-ML Operator Sets - protobuf v3](../onnx/onnx-operators-ml.proto3)
+
+### Versioning Conventions and Best Practices
+
+[Versioning](Versioning.md)
diff --git a/third_party/onnx/docs/ImplementingAnOnnxBackend.md b/third_party/onnx/docs/ImplementingAnOnnxBackend.md
new file mode 100644
index 0000000000..eb9e4ea6f6
--- /dev/null
+++ b/third_party/onnx/docs/ImplementingAnOnnxBackend.md
@@ -0,0 +1,123 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+### Implementing an ONNX backend
+
+#### What is an ONNX backend
+
+An ONNX backend is a library that can run ONNX models. Since many deep learning frameworks already exist, you likely won't need to create everything from scratch. Rather, you'll likely create a converter that converts ONNX models to the corresponding framework specific representation and then delegate the execution to the framework. For example, [onnx-caffe2 (as part of caffe2)](https://github.com/caffe2/caffe2/tree/master/caffe2/python/onnx) , [onnx-coreml](https://github.com/onnx/onnx-coreml), and [onnx-tensorflow](https://github.com/onnx/onnx-tensorflow) are all implemented as converters.
+
+#### Unified backend interface
+
+ONNX has defined a unified (Python) backend interface at [onnx/backend/base.py](/onnx/backend/base.py).
+
+There are three core concepts in this interface: `Device`, `Backend` and `BackendRep`.
+
+- `Device` is a lightweight abstraction over various hardware, e.g., CPU, GPU, etc.
+
+- `Backend` is the entity that will take an ONNX model with inputs, perform a computation, and then return the output.
+
+  For one-off execution, users can use `run_node` and `run_model` to obtain results quickly.
+
+  For repeated execution, users should use `prepare`, in which the `Backend` does all of the preparation work for executing the model repeatedly (e.g., loading initializers), and returns a `BackendRep` handle.
+
+- `BackendRep` is the handle that a `Backend` returns after preparing to execute a model repeatedly. Users will then pass inputs to the `run` function of `BackendRep` to retrieve the corresponding results.
+
+Note that even though the ONNX unified backend interface is defined in Python, your backend does not need to be implemented in Python. For example, yours can be created in C++, and tools such as [pybind11](https://github.com/pybind/pybind11) or [cython](http://cython.org/) can be used to fulfill the interface.
+
+#### ONNX backend test
+
+ONNX provides a standard backend test suite to assist backend implementation verification. It's strongly encouraged that each ONNX backend runs this test.
+
+Integrating the ONNX Backend Test suite into your CI is simple. The following are some examples demonstrating how a backend performs the integration:
+
+- [onnx-caffe2 onnx backend test](https://github.com/caffe2/caffe2/blob/master/caffe2/python/onnx/tests/onnx_backend_test.py)
+
+- [onnx-tensorflow onnx backend test](https://github.com/onnx/onnx-tensorflow/blob/master/test/backend/test_onnx_backend.py)
+
+- [onnx-coreml onnx backend test](https://github.com/onnx/onnx-coreml/blob/master/tests/onnx_backend_test.py)
+
+If you have [pytest](https://docs.pytest.org/en/latest/) installed, you can get a coverage report after running the ONNX backend test to see how well your backend is doing:
+
+```
+---------- onnx coverage: ----------
+Operators (passed/loaded/total): 21/21/70
+------------------------------------
+╒��������������������╤��������������������╕
+│ Operator           │ Attributes         │
+│                    │ (name: #values)    │
+╞��������������������╪��������������������╡
+│ Slice              │ axes: 2            │
+│                    │ ends: 3            │
+│                    │ starts: 3          │
+├────────────────────┼────────────────────┤
+│ Constant           │ value: 1           │
+├────────────────────┼────────────────────┤
+│ Concat             │ axis: 0            │
+├────────────────────┼────────────────────┤
+│ Conv               │ group: 6           │
+│                    │ kernel_shape: 5    │
+│                    │ pads: 4            │
+│                    │ strides: 3         │
+│                    │ auto_pad: 0        │
+│                    │ dilations: 0       │
+├────────────────────┼────────────────────┤
+│ Reshape            │ shape: 9           │
+├────────────────────┼────────────────────┤
+│ BatchNormalization │ consumed_inputs: 1 │
+│                    │ epsilon: 2         │
+│                    │ is_test: 1         │
+│                    │ momentum: 0        │
+│                    │ spatial: 0         │
+├────────────────────┼────────────────────┤
+│ Dropout            │ is_test: 1         │
+│                    │ ratio: 2           │
+├────────────────────┼────────────────────┤
+│ MaxPool            │ kernel_shape: 2    │
+│                    │ pads: 3            │
+│                    │ strides: 2         │
+│                    │ auto_pad: 0        │
+│                    │ dilations: 0       │
+├────────────────────┼────────────────────┤
+│ Transpose          │ perm: 1            │
+├────────────────────┼────────────────────┤
+│ MatMul             │ No attributes      │
+├────────────────────┼────────────────────┤
+│ Relu               │ No attributes      │
+├────────────────────┼────────────────────┤
+│ LRN                │ alpha: 2           │
+│                    │ beta: 1            │
+│                    │ bias: 2            │
+│                    │ size: 1            │
+├────────────────────┼────────────────────┤
+│ Add                │ axis: 1            │
+│                    │ broadcast: 1       │
+├────────────────────┼────────────────────┤
+│ Abs                │ No attributes      │
+├────────────────────┼────────────────────┤
+│ Pad                │ mode: 3            │
+│                    │ paddings: 2        │
+│                    │ value: 1           │
+├────────────────────┼────────────────────┤
+│ Softmax            │ axis: 0            │
+├────────────────────┼────────────────────┤
+│ GlobalAveragePool  │ No attributes      │
+├────────────────────┼────────────────────┤
+│ Mul                │ axis: 1            │
+│                    │ broadcast: 1       │
+├────────────────────┼────────────────────┤
+│ Sum                │ No attributes      │
+├────────────────────┼────────────────────┤
+│ Gemm               │ broadcast: 1       │
+│                    │ transB: 1          │
+│                    │ alpha: 0           │
+│                    │ beta: 0            │
+│                    │ transA: 0          │
+├────────────────────┼────────────────────┤
+│ AveragePool        │ kernel_shape: 3    │
+│                    │ pads: 3            │
+│                    │ strides: 2         │
+│                    │ auto_pad: 0        │
+╘��������������������╧��������������������╛
+```
+
+The numbers in the line `Operators (passed/loaded/total): 21/21/70` indicate 21 operators covered in all test cases of your backend have passed, 21 operators were covered in all test cases of the ONNX backend test, and ONNX has a total of 70 operators.
diff --git a/third_party/onnx/docs/ManagingExperimentalOps.md b/third_party/onnx/docs/ManagingExperimentalOps.md
new file mode 100644
index 0000000000..56e522f219
--- /dev/null
+++ b/third_party/onnx/docs/ManagingExperimentalOps.md
@@ -0,0 +1,34 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+## Deprecated Experimental Operators
+
+The following experimental operators were deprecated and removed from ONNX. They should be removed from models, either substituted with newer superseding operators or decomposed into functionally equivalent operators:
+
+Old operator        |New Operator
+--------------------|--------------------------
+`ATen`              |NA
+`Affine`            |`Add(Mul(X, alpha), beta)`
+`ConstantFill`      |`ConstantOfShape`
+`Crop`              |`Slice-1`
+`DynamicSlice`      |`Slice-10`
+`GRUUnit`           |NA
+`GivenTensorFill`   |`Const` or `ConstantOfShape`
+`ImageScaler`       |`Add(Mul(X, scale), Unsqueeze(bias, axes=[0, 2, 3]))`
+`ParametricSoftplus`|`Mul(alpha, Softplus(Mul(beta, X)))`
+`Scale`             |`Mul(X, scale)`
+`ScaledTanh`        |`Mul(Tanh(Mul(X, beta)), alpha)`
+
+## Adding Experimental Operators [Deprecated - as of v1.5 experimental ops are no longer supported]
+
+The experimental flag in ONNX operator definitions indicates that a customer of ONNX may not be able to take a long term dependency on that op. Ops in the ONNX namespace (ai.onnx) in the _main_ branch, whether experimental or not, go through the regular review process.
+
+Experimental ops that are being worked on that do not have consensus yet can be managed in one of 2 ways:
+1. Use a fork or branch – what you do in the fork or branch is entirely up to you. When you are ready, you can submit a PR using the normal process. This is the recommended way.
+2. If a fork/branch is not workable (for example due to complexity of mapping different branches between multiple repos), put the experimental ops in a custom namespace in the main branch.
+The specific process for this is:
+ * Submit an Issue with a proposal explaining the motivation and plan. It does not need to include detailed technical design. Issues will be tagged as "experimental op".
+ * Reviewers will generally approve by default unless the proposal directly conflicts with existing ops or somehow goes against general ONNX strategy. Approval is indicated by adding the "experiment approved" tag.
+ * The approval is good for 3 months, but can be renewed if needed.
+ * Experimental ops should be submitted in a PR in a custom namespace that is the name of the proposal, i.e. “proposal.controlflow�. The name should be descriptive rather than a company or entity name. These PRs will be approved by default as long as the parent proposal is approved and active.
+ * Once experimentation is done, the ops can be submitted for addition to the ONNX namespace via the regular process. The owner can also choose to end the experiment without promoting the ops.
+ * Either way, the custom namespace is deleted once experimentation is complete or when the approval expires.
diff --git a/third_party/onnx/docs/MetadataProps.md b/third_party/onnx/docs/MetadataProps.md
new file mode 100644
index 0000000000..21a7c4dfd3
--- /dev/null
+++ b/third_party/onnx/docs/MetadataProps.md
@@ -0,0 +1,35 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Metadata
+# Metadata
+
+In addition to the core metadata recommendations listed in the [extensibility documentation](IR.md#optional-metadata) there is additional experimental metadata to help provide information for model inputs and outputs.
+
+This metadata applies to all input and output tensors of a given category.  The first such category we define is: `Image`.
+
+## Motivation
+
+The motivation of such a mechanism is to allow model authors to convey to model consumers enough information for them to consume the model.
+
+In the case of images there are many option for providing valid image data.  However a model which consumes images was trained with a particular set of these options which must
+be used during inferencing.
+
+The goal is this proposal is to provide enough metadata that the model consumer can perform their own featurization prior to running the model and provide a compatible input or retrive an output and know what its format is.
+
+## Image Category Definition
+
+For every tensor in this model that uses [Type Denotation](TypeDenotation.md) to declare itself an `IMAGE`, you SHOULD provide metadata to assist the model consumer.  Note that any metadata provided using this mechanism is global to ALL types
+with the accompanying denotation.
+
+Keys and values are case insenstive.
+
+Specifically, we define here the following set image metadata:
+
+|Key|Value|Description|
+|-----|----|-----------|
+|`Image.BitmapPixelFormat`|__string__|Specifies the format of pixel data. Each enumeration value defines a channel ordering and bit depth. Possible values: <ul><li>`Gray8`: 1 channel image, the pixel data is 8 bpp grayscale.</li><li>`Rgb8`: 3 channel image, channel order is RGB, pixel data is 8bpp (No alpha)</li><li>`Bgr8`: 3 channel image, channel order is BGR, pixel data is 8bpp (No alpha)</li><li>`Rgba8`: 4 channel image, channel order is RGBA, pixel data is 8bpp (Straight alpha)</li><li>`Bgra8`: 4 channel image, channel order is BGRA, pixel data is 8bpp (Straight alpha)</li></ul>|
+|`Image.ColorSpaceGamma`|__string__|Specifies the gamma color space used. Possible values:<ul><li>`Linear`: Linear color space, gamma == 1.0</li><li>`SRGB`: sRGB color space, gamma == 2.2</li></ul>|
+|`Image.NominalPixelRange`|__string__|Specifies the range that pixel values are stored. Possible values: <ul><li>`NominalRange_0_255`:  [0...255] for 8bpp samples</li><li>`Normalized_0_1`: [0...1] pixel data is stored normalized</li><li>`Normalized_1_1`: [-1...1] pixel data is stored normalized</li><li>`NominalRange_16_235`: [16...235] for 8bpp samples</li></ul>|
+
+
+
diff --git a/third_party/onnx/docs/ONNXIFI.md b/third_party/onnx/docs/ONNXIFI.md
new file mode 100644
index 0000000000..011ac45ec7
--- /dev/null
+++ b/third_party/onnx/docs/ONNXIFI.md
@@ -0,0 +1,66 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Interface for Framework Integration (ONNXIFI)
+
+ONNXIFI is a cross-platform API for loading and executing ONNX graphs on optimized backends. High-level frameworks and applications can use this API to execute neural network and machine learning models. Hardware vendors can implement this API to expose specialized hardware accelerators and highly optimized software infrastructure to the users.
+
+## Core Features
+
+- Standardized interface for neural network inference on special-purpose accelerators (NPUs), CPUs, GPUs, DSPs, and FPGAs
+- Based on widely supported technologies
+  - C API for function calls
+  - ONNX format for passing model graphs
+  - NCHW tensor layout for passing inputs and outputs
+- Dynamic discovery of available backends for model execution
+  - Multiple backends from different vendors can co-exist on the same system
+- Dynamic discovery of supported ONNX Operators on each backend
+
+### Optional Features:
+
+- Graphs with variable-shape inputs and/or outputs
+- Graphs with data-dependendent output shapes
+
+## How to Use ONNX Interface for Framework Integration
+
+0. (Optional) Use `onnxifi_load` to dynamically load the ONNX Interface for Framework Integration library.
+1. Call `onnxGetBackendIDs` to get stable identifiers of available backends. Note: it is possible there are no backends installed in the system.
+2. Call `onnxGetBackendInfo` to check additional information about any available backend.
+3. Call `onnxGetBackendCompatibility` to check which operations within your model can run on the backend.
+4. Call `onnxInitBackend` to initialize a backend, then call `onnxInitGraph` to offload one or more model graphs to the backend.
+5. Call `onnxSetGraphIO` to set locations and shapes for inputs and outputs of a graph.
+6. Initialize an `inputFence` structure of type `onnxMemoryFenceV1`: set `tag` to `ONNXIFI_TAG_MEMORY_FENCE_V1`, `type` to `ONNXIFI_SYNCHRONIZATION_EVENT`, and call `onnxInitEvent` to initiaze the `event` member.
+7. Initialize an `outputFence` structure of type `onnxMemoryFenceV1`: set `tag` to `ONNXIFI_TAG_MEMORY_FENCE_V1`, `type` to `ONNXIFI_SYNCHRONIZATION_EVENT`, and `event` to null.
+8. Call `onnxRunGraph` with the initialized `inputFence` and `outputFence` structures to enable execution of the graph. The call to `onnxRunGraph` will populate `event` member of the `outputFence` with a newly created event object, asynchronously execute the graph once `inputFence`'s `event` is signalled, and then signal the `outputFence`'s `event`.
+9. Call `onnxSignalEvent` with `event` member of `inputFence` to signal to the backend that the inputs are ready to be consumed.
+10. Call `onnxWaitEvent` (alternatively, repeatedly call `onnxGetEventState` in a loop until the event state is `ONNXIFI_EVENT_STATE_SIGNALLED`) with `event` member of `outputFence` to wait until graph outputs are ready to be consumed. Release events for inputs and outputs using `onnxReleaseEvent`.
+11. If your model works with fixed-size inputs and outputs, and shape and location of inputs and outputs does not change, one call to `onnxSetGraphIO` is sufficient for multiple `onnxRunGraph` calls. The previous call to `onnxRunGraph`, however, must have finished before a user calls `onnxRunGraph` again, because concurrent execution with the same input and output locations is not allowed. For models with variable-size inputs or outputs, you'd need to call `onnxSetGraphIO` before each `onnxRunGraph` call.
+12. When done using the model, release the model graph(s) with `onnxReleaseGraph`, then release the backend with `onnxReleaseBackend` and backend ID with `onnxReleaseBackendID`.
+
+## How to Implement ONNX Interface for Framework Integration
+
+The minimum functionality an ONNXIFI implementation must provide is the following:
+
+- Support ONNX 1.0 model format.
+  - There is no minimum list of Operators a backend has to support.
+- Support graph inputs / outputs in CPU memory.
+- Support graph inputs / outputs with fixed shape, specified in GraphProto message.
+
+### Discovery
+
+Vendor-provided libraries should adhere to some rules to ensure discovery by ONNX-supported frameworks and applications:
+
+1. The libraries must be installed in the following directories:
+  - GNU/Linux: user-installed system library directory (typically /usr/lib)
+  - macOS: /opt/onnx/lib
+  - Windows: system directory (typically C:\Windows\System32)
+
+2. Filenames of vendor-specific libraries must follow the rule below:
+  - On Windows, library filename must match wildcard `onnxifi-*.dll`
+  - On macOS, library filename must match wildcard `libonnxifi-*.dylib`
+  - On Linux and other OSes, library filename must match wildcard `libonnxifi-*.so`
+
+### Extensions
+
+Hardware vendors are welcome to add their own extensions to ONNX backend interface. The backend interface offers several extension mechanisms:
+- Experimental, exotic, or vendor-specific operators can be supported in a private domain using NodeProto.domain attribute.
+- Vendor-provided ONNXIFI implementation can expose additional functions.
diff --git a/third_party/onnx/docs/ONNXTypes.md b/third_party/onnx/docs/ONNXTypes.md
new file mode 100644
index 0000000000..2edea6e424
--- /dev/null
+++ b/third_party/onnx/docs/ONNXTypes.md
@@ -0,0 +1,96 @@
+## Optional Type
+
+An optional type represents a reference to either an element (could be Tensor, Sequence, Map, or Sparse Tensor) or a null value. The optional type appears in model inputs, outputs, as well as intermediate values.  
+
+### Use-cases
+
+Optional type enables users to represent more dynamic typing senarios in ONNX. Similar to Optional[X] type hint in Python typing which is equivalent to Union[None, X], Optional types in ONNX may reference a single element, or null.
+
+### Examples in PyTorch
+Optional type only appears in TorchScript graphs generated by jit script compiler. Scripting a model captures dynamic types where an optional value can be assigned either None or a value. 
+
+- Example 1
+
+        class Model(torch.nn.Module): 
+            def forward(self, x, y:Optional[Tensor]=None): 
+                if y is not None: 
+                    return x + y 
+                return x 
+
+    Corresponding TorchScript graph:
+
+        Graph(
+            %self : __torch__.Model, 
+            %x.1 : Tensor, 
+            %y.1 : Tensor?
+        ): 
+            %11 : int = prim::Constant[value=1]() 
+            %4 : None = prim::Constant() 
+            %5 : bool = aten::__isnot__(%y.1, %4) 
+            %6 : Tensor = prim::If(%5) 
+                block0(): 
+                    %y.4 : Tensor = prim::unchecked_cast(%y.1) 
+                    %12 : Tensor = aten::add(%x.1, %y.4, %11) 
+                -> (%12) 
+                block1(): 
+                -> (%x.1) 
+            return (%6)
+
+    ONNX graph:
+
+        Graph(
+            %x.1 : Float(2, 3), 
+            %y.1 : Float(2, 3)
+        ): 
+            %2 : Bool(1) = onnx::OptionalHasElement(%y.1)
+            %5 : Float(2, 3) = onnx::If(%2) 
+                block0():
+                    %3 : Float(2, 3) = onnx::OptionalGetElement(%y.1) 
+                    %4 : Float(2, 3) = onnx::Add(%x.1, %3)
+                -> (%4) 
+                block1(): 
+                    %x.2 : Float(2, 3) = onnx::Identity(%x.1) 
+                -> (%x.2) 
+            return (%5)
+
+- Example 2
+
+        class Model(torch.nn.Module): 
+            def forward( 
+                    self, 
+                    src_tokens, 
+                    return_all_hiddens=torch.tensor([False]), 
+            ): 
+                encoder_states: Optional[Tensor] = None 
+                if return_all_hiddens: 
+                    encoder_states = src_tokens  
+
+                return src_tokens, encoder_states
+
+    Corresponding TorchScript graph:
+
+        Graph(
+            %src_tokens.1 : Float(3, 2, 4,), 
+            %return_all_hiddens.1 : Bool(1)
+        ): 
+            %3 : None = prim::Constant() 
+            %encoder_states : Tensor? = prim::If(%return_all_hiddens.1) 
+                block0(): 
+                -> (%src_tokens.1) 
+                block1(): 
+                -> (%3)
+            return (%src_tokens.1, %encoder_states) 
+
+    ONNX graph:
+
+        Graph(
+            %src_tokens.1 : Float(3, 2, 4), 
+            %return_all_hiddens.1 : Bool(1)
+        ): 
+            %2 : Float(3, 2, 4) = onnx::Optional[type=tensor(float)]()
+            %3 : Float(3, 2, 4) = onnx::If(%return_all_hiddens.1) 
+                block0():
+                -> (%src_tokens.1) 
+                block1(): 
+                -> (%2)
+            return (%3) 
\ No newline at end of file
diff --git a/third_party/onnx/docs/ONNX_logo_main.png b/third_party/onnx/docs/ONNX_logo_main.png
new file mode 100644
index 0000000000..24ed87b3b9
Binary files /dev/null and b/third_party/onnx/docs/ONNX_logo_main.png differ
diff --git a/third_party/onnx/docs/OnnxBackendTest.md b/third_party/onnx/docs/OnnxBackendTest.md
new file mode 100644
index 0000000000..b0787bec46
--- /dev/null
+++ b/third_party/onnx/docs/OnnxBackendTest.md
@@ -0,0 +1,20 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+### ONNX Backend Test
+
+#### What is ONNX Backend Test
+
+ONNX Backend Test is a test suite that each ONNX backend should run to verify whether it fulfills ONNX's standard. It serves both as a verification tool for backend implementations and one of the two ways to define each operator's expected behavior (the other way is to add it to the documentation).
+
+There are two types of tests in this suite – Node Tests and Model Tests:
+
+- **Node Tests** verify whether a backend is performing the correct computation, having the expected behavior of handling various attributes for each individual operator. In each test case, the backend will be given a node with some input, and the returned output will be compared with an expected output.
+- **Model Tests** verify the backend at the model level. The test cases are similar to those of Node Tests', but instead of a node, the backend will be given an ONNX model.
+
+#### Contributing
+
+As ONNX aims to become the spec of deep learning models format, it's important to ensure that there is no ambiguity in each ONNX operator's definition; adding more test cases is the only way to enforce this.
+
+Node Tests are created as Python/Numpy code in [onnx/backend/test/case/node](/onnx/backend/test/case/node), and then exported to protobuf files to [onnx/backend/test/data/node](/onnx/backend/test/data/node) as the source of truth by invoking the shell command `backend-test-tools generate-data`. Test cases of each operator lives in one standalone file, e.g. for the operator [Add](/docs/Operators.md#Add), its test cases are in [add.py](/onnx/backend/test/case/node/add.py), and each `expect(...)` statement in the code corresponds to one test case. The source code of all `export.*` functions will be also embedded as example code snippets in the [Operators documentation page](/docs/Operators.md). You are contributing to both the test and the documentation!
+
+For Model Tests, since each model protobuf file can be large in size, we don't place the file directly in the repo. Rather, we upload them to the cloud, and download them on demand when running the tests. Each test case consists of one model definition protobuf file, and several pairs of input and output files. Adding a new test case involves some manual work from admins (like uploading the files to the cloud), so if you have an ONNX model that you would like to contribute, please contact us.
diff --git a/third_party/onnx/docs/OnnxReleases.md b/third_party/onnx/docs/OnnxReleases.md
new file mode 100644
index 0000000000..828948b3b8
--- /dev/null
+++ b/third_party/onnx/docs/OnnxReleases.md
@@ -0,0 +1,123 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+The ONNX project, going forward, will plan to release roughly on a four month cadence. We follow the [Semver](https://semver.org/) versioning approach and will make decisions as a community on a release by release basis on whether to do a major or minor release.
+
+## Preparation
+
+* Install Twine, a utility tool to interact with PyPI. Do  - ``pip install twine``
+* Get hold of the username and password for the ‘onnx’ PyPI account. Release manager should get onnx pypi account credentials from steering committee or from previous release manager.
+* Pick a release tag (v.1.X.X) for the new release through mutual consent – Slack channel for Releases (https://lfaifoundation.slack.com/archives/C018VGGJUGK)
+* Prepare a change log for the release –
+    * ``git log --pretty=format:"%h - %s" <tag of the previous release>...<new tag>``
+    * And draft a new release statement - https://github.com/onnx/onnx/releases listing out the new features and bug fixes, and potential changes being introduced in the release.
+* Before creating the release branch, increase `VERSION_NUMBER` in the main branch. The following files will be updated: [VERSION_NUMBER file](/VERSION_NUMBER) and
+[version.h](/onnx/common/version.h)
+
+* Please use a VERSION_NUMBER smaller than the target (release VERSION_NUMBER) and larger than the previous one to test TestPyPI before using the target VERSION_NUMBER. 
+
+* Make sure that the IR version number and opset version numbers are up-to-date in
+[ONNX proto files](/onnx/onnx.in.proto),
+[Versioning.md](Versioning.md),
+[schema.h](/onnx/defs/schema.h),
+[helper.py](/onnx/helper.py) and [helper_test.py](/onnx/test/helper_test.py). Please note that this also needs to be happened in the main branch before creating the release branch.
+
+* Create a release branch (please use rel-* as the branch name) from main. Checkout the release tag in a clean branch on your local repo. Make sure all tests pass on that branch.
+* Create an issue in onnxruntime repo. See [a sample issue](https://github.com/microsoft/onnxruntime/issues/11108) for details. The issue is to request onnxruntime to update with the onnx release branch and to run all CI and packaging pipelines ([How_To_Update_ONNX_Dev_Notes](https://github.com/microsoft/onnxruntime/blob/master/docs/How_To_Update_ONNX_Dev_Notes.md)). It is possible that onnx bugs are detected with onnxruntime pipeline runs. In such case the bugs shall be fixed in the onnx main branch and cherry-picked into the release branch. Follow up with onnxruntime to ensure the issue is resolved in time before onnx release.
+
+## Upload to TestPyPI
+**Wheels**
+* In release branch update the version number in file [VERSION_NUMBER] to something like `1.x.0rc1` as release candidate for verification before finally using the targeted version number for this release.
+* Windows
+  * Use GitHub Action (`.github/workflows/release_win.yml`) under onnx repo to produce wheels for Windows.
+
+* Linux
+  * Use GitHub Action (`.github/workflows/release_linux_x86_64.yml`) and (`.github/workflows/release_linux_aarch64.yml`) under onnx repo to produce x64/aarch64 wheels for Linux.
+
+* Mac
+  * Use GitHub Action (`.github/workflows/release_mac.yml`) under onnx repo to produce wheels for Mac.
+
+* After success, upload the produced wheels manually to TestPyPI: `twine upload --verbose *.whl --repository-url https://test.pypi.org/legacy/ -u PYPI_USERNAME -p PYPI_PASSWORD`.
+
+
+**Source Distribution**
+* Make sure all the git submodules are updated
+    * ``git submodule update --init``
+* Make sure the git checkout is clean –
+    * Do ``git clean -nxd`` and make sure that none of the auto-generated header files *like* the following are not present.
+        * onnx/onnx-operators.pb.cc
+        * onnx/onnx-operator.pb.h
+        * onnx/onnx.pb.cc
+        * onnx/onnx.pb.h
+    * If they are present run ``git clean -ixd`` and remove those files from your local branch
+* Do ``python setup.py sdist`` to generate the source distribution.
+* Do ``twine upload dist/* --repository-url https://test.pypi.org/legacy/ -u PYPI_USERNAME -p PYPI_PASSWORD`` to upload it to the test instance of PyPI.
+
+## TestPyPI package verification
+**Test ONNX itself**
+* Test the PyPI package installation with different combinations of various Python versions, Protobuf versions and platforms.
+  * Python versions : Applicable python versions for the release.
+  * Protobuf versions : Latest protobuf version at the time of the release + protobuf version used for previous release
+  * Utilize the following matrix to check:
+
+    |   | 3.7 | 3.8 | 3.9 | 3.10 |
+    -- | -- | -- | -- | -- |
+    Linux |   |   |   |   |
+    Windows |   |   |   |   |
+    Mac |   |   |   |   |
+
+
+* After installing the PyPI package, run `pytest` in the release branch.
+
+**Partner Validation**
+
+ * Test with onnxruntime package: To test the interaction with onnxruntime, use ONNX functions like `load`, `checker.check_model`, `shape_inference.infer_shapes`, `save` with onnxruntime functions like `InferenceSession` and `InferenceSession.run` on certain example ONNX model. For example, run the test script from [test_with_ort.py](/onnx/test/test_with_ort.py) with installed onnxruntime package.
+
+ * Test with ONNX converters: Create GitHub issues in converters repos to provide them the package links and have them test the TestPyPI packages.
+   * https://github.com/pytorch/pytorch
+   * https://github.com/onnx/onnx-tensorflow
+   * https://github.com/onnx/tensorflow-onnx
+   * https://github.com/onnx/sklearn-onnx
+   * https://github.com/onnx/onnxmltools
+   * https://github.com/onnx/keras-onnx
+   * https://github.com/onnx/onnx-tensorrt
+   * https://github.com/onnx/onnx-coreml
+
+
+**Source distribution verification**
+* Test the source distribution by doing ``pip install --index-url https://test.pypi.org/simple --no-binary onnx onnx`` in a new environment.
+
+## Upload to official PyPI
+**NOTE: Once the packages are uploaded to PyPI, you cannot overwrite it on the same PyPI instance. Please make sure everything is good on TestPyPI before uploading to PyPI**
+
+**Wheels**
+* Windows/Linux/Mac
+  * Same as TestPyPI, use `twine upload --verbose *.whl --repository-url https://upload.pypi.org/legacy/ -u PYPI_USERNAME -p PYPI_PASSWORD` instead.
+
+**Source Distribution**
+* Follow the same process in TestPyPI to produce the source distribution.
+* Use ``twine upload --verbose dist/* --repository-url https://upload.pypi.org/legacy/`` instead to upload to the official PyPI.
+* Test with ``pip install --no-binary onnx onnx``
+
+## After PyPI Release
+
+**Release summary**
+* Upload the source distribution, `.tar.gz` and `.zip`, in the release summary.
+* Create release in github with the right tag and upload the release summary along with .tar.gz and .zip
+
+**Announce**
+* Announce in slack, for instance, `onnx-general` channel.
+* Notify ONNX partners like converter team and runtime team.
+* Create a news by updating `js/news.json` to announce ONNX release under [onnx/onnx.github.io](https://github.com/onnx/onnx.github.io) repo. For instance: https://github.com/onnx/onnx.github.io/pull/83.
+
+**Update conda-forge package with the new ONNX version**
+* Conda builds of ONNX are done via conda-forge, which runs infrastructure for building packages and uploading them to conda-forge. If it does not happen automatically, you need to submit a PR to https://github.com/conda-forge/onnx-feedstock (see https://github.com/conda-forge/onnx-feedstock/pull/1/files or https://github.com/conda-forge/onnx-feedstock/pull/50/files for example PRs) You will need to have uploaded to PyPI already, and update the version number and tarball hash of the PyPI uploaded tarball.
+
+**Merge into main branch**
+* After everything above is done, merge the release branch into the main branch to make it consistent.
+
+**Remove old onnx-weekly packages on TestPyPI**
+* Once ONNX has been released on PyPI, remove all previous versions of [onnx-weekly package](https://test.pypi.org/project/onnx-weekly/#history) on TestPyPI to save space.
+* Steps: Login and go [here](https://test.pypi.org/manage/project/onnx-weekly/releases/) -> Choose target package -> Options -> Delete.
+
+**Bump opset version for ai.onnx**
+* Bump opset version for ai.onnx domain in `onnx/defs/operator_sets.h` and `onnx/defs/schema.h` for use by future operator additions and changes. For example, this [demo PR](https://github.com/onnx/onnx/pull/4134/files).
\ No newline at end of file
diff --git a/third_party/onnx/docs/OpConventions.md b/third_party/onnx/docs/OpConventions.md
new file mode 100644
index 0000000000..ac4e531611
--- /dev/null
+++ b/third_party/onnx/docs/OpConventions.md
@@ -0,0 +1,12 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Operator Conventions
+
+To maintain consistency in operator signatures, we use the following principles:
+- All attribute names should be lower case and use underscores when it helps with readability
+- Any input/output represented by a single letter is capitalized (i.e. X)
+- Any input/output represented by a full word or multiple words is all lower case and uses underscores when it helps with readability
+- Any input/output representing a bias tensor will utilize the name "B"
+- Any input/output representing a weight tensor will utilize the name “W�
+- “axes� is used when an input, output or attribute is representing multiple axes
+- “axis� is used when an input, output or attribute is representing a single axis
diff --git a/third_party/onnx/docs/Operators-ml.md b/third_party/onnx/docs/Operators-ml.md
new file mode 100644
index 0000000000..9c3f413965
--- /dev/null
+++ b/third_party/onnx/docs/Operators-ml.md
@@ -0,0 +1,985 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+## Operator Schemas
+*This file is automatically generated from the
+            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).
+            Do not modify directly and instead edit operator definitions.*
+
+For an operator input/output's differentiability, it can be differentiable,
+            non-differentiable, or undefined. If a variable's differentiability
+            is not specified, that variable has undefined differentiability.
+
+### ai.onnx.ml
+|**Operator**|**Since version**|
+|-|-|
+|<a href="#ai.onnx.ml.ArrayFeatureExtractor">ai.onnx.ml.ArrayFeatureExtractor</a>|<a href="Changelog-ml.md#ai.onnx.ml.ArrayFeatureExtractor-1">1</a>|
+|<a href="#ai.onnx.ml.Binarizer">ai.onnx.ml.Binarizer</a>|<a href="Changelog-ml.md#ai.onnx.ml.Binarizer-1">1</a>|
+|<a href="#ai.onnx.ml.CastMap">ai.onnx.ml.CastMap</a>|<a href="Changelog-ml.md#ai.onnx.ml.CastMap-1">1</a>|
+|<a href="#ai.onnx.ml.CategoryMapper">ai.onnx.ml.CategoryMapper</a>|<a href="Changelog-ml.md#ai.onnx.ml.CategoryMapper-1">1</a>|
+|<a href="#ai.onnx.ml.DictVectorizer">ai.onnx.ml.DictVectorizer</a>|<a href="Changelog-ml.md#ai.onnx.ml.DictVectorizer-1">1</a>|
+|<a href="#ai.onnx.ml.FeatureVectorizer">ai.onnx.ml.FeatureVectorizer</a>|<a href="Changelog-ml.md#ai.onnx.ml.FeatureVectorizer-1">1</a>|
+|<a href="#ai.onnx.ml.Imputer">ai.onnx.ml.Imputer</a>|<a href="Changelog-ml.md#ai.onnx.ml.Imputer-1">1</a>|
+|<a href="#ai.onnx.ml.LabelEncoder">ai.onnx.ml.LabelEncoder</a>|<a href="Changelog-ml.md#ai.onnx.ml.LabelEncoder-2">2</a>, <a href="Changelog-ml.md#ai.onnx.ml.LabelEncoder-1">1</a>|
+|<a href="#ai.onnx.ml.LinearClassifier">ai.onnx.ml.LinearClassifier</a>|<a href="Changelog-ml.md#ai.onnx.ml.LinearClassifier-1">1</a>|
+|<a href="#ai.onnx.ml.LinearRegressor">ai.onnx.ml.LinearRegressor</a>|<a href="Changelog-ml.md#ai.onnx.ml.LinearRegressor-1">1</a>|
+|<a href="#ai.onnx.ml.Normalizer">ai.onnx.ml.Normalizer</a>|<a href="Changelog-ml.md#ai.onnx.ml.Normalizer-1">1</a>|
+|<a href="#ai.onnx.ml.OneHotEncoder">ai.onnx.ml.OneHotEncoder</a>|<a href="Changelog-ml.md#ai.onnx.ml.OneHotEncoder-1">1</a>|
+|<a href="#ai.onnx.ml.SVMClassifier">ai.onnx.ml.SVMClassifier</a>|<a href="Changelog-ml.md#ai.onnx.ml.SVMClassifier-1">1</a>|
+|<a href="#ai.onnx.ml.SVMRegressor">ai.onnx.ml.SVMRegressor</a>|<a href="Changelog-ml.md#ai.onnx.ml.SVMRegressor-1">1</a>|
+|<a href="#ai.onnx.ml.Scaler">ai.onnx.ml.Scaler</a>|<a href="Changelog-ml.md#ai.onnx.ml.Scaler-1">1</a>|
+|<a href="#ai.onnx.ml.TreeEnsembleClassifier">ai.onnx.ml.TreeEnsembleClassifier</a>|<a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembleClassifier-3">3</a>, <a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembleClassifier-1">1</a>|
+|<a href="#ai.onnx.ml.TreeEnsembleRegressor">ai.onnx.ml.TreeEnsembleRegressor</a>|<a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembleRegressor-3">3</a>, <a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembleRegressor-1">1</a>|
+|<a href="#ai.onnx.ml.ZipMap">ai.onnx.ml.ZipMap</a>|<a href="Changelog-ml.md#ai.onnx.ml.ZipMap-1">1</a>|
+
+
+## ai.onnx.ml
+### <a name="ai.onnx.ml.ArrayFeatureExtractor"></a><a name="ai.onnx.ml.arrayfeatureextractor">**ai.onnx.ml.ArrayFeatureExtractor**</a>
+
+  Select elements of the input tensor based on the indices passed.<br>
+      The indices are applied to the last axes of the tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be selected</dd>
+<dt><tt>Y</tt> : tensor(int64)</dt>
+<dd>The indices, based on 0 as the first index of any dimension.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>Selected output data as an array</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(string)</dt>
+<dd>The input must be a tensor of a numeric type or string. The output will be of the same tensor type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.Binarizer"></a><a name="ai.onnx.ml.binarizer">**ai.onnx.ml.Binarizer**</a>
+
+  Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>threshold</tt> : float (default is 0.0)</dt>
+<dd>Values greater than this are mapped to 1, others to 0.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be binarized</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Binarized output data</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type. The output will be of the same tensor type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.CastMap"></a><a name="ai.onnx.ml.castmap">**ai.onnx.ml.CastMap**</a>
+
+  Converts a map to a tensor.<br>The map key must be an int64 and the values will be ordered
+      in ascending order based on this key.<br>The operator supports dense packing or sparse packing.
+      If using sparse packing, the key cannot exceed the max_map-1 value.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>cast_to</tt> : string (default is TO_FLOAT)</dt>
+<dd>A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.</dd>
+<dt><tt>map_form</tt> : string (default is DENSE)</dt>
+<dd>Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'.</dd>
+<dt><tt>max_map</tt> : int (default is 1)</dt>
+<dd>If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>The input map that is to be cast to a tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>A tensor representing the same data as the input map, ordered by their keys</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : map(int64, string), map(int64, float)</dt>
+<dd>The input must be an integer map to either string or float.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(float), tensor(int64)</dt>
+<dd>The output is a 1-D tensor of string, float, or integer.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.CategoryMapper"></a><a name="ai.onnx.ml.categorymapper">**ai.onnx.ml.CategoryMapper**</a>
+
+  Converts strings to integers and vice versa.<br>
+      Two sequences of equal length are used to map between integers and strings,
+      with strings and integers at the same index detailing the mapping.<br>
+      Each operator converts either integers to strings or strings to integers, depending
+      on which default value attribute is provided. Only one default value attribute
+      should be defined.<br>
+      If the string default value is set, it will convert integers to strings.
+      If the int default value is set, it will convert strings to integers.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>cats_int64s</tt> : list of ints</dt>
+<dd>The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.</dd>
+<dt><tt>cats_strings</tt> : list of strings</dt>
+<dd>The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence</dd>
+<dt><tt>default_int64</tt> : int (default is -1)</dt>
+<dd>An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.</dd>
+<dt><tt>default_string</tt> : string (default is _Unused)</dt>
+<dd>A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Output data. If strings are input, the output values are integers, and vice versa.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(string), tensor(int64)</dt>
+<dd>The input must be a tensor of strings or integers, either [N,C] or [C].</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output is a tensor of strings or integers. Its shape will be the same as the input shape.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.DictVectorizer"></a><a name="ai.onnx.ml.dictvectorizer">**ai.onnx.ml.DictVectorizer**</a>
+
+  Uses an index mapping to convert a dictionary to an array.<br>
+      Given a dictionary, each key is looked up in the vocabulary attribute corresponding to
+      the key type. The index into the vocabulary array at which the key is found is then
+      used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.<br>
+      The key type of the input map must correspond to the element type of the defined vocabulary attribute.
+      Therefore, the output array will be equal in length to the index mapping vector parameter.
+      All keys in the input dictionary must be present in the index mapping vector.
+      For each item in the input dictionary, insert its value in the output array.
+      Any keys not present in the input dictionary, will be zero in the output array.<br>
+      For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``,
+      then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>int64_vocabulary</tt> : list of ints</dt>
+<dd>An integer vocabulary array.<br>One and only one of the vocabularies must be defined.</dd>
+<dt><tt>string_vocabulary</tt> : list of strings</dt>
+<dd>A string vocabulary array.<br>One and only one of the vocabularies must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>A dictionary.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>A 1-D tensor holding values from the input dictionary.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : map(string, int64), map(int64, string), map(int64, float), map(int64, double), map(string, float), map(string, double)</dt>
+<dd>The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.</dd>
+<dt><tt>T2</tt> : tensor(int64), tensor(float), tensor(double), tensor(string)</dt>
+<dd>The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.FeatureVectorizer"></a><a name="ai.onnx.ml.featurevectorizer">**ai.onnx.ml.FeatureVectorizer**</a>
+
+  Concatenates input tensors into one continuous output.<br>
+      All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C].
+      Inputs are copied to the output maintaining the order of the input arguments.<br>
+      All inputs must be integers or floats, while the output will be all floating point values.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>inputdimensions</tt> : list of ints</dt>
+<dd>The size of each input in the input list</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>X</tt> (variadic) : T1</dt>
+<dd>An ordered collection of tensors, all with the same element type.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>The output array, elements ordered as the inputs.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int32), tensor(int64), tensor(float), tensor(double)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.Imputer"></a><a name="ai.onnx.ml.imputer">**ai.onnx.ml.Imputer**</a>
+
+  Replaces inputs that equal one value with another, leaving all other elements alone.<br>
+      This operator is typically used to replace missing values in situations where they have a canonical
+      representation, such as -1, 0, NaN, or some extreme value.<br>
+      One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor
+      holds floats, integers if the input tensor holds integers. The imputed values must all fit within the
+      width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,
+      which one depends on whether floats or integers are being processed.<br>
+      The imputed_value attribute length can be 1 element, or it can have one element per input feature.<br>In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>imputed_value_floats</tt> : list of floats</dt>
+<dd>Value(s) to change to</dd>
+<dt><tt>imputed_value_int64s</tt> : list of ints</dt>
+<dd>Value(s) to change to.</dd>
+<dt><tt>replaced_value_float</tt> : float (default is 0.0)</dt>
+<dd>A value that needs replacing.</dd>
+<dt><tt>replaced_value_int64</tt> : int (default is 0)</dt>
+<dd>A value that needs replacing.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be processed.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Imputed output data</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.LabelEncoder"></a><a name="ai.onnx.ml.labelencoder">**ai.onnx.ml.LabelEncoder**</a>
+
+  Maps each element in the input tensor to another value.<br>
+      The mapping is determined by the two parallel attributes, 'keys_*' and
+      'values_*' attribute. The i-th value in the specified 'keys_*' attribute
+      would be mapped to the i-th value in the specified 'values_*' attribute. It
+      implies that input's element type and the element type of the specified
+      'keys_*' should be identical while the output type is identical to the
+      specified 'values_*' attribute. If an input element can not be found in the
+      specified 'keys_*' attribute, the 'default_*' that matches the specified
+      'values_*' attribute may be used as its output value.<br>
+      Let's consider an example which maps a string tensor to an integer tensor.
+      Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6],
+      and 'default_int64' is '-1'.  The input ["Dori", "Amy", "Amy", "Sally",
+      "Sally"] would be mapped to [-1, 5, 5, 6, 6].<br>
+      Since this operator is an one-to-one mapping, its input and output shapes
+      are the same. Notice that only one of 'keys_*'/'values_*' can be set.<br>
+      For key look-up, bit-wise comparison is used so even a float NaN can be
+      mapped to a value in 'values_*' attribute.<br>
+
+#### Version
+
+This version of the operator has been available since version 2 of the 'ai.onnx.ml' operator set.
+
+Other versions of this operator: <a href="Changelog-ml.md#ai.onnx.ml.LabelEncoder-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>default_float</tt> : float (default is -0.0)</dt>
+<dd>A float.</dd>
+<dt><tt>default_int64</tt> : int (default is -1)</dt>
+<dd>An integer.</dd>
+<dt><tt>default_string</tt> : string (default is _Unused)</dt>
+<dd>A string.</dd>
+<dt><tt>keys_floats</tt> : list of floats</dt>
+<dd>A list of floats.</dd>
+<dt><tt>keys_int64s</tt> : list of ints</dt>
+<dd>A list of ints.</dd>
+<dt><tt>keys_strings</tt> : list of strings</dt>
+<dd>A list of strings. One and only one of 'keys_*'s should be set.</dd>
+<dt><tt>values_floats</tt> : list of floats</dt>
+<dd>A list of floats.</dd>
+<dt><tt>values_int64s</tt> : list of ints</dt>
+<dd>A list of ints.</dd>
+<dt><tt>values_strings</tt> : list of strings</dt>
+<dd>A list of strings. One and only one of 'value_*'s should be set.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data. It can be either tensor or scalar.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Output data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(string), tensor(int64), tensor(float)</dt>
+<dd>The input type is a tensor of any shape.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64), tensor(float)</dt>
+<dd>Output type is determined by the specified 'values_*' attribute.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.LinearClassifier"></a><a name="ai.onnx.ml.linearclassifier">**ai.onnx.ml.LinearClassifier**</a>
+
+  Linear classifier
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classlabels_ints</tt> : list of ints</dt>
+<dd>Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels when using string labels. One and only one 'classlabels' attribute must be defined.</dd>
+<dt><tt>coefficients</tt> : list of floats (required)</dt>
+<dd>A collection of weights of the model(s).</dd>
+<dt><tt>intercepts</tt> : list of floats</dt>
+<dd>A collection of intercepts.</dd>
+<dt><tt>multi_class</tt> : int (default is 0)</dt>
+<dd>Indicates whether to do OvR or multinomial (0=OvR is the default).</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Data to be classified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Classification outputs (one class per example).</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>Classification scores ([N,E] - one score for each class and example</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output will be a tensor of strings or integers.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.LinearRegressor"></a><a name="ai.onnx.ml.linearregressor">**ai.onnx.ml.LinearRegressor**</a>
+
+  Generalized linear regression evaluation.<br>
+      If targets is set to 1 (default) then univariate regression is performed.<br>
+      If targets is set to M then M sets of coefficients must be passed in as a sequence
+      and M results will be output for each input n in N.<br>
+      The coefficients array is of length n, and the coefficients for each target are contiguous.
+      Intercepts are optional but if provided must match the number of targets.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coefficients</tt> : list of floats</dt>
+<dd>Weights of the model(s).</dd>
+<dt><tt>intercepts</tt> : list of floats</dt>
+<dd>Weights of the intercepts, if used.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>targets</tt> : int (default is 1)</dt>
+<dd>The total number of regression targets, 1 if not defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be regressed.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Regression outputs (one per target, per example).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.Normalizer"></a><a name="ai.onnx.ml.normalizer">**ai.onnx.ml.Normalizer**</a>
+
+  Normalize the input.  There are three normalization modes, which have the corresponding formulas,
+      defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':<br>
+  <br>
+      Max: Y = X / max(X)<br>
+      L1:  Y = X / sum(X)<br>
+      L2:  Y = sqrt(X^2 / sum(X^2)}<br>
+      In all modes, if the divisor is zero, Y == X.
+  <br>
+      For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row
+      of the batch is normalized independently.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>norm</tt> : string (default is MAX)</dt>
+<dd>One of 'MAX,' 'L1,' 'L2'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be encoded, a tensor of shape [N,C] or [C]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Encoded output data</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.OneHotEncoder"></a><a name="ai.onnx.ml.onehotencoder">**ai.onnx.ml.OneHotEncoder**</a>
+
+  Replace each input element with an array of ones and zeros, where a single
+      one is placed at the index of the category that was passed in. The total category count
+      will determine the size of the extra dimension of the output array Y.<br>
+      For example, if we pass a tensor with a single value of 4, and a category count of 8,
+      the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.<br>
+      This operator assumes every input feature is from the same set of categories.<br>
+      If the input is a tensor of float, int32, or double, the data will be cast
+      to integers and the cats_int64s category list will be used for the lookups.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>cats_int64s</tt> : list of ints</dt>
+<dd>List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.</dd>
+<dt><tt>cats_strings</tt> : list of strings</dt>
+<dd>List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined.</dd>
+<dt><tt>zeros</tt> : int (default is 1)</dt>
+<dd>If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be encoded.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Encoded output data, having one more dimension than X.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(string), tensor(int64), tensor(int32), tensor(float), tensor(double)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.SVMClassifier"></a><a name="ai.onnx.ml.svmclassifier">**ai.onnx.ml.SVMClassifier**</a>
+
+  Support Vector Machine classifier
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classlabels_ints</tt> : list of ints</dt>
+<dd>Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>coefficients</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>kernel_params</tt> : list of floats</dt>
+<dd>List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.</dd>
+<dt><tt>kernel_type</tt> : string (default is LINEAR)</dt>
+<dd>The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>prob_a</tt> : list of floats</dt>
+<dd>First set of probability coefficients.</dd>
+<dt><tt>prob_b</tt> : list of floats</dt>
+<dd>Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.</dd>
+<dt><tt>rho</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>support_vectors</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>vectors_per_class</tt> : list of ints</dt>
+<dd></dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Data to be classified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>Classification outputs (one class per example).</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type, either [C] or [N,C].</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.SVMRegressor"></a><a name="ai.onnx.ml.svmregressor">**ai.onnx.ml.SVMRegressor**</a>
+
+  Support Vector Machine regression prediction and one-class SVM anomaly detection.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>coefficients</tt> : list of floats</dt>
+<dd>Support vector coefficients.</dd>
+<dt><tt>kernel_params</tt> : list of floats</dt>
+<dd>List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.</dd>
+<dt><tt>kernel_type</tt> : string (default is LINEAR)</dt>
+<dd>The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.</dd>
+<dt><tt>n_supports</tt> : int (default is 0)</dt>
+<dd>The number of support vectors.</dd>
+<dt><tt>one_class</tt> : int (default is 0)</dt>
+<dd>Flag indicating whether the regression is a one-class SVM or not.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'</dd>
+<dt><tt>rho</tt> : list of floats</dt>
+<dd></dd>
+<dt><tt>support_vectors</tt> : list of floats</dt>
+<dd>Chosen support vectors</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be regressed.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Regression outputs (one score per target per example).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type, either [C] or [N,C].</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.Scaler"></a><a name="ai.onnx.ml.scaler">**ai.onnx.ml.Scaler**</a>
+
+  Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>offset</tt> : list of floats</dt>
+<dd>First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.</dd>
+<dt><tt>scale</tt> : list of floats</dt>
+<dd>Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Data to be scaled.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>Scaled output data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input must be a tensor of a numeric type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.TreeEnsembleClassifier"></a><a name="ai.onnx.ml.treeensembleclassifier">**ai.onnx.ml.TreeEnsembleClassifier**</a>
+
+  Tree Ensemble classifier. Returns the top class for each of N inputs.<br>
+      The attributes named 'nodes_X' form a sequence of tuples, associated by
+      index into the sequences, which must all be of equal length. These tuples
+      define the nodes.<br>
+      Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.
+      A leaf may have multiple votes, where each vote is weighted by
+      the associated class_weights index.<br>
+      One and only one of classlabels_strings or classlabels_int64s
+      will be defined. The class_ids are indices into this list.
+      All fields ending with <i>_as_tensor</i> can be used instead of the
+      same parameter without the suffix if the element type is double and not float.
+
+#### Version
+
+This version of the operator has been available since version 3 of the 'ai.onnx.ml' operator set.
+
+Other versions of this operator: <a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembleClassifier-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>base_values</tt> : list of floats</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>base_values_as_tensor</tt> : tensor</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>class_ids</tt> : list of ints</dt>
+<dd>The index of the class list that each weight is for.</dd>
+<dt><tt>class_nodeids</tt> : list of ints</dt>
+<dd>node id that this weight is for.</dd>
+<dt><tt>class_treeids</tt> : list of ints</dt>
+<dd>The id of the tree that this node is in.</dd>
+<dt><tt>class_weights</tt> : list of floats</dt>
+<dd>The weight for the class in class_id.</dd>
+<dt><tt>class_weights_as_tensor</tt> : tensor</dt>
+<dd>The weight for the class in class_id.</dd>
+<dt><tt>classlabels_int64s</tt> : list of ints</dt>
+<dd>Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>nodes_falsenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is false.</dd>
+<dt><tt>nodes_featureids</tt> : list of ints</dt>
+<dd>Feature id for each node.</dd>
+<dt><tt>nodes_hitrates</tt> : list of floats</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_hitrates_as_tensor</tt> : tensor</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_missing_value_tracks_true</tt> : list of ints</dt>
+<dd>For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.</dd>
+<dt><tt>nodes_modes</tt> : list of strings</dt>
+<dd>The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'</dd>
+<dt><tt>nodes_nodeids</tt> : list of ints</dt>
+<dd>Node id for each node. Ids may restart at zero for each tree, but it not required to.</dd>
+<dt><tt>nodes_treeids</tt> : list of ints</dt>
+<dd>Tree id for each node.</dd>
+<dt><tt>nodes_truenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is true.</dd>
+<dt><tt>nodes_values</tt> : list of floats</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>nodes_values_as_tensor</tt> : tensor</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input of shape [N,F]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T2</dt>
+<dd>N, Top class for each point</dd>
+<dt><tt>Z</tt> : tensor(float)</dt>
+<dd>The class score for each class, for each point, a tensor of shape [N,E].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+<dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
+<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.TreeEnsembleRegressor"></a><a name="ai.onnx.ml.treeensembleregressor">**ai.onnx.ml.TreeEnsembleRegressor**</a>
+
+  Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
+      All args with nodes_ are fields of a tuple of tree nodes, and
+      it is assumed they are the same length, and an index i will decode the
+      tuple across these inputs.  Each node id can appear only once
+      for each tree id.<br>
+      All fields prefixed with target_ are tuples of votes at the leaves.<br>
+      A leaf may have multiple votes, where each vote is weighted by
+      the associated target_weights index.<br>
+      All fields ending with <i>_as_tensor</i> can be used instead of the
+      same parameter without the suffix if the element type is double and not float.
+      All trees must have their node ids start at 0 and increment by 1.<br>
+      Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
+
+#### Version
+
+This version of the operator has been available since version 3 of the 'ai.onnx.ml' operator set.
+
+Other versions of this operator: <a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembleRegressor-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>aggregate_function</tt> : string (default is SUM)</dt>
+<dd>Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'</dd>
+<dt><tt>base_values</tt> : list of floats</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>base_values_as_tensor</tt> : tensor</dt>
+<dd>Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)</dd>
+<dt><tt>n_targets</tt> : int</dt>
+<dd>The total number of targets.</dd>
+<dt><tt>nodes_falsenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is false</dd>
+<dt><tt>nodes_featureids</tt> : list of ints</dt>
+<dd>Feature id for each node.</dd>
+<dt><tt>nodes_hitrates</tt> : list of floats</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_hitrates_as_tensor</tt> : tensor</dt>
+<dd>Popularity of each node, used for performance and may be omitted.</dd>
+<dt><tt>nodes_missing_value_tracks_true</tt> : list of ints</dt>
+<dd>For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.</dd>
+<dt><tt>nodes_modes</tt> : list of strings</dt>
+<dd>The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'</dd>
+<dt><tt>nodes_nodeids</tt> : list of ints</dt>
+<dd>Node id for each node. Node ids must restart at zero for each tree and increase sequentially.</dd>
+<dt><tt>nodes_treeids</tt> : list of ints</dt>
+<dd>Tree id for each node.</dd>
+<dt><tt>nodes_truenodeids</tt> : list of ints</dt>
+<dd>Child node if expression is true</dd>
+<dt><tt>nodes_values</tt> : list of floats</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>nodes_values_as_tensor</tt> : tensor</dt>
+<dd>Thresholds to do the splitting on for each node.</dd>
+<dt><tt>post_transform</tt> : string (default is NONE)</dt>
+<dd>Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'</dd>
+<dt><tt>target_ids</tt> : list of ints</dt>
+<dd>The index of the target that each weight is for</dd>
+<dt><tt>target_nodeids</tt> : list of ints</dt>
+<dd>The node id of each weight</dd>
+<dt><tt>target_treeids</tt> : list of ints</dt>
+<dd>The id of the tree that each node is in.</dd>
+<dt><tt>target_weights</tt> : list of floats</dt>
+<dd>The weight for each target</dd>
+<dt><tt>target_weights_as_tensor</tt> : tensor</dt>
+<dd>The weight for each target</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input of shape [N,F]</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(float)</dt>
+<dd>N classes</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
+<dd>The input type must be a tensor of a numeric type.</dd>
+</dl>
+
+
+### <a name="ai.onnx.ml.ZipMap"></a><a name="ai.onnx.ml.zipmap">**ai.onnx.ml.ZipMap**</a>
+
+  Creates a map from the input and the attributes.<br>
+      The values are provided by the input tensor, while the keys are specified by the attributes.
+      Must provide keys in either classlabels_strings or classlabels_int64s (but not both).<br>
+      The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.<br>
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>classlabels_int64s</tt> : list of ints</dt>
+<dd>The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+<dt><tt>classlabels_strings</tt> : list of strings</dt>
+<dd>The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : tensor(float)</dt>
+<dd>The input values</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> : T</dt>
+<dd>The output map</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : seq(map(string, float)), seq(map(int64, float))</dt>
+<dd>The output will be a sequence of string or integer maps to float.</dd>
+</dl>
+
+
diff --git a/third_party/onnx/docs/Operators.md b/third_party/onnx/docs/Operators.md
new file mode 100644
index 0000000000..36590c595c
--- /dev/null
+++ b/third_party/onnx/docs/Operators.md
@@ -0,0 +1,26199 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+## Operator Schemas
+*This file is automatically generated from the
+            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).
+            Do not modify directly and instead edit operator definitions.*
+
+For an operator input/output's differentiability, it can be differentiable,
+            non-differentiable, or undefined. If a variable's differentiability
+            is not specified, that variable has undefined differentiability.
+
+### ai.onnx (default)
+|**Operator**|**Since version**|
+|-|-|
+|<a href="#Abs">Abs</a>|<a href="Changelog.md#Abs-13">13</a>, <a href="Changelog.md#Abs-6">6</a>, <a href="Changelog.md#Abs-1">1</a>|
+|<a href="#Acos">Acos</a>|<a href="Changelog.md#Acos-7">7</a>|
+|<a href="#Acosh">Acosh</a>|<a href="Changelog.md#Acosh-9">9</a>|
+|<a href="#Add">Add</a>|<a href="Changelog.md#Add-14">14</a>, <a href="Changelog.md#Add-13">13</a>, <a href="Changelog.md#Add-7">7</a>, <a href="Changelog.md#Add-6">6</a>, <a href="Changelog.md#Add-1">1</a>|
+|<a href="#And">And</a>|<a href="Changelog.md#And-7">7</a>, <a href="Changelog.md#And-1">1</a>|
+|<a href="#ArgMax">ArgMax</a>|<a href="Changelog.md#ArgMax-13">13</a>, <a href="Changelog.md#ArgMax-12">12</a>, <a href="Changelog.md#ArgMax-11">11</a>, <a href="Changelog.md#ArgMax-1">1</a>|
+|<a href="#ArgMin">ArgMin</a>|<a href="Changelog.md#ArgMin-13">13</a>, <a href="Changelog.md#ArgMin-12">12</a>, <a href="Changelog.md#ArgMin-11">11</a>, <a href="Changelog.md#ArgMin-1">1</a>|
+|<a href="#Asin">Asin</a>|<a href="Changelog.md#Asin-7">7</a>|
+|<a href="#Asinh">Asinh</a>|<a href="Changelog.md#Asinh-9">9</a>|
+|<a href="#Atan">Atan</a>|<a href="Changelog.md#Atan-7">7</a>|
+|<a href="#Atanh">Atanh</a>|<a href="Changelog.md#Atanh-9">9</a>|
+|<a href="#AveragePool">AveragePool</a>|<a href="Changelog.md#AveragePool-11">11</a>, <a href="Changelog.md#AveragePool-10">10</a>, <a href="Changelog.md#AveragePool-7">7</a>, <a href="Changelog.md#AveragePool-1">1</a>|
+|<a href="#BatchNormalization">BatchNormalization</a>|<a href="Changelog.md#BatchNormalization-15">15</a>, <a href="Changelog.md#BatchNormalization-14">14</a>, <a href="Changelog.md#BatchNormalization-9">9</a>, <a href="Changelog.md#BatchNormalization-7">7</a>, <a href="Changelog.md#BatchNormalization-6">6</a>, <a href="Changelog.md#BatchNormalization-1">1</a>|
+|<a href="#BitShift">BitShift</a>|<a href="Changelog.md#BitShift-11">11</a>|
+|<a href="#Cast">Cast</a>|<a href="Changelog.md#Cast-13">13</a>, <a href="Changelog.md#Cast-9">9</a>, <a href="Changelog.md#Cast-6">6</a>, <a href="Changelog.md#Cast-1">1</a>|
+|<a href="#Ceil">Ceil</a>|<a href="Changelog.md#Ceil-13">13</a>, <a href="Changelog.md#Ceil-6">6</a>, <a href="Changelog.md#Ceil-1">1</a>|
+|<a href="#Clip">Clip</a>|<a href="Changelog.md#Clip-13">13</a>, <a href="Changelog.md#Clip-12">12</a>, <a href="Changelog.md#Clip-11">11</a>, <a href="Changelog.md#Clip-6">6</a>, <a href="Changelog.md#Clip-1">1</a>|
+|<a href="#Compress">Compress</a>|<a href="Changelog.md#Compress-11">11</a>, <a href="Changelog.md#Compress-9">9</a>|
+|<a href="#Concat">Concat</a>|<a href="Changelog.md#Concat-13">13</a>, <a href="Changelog.md#Concat-11">11</a>, <a href="Changelog.md#Concat-4">4</a>, <a href="Changelog.md#Concat-1">1</a>|
+|<a href="#ConcatFromSequence">ConcatFromSequence</a>|<a href="Changelog.md#ConcatFromSequence-11">11</a>|
+|<a href="#Constant">Constant</a>|<a href="Changelog.md#Constant-13">13</a>, <a href="Changelog.md#Constant-12">12</a>, <a href="Changelog.md#Constant-11">11</a>, <a href="Changelog.md#Constant-9">9</a>, <a href="Changelog.md#Constant-1">1</a>|
+|<a href="#ConstantOfShape">ConstantOfShape</a>|<a href="Changelog.md#ConstantOfShape-9">9</a>|
+|<a href="#Conv">Conv</a>|<a href="Changelog.md#Conv-11">11</a>, <a href="Changelog.md#Conv-1">1</a>|
+|<a href="#ConvInteger">ConvInteger</a>|<a href="Changelog.md#ConvInteger-10">10</a>|
+|<a href="#ConvTranspose">ConvTranspose</a>|<a href="Changelog.md#ConvTranspose-11">11</a>, <a href="Changelog.md#ConvTranspose-1">1</a>|
+|<a href="#Cos">Cos</a>|<a href="Changelog.md#Cos-7">7</a>|
+|<a href="#Cosh">Cosh</a>|<a href="Changelog.md#Cosh-9">9</a>|
+|<a href="#CumSum">CumSum</a>|<a href="Changelog.md#CumSum-14">14</a>, <a href="Changelog.md#CumSum-11">11</a>|
+|<a href="#DepthToSpace">DepthToSpace</a>|<a href="Changelog.md#DepthToSpace-13">13</a>, <a href="Changelog.md#DepthToSpace-11">11</a>, <a href="Changelog.md#DepthToSpace-1">1</a>|
+|<a href="#DequantizeLinear">DequantizeLinear</a>|<a href="Changelog.md#DequantizeLinear-13">13</a>, <a href="Changelog.md#DequantizeLinear-10">10</a>|
+|<a href="#Det">Det</a>|<a href="Changelog.md#Det-11">11</a>|
+|<a href="#Div">Div</a>|<a href="Changelog.md#Div-14">14</a>, <a href="Changelog.md#Div-13">13</a>, <a href="Changelog.md#Div-7">7</a>, <a href="Changelog.md#Div-6">6</a>, <a href="Changelog.md#Div-1">1</a>|
+|<a href="#Dropout">Dropout</a>|<a href="Changelog.md#Dropout-13">13</a>, <a href="Changelog.md#Dropout-12">12</a>, <a href="Changelog.md#Dropout-10">10</a>, <a href="Changelog.md#Dropout-7">7</a>, <a href="Changelog.md#Dropout-6">6</a>, <a href="Changelog.md#Dropout-1">1</a>|
+|<a href="#Einsum">Einsum</a>|<a href="Changelog.md#Einsum-12">12</a>|
+|<a href="#Elu">Elu</a>|<a href="Changelog.md#Elu-6">6</a>, <a href="Changelog.md#Elu-1">1</a>|
+|<a href="#Equal">Equal</a>|<a href="Changelog.md#Equal-13">13</a>, <a href="Changelog.md#Equal-11">11</a>, <a href="Changelog.md#Equal-7">7</a>, <a href="Changelog.md#Equal-1">1</a>|
+|<a href="#Erf">Erf</a>|<a href="Changelog.md#Erf-13">13</a>, <a href="Changelog.md#Erf-9">9</a>|
+|<a href="#Exp">Exp</a>|<a href="Changelog.md#Exp-13">13</a>, <a href="Changelog.md#Exp-6">6</a>, <a href="Changelog.md#Exp-1">1</a>|
+|<a href="#Expand">Expand</a>|<a href="Changelog.md#Expand-13">13</a>, <a href="Changelog.md#Expand-8">8</a>|
+|<a href="#EyeLike">EyeLike</a>|<a href="Changelog.md#EyeLike-9">9</a>|
+|<a href="#Flatten">Flatten</a>|<a href="Changelog.md#Flatten-13">13</a>, <a href="Changelog.md#Flatten-11">11</a>, <a href="Changelog.md#Flatten-9">9</a>, <a href="Changelog.md#Flatten-1">1</a>|
+|<a href="#Floor">Floor</a>|<a href="Changelog.md#Floor-13">13</a>, <a href="Changelog.md#Floor-6">6</a>, <a href="Changelog.md#Floor-1">1</a>|
+|<a href="#GRU">GRU</a>|<a href="Changelog.md#GRU-14">14</a>, <a href="Changelog.md#GRU-7">7</a>, <a href="Changelog.md#GRU-3">3</a>, <a href="Changelog.md#GRU-1">1</a>|
+|<a href="#Gather">Gather</a>|<a href="Changelog.md#Gather-13">13</a>, <a href="Changelog.md#Gather-11">11</a>, <a href="Changelog.md#Gather-1">1</a>|
+|<a href="#GatherElements">GatherElements</a>|<a href="Changelog.md#GatherElements-13">13</a>, <a href="Changelog.md#GatherElements-11">11</a>|
+|<a href="#GatherND">GatherND</a>|<a href="Changelog.md#GatherND-13">13</a>, <a href="Changelog.md#GatherND-12">12</a>, <a href="Changelog.md#GatherND-11">11</a>|
+|<a href="#Gemm">Gemm</a>|<a href="Changelog.md#Gemm-13">13</a>, <a href="Changelog.md#Gemm-11">11</a>, <a href="Changelog.md#Gemm-9">9</a>, <a href="Changelog.md#Gemm-7">7</a>, <a href="Changelog.md#Gemm-6">6</a>, <a href="Changelog.md#Gemm-1">1</a>|
+|<a href="#GlobalAveragePool">GlobalAveragePool</a>|<a href="Changelog.md#GlobalAveragePool-1">1</a>|
+|<a href="#GlobalLpPool">GlobalLpPool</a>|<a href="Changelog.md#GlobalLpPool-2">2</a>, <a href="Changelog.md#GlobalLpPool-1">1</a>|
+|<a href="#GlobalMaxPool">GlobalMaxPool</a>|<a href="Changelog.md#GlobalMaxPool-1">1</a>|
+|<a href="#Greater">Greater</a>|<a href="Changelog.md#Greater-13">13</a>, <a href="Changelog.md#Greater-9">9</a>, <a href="Changelog.md#Greater-7">7</a>, <a href="Changelog.md#Greater-1">1</a>|
+|<a href="#GridSample">GridSample</a>|<a href="Changelog.md#GridSample-16">16</a>|
+|<a href="#HardSigmoid">HardSigmoid</a>|<a href="Changelog.md#HardSigmoid-6">6</a>, <a href="Changelog.md#HardSigmoid-1">1</a>|
+|<a href="#Hardmax">Hardmax</a>|<a href="Changelog.md#Hardmax-13">13</a>, <a href="Changelog.md#Hardmax-11">11</a>, <a href="Changelog.md#Hardmax-1">1</a>|
+|<a href="#Identity">Identity</a>|<a href="Changelog.md#Identity-16">16</a>, <a href="Changelog.md#Identity-14">14</a>, <a href="Changelog.md#Identity-13">13</a>, <a href="Changelog.md#Identity-1">1</a>|
+|<a href="#If">If</a>|<a href="Changelog.md#If-16">16</a>, <a href="Changelog.md#If-13">13</a>, <a href="Changelog.md#If-11">11</a>, <a href="Changelog.md#If-1">1</a>|
+|<a href="#InstanceNormalization">InstanceNormalization</a>|<a href="Changelog.md#InstanceNormalization-6">6</a>, <a href="Changelog.md#InstanceNormalization-1">1</a>|
+|<a href="#IsInf">IsInf</a>|<a href="Changelog.md#IsInf-10">10</a>|
+|<a href="#IsNaN">IsNaN</a>|<a href="Changelog.md#IsNaN-13">13</a>, <a href="Changelog.md#IsNaN-9">9</a>|
+|<a href="#LRN">LRN</a>|<a href="Changelog.md#LRN-13">13</a>, <a href="Changelog.md#LRN-1">1</a>|
+|<a href="#LSTM">LSTM</a>|<a href="Changelog.md#LSTM-14">14</a>, <a href="Changelog.md#LSTM-7">7</a>, <a href="Changelog.md#LSTM-1">1</a>|
+|<a href="#LeakyRelu">LeakyRelu</a>|<a href="Changelog.md#LeakyRelu-16">16</a>, <a href="Changelog.md#LeakyRelu-6">6</a>, <a href="Changelog.md#LeakyRelu-1">1</a>|
+|<a href="#Less">Less</a>|<a href="Changelog.md#Less-13">13</a>, <a href="Changelog.md#Less-9">9</a>, <a href="Changelog.md#Less-7">7</a>, <a href="Changelog.md#Less-1">1</a>|
+|<a href="#Log">Log</a>|<a href="Changelog.md#Log-13">13</a>, <a href="Changelog.md#Log-6">6</a>, <a href="Changelog.md#Log-1">1</a>|
+|<a href="#Loop">Loop</a>|<a href="Changelog.md#Loop-16">16</a>, <a href="Changelog.md#Loop-13">13</a>, <a href="Changelog.md#Loop-11">11</a>, <a href="Changelog.md#Loop-1">1</a>|
+|<a href="#LpNormalization">LpNormalization</a>|<a href="Changelog.md#LpNormalization-1">1</a>|
+|<a href="#LpPool">LpPool</a>|<a href="Changelog.md#LpPool-11">11</a>, <a href="Changelog.md#LpPool-2">2</a>, <a href="Changelog.md#LpPool-1">1</a>|
+|<a href="#MatMul">MatMul</a>|<a href="Changelog.md#MatMul-13">13</a>, <a href="Changelog.md#MatMul-9">9</a>, <a href="Changelog.md#MatMul-1">1</a>|
+|<a href="#MatMulInteger">MatMulInteger</a>|<a href="Changelog.md#MatMulInteger-10">10</a>|
+|<a href="#Max">Max</a>|<a href="Changelog.md#Max-13">13</a>, <a href="Changelog.md#Max-12">12</a>, <a href="Changelog.md#Max-8">8</a>, <a href="Changelog.md#Max-6">6</a>, <a href="Changelog.md#Max-1">1</a>|
+|<a href="#MaxPool">MaxPool</a>|<a href="Changelog.md#MaxPool-12">12</a>, <a href="Changelog.md#MaxPool-11">11</a>, <a href="Changelog.md#MaxPool-10">10</a>, <a href="Changelog.md#MaxPool-8">8</a>, <a href="Changelog.md#MaxPool-1">1</a>|
+|<a href="#MaxRoiPool">MaxRoiPool</a>|<a href="Changelog.md#MaxRoiPool-1">1</a>|
+|<a href="#MaxUnpool">MaxUnpool</a>|<a href="Changelog.md#MaxUnpool-11">11</a>, <a href="Changelog.md#MaxUnpool-9">9</a>|
+|<a href="#Mean">Mean</a>|<a href="Changelog.md#Mean-13">13</a>, <a href="Changelog.md#Mean-8">8</a>, <a href="Changelog.md#Mean-6">6</a>, <a href="Changelog.md#Mean-1">1</a>|
+|<a href="#Min">Min</a>|<a href="Changelog.md#Min-13">13</a>, <a href="Changelog.md#Min-12">12</a>, <a href="Changelog.md#Min-8">8</a>, <a href="Changelog.md#Min-6">6</a>, <a href="Changelog.md#Min-1">1</a>|
+|<a href="#Mod">Mod</a>|<a href="Changelog.md#Mod-13">13</a>, <a href="Changelog.md#Mod-10">10</a>|
+|<a href="#Mul">Mul</a>|<a href="Changelog.md#Mul-14">14</a>, <a href="Changelog.md#Mul-13">13</a>, <a href="Changelog.md#Mul-7">7</a>, <a href="Changelog.md#Mul-6">6</a>, <a href="Changelog.md#Mul-1">1</a>|
+|<a href="#Multinomial">Multinomial</a>|<a href="Changelog.md#Multinomial-7">7</a>|
+|<a href="#Neg">Neg</a>|<a href="Changelog.md#Neg-13">13</a>, <a href="Changelog.md#Neg-6">6</a>, <a href="Changelog.md#Neg-1">1</a>|
+|<a href="#NonMaxSuppression">NonMaxSuppression</a>|<a href="Changelog.md#NonMaxSuppression-11">11</a>, <a href="Changelog.md#NonMaxSuppression-10">10</a>|
+|<a href="#NonZero">NonZero</a>|<a href="Changelog.md#NonZero-13">13</a>, <a href="Changelog.md#NonZero-9">9</a>|
+|<a href="#Not">Not</a>|<a href="Changelog.md#Not-1">1</a>|
+|<a href="#OneHot">OneHot</a>|<a href="Changelog.md#OneHot-11">11</a>, <a href="Changelog.md#OneHot-9">9</a>|
+|<a href="#Optional">Optional</a>|<a href="Changelog.md#Optional-15">15</a>|
+|<a href="#OptionalGetElement">OptionalGetElement</a>|<a href="Changelog.md#OptionalGetElement-15">15</a>|
+|<a href="#OptionalHasElement">OptionalHasElement</a>|<a href="Changelog.md#OptionalHasElement-15">15</a>|
+|<a href="#Or">Or</a>|<a href="Changelog.md#Or-7">7</a>, <a href="Changelog.md#Or-1">1</a>|
+|<a href="#PRelu">PRelu</a>|<a href="Changelog.md#PRelu-16">16</a>, <a href="Changelog.md#PRelu-9">9</a>, <a href="Changelog.md#PRelu-7">7</a>, <a href="Changelog.md#PRelu-6">6</a>, <a href="Changelog.md#PRelu-1">1</a>|
+|<a href="#Pad">Pad</a>|<a href="Changelog.md#Pad-13">13</a>, <a href="Changelog.md#Pad-11">11</a>, <a href="Changelog.md#Pad-2">2</a>, <a href="Changelog.md#Pad-1">1</a>|
+|<a href="#Pow">Pow</a>|<a href="Changelog.md#Pow-15">15</a>, <a href="Changelog.md#Pow-13">13</a>, <a href="Changelog.md#Pow-12">12</a>, <a href="Changelog.md#Pow-7">7</a>, <a href="Changelog.md#Pow-1">1</a>|
+|<a href="#QLinearConv">QLinearConv</a>|<a href="Changelog.md#QLinearConv-10">10</a>|
+|<a href="#QLinearMatMul">QLinearMatMul</a>|<a href="Changelog.md#QLinearMatMul-10">10</a>|
+|<a href="#QuantizeLinear">QuantizeLinear</a>|<a href="Changelog.md#QuantizeLinear-13">13</a>, <a href="Changelog.md#QuantizeLinear-10">10</a>|
+|<a href="#RNN">RNN</a>|<a href="Changelog.md#RNN-14">14</a>, <a href="Changelog.md#RNN-7">7</a>, <a href="Changelog.md#RNN-1">1</a>|
+|<a href="#RandomNormal">RandomNormal</a>|<a href="Changelog.md#RandomNormal-1">1</a>|
+|<a href="#RandomNormalLike">RandomNormalLike</a>|<a href="Changelog.md#RandomNormalLike-1">1</a>|
+|<a href="#RandomUniform">RandomUniform</a>|<a href="Changelog.md#RandomUniform-1">1</a>|
+|<a href="#RandomUniformLike">RandomUniformLike</a>|<a href="Changelog.md#RandomUniformLike-1">1</a>|
+|<a href="#Reciprocal">Reciprocal</a>|<a href="Changelog.md#Reciprocal-13">13</a>, <a href="Changelog.md#Reciprocal-6">6</a>, <a href="Changelog.md#Reciprocal-1">1</a>|
+|<a href="#ReduceL1">ReduceL1</a>|<a href="Changelog.md#ReduceL1-13">13</a>, <a href="Changelog.md#ReduceL1-11">11</a>, <a href="Changelog.md#ReduceL1-1">1</a>|
+|<a href="#ReduceL2">ReduceL2</a>|<a href="Changelog.md#ReduceL2-13">13</a>, <a href="Changelog.md#ReduceL2-11">11</a>, <a href="Changelog.md#ReduceL2-1">1</a>|
+|<a href="#ReduceLogSum">ReduceLogSum</a>|<a href="Changelog.md#ReduceLogSum-13">13</a>, <a href="Changelog.md#ReduceLogSum-11">11</a>, <a href="Changelog.md#ReduceLogSum-1">1</a>|
+|<a href="#ReduceLogSumExp">ReduceLogSumExp</a>|<a href="Changelog.md#ReduceLogSumExp-13">13</a>, <a href="Changelog.md#ReduceLogSumExp-11">11</a>, <a href="Changelog.md#ReduceLogSumExp-1">1</a>|
+|<a href="#ReduceMax">ReduceMax</a>|<a href="Changelog.md#ReduceMax-13">13</a>, <a href="Changelog.md#ReduceMax-12">12</a>, <a href="Changelog.md#ReduceMax-11">11</a>, <a href="Changelog.md#ReduceMax-1">1</a>|
+|<a href="#ReduceMean">ReduceMean</a>|<a href="Changelog.md#ReduceMean-13">13</a>, <a href="Changelog.md#ReduceMean-11">11</a>, <a href="Changelog.md#ReduceMean-1">1</a>|
+|<a href="#ReduceMin">ReduceMin</a>|<a href="Changelog.md#ReduceMin-13">13</a>, <a href="Changelog.md#ReduceMin-12">12</a>, <a href="Changelog.md#ReduceMin-11">11</a>, <a href="Changelog.md#ReduceMin-1">1</a>|
+|<a href="#ReduceProd">ReduceProd</a>|<a href="Changelog.md#ReduceProd-13">13</a>, <a href="Changelog.md#ReduceProd-11">11</a>, <a href="Changelog.md#ReduceProd-1">1</a>|
+|<a href="#ReduceSum">ReduceSum</a>|<a href="Changelog.md#ReduceSum-13">13</a>, <a href="Changelog.md#ReduceSum-11">11</a>, <a href="Changelog.md#ReduceSum-1">1</a>|
+|<a href="#ReduceSumSquare">ReduceSumSquare</a>|<a href="Changelog.md#ReduceSumSquare-13">13</a>, <a href="Changelog.md#ReduceSumSquare-11">11</a>, <a href="Changelog.md#ReduceSumSquare-1">1</a>|
+|<a href="#Relu">Relu</a>|<a href="Changelog.md#Relu-14">14</a>, <a href="Changelog.md#Relu-13">13</a>, <a href="Changelog.md#Relu-6">6</a>, <a href="Changelog.md#Relu-1">1</a>|
+|<a href="#Reshape">Reshape</a>|<a href="Changelog.md#Reshape-14">14</a>, <a href="Changelog.md#Reshape-13">13</a>, <a href="Changelog.md#Reshape-5">5</a>, <a href="Changelog.md#Reshape-1">1</a>|
+|<a href="#Resize">Resize</a>|<a href="Changelog.md#Resize-13">13</a>, <a href="Changelog.md#Resize-11">11</a>, <a href="Changelog.md#Resize-10">10</a>|
+|<a href="#ReverseSequence">ReverseSequence</a>|<a href="Changelog.md#ReverseSequence-10">10</a>|
+|<a href="#RoiAlign">RoiAlign</a>|<a href="Changelog.md#RoiAlign-16">16</a>, <a href="Changelog.md#RoiAlign-10">10</a>|
+|<a href="#Round">Round</a>|<a href="Changelog.md#Round-11">11</a>|
+|<a href="#Scan">Scan</a>|<a href="Changelog.md#Scan-16">16</a>, <a href="Changelog.md#Scan-11">11</a>, <a href="Changelog.md#Scan-9">9</a>, <a href="Changelog.md#Scan-8">8</a>|
+|<a href="#Scatter">Scatter</a> (deprecated)|<a href="Changelog.md#Scatter-11">11</a>, <a href="Changelog.md#Scatter-9">9</a>|
+|<a href="#ScatterElements">ScatterElements</a>|<a href="Changelog.md#ScatterElements-16">16</a>, <a href="Changelog.md#ScatterElements-13">13</a>, <a href="Changelog.md#ScatterElements-11">11</a>|
+|<a href="#ScatterND">ScatterND</a>|<a href="Changelog.md#ScatterND-16">16</a>, <a href="Changelog.md#ScatterND-13">13</a>, <a href="Changelog.md#ScatterND-11">11</a>|
+|<a href="#Selu">Selu</a>|<a href="Changelog.md#Selu-6">6</a>, <a href="Changelog.md#Selu-1">1</a>|
+|<a href="#SequenceAt">SequenceAt</a>|<a href="Changelog.md#SequenceAt-11">11</a>|
+|<a href="#SequenceConstruct">SequenceConstruct</a>|<a href="Changelog.md#SequenceConstruct-11">11</a>|
+|<a href="#SequenceEmpty">SequenceEmpty</a>|<a href="Changelog.md#SequenceEmpty-11">11</a>|
+|<a href="#SequenceErase">SequenceErase</a>|<a href="Changelog.md#SequenceErase-11">11</a>|
+|<a href="#SequenceInsert">SequenceInsert</a>|<a href="Changelog.md#SequenceInsert-11">11</a>|
+|<a href="#SequenceLength">SequenceLength</a>|<a href="Changelog.md#SequenceLength-11">11</a>|
+|<a href="#Shape">Shape</a>|<a href="Changelog.md#Shape-15">15</a>, <a href="Changelog.md#Shape-13">13</a>, <a href="Changelog.md#Shape-1">1</a>|
+|<a href="#Shrink">Shrink</a>|<a href="Changelog.md#Shrink-9">9</a>|
+|<a href="#Sigmoid">Sigmoid</a>|<a href="Changelog.md#Sigmoid-13">13</a>, <a href="Changelog.md#Sigmoid-6">6</a>, <a href="Changelog.md#Sigmoid-1">1</a>|
+|<a href="#Sign">Sign</a>|<a href="Changelog.md#Sign-13">13</a>, <a href="Changelog.md#Sign-9">9</a>|
+|<a href="#Sin">Sin</a>|<a href="Changelog.md#Sin-7">7</a>|
+|<a href="#Sinh">Sinh</a>|<a href="Changelog.md#Sinh-9">9</a>|
+|<a href="#Size">Size</a>|<a href="Changelog.md#Size-13">13</a>, <a href="Changelog.md#Size-1">1</a>|
+|<a href="#Slice">Slice</a>|<a href="Changelog.md#Slice-13">13</a>, <a href="Changelog.md#Slice-11">11</a>, <a href="Changelog.md#Slice-10">10</a>, <a href="Changelog.md#Slice-1">1</a>|
+|<a href="#Softplus">Softplus</a>|<a href="Changelog.md#Softplus-1">1</a>|
+|<a href="#Softsign">Softsign</a>|<a href="Changelog.md#Softsign-1">1</a>|
+|<a href="#SpaceToDepth">SpaceToDepth</a>|<a href="Changelog.md#SpaceToDepth-13">13</a>, <a href="Changelog.md#SpaceToDepth-1">1</a>|
+|<a href="#Split">Split</a>|<a href="Changelog.md#Split-13">13</a>, <a href="Changelog.md#Split-11">11</a>, <a href="Changelog.md#Split-2">2</a>, <a href="Changelog.md#Split-1">1</a>|
+|<a href="#SplitToSequence">SplitToSequence</a>|<a href="Changelog.md#SplitToSequence-11">11</a>|
+|<a href="#Sqrt">Sqrt</a>|<a href="Changelog.md#Sqrt-13">13</a>, <a href="Changelog.md#Sqrt-6">6</a>, <a href="Changelog.md#Sqrt-1">1</a>|
+|<a href="#Squeeze">Squeeze</a>|<a href="Changelog.md#Squeeze-13">13</a>, <a href="Changelog.md#Squeeze-11">11</a>, <a href="Changelog.md#Squeeze-1">1</a>|
+|<a href="#StringNormalizer">StringNormalizer</a>|<a href="Changelog.md#StringNormalizer-10">10</a>|
+|<a href="#Sub">Sub</a>|<a href="Changelog.md#Sub-14">14</a>, <a href="Changelog.md#Sub-13">13</a>, <a href="Changelog.md#Sub-7">7</a>, <a href="Changelog.md#Sub-6">6</a>, <a href="Changelog.md#Sub-1">1</a>|
+|<a href="#Sum">Sum</a>|<a href="Changelog.md#Sum-13">13</a>, <a href="Changelog.md#Sum-8">8</a>, <a href="Changelog.md#Sum-6">6</a>, <a href="Changelog.md#Sum-1">1</a>|
+|<a href="#Tan">Tan</a>|<a href="Changelog.md#Tan-7">7</a>|
+|<a href="#Tanh">Tanh</a>|<a href="Changelog.md#Tanh-13">13</a>, <a href="Changelog.md#Tanh-6">6</a>, <a href="Changelog.md#Tanh-1">1</a>|
+|<a href="#TfIdfVectorizer">TfIdfVectorizer</a>|<a href="Changelog.md#TfIdfVectorizer-9">9</a>|
+|<a href="#ThresholdedRelu">ThresholdedRelu</a>|<a href="Changelog.md#ThresholdedRelu-10">10</a>|
+|<a href="#Tile">Tile</a>|<a href="Changelog.md#Tile-13">13</a>, <a href="Changelog.md#Tile-6">6</a>, <a href="Changelog.md#Tile-1">1</a>|
+|<a href="#TopK">TopK</a>|<a href="Changelog.md#TopK-11">11</a>, <a href="Changelog.md#TopK-10">10</a>, <a href="Changelog.md#TopK-1">1</a>|
+|<a href="#Transpose">Transpose</a>|<a href="Changelog.md#Transpose-13">13</a>, <a href="Changelog.md#Transpose-1">1</a>|
+|<a href="#Trilu">Trilu</a>|<a href="Changelog.md#Trilu-14">14</a>|
+|<a href="#Unique">Unique</a>|<a href="Changelog.md#Unique-11">11</a>|
+|<a href="#Unsqueeze">Unsqueeze</a>|<a href="Changelog.md#Unsqueeze-13">13</a>, <a href="Changelog.md#Unsqueeze-11">11</a>, <a href="Changelog.md#Unsqueeze-1">1</a>|
+|<a href="#Upsample">Upsample</a> (deprecated)|<a href="Changelog.md#Upsample-10">10</a>, <a href="Changelog.md#Upsample-9">9</a>, <a href="Changelog.md#Upsample-7">7</a>|
+|<a href="#Where">Where</a>|<a href="Changelog.md#Where-16">16</a>, <a href="Changelog.md#Where-9">9</a>|
+|<a href="#Xor">Xor</a>|<a href="Changelog.md#Xor-7">7</a>, <a href="Changelog.md#Xor-1">1</a>|
+|**Function**|**Since version**|
+|<a href="#Bernoulli">Bernoulli</a>|<a href="Changelog.md#Bernoulli-15">15</a>|
+|<a href="#CastLike">CastLike</a>|<a href="Changelog.md#CastLike-15">15</a>|
+|<a href="#Celu">Celu</a>|<a href="Changelog.md#Celu-12">12</a>|
+|<a href="#DynamicQuantizeLinear">DynamicQuantizeLinear</a>|<a href="Changelog.md#DynamicQuantizeLinear-11">11</a>|
+|<a href="#GreaterOrEqual">GreaterOrEqual</a>|<a href="Changelog.md#GreaterOrEqual-16">16</a>, <a href="Changelog.md#GreaterOrEqual-12">12</a>|
+|<a href="#HardSwish">HardSwish</a>|<a href="Changelog.md#HardSwish-14">14</a>|
+|<a href="#LayerNormalization">LayerNormalization</a>|<a href="Changelog.md#LayerNormalization-17">17</a>|
+|<a href="#LessOrEqual">LessOrEqual</a>|<a href="Changelog.md#LessOrEqual-16">16</a>, <a href="Changelog.md#LessOrEqual-12">12</a>|
+|<a href="#LogSoftmax">LogSoftmax</a>|<a href="Changelog.md#LogSoftmax-13">13</a>, <a href="Changelog.md#LogSoftmax-11">11</a>, <a href="Changelog.md#LogSoftmax-1">1</a>|
+|<a href="#MeanVarianceNormalization">MeanVarianceNormalization</a>|<a href="Changelog.md#MeanVarianceNormalization-13">13</a>, <a href="Changelog.md#MeanVarianceNormalization-9">9</a>|
+|<a href="#NegativeLogLikelihoodLoss">NegativeLogLikelihoodLoss</a>|<a href="Changelog.md#NegativeLogLikelihoodLoss-13">13</a>, <a href="Changelog.md#NegativeLogLikelihoodLoss-12">12</a>|
+|<a href="#Range">Range</a>|<a href="Changelog.md#Range-11">11</a>|
+|<a href="#SequenceMap">SequenceMap</a>|<a href="Changelog.md#SequenceMap-17">17</a>|
+|<a href="#Softmax">Softmax</a>|<a href="Changelog.md#Softmax-13">13</a>, <a href="Changelog.md#Softmax-11">11</a>, <a href="Changelog.md#Softmax-1">1</a>|
+|<a href="#SoftmaxCrossEntropyLoss">SoftmaxCrossEntropyLoss</a>|<a href="Changelog.md#SoftmaxCrossEntropyLoss-13">13</a>, <a href="Changelog.md#SoftmaxCrossEntropyLoss-12">12</a>|
+
+### ai.onnx.preview.training
+|**Operator**|**Since version**|
+|-|-|
+|<a href="#ai.onnx.preview.training.Adagrad">ai.onnx.preview.training.Adagrad</a>|<a href="Changelog.md#ai.onnx.preview.training.Adagrad-1">1</a>|
+|<a href="#ai.onnx.preview.training.Adam">ai.onnx.preview.training.Adam</a>|<a href="Changelog.md#ai.onnx.preview.training.Adam-1">1</a>|
+|<a href="#ai.onnx.preview.training.Gradient">ai.onnx.preview.training.Gradient</a>|<a href="Changelog.md#ai.onnx.preview.training.Gradient-1">1</a>|
+|<a href="#ai.onnx.preview.training.Momentum">ai.onnx.preview.training.Momentum</a>|<a href="Changelog.md#ai.onnx.preview.training.Momentum-1">1</a>|
+
+
+## ai.onnx (default)
+### <a name="Abs"></a><a name="abs">**Abs**</a>
+
+  Absolute takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the absolute is, y = abs(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Abs-1">1</a>, <a href="Changelog.md#Abs-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>abs</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Abs',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = abs(x)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_abs')
+```
+
+</details>
+
+
+#### Sample Implementation
+
+<details>
+<summary>Abs</summary>
+
+```python
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+
+def abs(input: np.ndarray) -> np.ndarray:
+    return np.abs(input)
+
+```
+
+</details>
+
+
+### <a name="Acos"></a><a name="acos">**Acos**</a>
+
+  Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The arccosine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>acos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Acos',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+y = np.arccos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_acos_example')
+
+x = np.random.rand(3, 4, 5).astype(np.float32)
+y = np.arccos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_acos')
+```
+
+</details>
+
+
+### <a name="Acosh"></a><a name="acosh">**Acosh**</a>
+
+  Calculates the hyperbolic arccosine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic arccosine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>acosh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Acosh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([10, np.e, 1]).astype(np.float32)
+y = np.arccosh(x)  # expected output [2.99322295,  1.65745449,  0.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_acosh_example')
+
+x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32)
+y = np.arccosh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_acosh')
+```
+
+</details>
+
+
+### <a name="Add"></a><a name="add">**Add**</a>
+
+  Performs element-wise binary addition (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Add-1">1</a>, <a href="Changelog.md#Add-6">6</a>, <a href="Changelog.md#Add-7">7</a>, <a href="Changelog.md#Add-13">13</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>add</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Add',
+    inputs=['x', 'y'],
+    outputs=['sum'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+expect(node, inputs=[x, y], outputs=[x + y],
+       name='test_add')
+```
+
+</details>
+
+
+<details>
+<summary>add_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Add',
+    inputs=['x', 'y'],
+    outputs=['sum'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+expect(node, inputs=[x, y], outputs=[x + y],
+       name='test_add_bcast')
+```
+
+</details>
+
+
+<details>
+<summary>add_uint8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Add',
+    inputs=['x', 'y'],
+    outputs=['sum'],
+)
+
+x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+expect(node, inputs=[x, y], outputs=[x + y],
+       name='test_add_uint8')
+```
+
+</details>
+
+
+### <a name="And"></a><a name="and">**And**</a>
+
+  Returns the tensor resulted from performing the `and` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#And-1">1</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>and</summary>
+
+```python
+node = onnx.helper.make_node(
+    'And',
+    inputs=['x', 'y'],
+    outputs=['and'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+y = (np.random.randn(3, 4) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and4d')
+```
+
+</details>
+
+
+<details>
+<summary>and_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'And',
+    inputs=['x', 'y'],
+    outputs=['and'],
+)
+
+# 3d vs 1d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(5) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast3v1d')
+
+# 3d vs 2d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(4, 5) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast3v2d')
+
+# 4d vs 2d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast4v2d')
+
+# 4d vs 3d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast4v3d')
+
+# 4d vs 4d
+x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast4v4d')
+```
+
+</details>
+
+
+### <a name="ArgMax"></a><a name="argmax">**ArgMax**</a>
+
+  Computes the indices of the max elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
+  If select_last_index is True (default False), the index of the last occurrence of the max
+  is selected if the max appears more than once in the input. Otherwise the index of the
+  first occurrence is selected.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ArgMax-1">1</a>, <a href="Changelog.md#ArgMax-11">11</a>, <a href="Changelog.md#ArgMax-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>select_last_index</tt> : int (default is 0)</dt>
+<dd>Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims)
+
+# result: [[1, 1]]
+result = argmax_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmax_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_random')
+```
+
+</details>
+
+
+<details>
+<summary>default_axes_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims,
+    select_last_index=True)
+
+# result: [[1, 1]]
+result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_random_select_last_index')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# result: [[0], [1]]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [1]]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axis_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# result: [[0], [1]]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axis_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [1]]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+<details>
+<summary>no_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# result: [0, 1]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>no_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [1, 1]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+### <a name="ArgMin"></a><a name="argmin">**ArgMin**</a>
+
+  Computes the indices of the min elements of the input tensor's element along the
+  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+  If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
+  If select_last_index is True (default False), the index of the last occurrence of the min
+  is selected if the min appears more than once in the input. Otherwise the index of the
+  first occurrence is selected.
+  The type of the output tensor is integer.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ArgMin-1">1</a>, <a href="Changelog.md#ArgMin-11">11</a>, <a href="Changelog.md#ArgMin-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>select_last_index</tt> : int (default is 0)</dt>
+<dd>Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Reduced output tensor with integer data type.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims)
+
+# The content of result is : [[0], [0]]
+result = argmin_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmin_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_random')
+```
+
+</details>
+
+
+<details>
+<summary>default_axes_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims,
+    select_last_index=True)
+
+# result: [[0, 0]]
+result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_random_select_last_index')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# The content of result is : [[1], [0]]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [0]]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axis_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# The content of result is : [[1], [0]]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axis_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [0]]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+<details>
+<summary>no_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# The content of result is : [[1, 0]]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>no_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1, 0]]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+### <a name="Asin"></a><a name="asin">**Asin**</a>
+
+  Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The arcsine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>asin</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Asin',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+y = np.arcsin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_asin_example')
+
+x = np.random.rand(3, 4, 5).astype(np.float32)
+y = np.arcsin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_asin')
+```
+
+</details>
+
+
+### <a name="Asinh"></a><a name="asinh">**Asinh**</a>
+
+  Calculates the hyperbolic arcsine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic arcsine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>asinh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Asinh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.arcsinh(x)  # expected output [-0.88137358,  0.,  0.88137358]
+expect(node, inputs=[x], outputs=[y],
+       name='test_asinh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.arcsinh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_asinh')
+```
+
+</details>
+
+
+### <a name="Atan"></a><a name="atan">**Atan**</a>
+
+  Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The arctangent of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>atan</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Atan',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.arctan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_atan_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.arctan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_atan')
+```
+
+</details>
+
+
+### <a name="Atanh"></a><a name="atanh">**Atanh**</a>
+
+  Calculates the hyperbolic arctangent of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic arctangent values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>atanh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Atanh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+y = np.arctanh(x)  # expected output [-0.54930615,  0.,  0.54930615]
+expect(node, inputs=[x], outputs=[y],
+       name='test_atanh_example')
+
+x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32)
+y = np.arctanh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_atanh')
+```
+
+</details>
+
+
+### <a name="AveragePool"></a><a name="averagepool">**AveragePool**</a>
+
+  AveragePool consumes an input tensor X and applies average pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   average pooling consisting of computing the average on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+   ```
+   The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#AveragePool-1">1</a>, <a href="Changelog.md#AveragePool-7">7</a>, <a href="Changelog.md#AveragePool-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>count_include_pad</tt> : int (default is 0)</dt>
+<dd>Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>averagepool_1d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32]
+output_shape: [1, 3, 31]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2],
+)
+x = np.random.randn(1, 3, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2]
+strides = [1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_1d_default')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_ceil</summary>
+
+```python
+"""
+input_shape: [1, 1, 4, 4]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    ceil_mode=True
+)
+x = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]]).astype(np.float32)
+y = np.array([[[
+    [6, 7.5],
+    [12, 13.5]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_ceil')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_default')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_pads</summary>
+
+```python
+"""
+input_shape: [1, 3, 28, 28]
+output_shape: [1, 3, 30, 30]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[2, 2, 2, 2]
+)
+x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (3, 3)
+strides = (1, 1)
+pad_bottom = 2
+pad_top = 2
+pad_right = 2
+pad_left = 2
+pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_pads_count_include_pad</summary>
+
+```python
+"""
+input_shape: [1, 3, 28, 28]
+output_shape: [1, 3, 30, 30]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[2, 2, 2, 2],
+    count_include_pad=1,
+)
+x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (3, 3)
+strides = (1, 1)
+pad_bottom = 2
+pad_top = 2
+pad_right = 2
+pad_left = 2
+pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=0)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG', count_include_pad=1)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads_count_include_pad')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_precomputed_pads</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 7.5, 8, 8.5, 9],
+                [9.5, 10, 10.5, 11, 11.5],
+                [12, 12.5, 13, 13.5, 14],
+                [14.5, 15, 15.5, 16, 16.5],
+                [17, 17.5, 18, 18.5, 19]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_precomputed_pads_count_include_pad</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2],
+    count_include_pad=1
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
+                [4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
+                [7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
+                [6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
+                [6.1200, 8.4000, 10.8000, 8.8800, 6.8400]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads_count_include_pad')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_precomputed_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 3, 3]
+pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[4, 5.5, 7],
+                [11.5, 13, 14.5],
+                [19, 20.5, 22]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_same_upper')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_precomputed_strides</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[4, 6],
+                [14, 16]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_strides')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_same_lower</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_LOWER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_bottom = pad_shape[0] // 2
+pad_top = pad_shape[0] - pad_bottom
+pad_right = pad_shape[1] // 2
+pad_left = pad_shape[1] - pad_right
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_lower')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_top = pad_shape[0] // 2
+pad_bottom = pad_shape[0] - pad_top
+pad_left = pad_shape[1] // 2
+pad_right = pad_shape[1] - pad_left
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_upper')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_2d_strides</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 10, 10]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    strides=[3, 3]
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (5, 5)
+strides = (3, 3)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_strides')
+```
+
+</details>
+
+
+<details>
+<summary>averagepool_3d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32, 32]
+output_shape: [1, 3, 31, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2, 2],
+)
+x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2, 2, 2]
+strides = [1, 1, 1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_3d_default')
+```
+
+</details>
+
+
+### <a name="BatchNormalization"></a><a name="batchnormalization">**BatchNormalization**</a>
+
+  Carries out batch normalization as described in the paper
+  https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+  There are five required inputs 'X', 'scale', 'B', 'input_mean' and
+  'input_var'.
+  Note that 'input_mean' and 'input_var' are expected to be the estimated
+  statistics in inference mode (training_mode=False, default),
+  and the running statistics in training mode (training_mode=True).
+  There are multiple cases for the number of outputs, which we list below:
+
+  Output case #1: Y, running_mean, running_var (training_mode=True)
+  Output case #2: Y (training_mode=False)
+
+  When training_mode=False, extra outputs are invalid.
+  The outputs are updated as follows when training_mode=True:
+  ```
+  running_mean = input_mean * momentum + current_mean * (1 - momentum)
+  running_var = input_var * momentum + current_var * (1 - momentum)
+
+  Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
+
+  where:
+
+  current_mean = ReduceMean(X, axis=all_except_channel_index)
+  current_var =  ReduceVar(X, axis=all_except_channel_index)
+
+  Notice that ReduceVar refers to the population variance, and it equals to
+  sum(sqrd(x_i - x_avg)) / N
+  where N is the population size (this formula does not use sample size N - 1).
+
+  ```
+
+  The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs.
+
+  When training_mode=False:
+  ```
+  Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
+  ```
+
+  For previous (depreciated) non-spatial cases, implementors are suggested
+  to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op.
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#BatchNormalization-1">1</a>, <a href="Changelog.md#BatchNormalization-6">6</a>, <a href="Changelog.md#BatchNormalization-7">7</a>, <a href="Changelog.md#BatchNormalization-9">9</a>, <a href="Changelog.md#BatchNormalization-14">14</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>momentum</tt> : float (default is 0.9)</dt>
+<dd>Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).</dd>
+<dt><tt>training_mode</tt> : int (default is 0)</dt>
+<dd>If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1</dd>
+<dt><tt>scale</tt> (differentiable) : T1</dt>
+<dd>Scale tensor of shape (C).</dd>
+<dt><tt>B</tt> (differentiable) : T1</dt>
+<dd>Bias tensor of shape (C).</dd>
+<dt><tt>input_mean</tt> (differentiable) : T2</dt>
+<dd>running (training) or estimated (testing) mean tensor of shape (C).</dd>
+<dt><tt>input_var</tt> (differentiable) : T2</dt>
+<dd>running (training) or estimated (testing) variance tensor of shape (C).</dd>
+</dl>
+
+#### Outputs (1 - 3)
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>The output tensor of the same shape as X</dd>
+<dt><tt>running_mean</tt> (optional, non-differentiable) : T2</dt>
+<dd>The running mean after the BatchNormalization operator.</dd>
+<dt><tt>running_var</tt> (optional, non-differentiable) : T2</dt>
+<dd>The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain scale and bias types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain mean and variance types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>batchnormalization</summary>
+
+```python
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y'],
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
+       name='test_batchnorm_example')
+
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+epsilon = 1e-2
+y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y'],
+    epsilon=epsilon,
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
+       name='test_batchnorm_epsilon')
+```
+
+</details>
+
+
+<details>
+<summary>train</summary>
+
+```python
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+# using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'"
+# working around by using np.byte(1).astype(bool)
+training_mode = 1
+y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y', 'output_mean', 'output_var'],
+    training_mode=training_mode
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var],
+       outputs=[y, output_mean, output_var],
+       name='test_batchnorm_example_training_mode')
+
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+training_mode = 1
+momentum = 0.9
+epsilon = 1e-2
+y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var, momentum,
+                                                      epsilon)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y', 'output_mean', 'output_var'],
+    epsilon=epsilon,
+    training_mode=training_mode
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var],
+       outputs=[y, output_mean, output_var],
+       name='test_batchnorm_epsilon_training_mode')
+```
+
+</details>
+
+
+### <a name="Bernoulli"></a><a name="bernoulli">**Bernoulli**</a>
+
+  Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor
+  containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number,
+  where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p).
+
+  This operator is non-deterministic and may not produce the same values in different
+  implementations (even if a seed is specified).
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>All values in input have to be in the range:[0, 1].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>The returned output tensor only has values 0 or 1, same shape as input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bool)</dt>
+<dd>Constrain output types to all numeric tensors and bool tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>bernoulli_with_dtype</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Bernoulli',
+    inputs=['x'],
+    outputs=['y'],
+    dtype=onnx.TensorProto.DOUBLE,
+)
+
+x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+y = bernoulli_reference_implementation(x, np.float64)
+expect(node, inputs=[x], outputs=[y], name='test_bernoulli_double')
+```
+
+</details>
+
+
+<details>
+<summary>bernoulli_with_seed</summary>
+
+```python
+seed = np.float(0)
+node = onnx.helper.make_node(
+    'Bernoulli',
+    inputs=['x'],
+    outputs=['y'],
+    seed=seed,
+)
+
+x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+y = bernoulli_reference_implementation(x, np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_bernoulli_seed')
+```
+
+</details>
+
+
+<details>
+<summary>bernoulli_without_dtype</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Bernoulli',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.uniform(0.0, 1.0, 10).astype(np.float)
+y = bernoulli_reference_implementation(x, np.float)
+expect(node, inputs=[x], outputs=[y], name='test_bernoulli')
+```
+
+</details>
+
+
+### <a name="BitShift"></a><a name="bitshift">**BitShift**</a>
+
+  Bitwise shift operator performs element-wise operation. For each input element, if the
+   attribute "direction" is "RIGHT", this operator moves its binary representation toward
+   the right side so that the input value is effectively decreased. If the attribute "direction"
+   is "LEFT", bits of binary representation moves toward the left side, which results the
+   increase of its actual value. The input X is the tensor to be shifted and another input
+   Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4],
+   and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with
+   X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8].
+
+   Because this operator supports Numpy-style broadcasting, X's and Y's shapes are
+   not necessarily identical.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>direction</tt> : string (required)</dt>
+<dd>Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>First operand, input to be shifted.</dd>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Second operand, amounts of shift.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64)</dt>
+<dd>Constrain input and output types to integer tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>left_unit16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint16)
+y = np.array([1, 2, 3]).astype(np.uint16)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint16')
+```
+
+</details>
+
+
+<details>
+<summary>left_unit32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint32)
+y = np.array([1, 2, 3]).astype(np.uint32)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint32')
+```
+
+</details>
+
+
+<details>
+<summary>left_unit64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint64)
+y = np.array([1, 2, 3]).astype(np.uint64)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint64')
+```
+
+</details>
+
+
+<details>
+<summary>left_unit8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint8)
+y = np.array([1, 2, 3]).astype(np.uint8)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint8')
+```
+
+</details>
+
+
+<details>
+<summary>right_unit16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint16)
+y = np.array([1, 2, 3]).astype(np.uint16)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint16')
+```
+
+</details>
+
+
+<details>
+<summary>right_unit32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint32)
+y = np.array([1, 2, 3]).astype(np.uint32)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint32')
+```
+
+</details>
+
+
+<details>
+<summary>right_unit64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint64)
+y = np.array([1, 2, 3]).astype(np.uint64)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint64')
+```
+
+</details>
+
+
+<details>
+<summary>right_unit8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint8)
+y = np.array([1, 2, 3]).astype(np.uint8)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint8')
+```
+
+</details>
+
+
+### <a name="Cast"></a><a name="cast">**Cast**</a>
+
+  The operator casts the elements of a given input tensor to a data type
+  specified by the 'to' argument and returns an output tensor of the same size in
+  the converted type. The 'to' argument must be one of the data types specified
+  in the 'DataType' enum field in the TensorProto message.
+
+  Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
+  (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
+  result 100. There are some string literals reserved for special floating-point values;
+  "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
+  Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
+  this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
+  to string tensors, plain floating-point representation (such as "314.15926") would be used.
+  Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
+  of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
+
+  Conversion from a numerical type to any numerical type is always allowed.
+  User must be aware of precision loss and value change caused by range difference between two types.
+  For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
+  an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Cast-1">1</a>, <a href="Changelog.md#Cast-6">6</a>, <a href="Changelog.md#Cast-9">9</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>to</tt> : int (required)</dt>
+<dd>The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T1</dt>
+<dd>Input tensor to be cast.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T2</dt>
+<dd>Output tensor with the same shape as input with type specified by the 'to' argument</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain input types. Casting from complex is not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain output types. Casting to complex is not supported.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>cast</summary>
+
+```python
+shape = (3, 4)
+test_cases = [
+    ('FLOAT', 'FLOAT16'),
+    ('FLOAT', 'DOUBLE'),
+    ('FLOAT16', 'FLOAT'),
+    ('FLOAT16', 'DOUBLE'),
+    ('DOUBLE', 'FLOAT'),
+    ('DOUBLE', 'FLOAT16'),
+    ('FLOAT', 'STRING'),
+    ('STRING', 'FLOAT'),
+    ('FLOAT', 'BFLOAT16'),
+    ('BFLOAT16', 'FLOAT'),
+]
+
+for from_type, to_type in test_cases:
+    input_type_proto = None
+    output_type_proto = None
+    if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type:
+        np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32)
+        little_endisan = sys.byteorder == 'little'
+        np_uint16_view = np_fp32.view(dtype=np.uint16)
+        np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
+        if 'BFLOAT16' == to_type:
+            assert from_type == 'FLOAT'
+            input = np_fp32.reshape([3, 4])
+            output = np_bfp16.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), output.shape)
+        else:
+            assert to_type == 'FLOAT'
+            input = np_bfp16.reshape([3, 4])
+            #convert bfloat to FLOAT
+            np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
+            if little_endisan:
+                np_fp32_zeros[1::2] = np_bfp16
+            else:
+                np_fp32_zeros[0::2] = np_bfp16
+            np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
+            output = np_fp32_from_bfloat.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), output.shape)
+    elif 'STRING' != from_type:
+        input = np.random.random_sample(shape).astype(
+            TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)])
+        if ('STRING' == to_type):
+            # Converting input to str, then give it object dtype for generating script
+            ss = []
+            for i in input.flatten():
+                s = str(i).encode('utf-8')
+                su = s.decode('utf-8')
+                ss.append(su)
+
+            output = np.array(ss).astype(object).reshape([3, 4])
+        else:
+            output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    else:
+        input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(object)).reshape([3, 4])
+        output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    node = onnx.helper.make_node(
+        'Cast',
+        inputs=['input'],
+        outputs=['output'],
+        to=getattr(TensorProto, to_type),
+    )
+    if input_type_proto and output_type_proto:
+        expect(node, inputs=[input], outputs=[output],
+                   name='test_cast_' + from_type + '_to_' + to_type,
+                   input_type_protos=[input_type_proto],
+                   output_type_protos=[output_type_proto])
+    else:
+        expect(node, inputs=[input], outputs=[output],
+                   name='test_cast_' + from_type + '_to_' + to_type)
+```
+
+</details>
+
+
+### <a name="CastLike"></a><a name="castlike">**CastLike**</a>
+
+  The operator casts the elements of a given input tensor (the first input) to
+  the same data type as the elements of the second input tensor.
+  See documentation of the Cast operator for further details.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T1</dt>
+<dd>Input tensor to be cast.</dd>
+<dt><tt>target_type</tt> (non-differentiable) : T2</dt>
+<dd>The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T2</dt>
+<dd>Output tensor produced by casting the first input tensor to have the same type as the second input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain input types. Casting from complex is not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)</dt>
+<dd>Constrain output types. Casting to complex is not supported.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>castlike</summary>
+
+```python
+shape = (3, 4)
+test_cases = [
+    ('FLOAT', 'FLOAT16'),
+    ('FLOAT', 'DOUBLE'),
+    ('FLOAT16', 'FLOAT'),
+    ('FLOAT16', 'DOUBLE'),
+    ('DOUBLE', 'FLOAT'),
+    ('DOUBLE', 'FLOAT16'),
+    ('FLOAT', 'STRING'),
+    ('STRING', 'FLOAT'),
+    ('FLOAT', 'BFLOAT16'),
+    ('BFLOAT16', 'FLOAT'),
+]
+
+for from_type, to_type in test_cases:
+    input_type_proto = None
+    output_type_proto = None
+    if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type:
+        np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32)
+        little_endisan = sys.byteorder == 'little'
+        np_uint16_view = np_fp32.view(dtype=np.uint16)
+        np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
+        if 'BFLOAT16' == to_type:
+            assert from_type == 'FLOAT'
+            input = np_fp32.reshape([3, 4])
+            output = np_bfp16.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), output.shape)
+        else:
+            assert to_type == 'FLOAT'
+            input = np_bfp16.reshape([3, 4])
+            #convert bfloat to FLOAT
+            np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
+            if little_endisan:
+                np_fp32_zeros[1::2] = np_bfp16
+            else:
+                np_fp32_zeros[0::2] = np_bfp16
+            np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
+            output = np_fp32_from_bfloat.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), output.shape)
+    elif 'STRING' != from_type:
+        input = np.random.random_sample(shape).astype(
+            TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)])
+        if ('STRING' == to_type):
+            # Converting input to str, then give it np.object dtype for generating script
+            ss = []
+            for i in input.flatten():
+                s = str(i).encode('utf-8')
+                su = s.decode('utf-8')
+                ss.append(su)
+
+            output = np.array(ss).astype(np.object).reshape([3, 4])
+        else:
+            output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    else:
+        input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(np.object)).reshape([3, 4])
+        output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    like = output.flatten()[0:1]
+    node = onnx.helper.make_node(
+        'CastLike',
+        inputs=['input', 'like'],
+        outputs=['output'],
+    )
+    if input_type_proto and output_type_proto:
+        expect(node, inputs=[input, like], outputs=[output],
+                   name='test_castlike_' + from_type + '_to_' + to_type,
+                   input_type_protos=[input_type_proto, output_type_proto],
+                   output_type_protos=[output_type_proto])
+    else:
+        expect(node, inputs=[input, like], outputs=[output],
+                   name='test_castlike_' + from_type + '_to_' + to_type)
+```
+
+</details>
+
+
+### <a name="Ceil"></a><a name="ceil">**Ceil**</a>
+
+  Ceil takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the ceil is, y = ceil(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Ceil-1">1</a>, <a href="Changelog.md#Ceil-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>ceil</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Ceil',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1.5, 1.2]).astype(np.float32)
+y = np.ceil(x)  # expected output [-1., 2.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_ceil_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.ceil(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_ceil')
+```
+
+</details>
+
+
+### <a name="Celu"></a><a name="celu">**Celu**</a>
+
+  Continuously Differentiable Exponential Linear Units:
+  Perform the linear unit element-wise on the input tensor X
+  using formula:
+
+  ```
+  max(0,x) + min(0,alpha*(exp(x/alpha)-1))
+  ```
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float)</dt>
+<dd>Constrain input and output types to float32 tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>celu</summary>
+
+```python
+alpha = 2.0
+node = onnx.helper.make_node(
+    'Celu',
+    inputs=['X'],
+    outputs=['Y'],
+    alpha=alpha,
+)
+
+input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+    [[0.02916367], [0.12964272], [0.5060197]],
+    [[0.79538304], [0.9411346], [0.9546573]]],
+    [[[0.17730942], [0.46192095], [0.26480448]],
+    [[0.6746842], [0.01665257], [0.62473077]],
+    [[0.9240844], [0.9722341], [0.11965699]]],
+    [[[0.41356155], [0.9129373], [0.59330076]],
+    [[0.81929934], [0.7862604], [0.11799799]],
+    [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+# Calculate expected output data
+positive_input = np.maximum(0, input_data)
+negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
+expected_output = positive_input + negative_input
+
+expect(node, inputs=[input_data], outputs=[expected_output],
+       name='test_celu')
+```
+
+</details>
+
+
+### <a name="Clip"></a><a name="clip">**Clip**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified by the inputs 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max(), respectively.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Clip-1">1</a>, <a href="Changelog.md#Clip-6">6</a>, <a href="Changelog.md#Clip-11">11</a>, <a href="Changelog.md#Clip-12">12</a>
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+<dt><tt>min</tt> (optional, non-differentiable) : T</dt>
+<dd>Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).</dd>
+<dt><tt>max</tt> (optional, non-differentiable) : T</dt>
+<dd>Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>clip</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min', 'max'],
+    outputs=['y'],
+)
+
+x = np.array([-2, 0, 2]).astype(np.float32)
+min_val = np.float32(-1)
+max_val = np.float32(1)
+y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, min_val, max_val)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip')
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min', 'max'],
+    outputs=['y'],
+)
+
+min_val = np.float32(-5)
+max_val = np.float32(5)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.array([-1, 0, 1]).astype(np.float32)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_inbounds')
+
+x = np.array([-6, 0, 6]).astype(np.float32)
+y = np.array([-5, 0, 5]).astype(np.float32)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_outbounds')
+
+x = np.array([-1, 0, 6]).astype(np.float32)
+y = np.array([-1, 0, 5]).astype(np.float32)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_splitbounds')
+```
+
+</details>
+
+
+<details>
+<summary>clip_default</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min'],
+    outputs=['y'],
+)
+min_val = np.float32(0)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, min_val, np.inf)
+expect(node, inputs=[x, min_val], outputs=[y],
+       name='test_clip_default_min')
+
+no_min = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, 'max'],
+    outputs=['y'],
+)
+max_val = np.float32(0)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, -np.inf, max_val)
+expect(node, inputs=[x, max_val], outputs=[y],
+       name='test_clip_default_max')
+
+no_max = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, no_max],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.array([-1, 0, 1]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_clip_default_inbounds')
+```
+
+</details>
+
+
+<details>
+<summary>clip_default_int8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min'],
+    outputs=['y'],
+)
+min_val = np.int8(0)
+x = np.random.randn(3, 4, 5).astype(np.int8)
+y = np.clip(x, min_val, np.iinfo(np.int8).max)
+expect(node, inputs=[x, min_val], outputs=[y],
+       name='test_clip_default_int8_min')
+
+no_min = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, 'max'],
+    outputs=['y'],
+)
+max_val = np.int8(0)
+x = np.random.randn(3, 4, 5).astype(np.int8)
+y = np.clip(x, np.iinfo(np.int8).min, max_val)
+expect(node, inputs=[x, max_val], outputs=[y],
+       name='test_clip_default_int8_max')
+
+no_max = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, no_max],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.int8)
+y = np.array([-1, 0, 1]).astype(np.int8)
+expect(node, inputs=[x], outputs=[y],
+       name='test_clip_default_int8_inbounds')
+```
+
+</details>
+
+
+### <a name="Compress"></a><a name="compress">**Compress**</a>
+
+  Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index.
+      In case axis is not provided, input is flattened before elements are selected.
+      Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Compress-9">9</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>condition</tt> (non-differentiable) : T1</dt>
+<dd>Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain to boolean tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>compress_0</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+    axis=0,
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1, 1])
+output = np.compress(condition, input, axis=0)
+#print(output)
+#[[ 3.  4.]
+# [ 5.  6.]]
+
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_0')
+```
+
+</details>
+
+
+<details>
+<summary>compress_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+    axis=1,
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1])
+output = np.compress(condition, input, axis=1)
+#print(output)
+#[[ 2.]
+# [ 4.]
+# [ 6.]]
+
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_1')
+```
+
+</details>
+
+
+<details>
+<summary>compress_default_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1, 0, 0, 1])
+output = np.compress(condition, input)
+#print(output)
+#[ 2., 5.]
+
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_default_axis')
+```
+
+</details>
+
+
+<details>
+<summary>compress_negative_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+    axis=-1,
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1])
+output = np.compress(condition, input, axis=-1)
+# print(output)
+#[[ 2.]
+# [ 4.]
+# [ 6.]]
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_negative_axis')
+```
+
+</details>
+
+
+### <a name="Concat"></a><a name="concat">**Concat**</a>
+
+  Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Concat-1">1</a>, <a href="Changelog.md#Concat-4">4</a>, <a href="Changelog.md#Concat-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (required)</dt>
+<dd>Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> (differentiable) : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>concat</summary>
+
+```python
+test_cases: Dict[Text, Sequence[Any]] = {
+    '1d': ([1, 2],
+           [3, 4]),
+    '2d': ([[1, 2], [3, 4]],
+           [[5, 6], [7, 8]]),
+    '3d': ([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
+           [[[9, 10], [11, 12]], [[13, 14], [15, 16]]])
+}
+
+for test_case, values_ in test_cases.items():
+    values = [np.asarray(v, dtype=np.float32) for v in values_]
+    for i in range(len(values[0].shape)):
+        in_args = ['value' + str(k) for k in range(len(values))]
+        node = onnx.helper.make_node(
+            'Concat',
+            inputs=[s for s in in_args],
+            outputs=['output'],
+            axis=i
+        )
+        output = np.concatenate(values, i)
+        expect(node, inputs=[v for v in values], outputs=[output],
+               name='test_concat_' + test_case + '_axis_' + str(i))
+
+    for i in range(-len(values[0].shape), 0):
+        in_args = ['value' + str(k) for k in range(len(values))]
+        node = onnx.helper.make_node(
+            'Concat',
+            inputs=[s for s in in_args],
+            outputs=['output'],
+            axis=i
+        )
+        output = np.concatenate(values, i)
+        expect(node, inputs=[v for v in values], outputs=[output],
+               name='test_concat_' + test_case + '_axis_negative_' + str(abs(i)))
+```
+
+</details>
+
+
+### <a name="ConcatFromSequence"></a><a name="concatfromsequence">**ConcatFromSequence**</a>
+
+  Concatenate a sequence of tensors into a single tensor.
+  All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
+  By default 'new_axis' is 0, the behavior is similar to numpy.concatenate.
+  When 'new_axis' is 1, the behavior is similar to numpy.stack.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (required)</dt>
+<dd>Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. </dd>
+<dt><tt>new_axis</tt> : int (default is 0)</dt>
+<dd>Insert and concatenate on a new axis or not, default 0 means do not insert new axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Sequence of tensors for concatenation</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>concat_result</tt> : T</dt>
+<dd>Concatenated tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input types to any tensor type.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+
+### <a name="Constant"></a><a name="constant">**Constant**</a>
+
+  This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
+  or value_* must be specified.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Constant-1">1</a>, <a href="Changelog.md#Constant-9">9</a>, <a href="Changelog.md#Constant-11">11</a>, <a href="Changelog.md#Constant-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>sparse_value</tt> : sparse_tensor</dt>
+<dd>The value for the elements of the output tensor in sparse format.</dd>
+<dt><tt>value</tt> : tensor</dt>
+<dd>The value for the elements of the output tensor.</dd>
+<dt><tt>value_float</tt> : float</dt>
+<dd>The value for the sole element for the scalar, float32, output tensor.</dd>
+<dt><tt>value_floats</tt> : list of floats</dt>
+<dd>The values for the elements for the 1D, float32, output tensor.</dd>
+<dt><tt>value_int</tt> : int</dt>
+<dd>The value for the sole element for the scalar, int64, output tensor.</dd>
+<dt><tt>value_ints</tt> : list of ints</dt>
+<dd>The values for the elements for the 1D, int64, output tensor.</dd>
+<dt><tt>value_string</tt> : string</dt>
+<dd>The value for the sole element for the scalar, UTF-8 string, output tensor.</dd>
+<dt><tt>value_strings</tt> : list of strings</dt>
+<dd>The values for the elements for the 1D, UTF-8 string, output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor containing the same value of the provided tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>constant</summary>
+
+```python
+values = np.random.randn(5, 5).astype(np.float32)
+node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['values'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=values.shape,
+        vals=values.flatten().astype(float),
+    ),
+)
+
+expect(node, inputs=[], outputs=[values],
+       name='test_constant')
+```
+
+</details>
+
+
+### <a name="ConstantOfShape"></a><a name="constantofshape">**ConstantOfShape**</a>
+
+  Generate a tensor with given value and shape.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>value</tt> : tensor</dt>
+<dd>(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain input types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain output types to be numerics.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>float_ones</summary>
+
+```python
+x = np.array([4, 3, 2]).astype(np.int64)
+tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.FLOAT,
+                                       [1], [1])
+node = onnx.helper.make_node(
+    'ConstantOfShape',
+    inputs=['x'],
+    outputs=['y'],
+    value=tensor_value,
+)
+
+y = np.ones(x, dtype=np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_constantofshape_float_ones')
+```
+
+</details>
+
+
+<details>
+<summary>int32_shape_zero</summary>
+
+```python
+x = np.array([0, ]).astype(np.int64)
+tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.INT32,
+                                       [1], [0])
+node = onnx.helper.make_node(
+    'ConstantOfShape',
+    inputs=['x'],
+    outputs=['y'],
+    value=tensor_value,
+)
+y = np.zeros(x, dtype=np.int32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_constantofshape_int_shape_zero')
+```
+
+</details>
+
+
+<details>
+<summary>int32_zeros</summary>
+
+```python
+x = np.array([10, 6]).astype(np.int64)
+tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.INT32,
+                                       [1], [0])
+node = onnx.helper.make_node(
+    'ConstantOfShape',
+    inputs=['x'],
+    outputs=['y'],
+    value=tensor_value,
+)
+y = np.zeros(x, dtype=np.int32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_constantofshape_int_zeros')
+```
+
+</details>
+
+
+### <a name="Conv"></a><a name="conv">**Conv**</a>
+
+  The convolution operator consumes an input tensor and a filter, and
+  computes the output.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Conv-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>conv</summary>
+
+```python
+
+x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 5, 5) input tensor
+                [5., 6., 7., 8., 9.],
+                [10., 11., 12., 13., 14.],
+                [15., 16., 17., 18., 19.],
+                [20., 21., 22., 23., 24.]]]]).astype(np.float32)
+W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+# Convolution with padding
+node_with_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
+    pads=[1, 1, 1, 1],
+)
+y_with_padding = np.array([[[[12., 21., 27., 33., 24.],  # (1, 1, 5, 5) output tensor
+                             [33., 54., 63., 72., 51.],
+                             [63., 99., 108., 117., 81.],
+                             [93., 144., 153., 162., 111.],
+                             [72., 111., 117., 123., 84.]]]]).astype(np.float32)
+expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
+       name='test_basic_conv_with_padding')
+
+# Convolution without padding
+node_without_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
+    pads=[0, 0, 0, 0],
+)
+y_without_padding = np.array([[[[54., 63., 72.],  # (1, 1, 3, 3) output tensor
+                                [99., 108., 117.],
+                                [144., 153., 162.]]]]).astype(np.float32)
+expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
+       name='test_basic_conv_without_padding')
+```
+
+</details>
+
+
+<details>
+<summary>conv_with_autopad_same</summary>
+
+```python
+
+x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 5, 5) input tensor
+                [5., 6., 7., 8., 9.],
+                [10., 11., 12., 13., 14.],
+                [15., 16., 17., 18., 19.],
+                [20., 21., 22., 23., 24.]]]]).astype(np.float32)
+W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+# Convolution with auto_pad='SAME_LOWER' and strides=2
+node = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    auto_pad='SAME_LOWER',
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+)
+y = np.array([[[[12., 27., 24.],
+             [63., 108., 81.],
+             [72., 117., 84.]]]]).astype(np.float32)
+expect(node, inputs=[x, W], outputs=[y],
+       name='test_conv_with_autopad_same')
+```
+
+</details>
+
+
+<details>
+<summary>conv_with_strides</summary>
+
+```python
+
+x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 7, 5) input tensor
+                [5., 6., 7., 8., 9.],
+                [10., 11., 12., 13., 14.],
+                [15., 16., 17., 18., 19.],
+                [20., 21., 22., 23., 24.],
+                [25., 26., 27., 28., 29.],
+                [30., 31., 32., 33., 34.]]]]).astype(np.float32)
+W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+# Convolution with strides=2 and padding
+node_with_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[1, 1, 1, 1],
+    strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+)
+y_with_padding = np.array([[[[12., 27., 24.],  # (1, 1, 4, 3) output tensor
+                             [63., 108., 81.],
+                             [123., 198., 141.],
+                             [112., 177., 124.]]]]).astype(np.float32)
+expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
+       name='test_conv_with_strides_padding')
+
+# Convolution with strides=2 and no padding
+node_without_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[0, 0, 0, 0],
+    strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+)
+y_without_padding = np.array([[[[54., 72.],  # (1, 1, 3, 2) output tensor
+                                [144., 162.],
+                                [234., 252.]]]]).astype(np.float32)
+expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
+       name='test_conv_with_strides_no_padding')
+
+# Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
+node_with_asymmetric_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[1, 0, 1, 0],
+    strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+)
+y_with_asymmetric_padding = np.array([[[[21., 33.],  # (1, 1, 4, 2) output tensor
+                                        [99., 117.],
+                                        [189., 207.],
+                                        [171., 183.]]]]).astype(np.float32)
+expect(node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding],
+       name='test_conv_with_strides_and_asymmetric_padding')
+```
+
+</details>
+
+
+### <a name="ConvInteger"></a><a name="convinteger">**ConvInteger**</a>
+
+  The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point,
+  and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into. default is 1.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input 'w'.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.</dd>
+</dl>
+
+#### Inputs (2 - 4)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>w</tt> : T2</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. </dd>
+<dt><tt>x_zero_point</tt> (optional) : T1</dt>
+<dd>Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>w_zero_point</tt> (optional) : T2</dt>
+<dd>Zero point tensor for input 'w'. It's optional and default value is 0.  It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T3</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input x and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input w and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int32)</dt>
+<dd>Constrain output y data type to 32-bit integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>with_padding</summary>
+
+```python
+
+x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+x_zero_point = np.uint8(1)
+w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+y = np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9]).astype(np.int32).reshape((1, 1, 4, 4))
+
+# ConvInteger with padding
+convinteger_node_with_padding = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'],
+    pads=[1, 1, 1, 1],)
+
+expect(convinteger_node_with_padding, inputs=[x, w, x_zero_point], outputs=[y],
+       name='test_convinteger_with_padding')
+```
+
+</details>
+
+
+<details>
+<summary>without_padding</summary>
+
+```python
+
+x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+x_zero_point = np.uint8(1)
+w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
+
+# ConvInteger without padding
+convinteger_node = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'])
+
+expect(convinteger_node, inputs=[x, w, x_zero_point], outputs=[y],
+       name='test_convinteger_without_padding')
+```
+
+</details>
+
+
+### <a name="ConvTranspose"></a><a name="convtranspose">**ConvTranspose**</a>
+
+  The convolution transpose operator consumes an input tensor and a filter,
+  and computes the output.
+
+  If the pads parameter is provided the shape of the output is calculated via the following equation:
+
+    output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]
+
+  output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
+
+    total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
+    If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
+    Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
+
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ConvTranspose-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
+<dt><tt>output_padding</tt> : list of ints</dt>
+<dd>Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.</dd>
+<dt><tt>output_shape</tt> : list of ints</dt>
+<dd>The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>convtranspose</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+y = np.array([[[[0., 1., 3., 3., 2.],  # (1, 2, 5, 5)
+                [3., 8., 15., 12., 7.],
+                [9., 21., 36., 27., 15.],
+                [9., 20., 33., 24., 13.],
+                [6., 13., 21., 15., 8.]],
+
+               [[0., 1., 3., 3., 2.],
+                [3., 8., 15., 12., 7.],
+                [9., 21., 36., 27., 15.],
+                [9., 20., 33., 24., 13.],
+                [6., 13., 21., 15., 8.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose')
+```
+
+</details>
+
+
+<details>
+<summary>convtranspose_1d</summary>
+
+```python
+x = np.array([[[0., 1., 2.]]]).astype(np.float32)  # (1, 1, 3)
+
+W = np.array([[[1., 1., 1.],  # (1, 2, 3)
+               [1., 1., 1.]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+y = np.array([[[0., 1., 3., 3., 2.],  # (1, 2, 5)
+               [0., 1., 3., 3., 2.]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_1d')
+```
+
+</details>
+
+
+<details>
+<summary>convtranspose_3d</summary>
+
+```python
+x = np.array([[[[[0., 1., 2., 3., 4.],  # (1, 1, 3, 4, 5)
+                 [5., 6., 7., 8., 9.],
+                 [10., 11., 12., 13., 14.],
+                 [15., 16., 17., 18., 19.]],
+                [[20., 21., 22., 23., 24.],
+                 [25., 26., 27., 28., 29.],
+                 [30., 31., 32., 33., 34.],
+                 [35., 36., 37., 38., 39.]],
+                [[40., 41., 42., 43., 44.],
+                 [45., 46., 47., 48., 49.],
+                 [50., 51., 52., 53., 54.],
+                 [55., 56., 57., 58., 59.]]]]]).astype(np.float32)
+
+W = np.array([[[[[1., 1., 1.],  # (1, 2, 3, 3, 3)
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]]],
+               [[[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+y = np.array([[[[[0., 1., 3., 6., 9., 7., 4.],  # (1, 2, 5, 6, 7)
+                 [5., 12., 21., 27., 33., 24., 13.],
+                 [15., 33., 54., 63., 72., 51., 27.],
+                 [30., 63., 99., 108., 117., 81., 42.],
+                 [25., 52., 81., 87., 93., 64., 33.],
+                 [15., 31., 48., 51., 54., 37., 19.]],
+
+                [[20., 42., 66., 72., 78., 54., 28.],
+                 [50., 104., 162., 174., 186., 128., 66.],
+                 [90., 186., 288., 306., 324., 222., 114.],
+                 [120., 246., 378., 396., 414., 282., 144.],
+                 [90., 184., 282., 294., 306., 208., 106.],
+                 [50., 102., 156., 162., 168., 114., 58.]],
+
+                [[60., 123., 189., 198., 207., 141., 72.],
+                 [135., 276., 423., 441., 459., 312., 159.],
+                 [225., 459., 702., 729., 756., 513., 261.],
+                 [270., 549., 837., 864., 891., 603., 306.],
+                 [195., 396., 603., 621., 639., 432., 219.],
+                 [105., 213., 324., 333., 342., 231., 117.]],
+
+                [[60., 122., 186., 192., 198., 134., 68.],
+                 [130., 264., 402., 414., 426., 288., 146.],
+                 [210., 426., 648., 666., 684., 462., 234.],
+                 [240., 486., 738., 756., 774., 522., 264.],
+                 [170., 344., 522., 534., 546., 368., 186.],
+                 [90., 182., 276., 282., 288., 194., 98.]],
+
+                [[40., 81., 123., 126., 129., 87., 44.],
+                 [85., 172., 261., 267., 273., 184., 93.],
+                 [135., 273., 414., 423., 432., 291., 147.],
+                 [150., 303., 459., 468., 477., 321., 162.],
+                 [105., 212., 321., 327., 333., 224., 113.],
+                 [55., 111., 168., 171., 174., 117., 59.]]],
+
+               [[[0., 1., 3., 6., 9., 7., 4.],
+                 [5., 12., 21., 27., 33., 24., 13.],
+                 [15., 33., 54., 63., 72., 51., 27.],
+                 [30., 63., 99., 108., 117., 81., 42.],
+                 [25., 52., 81., 87., 93., 64., 33.],
+                 [15., 31., 48., 51., 54., 37., 19.]],
+
+                [[20., 42., 66., 72., 78., 54., 28.],
+                 [50., 104., 162., 174., 186., 128., 66.],
+                 [90., 186., 288., 306., 324., 222., 114.],
+                 [120., 246., 378., 396., 414., 282., 144.],
+                 [90., 184., 282., 294., 306., 208., 106.],
+                 [50., 102., 156., 162., 168., 114., 58.]],
+
+                [[60., 123., 189., 198., 207., 141., 72.],
+                 [135., 276., 423., 441., 459., 312., 159.],
+                 [225., 459., 702., 729., 756., 513., 261.],
+                 [270., 549., 837., 864., 891., 603., 306.],
+                 [195., 396., 603., 621., 639., 432., 219.],
+                 [105., 213., 324., 333., 342., 231., 117.]],
+
+                [[60., 122., 186., 192., 198., 134., 68.],
+                 [130., 264., 402., 414., 426., 288., 146.],
+                 [210., 426., 648., 666., 684., 462., 234.],
+                 [240., 486., 738., 756., 774., 522., 264.],
+                 [170., 344., 522., 534., 546., 368., 186.],
+                 [90., 182., 276., 282., 288., 194., 98.]],
+
+                [[40., 81., 123., 126., 129., 87., 44.],
+                 [85., 172., 261., 267., 273., 184., 93.],
+                 [135., 273., 414., 423., 432., 291., 147.],
+                 [150., 303., 459., 468., 477., 321., 162.],
+                 [105., 212., 321., 327., 333., 224., 113.],
+                 [55., 111., 168., 171., 174., 117., 59.]]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_3d')
+```
+
+</details>
+
+
+<details>
+<summary>convtranspose_attributes</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+y = np.array([[[[0., 0., 1., 1., 3., 2., 2., 0.],  # (1, 2, 10, 8)
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [0., 0., 0., 0., 0., 0., 0., 0.]],
+
+               [[0., 0., 1., 1., 3., 2., 2., 0.],
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [0., 0., 0., 0., 0., 0., 0., 0.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                             strides=[3, 2],
+                             output_shape=[10, 8])
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_output_shape')
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                             strides=[3, 2],
+                             output_padding=[1, 1])
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_pad')
+
+node = onnx.helper.make_node(
+    'ConvTranspose', ['X', 'W'], ['Y'],
+    name='test',
+    strides=[3, 2],
+    output_shape=[10, 8],
+    kernel_shape=[3, 3],
+    output_padding=[1, 1]
+)
+expect(node, inputs=[x, W], outputs=[y],
+       name='test_convtranspose_kernel_shape')
+```
+
+</details>
+
+
+<details>
+<summary>convtranspose_autopad_same</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2])
+
+y = np.array([[[[0., 0., 1., 1., 3., 2.],
+                [0., 0., 1., 1., 3., 2.],
+                [3., 3., 8., 5., 12., 7.],
+                [3., 3., 7., 4., 9., 5.],
+                [9., 9., 20., 11., 24., 13.],
+                [6., 6., 13., 7., 15., 8.]],
+
+               [[0., 0., 1., 1., 3., 2.],
+                [0., 0., 1., 1., 3., 2.],
+                [3., 3., 8., 5., 12., 7.],
+                [3., 3., 7., 4., 9., 5.],
+                [9., 9., 20., 11., 24., 13.],
+                [6., 6., 13., 7., 15., 8.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_autopad_same')
+```
+
+</details>
+
+
+<details>
+<summary>convtranspose_dilations</summary>
+
+```python
+x = np.array([[[[3., 8., 1.],  # (1, 1, 3, 3)
+                [9., 5., 7.],
+                [3., 2., 6.]]]]).astype(np.float32)
+W = np.array([[[[7., 2.],  # (1, 1, 2, 2)
+                [1., 9.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2])
+
+y = np.array([[[[21., 56., 13., 16., 2.],  # [1, 1, 5, 5]
+                [63., 35., 67., 10., 14.],
+                [24., 22., 76., 76., 21.],
+                [9., 5., 88., 45., 63.],
+                [3., 2., 33., 18., 54.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_dilations')
+```
+
+</details>
+
+
+<details>
+<summary>convtranspose_pads</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                             strides=[3, 2],
+                             pads=[1, 2, 1, 2])
+
+y = np.array([[[[1., 1., 3.],  # (1, 2, 7, 3)
+                [1., 1., 3.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [13., 7., 15.],
+                [13., 7., 15.]],
+
+               [[1., 1., 3.],
+                [1., 1., 3.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [13., 7., 15.],
+                [13., 7., 15.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_pads')
+```
+
+</details>
+
+
+### <a name="Cos"></a><a name="cos">**Cos**</a>
+
+  Calculates the cosine of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The cosine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>cos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Cos',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.cos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_cos_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.cos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_cos')
+```
+
+</details>
+
+
+### <a name="Cosh"></a><a name="cosh">**Cosh**</a>
+
+  Calculates the hyperbolic cosine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic cosine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>cosh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Cosh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.cosh(x)  # expected output [1.54308069,  1.,  1.54308069]
+expect(node, inputs=[x], outputs=[y],
+       name='test_cosh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.cosh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_cosh')
+```
+
+</details>
+
+
+### <a name="CumSum"></a><a name="cumsum">**CumSum**</a>
+
+  Performs cumulative sum of the input elements along the given axis.
+  By default, it will do the sum inclusively meaning the first element is copied as is.
+  Through an `exclusive` attribute, this behavior can change to exclude the first element.
+  It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.
+
+  Example:
+  ```
+  input_x = [1, 2, 3]
+  axis=0
+  output = [1, 3, 6]
+  exclusive=1
+  output = [0, 1, 3]
+  exclusive=0
+  reverse=1
+  output = [6, 5, 3]
+  exclusive=1
+  reverse=1
+  output = [5, 3, 0]
+  ```
+
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#CumSum-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>exclusive</tt> : int (default is 0)</dt>
+<dd>If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.</dd>
+<dt><tt>reverse</tt> : int (default is 0)</dt>
+<dd>If set to 1 will perform the sums in reverse direction.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>x</tt> (differentiable) : T</dt>
+<dd>An input tensor that is to be processed.</dd>
+<dt><tt>axis</tt> (non-differentiable) : T2</dt>
+<dd>A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same type as 'x' with cumulative sums of the x's elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+<dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
+<dd>axis tensor can be int32 or int64 only</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>cumsum_1d</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y']
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([1., 3., 6., 10., 15.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d')
+```
+
+</details>
+
+
+<details>
+<summary>cumsum_1d_exclusive</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+    exclusive=1
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([0., 1., 3., 6., 10.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d_exclusive')
+```
+
+</details>
+
+
+<details>
+<summary>cumsum_1d_reverse</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+    reverse=1
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([15., 14., 12., 9., 5.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d_reverse')
+```
+
+</details>
+
+
+<details>
+<summary>cumsum_1d_reverse_exclusive</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+    reverse=1,
+    exclusive=1
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([14., 12., 9., 5., 0.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d_reverse_exclusive')
+```
+
+</details>
+
+
+<details>
+<summary>cumsum_2d_axis_0</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+)
+x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+axis = np.int32(0)
+y = np.array([1., 2., 3., 5., 7., 9.]).astype(np.float64).reshape((2, 3))
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_2d_axis_0')
+```
+
+</details>
+
+
+<details>
+<summary>cumsum_2d_axis_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+)
+x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+axis = np.int32(1)
+y = np.array([1., 3., 6., 4., 9., 15.]).astype(np.float64).reshape((2, 3))
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_2d_axis_1')
+```
+
+</details>
+
+
+<details>
+<summary>cumsum_2d_negative_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+)
+x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+axis = np.int32(-1)
+y = np.array([1., 3., 6., 4., 9., 15.]).astype(np.float64).reshape((2, 3))
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_2d_negative_axis')
+```
+
+</details>
+
+
+### <a name="DepthToSpace"></a><a name="depthtospace">**DepthToSpace**</a>
+
+  DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+  This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+  the input tensor where values from the depth dimension are moved in spatial blocks to the height
+  and width dimensions. By default, `mode` = `DCR`.
+  In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
+  following order: depth, column, and then row. The output y is computed from the input x as below:
+
+  b, c, h, w = x.shape
+
+  tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+
+  tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+
+  y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
+
+
+  In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
+  following order: column, row, and the depth. The output y is computed from the input x as below:
+
+  b, c, h, w = x.shape
+
+  tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
+
+  tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+
+  y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#DepthToSpace-1">1</a>, <a href="Changelog.md#DepthToSpace-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+<dt><tt>mode</tt> : string (default is DCR)</dt>
+<dd>DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>crd_mode_example</summary>
+
+```python
+node = onnx.helper.make_node(
+    'DepthToSpace',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=2,
+    mode='CRD'
+)
+
+# (1, 8, 2, 3) input tensor
+x = np.array([[[[0., 1., 2.],
+                [3., 4., 5.]],
+               [[9., 10., 11.],
+                [12., 13., 14.]],
+               [[18., 19., 20.],
+                [21., 22., 23.]],
+               [[27., 28., 29.],
+                [30., 31., 32.]],
+               [[36., 37., 38.],
+                [39., 40., 41.]],
+               [[45., 46., 47.],
+                [48., 49., 50.]],
+               [[54., 55., 56.],
+                [57., 58., 59.]],
+               [[63., 64., 65.],
+                [66., 67., 68.]]]]).astype(np.float32)
+
+# (1, 2, 4, 6) output tensor
+y = np.array([[[[0., 9., 1., 10., 2., 11.],
+                [18., 27., 19., 28., 20., 29.],
+                [3., 12., 4., 13., 5., 14.],
+                [21., 30., 22., 31., 23., 32.]],
+               [[36., 45., 37., 46., 38., 47.],
+                [54., 63., 55., 64., 56., 65.],
+                [39., 48., 40., 49., 41., 50.],
+                [57., 66., 58., 67., 59., 68.]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_depthtospace_crd_mode_example')
+```
+
+</details>
+
+
+<details>
+<summary>default_mode_example</summary>
+
+```python
+node = onnx.helper.make_node(
+    'DepthToSpace',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=2,
+    mode='DCR'
+)
+
+# (1, 8, 2, 3) input tensor
+x = np.array([[[[0., 1., 2.],
+                [3., 4., 5.]],
+               [[9., 10., 11.],
+                [12., 13., 14.]],
+               [[18., 19., 20.],
+                [21., 22., 23.]],
+               [[27., 28., 29.],
+                [30., 31., 32.]],
+               [[36., 37., 38.],
+                [39., 40., 41.]],
+               [[45., 46., 47.],
+                [48., 49., 50.]],
+               [[54., 55., 56.],
+                [57., 58., 59.]],
+               [[63., 64., 65.],
+                [66., 67., 68.]]]]).astype(np.float32)
+
+# (1, 2, 4, 6) output tensor
+y = np.array([[[[0., 18., 1., 19., 2., 20.],
+                [36., 54., 37., 55., 38., 56.],
+                [3., 21., 4., 22., 5., 23.],
+                [39., 57., 40., 58., 41., 59.]],
+               [[9., 27., 10., 28., 11., 29.],
+                [45., 63., 46., 64., 47., 65.],
+                [12., 30., 13., 31., 14., 32.],
+                [48., 66., 49., 67., 50., 68.]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_depthtospace_example')
+```
+
+</details>
+
+
+### <a name="DequantizeLinear"></a><a name="dequantizelinear">**DequantizeLinear**</a>
+
+  The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor.
+  The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' must have same shape, and can be either a scalar
+  for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
+  'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
+  there's no zero point (zero point is supposed to be 0).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#DequantizeLinear-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>x</tt> : T</dt>
+<dd>N-D quantized input tensor to be de-quantized.</dd>
+<dt><tt>x_scale</tt> : tensor(float)</dt>
+<dd>Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.</dd>
+<dt><tt>x_zero_point</tt> (optional) : T</dt>
+<dd>Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : tensor(float)</dt>
+<dd>N-D full precision output tensor. It has same shape as input 'x'.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int8), tensor(uint8), tensor(int32)</dt>
+<dd>Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>axis</summary>
+
+```python
+node = onnx.helper.make_node('DequantizeLinear',
+                             inputs=['x', 'x_scale', 'x_zero_point'],
+                             outputs=['y'],)
+
+# 1-D tensor zero point and scale of size equal to axis 1 of the input tensor
+x = np.array([[[[3, 89],
+                [34, 200],
+                [74, 59]],
+
+               [[5, 24],
+                [24, 87],
+                [32, 13]],
+
+               [[245, 99],
+                [4, 142],
+                [121, 102]], ], ], dtype=np.uint8)
+x_scale = np.array([2, 4, 5], dtype=np.float32)
+x_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+y = (x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32)) * x_scale.reshape(1, 3, 1, 1)
+
+expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+       name='test_dequantizelinear_axis')
+```
+
+</details>
+
+
+<details>
+<summary>dequantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('DequantizeLinear',
+                             inputs=['x', 'x_scale', 'x_zero_point'],
+                             outputs=['y'],)
+
+# scalar zero point and scale
+x = np.array([0, 3, 128, 255]).astype(np.uint8)
+x_scale = np.float32(2)
+x_zero_point = np.uint8(128)
+y = np.array([-256, -250, 0, 254], dtype=np.float32)
+
+expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+       name='test_dequantizelinear')
+```
+
+</details>
+
+
+### <a name="Det"></a><a name="det">**Det**</a>
+
+  Det calculates determinant of a square matrix or batches of square matrices.
+  Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions,
+  and the inner-most 2 dimensions form square matrices.
+  The output is a tensor of shape `[*]`, containing the determinants of all input submatrices.
+  e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`).
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to floating-point tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>2d</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Det',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.arange(4).reshape(2, 2).astype(np.float32)
+y = np.linalg.det(x)  # expect -2
+expect(node, inputs=[x], outputs=[y],
+       name='test_det_2d')
+```
+
+</details>
+
+
+<details>
+<summary>nd</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Det',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype(np.float32)
+y = np.linalg.det(x)  # expect array([-2., -3., -8.])
+expect(node, inputs=[x], outputs=[y],
+       name='test_det_nd')
+```
+
+</details>
+
+
+### <a name="Div"></a><a name="div">**Div**</a>
+
+  Performs element-wise binary division (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Div-1">1</a>, <a href="Changelog.md#Div-6">6</a>, <a href="Changelog.md#Div-7">7</a>, <a href="Changelog.md#Div-13">13</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>div</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Div',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([3, 4]).astype(np.float32)
+y = np.array([1, 2]).astype(np.float32)
+z = x / y  # expected output [3., 2.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
+z = x / y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div')
+
+x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
+z = x // y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div_uint8')
+```
+
+</details>
+
+
+<details>
+<summary>div_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Div',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.rand(5).astype(np.float32) + 1.0
+z = x / y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div_bcast')
+```
+
+</details>
+
+
+### <a name="Dropout"></a><a name="dropout">**Dropout**</a>
+
+  Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
+  output (floating-point tensor) and mask (optional `Tensor<bool>`). If `training_mode` is true then the output Y will be a random dropout;
+  Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
+  the user can simply not pass `training_mode` input or set it to false.
+  ```
+  output = scale * data * mask,
+  ```
+  where
+  ```
+  scale = 1. / (1. - ratio).
+  ```
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Dropout-1">1</a>, <a href="Changelog.md#Dropout-6">6</a>, <a href="Changelog.md#Dropout-7">7</a>, <a href="Changelog.md#Dropout-10">10</a>, <a href="Changelog.md#Dropout-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>seed</tt> : int</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>The input data as Tensor.</dd>
+<dt><tt>ratio</tt> (optional, non-differentiable) : T1</dt>
+<dd>The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.</dd>
+<dt><tt>training_mode</tt> (optional, non-differentiable) : T2</dt>
+<dd>If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output.</dd>
+<dt><tt>mask</tt> (optional, non-differentiable) : T2</dt>
+<dd>The output mask.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input 'ratio' types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output 'mask' types to boolean tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = dropout(x)
+expect(node, inputs=[x], outputs=[y], name='test_dropout_default')
+```
+
+</details>
+
+
+<details>
+<summary>default_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y, z = dropout(x, return_mask=True)
+expect(node, inputs=[x], outputs=[y, z], name='test_dropout_default_mask')
+```
+
+</details>
+
+
+<details>
+<summary>default_mask_ratio</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+r = np.float32(0.1)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y, z = dropout(x, r, return_mask=True)
+expect(node, inputs=[x, r], outputs=[y, z], name='test_dropout_default_mask_ratio')
+```
+
+</details>
+
+
+<details>
+<summary>default_old</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = x
+expect(node, inputs=[x], outputs=[y],
+       name='test_dropout_default_old', opset_imports=[helper.make_opsetid("", 11)])
+```
+
+</details>
+
+
+<details>
+<summary>default_ratio</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r'],
+    outputs=['y'],
+    seed=seed
+)
+
+r = np.float32(0.1)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = dropout(x, r)
+expect(node, inputs=[x, r], outputs=[y], name='test_dropout_default_ratio')
+```
+
+</details>
+
+
+<details>
+<summary>random_old</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y'],
+    ratio=.2,
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = x
+expect(node, inputs=[x], outputs=[y],
+       name='test_dropout_random_old', opset_imports=[helper.make_opsetid("", 11)])
+```
+
+</details>
+
+
+<details>
+<summary>training</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.75)
+t = np.bool_(True)
+y = dropout(x, r, training_mode=t)
+expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout')
+```
+
+</details>
+
+
+<details>
+<summary>training_default</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.5)
+t = np.bool_(True)
+y = dropout(x, r, training_mode=t)
+expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout_default')
+```
+
+</details>
+
+
+<details>
+<summary>training_default_ratio_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.5)
+t = np.bool_(True)
+y, z = dropout(x, r, training_mode=t, return_mask=True)
+expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_default_mask')
+```
+
+</details>
+
+
+<details>
+<summary>training_default_zero_ratio</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.0)
+t = np.bool_(True)
+y = dropout(x, r, training_mode=t)
+expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout_zero_ratio')
+```
+
+</details>
+
+
+<details>
+<summary>training_default_zero_ratio_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.0)
+t = np.bool_(True)
+y, z = dropout(x, r, training_mode=t, return_mask=True)
+expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_zero_ratio_mask')
+```
+
+</details>
+
+
+<details>
+<summary>training_ratio_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.75)
+t = np.bool_(True)
+y, z = dropout(x, r, training_mode=t, return_mask=True)
+expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_mask')
+```
+
+</details>
+
+
+### <a name="DynamicQuantizeLinear"></a><a name="dynamicquantizelinear">**DynamicQuantizeLinear**</a>
+
+  A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data.
+  Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.
+  Scale is calculated as:
+  ```
+   y_scale = (max(x) - min(x))/(qmax - qmin)
+   * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
+   * data range is adjusted to include 0.
+  ```
+  Zero point is calculated as:
+  ```
+  intermediate_zero_point = qmin - min(x)/y_scale
+  y_zero_point = cast(round(saturate(itermediate_zero_point)))
+  * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
+  * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
+  * rounding to nearest ties to even.
+  ```
+  Data quantization formula is:
+  ```
+  y = saturate (round (x / y_scale) + y_zero_point)
+  * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
+  * rounding to nearest ties to even.
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T2</dt>
+<dd>Quantized output tensor</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Output scale. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>y_zero_point</tt> : T2</dt>
+<dd>Output zero point. It's a scalar, which means a per-tensor/layer quantization.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float)</dt>
+<dd>Constrain 'x' to float tensor.</dd>
+<dt><tt>T2</tt> : tensor(uint8)</dt>
+<dd>Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>dynamicquantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('DynamicQuantizeLinear',
+    inputs=['x'],
+    outputs=['y', 'y_scale', 'y_zero_point'],
+)
+
+# expected scale 0.0196078438 and zero point 153
+X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32)
+x_min = np.minimum(0, np.min(X))
+x_max = np.maximum(0, np.max(X))
+Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+       name='test_dynamicquantizelinear')
+
+# expected scale 0.0156862754 and zero point 255
+X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32)
+x_min = np.minimum(0, np.min(X))
+x_max = np.maximum(0, np.max(X))
+Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+       name='test_dynamicquantizelinear_max_adjusted')
+
+X = np.array([1, 2.1, 1.3, 2.5,
+              3.34, 4.0, 1.5, 2.6,
+              3.9, 4.0, 3.0, 2.345]).astype(np.float32).reshape((3, 4))
+
+# expected scale 0.0156862754 and zero point 0
+x_min = np.minimum(0, np.min(X))
+x_max = np.maximum(0, np.max(X))
+Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+       name='test_dynamicquantizelinear_min_adjusted')
+```
+
+</details>
+
+
+### <a name="Einsum"></a><a name="einsum">**Einsum**</a>
+
+  An einsum of the form ```term1, term2 -> output-term``` produces an output tensor using the following equation
+
+  ```output[output-term] = reduce-sum( input1[term1] * input2[term] )```
+
+  where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2)
+  that do not occur in the output-term.
+
+  The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation
+  convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to
+  an operand tensor, and the characters within the terms correspond to operands dimensions.
+
+  This sequence may be followed by "->" to separate the left and right hand side of the equation.
+  If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein
+  summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases,
+  output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the
+  equation.
+
+  When a dimension character is repeated in the left-hand side, it represents summation along the dimension.
+
+  The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions.
+  Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions.
+  The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the
+  beginning of the output. The equation string may contain space (U+0020) character.
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>equation</tt> : string (required)</dt>
+<dd>Einsum expression string.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>Inputs</tt> (variadic, differentiable) : T</dt>
+<dd>Operands</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Output</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to all numerical tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>einsum_batch_diagonal</summary>
+
+```python
+Eqn = '...ii ->...i'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x'],
+    outputs=['y'],
+    equation=Eqn
+)
+
+X = np.random.randn(3, 5, 5)
+Z = einsum_reference_implementation(Eqn, (X,))
+
+expect(node, inputs=[X], outputs=[Z], name='test_einsum_batch_diagonal')
+```
+
+</details>
+
+
+<details>
+<summary>einsum_batch_matmul</summary>
+
+```python
+Eqn = 'bij, bjk -> bik'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    equation=Eqn
+)
+
+X = np.random.randn(5, 2, 3)
+Y = np.random.randn(5, 3, 4)
+Z = einsum_reference_implementation(Eqn, (X, Y))
+
+expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_batch_matmul')
+```
+
+</details>
+
+
+<details>
+<summary>einsum_inner_prod</summary>
+
+```python
+Eqn = 'i,i'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    equation=Eqn
+)
+
+X = np.random.randn(5)
+Y = np.random.randn(5)
+Z = einsum_reference_implementation(Eqn, (X, Y))
+
+expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_inner_prod')
+```
+
+</details>
+
+
+<details>
+<summary>einsum_sum</summary>
+
+```python
+Eqn = 'ij->i'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x'],
+    outputs=['y'],
+    equation=Eqn
+)
+
+X = np.random.randn(3, 4)
+Z = einsum_reference_implementation(Eqn, (X,))
+
+expect(node, inputs=[X], outputs=[Z], name='test_einsum_sum')
+```
+
+</details>
+
+
+<details>
+<summary>einsum_transpose</summary>
+
+```python
+Eqn = 'ij->ji'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x'],
+    outputs=['y'],
+    equation=Eqn
+)
+
+X = np.random.randn(3, 4)
+Y = einsum_reference_implementation(Eqn, (X,))
+
+expect(node, inputs=[X], outputs=[Y], name='test_einsum_transpose')
+```
+
+</details>
+
+
+### <a name="Elu"></a><a name="elu">**Elu**</a>
+
+  Elu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
+  0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.
+
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Elu-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Coefficient of ELU.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>1D output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>elu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Elu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=2.0
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+# expected output [-1.2642411, 0., 1.]
+y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_elu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_elu')
+```
+
+</details>
+
+
+<details>
+<summary>elu_default</summary>
+
+```python
+default_alpha = 1.0
+node = onnx.helper.make_node(
+    'Elu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
+expect(node, inputs=[x], outputs=[y],
+       name='test_elu_default')
+```
+
+</details>
+
+
+### <a name="Equal"></a><a name="equal">**Equal**</a>
+
+  Returns the tensor resulted from performing the `equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Equal-1">1</a>, <a href="Changelog.md#Equal-7">7</a>, <a href="Changelog.md#Equal-11">11</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>equal</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Equal',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+z = np.equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_equal')
+```
+
+</details>
+
+
+<details>
+<summary>equal_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Equal',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+y = (np.random.randn(5) * 10).astype(np.int32)
+z = np.equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_equal_bcast')
+```
+
+</details>
+
+
+### <a name="Erf"></a><a name="erf">**Erf**</a>
+
+  Computes the error function of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Erf-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The error function of the input tensor computed element-wise. It has the same shape and type of the input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>erf</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Erf',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+y = np.vectorize(math.erf)(x).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_erf')
+```
+
+</details>
+
+
+### <a name="Exp"></a><a name="exp">**Exp**</a>
+
+  Calculates the exponential of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Exp-1">1</a>, <a href="Changelog.md#Exp-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The exponential of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>exp</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Exp',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.exp(x)  # expected output [0.36787945, 1., 2.71828175]
+expect(node, inputs=[x], outputs=[y],
+       name='test_exp_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.exp(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_exp')
+```
+
+</details>
+
+
+### <a name="Expand"></a><a name="expand">**Expand**</a>
+
+  Broadcast the input tensor following the given shape and the broadcast rule.
+  The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):
+  Dimensions are right alignment;
+  Two corresponding dimensions must have the same value, or one of them is equal to 1.
+  Also, this operator is similar to numpy.broadcast_to(input, shape),
+  but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size().
+  It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1,
+  or the shape.ndim < input.shape.ndim.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Expand-8">8</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>shape</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D tensor indicates the shape you want to expand to, following the broadcast rule</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>dim_changed</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Expand',
+    inputs=['data', 'new_shape'],
+    outputs=['expanded'],
+)
+shape = [3, 1]
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[1.], [2.], [3.]]
+new_shape = [2, 1, 6]
+expanded = data * np.ones(new_shape, dtype=np.float32)
+#print(expanded)
+#[[[1., 1., 1., 1., 1., 1.],
+#  [2., 2., 2., 2., 2., 2.],
+#  [3., 3., 3., 3., 3., 3.]],
+#
+# [[1., 1., 1., 1., 1., 1.],
+#  [2., 2., 2., 2., 2., 2.],
+#  [3., 3., 3., 3., 3., 3.]]]
+new_shape = np.array(new_shape, dtype=np.int64)
+expect(node, inputs=[data, new_shape], outputs=[expanded],
+       name='test_expand_dim_changed')
+```
+
+</details>
+
+
+<details>
+<summary>dim_unchanged</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Expand',
+    inputs=['data', 'new_shape'],
+    outputs=['expanded'],
+)
+shape = [3, 1]
+new_shape = [3, 4]
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[1.], [2.], [3.]]
+expanded = np.tile(data, 4)
+#print(expanded)
+#[[1., 1., 1., 1.],
+# [2., 2., 2., 2.],
+# [3., 3., 3., 3.]]
+new_shape = np.array(new_shape, dtype=np.int64)
+expect(node, inputs=[data, new_shape], outputs=[expanded],
+       name='test_expand_dim_unchanged')
+```
+
+</details>
+
+
+### <a name="EyeLike"></a><a name="eyelike">**EyeLike**</a>
+
+  Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D
+  tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the
+  same as the input tensor. The data type can be specified by the 'dtype' argument. If
+  'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal
+  is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals.
+  The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message and be valid as an output type.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.</dd>
+<dt><tt>k</tt> : int (default is 0)</dt>
+<dd>(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal,  and k < 0 populates a lower diagonal.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>2D input tensor to copy shape, and optionally, type information from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor, same shape as input tensor T1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain input types. Strings and complex are not supported.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)</dt>
+<dd>Constrain output types. Strings and complex are not supported.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>populate_off_main_diagonal</summary>
+
+```python
+shape = (4, 5)
+off_diagonal_offset = 1
+node = onnx.helper.make_node(
+    'EyeLike',
+    inputs=['x'],
+    outputs=['y'],
+    k=off_diagonal_offset,
+    dtype=onnx.TensorProto.FLOAT,
+)
+
+x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_eyelike_populate_off_main_diagonal')
+```
+
+</details>
+
+
+<details>
+<summary>with_dtype</summary>
+
+```python
+shape = (3, 4)
+node = onnx.helper.make_node(
+    'EyeLike',
+    inputs=['x'],
+    outputs=['y'],
+    dtype=onnx.TensorProto.DOUBLE,
+)
+
+x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+y = np.eye(shape[0], shape[1], dtype=np.float64)
+expect(node, inputs=[x], outputs=[y], name='test_eyelike_with_dtype')
+```
+
+</details>
+
+
+<details>
+<summary>without_dtype</summary>
+
+```python
+shape = (4, 4)
+node = onnx.helper.make_node(
+    'EyeLike',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+y = np.eye(shape[0], shape[1], dtype=np.int32)
+expect(node, inputs=[x], outputs=[y], name='test_eyelike_without_dtype')
+```
+
+</details>
+
+
+### <a name="Flatten"></a><a name="flatten">**Flatten**</a>
+
+  Flattens the input tensor into a 2D matrix. If input tensor has shape
+  (d_0, d_1, ... d_n) then the output will have shape
+  (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Flatten-1">1</a>, <a href="Changelog.md#Flatten-9">9</a>, <a href="Changelog.md#Flatten-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>A tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>flatten</summary>
+
+```python
+shape = (2, 3, 4, 5)
+a = np.random.random_sample(shape).astype(np.float32)
+
+for i in range(len(shape)):
+    node = onnx.helper.make_node(
+        'Flatten',
+        inputs=['a'],
+        outputs=['b'],
+        axis=i,
+    )
+
+    new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
+    b = np.reshape(a, new_shape)
+    expect(node, inputs=[a], outputs=[b],
+           name='test_flatten_axis' + str(i))
+```
+
+</details>
+
+
+<details>
+<summary>flatten_negative_axis</summary>
+
+```python
+shape = (2, 3, 4, 5)
+a = np.random.random_sample(shape).astype(np.float32)
+
+for i in range(-len(shape), 0):
+    node = onnx.helper.make_node(
+        'Flatten',
+        inputs=['a'],
+        outputs=['b'],
+        axis=i,
+    )
+
+    new_shape = (np.prod(shape[0:i]).astype(int), -1)
+    b = np.reshape(a, new_shape)
+    expect(node, inputs=[a], outputs=[b],
+           name='test_flatten_negative_axis' + str(abs(i)))
+```
+
+</details>
+
+
+<details>
+<summary>flatten_with_default_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Flatten',
+    inputs=['a'],
+    outputs=['b'],  # Default value for axis: axis=1
+)
+
+shape = (5, 4, 3, 2)
+a = np.random.random_sample(shape).astype(np.float32)
+new_shape = (5, 24)
+b = np.reshape(a, new_shape)
+expect(node, inputs=[a], outputs=[b],
+       name='test_flatten_default_axis')
+```
+
+</details>
+
+
+### <a name="Floor"></a><a name="floor">**Floor**</a>
+
+  Floor takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the floor is, y = floor(x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Floor-1">1</a>, <a href="Changelog.md#Floor-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>floor</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Floor',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1.5, 1.2, 2]).astype(np.float32)
+y = np.floor(x)  # expected output [-2., 1., 2.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_floor_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.floor(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_floor')
+```
+
+</details>
+
+
+### <a name="GRU"></a><a name="gru">**GRU**</a>
+
+  Computes an one-layer GRU. This operator is usually supported via some custom
+  implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `z` - update gate
+
+  `r` - reset gate
+
+  `h` - hidden gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+  `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+  `Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+  `Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+  `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+  `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+  `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+  `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh):
+
+    - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
+
+    - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
+
+    - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
+
+    - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
+
+    - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#GRU-1">1</a>, <a href="Changelog.md#GRU-3">3</a>, <a href="Changelog.md#GRU-7">7</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>layout</tt> : int (default is 0)</dt>
+<dd>The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].</dd>
+<dt><tt>linear_before_reset</tt> : int (default is 0)</dt>
+<dd>When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> (differentiable) : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0</dd>
+<dt><tt>sequence_lens</tt> (optional, non-differentiable) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional, differentiable) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>batchwise</summary>
+
+```python
+input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 6
+number_of_gates = 3
+weight_scale = 0.2
+layout = 1
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R'],
+    outputs=['Y', 'Y_h'],
+    hidden_size=hidden_size,
+    layout=layout
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+gru = GRU_Helper(X=input, W=W, R=R, layout=layout)
+Y, Y_h = gru.step()
+expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_gru_batchwise')
+```
+
+</details>
+
+
+<details>
+<summary>defaults</summary>
+
+```python
+input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 5
+weight_scale = 0.1
+number_of_gates = 3
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+gru = GRU_Helper(X=input, W=W, R=R)
+_, Y_h = gru.step()
+expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_gru_defaults')
+```
+
+</details>
+
+
+<details>
+<summary>initial_bias</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 3
+weight_scale = 0.1
+custom_bias = 0.1
+number_of_gates = 3
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+# Adding custom bias
+W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(np.float32)
+R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+gru = GRU_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = gru.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_gru_with_initial_bias')
+```
+
+</details>
+
+
+<details>
+<summary>seq_length</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
+                  [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 5
+number_of_gates = 3
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype(np.float32)
+R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(np.float32)
+
+# Adding custom bias
+W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+gru = GRU_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = gru.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_gru_seq_length')
+```
+
+</details>
+
+
+### <a name="Gather"></a><a name="gather">**Gather**</a>
+
+  Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+  entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+  them in an output tensor of rank q + (r - 1).
+
+  axis = 0 :
+
+  Let
+  k = indices[i_{0}, ..., i_{q-1}]
+  Then
+  output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]
+
+  ```
+    data = [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+    indices = [
+        [0, 1],
+        [1, 2],
+    ]
+    output = [
+        [
+            [1.0, 1.2],
+            [2.3, 3.4],
+        ],
+        [
+            [2.3, 3.4],
+            [4.5, 5.7],
+        ],
+    ]
+  ```
+  axis = 1 :
+
+  Let
+  k = indices[i_{0}, ..., i_{q-1}]
+  Then
+  output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]
+
+  ```
+    data = [
+        [1.0, 1.2, 1.9],
+        [2.3, 3.4, 3.9],
+        [4.5, 5.7, 5.9],
+    ]
+    indices = [
+        [0, 2],
+    ]
+    axis = 1,
+    output = [
+            [[1.0, 1.9]],
+            [[2.3, 3.9]],
+            [[4.5, 5.9]],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Gather-1">1</a>, <a href="Changelog.md#Gather-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + (r - 1).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>gather_0</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=0,
+)
+data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+indices = np.array([0, 1, 3])
+y = np.take(data, indices, axis=0)
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_0')
+```
+
+</details>
+
+
+<details>
+<summary>gather_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=1,
+)
+data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+indices = np.array([0, 1, 3])
+y = np.take(data, indices, axis=1)
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_1')
+```
+
+</details>
+
+
+<details>
+<summary>gather_2d_indices</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=1,
+)
+data = np.random.randn(3, 3).astype(np.float32)
+indices = np.array([[0, 2]])
+y = np.take(data, indices, axis=1)
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_2d_indices')
+```
+
+</details>
+
+
+<details>
+<summary>gather_negative_indices</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=0,
+)
+data = np.arange(10).astype(np.float32)
+indices = np.array([0, -9, -10])
+y = np.take(data, indices, axis=0)
+
+# print(y)
+# [0. 1. 0.]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_negative_indices')
+```
+
+</details>
+
+
+### <a name="GatherElements"></a><a name="gatherelements">**GatherElements**</a>
+
+  GatherElements takes two inputs `data` and `indices` of the same rank r >= 1
+  and an optional attribute `axis` that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). It is an indexing operation
+  that produces its output by indexing into the input data tensor at index
+  positions determined by elements of the `indices` tensor.
+  Its output shape is the same as the shape of `indices` and consists of one value
+  (gathered from the `data`) for each element in `indices`.
+
+  For instance, in the 3-D case (r = 3), the output produced is determined
+  by the following equations:
+  ```
+    out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
+    out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
+    out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
+  ```
+
+  This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation.
+
+  Example 1:
+  ```
+    data = [
+        [1, 2],
+        [3, 4],
+    ]
+    indices = [
+        [0, 0],
+        [1, 0],
+    ]
+    axis = 1
+    output = [
+        [1, 1],
+        [4, 3],
+    ]
+  ```
+  Example 2:
+  ```
+    data = [
+        [1, 2, 3],
+        [4, 5, 6],
+        [7, 8, 9],
+    ]
+    indices = [
+        [1, 2, 0],
+        [2, 0, 0],
+    ]
+    axis = 0
+    output = [
+        [4, 8, 3],
+        [7, 2, 3],
+    ]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#GatherElements-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of the same shape as indices.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>gather_elements_0</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'GatherElements',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1, 2],
+                 [3, 4]], dtype=np.float32)
+indices = np.array([[0, 0],
+                    [1, 0]], dtype=np.int32)
+
+y = gather_elements(data, indices, axis)
+# print(y) produces
+# [[1, 1],
+#  [4, 3]]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_elements_0')
+```
+
+</details>
+
+
+<details>
+<summary>gather_elements_1</summary>
+
+```python
+axis = 0
+node = onnx.helper.make_node(
+    'GatherElements',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1, 2, 3],
+                 [4, 5, 6],
+                 [7, 8, 9]], dtype=np.float32)
+indices = np.array([[1, 2, 0],
+                    [2, 0, 0]], dtype=np.int32)
+
+y = gather_elements(data, indices, axis)
+# print(y) produces
+# [[4, 8, 3],
+#  [7, 2, 3]]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_elements_1')
+```
+
+</details>
+
+
+<details>
+<summary>gather_elements_negative_indices</summary>
+
+```python
+axis = 0
+node = onnx.helper.make_node(
+    'GatherElements',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1, 2, 3],
+                 [4, 5, 6],
+                 [7, 8, 9]], dtype=np.float32)
+indices = np.array([[-1, -2, 0],
+                    [-2, 0, 0]], dtype=np.int32)
+
+y = gather_elements(data, indices, axis)
+# print(y) produces
+# [[7, 5, 3],
+#  [4, 2, 3]]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_elements_negative_indices')
+```
+
+</details>
+
+
+### <a name="GatherND"></a><a name="gathernd">**GatherND**</a>
+
+  Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers
+  slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`.
+
+  `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+  where each element defines a slice of `data`
+
+  `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of
+  `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension.
+
+  Some salient points about the inputs' rank and shape:
+
+  1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+  2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal.
+
+  3) b < min(q, r) is to be honored.
+
+  4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive)
+
+  5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+     It is an error if any of the index values are out of bounds.
+
+  The output is computed as follows:
+
+  The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+  1) If `indices_shape[-1] > r-b` => error condition
+
+  2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors
+     containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions
+     of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]`
+     is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below)
+
+  3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor
+     containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding
+     to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor
+     to form the `output` tensor (Examples 2, 3, 4 and 5 below)
+
+  This operator is the inverse of `ScatterND`.
+
+  `Example 1`
+
+    batch_dims = 0
+
+    data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+    indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+    output  = [0,3]           # output_shape = [2]
+
+  `Example 2`
+
+    batch_dims = 0
+
+    data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+    indices = [[1],[0]]      # indices_shape = [2, 1]
+
+    output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+  `Example 3`
+
+    batch_dims = 0
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+    output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+  `Example 4`
+
+    batch_dims = 0
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+    output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+  `Example 5`
+
+    batch_dims = 1
+
+    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+    indices = [[1],[0]]             # indices_shape = [2, 1]
+
+    output  = [[2,3],[4,5]]             # output_shape = [2, 2]
+
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#GatherND-11">11</a>, <a href="Changelog.md#GatherND-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>batch_dims</tt> : int (default is 0)</dt>
+<dd>The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>float32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GatherND',
+    inputs=['data', 'indices'],
+    outputs=['output'],
+)
+
+data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32)
+indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64)
+output = gather_nd_impl(data, indices, 0)
+expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32)
+assert (np.array_equal(output, expected_output))
+expect(node, inputs=[data, indices], outputs=[output],
+       name='test_gathernd_example_float32')
+```
+
+</details>
+
+
+<details>
+<summary>int32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GatherND',
+    inputs=['data', 'indices'],
+    outputs=['output'],
+)
+
+data = np.array([[0, 1], [2, 3]], dtype=np.int32)
+indices = np.array([[0, 0], [1, 1]], dtype=np.int64)
+output = gather_nd_impl(data, indices, 0)
+expected_output = np.array([0, 3], dtype=np.int32)
+assert (np.array_equal(output, expected_output))
+expect(node, inputs=[data, indices], outputs=[output],
+       name='test_gathernd_example_int32')
+```
+
+</details>
+
+
+<details>
+<summary>int32_batchdim_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GatherND',
+    inputs=['data', 'indices'],
+    outputs=['output'],
+    batch_dims=1,
+)
+
+data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32)
+indices = np.array([[1], [0]], dtype=np.int64)
+output = gather_nd_impl(data, indices, 1)
+expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32)
+assert (np.array_equal(output, expected_output))
+expect(node, inputs=[data, indices], outputs=[output],
+       name='test_gathernd_example_int32_batch_dim1')
+```
+
+</details>
+
+
+### <a name="Gemm"></a><a name="gemm">**Gemm**</a>
+
+  General Matrix multiplication:
+  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+  A' = transpose(A) if transA else A
+
+  B' = transpose(B) if transB else B
+
+  Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+  input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+  and output tensor Y has shape (M, N). A will be transposed before doing the
+  computation if attribute transA is non-zero, same for B and transB.
+  This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md).
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Gemm-1">1</a>, <a href="Changelog.md#Gemm-6">6</a>, <a href="Changelog.md#Gemm-7">7</a>, <a href="Changelog.md#Gemm-9">9</a>, <a href="Changelog.md#Gemm-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for the product of input tensors A * B.</dd>
+<dt><tt>beta</tt> : float (default is 1.0)</dt>
+<dd>Scalar multiplier for input tensor C.</dd>
+<dt><tt>transA</tt> : int (default is 0)</dt>
+<dd>Whether A should be transposed</dd>
+<dt><tt>transB</tt> : int (default is 0)</dt>
+<dd>Whether B should be transposed</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.</dd>
+<dt><tt>C</tt> (optional, differentiable) : T</dt>
+<dd>Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor of shape (M, N).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>all_attributes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    alpha=0.25,
+    beta=0.35,
+    transA=1,
+    transB=1
+)
+a = np.random.ranf([4, 3]).astype(np.float32)
+b = np.random.ranf([5, 4]).astype(np.float32)
+c = np.random.ranf([1, 5]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_all_attributes')
+```
+
+</details>
+
+
+<details>
+<summary>alpha</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    alpha=0.5
+)
+a = np.random.ranf([3, 5]).astype(np.float32)
+b = np.random.ranf([5, 4]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, alpha=0.5)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_alpha')
+```
+
+</details>
+
+
+<details>
+<summary>beta</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    beta=0.5
+)
+a = np.random.ranf([2, 7]).astype(np.float32)
+b = np.random.ranf([7, 4]).astype(np.float32)
+c = np.random.ranf([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, beta=0.5)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_beta')
+```
+
+</details>
+
+
+<details>
+<summary>default_matrix_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([3, 6]).astype(np.float32)
+b = np.random.ranf([6, 4]).astype(np.float32)
+c = np.random.ranf([3, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_matrix_bias')
+```
+
+</details>
+
+
+<details>
+<summary>default_no_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b'],
+    outputs=['y']
+)
+a = np.random.ranf([2, 10]).astype(np.float32)
+b = np.random.ranf([10, 3]).astype(np.float32)
+y = gemm_reference_implementation(a, b)
+expect(node, inputs=[a, b], outputs=[y],
+       name='test_gemm_default_no_bias')
+```
+
+</details>
+
+
+<details>
+<summary>default_scalar_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([2, 3]).astype(np.float32)
+b = np.random.ranf([3, 4]).astype(np.float32)
+c = np.array(3.14).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_scalar_bias')
+```
+
+</details>
+
+
+<details>
+<summary>default_single_elem_vector_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([3, 7]).astype(np.float32)
+b = np.random.ranf([7, 3]).astype(np.float32)
+c = np.random.ranf([1]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_single_elem_vector_bias')
+```
+
+</details>
+
+
+<details>
+<summary>default_vector_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([2, 7]).astype(np.float32)
+b = np.random.ranf([7, 4]).astype(np.float32)
+c = np.random.ranf([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_vector_bias')
+```
+
+</details>
+
+
+<details>
+<summary>default_zero_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([3, 5]).astype(np.float32)
+b = np.random.ranf([5, 4]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_zero_bias')
+```
+
+</details>
+
+
+<details>
+<summary>transposeA</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    transA=1
+)
+a = np.random.ranf([6, 3]).astype(np.float32)
+b = np.random.ranf([6, 4]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, transA=1)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_transposeA')
+```
+
+</details>
+
+
+<details>
+<summary>transposeB</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    transB=1
+)
+a = np.random.ranf([3, 6]).astype(np.float32)
+b = np.random.ranf([4, 6]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, transB=1)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_transposeB')
+```
+
+</details>
+
+
+### <a name="GlobalAveragePool"></a><a name="globalaveragepool">**GlobalAveragePool**</a>
+
+  GlobalAveragePool consumes an input tensor X and applies average pooling across
+   the values in the same channel. This is equivalent to AveragePool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>globalaveragepool</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GlobalAveragePool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+expect(node, inputs=[x], outputs=[y], name='test_globalaveragepool')
+```
+
+</details>
+
+
+<details>
+<summary>globalaveragepool_precomputed</summary>
+
+```python
+
+node = onnx.helper.make_node(
+    'GlobalAveragePool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[[
+    [1, 2, 3],
+    [4, 5, 6],
+    [7, 8, 9],
+]]]).astype(np.float32)
+y = np.array([[[[5]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_globalaveragepool_precomputed')
+```
+
+</details>
+
+
+### <a name="GlobalLpPool"></a><a name="globallppool">**GlobalLpPool**</a>
+
+  GlobalLpPool consumes an input tensor X and applies lp pool pooling across
+   the values in the same channel. This is equivalent to LpPool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 2 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#GlobalLpPool-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>p value of the Lp norm used to pool over the input data.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+### <a name="GlobalMaxPool"></a><a name="globalmaxpool">**GlobalMaxPool**</a>
+
+  GlobalMaxPool consumes an input tensor X and applies max pooling across
+   the values in the same channel. This is equivalent to MaxPool with kernel size
+   equal to the spatial dimension of input tensor.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>globalmaxpool</summary>
+
+```python
+
+node = onnx.helper.make_node(
+    'GlobalMaxPool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+expect(node, inputs=[x], outputs=[y], name='test_globalmaxpool')
+```
+
+</details>
+
+
+<details>
+<summary>globalmaxpool_precomputed</summary>
+
+```python
+
+node = onnx.helper.make_node(
+    'GlobalMaxPool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[[
+    [1, 2, 3],
+    [4, 5, 6],
+    [7, 8, 9],
+]]]).astype(np.float32)
+y = np.array([[[[9]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_globalmaxpool_precomputed')
+```
+
+</details>
+
+
+### <a name="Greater"></a><a name="greater">**Greater**</a>
+
+  Returns the tensor resulted from performing the `greater` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Greater-1">1</a>, <a href="Changelog.md#Greater-7">7</a>, <a href="Changelog.md#Greater-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>greater</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Greater',
+    inputs=['x', 'y'],
+    outputs=['greater'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.greater(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater')
+```
+
+</details>
+
+
+<details>
+<summary>greater</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GreaterOrEqual',
+    inputs=['x', 'y'],
+    outputs=['greater_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.greater_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater_equal')
+```
+
+</details>
+
+
+<details>
+<summary>greater_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Greater',
+    inputs=['x', 'y'],
+    outputs=['greater'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.greater(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater_bcast')
+```
+
+</details>
+
+
+<details>
+<summary>greater_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GreaterOrEqual',
+    inputs=['x', 'y'],
+    outputs=['greater_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.greater_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater_equal_bcast')
+```
+
+</details>
+
+
+### <a name="GreaterOrEqual"></a><a name="greaterorequal">**GreaterOrEqual**</a>
+
+  Returns the tensor resulted from performing the `greater_equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#GreaterOrEqual-12">12</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+### <a name="GridSample"></a><a name="gridsample">**GridSample**</a>
+
+  Given an `input` and a flow-field `grid`, computes the `output` using `input` values and pixel locations from `grid`.
+  Currently, only spatial (4-D) inputs are supported. For `input` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2),
+  the `output` will have shape (N, C, H_out, W_out).
+  For each output location `output[N, C, H_out, W_out]`, the size-2 vector `grid[N, H_out, W_out]` specifies `input` pixel locations `x` and `y`,
+  which are used to interpolate the output value `output[N, C, H_out, W_out]`.
+
+  The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025).
+  See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>align_corners</tt> : int (default is 0)</dt>
+<dd>If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.</dd>
+<dt><tt>mode</tt> : string (default is bilinear)</dt>
+<dd>Three interpolation modes: bilinear (default), nearest and bicubic.</dd>
+<dt><tt>padding_mode</tt> : string (default is zeros)</dt>
+<dd>Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T1</dt>
+<dd>4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.</dd>
+<dt><tt>grid</tt> (non-differentiable) : T1</dt>
+<dd>Input offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T2</dt>
+<dd>4-D tensor of shape (N, C, H_out, W_out).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input types to all tensor types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>gridsample</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bilinear',
+    padding_mode='zeros',
+    align_corners=0,
+)
+# X shape, [N, C, H, W] - [1, 1, 4, 4]
+X = np.array(
+    [
+        [
+            [
+                [0., 1., 2., 3.],
+                [4., 5., 6., 7.],
+                [8., 9., 10., 11.],
+                [12., 13., 14., 15.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
+Grid = np.array(
+    [
+        [
+            [
+                [-1.0000, -1.0000],
+                [-0.6000, -1.0000],
+                [-0.2000, -1.0000],
+                [0.2000, -1.0000],
+                [0.6000, -1.0000],
+                [1.0000, -1.0000]
+            ],
+            [
+                [-1.0000, -0.6000],
+                [-0.6000, -0.6000],
+                [-0.2000, -0.6000],
+                [0.2000, -0.6000],
+                [0.6000, -0.6000],
+                [1.0000, -0.6000]
+            ],
+            [
+                [-1.0000, -0.2000],
+                [-0.6000, -0.2000],
+                [-0.2000, -0.2000],
+                [0.2000, -0.2000],
+                [0.6000, -0.2000],
+                [1.0000, -0.2000]
+            ],
+            [
+                [-1.0000, 0.2000],
+                [-0.6000, 0.2000],
+                [-0.2000, 0.2000],
+                [0.2000, 0.2000],
+                [0.6000, 0.2000],
+                [1.0000, 0.2000]
+            ],
+            [
+                [-1.0000, 0.6000],
+                [-0.6000, 0.6000],
+                [-0.2000, 0.6000],
+                [0.2000, 0.6000],
+                [0.6000, 0.6000],
+                [1.0000, 0.6000]
+            ],
+            [
+                [-1.0000, 1.0000],
+                [-0.6000, 1.0000],
+                [-0.2000, 1.0000],
+                [0.2000, 1.0000],
+                [0.6000, 1.0000],
+                [1.0000, 1.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
+Y = np.array(
+    [
+        [
+            [
+                [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
+                [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
+                [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
+                [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
+                [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
+                [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+expect(node, inputs=[X, Grid], outputs=[Y],
+       name='test_gridsample')
+```
+
+</details>
+
+
+<details>
+<summary>gridsample_mode_aligncorners</summary>
+
+```python
+# X shape, [N, C, H, W] - [1, 1, 3, 2]
+X = np.array(
+    [
+        [
+            [
+                [0., 1.],
+                [2., 3.],
+                [4., 5.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+Grid = np.array(
+    [
+        [
+            [
+                [-1.0000, -1.0000],
+                [-0.5000, -0.5000],
+                [-0.2000, -0.2000],
+                [0.0000, 0.0000]
+            ],
+
+            [
+                [0.0000, 0.0000],
+                [-0.2000, -0.2000],
+                [0.5000, 0.5000],
+                [1.0000, 1.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+# setting mode = 'bilinear', default align_corners = 0
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bilinear',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_bilinear = np.array(
+    [
+        [
+            [
+                [0.0000, 0.5000, 1.7000, 2.5000],
+                [2.5000, 1.7000, 4.5000, 1.2500]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_bilinear],
+       name='test_gridsample_bilinear')
+
+# setting mode = 'bilinear', align_corners = 1
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bilinear',
+    align_corners=1,
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_align_corners = np.array(
+    [
+        [
+            [
+                [0.0000, 1.2500, 2.0000, 2.5000],
+                [2.5000, 2.0000, 3.7500, 5.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_align_corners],
+       name='test_gridsample_aligncorners_true')
+
+# setting mode = 'nearest'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='nearest',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_nearest = np.array(
+    [
+        [
+            [
+                [0., 0., 2., 2.],
+                [2., 2., 5., 0.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_nearest],
+       name='test_gridsample_nearest')
+
+# setting mode = 'bicubic'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bicubic',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_bicubic = np.array(
+    [
+        [
+            [
+                [-0.1406, 0.3828, 1.7556, 2.9688],
+                [2.9688, 1.7556, 5.1445, 1.3906]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_bicubic],
+       name='test_gridsample_bicubic')
+```
+
+</details>
+
+
+<details>
+<summary>gridsample_paddingmode</summary>
+
+```python
+# X shape, [N, C, H, W] - [1, 1, 3, 2]
+X = np.array(
+    [
+        [
+            [
+                [0., 1.],
+                [2., 3.],
+                [4., 5.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+Grid = np.array(
+    [
+        [
+            [
+                [-10.0000, -10.0000],
+                [-5.0000, -5.0000],
+                [-0.2000, -0.2000],
+                [10.0000, 10.0000]
+            ],
+
+            [
+                [10.0000, 10.0000],
+                [-0.2000, -0.2000],
+                [5.0000, 5.0000],
+                [10.0000, 10.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+# setting padding_mode = 'zeros'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    padding_mode='zeros',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_zeros = np.array(
+    [
+        [
+            [
+                [0.0000, 0.0000, 1.7000, 0.0000],
+                [0.0000, 1.7000, 0.0000, 0.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_zeros],
+       name='test_gridsample_zeros_padding')
+
+# setting padding_mode = 'border'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    padding_mode='border',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_border = np.array(
+    [
+        [
+            [
+                [0.0000, 0.0000, 1.7000, 5.0000],
+                [5.0000, 1.7000, 5.0000, 5.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_border],
+       name='test_gridsample_border_padding')
+
+# setting padding_mode = 'reflection'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    padding_mode='reflection',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_reflection = np.array(
+    [
+        [
+            [
+                [2.5000, 0.0000, 1.7000, 2.5000],
+                [2.5000, 1.7000, 5.0000, 2.5000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_reflection],
+       name='test_gridsample_reflection_padding')
+```
+
+</details>
+
+
+### <a name="HardSigmoid"></a><a name="hardsigmoid">**HardSigmoid**</a>
+
+  HardSigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#HardSigmoid-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.2)</dt>
+<dd>Value of alpha.</dd>
+<dt><tt>beta</tt> : float (default is 0.5)</dt>
+<dd>Value of beta.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>hardsigmoid</summary>
+
+```python
+node = onnx.helper.make_node(
+    'HardSigmoid',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=0.5,
+    beta=0.6
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardsigmoid_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x * 0.5 + 0.6, 0, 1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardsigmoid')
+```
+
+</details>
+
+
+<details>
+<summary>hardsigmoid_default</summary>
+
+```python
+default_alpha = 0.2
+default_beta = 0.5
+node = onnx.helper.make_node(
+    'HardSigmoid',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x * default_alpha + default_beta, 0, 1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardsigmoid_default')
+```
+
+</details>
+
+
+### <a name="HardSwish"></a><a name="hardswish">**HardSwish**</a>
+
+  HardSwish takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where
+  the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid<alpha, beta>(x),
+  where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>hardswish</summary>
+
+```python
+node = onnx.helper.make_node(
+    'HardSwish',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = hardswish(x)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardswish')
+```
+
+</details>
+
+
+### <a name="Hardmax"></a><a name="hardmax">**Hardmax**</a>
+
+  The operator computes the hardmax values for the given input:
+
+   Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise
+
+  The "axis" attribute indicates the dimension along which Hardmax
+  will be performed. The output tensor has the same shape
+  and contains the Hardmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Hardmax-1">1</a>, <a href="Changelog.md#Hardmax-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>
+Describes the dimension Hardmax will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output values with the same shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>hardmax</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2],
+              [0, 1, 2, 3]]).astype(np.float32)
+# expect result:
+# [[1. 0. 0. 0.]
+# [0. 1. 0. 0.]
+# [0. 0. 1. 0.]
+# [0. 0. 0. 1.]]
+y = hardmax(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_example')
+
+# For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
+x = np.array([[3, 3, 3, 1]]).astype(np.float32)
+# expect result:
+# [[1, 0, 0, 0]]
+y = hardmax(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_one_hot')
+```
+
+</details>
+
+
+<details>
+<summary>hardmax_axis</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=0,
+)
+y = hardmax(x, axis=0)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_axis_0')
+
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=1,
+)
+y = hardmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_axis_1')
+
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=2,
+)
+y = hardmax(x, axis=2)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_axis_2')
+
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=-1,
+)
+y = hardmax(x, axis=-1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_negative_axis')
+
+# default axis is -1
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_default_axis')
+```
+
+</details>
+
+
+### <a name="Identity"></a><a name="identity">**Identity**</a>
+
+  Identity operator
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Identity-1">1</a>, <a href="Changelog.md#Identity-13">13</a>, <a href="Changelog.md#Identity-14">14</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : V</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : V</dt>
+<dd>Tensor to copy input into.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain input and output types to all tensor, sequence, and optional types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>identity</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Identity',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+expect(node, inputs=[data], outputs=[data],
+       name='test_identity')
+```
+
+</details>
+
+
+<details>
+<summary>identity_opt</summary>
+
+```python
+ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['opt_in'],
+    outputs=['opt_out']
+)
+
+x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+
+expect(identity_node, inputs=[x], outputs=[x], name='test_identity_opt',
+       opset_imports=[onnx.helper.make_opsetid("", 16)],
+       input_type_protos=[opt_in_tp],
+       output_type_protos=[opt_in_tp])
+```
+
+</details>
+
+
+<details>
+<summary>sequence</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Identity',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+data = [
+    np.array([[[
+        [1, 2],
+        [3, 4],
+    ]]], dtype=np.float32),
+    np.array([[[
+        [2, 3],
+        [1, 5],
+    ]]], dtype=np.float32)]
+
+expect(node, inputs=[data], outputs=[data], name='test_identity_sequence')
+```
+
+</details>
+
+
+### <a name="If"></a><a name="if">**If**</a>
+
+  If conditional
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#If-1">1</a>, <a href="Changelog.md#If-11">11</a>, <a href="Changelog.md#If-13">13</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>else_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.</dd>
+<dt><tt>then_branch</tt> : graph (required)</dt>
+<dd>Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>cond</tt> : B</dt>
+<dd>Condition for the if</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Only bool</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>if</summary>
+
+```python
+# Given a bool scalar input cond.
+# return constant tensor x if cond is True, otherwise return constant tensor y.
+
+then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [5])
+else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [5])
+
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
+
+then_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['then_out'],
+    value=onnx.numpy_helper.from_array(x)
+)
+
+else_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['else_out'],
+    value=onnx.numpy_helper.from_array(y)
+)
+
+then_body = onnx.helper.make_graph(
+    [then_const_node],
+    'then_body',
+    [],
+    [then_out]
+)
+
+else_body = onnx.helper.make_graph(
+    [else_const_node],
+    'else_body',
+    [],
+    [else_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['cond'],
+    outputs=['res'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+cond = np.array(1).astype(bool)
+res = x if cond else y
+expect(if_node, inputs=[cond], outputs=[res], name='test_if',
+       opset_imports=[onnx.helper.make_opsetid("", 11)])
+```
+
+</details>
+
+
+<details>
+<summary>if_optional</summary>
+
+```python
+# Given a bool scalar input cond, return an empty optional sequence of
+# tensor if True, return an optional sequence with value x
+# (the input optional sequence) otherwise.
+
+ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+
+then_out_tensor_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp)
+then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp)
+then_out = onnx.helper.make_value_info('optional_empty', then_out_opt_tp)
+
+else_out_tensor_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp)
+else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp)
+else_out = onnx.helper.make_value_info('else_opt', else_out_opt_tp)
+
+x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+cond = np.array(0).astype(bool)
+res = compute_if_outputs(x, cond)
+
+opt_empty_in = onnx.helper.make_node(
+    'Optional',
+    inputs=[],
+    outputs=['optional_empty'],
+    type=seq_in_tp
+)
+
+then_body = onnx.helper.make_graph(
+    [opt_empty_in],
+    'then_body',
+    [],
+    [then_out]
+)
+
+else_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.numpy_helper.from_array(x[0])
+)
+
+else_seq_node = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['x'],
+    outputs=['else_seq']
+)
+
+else_optional_seq_node = onnx.helper.make_node(
+    'Optional',
+    inputs=['else_seq'],
+    outputs=['else_opt']
+)
+
+else_body = onnx.helper.make_graph(
+    [else_const_node, else_seq_node, else_optional_seq_node],
+    'else_body',
+    [],
+    [else_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['cond'],
+    outputs=['sequence'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+expect(if_node, inputs=[cond], outputs=[res], name='test_if_opt',
+       output_type_protos=[else_out_opt_tp],
+       opset_imports=[onnx.helper.make_opsetid("", 16)])
+```
+
+</details>
+
+
+<details>
+<summary>if_seq</summary>
+
+```python
+# Given a bool scalar input cond.
+# return constant sequence x if cond is True, otherwise return constant sequence y.
+
+then_out = onnx.helper.make_tensor_sequence_value_info('then_out', onnx.TensorProto.FLOAT, shape=[5])
+else_out = onnx.helper.make_tensor_sequence_value_info('else_out', onnx.TensorProto.FLOAT, shape=[5])
+
+x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)]
+
+then_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.numpy_helper.from_array(x[0])
+)
+
+then_seq_node = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['x'],
+    outputs=['then_out']
+)
+
+else_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['y'],
+    value=onnx.numpy_helper.from_array(y[0])
+)
+
+else_seq_node = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['y'],
+    outputs=['else_out']
+)
+
+then_body = onnx.helper.make_graph(
+    [then_const_node, then_seq_node],
+    'then_body',
+    [],
+    [then_out]
+)
+
+else_body = onnx.helper.make_graph(
+    [else_const_node, else_seq_node],
+    'else_body',
+    [],
+    [else_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['cond'],
+    outputs=['res'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+cond = np.array(1).astype(bool)
+res = x if cond else y
+expect(if_node, inputs=[cond], outputs=[res], name='test_if_seq',
+       opset_imports=[onnx.helper.make_opsetid("", 13)])
+```
+
+</details>
+
+
+### <a name="InstanceNormalization"></a><a name="instancenormalization">**InstanceNormalization**</a>
+
+  Carries out instance normalization as described in the paper
+  https://arxiv.org/abs/1607.08022.
+
+  y = scale * (x - mean) / sqrt(variance + epsilon) + B,
+  where mean and variance are computed per instance per channel.
+
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#InstanceNormalization-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+<dt><tt>scale</tt> (differentiable) : T</dt>
+<dd>The input 1-dimensional scale tensor of size C.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>The input 1-dimensional bias tensor of size C.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output tensor of the same shape as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>instancenormalization</summary>
+
+```python
+def _instancenorm_test_mode(x, s, bias, epsilon=1e-5):  # type: ignore
+    dims_x = len(x.shape)
+    axis = tuple(range(2, dims_x))
+    mean = np.mean(x, axis=axis, keepdims=True)
+    var = np.var(x, axis=axis, keepdims=True)
+    dim_ones = (1,) * (dims_x - 2)
+    s = s.reshape(-1, *dim_ones)
+    bias = bias.reshape(-1, *dim_ones)
+    return s * (x - mean) / np.sqrt(var + epsilon) + bias
+
+# input size: (1, 2, 1, 3)
+x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
+s = np.array([1.0, 1.5]).astype(np.float32)
+bias = np.array([0, 1]).astype(np.float32)
+y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'InstanceNormalization',
+    inputs=['x', 's', 'bias'],
+    outputs=['y'],
+)
+
+# output size: (1, 2, 1, 3)
+expect(node, inputs=[x, s, bias], outputs=[y],
+       name='test_instancenorm_example')
+
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+epsilon = 1e-2
+y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'InstanceNormalization',
+    inputs=['x', 's', 'bias'],
+    outputs=['y'],
+    epsilon=epsilon,
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias], outputs=[y],
+       name='test_instancenorm_epsilon')
+```
+
+</details>
+
+
+### <a name="IsInf"></a><a name="isinf">**IsInf**</a>
+
+  Map infinity to true and other values to false.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>detect_negative</tt> : int (default is 1)</dt>
+<dd>(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.</dd>
+<dt><tt>detect_positive</tt> : int (default is 1)</dt>
+<dd>(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T1</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T2</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output types to boolean tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>infinity</summary>
+
+```python
+node = onnx.helper.make_node('IsInf',
+                             inputs=['x'],
+                             outputs=['y'],
+                             )
+
+x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
+             dtype=np.float32)
+y = np.isinf(x)
+expect(node, inputs=[x], outputs=[y], name='test_isinf')
+```
+
+</details>
+
+
+<details>
+<summary>negative_infinity_only</summary>
+
+```python
+node = onnx.helper.make_node('IsInf',
+                             inputs=['x'],
+                             outputs=['y'],
+                             detect_positive=0
+                             )
+
+x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
+             dtype=np.float32)
+y = np.isneginf(x)
+expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')
+```
+
+</details>
+
+
+<details>
+<summary>positive_infinity_only</summary>
+
+```python
+node = onnx.helper.make_node('IsInf',
+                             inputs=['x'],
+                             outputs=['y'],
+                             detect_negative=0
+                             )
+
+x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
+             dtype=np.float32)
+y = np.isposinf(x)
+expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')
+```
+
+</details>
+
+
+### <a name="IsNaN"></a><a name="isnan">**IsNaN**</a>
+
+  Returns which elements of the input are NaN.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#IsNaN-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T1</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T2</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(bool)</dt>
+<dd>Constrain output types to boolean tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>isnan</summary>
+
+```python
+node = onnx.helper.make_node(
+    'IsNaN',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([3.0, np.nan, 4.0, np.nan], dtype=np.float32)
+y = np.isnan(x)
+expect(node, inputs=[x], outputs=[y], name='test_isnan')
+```
+
+</details>
+
+
+### <a name="LRN"></a><a name="lrn">**LRN**</a>
+
+  Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+  It normalizes over local input regions.
+  The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+  of shape (N x C x D1 x D2, ..., Dk), its region is
+  {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
+
+  square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+  where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
+
+  Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#LRN-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.0001)</dt>
+<dd>Scaling parameter.</dd>
+<dt><tt>beta</tt> : float (default is 0.75)</dt>
+<dd>The exponent.</dd>
+<dt><tt>bias</tt> : float (default is 1.0)</dt>
+<dd></dd>
+<dt><tt>size</tt> : int (required)</dt>
+<dd>The number of channels to sum over</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor, which has the shape and type as input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output  types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default</summary>
+
+```python
+alpha = 0.0001
+beta = 0.75
+bias = 1.0
+nsize = 3
+node = onnx.helper.make_node(
+    'LRN',
+    inputs=['x'],
+    outputs=['y'],
+    size=3
+)
+x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+for n, c, h, w in np.ndindex(x.shape):
+    square_sum[n, c, h, w] = sum(x[n,
+                                   max(0, c - int(math.floor((nsize - 1) / 2))):min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
+                                   h,
+                                   w] ** 2)
+y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+expect(node, inputs=[x], outputs=[y],
+       name='test_lrn_default')
+```
+
+</details>
+
+
+<details>
+<summary>lrn</summary>
+
+```python
+alpha = 0.0002
+beta = 0.5
+bias = 2.0
+nsize = 3
+node = onnx.helper.make_node(
+    'LRN',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=alpha,
+    beta=beta,
+    bias=bias,
+    size=nsize
+)
+x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+for n, c, h, w in np.ndindex(x.shape):
+    square_sum[n, c, h, w] = sum(x[n,
+                                   max(0, c - int(math.floor((nsize - 1) / 2))):min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
+                                   h,
+                                   w] ** 2)
+y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+expect(node, inputs=[x], outputs=[y],
+       name='test_lrn')
+```
+
+</details>
+
+
+### <a name="LSTM"></a><a name="lstm">**LSTM**</a>
+
+  Computes an one-layer LSTM. This operator is usually supported via some
+  custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `o` - output gate
+
+  `f` - forget gate
+
+  `c` - cell gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+  `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+  `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+  `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+  `P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+  `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+  `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+  `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+  `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+  `PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+    - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
+
+    - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
+
+    - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+
+    - Ct = ft (.) Ct-1 + it (.) ct
+
+    - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
+
+    - Ht = ot (.) h(Ct)
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#LSTM-1">1</a>, <a href="Changelog.md#LSTM-7">7</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings</dt>
+<dd>A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>input_forget</tt> : int (default is 0)</dt>
+<dd>Couple the input and forget gates if 1.</dd>
+<dt><tt>layout</tt> : int (default is 0)</dt>
+<dd>The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].</dd>
+</dl>
+
+#### Inputs (3 - 8)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> (differentiable) : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional, non-differentiable) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>initial_c</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>P</tt> (optional, differentiable) : T</dt>
+<dd>The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.</dd>
+</dl>
+
+#### Outputs (0 - 3)
+
+<dl>
+<dt><tt>Y</tt> (optional, differentiable) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+<dt><tt>Y_c</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>batchwise</summary>
+
+```python
+input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 7
+weight_scale = 0.3
+number_of_gates = 4
+layout = 1
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R'],
+    outputs=['Y', 'Y_h'],
+    hidden_size=hidden_size,
+    layout=layout
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+lstm = LSTM_Helper(X=input, W=W, R=R, layout=layout)
+Y, Y_h = lstm.step()
+expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_lstm_batchwise')
+```
+
+</details>
+
+
+<details>
+<summary>defaults</summary>
+
+```python
+input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 3
+weight_scale = 0.1
+number_of_gates = 4
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+lstm = LSTM_Helper(X=input, W=W, R=R)
+_, Y_h = lstm.step()
+expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_lstm_defaults')
+```
+
+</details>
+
+
+<details>
+<summary>initial_bias</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 4
+weight_scale = 0.1
+custom_bias = 0.1
+number_of_gates = 4
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+# Adding custom bias
+W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(np.float32)
+R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+B = np.concatenate((W_B, R_B), 1)
+
+lstm = LSTM_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = lstm.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_lstm_with_initial_bias')
+```
+
+</details>
+
+
+<details>
+<summary>peepholes</summary>
+
+```python
+input = np.array([[[1., 2., 3., 4.], [5., 6., 7., 8.]]]).astype(np.float32)
+
+input_size = 4
+hidden_size = 3
+weight_scale = 0.1
+number_of_gates = 4
+number_of_peepholes = 3
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R', 'B', 'sequence_lens', 'initial_h', 'initial_c', 'P'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+# Initializing Inputs
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32)
+seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32)
+init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype(np.float32)
+
+lstm = LSTM_Helper(X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h)
+_, Y_h = lstm.step()
+expect(node, inputs=[input, W, R, B, seq_lens, init_h, init_c, P], outputs=[Y_h.astype(np.float32)],
+       name='test_lstm_with_peepholes')
+```
+
+</details>
+
+
+### <a name="LayerNormalization"></a><a name="layernormalization">**LayerNormalization**</a>
+
+  This is layer normalization defined in ONNX as function.
+        The overall computation can be split into two stages.
+        The first stage is standardization, which makes the
+        normalized elements have zero mean and unit variances.
+        The computation required by standardization can be
+        described by the following equations.
+        ```
+        Mean = ReduceMean<axes=normalized_axes>(X)
+        D = Sub(X, Mean)
+        DD = Mul(Diff, Diff)
+        Var = ReduceMean<axes=normalized_axes>(DD)
+        VarEps = Add(Var, epsilon)
+        StdDev = Sqrt(VarEps)
+        InvStdDev = Reciprocal(StdDev)
+        Normalized = Mul(D, InvStdDev)
+        ```
+        where `normalized_axes` is `[axis, ..., rank of X - 1]`.
+        The variables `Var` and `StdDev` stand for variance and
+        standard deviation, respectively. The second output is
+        `Mean` and the last one is `InvStdDev`.
+        Depending on `stash_type` attribute, the actual computation
+        must happen in different floating-point precision.
+        For example, if `stash_type` is 1, this operator casts
+        all input variables to 32-bit float, perform the computation, and
+        finally cast `Normalized` back to the original type of `X`.
+        The second stage then scales and shifts the outcome of the
+        first stage using
+        ```
+        NormalizedScaled = Mul(Normalized, Scale)
+        Y = Add(NormalizedScaled, B)
+        ```
+        The second stage doesn't depends on `stash_type`.
+        All equations are in [this syntax](https://github.com/onnx/onnx/blob/main/docs/Syntax.md).
+        The same variable (i.e., input, output, and attribute) uses
+        the same name in the equations above and this operator's definition.
+        Let `d[i]` indicate the i-th dimension of `X`.
+        If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`,
+        the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`.
+        `Y` and `X` have the same shape.
+
+#### Version
+
+This version of the operator has been available since version 17 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.</dd>
+<dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
+<dd>The epsilon value to use to avoid division by zero.</dd>
+<dt><tt>stash_type</tt> : int (default is 1)</dt>
+<dd>Type of Mean and InvStdDev. This also specifies stage one's computation precision.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Tensor to be normalized.</dd>
+<dt><tt>Scale</tt> : T</dt>
+<dd>Scale tensor.</dd>
+<dt><tt>B</tt> (optional) : T</dt>
+<dd>Bias tensor.</dd>
+</dl>
+
+#### Outputs (1 - 3)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Normalized tensor.</dd>
+<dt><tt>Mean</tt> (optional) : U</dt>
+<dd>Saved mean used during training to speed up gradient computation</dd>
+<dt><tt>InvStdDev</tt> (optional) : U</dt>
+<dd>Saved inverse standard deviation used during training to speed up gradient computation.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types and output Y type to float tensors.</dd>
+<dt><tt>U</tt> : tensor(float), tensor(bfloat16)</dt>
+<dd>Type of Mean and InvStdDev tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>d</summary>
+
+```python
+X = np.random.randn(3, 4).astype(np.float32)
+
+def case(axis: int) -> None:
+    normalized_shape = calculate_normalized_shape(X.shape, axis)
+    W = np.random.randn(*normalized_shape).astype(np.float32)
+    B = np.random.randn(*normalized_shape).astype(np.float32)
+    Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
+
+    node = onnx.helper.make_node(
+        'LayerNormalization',
+        inputs=['X', 'W', 'B'],
+        outputs=['Y', 'Mean', 'InvStdDev'],
+        axis=axis,
+    )
+
+    if axis < 0:
+        name = f'test_layer_normalization_2d_axis_negative_{-axis}'
+    else:
+        name = f'test_layer_normalization_2d_axis{axis}'
+
+    expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+           name=name)
+
+for i in range(len(X.shape)):
+    case(i)
+    case(i - len(X.shape))
+```
+
+</details>
+
+
+<details>
+<summary>d_epsilon</summary>
+
+```python
+X = np.random.randn(2, 3, 5).astype(np.float32)
+
+def case(axis: int) -> None:
+    normalized_shape = calculate_normalized_shape(X.shape, axis)
+    W = np.random.randn(*normalized_shape).astype(np.float32)
+    B = np.random.randn(*normalized_shape).astype(np.float32)
+    Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+    node = onnx.helper.make_node(
+        'LayerNormalization',
+        inputs=['X', 'W', 'B'],
+        outputs=['Y', 'Mean', 'InvStdDev'],
+        axis=axis,
+        epsilon=1e-1
+    )
+
+    if axis < 0:
+        name = f'test_layer_normalization_3d_axis_negative_{-axis}_epsilon'
+    else:
+        name = f'test_layer_normalization_3d_axis{axis}_epsilon'
+
+    expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+           name=name)
+
+for i in range(len(X.shape)):
+    case(i)
+    case(i - len(X.shape))
+```
+
+</details>
+
+
+<details>
+<summary>default_axis</summary>
+
+```python
+X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+# Default axis in LayerNormalization is -1.
+normalized_shape = calculate_normalized_shape(X.shape, -1)
+W = np.random.randn(*normalized_shape).astype(np.float32)
+B = np.random.randn(*normalized_shape).astype(np.float32)
+# Axis is default to -1 in the reference implementation.
+Y, mean, inv_std_dev = _layer_normalization(X, W, B)
+
+# Not specifying axis attribute means -1.
+node = onnx.helper.make_node(
+    'LayerNormalization',
+    inputs=['X', 'W', 'B'],
+    outputs=['Y', 'Mean', 'InvStdDev']
+)
+
+expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+        name='test_layer_normalization_default_axis')
+```
+
+</details>
+
+
+<details>
+<summary>layernormalization</summary>
+
+```python
+X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+def case(axis: int) -> None:
+    normalized_shape = calculate_normalized_shape(X.shape, axis)
+    W = np.random.randn(*normalized_shape).astype(np.float32)
+    B = np.random.randn(*normalized_shape).astype(np.float32)
+    Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+
+    node = onnx.helper.make_node(
+        'LayerNormalization',
+        inputs=['X', 'W', 'B'],
+        outputs=['Y', 'Mean', 'InvStdDev'],
+        axis=axis,
+    )
+
+    if axis < 0:
+        name = f'test_layer_normalization_4d_axis_negative_{-axis}'
+    else:
+        name = f'test_layer_normalization_4d_axis{axis}'
+
+    expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+           name=name)
+
+for i in range(len(X.shape)):
+    case(i)
+    case(i - len(X.shape))
+```
+
+</details>
+
+
+### <a name="LeakyRelu"></a><a name="leakyrelu">**LeakyRelu**</a>
+
+  LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+  output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+  `f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+
+  **History**
+  - Version 16 adds bfloat16 to the types allowed.
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#LeakyRelu-1">1</a>, <a href="Changelog.md#LeakyRelu-6">6</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.01)</dt>
+<dd>Coefficient of leakage.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>leakyrelu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LeakyRelu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=0.1
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+# expected output [-0.1, 0., 1.]
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+expect(node, inputs=[x], outputs=[y],
+       name='test_leakyrelu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+expect(node, inputs=[x], outputs=[y],
+       name='test_leakyrelu')
+```
+
+</details>
+
+
+<details>
+<summary>leakyrelu_default</summary>
+
+```python
+default_alpha = 0.01
+node = onnx.helper.make_node(
+    'LeakyRelu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha
+expect(node, inputs=[x], outputs=[y],
+       name='test_leakyrelu_default')
+```
+
+</details>
+
+
+### <a name="Less"></a><a name="less">**Less**</a>
+
+  Returns the tensor resulted from performing the `less` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Less-1">1</a>, <a href="Changelog.md#Less-7">7</a>, <a href="Changelog.md#Less-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>less</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Less',
+    inputs=['x', 'y'],
+    outputs=['less'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.less(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less')
+```
+
+</details>
+
+
+<details>
+<summary>less</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LessOrEqual',
+    inputs=['x', 'y'],
+    outputs=['less_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.less_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less_equal')
+```
+
+</details>
+
+
+<details>
+<summary>less_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Less',
+    inputs=['x', 'y'],
+    outputs=['less'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.less(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less_bcast')
+```
+
+</details>
+
+
+<details>
+<summary>less_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LessOrEqual',
+    inputs=['x', 'y'],
+    outputs=['less_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.less_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less_equal_bcast')
+```
+
+</details>
+
+
+### <a name="LessOrEqual"></a><a name="lessorequal">**LessOrEqual**</a>
+
+  Returns the tensor resulted from performing the `less_equal` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#LessOrEqual-12">12</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input types to all numeric tensors.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+### <a name="Log"></a><a name="log">**Log**</a>
+
+  Calculates the natural log of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Log-1">1</a>, <a href="Changelog.md#Log-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The natural log of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>log</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Log',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([1, 10]).astype(np.float32)
+y = np.log(x)  # expected output [0., 2.30258512]
+expect(node, inputs=[x], outputs=[y],
+       name='test_log_example')
+
+x = np.exp(np.random.randn(3, 4, 5).astype(np.float32))
+y = np.log(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_log')
+```
+
+</details>
+
+
+### <a name="LogSoftmax"></a><a name="logsoftmax">**LogSoftmax**</a>
+
+  The operator computes the log of softmax values for the given input:
+
+   LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))
+
+  The "axis" attribute indicates the dimension along which LogSoftmax
+  will be performed. The output tensor has the same shape
+  and contains the LogSoftmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#LogSoftmax-1">1</a>, <a href="Changelog.md#LogSoftmax-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>
+Describes the dimension LogSoftmax will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output values with the same shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>logsoftmax</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[-1, 0, 1]]).astype(np.float32)
+# expected output
+# [[-2.4076061 -1.407606  -0.407606 ]]
+y = logsoftmax(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_example_1')
+```
+
+</details>
+
+
+<details>
+<summary>logsoftmax_axis</summary>
+
+```python
+x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]
+             ).astype(np.float32)
+# expected output
+# [[-3.4401896  -2.4401896  -1.4401896  -0.44018966]
+# [-3.4401896  -2.4401896  -1.4401896  -0.44018966]]
+y = logsoftmax(x)
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_large_number')
+
+x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=0,
+)
+y = logsoftmax(x, axis=0)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_axis_0')
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=1,
+)
+y = logsoftmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_axis_1')
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=2,
+)
+y = logsoftmax(x, axis=2)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_axis_2')
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=-1,
+)
+y = logsoftmax(x, axis=-1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_negative_axis')
+
+# default axis is -1
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_default_axis')
+```
+
+</details>
+
+
+### <a name="Loop"></a><a name="loop">**Loop**</a>
+
+  Generic Looping construct. This loop has multiple termination conditions:
+
+  1) Trip count. Iteration count specified at runtime. Set by
+     specifying the input M. Optional. Set to empty string to omit.
+     Note that a static trip count (specified at graph construction time) can be
+     specified by passing in a constant node for input M.
+  2) Loop termination condition. This is an input to the op that determines
+     whether to run the first iteration and also a loop-carried dependency for
+     the body graph. The body graph must yield a value for the condition variable,
+     whether this input is provided or not.
+
+  This table summarizes the operating modes of this operator with equivalent
+  C-style code:
+
+      Operator inputs defined as (max_trip_count, condition_var).
+
+      input ("", ""):
+          for (int i=0; ; ++i) {
+            cond = ... // Note this value is ignored, but is required in the body
+          }
+
+      input ("", cond) // Note this is analogous to a while loop
+          bool cond = ...;
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input ("", 1) // Note this is analogous to a do-while loop
+          bool cond = true
+          for (int i=0; cond; ++i) {
+            cond = ...;
+          }
+
+      input (trip_count, "") // Note this is analogous to a for loop
+          int trip_count = ...
+          for (int i=0; i < trip_count; ++i) {
+            cond = ...; // ignored
+          }
+
+      input (trip_count, cond)
+          int trip_count = ...;
+          bool cond = ...;
+          for (int i=0; i < trip_count && cond; ++i) {
+            cond = ...;
+          }
+
+
+  *Sample usage - cond as well as trip count*
+
+      graph predict-net {
+        %a = Constant[value = <Scalar Tensor [3]>]()
+        %b = Constant[value = <Scalar Tensor [6]>]()
+        %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+        %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+        %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+        return
+      }
+
+      graph body-net (
+        %i[INT32, scalar]           // iteration number
+        %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+        %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+      ) {
+        %my_local = Add(%a, %b_in)
+        %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+        %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+        %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+        return %keepgoing_out, %b_out, %user_defined_val
+      }
+
+  *Sample equivalent C code*
+
+      {
+        /* User-defined code (enclosing scope) */
+        int a = 3, b = 6;
+        bool keepgoing = true; // Analogous to input cond
+        /* End user-defined code */
+
+        /* Implicitly-defined code */
+        const int max_trip_count = 10; // Analogous to input M
+        int user_defined_vals[]; // Imagine this is resizable
+        /* End implicitly-defined code */
+        /* initialize loop-carried variables and scan-output variables */
+        bool keepgoing_out = keepgoing
+        int b_out = b
+
+        for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+          /* Implicitly-defined code: bind actual parameter values
+             to formal parameter variables of loop-body */
+          bool keepgoing_in = keepgoing_out;
+          bool b_in = b_out;
+
+          /* User-defined code (loop body) */
+          int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+          b_out = a - b_in;
+          keepgoing_out = my_local > b_out;
+          user_defined_val = b_in + b_in; // b_in and b_out are different variables
+          /* End user-defined code */
+
+          /* Implicitly defined-code */
+          user_defined_vals[i] = user_defined_val // accumulate scan-output values
+        }
+        // int t = my_local; // Can't do this. my_local is not accessible here.
+
+        // The values below are bound to the output variables of the loop and therefore accessible
+        // b_out; user_defined_vals; keepgoing_out;
+      }
+
+  There are several things of note in this code snippet:
+
+  1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+     be referenced in the inputs of the loop.
+  2) Any values computed in the loop body that needs to be used in a subsequent
+     iteration or after the loop are modelled using a pair of variables in the loop-body,
+     consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+     These are referred to as loop-carried dependences. The loop operation node
+     supplies the input value of the input variable for the first iteration, and
+     returns the output value of the output variable produced by the final
+     iteration.
+  3) Scan_output variables are used to implicitly concatenate values computed across
+     all the iterations. In the above example, the value of user_defined_val computed
+     over all iterations are concatenated and returned as the value of user_defined_vals
+     after the loop.
+  4) Values created in the body cannot be accessed in the enclosing scope,
+     except using the mechanism described above.
+
+  Note that the semantics of this op support "diagonal" or "wavefront" execution.
+  (See Step 3 here for an example:
+  https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+  Frontends should emit multi-layer RNNs as a series of While operators (with
+  time being the inner looping dimension), with each successive layer consuming
+  the scan_outputs from the previous layer, possibly going through several
+  point-wise operators (e.g. dropout, residual connections, linear layer).
+
+  The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Loop-1">1</a>, <a href="Changelog.md#Loop-11">11</a>, <a href="Changelog.md#Loop-13">13</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.</dd>
+</dl>
+
+#### Inputs (2 - &#8734;)
+
+<dl>
+<dt><tt>M</tt> (optional) : I</dt>
+<dd>A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.</dd>
+<dt><tt>cond</tt> (optional) : B</dt>
+<dd>A boolean termination condition. Optional. Pass empty string to skip.</dd>
+<dt><tt>v_initial</tt> (variadic, heterogeneous) : V</dt>
+<dd>The initial values of any loop-carried dependencies (values that change across loop iterations)</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>v_final_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>tensor of int64, which should be a scalar.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>tensor of bool, which should be a scalar.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>loop_11</summary>
+
+```python
+# Given a tensor x of values [x1, ..., xN], and initial tensor y
+# sum up its elements using a scan
+# returning the final state (y+x1+x2+...+xN) as well the scan_output
+# [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
+
+y_in = onnx.helper.make_tensor_value_info('y_in', onnx.TensorProto.FLOAT, [1])
+y_out = onnx.helper.make_tensor_value_info('y_out', onnx.TensorProto.FLOAT, [1])
+scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [1])
+cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+y = np.array([-2]).astype(np.float32)
+
+x_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_x',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=x.shape,
+        vals=x.flatten().astype(float),
+    )
+)
+
+one_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['one'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_one',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[1]
+    )
+)
+
+i_add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['iter_count', 'one'],
+    outputs=['end']
+)
+
+start_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['iter_count'],
+    outputs=['slice_start'],
+    axes=[0]
+)
+
+end_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['end'],
+    outputs=['slice_end'],
+    axes=[0]
+)
+
+slice_node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'slice_start', 'slice_end'],
+    outputs=['slice_out']
+)
+
+y_add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['y_in', 'slice_out'],
+    outputs=['y_out']
+)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['cond_in'],
+    outputs=['cond_out']
+)
+
+scan_identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['y_out'],
+    outputs=['scan_out']
+)
+
+loop_body = onnx.helper.make_graph(
+    [identity_node, x_const_node, one_const_node, i_add_node,
+     start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node,
+     scan_identity_node],
+    'loop_body',
+    [iter_count, cond_in, y_in],
+    [cond_out, y_out, scan_out]
+)
+
+node = onnx.helper.make_node(
+    'Loop',
+    inputs=['trip_count', 'cond', 'y'],
+    outputs=['res_y', 'res_scan'],
+    body=loop_body
+)
+
+trip_count = np.array(5).astype(np.int64)
+res_y = np.array([13]).astype(np.float32)
+cond = np.array(1).astype(bool)
+res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
+expect(node, inputs=[trip_count, cond, y], outputs=[res_y, res_scan],
+       name='test_loop11', opset_imports=[onnx.helper.make_opsetid("", 11)])
+```
+
+</details>
+
+
+<details>
+<summary>loop_13</summary>
+
+```python
+# Given a tensor x of values [x1, ..., xN],
+# Return a sequence of tensors of
+#   [[x1], [x1, x2], ..., [x1, ..., xN]]
+
+seq_in = onnx.helper.make_tensor_sequence_value_info('seq_in', onnx.TensorProto.FLOAT, None)
+seq_out = onnx.helper.make_tensor_sequence_value_info('seq_out', onnx.TensorProto.FLOAT, None)
+cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+x_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_x',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=x.shape,
+        vals=x.flatten().astype(float),
+    )
+)
+
+one_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['one'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_one',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[1]
+    )
+)
+
+zero_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['slice_start'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_zero',
+        data_type=onnx.TensorProto.INT64,
+        dims=(1,),
+        vals=[0]
+    )
+)
+
+axes_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['axes'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_axes',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[0]
+    )
+)
+
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['iter_count', 'one'],
+    outputs=['end']
+)
+
+end_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['end', 'axes'],
+    outputs=['slice_end']
+)
+
+slice_node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'slice_start', 'slice_end'],
+    outputs=['slice_out']
+)
+
+insert_node = onnx.helper.make_node(
+    'SequenceInsert',
+    inputs=['seq_in', 'slice_out'],
+    outputs=['seq_out']
+)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['cond_in'],
+    outputs=['cond_out']
+)
+
+loop_body = onnx.helper.make_graph(
+    [identity_node, x_const_node, one_const_node, zero_const_node, add_node,
+     axes_node, end_unsqueeze_node, slice_node, insert_node],
+    'loop_body',
+    [iter_count, cond_in, seq_in],
+    [cond_out, seq_out]
+)
+
+node = onnx.helper.make_node(
+    'Loop',
+    inputs=['trip_count', 'cond', 'seq_empty'],
+    outputs=['seq_res'],
+    body=loop_body
+)
+
+trip_count = np.array(5).astype(np.int64)
+seq_empty: List[Any] = []
+seq_res = [x[:int(i)] for i in x]
+cond = np.array(1).astype(bool)
+expect(node, inputs=[trip_count, cond, seq_empty], outputs=[seq_res],
+       name='test_loop13_seq', opset_imports=[onnx.helper.make_opsetid("", 13)],
+       input_type_protos=[onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, trip_count.shape),
+                          onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                          onnx.helper.make_sequence_type_proto(
+                              onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []))])
+```
+
+</details>
+
+
+<details>
+<summary>loop_16_none</summary>
+
+```python
+# Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
+# Return a concatenated sequence of tensors of
+#   [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
+
+ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
+seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+opt_in = onnx.helper.make_value_info('opt_seq_in', opt_in_tp)
+seq_out = onnx.helper.make_tensor_sequence_value_info('seq_out', onnx.TensorProto.FLOAT, [])
+cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+x0 = np.array(0).astype(np.float32)
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+optional_has_elem_node = onnx.helper.make_node(
+    'OptionalHasElement',
+    inputs=['opt_seq_in'],
+    outputs=['optional_has_elem']
+)
+
+optional_is_none = onnx.helper.make_node(
+    'Not',
+    inputs=['optional_has_elem'],
+    outputs=['optional_is_none']
+)
+
+optional_get_elem = onnx.helper.make_node(
+    'OptionalGetElement',
+    inputs=['opt_seq_in'],
+    outputs=['seq_in']
+)
+
+constant_in = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['constant_in'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=(),
+        vals=[0]
+    )
+)
+
+seq_const_in = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['constant_in'],
+    outputs=['init_seq_in']
+)
+
+then_seq_out = onnx.helper.make_tensor_sequence_value_info('init_seq_in', onnx.TensorProto.FLOAT, [])
+then_body = onnx.helper.make_graph(
+    [constant_in, seq_const_in],
+    'then_body',
+    [],
+    [then_seq_out]
+)
+
+else_seq_out = onnx.helper.make_tensor_sequence_value_info('seq_in', onnx.TensorProto.FLOAT, [])
+else_body = onnx.helper.make_graph(
+    [optional_get_elem],
+    'else_body',
+    [],
+    [else_seq_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['optional_is_none'],
+    outputs=['sequence'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+x_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_x',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=x.shape,
+        vals=x.flatten().astype(float),
+    )
+)
+
+one_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['one'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_one',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[1]
+    )
+)
+
+zero_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['slice_start'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_zero',
+        data_type=onnx.TensorProto.INT64,
+        dims=(1,),
+        vals=[0]
+    )
+)
+
+axes_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['axes'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_axes',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[0]
+    )
+)
+
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['iter_count', 'one'],
+    outputs=['end']
+)
+
+end_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['end', 'axes'],
+    outputs=['slice_end']
+)
+
+slice_node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'slice_start', 'slice_end'],
+    outputs=['slice_out']
+)
+
+insert_node = onnx.helper.make_node(
+    'SequenceInsert',
+    inputs=['sequence', 'slice_out'],
+    outputs=['seq_out']
+)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['cond_in'],
+    outputs=['cond_out']
+)
+
+loop_body = onnx.helper.make_graph(
+    [identity_node, optional_has_elem_node, optional_is_none, if_node, x_const_node, one_const_node,
+     zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node],
+    'loop_body',
+    [iter_count, cond_in, opt_in],
+    [cond_out, seq_out]
+)
+
+node = onnx.helper.make_node(
+    'Loop',
+    inputs=['trip_count', 'cond', 'opt_seq'],
+    outputs=['seq_res'],
+    body=loop_body
+)
+
+trip_count = np.array(5).astype(np.int64)
+cond = np.array(1).astype(bool)
+seq_res = compute_loop_outputs(x, [x0], trip_count)
+opt_seq_in: List[Any] = [x0]
+expect(node, inputs=[trip_count, cond, opt_seq_in], outputs=[seq_res],
+       name='test_loop16_seq_none', opset_imports=[onnx.helper.make_opsetid("", 16)],
+       input_type_protos=[onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, trip_count.shape),
+                          onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                          opt_in_tp])
+```
+
+</details>
+
+
+### <a name="LpNormalization"></a><a name="lpnormalization">**LpNormalization**</a>
+
+  Given a matrix, apply Lp-normalization along the provided axis.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>The axis on which to apply normalization, -1 mean last axis.</dd>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>The order of the normalization, only 1 or 2 are supported.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input matrix</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Matrix after normalization</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+### <a name="LpPool"></a><a name="lppool">**LpPool**</a>
+
+  LpPool consumes an input tensor X and applies Lp pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   Lp pooling consisting of computing the Lp norm on all values of a subset
+   of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#LpPool-1">1</a>, <a href="Changelog.md#LpPool-2">2</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>p</tt> : int (default is 2)</dt>
+<dd>p value of the Lp norm used to pool over the input data.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+### <a name="MatMul"></a><a name="matmul">**MatMul**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#MatMul-1">1</a>, <a href="Changelog.md#MatMul-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>N-dimensional matrix A</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>N-dimensional matrix B</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Matrix multiply results from A * B</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>matmul</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MatMul',
+    inputs=['a', 'b'],
+    outputs=['c'],
+)
+
+# 2d
+a = np.random.randn(3, 4).astype(np.float32)
+b = np.random.randn(4, 3).astype(np.float32)
+c = np.matmul(a, b)
+expect(node, inputs=[a, b], outputs=[c],
+       name='test_matmul_2d')
+
+# 3d
+a = np.random.randn(2, 3, 4).astype(np.float32)
+b = np.random.randn(2, 4, 3).astype(np.float32)
+c = np.matmul(a, b)
+expect(node, inputs=[a, b], outputs=[c],
+       name='test_matmul_3d')
+
+# 4d
+a = np.random.randn(1, 2, 3, 4).astype(np.float32)
+b = np.random.randn(1, 2, 4, 3).astype(np.float32)
+c = np.matmul(a, b)
+expect(node, inputs=[a, b], outputs=[c],
+       name='test_matmul_4d')
+```
+
+</details>
+
+
+### <a name="MatMulInteger"></a><a name="matmulinteger">**MatMulInteger**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+  The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs (2 - 4)
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T1</dt>
+<dd>N-dimensional matrix A</dd>
+<dt><tt>B</tt> (non-differentiable) : T2</dt>
+<dd>N-dimensional matrix B</dd>
+<dt><tt>a_zero_point</tt> (optional, non-differentiable) : T1</dt>
+<dd>Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. </dd>
+<dt><tt>b_zero_point</tt> (optional, non-differentiable) : T2</dt>
+<dd>Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T3</dt>
+<dd>Matrix multiply results from A * B</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input A data type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input B data type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int32)</dt>
+<dd>Constrain output Y data type as 32-bit integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>matmulinteger</summary>
+
+```python
+node = onnx.helper.make_node('MatMulInteger',
+    inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
+    outputs=['Y'],)
+
+A = np.array([[11, 7, 3],
+    [10, 6, 2],
+    [9, 5, 1],
+    [8, 4, 0], ], dtype=np.uint8)
+
+a_zero_point = np.array([12], dtype=np.uint8)
+
+B = np.array([[1, 4],
+    [2, 5],
+    [3, 6], ], dtype=np.uint8)
+
+b_zero_point = np.array([0], dtype=np.uint8)
+
+output = np.array([[-38, -83],
+    [-44, -98],
+    [-50, -113],
+    [-56, -128], ], dtype=np.int32)
+
+expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
+       name='test_matmulinteger')
+```
+
+</details>
+
+
+### <a name="Max"></a><a name="max">**Max**</a>
+
+  Element-wise max of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Max-1">1</a>, <a href="Changelog.md#Max-6">6</a>, <a href="Changelog.md#Max-8">8</a>, <a href="Changelog.md#Max-12">12</a>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for max.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>max</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>max</summary>
+
+```python
+data_0 = np.array([3, 2, 1]).astype(np.float32)
+data_1 = np.array([1, 4, 4]).astype(np.float32)
+data_2 = np.array([2, 5, 3]).astype(np.float32)
+result = np.array([3, 5, 4]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Max',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_max_example')
+
+node = onnx.helper.make_node(
+    'Max',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_max_one_input')
+
+result = np.maximum(data_0, data_1)
+node = onnx.helper.make_node(
+    'Max',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_max_two_inputs')
+```
+
+</details>
+
+
+<details>
+<summary>max_all_numeric_types</summary>
+
+```python
+for op_dtype in all_numeric_dtypes:
+    data_0 = np.array([3, 2, 1]).astype(op_dtype)
+    data_1 = np.array([1, 4, 4]).astype(op_dtype)
+    result = np.array([3, 4, 4]).astype(op_dtype)
+    node = onnx.helper.make_node(
+        'Max',
+        inputs=['data_0', 'data_1'],
+        outputs=['result'],
+    )
+    expect(node, inputs=[data_0, data_1], outputs=[result],
+           name='test_max_{0}'.format(np.dtype(op_dtype).name))
+```
+
+</details>
+
+
+### <a name="MaxPool"></a><a name="maxpool">**MaxPool**</a>
+
+  MaxPool consumes an input tensor X and applies max pooling across
+   the tensor according to kernel sizes, stride sizes, and pad lengths.
+   max pooling consisting of computing the max on all values of a
+   subset of the input tensor according to the kernel size and downsampling the
+   data into the output tensor Y for further processing. The output spatial shape will be following:
+   ```
+   output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   or
+   ```
+   output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+   ```
+   if ceil_mode is enabled
+
+   ```
+   * pad_shape[i] is sum of pads along axis i
+   ```
+
+   `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+   ```
+   VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
+   SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+   ```
+   And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+   ```
+   pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
+   ```
+   The output of each pooling window is maximum number of elements exclude pad.
+
+
+#### Version
+
+This version of the operator has been available since version 12 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#MaxPool-1">1</a>, <a href="Changelog.md#MaxPool-8">8</a>, <a href="Changelog.md#MaxPool-10">10</a>, <a href="Changelog.md#MaxPool-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>ceil_mode</tt> : int (default is 0)</dt>
+<dd>Whether to use ceil or floor (default) to compute the output shape.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>storage_order</tt> : int (default is 0)</dt>
+<dd>The storage order of the tensor. 0 is row major, and 1 is column major.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used</dd>
+<dt><tt>Indices</tt> (optional, non-differentiable) : I</dt>
+<dd>Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input and output types to float and 8 bit tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>maxpool_1d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32]
+output_shape: [1, 3, 31]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2],
+)
+x = np.random.randn(1, 3, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2]
+strides = [1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_1d_default')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_ceil</summary>
+
+```python
+"""
+input_shape: [1, 1, 4, 4]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    ceil_mode=True
+)
+x = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]]).astype(np.float32)
+y = np.array([[[
+    [11, 12],
+    [15, 16]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_ceil')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_default')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_dilations</summary>
+
+```python
+"""
+input_shape: [1, 1, 4, 4]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[1, 1],
+    dilations=[2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]]).astype(np.float32)
+y = np.array([[[
+    [11, 12],
+    [15, 16]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_dilations')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_pads</summary>
+
+```python
+"""
+input_shape: [1, 3, 28, 28]
+output_shape: [1, 3, 30, 30]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[2, 2, 2, 2]
+)
+x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (3, 3)
+strides = (1, 1)
+pad_bottom = pad_top = pad_right = pad_left = 2
+pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_pads')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_precomputed_pads</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[
+    [13, 14, 15, 15, 15],
+    [18, 19, 20, 20, 20],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_pads')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_precomputed_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 3, 3]
+pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 9, 10],
+                [17, 19, 20],
+                [22, 24, 25]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_same_upper')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_precomputed_strides</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 9],
+                [17, 19]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_strides')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_same_lower</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_LOWER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_bottom = pad_shape[0] // 2
+pad_top = pad_shape[0] - pad_bottom
+pad_right = pad_shape[1] // 2
+pad_left = pad_shape[1] - pad_right
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_same_lower')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_top = pad_shape[0] // 2
+pad_bottom = pad_shape[0] - pad_top
+pad_left = pad_shape[1] // 2
+pad_right = pad_shape[1] - pad_left
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_same_upper')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_strides</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 10, 10]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    strides=[3, 3]
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (5, 5)
+strides = (3, 3)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_strides')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_2d_uint8</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.uint8)
+y = np.array([[[
+    [13, 14, 15, 15, 15],
+    [18, 19, 20, 20, 20],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25]]]]).astype(np.uint8)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_uint8')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_3d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32, 32]
+output_shape: [1, 3, 31, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2, 2],
+)
+x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2, 2, 2]
+strides = [1, 1, 1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_3d_default')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_with_argmax_2d_precomputed_pads</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y', 'z'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[
+    [13, 14, 15, 15, 15],
+    [18, 19, 20, 20, 20],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25]]]]).astype(np.float32)
+z = np.array([[[
+    [12, 13, 14, 14, 14],
+    [17, 18, 19, 19, 19],
+    [22, 23, 24, 24, 24],
+    [22, 23, 24, 24, 24],
+    [22, 23, 24, 24, 24]]]]).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y, z], name='test_maxpool_with_argmax_2d_precomputed_pads')
+```
+
+</details>
+
+
+<details>
+<summary>maxpool_with_argmax_2d_precomputed_strides</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y', 'z'],
+    kernel_shape=[2, 2],
+    strides=[2, 2],
+    storage_order=1
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 9],
+                [17, 19]]]]).astype(np.float32)
+z = np.array([[[[6, 16],
+                [8, 18]]]]).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y, z], name='test_maxpool_with_argmax_2d_precomputed_strides')
+```
+
+</details>
+
+
+### <a name="MaxRoiPool"></a><a name="maxroipool">**MaxRoiPool**</a>
+
+  ROI max pool consumes an input tensor X and region of interests (RoIs) to
+   apply max pooling across each RoI, to produce output 4-D tensor of shape
+   (num_rois, channels, pooled_shape[0], pooled_shape[1]).
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>pooled_shape</tt> : list of ints (required)</dt>
+<dd>ROI pool output shape (height, width).</dd>
+<dt><tt>spatial_scale</tt> : float (default is 1.0)</dt>
+<dd>Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.</dd>
+<dt><tt>rois</tt> (non-differentiable) : T</dt>
+<dd>RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+### <a name="MaxUnpool"></a><a name="maxunpool">**MaxUnpool**</a>
+
+  MaxUnpool essentially computes the partial inverse of the MaxPool op.
+   The input information to this op is typically the the output information from a MaxPool op. The first
+   input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
+   from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
+   to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
+   The third (optional) input is a tensor that specifies the output size of the unpooling operation.
+
+  MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal
+   values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling
+   the result of an unpooling operation should give back the original input to the unpooling op.
+
+  MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.
+   The third input argument, output_size, is meant to disambiguate the op and produce output tensor of
+   known/predictable size.
+
+  In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,
+   which define the exact unpooling op. The attributes typically have the same values as the corrsponding
+   pooling op that the unpooling op is trying to invert.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#MaxUnpool-9">9</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>kernel_shape</tt> : list of ints (required)</dt>
+<dd>The size of the kernel along each axis.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T1</dt>
+<dd>Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>I</tt> (non-differentiable) : T2</dt>
+<dd>Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).</dd>
+<dt><tt>output_shape</tt> (optional, non-differentiable) : T2</dt>
+<dd>The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T1</dt>
+<dd>Output data tensor that contains the result of the unpooling.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>with_output_shape</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MaxUnpool',
+    inputs=['xT', 'xI', 'output_shape'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+xT = np.array([[[[5, 6],
+                 [7, 8]]]], dtype=np.float32)
+xI = np.array([[[[5, 7],
+                 [13, 15]]]], dtype=np.int64)
+output_shape = np.array((1, 1, 5, 5), dtype=np.int64)
+y = np.array([[[[0, 0, 0, 0, 0],
+                [0, 5, 0, 6, 0],
+                [0, 0, 0, 0, 0],
+                [0, 7, 0, 8, 0],
+                [0, 0, 0, 0, 0]]]], dtype=np.float32)
+expect(node, inputs=[xT, xI, output_shape], outputs=[y], name='test_maxunpool_export_with_output_shape')
+```
+
+</details>
+
+
+<details>
+<summary>without_output_shape</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MaxUnpool',
+    inputs=['xT', 'xI'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+xT = np.array([[[[1, 2],
+                 [3, 4]]]], dtype=np.float32)
+xI = np.array([[[[5, 7],
+                 [13, 15]]]], dtype=np.int64)
+y = np.array([[[[0, 0, 0, 0],
+                [0, 1, 0, 2],
+                [0, 0, 0, 0],
+                [0, 3, 0, 4]]]], dtype=np.float32)
+expect(node, inputs=[xT, xI], outputs=[y], name='test_maxunpool_export_without_output_shape')
+```
+
+</details>
+
+
+### <a name="Mean"></a><a name="mean">**Mean**</a>
+
+  Element-wise mean of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Mean-1">1</a>, <a href="Changelog.md#Mean-6">6</a>, <a href="Changelog.md#Mean-8">8</a>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for mean.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>mean</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>mean</summary>
+
+```python
+data_0 = np.array([3, 0, 2]).astype(np.float32)
+data_1 = np.array([1, 3, 4]).astype(np.float32)
+data_2 = np.array([2, 6, 6]).astype(np.float32)
+result = np.array([2, 3, 4]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Mean',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_mean_example')
+
+node = onnx.helper.make_node(
+    'Mean',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_mean_one_input')
+
+result = np.divide(np.add(data_0, data_1), 2.)
+node = onnx.helper.make_node(
+    'Mean',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_mean_two_inputs')
+```
+
+</details>
+
+
+### <a name="MeanVarianceNormalization"></a><a name="meanvariancenormalization">**MeanVarianceNormalization**</a>
+
+  A MeanVarianceNormalization Function: Perform mean variance normalization
+        on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#MeanVarianceNormalization-9">9</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints (default is ['0', '2', '3'])</dt>
+<dd>A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>meanvariancenormalization</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MeanVarianceNormalization',
+    inputs=['X'],
+    outputs=['Y']
+)
+
+input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+    [[0.02916367], [0.12964272], [0.5060197]],
+    [[0.79538304], [0.9411346], [0.9546573]]],
+    [[[0.17730942], [0.46192095], [0.26480448]],
+    [[0.6746842], [0.01665257], [0.62473077]],
+    [[0.9240844], [0.9722341], [0.11965699]]],
+    [[[0.41356155], [0.9129373], [0.59330076]],
+    [[0.81929934], [0.7862604], [0.11799799]],
+    [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+# Calculate expected output data
+data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
+data_mean_squared = np.power(data_mean, 2)
+data_squared = np.power(input_data, 2)
+data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
+std = np.sqrt(data_squared_mean - data_mean_squared)
+expected_output = (input_data - data_mean) / (std + 1e-9)
+
+expect(node, inputs=[input_data], outputs=[expected_output],
+       name='test_mvn')
+```
+
+</details>
+
+
+### <a name="Min"></a><a name="min">**Min**</a>
+
+  Element-wise min of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Min-1">1</a>, <a href="Changelog.md#Min-6">6</a>, <a href="Changelog.md#Min-8">8</a>, <a href="Changelog.md#Min-12">12</a>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for min.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>min</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>min</summary>
+
+```python
+data_0 = np.array([3, 2, 1]).astype(np.float32)
+data_1 = np.array([1, 4, 4]).astype(np.float32)
+data_2 = np.array([2, 5, 0]).astype(np.float32)
+result = np.array([1, 2, 0]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Min',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_min_example')
+
+node = onnx.helper.make_node(
+    'Min',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_min_one_input')
+
+result = np.minimum(data_0, data_1)
+node = onnx.helper.make_node(
+    'Min',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_min_two_inputs')
+```
+
+</details>
+
+
+<details>
+<summary>min_all_numeric_types</summary>
+
+```python
+for op_dtype in all_numeric_dtypes:
+    data_0 = np.array([3, 2, 1]).astype(op_dtype)
+    data_1 = np.array([1, 4, 4]).astype(op_dtype)
+    result = np.array([1, 2, 1]).astype(op_dtype)
+    node = onnx.helper.make_node(
+        'Min',
+        inputs=['data_0', 'data_1'],
+        outputs=['result'],
+    )
+    expect(node, inputs=[data_0, data_1], outputs=[result],
+           name='test_min_{0}'.format(np.dtype(op_dtype).name))
+```
+
+</details>
+
+
+### <a name="Mod"></a><a name="mod">**Mod**</a>
+
+  Performs element-wise binary modulus (with Numpy-style broadcasting support).
+      The sign of the remainder is the same as that of the Divisor.
+
+      Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
+      (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
+      This attribute is set to 0 by default causing the behavior to be like integer mod.
+      Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
+
+      If the input type is floating point, then `fmod` attribute must be set to 1.
+
+      In case of dividend being zero, the results will be platform dependent.
+
+    This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Mod-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>fmod</tt> : int (default is 0)</dt>
+<dd>Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>Dividend tensor</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Divisor tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Remainder tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>mod_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32)
+y = np.array([7]).astype(np.int32)
+z = np.mod(x, y)
+#   array([[[0, 1, 2, 3, 4],
+#     [5, 6, 0, 1, 2]],
+
+#    [[3, 4, 5, 6, 0],
+#     [1, 2, 3, 4, 5]],
+
+#    [[6, 0, 1, 2, 3],
+#     [4, 5, 6, 0, 1]]], dtype=int32)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_broadcast')
+```
+
+</details>
+
+
+<details>
+<summary>mod_int64_fmod</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+z = np.fmod(x, y)  # expected output [ 0,  1,  5,  0, -1,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_int64_fmod')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_float16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16)
+y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16)
+z = np.fmod(x, y)  # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 ,  3.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_float16')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_float32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32)
+y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32)
+z = np.fmod(x, y)  # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_float32')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_float64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64)
+y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64)
+z = np.fmod(x, y)  # expected output [-0.1,  0.4,  5. ,  0.1, -0.4,  3.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_float64')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_int16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int16')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_int32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int32')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_int64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int64')
+```
+
+</details>
+
+
+<details>
+<summary>mod_mixed_sign_int8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int8')
+```
+
+</details>
+
+
+<details>
+<summary>mod_uint16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint16)
+y = np.array([2, 3, 8]).astype(np.uint16)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint16')
+```
+
+</details>
+
+
+<details>
+<summary>mod_uint32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint32)
+y = np.array([2, 3, 8]).astype(np.uint32)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint32')
+```
+
+</details>
+
+
+<details>
+<summary>mod_uint64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint64)
+y = np.array([2, 3, 8]).astype(np.uint64)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint64')
+```
+
+</details>
+
+
+<details>
+<summary>mod_uint8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint8)
+y = np.array([2, 3, 8]).astype(np.uint8)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint8')
+```
+
+</details>
+
+
+### <a name="Mul"></a><a name="mul">**Mul**</a>
+
+  Performs element-wise binary multiplication (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Mul-1">1</a>, <a href="Changelog.md#Mul-6">6</a>, <a href="Changelog.md#Mul-7">7</a>, <a href="Changelog.md#Mul-13">13</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>mul</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mul',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = x * y  # expected output [4., 10., 18.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = x * y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul')
+
+x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+z = x * y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul_uint8')
+```
+
+</details>
+
+
+<details>
+<summary>mul_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mul',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = x * y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul_bcast')
+```
+
+</details>
+
+
+### <a name="Multinomial"></a><a name="multinomial">**Multinomial**</a>
+
+  Generate a tensor of samples from a multinomial distribution according to the probabilities
+  of each of the possible outcomes.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int (default is 6)</dt>
+<dd>(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.</dd>
+<dt><tt>sample_size</tt> : int (default is 1)</dt>
+<dd>Number of times to sample.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain output types to integral tensors.</dd>
+</dl>
+
+
+### <a name="Neg"></a><a name="neg">**Neg**</a>
+
+  Neg takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where each element flipped sign, y = -x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Neg-1">1</a>, <a href="Changelog.md#Neg-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to signed numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Neg',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-4, 2]).astype(np.float32)
+y = np.negative(x)  # expected output [4., -2.],
+expect(node, inputs=[x], outputs=[y],
+       name='test_neg_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.negative(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_neg')
+```
+
+</details>
+
+
+### <a name="NegativeLogLikelihoodLoss"></a><a name="negativeloglikelihoodloss">**NegativeLogLikelihoodLoss**</a>
+
+  A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss.
+  Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0.
+  The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C).
+  The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes)
+  or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples.
+  The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as:
+
+      loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].
+
+  When an optional "weight" is provided, the sample loss is calculated as:
+
+      loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].
+
+  loss is zero for the case when target-value equals ignore_index.
+
+      loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index
+
+  If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk).
+  If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged:
+
+      mean(loss), if "weight" is not provided,
+
+  or if weight is provided,
+
+      sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
+
+  If "reduction" attribute is set to "sum", the output is a scalar:
+      sum(loss).
+
+  See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
+
+  Example 1:
+
+      // negative log likelihood loss, "none" reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+               [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+
+      loss = np.zeros((N, d1))
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1]
+
+      // print(loss)
+      // [[-3. -2.]
+      //  [-0. -2.]]
+
+  Example 2:
+
+      // weighted negative log likelihood loss, sum reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+              [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      weight = [0.2, 0.3, 0.1]
+      loss = np.zeros((N, d1))
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1] * weight[c]
+
+      loss = np.sum(loss)
+      // print(loss)
+      // -1.1
+
+  Example 3:
+
+      // weighted negative log likelihood loss, mean reduction
+      N, C, d1 = 2, 3, 2
+      input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+              [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+      target = [[2, 1], [0, 2]]
+      weight = [0.2, 0.3, 0.1]
+      loss = np.zeros((N, d1))
+      weight_total = 0
+      for n in range(N):
+          for d_1 in range(d1):
+              c = target[n][d_1]
+              loss[n][d_1] = -input[n][c][d_1] * weight[c]
+              weight_total = weight_total + weight[c]
+
+      loss = np.sum(loss) / weight_total
+      // print(loss)
+      // -1.57
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#NegativeLogLikelihoodLoss-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>ignore_index</tt> : int</dt>
+<dd>Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.</dd>
+<dt><tt>reduction</tt> : string (default is mean)</dt>
+<dd>Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).</dd>
+<dt><tt>target</tt> (non-differentiable) : Tind</dt>
+<dd>Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.</dd>
+<dt><tt>weight</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>loss</tt> (differentiable) : T</dt>
+<dd>The negative log likelihood loss</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input, weight, and output types to floating-point tensors.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain target to integer types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>input_shape_is_NC</summary>
+
+```python
+reduction = 'none'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C = 3, 5
+np.random.seed(0)
+input = np.random.rand(N, C).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, )).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NC')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(1)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+target[0][0] = np.int64(1)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1_mean_weight_negative_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(-1)
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index)
+
+N, C, dim1 = 3, 5, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+target[0][0] = -1
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    weight=weight,
+                                                                    reduction=reduction,
+                                                                    ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_mean_weight_negative_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1_weight</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_weight')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1_weight_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(1)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+target[0][0] = np.int64(1)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_weight_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2</summary>
+
+```python
+reduction = 'none'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_no_weight_reduction_mean_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(1)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+target[0][0][0] = np.int64(1)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_no_weight_reduction_mean_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_reduction_mean</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_reduction_mean')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_reduction_sum</summary>
+
+```python
+reduction = 'sum'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2))
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_reduction_sum')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_with_weight</summary>
+
+```python
+reduction = 'none'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_with_weight_reduction_mean</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight_reduction_mean')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_with_weight_reduction_sum</summary>
+
+```python
+reduction = 'sum'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight_reduction_sum')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2_with_weight_reduction_sum_ii</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(0)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+target[0][0][0] = np.int64(0)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight_reduction_sum_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3_none_no_weight_negative_ii</summary>
+
+```python
+reduction = 'none'
+ignore_index = np.int64(-5)
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index)
+
+N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+target[0][0][0][0] = -5
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    reduction=reduction,
+                                                                    ignore_index=ignore_index)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3_none_no_weight_negative_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3_sum_weight_high_ii</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(10)
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index)
+
+N, C = 3, 5
+np.random.seed(0)
+input = np.random.rand(N, C).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N))
+target[0] = 10
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    weight=weight,
+                                                                    reduction=reduction,
+                                                                    ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3_sum_weight_high_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_mean_weight</summary>
+
+```python
+reduction = 'mean'
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    weight=weight,
+                                                                    reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3d4d5_mean_weight')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_none_no_weight</summary>
+
+```python
+reduction = 'none'
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3d4d5_none_no_weight')
+```
+
+</details>
+
+
+### <a name="NonMaxSuppression"></a><a name="nonmaxsuppression">**NonMaxSuppression**</a>
+
+  Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
+  Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box.
+  Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to
+  orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system
+  result in the same boxes being selected by the algorithm.
+  The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes.
+  The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#NonMaxSuppression-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>center_point_box</tt> : int (default is 0)</dt>
+<dd>Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.</dd>
+</dl>
+
+#### Inputs (2 - 5)
+
+<dl>
+<dt><tt>boxes</tt> : tensor(float)</dt>
+<dd>An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.</dd>
+<dt><tt>scores</tt> : tensor(float)</dt>
+<dd>An input tensor with shape [num_batches, num_classes, spatial_dimension]</dd>
+<dt><tt>max_output_boxes_per_class</tt> (optional) : tensor(int64)</dt>
+<dd>Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.</dd>
+<dt><tt>iou_threshold</tt> (optional) : tensor(float)</dt>
+<dd>Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.</dd>
+<dt><tt>score_threshold</tt> (optional) : tensor(float)</dt>
+<dd>Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>selected_indices</tt> : tensor(int64)</dt>
+<dd>selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].</dd>
+</dl>
+
+#### Type Constraints
+
+
+
+#### Examples
+
+<details>
+<summary>nonmaxsuppression_center_point_box_format</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices'],
+    center_point_box=1
+)
+boxes = np.array([[
+    [0.5, 0.5, 1.0, 1.0],
+    [0.5, 0.6, 1.0, 1.0],
+    [0.5, 0.4, 1.0, 1.0],
+    [0.5, 10.5, 1.0, 1.0],
+    [0.5, 10.6, 1.0, 1.0],
+    [0.5, 100.5, 1.0, 1.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_center_point_box_format')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_flipped_coordinates</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [1.0, 1.0, 0.0, 0.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, 0.9, 1.0, -0.1],
+    [0.0, 10.0, 1.0, 11.0],
+    [1.0, 10.1, 0.0, 11.1],
+    [1.0, 101.0, 0.0, 100.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_flipped_coordinates')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_identical_boxes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_identical_boxes')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_limit_output_size</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([2]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_limit_output_size')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_single_box</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_single_box')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_suppress_by_IOU</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_suppress_by_IOU')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_suppress_by_IOU_and_scores</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.4]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_suppress_by_IOU_and_scores')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_two_batches</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[[0.0, 0.0, 1.0, 1.0],
+                   [0.0, 0.1, 1.0, 1.1],
+                   [0.0, -0.1, 1.0, 0.9],
+                   [0.0, 10.0, 1.0, 11.0],
+                   [0.0, 10.1, 1.0, 11.1],
+                   [0.0, 100.0, 1.0, 101.0]],
+                  [[0.0, 0.0, 1.0, 1.0],
+                   [0.0, 0.1, 1.0, 1.1],
+                   [0.0, -0.1, 1.0, 0.9],
+                   [0.0, 10.0, 1.0, 11.0],
+                   [0.0, 10.1, 1.0, 11.1],
+                   [0.0, 100.0, 1.0, 101.0]]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]],
+                   [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([2]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_two_batches')
+```
+
+</details>
+
+
+<details>
+<summary>nonmaxsuppression_two_classes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3],
+                    [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([2]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_two_classes')
+```
+
+</details>
+
+
+### <a name="NonZero"></a><a name="nonzero">**NonZero**</a>
+
+  Returns the indices of the elements that are non-zero
+      (in row-major order - by dimension).
+      NonZero behaves similar to numpy.nonzero:
+      https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#NonZero-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>input</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>output</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>nonzero</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonZero',
+    inputs=['condition'],
+    outputs=['result'],
+)
+
+condition = np.array([[1, 0], [1, 1]], dtype=bool)
+result = np.array(np.nonzero(condition), dtype=np.int64)  # expected output [[0, 1, 1], [0, 0, 1]]
+expect(node, inputs=[condition], outputs=[result],
+       name='test_nonzero_example')
+```
+
+</details>
+
+
+### <a name="Not"></a><a name="not">**Not**</a>
+
+  Returns the negation of the input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input/output to boolean tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>not</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Not',
+    inputs=['x'],
+    outputs=['not'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+expect(node, inputs=[x], outputs=[np.logical_not(x)],
+       name='test_not_2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+expect(node, inputs=[x], outputs=[np.logical_not(x)],
+       name='test_not_3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+expect(node, inputs=[x], outputs=[np.logical_not(x)],
+       name='test_not_4d')
+```
+
+</details>
+
+
+### <a name="OneHot"></a><a name="onehot">**OneHot**</a>
+
+  Produces a one-hot tensor based on inputs.
+      The locations represented by the index values in the 'indices' input tensor will have 'on_value'
+      and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'
+      are specified as part of required input argument 'values', which is a two-element tensor of format
+      [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the
+      input tensor. The additional dimension is for one-hot representation. The additional dimension will
+      be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional
+      dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional
+      dimension is specified by required scalar input 'depth'. The type of the output tensor is the same
+      as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside
+      the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the
+      output tensor.
+
+      when axis = 0:
+      output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise.
+
+      when axis = -1:
+      output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise.
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#OneHot-9">9</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>indices</tt> (non-differentiable) : T1</dt>
+<dd>Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.</dd>
+<dt><tt>depth</tt> (non-differentiable) : T2</dt>
+<dd>Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.</dd>
+<dt><tt>values</tt> (non-differentiable) : T3</dt>
+<dd>Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (non-differentiable) : T3</dt>
+<dd>Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+<dt><tt>T2</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+<dt><tt>T3</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>with_axis</summary>
+
+```python
+axisValue = 1
+on_value = 3
+off_value = 1
+output_type = np.float32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y'],
+    axis=axisValue
+)
+indices = np.array([[1, 9],
+                    [2, 4]], dtype=np.float32)
+depth = np.float32(10)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_with_axis')
+```
+
+</details>
+
+
+<details>
+<summary>with_negative_axis</summary>
+
+```python
+axisValue = -2
+on_value = 3
+off_value = 1
+output_type = np.float32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y'],
+    axis=axisValue
+)
+indices = np.array([[1, 9],
+                    [2, 4]], dtype=np.float32)
+depth = np.float32(10)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_with_negative_axis')
+```
+
+</details>
+
+
+<details>
+<summary>with_negative_indices</summary>
+
+```python
+axisValue = 1
+on_value = 3
+off_value = 1
+output_type = np.float32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y'],
+    axis=axisValue
+)
+indices = np.array([0, -7, -8], dtype=np.int64)
+
+# print(y)
+# [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
+#  [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.]
+#  [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]]
+
+depth = np.float32(10)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_negative_indices')
+```
+
+</details>
+
+
+<details>
+<summary>without_axis</summary>
+
+```python
+on_value = 5
+off_value = 2
+output_type = np.int32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y']
+)
+indices = np.array([0, 7, 8], dtype=np.int64)
+depth = np.float32(12)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_without_axis')
+```
+
+</details>
+
+
+### <a name="Optional"></a><a name="optional">**Optional**</a>
+
+  Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute,
+  or a non-empty value containing the input element.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>type</tt> : type_proto</dt>
+<dd>Type of the element in the optional output</dd>
+</dl>
+
+#### Inputs (0 - 1)
+
+<dl>
+<dt><tt>input</tt> (optional) : V</dt>
+<dd>The input element.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : O</dt>
+<dd>The optional output enclosing the input element.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input type to all tensor and sequence types.</dd>
+<dt><tt>O</tt> : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain output type to all optional tensor or optional sequence types.</dd>
+</dl>
+
+
+### <a name="OptionalGetElement"></a><a name="optionalgetelement">**OptionalGetElement**</a>
+
+  Outputs the element in the optional-type input. It is an error if the input value does not have an element
+  and the behavior is undefined in this case.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : O</dt>
+<dd>The optional input.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : V</dt>
+<dd>Output element in the optional input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>O</tt> : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain input type to optional tensor and optional sequence types.</dd>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output type to all tensor or sequence types.</dd>
+</dl>
+
+
+### <a name="OptionalHasElement"></a><a name="optionalhaselement">**OptionalHasElement**</a>
+
+  Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns false.
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : O</dt>
+<dd>The optional input.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : B</dt>
+<dd>A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>O</tt> : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))</dt>
+<dd>Constrain input type to optional tensor and optional sequence types.</dd>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Constrain output to a boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>empty</summary>
+
+```python
+optional = None
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.INT32, shape=[])
+input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+node = onnx.helper.make_node(
+    'OptionalHasElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_has_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_has_element_empty')
+```
+
+</details>
+
+
+<details>
+<summary>get_element_sequence</summary>
+
+```python
+optional = [np.array([1, 2, 3, 4]).astype(np.int32)]
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.INT32, shape=[4, ])
+seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
+input_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto)
+
+node = onnx.helper.make_node(
+    'OptionalGetElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_get_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_get_element_sequence')
+```
+
+</details>
+
+
+<details>
+<summary>get_element_tensor</summary>
+
+```python
+optional = np.array([1, 2, 3, 4]).astype(np.float32)
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.FLOAT, shape=[4, ])
+input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+
+node = onnx.helper.make_node(
+    'OptionalGetElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_get_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_get_element')
+```
+
+</details>
+
+
+<details>
+<summary>optionalhaselement</summary>
+
+```python
+optional = np.array([1, 2, 3, 4]).astype(np.float32)
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.FLOAT, shape=[4, ])
+input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+node = onnx.helper.make_node(
+    'OptionalHasElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_has_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_has_element')
+```
+
+</details>
+
+
+### <a name="Or"></a><a name="or">**Or**</a>
+
+  Returns the tensor resulted from performing the `or` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Or-1">1</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>or</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Or',
+    inputs=['x', 'y'],
+    outputs=['or'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+y = (np.random.randn(3, 4) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or4d')
+```
+
+</details>
+
+
+<details>
+<summary>or_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Or',
+    inputs=['x', 'y'],
+    outputs=['or'],
+)
+
+# 3d vs 1d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(5) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast3v1d')
+
+# 3d vs 2d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(4, 5) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast3v2d')
+
+# 4d vs 2d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast4v2d')
+
+# 4d vs 3d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast4v3d')
+
+# 4d vs 4d
+x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast4v4d')
+```
+
+</details>
+
+
+### <a name="PRelu"></a><a name="prelu">**PRelu**</a>
+
+  PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+  output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+  `f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+
+  **History**
+  - Version 16 adds bfloat16 to the types allowed.
+  This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#PRelu-1">1</a>, <a href="Changelog.md#PRelu-6">6</a>, <a href="Changelog.md#PRelu-7">7</a>, <a href="Changelog.md#PRelu-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+<dt><tt>slope</tt> (differentiable) : T</dt>
+<dd>Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor (same size as X)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input and output types to float/int tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>prelu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'PRelu',
+    inputs=['x', 'slope'],
+    outputs=['y'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+slope = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+expect(node, inputs=[x, slope], outputs=[y],
+       name='test_prelu_example')
+```
+
+</details>
+
+
+<details>
+<summary>prelu_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'PRelu',
+    inputs=['x', 'slope'],
+    outputs=['y'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+slope = np.random.randn(5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+expect(node, inputs=[x, slope], outputs=[y],
+       name='test_prelu_broadcast')
+```
+
+</details>
+
+
+### <a name="Pad"></a><a name="pad">**Pad**</a>
+
+  Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`,
+  a padded tensor (`output`) is generated.
+
+  The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):
+
+  1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False)
+
+  2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
+
+  3) `edge` - pads with the edge values of array
+
+
+  Example 1 (`constant` mode):
+    Insert 0 pads to the beginning of the second dimension.
+
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'constant'
+
+    constant_value = 0.0
+
+    output =
+    [
+        [0.0, 0.0, 1.0, 1.2],
+        [0.0, 0.0, 2.3, 3.4],
+        [0.0, 0.0, 4.5, 5.7],
+    ]
+
+
+  Example 2 (`reflect` mode):
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'reflect'
+
+    output =
+    [
+        [1.0, 1.2, 1.0, 1.2],
+        [2.3, 3.4, 2.3, 3.4],
+        [4.5, 5.7, 4.5, 5.7],
+    ]
+
+
+  Example 3 (`edge` mode):
+    data =
+    [
+        [1.0, 1.2],
+        [2.3, 3.4],
+        [4.5, 5.7],
+    ]
+
+    pads = [0, 2, 0, 0]
+
+    mode = 'edge'
+
+    output =
+    [
+        [1.0, 1.0, 1.0, 1.2],
+        [2.3, 2.3, 2.3, 3.4],
+        [4.5, 4.5, 4.5, 5.7],
+    ]
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Pad-1">1</a>, <a href="Changelog.md#Pad-2">2</a>, <a href="Changelog.md#Pad-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>mode</tt> : string (default is constant)</dt>
+<dd>Supported modes: `constant`(default), `reflect`, `edge`</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Input tensor.</dd>
+<dt><tt>pads</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.</dd>
+<dt><tt>constant_value</tt> (optional, non-differentiable) : T</dt>
+<dd>(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor after padding.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>constant_pad</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pad',
+    inputs=['x', 'pads', 'value'],
+    outputs=['y'],
+    mode='constant'
+)
+x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+value = np.float32(1.2)
+y = pad_impl(
+    x,
+    pads,
+    'constant',
+    1.2
+)
+
+expect(node, inputs=[x, pads, value], outputs=[y],
+       name='test_constant_pad')
+```
+
+</details>
+
+
+<details>
+<summary>reflection_and_edge_pad</summary>
+
+```python
+for mode in ['edge', 'reflect']:
+    node = onnx.helper.make_node(
+        'Pad',
+        inputs=['x', 'pads'],
+        outputs=['y'],
+        mode=mode
+    )
+    x = np.random.randn(1, 3, 4, 5).astype(np.int32)
+    pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+    y = pad_impl(
+        x,
+        pads,
+        mode
+    )
+
+    expect(node, inputs=[x, pads], outputs=[y],
+           name='test_{}_pad'.format(mode))
+```
+
+</details>
+
+
+### <a name="Pow"></a><a name="pow">**Pow**</a>
+
+  Pow takes input data (Tensor<T>) and exponent Tensor, and
+  produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+  is applied to the data tensor elementwise.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Pow-1">1</a>, <a href="Changelog.md#Pow-7">7</a>, <a href="Changelog.md#Pow-12">12</a>, <a href="Changelog.md#Pow-13">13</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>First operand, base of the exponent.</dd>
+<dt><tt>Y</tt> (differentiable) : T1</dt>
+<dd>Second operand, power of the exponent.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Z</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input X and output types to float/int tensors.</dd>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input Y types to float/int tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>pow</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_example')
+
+x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = pow(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow')
+```
+
+</details>
+
+
+<details>
+<summary>pow_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array(2).astype(np.float32)
+z = pow(x, y)  # expected output [1., 4., 9.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_bcast_scalar')
+
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+y = np.array([1, 2, 3]).astype(np.float32)
+# expected output [[1, 4, 27], [4, 25, 216]]
+z = pow(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_bcast_array')
+```
+
+</details>
+
+
+<details>
+<summary>types</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.int64)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_int64')
+
+x = np.array([1, 2, 3]).astype(np.int64)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int64_float32')
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.int32)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_int32')
+
+x = np.array([1, 2, 3]).astype(np.int32)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int32_float32')
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.uint64)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_uint64')
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.uint32)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_uint32')
+
+x = np.array([1, 2, 3]).astype(np.int64)
+y = np.array([4, 5, 6]).astype(np.int64)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int64_int64')
+
+x = np.array([1, 2, 3]).astype(np.int32)
+y = np.array([4, 5, 6]).astype(np.int32)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int32_int32')
+```
+
+</details>
+
+
+### <a name="QLinearConv"></a><a name="qlinearconv">**QLinearConv**</a>
+
+  The convolution operator consumes a quantized input tensor, its scale and zero point,
+  a quantized filter, its scale and zero point, and output's scale and zero point,
+  and computes the quantized output. Each scale and zero-point pair must have same shape.
+  It means they must be either scalars (per tensor) or 1-D tensors (per output channel).
+  Each input or output and its related zero point must have same type.
+  When bias is present it must be quantized using scale = input scale * weight scale and
+  zero point as 0.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>auto_pad</tt> : string (default is NOTSET)</dt>
+<dd>auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.</dd>
+<dt><tt>dilations</tt> : list of ints</dt>
+<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.</dd>
+<dt><tt>group</tt> : int (default is 1)</dt>
+<dd>number of groups input channels and output channels are divided into. default is 1.</dd>
+<dt><tt>kernel_shape</tt> : list of ints</dt>
+<dd>The shape of the convolution kernel. If not present, should be inferred from input 'w'.</dd>
+<dt><tt>pads</tt> : list of ints</dt>
+<dd>Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.</dd>
+<dt><tt>strides</tt> : list of ints</dt>
+<dd>Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.</dd>
+</dl>
+
+#### Inputs (8 - 9)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
+<dt><tt>x_scale</tt> : tensor(float)</dt>
+<dd>Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>x_zero_point</tt> : T1</dt>
+<dd>Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>w</tt> : T2</dt>
+<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. </dd>
+<dt><tt>w_scale</tt> : tensor(float)</dt>
+<dd>Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).</dd>
+<dt><tt>w_zero_point</tt> : T2</dt>
+<dd>Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>y_zero_point</tt> : T3</dt>
+<dd>Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.</dd>
+<dt><tt>B</tt> (optional) : T4</dt>
+<dd>Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T3</dt>
+<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain filter type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain output type to 8-bit integer tensor.</dd>
+<dt><tt>T4</tt> : tensor(int32)</dt>
+<dd>Constrain bias type to 32-bit integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>qlinearconv</summary>
+
+```python
+node = onnx.helper.make_node('QLinearConv',
+    inputs=['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+x = np.array([[255, 174, 162, 25, 203, 168, 58],
+    [15, 59, 237, 95, 129, 0, 64],
+    [56, 242, 153, 221, 168, 12, 166],
+    [232, 178, 186, 195, 237, 162, 237],
+    [188, 39, 124, 77, 80, 102, 43],
+    [127, 230, 21, 83, 41, 40, 134],
+    [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+x_scale = np.float32(0.00369204697)
+x_zero_point = np.uint8(132)
+
+w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+w_scale = np.array([0.00172794575], dtype=np.float32)
+w_zero_point = np.array([255], dtype=np.uint8)
+
+y_scale = np.float32(0.00162681262)
+y_zero_point = np.uint8(123)
+
+output = np.array([[0, 81, 93, 230, 52, 87, 197],
+    [240, 196, 18, 160, 126, 255, 191],
+    [199, 13, 102, 34, 87, 243, 89],
+    [23, 77, 69, 60, 18, 93, 18],
+    [67, 216, 131, 178, 175, 153, 212],
+    [128, 25, 234, 172, 214, 215, 121],
+    [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+expect(node, inputs=[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearconv')
+```
+
+</details>
+
+
+### <a name="QLinearMatMul"></a><a name="qlinearmatmul">**QLinearMatMul**</a>
+
+  Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+  It consumes two quantized input tensors, their scales and zero points, scale and zero point of output,
+  and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).
+  For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.
+  Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor
+  (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row
+  or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be
+  an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]
+  for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may
+  have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.
+  Production must never overflow, and accumulation may overflow if and only if in 32 bits.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>a</tt> (non-differentiable) : T1</dt>
+<dd>N-dimensional quantized matrix a</dd>
+<dt><tt>a_scale</tt> (non-differentiable) : tensor(float)</dt>
+<dd>scale of quantized input a</dd>
+<dt><tt>a_zero_point</tt> (non-differentiable) : T1</dt>
+<dd>zero point of quantized input a</dd>
+<dt><tt>b</tt> (non-differentiable) : T2</dt>
+<dd>N-dimensional quantized matrix b</dd>
+<dt><tt>b_scale</tt> (non-differentiable) : tensor(float)</dt>
+<dd>scale of quantized input b</dd>
+<dt><tt>b_zero_point</tt> (non-differentiable) : T2</dt>
+<dd>zero point of quantized input b</dd>
+<dt><tt>y_scale</tt> (non-differentiable) : tensor(float)</dt>
+<dd>scale of quantized output y</dd>
+<dt><tt>y_zero_point</tt> (non-differentiable) : T3</dt>
+<dd>zero point of quantized output y</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> (non-differentiable) : T3</dt>
+<dd>Quantized matrix multiply results from a * b</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input a and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain input b and its zero point data type to 8-bit integer tensor.</dd>
+<dt><tt>T3</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain output y and its zero point data type to 8-bit integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>qlinearmatmul</summary>
+
+```python
+node = onnx.helper.make_node('QLinearMatMul',
+    inputs=['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+#2D
+a = np.array([[208, 236, 0, 238],
+    [3, 214, 255, 29], ], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[168, 115, 255],
+    [1, 66, 151], ], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_2D')
+
+#3D
+a = np.array([[[208, 236, 0, 238],
+    [3, 214, 255, 29]],
+    [[208, 236, 0, 238],
+    [3, 214, 255, 29]]], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]],
+    [[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[[168, 115, 255],
+    [1, 66, 151]],
+    [[168, 115, 255],
+    [1, 66, 151]]], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_3D')
+```
+
+</details>
+
+
+### <a name="QuantizeLinear"></a><a name="quantizelinear">**QuantizeLinear**</a>
+
+  The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
+  The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
+  The quantization formula is y = saturate ((x / y_scale) + y_zero_point).
+  For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
+  For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#QuantizeLinear-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 1)</dt>
+<dd>(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>x</tt> : T1</dt>
+<dd>N-D full precision Input tensor to be quantized.</dd>
+<dt><tt>y_scale</tt> : tensor(float)</dt>
+<dd>Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.</dd>
+<dt><tt>y_zero_point</tt> (optional) : T2</dt>
+<dd>Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>y</tt> : T2</dt>
+<dd>N-D quantized output tensor. It has same shape as input 'x'.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(int32)</dt>
+<dd>Constrain 'x' to float or int32 tensor.</dd>
+<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
+<dd>Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>axis</summary>
+
+```python
+node = onnx.helper.make_node('QuantizeLinear',
+                             inputs=['x', 'y_scale', 'y_zero_point'],
+                             outputs=['y'],)
+
+x = np.array([[[[-162, 10],
+                [-100, 232],
+                [-20, -50]],
+
+               [[-76, 0],
+                [0, 252],
+                [32, -44]],
+
+               [[245, -485],
+                [-960, -270],
+                [-375, -470]], ], ], dtype=np.float32)
+y_scale = np.array([2, 4, 5], dtype=np.float32)
+y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(np.uint8)
+
+expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+       name='test_quantizelinear_axis')
+```
+
+</details>
+
+
+<details>
+<summary>quantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('QuantizeLinear',
+                             inputs=['x', 'y_scale', 'y_zero_point'],
+                             outputs=['y'],)
+
+x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+y_scale = np.float32(2)
+y_zero_point = np.uint8(128)
+y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+       name='test_quantizelinear')
+```
+
+</details>
+
+
+### <a name="RNN"></a><a name="rnn">**RNN**</a>
+
+  Computes an one-layer simple RNN. This operator is usually supported
+  via some custom implementation such as CuDNN.
+
+  Notations:
+
+  `X` - input tensor
+
+  `i` - input gate
+
+  `t` - time step (t-1 means previous time step)
+
+  `Wi` - W parameter weight matrix for input gate
+
+  `Ri` - R recurrence weight matrix for input gate
+
+  `Wbi` - W parameter bias vector for input gate
+
+  `Rbi` - R parameter bias vector for input gate
+
+  `WBi` - W parameter weight matrix for backward input gate
+
+  `RBi` - R recurrence weight matrix for backward input gate
+
+  `WBbi` - WR bias vectors for backward input gate
+
+  `RBbi` - RR bias vectors for backward input gate
+
+  `H` - Hidden state
+
+  `num_directions` - 2 if direction == bidirectional else 1
+
+  Activation functions:
+
+    Relu(x)                - max(0, x)
+
+    Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+    Sigmoid(x)             - 1/(1 + e^{-x})
+
+    (NOTE: Below are optional)
+
+    Affine(x)              - alpha*x + beta
+
+    LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+    ThresholdedRelu(x)     - x if x >= alpha else 0
+
+    ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+    HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+    Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+    Softsign(x)            - x/(1 + |x|)
+
+    Softplus(x)            - log(1 + e^x)
+
+  Equations (Default: f=Tanh):
+
+    - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+  This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#RNN-1">1</a>, <a href="Changelog.md#RNN-7">7</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>activation_alpha</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.</dd>
+<dt><tt>activation_beta</tt> : list of floats</dt>
+<dd>Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.</dd>
+<dt><tt>activations</tt> : list of strings (default is ['Tanh', 'Tanh'])</dt>
+<dd>One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.</dd>
+<dt><tt>clip</tt> : float</dt>
+<dd>Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.</dd>
+<dt><tt>direction</tt> : string (default is forward)</dt>
+<dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
+<dt><tt>hidden_size</tt> : int</dt>
+<dd>Number of neurons in the hidden layer</dd>
+<dt><tt>layout</tt> : int (default is 0)</dt>
+<dd>The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].</dd>
+</dl>
+
+#### Inputs (3 - 6)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.</dd>
+<dt><tt>W</tt> (differentiable) : T</dt>
+<dd>The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.</dd>
+<dt><tt>R</tt> (differentiable) : T</dt>
+<dd>The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.</dd>
+<dt><tt>B</tt> (optional, differentiable) : T</dt>
+<dd>The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.</dd>
+<dt><tt>sequence_lens</tt> (optional, non-differentiable) : T1</dt>
+<dd>Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.</dd>
+<dt><tt>initial_h</tt> (optional, non-differentiable) : T</dt>
+<dd>Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Outputs (0 - 2)
+
+<dl>
+<dt><tt>Y</tt> (optional, differentiable) : T</dt>
+<dd>A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. </dd>
+<dt><tt>Y_h</tt> (optional, differentiable) : T</dt>
+<dd>The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T1</tt> : tensor(int32)</dt>
+<dd>Constrain seq_lens to integer tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>batchwise</summary>
+
+```python
+input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 4
+weight_scale = 0.5
+layout = 1
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R'],
+    outputs=['Y', 'Y_h'],
+    hidden_size=hidden_size,
+    layout=layout
+)
+
+W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+rnn = RNN_Helper(X=input, W=W, R=R, layout=layout)
+Y, Y_h = rnn.step()
+expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_simple_rnn_batchwise')
+```
+
+</details>
+
+
+<details>
+<summary>defaults</summary>
+
+```python
+input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 4
+weight_scale = 0.1
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+rnn = RNN_Helper(X=input, W=W, R=R)
+_, Y_h = rnn.step()
+expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_simple_rnn_defaults')
+```
+
+</details>
+
+
+<details>
+<summary>initial_bias</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 5
+custom_bias = 0.1
+weight_scale = 0.1
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+# Adding custom bias
+W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
+R_B = np.zeros((1, hidden_size)).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+rnn = RNN_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = rnn.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)],
+       name='test_simple_rnn_with_initial_bias')
+```
+
+</details>
+
+
+<details>
+<summary>seq_length</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
+                  [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 5
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
+R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
+
+# Adding custom bias
+W_B = np.random.randn(1, hidden_size).astype(np.float32)
+R_B = np.random.randn(1, hidden_size).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+rnn = RNN_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = rnn.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_rnn_seq_length')
+```
+
+</details>
+
+
+### <a name="RandomNormal"></a><a name="randomnormal">**RandomNormal**</a>
+
+  Generate a tensor with random values drawn from a normal distribution. The shape
+  of the tensor is specified by the `shape` argument and the parameter of the normal distribution
+  specified by `mean` and `scale`.
+
+  The data type is specified by the 'dtype' argument. The 'dtype' argument must
+  be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int (default is 1)</dt>
+<dd>The data type for the elements of the output tensor. Default is TensorProto::FLOAT.</dd>
+<dt><tt>mean</tt> : float (default is 0.0)</dt>
+<dd>The mean of the normal distribution.</dd>
+<dt><tt>scale</tt> : float (default is 1.0)</dt>
+<dd>The standard deviation of the normal distribution.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+<dt><tt>shape</tt> : list of ints (required)</dt>
+<dd>The shape of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of random values drawn from normal distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+
+### <a name="RandomNormalLike"></a><a name="randomnormallike">**RandomNormalLike**</a>
+
+  Generate a tensor with random values drawn from a normal distribution.
+  The shape of the output tensor is copied from the shape of the input tensor,
+  and the parameters of the normal distribution are specified by `mean` and `scale`.
+
+  The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
+  The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message, and be valid as an output type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.</dd>
+<dt><tt>mean</tt> : float (default is 0.0)</dt>
+<dd>The mean of the normal distribution.</dd>
+<dt><tt>scale</tt> : float (default is 1.0)</dt>
+<dd>The standard deviation of the normal distribution.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to copy shape and optionally type information from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor of random values drawn from normal distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+
+### <a name="RandomUniform"></a><a name="randomuniform">**RandomUniform**</a>
+
+  Generate a tensor with random values drawn from a uniform distribution. The shape
+  of the tensor is specified by the `shape` argument and the range by `low` and `high`.
+
+  The data type is specified by the 'dtype' argument. The 'dtype' argument must
+  be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int (default is 1)</dt>
+<dd>The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.</dd>
+<dt><tt>high</tt> : float (default is 1.0)</dt>
+<dd>Upper boundary of the output values.</dd>
+<dt><tt>low</tt> : float (default is 0.0)</dt>
+<dd>Lower boundary of the output values.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+<dt><tt>shape</tt> : list of ints (required)</dt>
+<dd>The shape of the output tensor.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor of random values drawn from uniform distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+
+### <a name="RandomUniformLike"></a><a name="randomuniformlike">**RandomUniformLike**</a>
+
+  Generate a tensor with random values drawn from a uniform distribution.
+  The shape of the output tensor is copied from the shape of the input tensor,
+  and the parameters of the uniform distribution are specified by `low` and `high`.
+
+  The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
+  The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+  TensorProto message and be valid as an output type.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.</dd>
+<dt><tt>high</tt> : float (default is 1.0)</dt>
+<dd>Upper boundary of the output values.</dd>
+<dt><tt>low</tt> : float (default is 0.0)</dt>
+<dd>Lower boundary of the output values.</dd>
+<dt><tt>seed</tt> : float</dt>
+<dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T1</dt>
+<dd>Input tensor to copy shape and optionally type information from.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T2</dt>
+<dd>Output tensor of random values drawn from uniform distribution</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain output types to float tensors.</dd>
+</dl>
+
+
+### <a name="Range"></a><a name="range">**Range**</a>
+
+  Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta`
+  up to `limit` (exclusive).
+
+  The number of elements in the output of range is computed as below-
+
+  `number_of_elements = max( ceil( (limit - start) / delta ) , 0 )`
+
+  The pseudocode determining the contents of the output is shown below-
+
+  `for(int i=0; i<number_of_elements; ++i)`
+
+  `{`
+
+  `    output[i] =  start + (i * delta);  `
+
+  `}`
+
+  `Example 1`
+  Inputs: start = 3, limit = 9, delta = 3
+  Output: [3, 6]
+
+  `Example 2`
+  Inputs: start = 10, limit = 4, delta = -2
+  Output: [10, 8, 6]
+
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>start</tt> : T</dt>
+<dd>Scalar. First entry for the range of output values.</dd>
+<dt><tt>limit</tt> : T</dt>
+<dd>Scalar. Exclusive upper limit for the range of output values.</dd>
+<dt><tt>delta</tt> : T</dt>
+<dd>Scalar. Value to step by.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>A 1-D tensor with same type as the inputs containing generated range of values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)</dt>
+<dd>Constrain input types to common numeric type tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>range_float_type_positive_delta</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Range',
+    inputs=['start', 'limit', 'delta'],
+    outputs=['output'],
+)
+
+start = np.float32(1)
+limit = np.float32(5)
+delta = np.float32(2)
+
+output = np.arange(start, limit, delta, dtype=np.float32)  # expected output [1.0, 3.0]
+expect(node, inputs=[start, limit, delta], outputs=[output],
+       name='test_range_float_type_positive_delta')
+```
+
+</details>
+
+
+<details>
+<summary>range_int32_type_negative_delta</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Range',
+    inputs=['start', 'limit', 'delta'],
+    outputs=['output'],
+)
+
+start = np.int32(10)
+limit = np.int32(6)
+delta = np.int32(-3)
+
+output = np.arange(start, limit, delta, dtype=np.int32)  # expected output [10, 7]
+expect(node, inputs=[start, limit, delta], outputs=[output],
+       name='test_range_int32_type_negative_delta')
+```
+
+</details>
+
+
+### <a name="Reciprocal"></a><a name="reciprocal">**Reciprocal**</a>
+
+  Reciprocal takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Reciprocal-1">1</a>, <a href="Changelog.md#Reciprocal-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>reciprocal</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Reciprocal',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-4, 2]).astype(np.float32)
+y = np.reciprocal(x)  # expected output [-0.25, 0.5],
+expect(node, inputs=[x], outputs=[y],
+       name='test_reciprocal_example')
+
+x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5
+y = np.reciprocal(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_reciprocal')
+```
+
+</details>
+
+
+### <a name="ReduceL1"></a><a name="reducel1">**ReduceL1**</a>
+
+  Computes the L1 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceL1-1">1</a>, <a href="Changelog.md#ReduceL1-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[78.]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[3., 7.], [11., 15.], [19., 23.]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_keep_dims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+# print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_negative_axes_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_negative_axes_keep_dims_random')
+```
+
+</details>
+
+
+### <a name="ReduceL2"></a><a name="reducel2">**ReduceL2**</a>
+
+  Computes the L2 norm of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceL2-1">1</a>, <a href="Changelog.md#ReduceL2-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=axes, keepdims=keepdims == 1))
+#print(reduced)
+#[[[25.49509757]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=axes, keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+#print(reduced)
+#[[2.23606798, 5.],
+# [7.81024968, 10.63014581],
+# [13.45362405, 16.2788206]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+#print(reduced)
+#[[[2.23606798], [5.]]
+# [[7.81024968], [10.63014581]]
+# [[13.45362405], [16.2788206 ]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_l2_keep_dims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+# print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+# print(reduced)
+#[[[2.23606798], [5.]]
+# [[7.81024968], [10.63014581]]
+# [[13.45362405], [16.2788206 ]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_negative_axes_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_negative_axes_keep_dims_random')
+```
+
+</details>
+
+
+### <a name="ReduceLogSum"></a><a name="reducelogsum">**ReduceLogSum**</a>
+
+  Computes the log sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceLogSum-1">1</a>, <a href="Changelog.md#ReduceLogSum-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>keepdims</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"]
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, keepdims=True))
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_default')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"],
+    axes=[-2]
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, axis=(-2), keepdims=True))
+# print(reduced)
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_negative_axes')
+```
+
+</details>
+
+
+<details>
+<summary>nokeepdims</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"],
+    axes=[2, 1],
+    keepdims=0
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, axis=(2, 1), keepdims=False))
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_desc_axes')
+
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"],
+    axes=[0, 1],
+    keepdims=0
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, axis=(0, 1), keepdims=False))
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_asc_axes')
+```
+
+</details>
+
+
+### <a name="ReduceLogSumExp"></a><a name="reducelogsumexp">**ReduceLogSumExp**</a>
+
+  Computes the log sum exponent of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceLogSumExp-1">1</a>, <a href="Changelog.md#ReduceLogSumExp-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=axes,
+                        keepdims=keepdims == 1))
+# print(reduced)
+# [[[60.00671387]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=axes,
+                        keepdims=keepdims == 1))
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(
+    np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+# print(reduced)
+#[[20., 2.31326175]
+# [40.00004578, 2.31326175]
+# [60.00671387, 2.31326175]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(
+    np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_log_sum_exp_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+# print(reduced)
+# [[[20., 2.31326175]]
+# [[40.00004578, 2.31326175]]
+# [[60.00671387, 2.31326175]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+# print(reduced)
+# [[[20., 2.31326175]]
+# [[40.00004578, 2.31326175]]
+# [[60.00671387, 2.31326175]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="ReduceMax"></a><a name="reducemax">**ReduceMax**</a>
+
+  Computes the max of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceMax-1">1</a>, <a href="Changelog.md#ReduceMax-11">11</a>, <a href="Changelog.md#ReduceMax-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)</dt>
+<dd>Constrain input and output types to high-precision and 8 bit numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+[[[60.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_default_axes_keepdim_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[20., 2.]
+# [40., 2.]
+# [60., 2.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[20., 2.]]
+# [[40., 2.]]
+# [[60., 2.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[20., 2.]]
+# [[40., 2.]]
+# [[60., 2.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="ReduceMean"></a><a name="reducemean">**ReduceMean**</a>
+
+  Computes the mean of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceMean-1">1</a>, <a href="Changelog.md#ReduceMean-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[18.25]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[12.5, 1.5]
+# [35., 1.5]
+# [57.5, 1.5]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[12.5, 1.5]]
+# [[35., 1.5]]
+# [[57.5, 1.5]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+# [[[12.5, 1.5]]
+# [[35., 1.5]]
+# [[57.5, 1.5]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="ReduceMin"></a><a name="reducemin">**ReduceMin**</a>
+
+  Computes the min of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceMin-1">1</a>, <a href="Changelog.md#ReduceMin-11">11</a>, <a href="Changelog.md#ReduceMin-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)</dt>
+<dd>Constrain input and output types to high-precision and 8 bit numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMin',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[1.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceMin',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[5., 1.]
+# [30., 1.]
+# [55., 1.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMin', inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[5., 1.]]
+# [[30., 1.]]
+# [[55., 1.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMin', inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[5., 1.]]
+# [[30., 1.]]
+# [[55., 1.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="ReduceProd"></a><a name="reduceprod">**ReduceProd**</a>
+
+  Computes the product of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceProd-1">1</a>, <a href="Changelog.md#ReduceProd-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[4.790016e+08]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[3., 8.]
+# [35., 48.]
+# [99., 120.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[3., 8.]]
+# [[35., 48.]]
+# [[99., 120.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[3., 8.]]
+# [[35., 48.]]
+# [[99., 120.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="ReduceSum"></a><a name="reducesum">**ReduceSum**</a>
+
+  Computes the sum of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceSum-1">1</a>, <a href="Changelog.md#ReduceSum-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+<dt><tt>noop_with_empty_axes</tt> : int (default is 0)</dt>
+<dd>Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+<dt><tt>axes</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([], dtype=np.int64)
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
+#print(reduced)
+#[[[78.]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([1], dtype=np.int64)
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+#print(reduced)
+#[[4., 6.]
+# [12., 14.]
+# [20., 22.]]
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>empty_axes_input_noop</summary>
+
+```python
+shape = [3, 2, 2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims,
+    noop_with_empty_axes=True)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+axes = np.array([], dtype=np.int64)
+reduced = np.array(data)
+#print(reduced)
+#[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced],
+       name='test_reduce_sum_empty_axes_input_noop_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.array(data)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([1], dtype=np.int64)
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+#print(reduced)
+#[[[4., 6.]]
+# [[12., 14.]]
+# [[20., 22.]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([-2], dtype=np.int64)
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+# print(reduced)
+#[[[4., 6.]]
+# [[12., 14.]]
+# [[20., 22.]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced],
+       name='test_reduce_sum_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=tuple(
+    axes.tolist()), keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced],
+       name='test_reduce_sum_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="ReduceSumSquare"></a><a name="reducesumsquare">**ReduceSumSquare**</a>
+
+  Computes the sum square of the input tensor's element along the provided axes. The resulting
+  tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+  the resulting tensor has the reduced dimension pruned.
+
+  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+  False instead of True.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ReduceSumSquare-1">1</a>, <a href="Changelog.md#ReduceSumSquare-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axes</tt> : list of ints</dt>
+<dd>A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the reduced dimension or not, default 1 means keep reduced dimension.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reduced</tt> (differentiable) : T</dt>
+<dd>Reduced output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to high-precision numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[650.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_default_axes_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[10., 20.]
+# [74., 100.]
+# [202., 244.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_do_not_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[10., 20.]]
+# [[74., 100.]]
+# [[202., 244.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_keepdims_random')
+```
+
+</details>
+
+
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[10., 20.s]]
+# [[74., 100.]]
+# [[202., 244.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### <a name="Relu"></a><a name="relu">**Relu**</a>
+
+  Relu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Relu-1">1</a>, <a href="Changelog.md#Relu-6">6</a>, <a href="Changelog.md#Relu-13">13</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to signed numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>relu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Relu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_relu')
+```
+
+</details>
+
+
+### <a name="Reshape"></a><a name="reshape">**Reshape**</a>
+
+  Reshape the input tensor similar to numpy.reshape.
+  First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+  At most one dimension of the new shape can be -1. In this case, the value is
+  inferred from the size of the tensor and the remaining dimensions. A dimension
+  could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+  from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
+  dimension will be set explicitly to zero (i.e. not taken from input tensor).
+  Shape (second input) could be an empty shape, which means converting to a scalar.
+  The input tensor's shape and the output tensor's shape are required to have the same number of elements.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Reshape-1">1</a>, <a href="Changelog.md#Reshape-5">5</a>, <a href="Changelog.md#Reshape-13">13</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>allowzero</tt> : int (default is 0)</dt>
+<dd>(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+<dt><tt>shape</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Specified shape for output.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>reshaped</tt> (differentiable) : T</dt>
+<dd>Reshaped data.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>allowzero</summary>
+
+```python
+original_shape = [0, 3, 4]
+test_cases = {
+    'allowzero_reordered': np.array([3, 4, 0], dtype=np.int64),
+}
+data = np.random.random_sample(original_shape).astype(np.float32)
+
+for test_name, shape in test_cases.items():
+    node = onnx.helper.make_node(
+        'Reshape',
+        inputs=['data', 'shape'],
+        outputs=['reshaped'],
+        allowzero=1,  # if allowzero=1, final shape = (3, 4, 0)
+                      # if allowzero=0, final shape = (3, 4, 4)
+    )
+
+    reshaped = reshape_reference_implementation(data, shape, allowzero=1)
+
+    expect(node, inputs=[data, shape], outputs=[reshaped],
+           name='test_reshape_' + test_name)
+```
+
+</details>
+
+
+<details>
+<summary>reshape</summary>
+
+```python
+original_shape = [2, 3, 4]
+test_cases = {
+    'reordered_all_dims': np.array([4, 2, 3], dtype=np.int64),
+    'reordered_last_dims': np.array([2, 4, 3], dtype=np.int64),
+    'reduced_dims': np.array([2, 12], dtype=np.int64),
+    'extended_dims': np.array([2, 3, 2, 2], dtype=np.int64),
+    'one_dim': np.array([24], dtype=np.int64),
+    'negative_dim': np.array([2, -1, 2], dtype=np.int64),
+    'negative_extended_dims': np.array([-1, 2, 3, 4], dtype=np.int64),
+    'zero_dim': np.array([2, 0, 4, 1], dtype=np.int64),
+    'zero_and_negative_dim': np.array([2, 0, 1, -1], dtype=np.int64),
+}
+data = np.random.random_sample(original_shape).astype(np.float32)
+
+for test_name, shape in test_cases.items():
+    node = onnx.helper.make_node(
+        'Reshape',
+        inputs=['data', 'shape'],
+        outputs=['reshaped'],
+    )
+
+    reshaped = reshape_reference_implementation(data, shape)
+
+    expect(node, inputs=[data, shape], outputs=[reshaped],
+           name='test_reshape_' + test_name)
+```
+
+</details>
+
+
+### <a name="Resize"></a><a name="resize">**Resize**</a>
+
+  Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Resize-10">10</a>, <a href="Changelog.md#Resize-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>coordinate_transformation_mode</tt> : string (default is half_pixel)</dt>
+<dd>
+This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
+
+The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example.
+Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/>
+
+if coordinate_transformation_mode is "half_pixel", <br/>
+x_original = (x_resized + 0.5) / scale - 0.5, <br/>
+
+if coordinate_transformation_mode is "pytorch_half_pixel", <br/>
+x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/>
+
+if coordinate_transformation_mode is "align_corners", <br/>
+x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
+
+if coordinate_transformation_mode is "asymmetric", <br/>
+x_original = x_resized / scale, <br/>
+
+if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).</dd>
+<dt><tt>cubic_coeff_a</tt> : float (default is -0.75)</dt>
+<dd>The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".</dd>
+<dt><tt>exclude_outside</tt> : int (default is 0)</dt>
+<dd>If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.</dd>
+<dt><tt>extrapolation_value</tt> : float (default is 0.0)</dt>
+<dd>When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.</dd>
+<dt><tt>mode</tt> : string (default is nearest)</dt>
+<dd>Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).</dd>
+<dt><tt>nearest_mode</tt> : string (default is round_prefer_floor)</dt>
+<dd>Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".</dd>
+</dl>
+
+#### Inputs (1 - 4)
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T1</dt>
+<dd>N-D tensor</dd>
+<dt><tt>roi</tt> (optional, non-differentiable) : T2</dt>
+<dd>1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"</dd>
+<dt><tt>scales</tt> (optional, non-differentiable) : tensor(float)</dt>
+<dd>The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.</dd>
+<dt><tt>sizes</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T1</dt>
+<dd>N-D tensor after resizing</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input 'X' and output 'Y' to all tensor types.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain roi type to float or double.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>resize_downsample_scales_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+# [[[[ 1.47119141  2.78125     4.08251953]
+#    [ 6.71142578  8.02148438  9.32275391]
+#    [11.91650391 13.2265625  14.52783203]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_cubic')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_scales_cubic_A_n0p5_exclude_outside</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    cubic_coeff_a=-0.5,
+    exclude_outside=True
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+# [[[[ 1.36812675  2.6695014   4.0133367 ]
+#    [ 6.57362535  7.875       9.2188353 ]
+#    [11.94896657 13.25034122 14.59417652]]]]
+output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.5), scale_factors=scales,
+                        exclude_outside=True).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_cubic_A_n0p5_exclude_outside')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_scales_cubic_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+# [[[[ 1.          2.39519159  3.79038317]
+#    [ 6.58076634  7.97595793  9.37114951]
+#    [12.16153268 13.55672427 14.95191585]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_cubic_align_corners')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_scales_linear</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+# [[[[2.6666665 4.3333331]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_linear')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_scales_linear_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+# [[[[1.       3.142857]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_linear_align_corners')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_scales_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+# [[[[1. 3.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_nearest')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_sizes_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+# [[[[ 1.63078704  3.00462963  4.37847222]
+#    [ 7.12615741  8.5         9.87384259]
+#    [12.62152778 13.99537037 15.36921296]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_downsample_sizes_cubic')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_sizes_linear_pytorch_half_pixel</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='pytorch_half_pixel'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 3, 1], dtype=np.int64)
+
+# [[[[ 1.6666666]
+#    [ 7.       ]
+#    [12.333333 ]]]]
+output = interpolate_nd(
+    data, linear_coeffs, output_size=sizes, coordinate_transformation_mode='pytorch_half_pixel').astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_downsample_sizes_linear_pytorch_half_pixel')
+```
+
+</details>
+
+
+<details>
+<summary>resize_downsample_sizes_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 1, 3], dtype=np.int64)
+
+# [[[[1. 3.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_downsample_sizes_nearest')
+```
+
+</details>
+
+
+<details>
+<summary>resize_tf_crop_and_resize</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', 'roi', '', 'sizes'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='tf_crop_and_resize'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+# Note: for some rois, the result may be different with that of TF for inaccurate floating point
+roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
+sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+# [[[[ 7.6000004  7.9        8.2      ]
+#    [ 8.8        9.1        9.400001 ]
+#    [10.        10.3       10.6      ]]]]
+output = interpolate_nd(data, linear_coeffs, output_size=sizes, roi=roi,
+                        coordinate_transformation_mode='tf_crop_and_resize').astype(np.float32)
+
+expect(node, inputs=[data, roi, sizes], outputs=[output],
+       name='test_resize_tf_crop_and_resize')
+```
+
+</details>
+
+
+<details>
+<summary>resize_tf_crop_and_resize_extrapolation_value</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', 'roi', '', 'sizes'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='tf_crop_and_resize',
+    extrapolation_value=10.0
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+# Note: for some rois, the result may be different with that of TF for inaccurate floating point
+roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
+sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+# [[[[ 7.6000004 10.        10.       ]
+#    [12.400001  10.        10.       ]
+#    [10.        10.        10.       ]]]]
+output = interpolate_nd(data, linear_coeffs, output_size=sizes, roi=roi,
+                        coordinate_transformation_mode='tf_crop_and_resize', extrapolation_value=10.0).astype(np.float32)
+
+expect(node, inputs=[data, roi, sizes], outputs=[output],
+       name='test_resize_tf_crop_and_resize')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 0.47265625  0.76953125  1.24609375  1.875       2.28125
+#      2.91015625  3.38671875  3.68359375]
+#    [ 1.66015625  1.95703125  2.43359375  3.0625      3.46875
+#      4.09765625  4.57421875  4.87109375]
+#    [ 3.56640625  3.86328125  4.33984375  4.96875     5.375
+#      6.00390625  6.48046875  6.77734375]
+#    [ 6.08203125  6.37890625  6.85546875  7.484375    7.890625
+#      8.51953125  8.99609375  9.29296875]
+#    [ 7.70703125  8.00390625  8.48046875  9.109375    9.515625
+#     10.14453125 10.62109375 10.91796875]
+#    [10.22265625 10.51953125 10.99609375 11.625      12.03125
+#     12.66015625 13.13671875 13.43359375]
+#    [12.12890625 12.42578125 12.90234375 13.53125    13.9375
+#     14.56640625 15.04296875 15.33984375]
+#    [13.31640625 13.61328125 14.08984375 14.71875    15.125
+#     15.75390625 16.23046875 16.52734375]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_cubic_A_n0p5_exclude_outside</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    cubic_coeff_a=-0.5,
+    exclude_outside=True
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 0.55882353  0.81494204  1.35698249  1.89705882  2.39705882
+#      2.93713516  3.47917561  3.73529412]
+#    [ 1.58329755  1.83941606  2.38145651  2.92153285  3.42153285
+#      3.96160918  4.50364964  4.75976814]
+#    [ 3.75145936  4.00757787  4.54961832  5.08969466  5.58969466
+#      6.12977099  6.67181144  6.92792995]
+#    [ 5.91176471  6.16788321  6.70992366  7.25        7.75
+#      8.29007634  8.83211679  9.08823529]
+#    [ 7.91176471  8.16788321  8.70992366  9.25        9.75
+#     10.29007634 10.83211679 11.08823529]
+#    [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534
+#     12.45038168 12.99242213 13.24854064]
+#    [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715
+#     14.61854349 15.16058394 15.41670245]
+#    [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118
+#     15.64301751 16.18505796 16.44117647]]]]
+output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.5), scale_factors=scales,
+                        exclude_outside=True).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic_A_n0p5_exclude_outside')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_cubic_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 1.          1.34110787  1.80029155  2.32944606  2.67055394
+#      3.19970845  3.65889213  4.        ]
+#    [ 2.36443149  2.70553936  3.16472303  3.69387755  4.03498542
+#      4.56413994  5.02332362  5.36443149]
+#    [ 4.20116618  4.54227405  5.00145773  5.53061224  5.87172012
+#      6.40087464  6.86005831  7.20116618]
+#    [ 6.31778426  6.65889213  7.1180758   7.64723032  7.98833819
+#      8.51749271  8.97667638  9.31778426]
+#    [ 7.68221574  8.02332362  8.48250729  9.01166181  9.35276968
+#      9.8819242  10.34110787 10.68221574]
+#    [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776
+#     11.99854227 12.45772595 12.79883382]
+#    [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245
+#     13.83527697 14.29446064 14.63556851]
+#    [13.         13.34110787 13.80029155 14.32944606 14.67055394
+#     15.19970845 15.65889213 16.        ]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic_align_corners')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_cubic_asymmetric</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    coordinate_transformation_mode='asymmetric'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 1.       1.40625  2.       2.5      3.       3.59375  4.
+#      4.09375]
+#    [ 2.625    3.03125  3.625    4.125    4.625    5.21875  5.625
+#      5.71875]
+#    [ 5.       5.40625  6.       6.5      7.       7.59375  8.
+#      8.09375]
+#    [ 7.       7.40625  8.       8.5      9.       9.59375 10.
+#     10.09375]
+#    [ 9.       9.40625 10.      10.5     11.      11.59375 12.
+#     12.09375]
+#    [11.375   11.78125 12.375   12.875   13.375   13.96875 14.375
+#     14.46875]
+#    [13.      13.40625 14.      14.5     15.      15.59375 16.
+#     16.09375]
+#    [13.375   13.78125 14.375   14.875   15.375   15.96875 16.375
+#     16.46875]]]]
+output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.75), scale_factors=scales,
+                        coordinate_transformation_mode='asymmetric').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic_asymmetric')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_linear</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[1.   1.25 1.75 2.  ]
+#    [1.5  1.75 2.25 2.5 ]
+#    [2.5  2.75 3.25 3.5 ]
+#    [3.   3.25 3.75 4.  ]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_linear')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_linear_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[1.         1.33333333 1.66666667 2.        ]
+#    [1.66666667 2.         2.33333333 2.66666667]
+#    [2.33333333 2.66666667 3.         3.33333333]
+#    [3.         3.33333333 3.66666667 4.        ]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_linear_align_corners')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_scales_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+# [[[[1. 1. 1. 2. 2. 2.]
+#    [1. 1. 1. 2. 2. 2.]
+#    [3. 3. 3. 4. 4. 4.]
+#    [3. 3. 3. 4. 4. 4.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_nearest')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_sizes_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 9, 10], dtype=np.int64)
+
+# [[[[ 0.45507922  0.64057922  0.97157922  1.42257922  1.90732922
+#      2.22332922  2.70807922  3.15907922  3.49007922  3.67557922]
+#    [ 1.39437963  1.57987963  1.91087963  2.36187963  2.84662963
+#      3.16262963  3.64737963  4.09837963  4.42937963  4.61487963]
+#    [ 2.95130693  3.13680693  3.46780693  3.91880693  4.40355693
+#      4.71955693  5.20430693  5.65530693  5.98630693  6.17180693]
+#    [ 5.20525069  5.39075069  5.72175069  6.17275069  6.65750069
+#      6.97350069  7.45825069  7.90925069  8.24025069  8.42575069]
+#    [ 6.88975     7.07525     7.40625     7.85725     8.342
+#      8.658       9.14275     9.59375     9.92475    10.11025   ]
+#    [ 8.57424931  8.75974931  9.09074931  9.54174931 10.02649931
+#     10.34249931 10.82724931 11.27824931 11.60924931 11.79474931]
+#    [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307
+#     12.59644307 13.08119307 13.53219307 13.86319307 14.04869307]
+#    [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037
+#     14.15337037 14.63812037 15.08912037 15.42012037 15.60562037]
+#    [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078
+#     15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_cubic')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_sizes_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 7, 8], dtype=np.int64)
+
+# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+#    [1. 1. 1. 1. 2. 2. 2. 2.]
+#    [1. 1. 1. 1. 2. 2. 2. 2.]
+#    [1. 1. 1. 1. 2. 2. 2. 2.]
+#    [3. 3. 3. 3. 4. 4. 4. 4.]
+#    [3. 3. 3. 3. 4. 4. 4. 4.]
+#    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_sizes_nearest_ceil_half_pixel</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+    coordinate_transformation_mode='half_pixel',
+    nearest_mode='ceil'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+# [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+output = interpolate_nd(
+    data, lambda x: nearest_coeffs(x, mode='ceil'), output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest_ceil_half_pixel')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_sizes_nearest_floor_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+    coordinate_transformation_mode='align_corners',
+    nearest_mode='floor'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+# [[[[ 1.  1.  1.  2.  2.  3.  3.  4.]
+#    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+#    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+#    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+#    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+#    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+#    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+#    [13. 13. 13. 14. 14. 15. 15. 16.]]]]
+output = interpolate_nd(
+    data, lambda x: nearest_coeffs(x, mode='floor'), output_size=sizes, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest_floor_align_corners')
+```
+
+</details>
+
+
+<details>
+<summary>resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+    coordinate_transformation_mode='asymmetric',
+    nearest_mode='round_prefer_ceil'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+# [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+output = interpolate_nd(
+    data, lambda x: nearest_coeffs(x, mode='round_prefer_ceil'),
+    output_size=sizes, coordinate_transformation_mode='asymmetric').astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric')
+```
+
+</details>
+
+
+### <a name="ReverseSequence"></a><a name="reversesequence">**ReverseSequence**</a>
+
+  Reverse batch of sequences having different lengths specified by `sequence_lens`.
+
+  For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis,
+  and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed
+  sequences on the first sequence_lens[i] elements, then have original values copied for the other elements.
+
+  Example 1:
+    input = [[0.0, 4.0, 8.0,  12.0],
+             [1.0, 5.0, 9.0,  13.0],
+             [2.0, 6.0, 10.0, 14.0],
+             [3.0, 7.0, 11.0, 15.0]]
+    sequence_lens = [4, 3, 2, 1]
+    time_axis = 0
+    batch_axis = 1
+
+    output = [[3.0, 6.0, 9.0,  12.0],
+              [2.0, 5.0, 8.0,  13.0],
+              [1.0, 4.0, 10.0, 14.0],
+              [0.0, 7.0, 11.0, 15.0]]
+
+  Example 2:
+    input = [[0.0,  1.0,  2.0,  3.0 ],
+             [4.0,  5.0,  6.0,  7.0 ],
+             [8.0,  9.0,  10.0, 11.0],
+             [12.0, 13.0, 14.0, 15.0]]
+    sequence_lens = [1, 2, 3, 4]
+    time_axis = 1
+    batch_axis = 0
+
+    output = [[0.0,  1.0,  2.0,  3.0 ],
+              [5.0,  4.0,  6.0,  7.0 ],
+              [10.0, 9.0,  8.0,  11.0],
+              [15.0, 14.0, 13.0, 12.0]]
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>batch_axis</tt> : int (default is 1)</dt>
+<dd>(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.</dd>
+<dt><tt>time_axis</tt> : int (default is 0)</dt>
+<dd>(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Tensor of rank r >= 2.</dd>
+<dt><tt>sequence_lens</tt> : tensor(int64)</dt>
+<dd>Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>Tensor with same shape of input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>reversesequence_batch</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReverseSequence',
+    inputs=['x', 'sequence_lens'],
+    outputs=['y'],
+    time_axis=1,
+    batch_axis=0,
+)
+x = np.array([[0.0, 1.0, 2.0, 3.0],
+              [4.0, 5.0, 6.0, 7.0],
+              [8.0, 9.0, 10.0, 11.0],
+              [12.0, 13.0, 14.0, 15.0]], dtype=np.float32)
+sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64)
+
+y = np.array([[0.0, 1.0, 2.0, 3.0],
+              [5.0, 4.0, 6.0, 7.0],
+              [10.0, 9.0, 8.0, 11.0],
+              [15.0, 14.0, 13.0, 12.0]], dtype=np.float32)
+
+expect(node, inputs=[x, sequence_lens], outputs=[y],
+       name='test_reversesequence_batch')
+```
+
+</details>
+
+
+<details>
+<summary>reversesequence_time</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReverseSequence',
+    inputs=['x', 'sequence_lens'],
+    outputs=['y'],
+    time_axis=0,
+    batch_axis=1,
+)
+x = np.array([[0.0, 4.0, 8.0, 12.0],
+              [1.0, 5.0, 9.0, 13.0],
+              [2.0, 6.0, 10.0, 14.0],
+              [3.0, 7.0, 11.0, 15.0]], dtype=np.float32)
+sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)
+
+y = np.array([[3.0, 6.0, 9.0, 12.0],
+              [2.0, 5.0, 8.0, 13.0],
+              [1.0, 4.0, 10.0, 14.0],
+              [0.0, 7.0, 11.0, 15.0]], dtype=np.float32)
+
+expect(node, inputs=[x, sequence_lens], outputs=[y],
+       name='test_reversesequence_time')
+```
+
+</details>
+
+
+### <a name="RoiAlign"></a><a name="roialign">**RoiAlign**</a>
+
+  Region of Interest (RoI) align operation described in the
+  [Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
+  RoiAlign consumes an input tensor X and region of interests (rois)
+  to apply pooling across each RoI; it produces a 4-D tensor of shape
+  (num_rois, C, output_height, output_width).
+
+  RoiAlign is proposed to avoid the misalignment by removing
+  quantizations while converting from original image into feature
+  map and from feature map into RoI feature; in each ROI bin,
+  the value of the sampled locations are computed directly
+  through bilinear interpolation.
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#RoiAlign-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>coordinate_transformation_mode</tt> : string (default is half_pixel)</dt>
+<dd>Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).</dd>
+<dt><tt>mode</tt> : string (default is avg)</dt>
+<dd>The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.</dd>
+<dt><tt>output_height</tt> : int (default is 1)</dt>
+<dd>default 1; Pooled output Y's height.</dd>
+<dt><tt>output_width</tt> : int (default is 1)</dt>
+<dd>default 1; Pooled output Y's width.</dd>
+<dt><tt>sampling_ratio</tt> : int (default is 0)</dt>
+<dd>Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.</dd>
+<dt><tt>spatial_scale</tt> : float (default is 1.0)</dt>
+<dd>Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. </dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T1</dt>
+<dd>Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.</dd>
+<dt><tt>rois</tt> : T1</dt>
+<dd>RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.</dd>
+<dt><tt>batch_indices</tt> : T2</dt>
+<dd>1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T1</dt>
+<dd>RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain types to int tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>roialign_aligned_false</summary>
+
+```python
+node = onnx.helper.make_node(
+    "RoiAlign",
+    inputs=["X", "rois", "batch_indices"],
+    outputs=["Y"],
+    spatial_scale=1.0,
+    output_height=5,
+    output_width=5,
+    sampling_ratio=2,
+    coordinate_transformation_mode="output_half_pixel",
+)
+
+X, batch_indices, rois = get_roi_align_input_values()
+# (num_rois, C, output_height, output_width)
+Y = np.array(
+    [
+        [
+            [
+                [0.4664, 0.4466, 0.3405, 0.5688, 0.6068],
+                [0.3714, 0.4296, 0.3835, 0.5562, 0.3510],
+                [0.2768, 0.4883, 0.5222, 0.5528, 0.4171],
+                [0.4713, 0.4844, 0.6904, 0.4920, 0.8774],
+                [0.6239, 0.7125, 0.6289, 0.3355, 0.3495],
+            ]
+        ],
+        [
+            [
+                [0.3022, 0.4305, 0.4696, 0.3978, 0.5423],
+                [0.3656, 0.7050, 0.5165, 0.3172, 0.7015],
+                [0.2912, 0.5059, 0.6476, 0.6235, 0.8299],
+                [0.5916, 0.7389, 0.7048, 0.8372, 0.8893],
+                [0.6227, 0.6153, 0.7097, 0.6154, 0.4585],
+            ]
+        ],
+        [
+            [
+                [0.2384, 0.3379, 0.3717, 0.6100, 0.7601],
+                [0.3767, 0.3785, 0.7147, 0.9243, 0.9727],
+                [0.5749, 0.5826, 0.5709, 0.7619, 0.8770],
+                [0.5355, 0.2566, 0.2141, 0.2796, 0.3600],
+                [0.4365, 0.3504, 0.2887, 0.3661, 0.2349],
+            ]
+        ],
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_false")
+```
+
+</details>
+
+
+<details>
+<summary>roialign_aligned_true</summary>
+
+```python
+node = onnx.helper.make_node(
+    "RoiAlign",
+    inputs=["X", "rois", "batch_indices"],
+    outputs=["Y"],
+    spatial_scale=1.0,
+    output_height=5,
+    output_width=5,
+    sampling_ratio=2,
+    coordinate_transformation_mode="half_pixel",
+)
+
+X, batch_indices, rois = get_roi_align_input_values()
+# (num_rois, C, output_height, output_width)
+Y = np.array(
+    [
+        [
+            [
+                [0.5178, 0.3434, 0.3229, 0.4474, 0.6344],
+                [0.4031, 0.5366, 0.4428, 0.4861, 0.4023],
+                [0.2512, 0.4002, 0.5155, 0.6954, 0.3465],
+                [0.3350, 0.4601, 0.5881, 0.3439, 0.6849],
+                [0.4932, 0.7141, 0.8217, 0.4719, 0.4039],
+            ]
+        ],
+        [
+            [
+                [0.3070, 0.2187, 0.3337, 0.4880, 0.4870],
+                [0.1871, 0.4914, 0.5561, 0.4192, 0.3686],
+                [0.1433, 0.4608, 0.5971, 0.5310, 0.4982],
+                [0.2788, 0.4386, 0.6022, 0.7000, 0.7524],
+                [0.5774, 0.7024, 0.7251, 0.7338, 0.8163],
+            ]
+        ],
+        [
+            [
+                [0.2393, 0.4075, 0.3379, 0.2525, 0.4743],
+                [0.3671, 0.2702, 0.4105, 0.6419, 0.8308],
+                [0.5556, 0.4543, 0.5564, 0.7502, 0.9300],
+                [0.6626, 0.5617, 0.4813, 0.4954, 0.6663],
+                [0.6636, 0.3721, 0.2056, 0.1928, 0.2478],
+            ]
+        ],
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_true")
+```
+
+</details>
+
+
+### <a name="Round"></a><a name="round">**Round**</a>
+
+  Round takes one input Tensor and rounds the values, element-wise, meaning
+  it finds the nearest integer for each value.
+  In case of halfs, the rule is to round them to the nearest even integer.
+  The output tensor has the same shape and type as the input.
+
+  Examples:
+  ```
+  round([0.9]) = [1.0]
+  round([2.5]) = [2.0]
+  round([2.3]) = [2.0]
+  round([1.5]) = [2.0]
+  round([-4.5]) = [-4.0]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>round</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Round',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([0.1, 0.5, 0.9, 1.2, 1.5,
+            1.8, 2.3, 2.5, 2.7, -1.1,
+            -1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float32)
+y = np.array([0., 0., 1., 1., 2.,
+            2., 2., 2., 3., -1.,
+            -2., -2., -2., -2., -3.]).astype(np.float32)  # expected output
+expect(node, inputs=[x], outputs=[y],
+       name='test_round')
+```
+
+</details>
+
+
+### <a name="Scan"></a><a name="scan">**Scan**</a>
+
+  Scan can be used to iterate over one or more scan_input tensors,
+  constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+  functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+  generalizations of RNN-like constructs for sequence-to-sequence processing.
+  Other tensors (referred to as state_variables here) can be used to carry a state
+  when iterating from one element to another (similar to hidden-state in RNNs, also referred
+  to as loop-carried dependences in the context of loops).
+  Many common usages involve a single scan_input tensor (where functionality
+  similar to scan, fold and map can be obtained). When more than one scan_input is used,
+  a behavior similar to zip is obtained.
+
+  The attribute body must be a graph, specifying the computation to be performed in
+  every iteration. It takes as input the current values of the state_variables and
+  the current iterated element of the scan_inputs. It must return the (updated) values
+  of the state_variables and zero or more scan_output_element tensors. The values of the
+  scan_output_element tensors are concatenated over all the iterations to produce the
+  scan_output values of the scan construct (similar to the concatenated intermediate
+  hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+  well as scan_output_element tensors) are required to have the same shape in each iteration
+  of the loop (a restriction imposed to enable efficient memory allocation).
+
+  Note that the iterated element passed to the body subgraph does not have a sequence
+  axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+  The scan operation returns the final values of the state_variables as well as the
+  scan_outputs.
+
+  The optional attribute scan_input_directions specifies the direction (forward or backward)
+  for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+  direction. A bidirectional scan may be performed by specifying the same tensor input twice
+  in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+  The scan_output of the operation is produced by concatenating the scan_output_element
+  values produced by the body in each iteration.  The optional attribute scan_output_directions
+  specifies the direction in which scan_output is constructed (by appending or prepending the
+  scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+  is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+  The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+  If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+  batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+  Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+  The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+  are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+  scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+  value of 1.
+
+  Note that because of the ONNX restriction that only the last parameter of an operator can
+  be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+  Similarly, the final-states and scan-outputs are listed together as one output parameter.
+  The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+  The behavior of
+
+      Scan <
+          num_scan_inputs = m,
+          body = loop-body,
+          scan_input_axes = [axis_1, ..., axis_m]
+      > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+  is equivalent to the following pseudo-code:
+
+      // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+      // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+      sequence_length = scan_1.shape[axis_1];
+
+      // initialize state-variables
+      st_1 = init_1; ... st_n = init_n;
+      // initialize scan-output variables: [] denotes an empty tensor
+      scan_out_1 = []; ...; scan_out_k = [];
+      // identify number of iterations:
+
+      // execute loop
+      for (int t = 0; t < sequence_length; ++t) {
+          // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+          // of rank one less than T obtained by indexing T at position t along axis k.
+          si_1 = scan_1<axis=axis_1>[t];
+          ... ;
+          si_m = scan_m<axis=axis_m>[t];
+          // execute loop-body
+          st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+          // accumulate the scan-output elements
+          scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+      }
+
+      return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+  *Sample usage: Encoding RNN using a Scan*
+
+  The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+  recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+  be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+  %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+  values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+      graph rnn-encoding {
+        %H_0 = ...
+        %X = ...
+        %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+        return %Y, %Y_h
+      }
+
+      graph rnn-cell-1 (
+        %H_tminus1[FLOAT, tensor]
+        %X_t[FLOAT, tensor]
+      ) {
+        %Wi = ...
+        %Ri = ...
+        %Wbi = ...
+        %Rbi = ...
+        %t1 = X_t * (Wi^T)
+        %t2 = H_tminus1*(Ri^T)
+        %t3 = Add(%t1, %t2)
+        %t4 = Add(%t3, %Wbi)
+        %t5 = Add(%t4, %Rbi)
+        %Ht = Tanh(%t5)
+        %Accumulate = Identity(%Ht)
+        return %Ht, %Accumulate
+      }
+
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Scan-8">8</a>, <a href="Changelog.md#Scan-9">9</a>, <a href="Changelog.md#Scan-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.</dd>
+<dt><tt>num_scan_inputs</tt> : int (required)</dt>
+<dd>An attribute specifying the number of scan_inputs M. </dd>
+<dt><tt>scan_input_axes</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>scan_input_directions</tt> : list of ints</dt>
+<dd>An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.</dd>
+<dt><tt>scan_output_axes</tt> : list of ints</dt>
+<dd>An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].</dd>
+<dt><tt>scan_output_directions</tt> : list of ints</dt>
+<dd>An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>initial_state_and_scan_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Initial values of the loop's N state variables followed by M scan_inputs</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>final_state_and_scan_outputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Final values of the loop's N state variables followed by K scan_outputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>All Tensor types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>scan_8</summary>
+
+```python
+# Given an input sequence [x1, ..., xN], sum up its elements using a scan
+# returning the final state (x1+x2+...+xN) as well the scan_output
+# [x1, x1+x2, ..., x1+x2+...+xN]
+#
+# create graph to represent scan body
+sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+next = onnx.helper.make_tensor_value_info('next', onnx.TensorProto.FLOAT, [2])
+sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['sum_in', 'next'],
+    outputs=['sum_out']
+)
+id_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['sum_out'],
+    outputs=['scan_out']
+)
+scan_body = onnx.helper.make_graph(
+    [add_node, id_node],
+    'scan_body',
+    [sum_in, next],
+    [sum_out, scan_out]
+)
+# create scan op node
+no_sequence_lens = ''   # optional input, not supplied
+node = onnx.helper.make_node(
+    'Scan',
+    inputs=[no_sequence_lens, 'initial', 'x'],
+    outputs=['y', 'z'],
+    num_scan_inputs=1,
+    body=scan_body
+)
+# create inputs for batch-size 1, sequence-length 3, inner dimension 2
+initial = np.array([0, 0]).astype(np.float32).reshape((1, 2))
+x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2))
+# final state computed = [1 + 3 + 5, 2 + 4 + 6]
+y = np.array([9, 12]).astype(np.float32).reshape((1, 2))
+# scan-output computed
+z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2))
+
+expect(node, inputs=[initial, x], outputs=[y, z],
+       name='test_scan_sum', opset_imports=[onnx.helper.make_opsetid("", 8)])
+```
+
+</details>
+
+
+<details>
+<summary>scan_9</summary>
+
+```python
+# Given an input sequence [x1, ..., xN], sum up its elements using a scan
+# returning the final state (x1+x2+...+xN) as well the scan_output
+# [x1, x1+x2, ..., x1+x2+...+xN]
+#
+# create graph to represent scan body
+sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+next = onnx.helper.make_tensor_value_info('next', onnx.TensorProto.FLOAT, [2])
+sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['sum_in', 'next'],
+    outputs=['sum_out']
+)
+id_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['sum_out'],
+    outputs=['scan_out']
+)
+scan_body = onnx.helper.make_graph(
+    [add_node, id_node],
+    'scan_body',
+    [sum_in, next],
+    [sum_out, scan_out]
+)
+# create scan op node
+node = onnx.helper.make_node(
+    'Scan',
+    inputs=['initial', 'x'],
+    outputs=['y', 'z'],
+    num_scan_inputs=1,
+    body=scan_body
+)
+# create inputs for sequence-length 3, inner dimension 2
+initial = np.array([0, 0]).astype(np.float32).reshape((2,))
+x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
+# final state computed = [1 + 3 + 5, 2 + 4 + 6]
+y = np.array([9, 12]).astype(np.float32).reshape((2,))
+# scan-output computed
+z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
+
+expect(node, inputs=[initial, x], outputs=[y, z],
+       name='test_scan9_sum', opset_imports=[onnx.helper.make_opsetid("", 9)])
+```
+
+</details>
+
+
+### <a name="Scatter"></a><a name="scatter">**Scatter** (deprecated)</a>
+
+  This operator is deprecated. Please use ScatterElements, which provides the same functionality.
+
+  Scatter takes three inputs `data`, `updates`, and `indices` of the same
+  rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value
+  to values specified by `updates` at specific index positions specified by
+  `indices`. Its output shape is the same as the shape of `data`.
+
+  For each entry in `updates`, the target index in `data` is obtained by combining
+  the corresponding entry in `indices` with the index of the entry itself: the
+  index-value for dimension = axis is obtained from the value of the corresponding
+  entry in `indices` and the index-value for dimension != axis is obtained from the
+  index of the entry itself.
+
+  For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+  is performed as below:
+  ```
+    output[indices[i][j]][j] = updates[i][j] if axis = 0,
+    output[i][indices[i][j]] = updates[i][j] if axis = 1,
+  ```
+
+  This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+  Example 1:
+  ```
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  ```
+  Example 2:
+  ```
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+  ```
+
+#### Version
+
+This version of the operator has been deprecated since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Scatter-9">9</a>
+
+
+#### Examples
+
+<details>
+<summary>scatter_with_axis</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'Scatter',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, 3]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter(data, indices, updates, axis=axis)
+# print(y) produces
+# [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_with_axis', opset_imports=[helper.make_opsetid("", 10)])
+```
+
+</details>
+
+
+<details>
+<summary>scatter_without_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Scatter',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+)
+data = np.zeros((3, 3), dtype=np.float32)
+indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+y = scatter(data, indices, updates)
+# print(y) produces
+# [[2.0, 1.1, 0.0],
+#  [1.0, 0.0, 2.2],
+#  [0.0, 2.1, 1.2]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_without_axis', opset_imports=[helper.make_opsetid("", 10)])
+```
+
+</details>
+
+
+### <a name="ScatterElements"></a><a name="scatterelements">**ScatterElements**</a>
+
+  ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+  rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+  (by default, the outer-most axis, that is axis 0). The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value
+  to values specified by `updates` at specific index positions specified by
+  `indices`. Its output shape is the same as the shape of `data`.
+
+  For each entry in `updates`, the target index in `data` is obtained by combining
+  the corresponding entry in `indices` with the index of the entry itself: the
+  index-value for dimension = axis is obtained from the value of the corresponding
+  entry in `indices` and the index-value for dimension != axis is obtained from the
+  index of the entry itself.
+
+  `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates`
+  tensor into `output` at the specified `indices`.
+  In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2,
+  then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update
+  corresponding to the [i][j] entry is performed as below:
+  ```
+    output[indices[i][j]][j] = updates[i][j] if axis = 0,
+    output[i][indices[i][j]] = updates[i][j] if axis = 1,
+  ```
+  When `reduction` is set to "add", the update corresponding to the [i][j] entry is performed as below:
+  ```
+    output[indices[i][j]][j] += updates[i][j] if axis = 0,
+    output[i][indices[i][j]] += updates[i][j] if axis = 1,
+  ```
+  When `reduction` is set to "mul", the update corresponding to the [i][j] entry is performed as below:
+  ```
+    output[indices[i][j]][j] *= updates[i][j] if axis = 0,
+    output[i][indices[i][j]] *= updates[i][j] if axis = 1,
+  ```
+
+  This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+  Example 1:
+  ```
+    data = [
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+    ]
+    indices = [
+        [1, 0, 2],
+        [0, 2, 1],
+    ]
+    updates = [
+        [1.0, 1.1, 1.2],
+        [2.0, 2.1, 2.2],
+    ]
+    output = [
+        [2.0, 1.1, 0.0]
+        [1.0, 0.0, 2.2]
+        [0.0, 2.1, 1.2]
+    ]
+  ```
+  Example 2:
+  ```
+    data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+    indices = [[1, 3]]
+    updates = [[1.1, 2.1]]
+    axis = 1
+    output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ScatterElements-11">11</a>, <a href="Changelog.md#ScatterElements-13">13</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+<dt><tt>reduction</tt> : string (default is none)</dt>
+<dd>Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add':  reduction using the addition operation. 'mul': reduction using the multiplication operation.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : Tind</dt>
+<dd>Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.</dd>
+<dt><tt>updates</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >=1 (same rank and shape as indices)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1 (same rank as input).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input and output types can be of any tensor type.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>scatter_elements_with_axis</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, 3]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates, axis)
+# print(y) produces
+# [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_elements_with_axis')
+```
+
+</details>
+
+
+<details>
+<summary>scatter_elements_with_duplicate_indices</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+    reduction='add',
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, 1]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates, axis, reduction='add')
+# print(y) produces
+# [[1.0, 5.2, 3.0, 4.0, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+        name='test_scatter_elements_with_duplicate_indices')
+```
+
+</details>
+
+
+<details>
+<summary>scatter_elements_with_negative_indices</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, -3]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates, axis)
+# print(y) produces
+# [[1.0, 1.1, 2.1, 4.0, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_elements_with_negative_indices')
+```
+
+</details>
+
+
+<details>
+<summary>scatter_elements_without_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+)
+data = np.zeros((3, 3), dtype=np.float32)
+indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates)
+# print(y) produces
+# [[2.0, 1.1, 0.0],
+#  [1.0, 0.0, 2.2],
+#  [0.0, 2.1, 1.2]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_elements_without_axis')
+```
+
+</details>
+
+
+### <a name="ScatterND"></a><a name="scatternd">**ScatterND**</a>
+
+  ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+  and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+  is produced by creating a copy of the input `data`, and then updating its value to values
+  specified by `updates` at specific index positions specified by `indices`. Its output shape
+  is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+  That is, two or more `updates` for the same index-location is not supported.
+
+  `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+   `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+  Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+  update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+  update to a slice of the tensor.
+
+  `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+  first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+  The remaining dimensions of `updates` correspond to the dimensions of the
+  replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+  corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+  must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+  of shapes.
+
+  The `output` is calculated via the following equation:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] = updates[idx]
+
+  The order of iteration in the above loop is not specified.
+  In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+  This ensures that the output value does not depend on the iteration order.
+
+  `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates`
+  tensor into `output` at the specified `indices`.
+  In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2,
+  then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order.
+  When `reduction` is set to "add", `output` is calculated as follows:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] += updates[idx]
+
+  When `reduction` is set to "mul", `output` is calculated as follows:
+
+      output = np.copy(data)
+      update_indices = indices.shape[:-1]
+      for idx in np.ndindex(update_indices):
+          output[indices[idx]] *= updates[idx]
+
+  This operator is the inverse of GatherND.
+
+  Example 1:
+  ```
+    data    = [1, 2, 3, 4, 5, 6, 7, 8]
+    indices = [[4], [3], [1], [7]]
+    updates = [9, 10, 11, 12]
+    output  = [1, 11, 3, 10, 9, 6, 7, 12]
+  ```
+
+  Example 2:
+  ```
+    data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+    indices = [[0], [2]]
+    updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+    output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+               [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+               [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+               [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  ```
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#ScatterND-11">11</a>, <a href="Changelog.md#ScatterND-13">13</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>reduction</tt> : string (default is none)</dt>
+<dd>Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add':  reduction using the addition operation. 'mul': reduction using the multiplication operation.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+<dt><tt>indices</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>Tensor of rank q >= 1.</dd>
+<dt><tt>updates</tt> (differentiable) : T</dt>
+<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of rank r >= 1.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to any tensor type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>scatternd</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterND',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+)
+data = np.array(
+    [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+indices = np.array([[0], [2]], dtype=np.int64)
+updates = np.array(
+    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+# Expecting output as np.array(
+#    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+#     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+#     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+#     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+output = scatter_nd_impl(data, indices, updates)
+expect(node, inputs=[data, indices, updates], outputs=[output],
+       name='test_scatternd')
+```
+
+</details>
+
+
+<details>
+<summary>scatternd_add</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterND',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    reduction='add',
+)
+data = np.array(
+    [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+indices = np.array([[0], [0]], dtype=np.int64)
+updates = np.array(
+    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+        [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+# Expecting output as np.array(
+#    [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]],
+#     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+#     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+#     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+output = scatter_nd_impl(data, indices, updates, reduction='add')
+expect(node, inputs=[data, indices, updates], outputs=[output],
+       name='test_scatternd_add')
+```
+
+</details>
+
+
+<details>
+<summary>scatternd_multiply</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterND',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    reduction='mul',
+)
+data = np.array(
+    [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+indices = np.array([[0], [0]], dtype=np.int64)
+updates = np.array(
+    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+        [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+# Expecting output as np.array(
+#    [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]],
+#     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+#     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+#     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+output = scatter_nd_impl(data, indices, updates, reduction='mul')
+expect(node, inputs=[data, indices, updates], outputs=[output],
+       name='test_scatternd_multiply')
+```
+
+</details>
+
+
+### <a name="Selu"></a><a name="selu">**Selu**</a>
+
+  Selu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the scaled exponential linear unit function,
+  `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 6 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Selu-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.67326)</dt>
+<dd>Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).</dd>
+<dt><tt>gamma</tt> : float (default is 1.0507)</dt>
+<dd>Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>selu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Selu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=2.0,
+    gamma=3.0
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+# expected output [-3.79272318, 0., 3.]
+y = np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_selu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_selu')
+```
+
+</details>
+
+
+<details>
+<summary>selu_default</summary>
+
+```python
+default_alpha = 1.67326319217681884765625
+default_gamma = 1.05070102214813232421875
+node = onnx.helper.make_node(
+    'Selu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) * default_gamma + \
+    (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma
+expect(node, inputs=[x], outputs=[y],
+       name='test_selu_default')
+```
+
+</details>
+
+
+### <a name="SequenceAt"></a><a name="sequenceat">**SequenceAt**</a>
+
+  Outputs a tensor copy from the tensor at 'position' in 'input_sequence'.
+  Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
+  Negative value means counting positions from the back.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>position</tt> : I</dt>
+<dd>Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>tensor</tt> : T</dt>
+<dd>Output tensor at the specified position in the input sequence.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain position to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+
+### <a name="SequenceConstruct"></a><a name="sequenceconstruct">**SequenceConstruct**</a>
+
+  Construct a tensor sequence containing 'inputs' tensors.
+  All tensors in 'inputs' must have the same data type.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>inputs</tt> (variadic) : T</dt>
+<dd>Tensors.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>Sequence enclosing the input tensors.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input types to any tensor type.</dd>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+
+### <a name="SequenceEmpty"></a><a name="sequenceempty">**SequenceEmpty**</a>
+
+  Construct an empty tensor sequence, with given data type.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>dtype</tt> : int</dt>
+<dd>(Optional) The data type of the tensors in the output sequence. The default type is 'float'.</dd>
+</dl>
+
+#### Inputs
+
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : S</dt>
+<dd>Empty sequence.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output types to any tensor type.</dd>
+</dl>
+
+
+### <a name="SequenceErase"></a><a name="sequenceerase">**SequenceErase**</a>
+
+  Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'.
+  Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
+  Negative value means counting positions from the back.
+  'position' is optional, by default it erases the last tensor from 'input_sequence'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>position</tt> (optional) : I</dt>
+<dd>Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>Output sequence that has the tensor at the specified position removed.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain position to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+
+### <a name="SequenceInsert"></a><a name="sequenceinsert">**SequenceInsert**</a>
+
+  Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'.
+  'tensor' must have the same data type as 'input_sequence'.
+  Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'.
+  Negative value means counting positions from the back.
+  'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>tensor</tt> : T</dt>
+<dd>Input tensor to be inserted into the input sequence.</dd>
+<dt><tt>position</tt> (optional) : I</dt>
+<dd>Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>Output sequence that contains the inserted tensor at given position.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain position to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sequenceinsert</summary>
+
+```python
+test_cases = {
+    'at_back': [np.array([10, 11, 12]).astype(np.int64)],
+    'at_front': [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)]
+}
+sequence = [np.array([1, 2, 3, 4]).astype(np.int64), np.array([5, 6, 7]).astype(np.int64), np.array([8, 9]).astype(np.int64)]
+
+for test_name, test_inputs in test_cases.items():
+    tensor = test_inputs[0].astype(np.int64)
+
+    if len(test_inputs) > 1:
+        node = onnx.helper.make_node(
+            'SequenceInsert',
+            inputs=['sequence', 'tensor', 'position'],
+            outputs=['output_sequence']
+        )
+        position = test_inputs[1]
+        inserted = sequence_insert_reference_implementation(sequence, tensor, position)
+        expect(node, inputs=[sequence, tensor, position], outputs=[inserted],
+               name='test_sequence_insert_' + test_name)
+    else:
+        node = onnx.helper.make_node(
+            'SequenceInsert',
+            inputs=['sequence', 'tensor'],
+            outputs=['output_sequence']
+        )
+        inserted = sequence_insert_reference_implementation(sequence, tensor)
+        expect(node, inputs=[sequence, tensor], outputs=[inserted],
+               name='test_sequence_insert_' + test_name)
+```
+
+</details>
+
+
+### <a name="SequenceLength"></a><a name="sequencelength">**SequenceLength**</a>
+
+  Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>length</tt> : I</dt>
+<dd>Length of input sequence. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor type.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain output to integral tensor. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+
+### <a name="SequenceMap"></a><a name="sequencemap">**SequenceMap**</a>
+
+  Applies a sub-graph to each sample in the input sequence(s).
+
+  Inputs can be either tensors or sequences, with the exception of the first input which must
+  be a sequence. The length of the first input sequence will determine the number of samples in the
+  outputs. Any other sequence inputs should have the same number of samples. The number of inputs
+  and outputs, should match the one of the subgraph.
+
+  For each i-th element in the output, a sample will be extracted from the input sequence(s) at
+  the i-th position and the sub-graph will be applied to it.
+  The outputs will contain the outputs of the sub-graph for each sample, in the same order as in
+  the input.
+
+  This operator assumes that processing each sample is independent and could executed in parallel
+  or in any order. Users cannot expect any specific ordering in which each subgraph is computed.
+
+#### Version
+
+This version of the operator has been available since version 17 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>body</tt> : graph (required)</dt>
+<dd>The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>input_sequence</tt> : S</dt>
+<dd>Input sequence.</dd>
+<dt><tt>additional_inputs</tt> (variadic, heterogeneous) : V</dt>
+<dd>Additional inputs to the graph</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>out_sequence</tt> (variadic, heterogeneous) : S</dt>
+<dd>Output sequence(s)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain input types to any sequence type.</dd>
+<dt><tt>V</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain to any tensor or sequence type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sequence_map_add_1_sequence_1_tensor</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Add', ['in0', 'in1'], ['out0'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['N'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['N'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0'],
+    body=body
+)
+
+x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
+x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+y0 = [x0[i] + x1 for i in range(3)]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N']),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_add_1_sequence_1_tensor')
+```
+
+</details>
+
+
+<details>
+<summary>sequence_map_add_2_sequences</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Add', ['in0', 'in1'], ['out0'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['N'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['N'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0'],
+    body=body
+)
+
+N = [np.random.randint(1, 10) for _ in range(3)]
+x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32)
+      for k in range(3)]
+x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32)
+      for k in range(3)]
+y0 = [x0[k] + x1[k] for k in range(3)]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_add_2_sequences')
+```
+
+</details>
+
+
+<details>
+<summary>sequence_map_extract_shapes</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Shape', ['x'], ['shape'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, ['H', 'W', 'C'])],
+    [onnx.helper.make_tensor_value_info('shape', onnx.TensorProto.INT64, [3])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['in_seq'],
+    outputs=['shapes'],
+    body=body
+)
+
+shapes = [
+    np.array([40, 30, 3], dtype=np.int64),
+    np.array([20, 10, 3], dtype=np.int64),
+    np.array([10, 5, 3], dtype=np.int64),
+]
+x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['H', 'W', 'C'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])),
+]
+expect(node, inputs=[x0], outputs=[shapes],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_extract_shapes')
+```
+
+</details>
+
+
+<details>
+<summary>sequence_map_identity_1_sequence</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Identity', ['in0'], ['out0'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info(
+        'in0', onnx.TensorProto.FLOAT, ['N'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['M'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x'],
+    outputs=['y'],
+    body=body
+)
+
+x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+     for _ in range(3)]
+y = x
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+expect(node, inputs=[x], outputs=[y],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_identity_1_sequence')
+```
+
+</details>
+
+
+<details>
+<summary>sequence_map_identity_1_sequence_1_tensor</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Identity', ['in0'], ['out0']),
+     onnx.helper.make_node('Identity', ['in1'], ['out1'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['M'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info(
+        'out1', onnx.TensorProto.FLOAT, ['M'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0', 'y1'],
+    body=body
+)
+
+x0 = [np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32) for _ in range(3)]
+x1 = np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32)
+y0 = x0
+y1 = [x1 for _ in range(3)]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M']),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0, y1],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_identity_1_sequence_1_tensor')
+```
+
+</details>
+
+
+<details>
+<summary>sequence_map_identity_2_sequences</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Identity', ['in0'], ['out0']),
+     onnx.helper.make_node('Identity', ['in1'], ['out1'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['M'])],
+    [onnx.helper.make_tensor_value_info('out0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('out1', onnx.TensorProto.FLOAT, ['M'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0', 'y1'],
+    body=body
+)
+
+x0 = [np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32) for _ in range(3)]
+x1 = [np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32) for _ in range(3)]
+y0 = x0
+y1 = x1
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0, y1],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_identity_2_sequences')
+```
+
+</details>
+
+
+### <a name="Shape"></a><a name="shape">**Shape**</a>
+
+  Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+  Optional attributes start and end can be used to compute a slice of the input tensor's shape.
+  If start axis is omitted, the slice starts from axis 0.
+  The end axis, if specified, is exclusive (and the returned value will not include the size of that axis).
+  If the end axis is omitted, the axes upto the last one will be included.
+  Negative axes indicate counting back from the last axis.
+  Note that axes will be clamped to the range [0, r-1], where r is the
+  rank of the input tensor if they are out-of-range (after adding r in the case of
+  negative axis). Thus, specifying any end value > r is equivalent to specifying an end
+  value of r, and specifying any start value < -r is equivalent to specifying a start
+  value of 0.
+
+  For example:
+  Input tensor with shape: [2, 3, 4]
+  No attributes specified.
+  Output: [2, 3, 4]
+
+  Input tensor with shape: [2, 3, 4]
+  start: -1
+  Output: [4]
+
+  Input tensor with shape: [2, 3, 4]
+  end: -1
+  Output: [2, 3]
+
+  Input tensor with shape: [2, 3, 4]
+  start: 1
+  end: 2
+  Output: [3]
+
+#### Version
+
+This version of the operator has been available since version 15 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Shape-1">1</a>, <a href="Changelog.md#Shape-13">13</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>end</tt> : int</dt>
+<dd>(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.</dd>
+<dt><tt>start</tt> : int (default is 0)</dt>
+<dd>(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>shape</tt> (non-differentiable) : T1</dt>
+<dd>Shape of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>shape</summary>
+
+```python
+x = np.array([
+    [1, 2, 3],
+    [4, 5, 6],
+]).astype(np.float32)
+test_shape('_example', x)  # preserve names of original test cases
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+
+test_shape('', x)  # preserve names of original test cases
+
+test_shape('_start_1', x, start=1)
+
+test_shape('_end_1', x, end=1)
+
+test_shape('_start_negative_1', x, start=-1)
+
+test_shape('_end_negative_1', x, end=-1)
+
+test_shape('_start_1_end_negative_1', x, start=1, end=-1)
+
+test_shape('_start_1_end_2', x, start=1, end=2)
+
+test_shape('_clip_start', x, start=-10)
+
+test_shape('_clip_end', x, end=10)
+```
+
+</details>
+
+
+### <a name="Shrink"></a><a name="shrink">**Shrink**</a>
+
+  Shrink takes one input data (Tensor<numeric>) and produces one Tensor output,
+  having same datatype and shape with input. It has two attributes, lambd and
+  bias. The formula of this operator is: If x < -lambd, y = x + bias;
+  If x > lambd, y = x - bias; Otherwise, y = 0.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>bias</tt> : float (default is 0.0)</dt>
+<dd>The bias value added to output. Default is 0.</dd>
+<dt><tt>lambd</tt> : float (default is 0.5)</dt>
+<dd>The lambd value for the Shrink formulation. Default is 0.5.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input data as Tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input to only numeric types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>hard_shrink</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Shrink',
+    inputs=['x'],
+    outputs=['y'],
+    lambd=1.5,
+)
+X = np.arange(-2.0, 2.1, dtype=np.float32)
+Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
+expect(node, inputs=[X], outputs=[Y],
+       name='test_shrink_hard')
+```
+
+</details>
+
+
+<details>
+<summary>soft_shrink</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Shrink',
+    inputs=['x'],
+    outputs=['y'],
+    lambd=1.5,
+    bias=1.5,
+)
+X = np.arange(-2.0, 2.1, dtype=np.float32)
+Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
+expect(node, inputs=[X], outputs=[Y],
+       name='test_shrink_soft')
+```
+
+</details>
+
+
+### <a name="Sigmoid"></a><a name="sigmoid">**Sigmoid**</a>
+
+  Sigmoid takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+  tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Sigmoid-1">1</a>, <a href="Changelog.md#Sigmoid-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sigmoid</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sigmoid',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = 1.0 / (1.0 + np.exp(np.negative(x)))  # expected output [0.26894143, 0.5, 0.7310586]
+expect(node, inputs=[x], outputs=[y],
+       name='test_sigmoid_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = 1.0 / (1.0 + np.exp(np.negative(x)))
+expect(node, inputs=[x], outputs=[y],
+       name='test_sigmoid')
+```
+
+</details>
+
+
+### <a name="Sign"></a><a name="sign">**Sign**</a>
+
+  Calculate the sign of the given input tensor element-wise.
+  If input > 0, output 1. if input < 0, output -1. if input == 0, output 0.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Sign-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (non-differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (non-differentiable) : T</dt>
+<dd>The sign of the input tensor computed element-wise. It has the same shape and type of the input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sign</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sign',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array(range(-5, 6)).astype(np.float32)
+y = np.sign(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sign')
+```
+
+</details>
+
+
+### <a name="Sin"></a><a name="sin">**Sin**</a>
+
+  Calculates the sine of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The sine of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sin</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sin',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.sin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sin_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.sin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sin')
+```
+
+</details>
+
+
+### <a name="Sinh"></a><a name="sinh">**Sinh**</a>
+
+  Calculates the hyperbolic sine of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic sine values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sinh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sinh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.sinh(x)  # expected output [-1.17520118,  0.,  1.17520118]
+expect(node, inputs=[x], outputs=[y],
+       name='test_sinh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.sinh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sinh')
+```
+
+</details>
+
+
+### <a name="Size"></a><a name="size">**Size**</a>
+
+  Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Size-1">1</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (non-differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>size</tt> (non-differentiable) : T1</dt>
+<dd>Total number of elements of the input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input tensor can be of arbitrary type.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain output to int64 tensor, which should be a scalar though.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>size</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Size',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([
+    [1, 2, 3],
+    [4, 5, 6],
+]).astype(np.float32)
+y = np.array(6).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_size_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.array(x.size).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_size')
+```
+
+</details>
+
+
+### <a name="Slice"></a><a name="slice">**Slice**</a>
+
+  Produces a slice of the input tensor along multiple axes. Similar to numpy:
+  https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding
+
+  Slice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor
+  of its input `data` tensor.
+
+  An effective `start[i]`, `end[i]`, and `step[i]` must be computed for each `i`
+  in `[0, ... r-1]` where `r = rank(input)` as follows:
+
+  If `axes` are omitted, they are set to `[0, ..., r-1]`.
+  If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+
+  The effective values are initialized as `start[i] = 0`, `end[i] = dims[i]` where
+  `dims` are the dimensions of `input` and `step[i] = `1.
+
+  All negative elements of `axes` are made non-negatve by adding `r` to them, where
+  `r =rank(input)`.
+
+  All negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them,
+  where `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted
+  `starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping
+  and `[0, dims[axes[i]]-1]` for negative stepping.
+
+  The clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must
+  accommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping
+  `end[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it
+  is clamped to `[-1, dims[axes[i]]-1]`.
+
+  Finally, `step[axes[i]] = steps[i]`.
+
+  For slicing to the end of a dimension with unknown size, it is recommended to pass
+  in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward.
+
+  Example 1:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    axes = [0, 1]
+    starts = [1, 0]
+    ends = [2, 3]
+    steps = [1, 2]
+    result = [
+        [5, 7],
+    ]
+  Example 2:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    starts = [0, 1]
+    ends = [-1, 1000]
+    result = [
+        [2, 3, 4],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Slice-1">1</a>, <a href="Changelog.md#Slice-10">10</a>, <a href="Changelog.md#Slice-11">11</a>
+
+#### Inputs (3 - 5)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensor of data to extract slices from.</dd>
+<dt><tt>starts</tt> (non-differentiable) : Tind</dt>
+<dd>1-D tensor of starting indices of corresponding axis in `axes`</dd>
+<dt><tt>ends</tt> (non-differentiable) : Tind</dt>
+<dd>1-D tensor of ending indices (exclusive) of corresponding axis in `axes`</dd>
+<dt><tt>axes</tt> (optional, non-differentiable) : Tind</dt>
+<dd>1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.</dd>
+<dt><tt>steps</tt> (optional, non-differentiable) : Tind</dt>
+<dd>1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Sliced data tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>slice</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+y = x[0:3, 0:10]
+starts = np.array([0, 0], dtype=np.int64)
+ends = np.array([3, 10], dtype=np.int64)
+axes = np.array([0, 1], dtype=np.int64)
+steps = np.array([1, 1], dtype=np.int64)
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice')
+```
+
+</details>
+
+
+<details>
+<summary>slice_default_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0, 0, 3], dtype=np.int64)
+ends = np.array([20, 10, 4], dtype=np.int64)
+y = x[:, :, 3:4]
+
+expect(node, inputs=[x, starts, ends], outputs=[y],
+       name='test_slice_default_axes')
+```
+
+</details>
+
+
+<details>
+<summary>slice_default_steps</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0, 0, 3], dtype=np.int64)
+ends = np.array([20, 10, 4], dtype=np.int64)
+axes = np.array([0, 1, 2], dtype=np.int64)
+y = x[:, :, 3:4]
+
+expect(node, inputs=[x, starts, ends, axes], outputs=[y],
+       name='test_slice_default_steps')
+```
+
+</details>
+
+
+<details>
+<summary>slice_end_out_of_bounds</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([1], dtype=np.int64)
+ends = np.array([1000], dtype=np.int64)
+axes = np.array([1], dtype=np.int64)
+steps = np.array([1], dtype=np.int64)
+y = x[:, 1:1000]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_end_out_of_bounds')
+```
+
+</details>
+
+
+<details>
+<summary>slice_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0], dtype=np.int64)
+ends = np.array([-1], dtype=np.int64)
+axes = np.array([1], dtype=np.int64)
+steps = np.array([1], dtype=np.int64)
+y = x[:, 0:-1]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_neg')
+```
+
+</details>
+
+
+<details>
+<summary>slice_neg_steps</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([20, 10, 4], dtype=np.int64)
+ends = np.array([0, 0, 1], dtype=np.int64)
+axes = np.array([0, 1, 2], dtype=np.int64)
+steps = np.array([-1, -3, -2]).astype(np.int64)
+y = x[20:0:-1, 10:0:-3, 4:1:-2]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_neg_steps')
+```
+
+</details>
+
+
+<details>
+<summary>slice_negative_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0, 0, 3], dtype=np.int64)
+ends = np.array([20, 10, 4], dtype=np.int64)
+axes = np.array([0, -2, -1], dtype=np.int64)
+y = x[:, :, 3:4]
+
+expect(node, inputs=[x, starts, ends, axes], outputs=[y],
+       name='test_slice_negative_axes')
+```
+
+</details>
+
+
+<details>
+<summary>slice_start_out_of_bounds</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([1000], dtype=np.int64)
+ends = np.array([1000], dtype=np.int64)
+axes = np.array([1], dtype=np.int64)
+steps = np.array([1], dtype=np.int64)
+y = x[:, 1000:1000]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_start_out_of_bounds')
+```
+
+</details>
+
+
+### <a name="Softmax"></a><a name="softmax">**Softmax**</a>
+
+  The operator computes the normalized exponential values for the given input:
+
+   Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1)
+
+  The "axis" attribute indicates the dimension along which Softmax
+  will be performed. The output tensor has the same shape
+  and contains the Softmax values of the corresponding input.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Softmax-1">1</a>, <a href="Changelog.md#Softmax-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>
+Describes the dimension Softmax will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The input tensor of rank >= axis.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The output values with the same shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>softmax</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[-1, 0, 1]]).astype(np.float32)
+# expected output [[0.09003058, 0.24472848, 0.66524094]]
+y = softmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_example')
+```
+
+</details>
+
+
+<details>
+<summary>softmax_axis</summary>
+
+```python
+x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]
+             ).astype(np.float32)
+# expected output
+# [[0.032058604 0.08714432  0.23688284  0.6439143  ]
+# [0.032058604 0.08714432  0.23688284  0.6439143  ]]
+y = softmax(x)
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_large_number')
+
+x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=0,
+)
+y = softmax(x, axis=0)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_axis_0')
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=1,
+)
+y = softmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_axis_1')
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=2,
+)
+y = softmax(x, axis=2)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_axis_2')
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=-1,
+)
+y = softmax(x, axis=-1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_negative_axis')
+
+# default axis is -1
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_default_axis')
+```
+
+</details>
+
+
+### <a name="SoftmaxCrossEntropyLoss"></a><a name="softmaxcrossentropyloss">**SoftmaxCrossEntropyLoss**</a>
+
+  Loss function that measures the softmax cross entropy
+  between 'scores' and 'labels'.
+  This operator first computes a loss tensor whose shape is identical to the labels input.
+  If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).
+  If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),
+  the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.
+  After L is available, this operator can optionally do a reduction operator.
+
+  shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),
+          with K >= 1 in case of K-dimensional loss.
+  shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),
+          with K >= 1 in case of K-dimensional loss.
+
+  The loss for one sample, l_i, can caculated as follows:
+      l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.
+  or
+      l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.
+
+  loss is zero for the case when label-value equals ignore_index.
+      l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index
+
+  where:
+      p = Softmax(scores)
+      y = Log(p)
+      c = labels[i][d1][d2]...[dk]
+
+  Finally, L is optionally reduced:
+  If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).
+  If reduction = 'sum', the output is scalar: Sum(L).
+  If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),
+  where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#SoftmaxCrossEntropyLoss-12">12</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>ignore_index</tt> : int</dt>
+<dd>Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.</dd>
+<dt><tt>reduction</tt> : string (default is mean)</dt>
+<dd>Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.</dd>
+</dl>
+
+#### Inputs (2 - 3)
+
+<dl>
+<dt><tt>scores</tt> (differentiable) : T</dt>
+<dd>The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.</dd>
+<dt><tt>labels</tt> (non-differentiable) : Tind</dt>
+<dd>The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.</dd>
+<dt><tt>weights</tt> (optional, non-differentiable) : T</dt>
+<dd>A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.</dd>
+<dt><tt>log_prob</tt> (optional, differentiable) : T</dt>
+<dd>Log probability tensor. If the output of softmax is prob, its value is log(prob).</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain target to integer types</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>input_shape_is_NCd1_mean_weight_negative_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(-1)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1 = 3, 5, 6
+np.random.seed(0)
+x = np.random.rand(N, C, dim1).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+labels[0][0] = -1
+weight = np.random.rand(C).astype(np.float32)
+
+sce = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index)
+
+expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1_mean_weight_negative_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1_mean_weight_negative_ii_log_prob</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(-1)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1 = 3, 5, 6
+np.random.seed(0)
+x = np.random.rand(N, C, dim1).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+labels[0][0] = -1
+weight = np.random.rand(C).astype(np.float32)
+
+loss, log_prob = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index,
+                          get_log_prob=True)
+
+expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1_mean_weight_negative_ii_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3_none_no_weight_negative_ii</summary>
+
+```python
+reduction = 'none'
+ignore_index = np.int64(-5)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+labels[0][0][0][0] = -5
+
+sce = softmaxcrossentropy(x,
+                          labels,
+                          reduction=reduction,
+                          ignore_index=ignore_index)
+
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_NCd1d2d3_none_no_weight_negative_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob</summary>
+
+```python
+reduction = 'none'
+ignore_index = np.int64(-5)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+labels[0][0][0][0] = -5
+
+loss, log_prob = softmaxcrossentropy(x,
+                          labels,
+                          reduction=reduction,
+                          ignore_index=ignore_index,
+                          get_log_prob=True)
+
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3_sum_weight_high_ii</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(10)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C = 3, 5
+np.random.seed(0)
+x = np.random.rand(N, C).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+labels[0] = 10
+weight = np.random.rand(C).astype(np.float32)
+
+sce = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index)
+
+expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1d2d3_sum_weight_high_ii')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(10)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C = 3, 5
+np.random.seed(0)
+x = np.random.rand(N, C).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+labels[0] = 10
+weight = np.random.rand(C).astype(np.float32)
+
+loss, log_prob = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index,
+                          get_log_prob=True)
+
+expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1d2d3_sum_weight_high_ii_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_mean_weight</summary>
+
+```python
+reduction = 'mean'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+sce = softmaxcrossentropy(x,
+                        labels,
+                        weight=weight,
+                        reduction=reduction)
+
+expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1d2d3d4d5_mean_weight')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob</summary>
+
+```python
+reduction = 'mean'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+loss, log_prob = softmaxcrossentropy(x,
+                        labels,
+                        weight=weight,
+                        reduction=reduction,
+                        get_log_prob=True)
+
+expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1d2d3d4d5_mean_weight_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_none_no_weight</summary>
+
+```python
+reduction = 'none'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+sce = softmaxcrossentropy(x,
+                        labels,
+                        reduction=reduction)
+
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_NCd1d2d3d4d5_none_no_weight')
+```
+
+</details>
+
+
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob</summary>
+
+```python
+reduction = 'none'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+loss, log_prob = softmaxcrossentropy(x,
+                        labels,
+                        reduction=reduction,
+                        get_log_prob=True)
+
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_NCd1d2d3d4d5_none_no_weight_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_3d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, y)
+
+# Check results
+expect(node, inputs=[x, y], outputs=[sce], name='test_sce_mean_3d')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_3d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, y], outputs=[loss, log_prob], name='test_sce_mean_3d_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_3d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii_3d')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_3d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_3d_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_4d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction=reduction, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii_4d')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_4d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_4d_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(0)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(0)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_3d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(1)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(1)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii_3d')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_3d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(1)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(1)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_3d_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_4d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii_4d')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_4d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_4d_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(0)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(0)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_mean_weights_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_none</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction='none')
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_none')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_none_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction='none', get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_none_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_none_weights</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights, reduction='none')
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_none_weights')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_none_weights_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, reduction='none', get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_none_weights_log_prob')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_sum</summary>
+
+```python
+# Define operator attributes.
+reduction = 'sum'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction='sum')
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_sum')
+```
+
+</details>
+
+
+<details>
+<summary>softmaxcrossentropy_sum_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'sum'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction='sum', get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_sum_log_prob')
+```
+
+</details>
+
+
+### <a name="Softplus"></a><a name="softplus">**Softplus**</a>
+
+  Softplus takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to
+  the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>1D input tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>softplus</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Softplus',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.log(np.exp(x) + 1)  # expected output [0.31326166, 0.69314718, 1.31326163]
+expect(node, inputs=[x], outputs=[y],
+       name='test_softplus_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.log(np.exp(x) + 1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softplus')
+```
+
+</details>
+
+
+### <a name="Softsign"></a><a name="softsign">**Softsign**</a>
+
+  Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 1 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The softsign (x/(1+|x|)) values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>softsign</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Softsign',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.array([-0.5, 0, 0.5]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softsign_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = x / (1 + np.abs(x))
+expect(node, inputs=[x], outputs=[y],
+       name='test_softsign')
+```
+
+</details>
+
+
+### <a name="SpaceToDepth"></a><a name="spacetodepth">**SpaceToDepth**</a>
+
+  SpaceToDepth rearranges blocks of spatial data into depth. More specifically,
+  this op outputs a copy of the input tensor where values from the height and width dimensions
+  are moved to the depth dimension.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#SpaceToDepth-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>blocksize</tt> : int (required)</dt>
+<dd>Blocks of [blocksize, blocksize] are moved.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>example</summary>
+
+```python
+node = onnx.helper.make_node(
+    'SpaceToDepth',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=2,
+)
+
+# (1, 1, 4, 6) input tensor
+x = np.array([[[[0, 6, 1, 7, 2, 8],
+                [12, 18, 13, 19, 14, 20],
+                [3, 9, 4, 10, 5, 11],
+                [15, 21, 16, 22, 17, 23]]]]).astype(np.float32)
+
+# (1, 4, 2, 3) output tensor
+y = np.array([[[[0, 1, 2],
+                [3, 4, 5]],
+               [[6, 7, 8],
+                [9, 10, 11]],
+               [[12, 13, 14],
+                [15, 16, 17]],
+               [[18, 19, 20],
+                [21, 22, 23]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_spacetodepth_example')
+```
+
+</details>
+
+
+<details>
+<summary>spacetodepth</summary>
+
+```python
+b, c, h, w = shape = (2, 2, 6, 6)
+blocksize = 2
+node = onnx.helper.make_node(
+    'SpaceToDepth',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=blocksize,
+)
+x = np.random.random_sample(shape).astype(np.float32)
+tmp = np.reshape(x, [b, c,
+                     h // blocksize, blocksize,
+                     w // blocksize, blocksize])
+tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
+y = np.reshape(tmp, [b, c * (blocksize**2),
+                     h // blocksize,
+                     w // blocksize])
+expect(node, inputs=[x], outputs=[y],
+       name='test_spacetodepth')
+```
+
+</details>
+
+
+### <a name="Split"></a><a name="split">**Split**</a>
+
+  Split a tensor into a list of tensors, along the specified
+  'axis'. Lengths of the parts can be specified using input 'split'.
+  Otherwise, the tensor is split to equal sized parts.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Split-1">1</a>, <a href="Changelog.md#Split-2">2</a>, <a href="Changelog.md#Split-11">11</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>The tensor to split</dd>
+<dt><tt>split</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, differentiable) : T</dt>
+<dd>One or more outputs forming list of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>1d</summary>
+
+```python
+input = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input'],
+    outputs=['output_1', 'output_2', 'output_3'],
+    axis=0
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4.]).astype(np.float32), np.array([5., 6.]).astype(np.float32)]
+expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_1d')
+
+split = np.array([2, 4]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2'],
+    axis=0,
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4., 5., 6.]).astype(np.float32)]
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_1d')
+```
+
+</details>
+
+
+<details>
+<summary>2d</summary>
+
+```python
+input = np.array([[1., 2., 3., 4., 5., 6.],
+                  [7., 8., 9., 10., 11., 12.]]).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input'],
+    outputs=['output_1', 'output_2'],
+    axis=1
+)
+
+expected_outputs = [np.array([[1., 2., 3.], [7., 8., 9.]]).astype(np.float32),
+                    np.array([[4., 5., 6.], [10., 11., 12.]]).astype(np.float32)]
+
+expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_2d')
+
+split = np.array([2, 4]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2'],
+    axis=1,
+)
+
+expected_outputs = [np.array([[1., 2.], [7., 8.]]).astype(np.float32),
+                    np.array([[3., 4., 5., 6.], [9., 10., 11., 12.]]).astype(np.float32)]
+
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_2d')
+```
+
+</details>
+
+
+<details>
+<summary>default_values</summary>
+
+```python
+input = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float32)
+
+# If axis is not specified, split is applied on default axis 0
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input'],
+    outputs=['output_1', 'output_2', 'output_3']
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4.]).astype(np.float32), np.array([5., 6.]).astype(np.float32)]
+expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_default_axis')
+
+split = np.array([2, 4]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2']
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4., 5., 6.]).astype(np.float32)]
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_default_axis')
+```
+
+</details>
+
+
+<details>
+<summary>zero_size_splits</summary>
+
+```python
+input = np.array([]).astype(np.float32)
+
+# Split emtpy tensor to tensors of size zero
+split = np.array([0, 0, 0]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2', 'output_3']
+)
+
+expected_outputs = [np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32)]
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_zero_size_splits')
+```
+
+</details>
+
+
+### <a name="SplitToSequence"></a><a name="splittosequence">**SplitToSequence**</a>
+
+  Split a tensor into a sequence of tensors, along the specified
+  'axis'. Lengths of the parts can be specified using argument 'split'.
+  'split' must contain only positive numbers.
+  'split' is either a scalar (tensor of empty shape), or a 1-D tensor.
+  If 'split' is a scalar, then 'input' will be split into equally sized chunks(if possible).
+  Last chunk will be smaller if the 'input' size along the given axis 'axis' is not divisible
+  by 'split'.
+  Otherwise, the tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis'
+  specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the
+  dimension size of input tensor on 'axis'.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is 0)</dt>
+<dd>Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].</dd>
+<dt><tt>keepdims</tt> : int (default is 1)</dt>
+<dd>Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>The tensor to split</dd>
+<dt><tt>split</tt> (optional) : I</dt>
+<dd>Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output_sequence</tt> : S</dt>
+<dd>One or more outputs forming a sequence of tensors after splitting</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input types to all tensor types.</dd>
+<dt><tt>I</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain split size to integral tensor.</dd>
+<dt><tt>S</tt> : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))</dt>
+<dd>Constrain output types to all tensor types.</dd>
+</dl>
+
+
+### <a name="Sqrt"></a><a name="sqrt">**Sqrt**</a>
+
+  Square root takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the square root is, y = x^0.5, is applied to
+  the tensor elementwise. If x is negative, then it will return NaN.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Sqrt-1">1</a>, <a href="Changelog.md#Sqrt-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sqrt</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sqrt',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([1, 4, 9]).astype(np.float32)
+y = np.sqrt(x)  # expected output [1., 2., 3.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_sqrt_example')
+
+x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+y = np.sqrt(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sqrt')
+```
+
+</details>
+
+
+### <a name="Squeeze"></a><a name="squeeze">**Squeeze**</a>
+
+  Remove single-dimensional entries from the shape of a tensor.
+  Takes an input `axes` with a list of axes to squeeze.
+  If `axes` is not provided, all the single dimensions will be removed from
+  the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Squeeze-1">1</a>, <a href="Changelog.md#Squeeze-11">11</a>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Tensors with at least max(dims) dimensions.</dd>
+<dt><tt>axes</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>squeezed</tt> (differentiable) : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>squeeze</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Squeeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+axes = np.array([0], dtype=np.int64)
+y = np.squeeze(x, axis=0)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_squeeze')
+```
+
+</details>
+
+
+<details>
+<summary>squeeze_negative_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Squeeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+axes = np.array([-2], dtype=np.int64)
+y = np.squeeze(x, axis=-2)
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_squeeze_negative_axes')
+```
+
+</details>
+
+
+### <a name="StringNormalizer"></a><a name="stringnormalizer">**StringNormalizer**</a>
+
+  StringNormalization performs string operations for basic cleaning.
+  This operator has only one input (denoted by X) and only one output
+  (denoted by Y). This operator first examines the elements in the X,
+  and removes elements specified in "stopwords" attribute.
+  After removing stop words, the intermediate result can be further lowercased,
+  uppercased, or just returned depending the "case_change_action" attribute.
+  This operator only accepts [C]- and [1, C]-tensor.
+  If all elements in X are dropped, the output will be the empty value of string tensor with shape [1]
+  if input shape is [C] and shape [1, 1] if input shape is [1, C].
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>case_change_action</tt> : string (default is NONE)</dt>
+<dd>string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"</dd>
+<dt><tt>is_case_sensitive</tt> : int (default is 0)</dt>
+<dd>Boolean. Whether the identification of stop words in X is case-sensitive. Default is false</dd>
+<dt><tt>locale</tt> : string</dt>
+<dd>Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.</dd>
+<dt><tt>stopwords</tt> : list of strings</dt>
+<dd>List of stop words. If not set, no word would be removed from X.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : tensor(string)</dt>
+<dd>UTF-8 strings to normalize</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : tensor(string)</dt>
+<dd>UTF-8 Normalized strings</dd>
+</dl>
+
+#### Type Constraints
+
+
+
+#### Examples
+
+<details>
+<summary>monday_casesensintive_lower</summary>
+
+```python
+input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='LOWER',
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_lower')
+```
+
+</details>
+
+
+<details>
+<summary>monday_casesensintive_nochangecase</summary>
+
+```python
+input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_nochangecase')
+```
+
+</details>
+
+
+<details>
+<summary>monday_casesensintive_upper</summary>
+
+```python
+input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+output = np.array([u'TUESDAY', u'WEDNESDAY', u'THURSDAY']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='UPPER',
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_upper')
+```
+
+</details>
+
+
+<details>
+<summary>monday_empty_output</summary>
+
+```python
+input = np.array([u'monday', u'monday']).astype(object)
+output = np.array([u'']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='UPPER',
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_empty_output')
+```
+
+</details>
+
+
+<details>
+<summary>monday_insensintive_upper_twodim</summary>
+
+```python
+input = np.array([u'Monday', u'tuesday', u'wednesday', u'Monday', u'tuesday', u'wednesday']).astype(object).reshape([1, 6])
+
+# It does upper case cecedille, accented E
+# and german umlaut but fails
+# with german eszett
+output = np.array([u'TUESDAY', u'WEDNESDAY', u'TUESDAY', u'WEDNESDAY']).astype(object).reshape([1, 4])
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='UPPER',
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_insensintive_upper_twodim')
+```
+
+</details>
+
+
+<details>
+<summary>nostopwords_nochangecase</summary>
+
+```python
+input = np.array([u'monday', u'tuesday']).astype(object)
+output = input
+
+# No stopwords. This is a NOOP
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    is_case_sensitive=1,
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_nostopwords_nochangecase')
+```
+
+</details>
+
+
+### <a name="Sub"></a><a name="sub">**Sub**</a>
+
+  Performs element-wise binary subtraction (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Sub-1">1</a>, <a href="Changelog.md#Sub-6">6</a>, <a href="Changelog.md#Sub-7">7</a>, <a href="Changelog.md#Sub-13">13</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (differentiable) : T</dt>
+<dd>First operand.</dd>
+<dt><tt>B</tt> (differentiable) : T</dt>
+<dd>Second operand.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (differentiable) : T</dt>
+<dd>Result, has same element type as two inputs</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to all numeric tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sub</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sub',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([3, 2, 1]).astype(np.float32)
+z = x - y  # expected output [-2., 0., 2.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = x - y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub')
+
+x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8)
+z = x - y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub_uint8')
+```
+
+</details>
+
+
+<details>
+<summary>sub_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sub',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = x - y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub_bcast')
+```
+
+</details>
+
+
+### <a name="Sum"></a><a name="sum">**Sum**</a>
+
+  Element-wise sum of each of the input tensors (with Numpy-style broadcasting support).
+  All inputs and outputs must have the same data type.
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Sum-1">1</a>, <a href="Changelog.md#Sum-6">6</a>, <a href="Changelog.md#Sum-8">8</a>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>data_0</tt> (variadic, differentiable) : T</dt>
+<dd>List of tensors for sum.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>sum</tt> (differentiable) : T</dt>
+<dd>Output tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>sum</summary>
+
+```python
+data_0 = np.array([3, 0, 2]).astype(np.float32)
+data_1 = np.array([1, 3, 4]).astype(np.float32)
+data_2 = np.array([2, 6, 6]).astype(np.float32)
+result = np.array([6, 9, 12]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Sum',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_sum_example')
+
+node = onnx.helper.make_node(
+    'Sum',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_sum_one_input')
+
+result = np.add(data_0, data_1)
+node = onnx.helper.make_node(
+    'Sum',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_sum_two_inputs')
+```
+
+</details>
+
+
+### <a name="Tan"></a><a name="tan">**Tan**</a>
+
+  Calculates the tangent of the given input tensor, element-wise.
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The tangent of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>tan</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tan',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.tan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_tan_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.tan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_tan')
+```
+
+</details>
+
+
+### <a name="Tanh"></a><a name="tanh">**Tanh**</a>
+
+  Calculates the hyperbolic tangent of the given input tensor element-wise.
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Tanh-1">1</a>, <a href="Changelog.md#Tanh-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>The hyperbolic tangent values of the input tensor computed element-wise</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>tanh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tanh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.tanh(x)  # expected output [-0.76159418, 0., 0.76159418]
+expect(node, inputs=[x], outputs=[y],
+       name='test_tanh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.tanh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_tanh')
+```
+
+</details>
+
+
+### <a name="TfIdfVectorizer"></a><a name="tfidfvectorizer">**TfIdfVectorizer**</a>
+
+  This transform extracts n-grams from the input sequence and save them as a vector. Input can
+  be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input.
+  For 2-D input, the output is also a  2-D tensor whose i-th row is the n-gram representation of the i-th input row.
+  More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1].
+  If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor.
+
+  In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original
+  sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips.
+  If the number of skips is 2, we should skip two tokens when scanning through the original sequence.
+  Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2.
+  The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4].
+  If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]
+  indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively.
+
+  The output vector (denoted by Y) stores the count of each n-gram;
+  Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping
+  between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0],
+  ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17],
+  respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output.
+  Note that we may consider all skips up to S when generating the n-grams.
+
+  The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and
+  the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF",
+  this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute.
+
+  Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor.
+  If pool_strings is set, the input must be a string tensor.
+
+#### Version
+
+This version of the operator has been available since version 9 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>max_gram_length</tt> : int (required)</dt>
+<dd>Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.</dd>
+<dt><tt>max_skip_count</tt> : int (required)</dt>
+<dd>Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1</dd>
+<dt><tt>min_gram_length</tt> : int (required)</dt>
+<dd>Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.</dd>
+<dt><tt>mode</tt> : string (required)</dt>
+<dd>The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)</dd>
+<dt><tt>ngram_counts</tt> : list of ints (required)</dt>
+<dd>The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.</dd>
+<dt><tt>ngram_indexes</tt> : list of ints (required)</dt>
+<dd>list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.</dd>
+<dt><tt>pool_int64s</tt> : list of ints</dt>
+<dd>List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.</dd>
+<dt><tt>pool_strings</tt> : list of strings</dt>
+<dd>List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.</dd>
+<dt><tt>weights</tt> : list of floats</dt>
+<dd>list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>Input for n-gram extraction</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T1</dt>
+<dd>Ngram results</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(string), tensor(int32), tensor(int64)</dt>
+<dd>Input is ether string UTF-8 or int32/int64</dd>
+<dt><tt>T1</tt> : tensor(float)</dt>
+<dd>1-D tensor of floats</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>tf_batch_onlybigrams_skip0</summary>
+
+```python
+input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+output = np.array([[0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 1.]]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=0,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_onlybigrams_skip0')
+```
+
+</details>
+
+
+<details>
+<summary>tf_batch_onlybigrams_skip5</summary>
+
+```python
+input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+output = np.array([[0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 1., 1.]]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_onlybigrams_skip5')
+```
+
+</details>
+
+
+<details>
+<summary>tf_batch_uniandbigrams_skip5</summary>
+
+```python
+input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+output = np.array([[0., 3., 0., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 1., 1.]]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=1,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_uniandbigrams_skip5')
+```
+
+</details>
+
+
+<details>
+<summary>tf_only_bigrams_skip0</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([0., 0., 0., 0., 1., 1., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=0,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_only_bigrams_skip0')
+```
+
+</details>
+
+
+<details>
+<summary>tf_onlybigrams_levelempty</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([1., 1., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 0]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2]).astype(np.int64)
+pool_int64s = np.array([    # unigrams none
+                       5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=0,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_onlybigrams_levelempty')
+```
+
+</details>
+
+
+<details>
+<summary>tf_onlybigrams_skip5</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([0., 0., 0., 0., 1., 3., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_onlybigrams_skip5')
+```
+
+</details>
+
+
+<details>
+<summary>tf_uniandbigrams_skip5</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([0., 3., 1., 0., 1., 3., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=1,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_uniandbigrams_skip5')
+```
+
+</details>
+
+
+### <a name="ThresholdedRelu"></a><a name="thresholdedrelu">**ThresholdedRelu**</a>
+
+  ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
+  (Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
+  is applied to the tensor elementwise.
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 1.0)</dt>
+<dd>Threshold value</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Input tensor</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>Output tensor</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>default</summary>
+
+```python
+default_alpha = 1.0
+node = onnx.helper.make_node(
+    'ThresholdedRelu',
+    inputs=['x'],
+    outputs=['y']
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, default_alpha, np.inf)
+y[y == default_alpha] = 0
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_thresholdedrelu_default')
+```
+
+</details>
+
+
+<details>
+<summary>thresholdedrelu</summary>
+
+```python
+alpha = 2.0
+node = onnx.helper.make_node(
+    'ThresholdedRelu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=alpha
+)
+
+x = np.array([-1.5, 0., 1.2, 2.0, 2.2]).astype(np.float32)
+y = np.clip(x, alpha, np.inf)  # expected output [0., 0., 0., 0., 2.2]
+y[y == alpha] = 0
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_thresholdedrelu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, alpha, np.inf)
+y[y == alpha] = 0
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_thresholdedrelu')
+```
+
+</details>
+
+
+### <a name="Tile"></a><a name="tile">**Tile**</a>
+
+  Constructs a tensor by tiling a given tensor.
+  This is the same as function `tile` in Numpy, but no broadcast.
+  For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Tile-1">1</a>, <a href="Changelog.md#Tile-6">6</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of any shape.</dd>
+<dt><tt>repeats</tt> (non-differentiable) : T1</dt>
+<dd>1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>T1</tt> : tensor(int64)</dt>
+<dd>Constrain repeat's type to int64 tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>tile</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tile',
+    inputs=['x', 'y'],
+    outputs=['z']
+)
+
+x = np.random.rand(2, 3, 4, 5).astype(np.float32)
+
+repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
+
+z = np.tile(x, repeats)
+
+expect(node,
+       inputs=[x, repeats],
+       outputs=[z],
+       name='test_tile')
+```
+
+</details>
+
+
+<details>
+<summary>tile_precomputed</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tile',
+    inputs=['x', 'y'],
+    outputs=['z']
+)
+
+x = np.array([
+    [0, 1],
+    [2, 3]
+], dtype=np.float32)
+
+repeats = np.array([2, 2], dtype=np.int64)
+
+z = np.array([
+    [0, 1, 0, 1],
+    [2, 3, 2, 3],
+    [0, 1, 0, 1],
+    [2, 3, 2, 3]
+], dtype=np.float32)
+
+expect(node,
+       inputs=[x, repeats],
+       outputs=[z],
+       name='test_tile_precomputed')
+```
+
+</details>
+
+
+### <a name="TopK"></a><a name="topk">**TopK**</a>
+
+  Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of
+  shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+    -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+      which contains the values of the top k elements along the specified axis
+    -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+     contains the indices of the top k elements (original indices from the input
+     tensor).
+
+  If "largest" is 1 (the default value) then the k largest elements are returned.
+  If "sorted" is 1 (the default value) then the resulting k elements will be sorted.
+  If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined.
+
+  Given two equivalent values, this operator uses the indices along the axis as
+   a tiebreaker. That is, the element with the lower index will appear first.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#TopK-1">1</a>, <a href="Changelog.md#TopK-10">10</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int (default is -1)</dt>
+<dd>Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>largest</tt> : int (default is 1)</dt>
+<dd>Whether to return the top-K largest or smallest elements.</dd>
+<dt><tt>sorted</tt> : int (default is 1)</dt>
+<dd>Whether to return the elements in sorted order.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_n, r]</dd>
+<dt><tt>K</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Values</tt> (differentiable) : T</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor</dd>
+<dt><tt>Indices</tt> (non-differentiable) : I</dt>
+<dd>Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to numeric tensors.</dd>
+<dt><tt>I</tt> : tensor(int64)</dt>
+<dd>Constrain index tensor to int64</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>top_k</summary>
+
+```python
+axis = 1
+largest = 1
+
+k = 3
+node = onnx.helper.make_node(
+    'TopK',
+    inputs=['x', 'k'],
+    outputs=['values', 'indices'],
+    axis=axis
+)
+X = np.array([
+    [0, 1, 2, 3],
+    [4, 5, 6, 7],
+    [8, 9, 10, 11],
+], dtype=np.float32)
+K = np.array([k], dtype=np.int64)
+values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+#print(values_ref)
+#[[ 3.  2.  1.]
+# [ 7.  6.  5.]
+# [11. 10.  9.]]
+#print(indices_ref)
+#[[3 2 1]
+# [3 2 1]
+# [3 2 1]]
+
+expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+       name='test_top_k')
+```
+
+</details>
+
+
+<details>
+<summary>top_k_negative_axis</summary>
+
+```python
+axis = -1
+largest = 1
+
+k = 3
+node = onnx.helper.make_node(
+    'TopK',
+    inputs=['x', 'k'],
+    outputs=['values', 'indices'],
+    axis=axis
+)
+X = np.array([
+    [0, 1, 2, 3],
+    [4, 5, 6, 7],
+    [8, 9, 10, 11],
+], dtype=np.float32)
+K = np.array([k], dtype=np.int64)
+values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+# print(values_ref)
+#[[ 3.  2.  1.]
+# [ 7.  6.  5.]
+# [11. 10.  9.]]
+# print(indices_ref)
+#[[3 2 1]
+# [3 2 1]
+# [3 2 1]]
+
+expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+       name='test_top_k_negative_axis')
+```
+
+</details>
+
+
+<details>
+<summary>top_k_smallest</summary>
+
+```python
+axis = 1
+largest = 0
+sorted = 1
+k = 3
+
+node = onnx.helper.make_node(
+    'TopK',
+    inputs=['x', 'k'],
+    outputs=['values', 'indices'],
+    axis=axis,
+    largest=largest,
+    sorted=sorted
+)
+
+X = np.array([
+    [0, 1, 2, 3],
+    [4, 5, 6, 7],
+    [11, 10, 9, 8],
+], dtype=np.float32)
+K = np.array([k], dtype=np.int64)
+values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+#print(values_ref)
+#[[ 0.  1.  2.]
+# [ 4.  5.  6.]
+# [ 8.  9. 10.]]
+#print(indices_ref)
+#[[0 1 2]
+# [0 1 2]
+# [3 2 1]]
+
+expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+       name='test_top_k_smallest')
+```
+
+</details>
+
+
+### <a name="Transpose"></a><a name="transpose">**Transpose**</a>
+
+  Transpose the input tensor similar to numpy.transpose. For example, when
+  perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
+  will be (2, 1, 3).
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Transpose-1">1</a>
+
+#### Attributes
+
+<dl>
+<dt><tt>perm</tt> : list of ints</dt>
+<dd>A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>An input tensor.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>transposed</tt> (differentiable) : T</dt>
+<dd>Transposed output.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>all_permutations</summary>
+
+```python
+shape = (2, 3, 4)
+data = np.random.random_sample(shape).astype(np.float32)
+permutations = list(itertools.permutations(np.arange(len(shape))))
+
+for i in range(len(permutations)):
+    node = onnx.helper.make_node(
+        'Transpose',
+        inputs=['data'],
+        outputs=['transposed'],
+        perm=permutations[i]
+    )
+    transposed = np.transpose(data, permutations[i])
+    expect(node, inputs=[data], outputs=[transposed],
+           name='test_transpose_all_permutations_' + str(i))
+```
+
+</details>
+
+
+<details>
+<summary>default</summary>
+
+```python
+shape = (2, 3, 4)
+data = np.random.random_sample(shape).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'Transpose',
+    inputs=['data'],
+    outputs=['transposed']
+)
+
+transposed = np.transpose(data)
+expect(node, inputs=[data], outputs=[transposed],
+       name='test_transpose_default')
+```
+
+</details>
+
+
+### <a name="Trilu"></a><a name="trilu">**Trilu**</a>
+
+  Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s).
+  The attribute "upper" determines whether the upper or lower part is retained. If set to true,
+  the upper triangular matrix is retained. Lower triangular matrix is retained otherwise.
+  Default value for the "upper" attribute is true.
+  Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists
+  of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal.
+  All other elements in the matrix are set to zero.
+  If k = 0, the triangular part on and above/below the main diagonal is retained.
+  If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it.
+  A negative k value retains the main diagonal and |k| diagonals below it.
+  If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it.
+  A negative k value excludes the main diagonal and (|k|-1) diagonals below it.
+
+#### Version
+
+This version of the operator has been available since version 14 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>upper</tt> : int (default is 1)</dt>
+<dd>Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.</dd>
+</dl>
+
+#### Inputs (1 - 2)
+
+<dl>
+<dt><tt>input</tt> (differentiable) : T</dt>
+<dd>Input tensor of rank 2 or higher.</dd>
+<dt><tt>k</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Output tensor of the same type and shape as the input tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>tril</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 0, 0, 0, 0],
+#   [1, 2, 0, 0, 0],
+#   [9, 4, 1, 0, 0],
+#   [4, 3, 4, 2, 0]]
+y = tril_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_tril')
+```
+
+</details>
+
+
+<details>
+<summary>tril_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 0, 0, 0],
+#   [1, 0, 0, 0, 0],
+#   [9, 4, 0, 0, 0],
+#   [4, 3, 4, 0, 0]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_neg')
+```
+
+</details>
+
+
+<details>
+<summary>tril_one_row</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+# X:
+# [[[6, 2, 4, 1, 6]],
+#
+#  [[8, 3, 8, 7, 0]],
+#
+#  [[2, 2, 9, 5, 9]]]
+# expect result:
+# [[[6, 0, 0, 0, 0]],
+#
+#  [[8, 0, 0, 0, 0]],
+#
+#  [[2, 0, 0, 0, 0]]]
+y = tril_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_tril_one_row_neg')
+```
+
+</details>
+
+
+<details>
+<summary>tril_out_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-7).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_neg')
+```
+
+</details>
+
+
+<details>
+<summary>tril_out_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_pos')
+```
+
+</details>
+
+
+<details>
+<summary>tril_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(2).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 0, 0],
+#   [1, 2, 8, 6, 0],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_pos')
+```
+
+</details>
+
+
+<details>
+<summary>tril_square</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+# X:
+# [[[0, 4, 3],
+#   [2, 0, 9],
+#   [8, 2, 5]],
+#
+#  [[2, 7, 2],
+#   [2, 6, 0],
+#   [2, 6, 5]]]
+# expect result:
+# [[[0, 0, 0],
+#   [2, 0, 0],
+#   [8, 2, 5]],
+#
+#  [[2, 0, 0],
+#   [2, 6, 0],
+#   [2, 6, 5]]]
+y = tril_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_tril_square')
+```
+
+</details>
+
+
+<details>
+<summary>tril_square_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+# [[[0, 4, 3],
+#   [2, 0, 9],
+#   [8, 2, 5]],
+#
+#  [[2, 7, 2],
+#   [2, 6, 0],
+#   [2, 6, 5]]]
+# expect result:
+# [[[0, 0, 0],
+#   [2, 0, 0],
+#   [8, 2, 0]],
+#
+#  [[0, 0, 0],
+#   [2, 0, 0],
+#   [2, 6, 0]]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_square_neg')
+```
+
+</details>
+
+
+<details>
+<summary>tril_zero</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+# []
+# expect result:
+# []
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_zero')
+```
+
+</details>
+
+
+<details>
+<summary>triu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [0, 2, 8, 6, 9],
+#   [0, 0, 0, 8, 7],
+#   [0, 0, 0, 2, 4]]
+y = triu_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_triu')
+```
+
+</details>
+
+
+<details>
+<summary>triu_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [0, 4, 0, 8, 7],
+#   [0, 0, 4, 2, 4]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_neg')
+```
+
+</details>
+
+
+<details>
+<summary>triu_one_row</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+k = np.array(1).astype(np.int64)
+# X:
+# [[[1, 4, 9, 7, 1]],
+#
+#  [[9, 2, 8, 8, 4]],
+#
+#  [[3, 9, 7, 4, 2]]]
+# expect result:
+# [[[0, 4, 9, 7, 1]],
+#
+#  [[0, 2, 8, 8, 4]],
+#
+#  [[0, 9, 7, 4, 2]]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_one_row')
+```
+
+</details>
+
+
+<details>
+<summary>triu_out_neg_out</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-7).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_neg_out')
+```
+
+</details>
+
+
+<details>
+<summary>triu_out_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_pos')
+```
+
+</details>
+
+
+<details>
+<summary>triu_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(2).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 3, 7, 9],
+#   [0, 0, 0, 6, 9],
+#   [0, 0, 0, 0, 7],
+#   [0, 0, 0, 0, 0]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_pos')
+```
+
+</details>
+
+
+<details>
+<summary>triu_square</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+y = triu_reference_implementation(x)
+# X:
+# [[[4, 6, 9],
+#   [7, 5, 4],
+#   [8, 1, 2]],
+#
+#  [[1, 4, 9],
+#   [9, 6, 3],
+#   [8, 9, 8]]]
+# expect result:
+# [[[4, 6, 9],
+#   [0, 5, 4],
+#   [0, 0, 2]],
+#
+#  [[1, 4, 9],
+#   [0, 6, 3],
+#   [0, 0, 8]]]
+expect(node, inputs=[x], outputs=[y], name='test_triu_square')
+```
+
+</details>
+
+
+<details>
+<summary>triu_square_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+# [[[4, 6, 9],
+#   [7, 5, 4],
+#   [8, 1, 2]],
+#
+#  [[1, 4, 9],
+#   [9, 6, 3],
+#   [8, 9, 8]]]
+# expect result:
+# [[[4, 6, 9],
+#   [7, 5, 4],
+#   [0, 1, 2]],
+#
+#  [[1, 4, 9],
+#   [9, 6, 3],
+#   [0, 9, 8]]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_square_neg')
+```
+
+</details>
+
+
+<details>
+<summary>triu_zero</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(0, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+# []
+# expect result:
+# []
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_zero')
+```
+
+</details>
+
+
+### <a name="Unique"></a><a name="unique">**Unique**</a>
+
+  Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned.
+  Otherwise the input tensor is flattened and unique values of the flattened tensor are returned.
+
+  This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs.
+  The first output tensor 'Y' contains all unique values or subtensors of the input.
+  The second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'..
+  The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. ".
+  The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input.
+
+  Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.
+
+  https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html
+
+  Example 1:
+    input_X = [2, 1, 1, 3, 4, 3]
+    attribute_sorted = 0
+    attribute_axis = None
+    output_Y = [2, 1, 3, 4]
+    output_indices = [0, 1, 3, 4]
+    output_inverse_indices = [0, 1, 1, 2, 3, 2]
+    output_counts = [1, 2, 2, 1]
+
+  Example 2:
+    input_X = [[1, 3], [2, 3]]
+    attribute_sorted = 1
+    attribute_axis = None
+    output_Y = [1, 2, 3]
+    output_indices = [0, 2, 1]
+    output_inverse_indices = [0, 2, 1, 2]
+    output_counts = [1, 1, 2]
+
+  Example 3:
+    input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]
+    attribute_sorted = 1
+    attribute_axis = 0
+    output_Y = [[1, 0, 0], [2, 3, 4]]
+    output_indices = [0, 2]
+    output_inverse_indices = [0, 0, 1]
+    output_counts = [2, 1]
+
+  Example 4:
+    input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+               [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]
+    attribute_sorted = 1
+    attribute_axis = 1
+
+    intermediate data are presented below for better understanding:
+
+    there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):
+    A: [[1, 1], [1, 1]],
+       [[0, 1], [0, 1]],
+       [[2, 1], [2, 1]],
+       [[0, 1], [0, 1]].
+
+    there are 3 unique subtensors:
+    [[1, 1], [1, 1]],
+    [[0, 1], [0, 1]],
+    [[2, 1], [2, 1]].
+
+    sorted unique subtensors:
+    B: [[0, 1], [0, 1]],
+       [[1, 1], [1, 1]],
+       [[2, 1], [2, 1]].
+
+    output_Y is constructed from B:
+    [[[0. 1.], [1. 1.], [2. 1.]],
+     [[0. 1.], [1. 1.], [2. 1.]]]
+
+    output_indices is to map from B to A:
+    [1, 0, 2]
+
+    output_inverse_indices is to map from A to B:
+    [1, 0, 2, 0]
+
+    output_counts = [2 1 1]
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>axis</tt> : int</dt>
+<dd>(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).</dd>
+<dt><tt>sorted</tt> : int (default is 1)</dt>
+<dd>(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).</dd>
+</dl>
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> (non-differentiable) : T</dt>
+<dd>A N-D input tensor that is to be processed.</dd>
+</dl>
+
+#### Outputs (1 - 4)
+
+<dl>
+<dt><tt>Y</tt> (non-differentiable) : T</dt>
+<dd>A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'</dd>
+<dt><tt>indices</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. </dd>
+<dt><tt>inverse_indices</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. </dd>
+<dt><tt>counts</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Input can be of any tensor type.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>not_sorted_without_axis</summary>
+
+```python
+node_not_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=0
+)
+# numpy unique does not retain original order (it sorts the output unique values)
+# https://github.com/numpy/numpy/issues/8621
+# we need to recover unsorted output and indices
+x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+
+# prepare index mapping from sorted to unsorted
+argsorted_indices = np.argsort(indices)
+inverse_indices_map = {i: si for i, si in zip(argsorted_indices, np.arange(len(argsorted_indices)))}
+
+indices = indices[argsorted_indices]
+y = np.take(x, indices, axis=0)
+inverse_indices = np.asarray([inverse_indices_map[i] for i in inverse_indices], dtype=np.int64)
+counts = counts[argsorted_indices]
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [2.0, 1.0, 3.0, 4.0]
+# print(indices)
+# [0 1 3 4]
+# print(inverse_indices)
+# [0, 1, 1, 2, 3, 2]
+# print(counts)
+# [1, 2, 2, 1]
+
+expect(node_not_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_not_sorted_without_axis')
+```
+
+</details>
+
+
+<details>
+<summary>sorted_with_axis</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=1,
+    axis=0
+)
+
+x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [[1. 0. 0.]
+#  [2. 3. 4.]]
+# print(indices)
+# [0 2]
+# print(inverse_indices)
+# [0 0 1]
+# print(counts)
+# [2 1]
+
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_axis')
+```
+
+</details>
+
+
+<details>
+<summary>sorted_with_axis_3d</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=1,
+    axis=1
+)
+
+x = np.array([[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+              [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [[[0. 1.]
+#  [1. 1.]
+#  [2. 1.]]
+# [[0. 1.]
+#  [1. 1.]
+#  [2. 1.]]]
+# print(indices)
+# [1 0 2]
+# print(inverse_indices)
+# [1 0 2 0]
+# print(counts)
+# [2 1 1]
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_axis_3d')
+```
+
+</details>
+
+
+<details>
+<summary>sorted_with_negative_axis</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=1,
+    axis=-1
+)
+
+x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [[0. 1.]
+#  [0. 1.]
+#  [3. 2.]]
+# print(indices)
+# [1 0]
+# print(inverse_indices)
+# [1 0 0]
+# print(counts)
+# [2 1]
+
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_negative_axis')
+```
+
+</details>
+
+
+<details>
+<summary>sorted_without_axis</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts']
+)
+
+x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_without_axis')
+```
+
+</details>
+
+
+### <a name="Unsqueeze"></a><a name="unsqueeze">**Unsqueeze**</a>
+
+  Insert single-dimensional entries to the shape of an input tensor (`data`).
+  Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).
+
+  For example:
+    Given an input tensor (`data`) of shape [3, 4, 5], then
+    Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].
+
+  The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
+  The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
+  Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
+  The order of values in `axes` does not matter and can come in any order.
+
+
+#### Version
+
+This version of the operator has been available since version 13 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Unsqueeze-1">1</a>, <a href="Changelog.md#Unsqueeze-11">11</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>data</tt> (differentiable) : T</dt>
+<dd>Original tensor</dd>
+<dt><tt>axes</tt> (non-differentiable) : tensor(int64)</dt>
+<dd>List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>expanded</tt> (differentiable) : T</dt>
+<dd>Reshaped tensor with same data as input.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>unsqueeze_negative_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+axes = np.array([-2]).astype(np.int64)
+y = np.expand_dims(x, axis=-2)
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_negative_axes')
+```
+
+</details>
+
+
+<details>
+<summary>unsqueeze_one_axis</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+
+for i in range(x.ndim):
+    axes = np.array([i]).astype(np.int64)
+    node = onnx.helper.make_node(
+        'Unsqueeze',
+        inputs=['x', 'axes'],
+        outputs=['y'],
+    )
+    y = np.expand_dims(x, axis=i)
+
+    expect(node, inputs=[x, axes], outputs=[y],
+           name='test_unsqueeze_axis_' + str(i))
+```
+
+</details>
+
+
+<details>
+<summary>unsqueeze_three_axes</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+axes = np.array([2, 4, 5]).astype(np.int64)
+
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+y = np.expand_dims(x, axis=2)
+y = np.expand_dims(y, axis=4)
+y = np.expand_dims(y, axis=5)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_three_axes')
+```
+
+</details>
+
+
+<details>
+<summary>unsqueeze_two_axes</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+axes = np.array([1, 4]).astype(np.int64)
+
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+y = np.expand_dims(x, axis=1)
+y = np.expand_dims(y, axis=4)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_two_axes')
+```
+
+</details>
+
+
+<details>
+<summary>unsqueeze_unsorted_axes</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+axes = np.array([5, 4, 2]).astype(np.int64)
+
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+y = np.expand_dims(x, axis=2)
+y = np.expand_dims(y, axis=4)
+y = np.expand_dims(y, axis=5)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_unsorted_axes')
+```
+
+</details>
+
+
+### <a name="Upsample"></a><a name="upsample">**Upsample** (deprecated)</a>
+
+  Upsample the input tensor.
+  Each dimension value of the output tensor is:
+    output_dimension = floor(input_dimension * scale).
+
+#### Version
+
+This version of the operator has been deprecated since version 10 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Upsample-7">7</a>, <a href="Changelog.md#Upsample-9">9</a>
+
+
+#### Examples
+
+<details>
+<summary>nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Upsample',
+    inputs=['X', 'scales'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+output = np.array([[[
+    [1, 1, 1, 2, 2, 2],
+    [1, 1, 1, 2, 2, 2],
+    [3, 3, 3, 4, 4, 4],
+    [3, 3, 3, 4, 4, 4],
+]]], dtype=np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_upsample_nearest', opset_imports=[helper.make_opsetid("", 9)])
+```
+
+</details>
+
+
+### <a name="Where"></a><a name="where">**Where**</a>
+
+  Return elements, either from X or Y, depending on condition.
+  Where behaves like
+  [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
+  with three parameters.
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+  **History**
+  - Version 16 adds bfloat16 to the types allowed (for the second and third parameter).
+
+#### Version
+
+This version of the operator has been available since version 16 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Where-9">9</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>condition</tt> (non-differentiable) : B</dt>
+<dd>When True (nonzero), yield X, otherwise yield Y</dd>
+<dt><tt>X</tt> (differentiable) : T</dt>
+<dd>values selected at indices where condition is True</dd>
+<dt><tt>Y</tt> (differentiable) : T</dt>
+<dd>values selected at indices where condition is False</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> (differentiable) : T</dt>
+<dd>Tensor of shape equal to the broadcasted shape of condition, X, and Y.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>B</tt> : tensor(bool)</dt>
+<dd>Constrain to boolean tensors.</dd>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types (including bfloat).</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>long</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Where',
+    inputs=['condition', 'x', 'y'],
+    outputs=['z'],
+)
+
+condition = np.array([[1, 0], [1, 1]], dtype=bool)
+x = np.array([[1, 2], [3, 4]], dtype=np.int64)
+y = np.array([[9, 8], [7, 6]], dtype=np.int64)
+z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+expect(node, inputs=[condition, x, y], outputs=[z],
+       name='test_where_long_example')
+```
+
+</details>
+
+
+<details>
+<summary>where</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Where',
+    inputs=['condition', 'x', 'y'],
+    outputs=['z'],
+)
+
+condition = np.array([[1, 0], [1, 1]], dtype=bool)
+x = np.array([[1, 2], [3, 4]], dtype=np.float32)
+y = np.array([[9, 8], [7, 6]], dtype=np.float32)
+z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+expect(node, inputs=[condition, x, y], outputs=[z],
+       name='test_where_example')
+```
+
+</details>
+
+
+### <a name="Xor"></a><a name="xor">**Xor**</a>
+
+  Returns the tensor resulted from performing the `xor` logical operation
+  elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+
+#### Version
+
+This version of the operator has been available since version 7 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#Xor-1">1</a>
+
+#### Inputs
+
+<dl>
+<dt><tt>A</tt> (non-differentiable) : T</dt>
+<dd>First input operand for the logical operator.</dd>
+<dt><tt>B</tt> (non-differentiable) : T</dt>
+<dd>Second input operand for the logical operator.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>C</tt> (non-differentiable) : T1</dt>
+<dd>Result tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(bool)</dt>
+<dd>Constrain input to boolean tensor.</dd>
+<dt><tt>T1</tt> : tensor(bool)</dt>
+<dd>Constrain output to boolean tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>xor</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Xor',
+    inputs=['x', 'y'],
+    outputs=['xor'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+y = (np.random.randn(3, 4) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor4d')
+```
+
+</details>
+
+
+<details>
+<summary>xor_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Xor',
+    inputs=['x', 'y'],
+    outputs=['xor'],
+)
+
+# 3d vs 1d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(5) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast3v1d')
+
+# 3d vs 2d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(4, 5) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast3v2d')
+
+# 4d vs 2d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast4v2d')
+
+# 4d vs 3d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast4v3d')
+
+# 4d vs 4d
+x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast4v4d')
+```
+
+</details>
+
+
+## ai.onnx.preview.training
+### <a name="ai.onnx.preview.training.Adagrad"></a><a name="ai.onnx.preview.training.adagrad">**ai.onnx.preview.training.Adagrad**</a>
+
+  Compute one iteration of ADAGRAD, a stochastic gradient based optimization
+      algorithm. This operator can conduct the optimization of multiple tensor variables.
+
+      Let's define the behavior of this operator. As you can imagine, ADAGRAD requires
+      some parameters:
+
+       - The initial learning-rate "R".
+       - The update count "T". That is, the number of training iterations conducted.
+       - A L2-norm regularization coefficient "norm_coefficient".
+       - A learning-rate decay factor "decay_factor".
+       - A small constant "epsilon" to avoid dividing-by-zero.
+
+      At each ADAGRAD iteration, the optimized tensors are moved along a direction
+      computed based on their estimated gradient and accumulated squared gradient. Assume
+      that only a single tensor "X" is updated by this operator. We need the value of "X",
+      its gradient "G", and its accumulated squared gradient "H". Therefore, variables in
+      this operator's input list are sequentially "R", "T", "X", "G", and "H". Other
+      parameters are given as attributes because they are usually constants. Also, the
+      corresponding output tensors are the new value of "X" (called "X_new"), and then
+      the new accumulated squared gradient (called "H_new"). Those outputs are computed
+      from the given inputs following the pseudo code below.
+
+      Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
+      numpy-style broadcasting support. The pseudo code to compute those outputs is:
+
+        // Compute a scalar learning-rate factor. At the first update of X, T is generally
+        // 0 (0-based update index) or 1 (1-based update index).
+        r = R / (1 + T * decay_factor);
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
+        G_regularized = norm_coefficient * X + G;
+
+        // Compute new accumulated squared gradient.
+        H_new = H + G_regularized * G_regularized;
+
+        // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...)
+        // computes element-wise square-root.
+        H_adaptive = Sqrt(H_new) + epsilon
+
+        // Compute the new value of "X".
+        X_new = X - r * G_regularized / H_adaptive;
+
+      If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same
+      pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a
+      concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
+      be concatenated too) and then just reuse the entire pseudo code.
+
+      Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
+      In that reference paper, this operator is a special case of the Figure 1's composite mirror
+      descent update.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>decay_factor</tt> : float (default is 0.0)</dt>
+<dd>The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.</dd>
+<dt><tt>epsilon</tt> : float (default is 0.0)</dt>
+<dd>Small scalar to avoid dividing by zero.</dd>
+<dt><tt>norm_coefficient</tt> : float (default is 0.0)</dt>
+<dd>Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>R</tt> : T1</dt>
+<dd>The initial learning rate.</dd>
+<dt><tt>T</tt> : T2</dt>
+<dd>The update count of "X". It should be a scalar.</dd>
+<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float scalars.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain input types to 64-bit integer scalars.</dd>
+<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>adagrad</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+epsilon = 1e-5
+decay_factor = 0.1
+
+# Create operator.
+node = onnx.helper.make_node('Adagrad',
+                             inputs=['R', 'T', 'X', 'G', 'H'],
+                             outputs=['X_new', 'H_new'],
+                             norm_coefficient=norm_coefficient,
+                             epsilon=epsilon,
+                             decay_factor=decay_factor,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.0], dtype=np.float32)
+g = np.array([-1.0], dtype=np.float32)
+h = np.array([2.0], dtype=np.float32)
+
+# Compute expected outputs of Adagrad.
+x_new, h_new = apply_adagrad(r, t, x, g, h,
+                             norm_coefficient, epsilon, decay_factor)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, h],
+       outputs=[x_new, h_new], name='test_adagrad',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+<details>
+<summary>adagrad_multiple</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+epsilon = 1e-5
+decay_factor = 0.1
+
+node = onnx.helper.make_node('Adagrad',
+                             inputs=['R', 'T', 'X1', 'X2',
+                                     'G1', 'G2', 'H1', 'H2'],
+                             outputs=['X1_new', 'X2_new',
+                                      'H1_new', 'H2_new'],
+                             norm_coefficient=norm_coefficient,
+                             epsilon=epsilon,
+                             decay_factor=decay_factor,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+
+x1 = np.array([1.0], dtype=np.float32)
+g1 = np.array([-1.0], dtype=np.float32)
+h1 = np.array([2.0], dtype=np.float32)
+
+x2 = np.array([1.0, 2.0], dtype=np.float32)
+g2 = np.array([-1.0, -3.0], dtype=np.float32)
+h2 = np.array([4.0, 1.0], dtype=np.float32)
+
+# Compute expected outputs of Adagrad.
+x1_new, h1_new = apply_adagrad(r, t, x1, g1, h1,
+                               norm_coefficient, epsilon, decay_factor)
+x2_new, h2_new = apply_adagrad(r, t, x2, g2, h2,
+                               norm_coefficient, epsilon, decay_factor)
+
+# Check results.
+expect(node, inputs=[r, t, x1, x2, g1, g2, h1, h2],
+       outputs=[x1_new, x2_new, h1_new, h2_new], name='test_adagrad_multiple',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+### <a name="ai.onnx.preview.training.Adam"></a><a name="ai.onnx.preview.training.adam">**ai.onnx.preview.training.Adam**</a>
+
+  Compute one iteration of Adam, a stochastic gradient based optimization
+      algorithm. This operator can conduct the optimization of multiple tensor variables.
+
+      Let's define the behavior of this operator. First of all, Adam requires
+      some parameters:
+
+       - The learning-rate "R".
+       - The update count "T". That is, the number of training iterations conducted.
+       - A L2-norm regularization coefficient "norm_coefficient".
+       - A small constant "epsilon" to avoid dividing-by-zero.
+       - Two coefficients, "alpha" and "beta".
+
+      At each Adam iteration, the optimized tensors are moved along a direction
+      computed based on their exponentially-averaged historical gradient and
+      exponentially-averaged historical squared gradient. Assume that only a tensor
+      "X" is being optimized. The rest of required information is
+
+       - the value of "X",
+       - "X"'s gradient (denoted by "G"),
+       - "X"'s exponentially-averaged historical gradient (denoted by "V"), and
+       - "X"'s exponentially-averaged historical squared gradient (denoted by "H").
+
+      Some of those parameters are passed into this operator as input tensors and others
+      are stored as this operator's attributes. Specifically, this operator's input tensor
+      list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is
+      the second input, and so on. Other parameters are given as attributes because they
+      are constants. Moreover, the corresponding output tensors are
+
+       - the new value of "X" (called "X_new"),
+       - the new exponentially-averaged historical gradient (denoted by "V_new"), and
+       - the new exponentially-averaged historical squared gradient (denoted by "H_new").
+
+      Those outputs are computed following the pseudo code below.
+
+      Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
+      numpy-style broadcasting support. The pseudo code to compute those outputs is:
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
+        G_regularized = norm_coefficient * X + G
+
+        // Update exponentially-averaged historical gradient.
+        V_new = alpha * V + (1 - alpha) * G_regularized
+
+        // Update exponentially-averaged historical squared gradient.
+        H_new = beta * H + (1 - beta) * G_regularized * G_regularized
+
+        // Compute the element-wise square-root of H_new. V_new will be element-wisely
+        // divided by H_sqrt for a better update direction.
+        H_sqrt = Sqrt(H_new) + epsilon
+
+        // Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power.
+        R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R
+
+        // Compute new value of "X".
+        X_new = X - R_adjusted * V_new / H_sqrt
+
+        // Post-update regularization.
+        X_final = (1 - norm_coefficient_post) * X_new
+
+      If there are multiple inputs to be optimized, the pseudo code will be applied
+      independently to each of them.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (default is 0.9)</dt>
+<dd>Coefficient of previously accumulated gradient in running average. Default to 0.9.</dd>
+<dt><tt>beta</tt> : float (default is 0.999)</dt>
+<dd>Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.</dd>
+<dt><tt>epsilon</tt> : float (default is 0.0)</dt>
+<dd>Small scalar to avoid dividing by zero.</dd>
+<dt><tt>norm_coefficient</tt> : float (default is 0.0)</dt>
+<dd>Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
+<dt><tt>norm_coefficient_post</tt> : float (default is 0.0)</dt>
+<dd>Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>R</tt> : T1</dt>
+<dd>The initial learning rate.</dd>
+<dt><tt>T</tt> : T2</dt>
+<dd>The update count of "X". It should be a scalar.</dd>
+<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>The tensors to be optimized, followed by their respective gradients, followed by their respective accumulated gradients (aka momentum), followed by their respective accumulated squared gradients. For example, to optimize tensors "X_1" and "X_2,", the input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated gradient of "X_1", accumulated gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>New values of optimized tensors, followed by their respective new accumulated gradients, followed by their respective new accumulated squared gradients. For example, if two tensors "X_1" and "X_2" are optimized, the outputs list would be [new value of "X_1", new value of "X_2", new accumulated gradient of "X_1", new accumulated gradient of "X_2", new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float scalars.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain input types to 64-bit integer scalars.</dd>
+<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>adam</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.1
+epsilon = 1e-7
+
+# Create operator.
+node = onnx.helper.make_node('Adam',
+                             inputs=['R', 'T', 'X', 'G', 'V', 'H'],
+                             outputs=['X_new', 'V_new', 'H_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             epsilon=epsilon,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.2, 2.8], dtype=np.float32)
+g = np.array([-0.94, -2.5], dtype=np.float32)
+v = np.array([1.7, 3.6], dtype=np.float32)
+h = np.array([0.1, 0.1], dtype=np.float32)
+
+# Compute expected outputs of Adam.
+x_new, v_new, h_new = apply_adam(r, t, x, g, v, h,
+                                 norm_coefficient, 0.0, alpha, beta,
+                                 epsilon)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, v, h],
+       outputs=[x_new, v_new, h_new], name='test_adam',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+<details>
+<summary>adam_multiple</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.85
+epsilon = 1e-2
+
+node = onnx.helper.make_node('Adam',
+                             inputs=['R', 'T', 'X1', 'X2',
+                                     'G1', 'G2', 'V1', 'V2',
+                                     'H1', 'H2'],
+                             outputs=['X1_new', 'X2_new',
+                                      'V1_new', 'V2_new',
+                                      'H1_new', 'H2_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+
+x1 = np.array([1.0], dtype=np.float32)
+g1 = np.array([-1.0], dtype=np.float32)
+v1 = np.array([2.0], dtype=np.float32)
+h1 = np.array([0.5], dtype=np.float32)
+
+x2 = np.array([1.0, 2.0], dtype=np.float32)
+g2 = np.array([-1.0, -3.0], dtype=np.float32)
+v2 = np.array([4.0, 1.0], dtype=np.float32)
+h2 = np.array([1.0, 10.0], dtype=np.float32)
+
+# Compute expected outputs of Adam.
+x1_new, v1_new, h1_new = apply_adam(r, t, x1, g1, v1, h1,
+                            norm_coefficient, 0.0, alpha, beta,
+                            epsilon)
+x2_new, v2_new, h2_new = apply_adam(r, t, x2, g2, v2, h2,
+                            norm_coefficient, 0.0, alpha, beta,
+                            epsilon)
+
+# Check results.
+expect(node, inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
+       outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
+       name='test_adam_multiple',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+### <a name="ai.onnx.preview.training.Gradient"></a><a name="ai.onnx.preview.training.gradient">**ai.onnx.preview.training.Gradient**</a>
+
+  Gradient operator computes the partial derivatives of a specific tensor w.r.t.
+  some other tensors. This operator is widely used in gradient-based training
+  algorithms. To illustrate its use, let's consider a computation graph,
+
+  ```
+  X -----.
+         |
+         v
+  W --> Conv --> H --> Gemm --> Y
+                        ^
+                        |
+                        Z
+  ```
+
+  , where W and Z are trainable tensors. Note that operators' attributes are
+  omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of
+  Y with respect to W (Z). The user can compute gradient by inserting Gradient
+  operator to form another graph shown below.
+
+  ```
+  W --> Conv --> H --> Gemm --> Y
+  |      ^              ^
+  |      |              |
+  |      X              Z
+  |      |              |
+  |      |   .----------'
+  |      |   |  (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in
+  |      |   |   "xs" followed by "zs")
+  |      v   v
+  '---> Gradient(xs=["W", "Z"], zs=["X"], y="Y")
+         |   |
+         |   '-----------------------------------> dY/dW (1st output of Gradient)
+         |
+         '---------------------------------------> dY/dZ (2nd output of Gradient)
+  ```
+
+  By definition, the tensor "y" is a function of independent variables in "xs"
+  and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable
+  variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H"
+  cannot appear in "xs" and "zs". The reason is that "H" can be determined by
+  tensors "W" and "X" and therefore "H" is not an independent variable.
+
+  All outputs are optional. If needed, for example, user can assign an empty
+  string to the 1st output name of that Gradient to skip the generation of dY/dW.
+  Note that the concept of optional outputs can also be found in ONNX's RNN, GRU,
+  and LSTM.
+
+  Gradient operator can compute derivative against intermediate tensors. For
+  example, the gradient of Y with respect to H can be done via
+
+  ```
+  W --> Conv --> H --> Gemm --> Y
+         ^       |      ^
+         |       |      |
+         X       |      Z
+         .-------'      |
+         |   .----------'
+         |   | (H/Z is the 1st/2nd input of Gradient as shown in "xs")
+         v   v
+        Gradient(xs=["H", "Z"], y="Y")
+         |   |
+         |   '-----------------------------------> dY/dH (1st output of Gradient)
+         |
+         '---------------------------------------> dY/dZ (2nd output of Gradient)
+  ```
+
+  It is possible to represent high-order differentiation using Gradient operators.
+  For example, given the following linear model:
+
+  ```
+  W --> Gemm --> Y --> Loss --> O
+         ^              ^
+         |              |
+         X              L
+  ```
+
+  To compute the 2nd order derivative of O with respect to W (denoted by
+  d^2O/dW^2), one can do
+
+  ```
+  W --> Gemm --> Y --> Loss --> O
+  |      ^              ^
+  |      |              |
+  |      X .------------L
+  |      | |            |
+  |      | |            v
+  +------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient)
+  |      | |    |
+  |      | |    '---> dO/dW (2nd output of Gradient)
+  |      v v
+  '---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of
+         |                                                  Gradient)
+         |
+         |
+         '---> d^2O/dW^2 (2nd output of Gradient)
+  ```
+
+  The tensors named in attributes "xs", "zs", and "y" define the differentiated
+  computation graph, and the inputs to Gradient node define the values at
+  which the gradient is computed. We can feed different tensors to the identified
+  graph. For example, one can compute the gradient of Y with respect to H at
+  a specific value of H, H_1, by providing that value as an input to the Gradient
+  node.
+
+  ```
+  W --> Conv --> H --> Gemm --> Y
+         ^              ^
+         |              |
+         X              Z
+
+            Z_1 (2nd input of Gradient)
+             |
+             v
+  H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1.
+             |
+             '------------------------------> dY/dZ (2nd output of Gradient)
+  ```
+
+  When the inputs of Gradient are the tensors named in "xs" and "zs", the
+  computation can be optimized. More specifically, intermediate variables in
+  forward pass can be reused if the gradient is computed via reverse-mode
+  auto-differentiation.
+
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>xs</tt> : list of strings (required)</dt>
+<dd>Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.</dd>
+<dt><tt>y</tt> : string (required)</dt>
+<dd>The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".</dd>
+<dt><tt>zs</tt> : list of strings</dt>
+<dd>Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.</dd>
+</dl>
+
+#### Inputs (1 - &#8734;)
+
+<dl>
+<dt><tt>Inputs</tt> (variadic, heterogeneous) : T1</dt>
+<dd>The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute  "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>Outputs</tt> (variadic, heterogeneous) : T2</dt>
+<dd>The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Allow outputs to be any kind of tensor.</dd>
+<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Allow inputs to be any kind of floating-point tensor.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>gradient_scalar_add</summary>
+
+```python
+add_node = onnx.helper.make_node('Add',
+                                 ['a', 'b'], ['c'], name='my_add')
+gradient_node = onnx.helper.make_node(
+    'Gradient', ['a', 'b'],
+    ['dc_da', 'dc_db'], name='my_gradient',
+    domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+    xs=['a', 'b'], y='c')
+
+a = np.array(1.0).astype(np.float32)
+b = np.array(2.0).astype(np.float32)
+c = a + b
+# dc / da = d(a+b) / da = 1
+dc_da = np.array(1).astype(np.float32)
+# db / db = d(a+b) / db = 1
+dc_db = np.array(1).astype(np.float32)
+
+graph = onnx.helper.make_graph(
+    nodes=[add_node, gradient_node],
+    name='GradientOfAdd',
+    inputs=[
+        onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
+                                           [])],
+    outputs=[
+        onnx.helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('dc_da',
+                                           onnx.TensorProto.FLOAT, []),
+        onnx.helper.make_tensor_value_info('dc_db',
+                                           onnx.TensorProto.FLOAT, [])])
+opsets = [
+    onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+    onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]
+model = onnx.helper.make_model(
+    graph,
+    producer_name='backend-test',
+    opset_imports=opsets)
+expect(model, inputs=[a, b], outputs=[c, dc_da, dc_db],
+       name='test_gradient_of_add')
+```
+
+</details>
+
+
+<details>
+<summary>gradient_scalar_add_and_mul</summary>
+
+```python
+add_node = onnx.helper.make_node('Add',
+                                 ['a', 'b'], ['c'], name='my_add')
+mul_node = onnx.helper.make_node('Mul',
+                                 ['c', 'a'], ['d'], name='my_mul')
+gradient_node = onnx.helper.make_node(
+    'Gradient', ['a', 'b'],
+    ['dd_da', 'dd_db'], name='my_gradient',
+    domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+    xs=['a', 'b'], y='d')
+
+a = np.array(1.0).astype(np.float32)
+b = np.array(2.0).astype(np.float32)
+c = a + b
+# d = a * c = a * (a + b)
+d = a * c
+# dd / da = d(a*a+a*b) / da = 2 * a + b
+dd_da = (2 * a + b).astype(np.float32)
+# dd / db = d(a*a+a*b) / db = a
+dd_db = a
+
+graph = onnx.helper.make_graph(
+    nodes=[add_node, mul_node, gradient_node],
+    name='GradientOfTwoOperators',
+    inputs=[
+        onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
+                                           [])],
+    outputs=[
+        onnx.helper.make_tensor_value_info('d', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('dd_da',
+                                           onnx.TensorProto.FLOAT, []),
+        onnx.helper.make_tensor_value_info('dd_db',
+                                           onnx.TensorProto.FLOAT, [])])
+
+opsets = [
+    onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+    onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]
+model = onnx.helper.make_model(graph,
+    producer_name='backend-test',
+    opset_imports=opsets)
+expect(model, inputs=[a, b], outputs=[d, dd_da, dd_db],
+       name='test_gradient_of_add_and_mul')
+```
+
+</details>
+
+
+### <a name="ai.onnx.preview.training.Momentum"></a><a name="ai.onnx.preview.training.momentum">**ai.onnx.preview.training.Momentum**</a>
+
+  Compute one iteration of stochastic gradient update with momentum.
+      This operator can conduct the optimization of multiple tensor variables.
+
+      Let's define the behavior of this operator. As you can imagine, SG with momentum requires
+      several parameters:
+
+       - The learning-rate "R".
+       - The update count "T". That is, the number of conducted training iterations. It should
+         be zero in the first training iteration.
+       - A L2-norm regularization coefficient "norm_coefficient".
+       - A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha".
+       - The scaling coefficient of current gradient "beta".
+       - An attribute to choose either standard momentum or Nesterov's momentum "mode" should
+         be used.
+
+      For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized.
+      Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This
+      Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new
+      momentum (called "V_new").
+
+      This operator supports two different momentum algorithms. Set the attribute "mode" to
+      "nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to
+      "standard" to use standard momentum. Computation details are described subsequently.
+
+      Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting.
+
+      Pseudo code for SG with standard momentum:
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
+        // values of all elements in X.
+        G_regularized = norm_coefficient * X + G
+
+        // In the first training iteration, beta should always be 1.
+        beta_adjusted = T > 0 ? beta : 1
+
+        // Compute the current momentum based on previous momentum and the current gradient.
+        V_new = alpha * V + beta_adjusted * G_regularized
+
+        // Update X.
+        X_new = X - R * V_new
+
+      Pseudo code for SG with Nesterov's momentum:
+
+        // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
+        // values of all elements in X.
+        G_regularized = norm_coefficient * X + G;
+
+        // In the first training iteration, beta should always be 1.
+        beta_adjusted = T > 0 ? beta : 1
+
+        // Compute the current momentum based on previous momentum and the current gradient.
+        V_new = alpha * V + beta_adjusted * G_regularized;
+
+        // Compute final update direction and then update X.
+        X_new = X - R * (G_regularized + alpha * V_new)
+
+      If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same
+      pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a
+      concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
+      be concatenated too) and then our pseudo code becomes applicable.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>alpha</tt> : float (required)</dt>
+<dd>The decay factor of momentum. It should be a scalar.</dd>
+<dt><tt>beta</tt> : float (required)</dt>
+<dd>The coefficient of gradient in computing new momentum. It should be a scalar.</dd>
+<dt><tt>mode</tt> : string (required)</dt>
+<dd>Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentum</dd>
+<dt><tt>norm_coefficient</tt> : float (required)</dt>
+<dd>Coefficient of 0.5 * norm_coefficient * ||X||^2.</dd>
+</dl>
+
+#### Inputs (3 - &#8734;)
+
+<dl>
+<dt><tt>R</tt> : T1</dt>
+<dd>The learning rate.</dd>
+<dt><tt>T</tt> : T2</dt>
+<dd>Update count of "X". It should be a scalar.</dd>
+<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].</dd>
+</dl>
+
+#### Outputs (1 - &#8734;)
+
+<dl>
+<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
+<dd>It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float scalars.</dd>
+<dt><tt>T2</tt> : tensor(int64)</dt>
+<dd>Constrain input types to 64-bit integer scalars.</dd>
+<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
+<dd>Constrain input types to float tensors.</dd>
+</dl>
+
+
+#### Examples
+
+<details>
+<summary>momentum</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.1
+
+# Create operator.
+node = onnx.helper.make_node('Momentum',
+                             inputs=['R', 'T', 'X', 'G', 'V'],
+                             outputs=['X_new', 'V_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             mode='standard',
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.2, 2.8], dtype=np.float32)
+g = np.array([-0.94, -2.5], dtype=np.float32)
+v = np.array([1.7, 3.6], dtype=np.float32)
+
+# Compute expected outputs of Momentum.
+x_new, v_new = apply_momentum(r, t, x, g, v,
+                              norm_coefficient, alpha, beta)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, v],
+       outputs=[x_new, v_new], name='test_momentum',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+<details>
+<summary>momentum_multiple</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.85
+
+node = onnx.helper.make_node('Momentum',
+                             inputs=['R', 'T', 'X1', 'X2',
+                                     'G1', 'G2', 'H1', 'H2'],
+                             outputs=['X1_new', 'X2_new',
+                                      'V1_new', 'V2_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             mode='standard',
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+
+x1 = np.array([1.0], dtype=np.float32)
+g1 = np.array([-1.0], dtype=np.float32)
+v1 = np.array([2.0], dtype=np.float32)
+
+x2 = np.array([1.0, 2.0], dtype=np.float32)
+g2 = np.array([-1.0, -3.0], dtype=np.float32)
+v2 = np.array([4.0, 1.0], dtype=np.float32)
+
+# Compute expected outputs of Momentum.
+x1_new, v1_new = apply_momentum(r, t, x1, g1, v1,
+                                norm_coefficient, alpha, beta)
+x2_new, v2_new = apply_momentum(r, t, x2, g2, v2,
+                                norm_coefficient, alpha, beta)
+
+# Check results.
+expect(node, inputs=[r, t, x1, x2, g1, g2, v1, v2],
+       outputs=[x1_new, x2_new, v1_new, v2_new], name='test_momentum_multiple',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+<details>
+<summary>nesterov_momentum</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.01
+alpha = 0.95
+beta = 1.0
+
+# Create operator.
+node = onnx.helper.make_node('Momentum',
+                             inputs=['R', 'T', 'X', 'G', 'V'],
+                             outputs=['X_new', 'V_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             mode='nesterov',
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.2, 2.8], dtype=np.float32)
+g = np.array([-0.94, -2.5], dtype=np.float32)
+v = np.array([1.7, 3.6], dtype=np.float32)
+
+# Compute expected outputs of Momentum.
+x_new, v_new = apply_nesterov(r, t, x, g, v,
+                              norm_coefficient, alpha, beta)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, v],
+       outputs=[x_new, v_new], name='test_nesterov_momentum',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
diff --git a/third_party/onnx/docs/Overview.md b/third_party/onnx/docs/Overview.md
new file mode 100644
index 0000000000..c5062e7638
--- /dev/null
+++ b/third_party/onnx/docs/Overview.md
@@ -0,0 +1,22 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+Overview
+========
+
+Deep learning with neural networks is accomplished through computation over dataflow graphs. Some frameworks (such as CNTK, Caffe2, Theano, and TensorFlow) make use of static graphs, while others (such as PyTorch and Chainer) use dynamic graphs. However, they all provide interfaces that make it simple for developers to construct computation graphs and runtimes that process the graphs in an optimized way. The graph serves as an Intermediate Representation (IR) that captures the specific intent of the developer's source code, and is conducive for optimization and translation to run on specific devices (CPU, GPU, FPGA, etc.).
+
+Why a common IR?
+----------------
+
+Today, each framework has its own proprietary representation of the graph, though they all provide similar capabilities – meaning each framework is a siloed stack of API, graph, and runtime. Furthermore, frameworks are typically optimized for some characteristic, such as fast training, supporting complicated network architectures, inference on mobile devices, etc. It's up to the developer to select a framework that is optimized for one of these characteristics. Additionally, these optimizations may be better suited for particular stages of development. This leads to significant delays between research and production due to the necessity of conversion.
+
+With the goal of democratizing AI, we envision empowering developers to select the framework that works best for their project, at any stage of development or deployment. The Open Neural Network Exchange (ONNX) format is a common IR to help establish this powerful ecosystem.
+
+By providing a common representation of the computation graph, ONNX helps developers choose the right framework for their task, allows authors to focus on innovative enhancements, and enables hardware vendors to streamline optimizations for their platforms.
+
+ONNX is designed to be an open format. We welcome contributions from the community and encourage everyone to adopt ONNX in their ecosystem.
+
+Why two variants?
+-----------------
+
+The base definition of ONNX includes the necessary support for machine learning algorithms based on neural network technologies. ONNX-ML includes additional types and standard operators commonly used in classical machine learning algorithms. The two variants were created in order to explicitly recognize the desire for some frameworks to go beyond neural network algorithms in a standardized fashion, while allowing other frameworks to support only neural networks.
diff --git a/third_party/onnx/docs/PythonAPIOverview.md b/third_party/onnx/docs/PythonAPIOverview.md
new file mode 100644
index 0000000000..78ce4d4383
--- /dev/null
+++ b/third_party/onnx/docs/PythonAPIOverview.md
@@ -0,0 +1,412 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Python API Overview
+
+## Loading an ONNX Model
+```python
+import onnx
+
+# onnx_model is an in-memory ModelProto
+onnx_model = onnx.load('path/to/the/model.onnx')
+```
+Runnable IPython notebooks:
+- [load_model.ipynb](/onnx/examples/load_model.ipynb)
+
+## Loading an ONNX Model with External Data
+
+
+* [Default] If the external data is under the same directory of the model, simply use `onnx.load()`
+```python
+import onnx
+
+onnx_model = onnx.load('path/to/the/model.onnx')
+```
+
+* If the external data is under another directory, use `load_external_data_for_model()` to specify the directory path and load after using `onnx.load()`
+
+```python
+import onnx
+from onnx.external_data_helper import load_external_data_for_model
+
+onnx_model = onnx.load('path/to/the/model.onnx', load_external_data=False)
+load_external_data_for_model(onnx_model, 'data/directory/path/')
+# Then the onnx_model has loaded the external data from the specific directory
+```
+
+## Converting an ONNX Model to External Data
+```python
+from onnx.external_data_helper import convert_model_to_external_data
+
+# onnx_model is an in-memory ModelProto
+onnx_model = ...
+convert_model_to_external_data(onnx_model, all_tensors_to_one_file=True, location='filename', size_threshold=1024, convert_attribute=False)
+# Then the onnx_model has converted raw data as external data
+# Must be followed by save
+```
+
+## Saving an ONNX Model
+```python
+import onnx
+
+# onnx_model is an in-memory ModelProto
+onnx_model = ...
+
+# Save the ONNX model
+onnx.save(onnx_model, 'path/to/the/model.onnx')
+```
+Runnable IPython notebooks:
+- [save_model.ipynb](/onnx/examples/save_model.ipynb)
+
+
+## Converting and Saving an ONNX Model to External Data
+```python
+import onnx
+
+# onnx_model is an in-memory ModelProto
+onnx_model = ...
+onnx.save_model(onnx_model, 'path/to/save/the/model.onnx', save_as_external_data=True, all_tensors_to_one_file=True, location='filename', size_threshold=1024, convert_attribute=False)
+# Then the onnx_model has converted raw data as external data and saved to specific directory
+```
+
+
+## Manipulating TensorProto and Numpy Array
+```python
+import numpy
+import onnx
+from onnx import numpy_helper
+
+# Preprocessing: create a Numpy array
+numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float)
+print('Original Numpy array:\n{}\n'.format(numpy_array))
+
+# Convert the Numpy array to a TensorProto
+tensor = numpy_helper.from_array(numpy_array)
+print('TensorProto:\n{}'.format(tensor))
+
+# Convert the TensorProto to a Numpy array
+new_array = numpy_helper.to_array(tensor)
+print('After round trip, Numpy array:\n{}\n'.format(new_array))
+
+# Save the TensorProto
+with open('tensor.pb', 'wb') as f:
+    f.write(tensor.SerializeToString())
+
+# Load a TensorProto
+new_tensor = onnx.TensorProto()
+with open('tensor.pb', 'rb') as f:
+    new_tensor.ParseFromString(f.read())
+print('After saving and loading, new TensorProto:\n{}'.format(new_tensor))
+```
+Runnable IPython notebooks:
+- [np_array_tensorproto.ipynb](/onnx/examples/np_array_tensorproto.ipynb)
+
+## Creating an ONNX Model Using Helper Functions
+```python
+import onnx
+from onnx import helper
+from onnx import AttributeProto, TensorProto, GraphProto
+
+
+# The protobuf definition can be found here:
+# https://github.com/onnx/onnx/blob/main/onnx/onnx.proto
+
+
+# Create one input (ValueInfoProto)
+X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [3, 2])
+pads = helper.make_tensor_value_info('pads', TensorProto.FLOAT, [1, 4])
+
+value = helper.make_tensor_value_info('value', AttributeProto.FLOAT, [1])
+
+
+# Create one output (ValueInfoProto)
+Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [3, 4])
+
+# Create a node (NodeProto) - This is based on Pad-11
+node_def = helper.make_node(
+    'Pad',                  # name
+    ['X', 'pads', 'value'], # inputs
+    ['Y'],                  # outputs
+    mode='constant',        # attributes
+)
+
+# Create the graph (GraphProto)
+graph_def = helper.make_graph(
+    [node_def],        # nodes
+    'test-model',      # name
+    [X, pads, value],  # inputs
+    [Y],               # outputs
+)
+
+# Create the model (ModelProto)
+model_def = helper.make_model(graph_def, producer_name='onnx-example')
+
+print('The model is:\n{}'.format(model_def))
+onnx.checker.check_model(model_def)
+print('The model is checked!')
+```
+Runnable IPython notebooks:
+- [make_model.ipynb](/onnx/examples/make_model.ipynb)
+- [Protobufs.ipynb](/onnx/examples/Protobufs.ipynb)
+
+## Checking an ONNX Model
+```python
+import onnx
+
+# Preprocessing: load the ONNX model
+model_path = 'path/to/the/model.onnx'
+onnx_model = onnx.load(model_path)
+
+print('The model is:\n{}'.format(onnx_model))
+
+# Check the model
+try:
+    onnx.checker.check_model(onnx_model)
+except onnx.checker.ValidationError as e:
+    print('The model is invalid: %s' % e)
+else:
+    print('The model is valid!')
+```
+Runnable IPython notebooks:
+- [check_model.ipynb](/onnx/examples/check_model.ipynb)
+
+### Checking a Large ONNX Model >2GB
+Current checker supports checking models with external data, but for those models larger than 2GB, please use the model path for onnx.checker and the external data needs to be under the same directory.
+
+```python
+import onnx
+
+onnx.checker.check_model('path/to/the/model.onnx')
+# onnx.checker.check_model(loaded_onnx_model) will fail if given >2GB model
+```
+
+## Running Shape Inference on an ONNX Model
+```python
+import onnx
+from onnx import helper, shape_inference
+from onnx import TensorProto
+
+
+# Preprocessing: create a model with two nodes, Y's shape is unknown
+node1 = helper.make_node('Transpose', ['X'], ['Y'], perm=[1, 0, 2])
+node2 = helper.make_node('Transpose', ['Y'], ['Z'], perm=[1, 0, 2])
+
+graph = helper.make_graph(
+    [node1, node2],
+    'two-transposes',
+    [helper.make_tensor_value_info('X', TensorProto.FLOAT, (2, 3, 4))],
+    [helper.make_tensor_value_info('Z', TensorProto.FLOAT, (2, 3, 4))],
+)
+
+original_model = helper.make_model(graph, producer_name='onnx-examples')
+
+# Check the model and print Y's shape information
+onnx.checker.check_model(original_model)
+print('Before shape inference, the shape info of Y is:\n{}'.format(original_model.graph.value_info))
+
+# Apply shape inference on the model
+inferred_model = shape_inference.infer_shapes(original_model)
+
+# Check the model and print Y's shape information
+onnx.checker.check_model(inferred_model)
+print('After shape inference, the shape info of Y is:\n{}'.format(inferred_model.graph.value_info))
+```
+Runnable IPython notebooks:
+- [shape_inference.ipynb](/onnx/examples/shape_inference.ipynb)
+
+### Shape inference a Large ONNX Model >2GB
+Current shape_inference supports models with external data, but for those models larger than 2GB, please use the model path for onnx.shape_inference.infer_shapes_path and the external data needs to be under the same directory. You can specify the output path for saving the inferred model; otherwise, the default output path is same as the original model path.
+
+```python
+import onnx
+
+# output the inferred model to the original model path
+onnx.shape_inference.infer_shapes_path('path/to/the/model.onnx')
+
+# output the inferred model to the specified model path
+onnx.shape_inference.infer_shapes_path('path/to/the/model.onnx', 'output/inferred/model.onnx')
+
+# inferred_model = onnx.shape_inference.infer_shapes(loaded_onnx_model) will fail if given >2GB model
+```
+
+## Converting Version of an ONNX Model within Default Domain (""/"ai.onnx")
+```python
+import onnx
+from onnx import version_converter, helper
+
+# Preprocessing: load the model to be converted.
+model_path = 'path/to/the/model.onnx'
+original_model = onnx.load(model_path)
+
+print('The model before conversion:\n{}'.format(original_model))
+
+# A full list of supported adapters can be found here:
+# https://github.com/onnx/onnx/blob/main/onnx/version_converter.py#L21
+# Apply the version conversion on the original model
+converted_model = version_converter.convert_version(original_model, <int target_version>)
+
+print('The model after conversion:\n{}'.format(converted_model))
+```
+
+## Utility Functions
+### Extracting Sub-model with Inputs Outputs Tensor Names
+
+Function `extract_model()` extracts sub-model from an ONNX model.
+The sub-model is defined by the names of the input and output tensors *exactly*.
+
+```python
+import onnx
+
+input_path = 'path/to/the/original/model.onnx'
+output_path = 'path/to/save/the/extracted/model.onnx'
+input_names = ['input_0', 'input_1', 'input_2']
+output_names = ['output_0', 'output_1']
+
+onnx.utils.extract_model(input_path, output_path, input_names, output_names)
+```
+
+Note: For control-flow operators, e.g. If and Loop, the _boundary of sub-model_,
+which is defined by the input and output tensors, should not _cut through_ the
+subgraph that is connected to the _main graph_ as attributes of these operators.
+
+### ONNX Compose
+
+`onnx.compose` module provides tools to create combined models.
+
+`onnx.compose.merge_models` can be used to merge two models, by connecting some of the outputs
+from the first model with inputs from the second model. By default, inputs/outputs not present in the
+`io_map` argument will remain as inputs/outputs of the combined model.
+
+In this example we merge two models by connecting each output of the first model to an input in the second. The resulting model will have the same inputs as the first model and the same outputs as the second:
+```python
+import onnx
+
+model1 = onnx.load('path/to/model1.onnx')
+# agraph (float[N] A, float[N] B) => (float[N] C, float[N] D)
+#   {
+#      C = Add(A, B)
+#      D = Sub(A, B)
+#   }
+
+model2 = onnx.load('path/to/model2.onnx')
+#   agraph (float[N] X, float[N] Y) => (float[N] Z)
+#   {
+#      Z = Mul(X, Y)
+#   }
+
+combined_model = onnx.compose.merge_models(
+    model1, model2,
+    io_map=[('C', 'X'), ('D', 'Y')]
+)
+```
+
+Additionally, a user can specify a list of `inputs`/`outputs` to be included in the combined model,
+effectively dropping the part of the graph that doesn't contribute to the combined model outputs.
+In the following example, we are connecting only one of the two outputs in the first model
+to both inputs in the second. By specifying the outputs of the combined model explicitly, we are dropping the output not consumed from the first model, and the relevant part of the graph:
+```python
+import onnx
+
+# Default case. Include all outputs in the combined model
+combined_model = onnx.compose.merge_models(
+    model1, model2,
+    io_map=[('C', 'X'), ('C', 'Y')],
+)  # outputs: 'D', 'Z'
+
+# Explicit outputs. 'Y' output and the Sub node are not present in the combined model
+combined_model = onnx.compose.merge_models(
+    model1, model2,
+    io_map=[('C', 'X'), ('C', 'Y')],
+    outputs=['Z'],
+)  # outputs: 'Z'
+```
+
+`onnx.compose.add_prefix` allows you to add a prefix to names in the model, to avoid a name collision
+when merging them. By default, it renames all names in the graph: inputs, outputs, edges, nodes,
+initializers, sparse initializers and value infos.
+
+```python
+import onnx
+
+model = onnx.load('path/to/the/model.onnx')
+# model - outputs: ['out0', 'out1'], inputs: ['in0', 'in1']
+
+new_model = onnx.compose.add_prefix(model, prefix='m1/')
+# new_model - outputs: ['m1/out0', 'm1/out1'], inputs: ['m1/in0', 'm1/in1']
+
+# Can also be run in-place
+onnx.compose.add_prefix(model, prefix='m1/', inplace=True)
+```
+
+`onnx.compose.expand_out_dim` can be used to connect models that expect a different number
+ of dimensions by inserting dimensions with extent one. This can be useful, when combining a
+ model producing samples with a model that works with batches of samples.
+
+```python
+import onnx
+
+# outputs: 'out0', shape=[200, 200, 3]
+model1 = onnx.load('path/to/the/model1.onnx')
+
+# outputs: 'in0', shape=[N, 200, 200, 3]
+model2 = onnx.load('path/to/the/model2.onnx')
+
+# outputs: 'out0', shape=[1, 200, 200, 3]
+new_model1 = onnx.compose.expand_out_dims(model1, dim_idx=0)
+
+# Models can now be merged
+combined_model = onnx.compose.merge_models(
+    new_model1, model2, io_map=[('out0', 'in0')]
+)
+
+# Can also be run in-place
+onnx.compose.expand_out_dims(model1, dim_idx=0, inplace=True)
+```
+
+## Tools
+### Updating Model's Inputs Outputs Dimension Sizes with Variable Length
+Function `update_inputs_outputs_dims` updates the dimension of the inputs and outputs of the model,
+to the provided values in the parameter. You could provide both static and dynamic dimension size,
+by using dim_param. For more information on static and dynamic dimension size, checkout [Tensor Shapes](IR.md#tensor-shapes).
+
+The function runs model checker after the input/output sizes are updated.
+```python
+import onnx
+from onnx.tools import update_model_dims
+
+model = onnx.load('path/to/the/model.onnx')
+# Here both 'seq', 'batch' and -1 are dynamic using dim_param.
+variable_length_model = update_model_dims.update_inputs_outputs_dims(model, {'input_name': ['seq', 'batch', 3, -1]}, {'output_name': ['seq', 'batch', 1, -1]})
+```
+
+## ONNX Parser
+
+Functions `onnx.parser.parse_model` and `onnx.parser.parse_graph` can be used to create an ONNX model
+or graph from a textual representation as shown below. See [Language Syntax](Syntax.md) for more details
+about the language syntax.
+
+```python
+input = '''
+   agraph (float[N, 128] X, float[128, 10] W, float[10] B) => (float[N, 10] C)
+   {
+        T = MatMul(X, W)
+        S = Add(T, B)
+        C = Softmax(S)
+   }
+'''
+graph = onnx.parser.parse_graph(input)
+
+input = '''
+   <
+     ir_version: 7,
+     opset_import: ["" : 10]
+   >
+   agraph (float[N, 128] X, float[128, 10] W, float[10] B) => (float[N, 10] C)
+   {
+      T = MatMul(X, W)
+      S = Add(T, B)
+      C = Softmax(S)
+   }
+'''
+model = onnx.parser.parse_model(input)
+
+```
diff --git a/third_party/onnx/docs/Relicensing.md b/third_party/onnx/docs/Relicensing.md
new file mode 100644
index 0000000000..3a65903fb3
--- /dev/null
+++ b/third_party/onnx/docs/Relicensing.md
@@ -0,0 +1,11 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Relicensing MIT to Apache-2.0
+
+The following copyright holders agree that all of their contributions originally submitted to this project under the MIT license are hereby relicensed to Apache-2.0, and are submitted pursuant to the Developer Certificate of Origin, version 1.1:
+
+Intel Corporation
+Microsoft Corporation
+NVIDIA Corporation
+IBM Corporation
+Facebook Inc.
diff --git a/third_party/onnx/docs/ShapeInference.md b/third_party/onnx/docs/ShapeInference.md
new file mode 100644
index 0000000000..9c51e51e51
--- /dev/null
+++ b/third_party/onnx/docs/ShapeInference.md
@@ -0,0 +1,137 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Shape Inference
+
+ONNX provides an optional implementation of shape inference on ONNX
+graphs. This implementation covers each of the core operators, as well
+as provides an interface for extensibility. Therefore, you may choose
+to invoke the existing shape inference functionality on your graphs,
+or to define shape inference implementations to go along with your
+custom operators (or both!). Shape inference functions are stored as a
+member of the OpSchema objects.
+
+In ONNX 1.10 release, symbol generation and propagation along with shape 
+data propagation was added to ONNX graph level shape inference. 
+Detailed proposal is [here](proposals/SymbolicShapeInfProposal.md)
+
+## Background
+
+Please see this [section](IR.md#static-tensor-shapes) of IR.md for a review of static tensor shapes.
+In particular, a static tensor shape (represented by a `TensorShapeProto`) is distinct from
+a runtime tensor shape. This feature is commonly used when the exact runtime tensor shape is
+not known statically (that is, at compile time).
+
+* A `Tensor` with an undefined `shape` field is used to represent a tensor of unknown rank.
+* A `Tensor` with a defined `shape` represents a tensor of known rank.
+* Each `Dimension` of a `TensorShapeProto` can have a known integer value
+(represented by the `dim_value` field) or it can have an unknown value
+represented by a symbolic identified (the `dim_param` field) or it
+may have neither field defined (in which case it represents an anonymous
+unknown value).
+
+## Invoking Shape Inference
+
+Shape inference can be invoked either via C++ or Python. The Python
+API is described, with example,
+[here](PythonAPIOverview.md#running-shape-inference-on-an-onnx-model).
+
+The C++ API consists of a single function
+
+```
+shape_inference::InferShapes(
+    ModelProto& m,
+    const ISchemaRegistry* schema_registry);
+```
+
+The first argument is a `ModelProto` to perform shape inference on,
+which is annotated in-place with shape information. The second
+argument is optional.
+
+## Limitations
+
+Shape inference is not guaranteed to be complete. In particular, some
+dynamic behaviors block the flow of shape inference, for example a
+Reshape to a dynamically-provide shape. Also, all operators are not
+required to have a shape inference implementation.
+
+Shape inference works only with constants and simple variables. It
+does not support arithmetic expressions containing variables. For
+example, `Concat` on tensors of shapes `(5, 2)` and `(7, 2)` can be
+inferred to produce a result of shape `(12, 2)`, but `Concat` on
+tensors of shapes `(5, 2)` and `(N, 2)` will simply produce `(M, 2)`,
+rather than containing a representation of `N+5`. Note that differing
+unknown symbolic values will be propagated, so the `M` here represents
+an unknown quantity that is the same as other occurrences of `M`.
+
+These limitations are a property of the current implementation, not
+fundamental constraints - if you are in need of something more
+advanced, do let us know!
+
+## Implementing Shape Inference For Operators
+
+You can add a shape inference function to your operator's Schema with
+
+```
+OpSchema& Opschema::TypeAndShapeInferenceFunction(InferenceFunction inferenceFunction);
+```
+
+`InferenceFunction` is defined in
+[shape_inference.h](/onnx/defs/shape_inference.h), along with the core
+interface struct `InferenceContext` and an assortment of helper
+methods. `InferenceContext` is the core struct which is provided to
+your inference function. It allows accessing information about the
+operator's inputs, and also allows writing out inferred information.
+
+To see numerous examples, search for occurrences of
+`TypeAndShapeInferenceFunction` in the codebase. One that is
+relatively involved is the implementation for `Concat`, in
+onnx/defs/tensor/defs.cc.
+
+Please note the following points when implementing the shape-inference method for
+operators to avoid common errors:
+
+* Before accessing the `shape` of any input, the code must check that
+the shape is available. If unavailable, it should be treated as a dynamic
+tensor whose rank is unknown and handled appropriately. Usually, the
+shape-inference logic is guarded by a call to `hasInputShape` or
+`hasNInputShapes`.
+
+* Before accessing the `dim_value` or `dim_param` of any dimension, the
+code must check if these fields have a value. In particular, the code must
+handle the possibility that the dimension may not have a statically
+known value.
+
+There are several utility functions in [shape_inference.h](/onnx/defs/shape_inference.h)
+to handle various common situations.
+
+* Use `checkInputRank` for inputs that must have a fixed rank. (See the
+inference for `RoiAlign` as an example.)
+
+* `unifyInputDim` and `unifyDim` and `updateOutputShape` can be used
+when multiple input dims are expected to be the same, and when input
+dimensions are propagated to specific output dimensions. (See the inference
+for `RoiAlign` for an example.)
+
+* Overloaded operators `*` and `/` can be used on symbolic dimensions when output
+dimensions are computed from input dimensions using arithmetic. (See the inference
+for `SpaceToDepth` for an example.)
+
+These utilities handle missing shapes and dimensions safely.
+
+_Example_: Consider a simple matrix-multiplication op that expects inputs of shape
+`[M,K]` and `[K,N]` and returns an output of shape `[M,N]`. This can be coded
+up as below:
+```cpp
+   // Check that input 0 has rank 2 (if its rank is known).
+   checkInputRank(ctx, 0, 2);
+   // Check that input 1 has rank 2 (if its rank is known).
+   checkInputRank(ctx, 1, 2);
+   Dim M, K, N;
+   // Check various dimensions, handling missing dimensions/shapes safely.
+   unifyInputDim(ctx, 0, 0, M);
+   unifyInputDim(ctx, 0, 1, K);
+   unifyInputDim(ctx, 1, 0, K);
+   unifyInputDim(ctx, 1, 1, N);
+   updateOutputShape(ctx, 0, {M. N});
+```
+
diff --git a/third_party/onnx/docs/Syntax.md b/third_party/onnx/docs/Syntax.md
new file mode 100644
index 0000000000..3c11d1f552
--- /dev/null
+++ b/third_party/onnx/docs/Syntax.md
@@ -0,0 +1,77 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+Overview
+========
+
+This document describes a textual syntax for ONNX models, which is currently an experimental feature.
+The syntax enables a compact and readable representation of ONNX models. It is motivated by a couple
+of use-cases. One is to enable compact description of test-cases and its use in CI (both in the ONNX
+repo as well as in other dependent repos such as ONNX-MLIR). The second is to help simplify the
+definition of ONNX functions. Several of the existing function-definitions are verbose, and the
+use of this syntax will lead to more compact, readable, and easier-to-maintain function definitions.
+Efficient representation and efficient parsing of very large tensor-constants is *not* a goal.
+Alternative methods should be used for that.
+
+The API
+-------
+
+The key parser methods are the ```OnnxParser::Parse``` methods, used as below.
+
+```cpp
+  const char* code = R"ONNX(
+<
+  ir_version: 7,
+  opset_import: [ "" : 10 ]
+>
+agraph (float[N, 128] X, float[128, 10] W, float[10] B) => (float[N, 10] C)
+{
+    T = MatMul(X, W)
+    S = Add(T, B)
+    C = Softmax(S)
+}
+)ONNX";
+
+  ModelProto model;
+  OnnxParser::Parse(model, code);
+
+  checker::check_model(model);
+```
+
+See the [test-cases](../onnx/test/cpp/parser_test.cc) for more examples illustrating the API and syntax.
+
+The Syntax
+----------
+
+The grammar below describes the syntax:
+
+```
+   id-list ::= id (',' id)*
+   tensor-dim ::= '?' | id | int-constant
+   tensor-dims ::= tensor-dim (',' tensor-dim)*
+   tensor-type ::= prim-type | prim-type '[' ']' | prim-type '[' tensor-dims ']'
+   type ::= tensor-type | 'seq' '(' type ')' | 'map' '(' prim-type ',' type ')'
+            | 'optional' '(' type ')' | 'sparse_tensor' '(' tensor-type ')'
+   value-info ::= type id
+   value-infos ::= value-info (',' value-info)*
+   value-info-list ::= '(' value-infos? ')'
+   prim-constants ::= prim-constant (',' prim-constant)*
+   tensor-constant ::= tensor-type (id)? ('=')? '{' prim-constants '}'
+   attr-ref ::= '@' id
+   single-attr-value ::= tensor-constant | graph | prim-constant | attr-ref
+   attr-value-list ::= '[' single-attr-value (',' single-attr-value)* ']'
+   attr-value ::= single-attr-value | attr-value-list
+   attr-type ::= ':' id
+   attr ::= id attr-type? '=' attr-value
+   attr-list ::= '<' attr (',' attr)* '>'
+   node ::= id-list? '=' qualified-id attr-list? '(' id-list? ')'
+         |  id-list? '=' qualified-id '(' id-list? ')' attr-list
+   node-list ::= '{' node* '}'
+   graph ::= id value-info-list '=>' value-info-list node-list
+   other-data ::= id ':' value
+   other-data-list ::= '<' other-data (',' other-data)* '>'
+   fun-attr-list ::= '<' id-list '>'
+   fun-input-list ::= '(' id-list ')'
+   fun-output-list ::= '(' id-list ')'
+   function ::= other-data-list? id fun-attr-list?  fun-input-list '=>' fun-output-list  node-list
+   model ::= other-data-list? graph function*
+```
diff --git a/third_party/onnx/docs/TestCoverage-ml.md b/third_party/onnx/docs/TestCoverage-ml.md
new file mode 100644
index 0000000000..1f4e2e0049
--- /dev/null
+++ b/third_party/onnx/docs/TestCoverage-ml.md
@@ -0,0 +1,87 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+# Test Coverage Report (ONNX-ML Operators)
+## Outlines
+* [Node Test Coverage](#node-test-coverage)
+* [Model Test Coverage](#model-test-coverage)
+* [Overall Test Coverage](#overall-test-coverage)
+# Node Test Coverage
+## Summary
+Node tests have covered 0/18 (0.00%, 0 generators excluded) common operators.
+
+Node tests have covered 0/0 (N/A) experimental operators.
+
+* [Covered Common Operators](#covered-common-operators)
+* [No Cover Common Operators](#no-cover-common-operators)
+* [Covered Experimental Operators](#covered-experimental-operators)
+* [No Cover Experimental Operators](#no-cover-experimental-operators)
+
+## &#x1F49A;Covered Common Operators
+<br/>
+
+## &#x1F494;No Cover Common Operators
+### ArrayFeatureExtractor (call for test cases)
+
+
+### Binarizer (call for test cases)
+
+
+### CastMap (call for test cases)
+
+
+### CategoryMapper (call for test cases)
+
+
+### DictVectorizer (call for test cases)
+
+
+### FeatureVectorizer (call for test cases)
+
+
+### Imputer (call for test cases)
+
+
+### LabelEncoder (call for test cases)
+
+
+### LinearClassifier (call for test cases)
+
+
+### LinearRegressor (call for test cases)
+
+
+### Normalizer (call for test cases)
+
+
+### OneHotEncoder (call for test cases)
+
+
+### SVMClassifier (call for test cases)
+
+
+### SVMRegressor (call for test cases)
+
+
+### Scaler (call for test cases)
+
+
+### TreeEnsembleClassifier (call for test cases)
+
+
+### TreeEnsembleRegressor (call for test cases)
+
+
+### ZipMap (call for test cases)
+
+
+<br/>
+
+## &#x1F49A;Covered Experimental Operators
+<br/>
+
+## &#x1F494;No Cover Experimental Operators
+<br/>
+
+# Model Test Coverage
+No model tests present for selected domain
+# Overall Test Coverage
+## To be filled.
diff --git a/third_party/onnx/docs/TestCoverage.md b/third_party/onnx/docs/TestCoverage.md
new file mode 100644
index 0000000000..34f116dadc
--- /dev/null
+++ b/third_party/onnx/docs/TestCoverage.md
@@ -0,0 +1,16718 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+# Test Coverage Report (ONNX Core Operators)
+## Outlines
+* [Node Test Coverage](#node-test-coverage)
+* [Model Test Coverage](#model-test-coverage)
+* [Overall Test Coverage](#overall-test-coverage)
+# Node Test Coverage
+## Summary
+Node tests have covered 156/171 (91.23%, 5 generators excluded) common operators.
+
+Node tests have covered 0/0 (N/A) experimental operators.
+
+* [Covered Common Operators](#covered-common-operators)
+* [No Cover Common Operators](#no-cover-common-operators)
+* [Covered Experimental Operators](#covered-experimental-operators)
+* [No Cover Experimental Operators](#no-cover-experimental-operators)
+
+## &#x1F49A;Covered Common Operators
+### Abs
+There are 1 test cases, listed as following:
+<details>
+<summary>abs</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Abs',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = abs(x)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_abs')
+```
+
+</details>
+
+
+### Acos
+There are 1 test cases, listed as following:
+<details>
+<summary>acos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Acos',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+y = np.arccos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_acos_example')
+
+x = np.random.rand(3, 4, 5).astype(np.float32)
+y = np.arccos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_acos')
+```
+
+</details>
+
+
+### Acosh
+There are 1 test cases, listed as following:
+<details>
+<summary>acosh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Acosh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([10, np.e, 1]).astype(np.float32)
+y = np.arccosh(x)  # expected output [2.99322295,  1.65745449,  0.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_acosh_example')
+
+x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32)
+y = np.arccosh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_acosh')
+```
+
+</details>
+
+
+### Adagrad
+There are 2 test cases, listed as following:
+<details>
+<summary>adagrad</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+epsilon = 1e-5
+decay_factor = 0.1
+
+# Create operator.
+node = onnx.helper.make_node('Adagrad',
+                             inputs=['R', 'T', 'X', 'G', 'H'],
+                             outputs=['X_new', 'H_new'],
+                             norm_coefficient=norm_coefficient,
+                             epsilon=epsilon,
+                             decay_factor=decay_factor,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.0], dtype=np.float32)
+g = np.array([-1.0], dtype=np.float32)
+h = np.array([2.0], dtype=np.float32)
+
+# Compute expected outputs of Adagrad.
+x_new, h_new = apply_adagrad(r, t, x, g, h,
+                             norm_coefficient, epsilon, decay_factor)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, h],
+       outputs=[x_new, h_new], name='test_adagrad',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+<details>
+<summary>adagrad_multiple</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+epsilon = 1e-5
+decay_factor = 0.1
+
+node = onnx.helper.make_node('Adagrad',
+                             inputs=['R', 'T', 'X1', 'X2',
+                                     'G1', 'G2', 'H1', 'H2'],
+                             outputs=['X1_new', 'X2_new',
+                                      'H1_new', 'H2_new'],
+                             norm_coefficient=norm_coefficient,
+                             epsilon=epsilon,
+                             decay_factor=decay_factor,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+
+x1 = np.array([1.0], dtype=np.float32)
+g1 = np.array([-1.0], dtype=np.float32)
+h1 = np.array([2.0], dtype=np.float32)
+
+x2 = np.array([1.0, 2.0], dtype=np.float32)
+g2 = np.array([-1.0, -3.0], dtype=np.float32)
+h2 = np.array([4.0, 1.0], dtype=np.float32)
+
+# Compute expected outputs of Adagrad.
+x1_new, h1_new = apply_adagrad(r, t, x1, g1, h1,
+                               norm_coefficient, epsilon, decay_factor)
+x2_new, h2_new = apply_adagrad(r, t, x2, g2, h2,
+                               norm_coefficient, epsilon, decay_factor)
+
+# Check results.
+expect(node, inputs=[r, t, x1, x2, g1, g2, h1, h2],
+       outputs=[x1_new, x2_new, h1_new, h2_new], name='test_adagrad_multiple',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+### Adam
+There are 2 test cases, listed as following:
+<details>
+<summary>adam</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.1
+epsilon = 1e-7
+
+# Create operator.
+node = onnx.helper.make_node('Adam',
+                             inputs=['R', 'T', 'X', 'G', 'V', 'H'],
+                             outputs=['X_new', 'V_new', 'H_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             epsilon=epsilon,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.2, 2.8], dtype=np.float32)
+g = np.array([-0.94, -2.5], dtype=np.float32)
+v = np.array([1.7, 3.6], dtype=np.float32)
+h = np.array([0.1, 0.1], dtype=np.float32)
+
+# Compute expected outputs of Adam.
+x_new, v_new, h_new = apply_adam(r, t, x, g, v, h,
+                                 norm_coefficient, 0.0, alpha, beta,
+                                 epsilon)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, v, h],
+       outputs=[x_new, v_new, h_new], name='test_adam',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+<details>
+<summary>adam_multiple</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.85
+epsilon = 1e-2
+
+node = onnx.helper.make_node('Adam',
+                             inputs=['R', 'T', 'X1', 'X2',
+                                     'G1', 'G2', 'V1', 'V2',
+                                     'H1', 'H2'],
+                             outputs=['X1_new', 'X2_new',
+                                      'V1_new', 'V2_new',
+                                      'H1_new', 'H2_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+
+x1 = np.array([1.0], dtype=np.float32)
+g1 = np.array([-1.0], dtype=np.float32)
+v1 = np.array([2.0], dtype=np.float32)
+h1 = np.array([0.5], dtype=np.float32)
+
+x2 = np.array([1.0, 2.0], dtype=np.float32)
+g2 = np.array([-1.0, -3.0], dtype=np.float32)
+v2 = np.array([4.0, 1.0], dtype=np.float32)
+h2 = np.array([1.0, 10.0], dtype=np.float32)
+
+# Compute expected outputs of Adam.
+x1_new, v1_new, h1_new = apply_adam(r, t, x1, g1, v1, h1,
+                            norm_coefficient, 0.0, alpha, beta,
+                            epsilon)
+x2_new, v2_new, h2_new = apply_adam(r, t, x2, g2, v2, h2,
+                            norm_coefficient, 0.0, alpha, beta,
+                            epsilon)
+
+# Check results.
+expect(node, inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
+       outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
+       name='test_adam_multiple',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+### Add
+There are 3 test cases, listed as following:
+<details>
+<summary>add</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Add',
+    inputs=['x', 'y'],
+    outputs=['sum'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+expect(node, inputs=[x, y], outputs=[x + y],
+       name='test_add')
+```
+
+</details>
+<details>
+<summary>add_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Add',
+    inputs=['x', 'y'],
+    outputs=['sum'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+expect(node, inputs=[x, y], outputs=[x + y],
+       name='test_add_bcast')
+```
+
+</details>
+<details>
+<summary>add_uint8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Add',
+    inputs=['x', 'y'],
+    outputs=['sum'],
+)
+
+x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+expect(node, inputs=[x, y], outputs=[x + y],
+       name='test_add_uint8')
+```
+
+</details>
+
+
+### And
+There are 2 test cases, listed as following:
+<details>
+<summary>and</summary>
+
+```python
+node = onnx.helper.make_node(
+    'And',
+    inputs=['x', 'y'],
+    outputs=['and'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+y = (np.random.randn(3, 4) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and4d')
+```
+
+</details>
+<details>
+<summary>and_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'And',
+    inputs=['x', 'y'],
+    outputs=['and'],
+)
+
+# 3d vs 1d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(5) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast3v1d')
+
+# 3d vs 2d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(4, 5) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast3v2d')
+
+# 4d vs 2d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast4v2d')
+
+# 4d vs 3d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast4v3d')
+
+# 4d vs 4d
+x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+z = np.logical_and(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_and_bcast4v4d')
+```
+
+</details>
+
+
+### ArgMax
+There are 8 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims)
+
+# result: [[1, 1]]
+result = argmax_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmax_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_random')
+```
+
+</details>
+<details>
+<summary>default_axes_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims,
+    select_last_index=True)
+
+# result: [[1, 1]]
+result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_random_select_last_index')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# result: [[0], [1]]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [1]]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_random_select_last_index')
+```
+
+</details>
+<details>
+<summary>negative_axis_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# result: [[0], [1]]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axis_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [1]]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_random_select_last_index')
+```
+
+</details>
+<details>
+<summary>no_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# result: [0, 1]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_random')
+```
+
+</details>
+<details>
+<summary>no_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMax',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [1, 1]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+### ArgMin
+There are 8 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims)
+
+# The content of result is : [[0], [0]]
+result = argmin_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmin_use_numpy(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_random')
+```
+
+</details>
+<details>
+<summary>default_axes_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    keepdims=keepdims,
+    select_last_index=True)
+
+# result: [[0, 0]]
+result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [1, 3, 4]
+result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_random_select_last_index')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# The content of result is : [[1], [0]]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [0]]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 1, 4]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_random_select_last_index')
+```
+
+</details>
+<details>
+<summary>negative_axis_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# The content of result is : [[1], [0]]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axis_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = -1
+keepdims = 1
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1], [0]]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 3, 1]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_random_select_last_index')
+```
+
+</details>
+<details>
+<summary>no_keepdims</summary>
+
+```python
+data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims)
+# The content of result is : [[1, 0]]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_example')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_random')
+```
+
+</details>
+<details>
+<summary>no_keepdims_select_last_index</summary>
+
+```python
+data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+axis = 1
+keepdims = 0
+node = onnx.helper.make_node(
+    'ArgMin',
+    inputs=['data'],
+    outputs=['result'],
+    axis=axis,
+    keepdims=keepdims,
+    select_last_index=True)
+# result: [[1, 0]]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_example_select_last_index')
+
+data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+# result's shape: [2, 4]
+result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_random_select_last_index')
+```
+
+</details>
+
+
+### Asin
+There are 1 test cases, listed as following:
+<details>
+<summary>asin</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Asin',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+y = np.arcsin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_asin_example')
+
+x = np.random.rand(3, 4, 5).astype(np.float32)
+y = np.arcsin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_asin')
+```
+
+</details>
+
+
+### Asinh
+There are 1 test cases, listed as following:
+<details>
+<summary>asinh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Asinh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.arcsinh(x)  # expected output [-0.88137358,  0.,  0.88137358]
+expect(node, inputs=[x], outputs=[y],
+       name='test_asinh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.arcsinh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_asinh')
+```
+
+</details>
+
+
+### Atan
+There are 1 test cases, listed as following:
+<details>
+<summary>atan</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Atan',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.arctan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_atan_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.arctan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_atan')
+```
+
+</details>
+
+
+### Atanh
+There are 1 test cases, listed as following:
+<details>
+<summary>atanh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Atanh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+y = np.arctanh(x)  # expected output [-0.54930615,  0.,  0.54930615]
+expect(node, inputs=[x], outputs=[y],
+       name='test_atanh_example')
+
+x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32)
+y = np.arctanh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_atanh')
+```
+
+</details>
+
+
+### AveragePool
+There are 13 test cases, listed as following:
+<details>
+<summary>averagepool_1d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32]
+output_shape: [1, 3, 31]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2],
+)
+x = np.random.randn(1, 3, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2]
+strides = [1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_1d_default')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_ceil</summary>
+
+```python
+"""
+input_shape: [1, 1, 4, 4]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    ceil_mode=True
+)
+x = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]]).astype(np.float32)
+y = np.array([[[
+    [6, 7.5],
+    [12, 13.5]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_ceil')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_default')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_pads</summary>
+
+```python
+"""
+input_shape: [1, 3, 28, 28]
+output_shape: [1, 3, 30, 30]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[2, 2, 2, 2]
+)
+x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (3, 3)
+strides = (1, 1)
+pad_bottom = 2
+pad_top = 2
+pad_right = 2
+pad_left = 2
+pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_pads_count_include_pad</summary>
+
+```python
+"""
+input_shape: [1, 3, 28, 28]
+output_shape: [1, 3, 30, 30]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[2, 2, 2, 2],
+    count_include_pad=1,
+)
+x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (3, 3)
+strides = (1, 1)
+pad_bottom = 2
+pad_top = 2
+pad_right = 2
+pad_left = 2
+pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=0)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG', count_include_pad=1)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads_count_include_pad')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_precomputed_pads</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 7.5, 8, 8.5, 9],
+                [9.5, 10, 10.5, 11, 11.5],
+                [12, 12.5, 13, 13.5, 14],
+                [14.5, 15, 15.5, 16, 16.5],
+                [17, 17.5, 18, 18.5, 19]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_precomputed_pads_count_include_pad</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2],
+    count_include_pad=1
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
+                [4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
+                [7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
+                [6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
+                [6.1200, 8.4000, 10.8000, 8.8800, 6.8400]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads_count_include_pad')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_precomputed_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 3, 3]
+pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[4, 5.5, 7],
+                [11.5, 13, 14.5],
+                [19, 20.5, 22]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_same_upper')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_precomputed_strides</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[4, 6],
+                [14, 16]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_strides')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_same_lower</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_LOWER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_bottom = pad_shape[0] // 2
+pad_top = pad_shape[0] - pad_bottom
+pad_right = pad_shape[1] // 2
+pad_left = pad_shape[1] - pad_right
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_lower')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_top = pad_shape[0] // 2
+pad_bottom = pad_shape[0] - pad_top
+pad_left = pad_shape[1] // 2
+pad_right = pad_shape[1] - pad_left
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_upper')
+```
+
+</details>
+<details>
+<summary>averagepool_2d_strides</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 10, 10]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    strides=[3, 3]
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (5, 5)
+strides = (3, 3)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_strides')
+```
+
+</details>
+<details>
+<summary>averagepool_3d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32, 32]
+output_shape: [1, 3, 31, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'AveragePool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2, 2],
+)
+x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2, 2, 2]
+strides = [1, 1, 1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'AVG')
+
+expect(node, inputs=[x], outputs=[y], name='test_averagepool_3d_default')
+```
+
+</details>
+
+
+### BatchNormalization
+There are 2 test cases, listed as following:
+<details>
+<summary>batchnormalization</summary>
+
+```python
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y'],
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
+       name='test_batchnorm_example')
+
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+epsilon = 1e-2
+y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y'],
+    epsilon=epsilon,
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
+       name='test_batchnorm_epsilon')
+```
+
+</details>
+<details>
+<summary>train</summary>
+
+```python
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+# using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'"
+# working around by using np.byte(1).astype(bool)
+training_mode = 1
+y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y', 'output_mean', 'output_var'],
+    training_mode=training_mode
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var],
+       outputs=[y, output_mean, output_var],
+       name='test_batchnorm_example_training_mode')
+
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+mean = np.random.randn(3).astype(np.float32)
+var = np.random.rand(3).astype(np.float32)
+training_mode = 1
+momentum = 0.9
+epsilon = 1e-2
+y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var, momentum,
+                                                      epsilon)
+
+node = onnx.helper.make_node(
+    'BatchNormalization',
+    inputs=['x', 's', 'bias', 'mean', 'var'],
+    outputs=['y', 'output_mean', 'output_var'],
+    epsilon=epsilon,
+    training_mode=training_mode
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias, mean, var],
+       outputs=[y, output_mean, output_var],
+       name='test_batchnorm_epsilon_training_mode')
+```
+
+</details>
+
+
+### Bernoulli
+There are 3 test cases, listed as following:
+<details>
+<summary>bernoulli_with_dtype</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Bernoulli',
+    inputs=['x'],
+    outputs=['y'],
+    dtype=onnx.TensorProto.DOUBLE,
+)
+
+x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+y = bernoulli_reference_implementation(x, np.float64)
+expect(node, inputs=[x], outputs=[y], name='test_bernoulli_double')
+```
+
+</details>
+<details>
+<summary>bernoulli_with_seed</summary>
+
+```python
+seed = np.float(0)
+node = onnx.helper.make_node(
+    'Bernoulli',
+    inputs=['x'],
+    outputs=['y'],
+    seed=seed,
+)
+
+x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+y = bernoulli_reference_implementation(x, np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_bernoulli_seed')
+```
+
+</details>
+<details>
+<summary>bernoulli_without_dtype</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Bernoulli',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.uniform(0.0, 1.0, 10).astype(np.float)
+y = bernoulli_reference_implementation(x, np.float)
+expect(node, inputs=[x], outputs=[y], name='test_bernoulli')
+```
+
+</details>
+
+
+### BitShift
+There are 8 test cases, listed as following:
+<details>
+<summary>left_unit16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint16)
+y = np.array([1, 2, 3]).astype(np.uint16)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint16')
+```
+
+</details>
+<details>
+<summary>left_unit32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint32)
+y = np.array([1, 2, 3]).astype(np.uint32)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint32')
+```
+
+</details>
+<details>
+<summary>left_unit64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint64)
+y = np.array([1, 2, 3]).astype(np.uint64)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint64')
+```
+
+</details>
+<details>
+<summary>left_unit8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="LEFT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint8)
+y = np.array([1, 2, 3]).astype(np.uint8)
+z = x << y  # expected output [32, 16, 8]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_left_uint8')
+```
+
+</details>
+<details>
+<summary>right_unit16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint16)
+y = np.array([1, 2, 3]).astype(np.uint16)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint16')
+```
+
+</details>
+<details>
+<summary>right_unit32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint32)
+y = np.array([1, 2, 3]).astype(np.uint32)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint32')
+```
+
+</details>
+<details>
+<summary>right_unit64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint64)
+y = np.array([1, 2, 3]).astype(np.uint64)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint64')
+```
+
+</details>
+<details>
+<summary>right_unit8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'BitShift',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    direction="RIGHT"
+)
+
+x = np.array([16, 4, 1]).astype(np.uint8)
+y = np.array([1, 2, 3]).astype(np.uint8)
+z = x >> y  # expected output [8, 1, 0]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_bitshift_right_uint8')
+```
+
+</details>
+
+
+### Cast
+There are 1 test cases, listed as following:
+<details>
+<summary>cast</summary>
+
+```python
+shape = (3, 4)
+test_cases = [
+    ('FLOAT', 'FLOAT16'),
+    ('FLOAT', 'DOUBLE'),
+    ('FLOAT16', 'FLOAT'),
+    ('FLOAT16', 'DOUBLE'),
+    ('DOUBLE', 'FLOAT'),
+    ('DOUBLE', 'FLOAT16'),
+    ('FLOAT', 'STRING'),
+    ('STRING', 'FLOAT'),
+    ('FLOAT', 'BFLOAT16'),
+    ('BFLOAT16', 'FLOAT'),
+]
+
+for from_type, to_type in test_cases:
+    input_type_proto = None
+    output_type_proto = None
+    if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type:
+        np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32)
+        little_endisan = sys.byteorder == 'little'
+        np_uint16_view = np_fp32.view(dtype=np.uint16)
+        np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
+        if 'BFLOAT16' == to_type:
+            assert from_type == 'FLOAT'
+            input = np_fp32.reshape([3, 4])
+            output = np_bfp16.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), output.shape)
+        else:
+            assert to_type == 'FLOAT'
+            input = np_bfp16.reshape([3, 4])
+            #convert bfloat to FLOAT
+            np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
+            if little_endisan:
+                np_fp32_zeros[1::2] = np_bfp16
+            else:
+                np_fp32_zeros[0::2] = np_bfp16
+            np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
+            output = np_fp32_from_bfloat.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), output.shape)
+    elif 'STRING' != from_type:
+        input = np.random.random_sample(shape).astype(
+            TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)])
+        if ('STRING' == to_type):
+            # Converting input to str, then give it object dtype for generating script
+            ss = []
+            for i in input.flatten():
+                s = str(i).encode('utf-8')
+                su = s.decode('utf-8')
+                ss.append(su)
+
+            output = np.array(ss).astype(object).reshape([3, 4])
+        else:
+            output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    else:
+        input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(object)).reshape([3, 4])
+        output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    node = onnx.helper.make_node(
+        'Cast',
+        inputs=['input'],
+        outputs=['output'],
+        to=getattr(TensorProto, to_type),
+    )
+    if input_type_proto and output_type_proto:
+        expect(node, inputs=[input], outputs=[output],
+                   name='test_cast_' + from_type + '_to_' + to_type,
+                   input_type_protos=[input_type_proto],
+                   output_type_protos=[output_type_proto])
+    else:
+        expect(node, inputs=[input], outputs=[output],
+                   name='test_cast_' + from_type + '_to_' + to_type)
+```
+
+</details>
+
+
+### CastLike
+There are 1 test cases, listed as following:
+<details>
+<summary>castlike</summary>
+
+```python
+shape = (3, 4)
+test_cases = [
+    ('FLOAT', 'FLOAT16'),
+    ('FLOAT', 'DOUBLE'),
+    ('FLOAT16', 'FLOAT'),
+    ('FLOAT16', 'DOUBLE'),
+    ('DOUBLE', 'FLOAT'),
+    ('DOUBLE', 'FLOAT16'),
+    ('FLOAT', 'STRING'),
+    ('STRING', 'FLOAT'),
+    ('FLOAT', 'BFLOAT16'),
+    ('BFLOAT16', 'FLOAT'),
+]
+
+for from_type, to_type in test_cases:
+    input_type_proto = None
+    output_type_proto = None
+    if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type:
+        np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32)
+        little_endisan = sys.byteorder == 'little'
+        np_uint16_view = np_fp32.view(dtype=np.uint16)
+        np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
+        if 'BFLOAT16' == to_type:
+            assert from_type == 'FLOAT'
+            input = np_fp32.reshape([3, 4])
+            output = np_bfp16.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), output.shape)
+        else:
+            assert to_type == 'FLOAT'
+            input = np_bfp16.reshape([3, 4])
+            #convert bfloat to FLOAT
+            np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
+            if little_endisan:
+                np_fp32_zeros[1::2] = np_bfp16
+            else:
+                np_fp32_zeros[0::2] = np_bfp16
+            np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
+            output = np_fp32_from_bfloat.reshape([3, 4])
+            input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), input.shape)
+            output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), output.shape)
+    elif 'STRING' != from_type:
+        input = np.random.random_sample(shape).astype(
+            TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)])
+        if ('STRING' == to_type):
+            # Converting input to str, then give it np.object dtype for generating script
+            ss = []
+            for i in input.flatten():
+                s = str(i).encode('utf-8')
+                su = s.decode('utf-8')
+                ss.append(su)
+
+            output = np.array(ss).astype(np.object).reshape([3, 4])
+        else:
+            output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    else:
+        input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+            u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+            u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(np.object)).reshape([3, 4])
+        output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+    like = output.flatten()[0:1]
+    node = onnx.helper.make_node(
+        'CastLike',
+        inputs=['input', 'like'],
+        outputs=['output'],
+    )
+    if input_type_proto and output_type_proto:
+        expect(node, inputs=[input, like], outputs=[output],
+                   name='test_castlike_' + from_type + '_to_' + to_type,
+                   input_type_protos=[input_type_proto, output_type_proto],
+                   output_type_protos=[output_type_proto])
+    else:
+        expect(node, inputs=[input, like], outputs=[output],
+                   name='test_castlike_' + from_type + '_to_' + to_type)
+```
+
+</details>
+
+
+### Ceil
+There are 1 test cases, listed as following:
+<details>
+<summary>ceil</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Ceil',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1.5, 1.2]).astype(np.float32)
+y = np.ceil(x)  # expected output [-1., 2.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_ceil_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.ceil(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_ceil')
+```
+
+</details>
+
+
+### Celu
+There are 1 test cases, listed as following:
+<details>
+<summary>celu</summary>
+
+```python
+alpha = 2.0
+node = onnx.helper.make_node(
+    'Celu',
+    inputs=['X'],
+    outputs=['Y'],
+    alpha=alpha,
+)
+
+input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+    [[0.02916367], [0.12964272], [0.5060197]],
+    [[0.79538304], [0.9411346], [0.9546573]]],
+    [[[0.17730942], [0.46192095], [0.26480448]],
+    [[0.6746842], [0.01665257], [0.62473077]],
+    [[0.9240844], [0.9722341], [0.11965699]]],
+    [[[0.41356155], [0.9129373], [0.59330076]],
+    [[0.81929934], [0.7862604], [0.11799799]],
+    [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+# Calculate expected output data
+positive_input = np.maximum(0, input_data)
+negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
+expected_output = positive_input + negative_input
+
+expect(node, inputs=[input_data], outputs=[expected_output],
+       name='test_celu')
+```
+
+</details>
+
+
+### Clip
+There are 3 test cases, listed as following:
+<details>
+<summary>clip</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min', 'max'],
+    outputs=['y'],
+)
+
+x = np.array([-2, 0, 2]).astype(np.float32)
+min_val = np.float32(-1)
+max_val = np.float32(1)
+y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, min_val, max_val)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip')
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min', 'max'],
+    outputs=['y'],
+)
+
+min_val = np.float32(-5)
+max_val = np.float32(5)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.array([-1, 0, 1]).astype(np.float32)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_inbounds')
+
+x = np.array([-6, 0, 6]).astype(np.float32)
+y = np.array([-5, 0, 5]).astype(np.float32)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_outbounds')
+
+x = np.array([-1, 0, 6]).astype(np.float32)
+y = np.array([-1, 0, 5]).astype(np.float32)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
+       name='test_clip_splitbounds')
+```
+
+</details>
+<details>
+<summary>clip_default</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min'],
+    outputs=['y'],
+)
+min_val = np.float32(0)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, min_val, np.inf)
+expect(node, inputs=[x, min_val], outputs=[y],
+       name='test_clip_default_min')
+
+no_min = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, 'max'],
+    outputs=['y'],
+)
+max_val = np.float32(0)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, -np.inf, max_val)
+expect(node, inputs=[x, max_val], outputs=[y],
+       name='test_clip_default_max')
+
+no_max = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, no_max],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.array([-1, 0, 1]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_clip_default_inbounds')
+```
+
+</details>
+<details>
+<summary>clip_default_int8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', 'min'],
+    outputs=['y'],
+)
+min_val = np.int8(0)
+x = np.random.randn(3, 4, 5).astype(np.int8)
+y = np.clip(x, min_val, np.iinfo(np.int8).max)
+expect(node, inputs=[x, min_val], outputs=[y],
+       name='test_clip_default_int8_min')
+
+no_min = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, 'max'],
+    outputs=['y'],
+)
+max_val = np.int8(0)
+x = np.random.randn(3, 4, 5).astype(np.int8)
+y = np.clip(x, np.iinfo(np.int8).min, max_val)
+expect(node, inputs=[x, max_val], outputs=[y],
+       name='test_clip_default_int8_max')
+
+no_max = ""  # optional input, not supplied
+node = onnx.helper.make_node(
+    'Clip',
+    inputs=['x', no_min, no_max],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.int8)
+y = np.array([-1, 0, 1]).astype(np.int8)
+expect(node, inputs=[x], outputs=[y],
+       name='test_clip_default_int8_inbounds')
+```
+
+</details>
+
+
+### Compress
+There are 4 test cases, listed as following:
+<details>
+<summary>compress_0</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+    axis=0,
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1, 1])
+output = np.compress(condition, input, axis=0)
+#print(output)
+#[[ 3.  4.]
+# [ 5.  6.]]
+
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_0')
+```
+
+</details>
+<details>
+<summary>compress_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+    axis=1,
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1])
+output = np.compress(condition, input, axis=1)
+#print(output)
+#[[ 2.]
+# [ 4.]
+# [ 6.]]
+
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_1')
+```
+
+</details>
+<details>
+<summary>compress_default_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1, 0, 0, 1])
+output = np.compress(condition, input)
+#print(output)
+#[ 2., 5.]
+
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_default_axis')
+```
+
+</details>
+<details>
+<summary>compress_negative_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Compress',
+    inputs=['input', 'condition'],
+    outputs=['output'],
+    axis=-1,
+)
+input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+condition = np.array([0, 1])
+output = np.compress(condition, input, axis=-1)
+# print(output)
+#[[ 2.]
+# [ 4.]
+# [ 6.]]
+expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+       name='test_compress_negative_axis')
+```
+
+</details>
+
+
+### Concat
+There are 1 test cases, listed as following:
+<details>
+<summary>concat</summary>
+
+```python
+test_cases: Dict[Text, Sequence[Any]] = {
+    '1d': ([1, 2],
+           [3, 4]),
+    '2d': ([[1, 2], [3, 4]],
+           [[5, 6], [7, 8]]),
+    '3d': ([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
+           [[[9, 10], [11, 12]], [[13, 14], [15, 16]]])
+}
+
+for test_case, values_ in test_cases.items():
+    values = [np.asarray(v, dtype=np.float32) for v in values_]
+    for i in range(len(values[0].shape)):
+        in_args = ['value' + str(k) for k in range(len(values))]
+        node = onnx.helper.make_node(
+            'Concat',
+            inputs=[s for s in in_args],
+            outputs=['output'],
+            axis=i
+        )
+        output = np.concatenate(values, i)
+        expect(node, inputs=[v for v in values], outputs=[output],
+               name='test_concat_' + test_case + '_axis_' + str(i))
+
+    for i in range(-len(values[0].shape), 0):
+        in_args = ['value' + str(k) for k in range(len(values))]
+        node = onnx.helper.make_node(
+            'Concat',
+            inputs=[s for s in in_args],
+            outputs=['output'],
+            axis=i
+        )
+        output = np.concatenate(values, i)
+        expect(node, inputs=[v for v in values], outputs=[output],
+               name='test_concat_' + test_case + '_axis_negative_' + str(abs(i)))
+```
+
+</details>
+
+
+### Constant
+There are 1 test cases, listed as following:
+<details>
+<summary>constant</summary>
+
+```python
+values = np.random.randn(5, 5).astype(np.float32)
+node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['values'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=values.shape,
+        vals=values.flatten().astype(float),
+    ),
+)
+
+expect(node, inputs=[], outputs=[values],
+       name='test_constant')
+```
+
+</details>
+
+
+### ConstantOfShape
+There are 3 test cases, listed as following:
+<details>
+<summary>float_ones</summary>
+
+```python
+x = np.array([4, 3, 2]).astype(np.int64)
+tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.FLOAT,
+                                       [1], [1])
+node = onnx.helper.make_node(
+    'ConstantOfShape',
+    inputs=['x'],
+    outputs=['y'],
+    value=tensor_value,
+)
+
+y = np.ones(x, dtype=np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_constantofshape_float_ones')
+```
+
+</details>
+<details>
+<summary>int32_shape_zero</summary>
+
+```python
+x = np.array([0, ]).astype(np.int64)
+tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.INT32,
+                                       [1], [0])
+node = onnx.helper.make_node(
+    'ConstantOfShape',
+    inputs=['x'],
+    outputs=['y'],
+    value=tensor_value,
+)
+y = np.zeros(x, dtype=np.int32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_constantofshape_int_shape_zero')
+```
+
+</details>
+<details>
+<summary>int32_zeros</summary>
+
+```python
+x = np.array([10, 6]).astype(np.int64)
+tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.INT32,
+                                       [1], [0])
+node = onnx.helper.make_node(
+    'ConstantOfShape',
+    inputs=['x'],
+    outputs=['y'],
+    value=tensor_value,
+)
+y = np.zeros(x, dtype=np.int32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_constantofshape_int_zeros')
+```
+
+</details>
+
+
+### Conv
+There are 3 test cases, listed as following:
+<details>
+<summary>conv</summary>
+
+```python
+
+x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 5, 5) input tensor
+                [5., 6., 7., 8., 9.],
+                [10., 11., 12., 13., 14.],
+                [15., 16., 17., 18., 19.],
+                [20., 21., 22., 23., 24.]]]]).astype(np.float32)
+W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+# Convolution with padding
+node_with_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
+    pads=[1, 1, 1, 1],
+)
+y_with_padding = np.array([[[[12., 21., 27., 33., 24.],  # (1, 1, 5, 5) output tensor
+                             [33., 54., 63., 72., 51.],
+                             [63., 99., 108., 117., 81.],
+                             [93., 144., 153., 162., 111.],
+                             [72., 111., 117., 123., 84.]]]]).astype(np.float32)
+expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
+       name='test_basic_conv_with_padding')
+
+# Convolution without padding
+node_without_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
+    pads=[0, 0, 0, 0],
+)
+y_without_padding = np.array([[[[54., 63., 72.],  # (1, 1, 3, 3) output tensor
+                                [99., 108., 117.],
+                                [144., 153., 162.]]]]).astype(np.float32)
+expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
+       name='test_basic_conv_without_padding')
+```
+
+</details>
+<details>
+<summary>conv_with_autopad_same</summary>
+
+```python
+
+x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 5, 5) input tensor
+                [5., 6., 7., 8., 9.],
+                [10., 11., 12., 13., 14.],
+                [15., 16., 17., 18., 19.],
+                [20., 21., 22., 23., 24.]]]]).astype(np.float32)
+W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+# Convolution with auto_pad='SAME_LOWER' and strides=2
+node = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    auto_pad='SAME_LOWER',
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+)
+y = np.array([[[[12., 27., 24.],
+             [63., 108., 81.],
+             [72., 117., 84.]]]]).astype(np.float32)
+expect(node, inputs=[x, W], outputs=[y],
+       name='test_conv_with_autopad_same')
+```
+
+</details>
+<details>
+<summary>conv_with_strides</summary>
+
+```python
+
+x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 7, 5) input tensor
+                [5., 6., 7., 8., 9.],
+                [10., 11., 12., 13., 14.],
+                [15., 16., 17., 18., 19.],
+                [20., 21., 22., 23., 24.],
+                [25., 26., 27., 28., 29.],
+                [30., 31., 32., 33., 34.]]]]).astype(np.float32)
+W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+# Convolution with strides=2 and padding
+node_with_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[1, 1, 1, 1],
+    strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+)
+y_with_padding = np.array([[[[12., 27., 24.],  # (1, 1, 4, 3) output tensor
+                             [63., 108., 81.],
+                             [123., 198., 141.],
+                             [112., 177., 124.]]]]).astype(np.float32)
+expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
+       name='test_conv_with_strides_padding')
+
+# Convolution with strides=2 and no padding
+node_without_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[0, 0, 0, 0],
+    strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+)
+y_without_padding = np.array([[[[54., 72.],  # (1, 1, 3, 2) output tensor
+                                [144., 162.],
+                                [234., 252.]]]]).astype(np.float32)
+expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
+       name='test_conv_with_strides_no_padding')
+
+# Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
+node_with_asymmetric_padding = onnx.helper.make_node(
+    'Conv',
+    inputs=['x', 'W'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[1, 0, 1, 0],
+    strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+)
+y_with_asymmetric_padding = np.array([[[[21., 33.],  # (1, 1, 4, 2) output tensor
+                                        [99., 117.],
+                                        [189., 207.],
+                                        [171., 183.]]]]).astype(np.float32)
+expect(node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding],
+       name='test_conv_with_strides_and_asymmetric_padding')
+```
+
+</details>
+
+
+### ConvInteger
+There are 2 test cases, listed as following:
+<details>
+<summary>with_padding</summary>
+
+```python
+
+x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+x_zero_point = np.uint8(1)
+w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+y = np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9]).astype(np.int32).reshape((1, 1, 4, 4))
+
+# ConvInteger with padding
+convinteger_node_with_padding = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'],
+    pads=[1, 1, 1, 1],)
+
+expect(convinteger_node_with_padding, inputs=[x, w, x_zero_point], outputs=[y],
+       name='test_convinteger_with_padding')
+```
+
+</details>
+<details>
+<summary>without_padding</summary>
+
+```python
+
+x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+x_zero_point = np.uint8(1)
+w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
+
+# ConvInteger without padding
+convinteger_node = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'])
+
+expect(convinteger_node, inputs=[x, w, x_zero_point], outputs=[y],
+       name='test_convinteger_without_padding')
+```
+
+</details>
+
+
+### ConvTranspose
+There are 7 test cases, listed as following:
+<details>
+<summary>convtranspose</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+y = np.array([[[[0., 1., 3., 3., 2.],  # (1, 2, 5, 5)
+                [3., 8., 15., 12., 7.],
+                [9., 21., 36., 27., 15.],
+                [9., 20., 33., 24., 13.],
+                [6., 13., 21., 15., 8.]],
+
+               [[0., 1., 3., 3., 2.],
+                [3., 8., 15., 12., 7.],
+                [9., 21., 36., 27., 15.],
+                [9., 20., 33., 24., 13.],
+                [6., 13., 21., 15., 8.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose')
+```
+
+</details>
+<details>
+<summary>convtranspose_1d</summary>
+
+```python
+x = np.array([[[0., 1., 2.]]]).astype(np.float32)  # (1, 1, 3)
+
+W = np.array([[[1., 1., 1.],  # (1, 2, 3)
+               [1., 1., 1.]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+y = np.array([[[0., 1., 3., 3., 2.],  # (1, 2, 5)
+               [0., 1., 3., 3., 2.]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_1d')
+```
+
+</details>
+<details>
+<summary>convtranspose_3d</summary>
+
+```python
+x = np.array([[[[[0., 1., 2., 3., 4.],  # (1, 1, 3, 4, 5)
+                 [5., 6., 7., 8., 9.],
+                 [10., 11., 12., 13., 14.],
+                 [15., 16., 17., 18., 19.]],
+                [[20., 21., 22., 23., 24.],
+                 [25., 26., 27., 28., 29.],
+                 [30., 31., 32., 33., 34.],
+                 [35., 36., 37., 38., 39.]],
+                [[40., 41., 42., 43., 44.],
+                 [45., 46., 47., 48., 49.],
+                 [50., 51., 52., 53., 54.],
+                 [55., 56., 57., 58., 59.]]]]]).astype(np.float32)
+
+W = np.array([[[[[1., 1., 1.],  # (1, 2, 3, 3, 3)
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]]],
+               [[[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]],
+                [[1., 1., 1.],
+                 [1., 1., 1.],
+                 [1., 1., 1.]]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+y = np.array([[[[[0., 1., 3., 6., 9., 7., 4.],  # (1, 2, 5, 6, 7)
+                 [5., 12., 21., 27., 33., 24., 13.],
+                 [15., 33., 54., 63., 72., 51., 27.],
+                 [30., 63., 99., 108., 117., 81., 42.],
+                 [25., 52., 81., 87., 93., 64., 33.],
+                 [15., 31., 48., 51., 54., 37., 19.]],
+
+                [[20., 42., 66., 72., 78., 54., 28.],
+                 [50., 104., 162., 174., 186., 128., 66.],
+                 [90., 186., 288., 306., 324., 222., 114.],
+                 [120., 246., 378., 396., 414., 282., 144.],
+                 [90., 184., 282., 294., 306., 208., 106.],
+                 [50., 102., 156., 162., 168., 114., 58.]],
+
+                [[60., 123., 189., 198., 207., 141., 72.],
+                 [135., 276., 423., 441., 459., 312., 159.],
+                 [225., 459., 702., 729., 756., 513., 261.],
+                 [270., 549., 837., 864., 891., 603., 306.],
+                 [195., 396., 603., 621., 639., 432., 219.],
+                 [105., 213., 324., 333., 342., 231., 117.]],
+
+                [[60., 122., 186., 192., 198., 134., 68.],
+                 [130., 264., 402., 414., 426., 288., 146.],
+                 [210., 426., 648., 666., 684., 462., 234.],
+                 [240., 486., 738., 756., 774., 522., 264.],
+                 [170., 344., 522., 534., 546., 368., 186.],
+                 [90., 182., 276., 282., 288., 194., 98.]],
+
+                [[40., 81., 123., 126., 129., 87., 44.],
+                 [85., 172., 261., 267., 273., 184., 93.],
+                 [135., 273., 414., 423., 432., 291., 147.],
+                 [150., 303., 459., 468., 477., 321., 162.],
+                 [105., 212., 321., 327., 333., 224., 113.],
+                 [55., 111., 168., 171., 174., 117., 59.]]],
+
+               [[[0., 1., 3., 6., 9., 7., 4.],
+                 [5., 12., 21., 27., 33., 24., 13.],
+                 [15., 33., 54., 63., 72., 51., 27.],
+                 [30., 63., 99., 108., 117., 81., 42.],
+                 [25., 52., 81., 87., 93., 64., 33.],
+                 [15., 31., 48., 51., 54., 37., 19.]],
+
+                [[20., 42., 66., 72., 78., 54., 28.],
+                 [50., 104., 162., 174., 186., 128., 66.],
+                 [90., 186., 288., 306., 324., 222., 114.],
+                 [120., 246., 378., 396., 414., 282., 144.],
+                 [90., 184., 282., 294., 306., 208., 106.],
+                 [50., 102., 156., 162., 168., 114., 58.]],
+
+                [[60., 123., 189., 198., 207., 141., 72.],
+                 [135., 276., 423., 441., 459., 312., 159.],
+                 [225., 459., 702., 729., 756., 513., 261.],
+                 [270., 549., 837., 864., 891., 603., 306.],
+                 [195., 396., 603., 621., 639., 432., 219.],
+                 [105., 213., 324., 333., 342., 231., 117.]],
+
+                [[60., 122., 186., 192., 198., 134., 68.],
+                 [130., 264., 402., 414., 426., 288., 146.],
+                 [210., 426., 648., 666., 684., 462., 234.],
+                 [240., 486., 738., 756., 774., 522., 264.],
+                 [170., 344., 522., 534., 546., 368., 186.],
+                 [90., 182., 276., 282., 288., 194., 98.]],
+
+                [[40., 81., 123., 126., 129., 87., 44.],
+                 [85., 172., 261., 267., 273., 184., 93.],
+                 [135., 273., 414., 423., 432., 291., 147.],
+                 [150., 303., 459., 468., 477., 321., 162.],
+                 [105., 212., 321., 327., 333., 224., 113.],
+                 [55., 111., 168., 171., 174., 117., 59.]]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_3d')
+```
+
+</details>
+<details>
+<summary>convtranspose_attributes</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+y = np.array([[[[0., 0., 1., 1., 3., 2., 2., 0.],  # (1, 2, 10, 8)
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [0., 0., 0., 0., 0., 0., 0., 0.]],
+
+               [[0., 0., 1., 1., 3., 2., 2., 0.],
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [0., 0., 1., 1., 3., 2., 2., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [3., 3., 7., 4., 9., 5., 5., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [6., 6., 13., 7., 15., 8., 8., 0.],
+                [0., 0., 0., 0., 0., 0., 0., 0.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                             strides=[3, 2],
+                             output_shape=[10, 8])
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_output_shape')
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                             strides=[3, 2],
+                             output_padding=[1, 1])
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_pad')
+
+node = onnx.helper.make_node(
+    'ConvTranspose', ['X', 'W'], ['Y'],
+    name='test',
+    strides=[3, 2],
+    output_shape=[10, 8],
+    kernel_shape=[3, 3],
+    output_padding=[1, 1]
+)
+expect(node, inputs=[x, W], outputs=[y],
+       name='test_convtranspose_kernel_shape')
+```
+
+</details>
+<details>
+<summary>convtranspose_autopad_same</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2])
+
+y = np.array([[[[0., 0., 1., 1., 3., 2.],
+                [0., 0., 1., 1., 3., 2.],
+                [3., 3., 8., 5., 12., 7.],
+                [3., 3., 7., 4., 9., 5.],
+                [9., 9., 20., 11., 24., 13.],
+                [6., 6., 13., 7., 15., 8.]],
+
+               [[0., 0., 1., 1., 3., 2.],
+                [0., 0., 1., 1., 3., 2.],
+                [3., 3., 8., 5., 12., 7.],
+                [3., 3., 7., 4., 9., 5.],
+                [9., 9., 20., 11., 24., 13.],
+                [6., 6., 13., 7., 15., 8.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_autopad_same')
+```
+
+</details>
+<details>
+<summary>convtranspose_dilations</summary>
+
+```python
+x = np.array([[[[3., 8., 1.],  # (1, 1, 3, 3)
+                [9., 5., 7.],
+                [3., 2., 6.]]]]).astype(np.float32)
+W = np.array([[[[7., 2.],  # (1, 1, 2, 2)
+                [1., 9.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2])
+
+y = np.array([[[[21., 56., 13., 16., 2.],  # [1, 1, 5, 5]
+                [63., 35., 67., 10., 14.],
+                [24., 22., 76., 76., 21.],
+                [9., 5., 88., 45., 63.],
+                [3., 2., 33., 18., 54.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_dilations')
+```
+
+</details>
+<details>
+<summary>convtranspose_pads</summary>
+
+```python
+x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                [3., 4., 5.],
+                [6., 7., 8.]]]]).astype(np.float32)
+
+W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                [1., 1., 1.],
+                [1., 1., 1.]],
+               [[1., 1., 1.],
+                [1., 1., 1.],
+                [1., 1., 1.]]]]).astype(np.float32)
+
+node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                             strides=[3, 2],
+                             pads=[1, 2, 1, 2])
+
+y = np.array([[[[1., 1., 3.],  # (1, 2, 7, 3)
+                [1., 1., 3.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [13., 7., 15.],
+                [13., 7., 15.]],
+
+               [[1., 1., 3.],
+                [1., 1., 3.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [7., 4., 9.],
+                [13., 7., 15.],
+                [13., 7., 15.]]]]).astype(np.float32)
+
+expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_pads')
+```
+
+</details>
+
+
+### Cos
+There are 1 test cases, listed as following:
+<details>
+<summary>cos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Cos',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.cos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_cos_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.cos(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_cos')
+```
+
+</details>
+
+
+### Cosh
+There are 1 test cases, listed as following:
+<details>
+<summary>cosh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Cosh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.cosh(x)  # expected output [1.54308069,  1.,  1.54308069]
+expect(node, inputs=[x], outputs=[y],
+       name='test_cosh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.cosh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_cosh')
+```
+
+</details>
+
+
+### CumSum
+There are 7 test cases, listed as following:
+<details>
+<summary>cumsum_1d</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y']
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([1., 3., 6., 10., 15.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d')
+```
+
+</details>
+<details>
+<summary>cumsum_1d_exclusive</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+    exclusive=1
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([0., 1., 3., 6., 10.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d_exclusive')
+```
+
+</details>
+<details>
+<summary>cumsum_1d_reverse</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+    reverse=1
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([15., 14., 12., 9., 5.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d_reverse')
+```
+
+</details>
+<details>
+<summary>cumsum_1d_reverse_exclusive</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+    reverse=1,
+    exclusive=1
+)
+x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+axis = np.int32(0)
+y = np.array([14., 12., 9., 5., 0.]).astype(np.float64)
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_1d_reverse_exclusive')
+```
+
+</details>
+<details>
+<summary>cumsum_2d_axis_0</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+)
+x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+axis = np.int32(0)
+y = np.array([1., 2., 3., 5., 7., 9.]).astype(np.float64).reshape((2, 3))
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_2d_axis_0')
+```
+
+</details>
+<details>
+<summary>cumsum_2d_axis_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+)
+x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+axis = np.int32(1)
+y = np.array([1., 3., 6., 4., 9., 15.]).astype(np.float64).reshape((2, 3))
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_2d_axis_1')
+```
+
+</details>
+<details>
+<summary>cumsum_2d_negative_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'CumSum',
+    inputs=['x', 'axis'],
+    outputs=['y'],
+)
+x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+axis = np.int32(-1)
+y = np.array([1., 3., 6., 4., 9., 15.]).astype(np.float64).reshape((2, 3))
+expect(node, inputs=[x, axis], outputs=[y],
+       name='test_cumsum_2d_negative_axis')
+```
+
+</details>
+
+
+### DepthToSpace
+There are 2 test cases, listed as following:
+<details>
+<summary>crd_mode_example</summary>
+
+```python
+node = onnx.helper.make_node(
+    'DepthToSpace',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=2,
+    mode='CRD'
+)
+
+# (1, 8, 2, 3) input tensor
+x = np.array([[[[0., 1., 2.],
+                [3., 4., 5.]],
+               [[9., 10., 11.],
+                [12., 13., 14.]],
+               [[18., 19., 20.],
+                [21., 22., 23.]],
+               [[27., 28., 29.],
+                [30., 31., 32.]],
+               [[36., 37., 38.],
+                [39., 40., 41.]],
+               [[45., 46., 47.],
+                [48., 49., 50.]],
+               [[54., 55., 56.],
+                [57., 58., 59.]],
+               [[63., 64., 65.],
+                [66., 67., 68.]]]]).astype(np.float32)
+
+# (1, 2, 4, 6) output tensor
+y = np.array([[[[0., 9., 1., 10., 2., 11.],
+                [18., 27., 19., 28., 20., 29.],
+                [3., 12., 4., 13., 5., 14.],
+                [21., 30., 22., 31., 23., 32.]],
+               [[36., 45., 37., 46., 38., 47.],
+                [54., 63., 55., 64., 56., 65.],
+                [39., 48., 40., 49., 41., 50.],
+                [57., 66., 58., 67., 59., 68.]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_depthtospace_crd_mode_example')
+```
+
+</details>
+<details>
+<summary>default_mode_example</summary>
+
+```python
+node = onnx.helper.make_node(
+    'DepthToSpace',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=2,
+    mode='DCR'
+)
+
+# (1, 8, 2, 3) input tensor
+x = np.array([[[[0., 1., 2.],
+                [3., 4., 5.]],
+               [[9., 10., 11.],
+                [12., 13., 14.]],
+               [[18., 19., 20.],
+                [21., 22., 23.]],
+               [[27., 28., 29.],
+                [30., 31., 32.]],
+               [[36., 37., 38.],
+                [39., 40., 41.]],
+               [[45., 46., 47.],
+                [48., 49., 50.]],
+               [[54., 55., 56.],
+                [57., 58., 59.]],
+               [[63., 64., 65.],
+                [66., 67., 68.]]]]).astype(np.float32)
+
+# (1, 2, 4, 6) output tensor
+y = np.array([[[[0., 18., 1., 19., 2., 20.],
+                [36., 54., 37., 55., 38., 56.],
+                [3., 21., 4., 22., 5., 23.],
+                [39., 57., 40., 58., 41., 59.]],
+               [[9., 27., 10., 28., 11., 29.],
+                [45., 63., 46., 64., 47., 65.],
+                [12., 30., 13., 31., 14., 32.],
+                [48., 66., 49., 67., 50., 68.]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_depthtospace_example')
+```
+
+</details>
+
+
+### DequantizeLinear
+There are 2 test cases, listed as following:
+<details>
+<summary>axis</summary>
+
+```python
+node = onnx.helper.make_node('DequantizeLinear',
+                             inputs=['x', 'x_scale', 'x_zero_point'],
+                             outputs=['y'],)
+
+# 1-D tensor zero point and scale of size equal to axis 1 of the input tensor
+x = np.array([[[[3, 89],
+                [34, 200],
+                [74, 59]],
+
+               [[5, 24],
+                [24, 87],
+                [32, 13]],
+
+               [[245, 99],
+                [4, 142],
+                [121, 102]], ], ], dtype=np.uint8)
+x_scale = np.array([2, 4, 5], dtype=np.float32)
+x_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+y = (x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32)) * x_scale.reshape(1, 3, 1, 1)
+
+expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+       name='test_dequantizelinear_axis')
+```
+
+</details>
+<details>
+<summary>dequantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('DequantizeLinear',
+                             inputs=['x', 'x_scale', 'x_zero_point'],
+                             outputs=['y'],)
+
+# scalar zero point and scale
+x = np.array([0, 3, 128, 255]).astype(np.uint8)
+x_scale = np.float32(2)
+x_zero_point = np.uint8(128)
+y = np.array([-256, -250, 0, 254], dtype=np.float32)
+
+expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+       name='test_dequantizelinear')
+```
+
+</details>
+
+
+### Det
+There are 2 test cases, listed as following:
+<details>
+<summary>2d</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Det',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.arange(4).reshape(2, 2).astype(np.float32)
+y = np.linalg.det(x)  # expect -2
+expect(node, inputs=[x], outputs=[y],
+       name='test_det_2d')
+```
+
+</details>
+<details>
+<summary>nd</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Det',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype(np.float32)
+y = np.linalg.det(x)  # expect array([-2., -3., -8.])
+expect(node, inputs=[x], outputs=[y],
+       name='test_det_nd')
+```
+
+</details>
+
+
+### Div
+There are 2 test cases, listed as following:
+<details>
+<summary>div</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Div',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([3, 4]).astype(np.float32)
+y = np.array([1, 2]).astype(np.float32)
+z = x / y  # expected output [3., 2.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
+z = x / y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div')
+
+x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
+z = x // y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div_uint8')
+```
+
+</details>
+<details>
+<summary>div_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Div',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.rand(5).astype(np.float32) + 1.0
+z = x / y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_div_bcast')
+```
+
+</details>
+
+
+### Dropout
+There are 12 test cases, listed as following:
+<details>
+<summary>default</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = dropout(x)
+expect(node, inputs=[x], outputs=[y], name='test_dropout_default')
+```
+
+</details>
+<details>
+<summary>default_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y, z = dropout(x, return_mask=True)
+expect(node, inputs=[x], outputs=[y, z], name='test_dropout_default_mask')
+```
+
+</details>
+<details>
+<summary>default_mask_ratio</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+r = np.float32(0.1)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y, z = dropout(x, r, return_mask=True)
+expect(node, inputs=[x, r], outputs=[y, z], name='test_dropout_default_mask_ratio')
+```
+
+</details>
+<details>
+<summary>default_old</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = x
+expect(node, inputs=[x], outputs=[y],
+       name='test_dropout_default_old', opset_imports=[helper.make_opsetid("", 11)])
+```
+
+</details>
+<details>
+<summary>default_ratio</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r'],
+    outputs=['y'],
+    seed=seed
+)
+
+r = np.float32(0.1)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = dropout(x, r)
+expect(node, inputs=[x, r], outputs=[y], name='test_dropout_default_ratio')
+```
+
+</details>
+<details>
+<summary>random_old</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x'],
+    outputs=['y'],
+    ratio=.2,
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = x
+expect(node, inputs=[x], outputs=[y],
+       name='test_dropout_random_old', opset_imports=[helper.make_opsetid("", 11)])
+```
+
+</details>
+<details>
+<summary>training</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.75)
+t = np.bool_(True)
+y = dropout(x, r, training_mode=t)
+expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout')
+```
+
+</details>
+<details>
+<summary>training_default</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.5)
+t = np.bool_(True)
+y = dropout(x, r, training_mode=t)
+expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout_default')
+```
+
+</details>
+<details>
+<summary>training_default_ratio_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.5)
+t = np.bool_(True)
+y, z = dropout(x, r, training_mode=t, return_mask=True)
+expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_default_mask')
+```
+
+</details>
+<details>
+<summary>training_default_zero_ratio</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.0)
+t = np.bool_(True)
+y = dropout(x, r, training_mode=t)
+expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout_zero_ratio')
+```
+
+</details>
+<details>
+<summary>training_default_zero_ratio_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.0)
+t = np.bool_(True)
+y, z = dropout(x, r, training_mode=t, return_mask=True)
+expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_zero_ratio_mask')
+```
+
+</details>
+<details>
+<summary>training_ratio_mask</summary>
+
+```python
+seed = np.int64(0)
+node = onnx.helper.make_node(
+    'Dropout',
+    inputs=['x', 'r', 't'],
+    outputs=['y', 'z'],
+    seed=seed
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+r = np.float32(0.75)
+t = np.bool_(True)
+y, z = dropout(x, r, training_mode=t, return_mask=True)
+expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_mask')
+```
+
+</details>
+
+
+### DynamicQuantizeLinear
+There are 1 test cases, listed as following:
+<details>
+<summary>dynamicquantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('DynamicQuantizeLinear',
+    inputs=['x'],
+    outputs=['y', 'y_scale', 'y_zero_point'],
+)
+
+# expected scale 0.0196078438 and zero point 153
+X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32)
+x_min = np.minimum(0, np.min(X))
+x_max = np.maximum(0, np.max(X))
+Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+       name='test_dynamicquantizelinear')
+
+# expected scale 0.0156862754 and zero point 255
+X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32)
+x_min = np.minimum(0, np.min(X))
+x_max = np.maximum(0, np.max(X))
+Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+       name='test_dynamicquantizelinear_max_adjusted')
+
+X = np.array([1, 2.1, 1.3, 2.5,
+              3.34, 4.0, 1.5, 2.6,
+              3.9, 4.0, 3.0, 2.345]).astype(np.float32).reshape((3, 4))
+
+# expected scale 0.0156862754 and zero point 0
+x_min = np.minimum(0, np.min(X))
+x_max = np.maximum(0, np.max(X))
+Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+       name='test_dynamicquantizelinear_min_adjusted')
+```
+
+</details>
+
+
+### Einsum
+There are 5 test cases, listed as following:
+<details>
+<summary>einsum_batch_diagonal</summary>
+
+```python
+Eqn = '...ii ->...i'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x'],
+    outputs=['y'],
+    equation=Eqn
+)
+
+X = np.random.randn(3, 5, 5)
+Z = einsum_reference_implementation(Eqn, (X,))
+
+expect(node, inputs=[X], outputs=[Z], name='test_einsum_batch_diagonal')
+```
+
+</details>
+<details>
+<summary>einsum_batch_matmul</summary>
+
+```python
+Eqn = 'bij, bjk -> bik'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    equation=Eqn
+)
+
+X = np.random.randn(5, 2, 3)
+Y = np.random.randn(5, 3, 4)
+Z = einsum_reference_implementation(Eqn, (X, Y))
+
+expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_batch_matmul')
+```
+
+</details>
+<details>
+<summary>einsum_inner_prod</summary>
+
+```python
+Eqn = 'i,i'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    equation=Eqn
+)
+
+X = np.random.randn(5)
+Y = np.random.randn(5)
+Z = einsum_reference_implementation(Eqn, (X, Y))
+
+expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_inner_prod')
+```
+
+</details>
+<details>
+<summary>einsum_sum</summary>
+
+```python
+Eqn = 'ij->i'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x'],
+    outputs=['y'],
+    equation=Eqn
+)
+
+X = np.random.randn(3, 4)
+Z = einsum_reference_implementation(Eqn, (X,))
+
+expect(node, inputs=[X], outputs=[Z], name='test_einsum_sum')
+```
+
+</details>
+<details>
+<summary>einsum_transpose</summary>
+
+```python
+Eqn = 'ij->ji'
+node = onnx.helper.make_node(
+    'Einsum',
+    inputs=['x'],
+    outputs=['y'],
+    equation=Eqn
+)
+
+X = np.random.randn(3, 4)
+Y = einsum_reference_implementation(Eqn, (X,))
+
+expect(node, inputs=[X], outputs=[Y], name='test_einsum_transpose')
+```
+
+</details>
+
+
+### Elu
+There are 2 test cases, listed as following:
+<details>
+<summary>elu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Elu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=2.0
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+# expected output [-1.2642411, 0., 1.]
+y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_elu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_elu')
+```
+
+</details>
+<details>
+<summary>elu_default</summary>
+
+```python
+default_alpha = 1.0
+node = onnx.helper.make_node(
+    'Elu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
+expect(node, inputs=[x], outputs=[y],
+       name='test_elu_default')
+```
+
+</details>
+
+
+### Equal
+There are 2 test cases, listed as following:
+<details>
+<summary>equal</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Equal',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+z = np.equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_equal')
+```
+
+</details>
+<details>
+<summary>equal_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Equal',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+y = (np.random.randn(5) * 10).astype(np.int32)
+z = np.equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_equal_bcast')
+```
+
+</details>
+
+
+### Erf
+There are 1 test cases, listed as following:
+<details>
+<summary>erf</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Erf',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+y = np.vectorize(math.erf)(x).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_erf')
+```
+
+</details>
+
+
+### Exp
+There are 1 test cases, listed as following:
+<details>
+<summary>exp</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Exp',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.exp(x)  # expected output [0.36787945, 1., 2.71828175]
+expect(node, inputs=[x], outputs=[y],
+       name='test_exp_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.exp(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_exp')
+```
+
+</details>
+
+
+### Expand
+There are 2 test cases, listed as following:
+<details>
+<summary>dim_changed</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Expand',
+    inputs=['data', 'new_shape'],
+    outputs=['expanded'],
+)
+shape = [3, 1]
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[1.], [2.], [3.]]
+new_shape = [2, 1, 6]
+expanded = data * np.ones(new_shape, dtype=np.float32)
+#print(expanded)
+#[[[1., 1., 1., 1., 1., 1.],
+#  [2., 2., 2., 2., 2., 2.],
+#  [3., 3., 3., 3., 3., 3.]],
+#
+# [[1., 1., 1., 1., 1., 1.],
+#  [2., 2., 2., 2., 2., 2.],
+#  [3., 3., 3., 3., 3., 3.]]]
+new_shape = np.array(new_shape, dtype=np.int64)
+expect(node, inputs=[data, new_shape], outputs=[expanded],
+       name='test_expand_dim_changed')
+```
+
+</details>
+<details>
+<summary>dim_unchanged</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Expand',
+    inputs=['data', 'new_shape'],
+    outputs=['expanded'],
+)
+shape = [3, 1]
+new_shape = [3, 4]
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[1.], [2.], [3.]]
+expanded = np.tile(data, 4)
+#print(expanded)
+#[[1., 1., 1., 1.],
+# [2., 2., 2., 2.],
+# [3., 3., 3., 3.]]
+new_shape = np.array(new_shape, dtype=np.int64)
+expect(node, inputs=[data, new_shape], outputs=[expanded],
+       name='test_expand_dim_unchanged')
+```
+
+</details>
+
+
+### EyeLike
+There are 3 test cases, listed as following:
+<details>
+<summary>populate_off_main_diagonal</summary>
+
+```python
+shape = (4, 5)
+off_diagonal_offset = 1
+node = onnx.helper.make_node(
+    'EyeLike',
+    inputs=['x'],
+    outputs=['y'],
+    k=off_diagonal_offset,
+    dtype=onnx.TensorProto.FLOAT,
+)
+
+x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_eyelike_populate_off_main_diagonal')
+```
+
+</details>
+<details>
+<summary>with_dtype</summary>
+
+```python
+shape = (3, 4)
+node = onnx.helper.make_node(
+    'EyeLike',
+    inputs=['x'],
+    outputs=['y'],
+    dtype=onnx.TensorProto.DOUBLE,
+)
+
+x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+y = np.eye(shape[0], shape[1], dtype=np.float64)
+expect(node, inputs=[x], outputs=[y], name='test_eyelike_with_dtype')
+```
+
+</details>
+<details>
+<summary>without_dtype</summary>
+
+```python
+shape = (4, 4)
+node = onnx.helper.make_node(
+    'EyeLike',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+y = np.eye(shape[0], shape[1], dtype=np.int32)
+expect(node, inputs=[x], outputs=[y], name='test_eyelike_without_dtype')
+```
+
+</details>
+
+
+### Flatten
+There are 3 test cases, listed as following:
+<details>
+<summary>flatten</summary>
+
+```python
+shape = (2, 3, 4, 5)
+a = np.random.random_sample(shape).astype(np.float32)
+
+for i in range(len(shape)):
+    node = onnx.helper.make_node(
+        'Flatten',
+        inputs=['a'],
+        outputs=['b'],
+        axis=i,
+    )
+
+    new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
+    b = np.reshape(a, new_shape)
+    expect(node, inputs=[a], outputs=[b],
+           name='test_flatten_axis' + str(i))
+```
+
+</details>
+<details>
+<summary>flatten_negative_axis</summary>
+
+```python
+shape = (2, 3, 4, 5)
+a = np.random.random_sample(shape).astype(np.float32)
+
+for i in range(-len(shape), 0):
+    node = onnx.helper.make_node(
+        'Flatten',
+        inputs=['a'],
+        outputs=['b'],
+        axis=i,
+    )
+
+    new_shape = (np.prod(shape[0:i]).astype(int), -1)
+    b = np.reshape(a, new_shape)
+    expect(node, inputs=[a], outputs=[b],
+           name='test_flatten_negative_axis' + str(abs(i)))
+```
+
+</details>
+<details>
+<summary>flatten_with_default_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Flatten',
+    inputs=['a'],
+    outputs=['b'],  # Default value for axis: axis=1
+)
+
+shape = (5, 4, 3, 2)
+a = np.random.random_sample(shape).astype(np.float32)
+new_shape = (5, 24)
+b = np.reshape(a, new_shape)
+expect(node, inputs=[a], outputs=[b],
+       name='test_flatten_default_axis')
+```
+
+</details>
+
+
+### Floor
+There are 1 test cases, listed as following:
+<details>
+<summary>floor</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Floor',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1.5, 1.2, 2]).astype(np.float32)
+y = np.floor(x)  # expected output [-2., 1., 2.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_floor_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.floor(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_floor')
+```
+
+</details>
+
+
+### GRU
+There are 4 test cases, listed as following:
+<details>
+<summary>batchwise</summary>
+
+```python
+input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 6
+number_of_gates = 3
+weight_scale = 0.2
+layout = 1
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R'],
+    outputs=['Y', 'Y_h'],
+    hidden_size=hidden_size,
+    layout=layout
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+gru = GRU_Helper(X=input, W=W, R=R, layout=layout)
+Y, Y_h = gru.step()
+expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_gru_batchwise')
+```
+
+</details>
+<details>
+<summary>defaults</summary>
+
+```python
+input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 5
+weight_scale = 0.1
+number_of_gates = 3
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+gru = GRU_Helper(X=input, W=W, R=R)
+_, Y_h = gru.step()
+expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_gru_defaults')
+```
+
+</details>
+<details>
+<summary>initial_bias</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 3
+weight_scale = 0.1
+custom_bias = 0.1
+number_of_gates = 3
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+# Adding custom bias
+W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(np.float32)
+R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+gru = GRU_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = gru.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_gru_with_initial_bias')
+```
+
+</details>
+<details>
+<summary>seq_length</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
+                  [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 5
+number_of_gates = 3
+
+node = onnx.helper.make_node(
+    'GRU',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype(np.float32)
+R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(np.float32)
+
+# Adding custom bias
+W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+gru = GRU_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = gru.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_gru_seq_length')
+```
+
+</details>
+
+
+### Gather
+There are 4 test cases, listed as following:
+<details>
+<summary>gather_0</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=0,
+)
+data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+indices = np.array([0, 1, 3])
+y = np.take(data, indices, axis=0)
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_0')
+```
+
+</details>
+<details>
+<summary>gather_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=1,
+)
+data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+indices = np.array([0, 1, 3])
+y = np.take(data, indices, axis=1)
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_1')
+```
+
+</details>
+<details>
+<summary>gather_2d_indices</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=1,
+)
+data = np.random.randn(3, 3).astype(np.float32)
+indices = np.array([[0, 2]])
+y = np.take(data, indices, axis=1)
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_2d_indices')
+```
+
+</details>
+<details>
+<summary>gather_negative_indices</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gather',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=0,
+)
+data = np.arange(10).astype(np.float32)
+indices = np.array([0, -9, -10])
+y = np.take(data, indices, axis=0)
+
+# print(y)
+# [0. 1. 0.]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_negative_indices')
+```
+
+</details>
+
+
+### GatherElements
+There are 3 test cases, listed as following:
+<details>
+<summary>gather_elements_0</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'GatherElements',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1, 2],
+                 [3, 4]], dtype=np.float32)
+indices = np.array([[0, 0],
+                    [1, 0]], dtype=np.int32)
+
+y = gather_elements(data, indices, axis)
+# print(y) produces
+# [[1, 1],
+#  [4, 3]]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_elements_0')
+```
+
+</details>
+<details>
+<summary>gather_elements_1</summary>
+
+```python
+axis = 0
+node = onnx.helper.make_node(
+    'GatherElements',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1, 2, 3],
+                 [4, 5, 6],
+                 [7, 8, 9]], dtype=np.float32)
+indices = np.array([[1, 2, 0],
+                    [2, 0, 0]], dtype=np.int32)
+
+y = gather_elements(data, indices, axis)
+# print(y) produces
+# [[4, 8, 3],
+#  [7, 2, 3]]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_elements_1')
+```
+
+</details>
+<details>
+<summary>gather_elements_negative_indices</summary>
+
+```python
+axis = 0
+node = onnx.helper.make_node(
+    'GatherElements',
+    inputs=['data', 'indices'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1, 2, 3],
+                 [4, 5, 6],
+                 [7, 8, 9]], dtype=np.float32)
+indices = np.array([[-1, -2, 0],
+                    [-2, 0, 0]], dtype=np.int32)
+
+y = gather_elements(data, indices, axis)
+# print(y) produces
+# [[7, 5, 3],
+#  [4, 2, 3]]
+
+expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+       name='test_gather_elements_negative_indices')
+```
+
+</details>
+
+
+### GatherND
+There are 3 test cases, listed as following:
+<details>
+<summary>float32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GatherND',
+    inputs=['data', 'indices'],
+    outputs=['output'],
+)
+
+data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32)
+indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64)
+output = gather_nd_impl(data, indices, 0)
+expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32)
+assert (np.array_equal(output, expected_output))
+expect(node, inputs=[data, indices], outputs=[output],
+       name='test_gathernd_example_float32')
+```
+
+</details>
+<details>
+<summary>int32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GatherND',
+    inputs=['data', 'indices'],
+    outputs=['output'],
+)
+
+data = np.array([[0, 1], [2, 3]], dtype=np.int32)
+indices = np.array([[0, 0], [1, 1]], dtype=np.int64)
+output = gather_nd_impl(data, indices, 0)
+expected_output = np.array([0, 3], dtype=np.int32)
+assert (np.array_equal(output, expected_output))
+expect(node, inputs=[data, indices], outputs=[output],
+       name='test_gathernd_example_int32')
+```
+
+</details>
+<details>
+<summary>int32_batchdim_1</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GatherND',
+    inputs=['data', 'indices'],
+    outputs=['output'],
+    batch_dims=1,
+)
+
+data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32)
+indices = np.array([[1], [0]], dtype=np.int64)
+output = gather_nd_impl(data, indices, 1)
+expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32)
+assert (np.array_equal(output, expected_output))
+expect(node, inputs=[data, indices], outputs=[output],
+       name='test_gathernd_example_int32_batch_dim1')
+```
+
+</details>
+
+
+### Gemm
+There are 11 test cases, listed as following:
+<details>
+<summary>all_attributes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    alpha=0.25,
+    beta=0.35,
+    transA=1,
+    transB=1
+)
+a = np.random.ranf([4, 3]).astype(np.float32)
+b = np.random.ranf([5, 4]).astype(np.float32)
+c = np.random.ranf([1, 5]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_all_attributes')
+```
+
+</details>
+<details>
+<summary>alpha</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    alpha=0.5
+)
+a = np.random.ranf([3, 5]).astype(np.float32)
+b = np.random.ranf([5, 4]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, alpha=0.5)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_alpha')
+```
+
+</details>
+<details>
+<summary>beta</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    beta=0.5
+)
+a = np.random.ranf([2, 7]).astype(np.float32)
+b = np.random.ranf([7, 4]).astype(np.float32)
+c = np.random.ranf([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, beta=0.5)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_beta')
+```
+
+</details>
+<details>
+<summary>default_matrix_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([3, 6]).astype(np.float32)
+b = np.random.ranf([6, 4]).astype(np.float32)
+c = np.random.ranf([3, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_matrix_bias')
+```
+
+</details>
+<details>
+<summary>default_no_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b'],
+    outputs=['y']
+)
+a = np.random.ranf([2, 10]).astype(np.float32)
+b = np.random.ranf([10, 3]).astype(np.float32)
+y = gemm_reference_implementation(a, b)
+expect(node, inputs=[a, b], outputs=[y],
+       name='test_gemm_default_no_bias')
+```
+
+</details>
+<details>
+<summary>default_scalar_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([2, 3]).astype(np.float32)
+b = np.random.ranf([3, 4]).astype(np.float32)
+c = np.array(3.14).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_scalar_bias')
+```
+
+</details>
+<details>
+<summary>default_single_elem_vector_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([3, 7]).astype(np.float32)
+b = np.random.ranf([7, 3]).astype(np.float32)
+c = np.random.ranf([1]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_single_elem_vector_bias')
+```
+
+</details>
+<details>
+<summary>default_vector_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([2, 7]).astype(np.float32)
+b = np.random.ranf([7, 4]).astype(np.float32)
+c = np.random.ranf([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_vector_bias')
+```
+
+</details>
+<details>
+<summary>default_zero_bias</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y']
+)
+a = np.random.ranf([3, 5]).astype(np.float32)
+b = np.random.ranf([5, 4]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_default_zero_bias')
+```
+
+</details>
+<details>
+<summary>transposeA</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    transA=1
+)
+a = np.random.ranf([6, 3]).astype(np.float32)
+b = np.random.ranf([6, 4]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, transA=1)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_transposeA')
+```
+
+</details>
+<details>
+<summary>transposeB</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Gemm',
+    inputs=['a', 'b', 'c'],
+    outputs=['y'],
+    transB=1
+)
+a = np.random.ranf([3, 6]).astype(np.float32)
+b = np.random.ranf([4, 6]).astype(np.float32)
+c = np.zeros([1, 4]).astype(np.float32)
+y = gemm_reference_implementation(a, b, c, transB=1)
+expect(node, inputs=[a, b, c], outputs=[y],
+       name='test_gemm_transposeB')
+```
+
+</details>
+
+
+### GlobalAveragePool
+There are 2 test cases, listed as following:
+<details>
+<summary>globalaveragepool</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GlobalAveragePool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+expect(node, inputs=[x], outputs=[y], name='test_globalaveragepool')
+```
+
+</details>
+<details>
+<summary>globalaveragepool_precomputed</summary>
+
+```python
+
+node = onnx.helper.make_node(
+    'GlobalAveragePool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[[
+    [1, 2, 3],
+    [4, 5, 6],
+    [7, 8, 9],
+]]]).astype(np.float32)
+y = np.array([[[[5]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_globalaveragepool_precomputed')
+```
+
+</details>
+
+
+### GlobalMaxPool
+There are 2 test cases, listed as following:
+<details>
+<summary>globalmaxpool</summary>
+
+```python
+
+node = onnx.helper.make_node(
+    'GlobalMaxPool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+expect(node, inputs=[x], outputs=[y], name='test_globalmaxpool')
+```
+
+</details>
+<details>
+<summary>globalmaxpool_precomputed</summary>
+
+```python
+
+node = onnx.helper.make_node(
+    'GlobalMaxPool',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[[
+    [1, 2, 3],
+    [4, 5, 6],
+    [7, 8, 9],
+]]]).astype(np.float32)
+y = np.array([[[[9]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y], name='test_globalmaxpool_precomputed')
+```
+
+</details>
+
+
+### Gradient
+There are 2 test cases, listed as following:
+<details>
+<summary>gradient_scalar_add</summary>
+
+```python
+add_node = onnx.helper.make_node('Add',
+                                 ['a', 'b'], ['c'], name='my_add')
+gradient_node = onnx.helper.make_node(
+    'Gradient', ['a', 'b'],
+    ['dc_da', 'dc_db'], name='my_gradient',
+    domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+    xs=['a', 'b'], y='c')
+
+a = np.array(1.0).astype(np.float32)
+b = np.array(2.0).astype(np.float32)
+c = a + b
+# dc / da = d(a+b) / da = 1
+dc_da = np.array(1).astype(np.float32)
+# db / db = d(a+b) / db = 1
+dc_db = np.array(1).astype(np.float32)
+
+graph = onnx.helper.make_graph(
+    nodes=[add_node, gradient_node],
+    name='GradientOfAdd',
+    inputs=[
+        onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
+                                           [])],
+    outputs=[
+        onnx.helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('dc_da',
+                                           onnx.TensorProto.FLOAT, []),
+        onnx.helper.make_tensor_value_info('dc_db',
+                                           onnx.TensorProto.FLOAT, [])])
+opsets = [
+    onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+    onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]
+model = onnx.helper.make_model(
+    graph,
+    producer_name='backend-test',
+    opset_imports=opsets)
+expect(model, inputs=[a, b], outputs=[c, dc_da, dc_db],
+       name='test_gradient_of_add')
+```
+
+</details>
+<details>
+<summary>gradient_scalar_add_and_mul</summary>
+
+```python
+add_node = onnx.helper.make_node('Add',
+                                 ['a', 'b'], ['c'], name='my_add')
+mul_node = onnx.helper.make_node('Mul',
+                                 ['c', 'a'], ['d'], name='my_mul')
+gradient_node = onnx.helper.make_node(
+    'Gradient', ['a', 'b'],
+    ['dd_da', 'dd_db'], name='my_gradient',
+    domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+    xs=['a', 'b'], y='d')
+
+a = np.array(1.0).astype(np.float32)
+b = np.array(2.0).astype(np.float32)
+c = a + b
+# d = a * c = a * (a + b)
+d = a * c
+# dd / da = d(a*a+a*b) / da = 2 * a + b
+dd_da = (2 * a + b).astype(np.float32)
+# dd / db = d(a*a+a*b) / db = a
+dd_db = a
+
+graph = onnx.helper.make_graph(
+    nodes=[add_node, mul_node, gradient_node],
+    name='GradientOfTwoOperators',
+    inputs=[
+        onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
+                                           [])],
+    outputs=[
+        onnx.helper.make_tensor_value_info('d', onnx.TensorProto.FLOAT,
+                                           []),
+        onnx.helper.make_tensor_value_info('dd_da',
+                                           onnx.TensorProto.FLOAT, []),
+        onnx.helper.make_tensor_value_info('dd_db',
+                                           onnx.TensorProto.FLOAT, [])])
+
+opsets = [
+    onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+    onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]
+model = onnx.helper.make_model(graph,
+    producer_name='backend-test',
+    opset_imports=opsets)
+expect(model, inputs=[a, b], outputs=[d, dd_da, dd_db],
+       name='test_gradient_of_add_and_mul')
+```
+
+</details>
+
+
+### Greater
+There are 4 test cases, listed as following:
+<details>
+<summary>greater</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Greater',
+    inputs=['x', 'y'],
+    outputs=['greater'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.greater(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater')
+```
+
+</details>
+<details>
+<summary>greater</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GreaterOrEqual',
+    inputs=['x', 'y'],
+    outputs=['greater_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.greater_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater_equal')
+```
+
+</details>
+<details>
+<summary>greater_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Greater',
+    inputs=['x', 'y'],
+    outputs=['greater'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.greater(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater_bcast')
+```
+
+</details>
+<details>
+<summary>greater_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GreaterOrEqual',
+    inputs=['x', 'y'],
+    outputs=['greater_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.greater_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_greater_equal_bcast')
+```
+
+</details>
+
+
+### GridSample
+There are 3 test cases, listed as following:
+<details>
+<summary>gridsample</summary>
+
+```python
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bilinear',
+    padding_mode='zeros',
+    align_corners=0,
+)
+# X shape, [N, C, H, W] - [1, 1, 4, 4]
+X = np.array(
+    [
+        [
+            [
+                [0., 1., 2., 3.],
+                [4., 5., 6., 7.],
+                [8., 9., 10., 11.],
+                [12., 13., 14., 15.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
+Grid = np.array(
+    [
+        [
+            [
+                [-1.0000, -1.0000],
+                [-0.6000, -1.0000],
+                [-0.2000, -1.0000],
+                [0.2000, -1.0000],
+                [0.6000, -1.0000],
+                [1.0000, -1.0000]
+            ],
+            [
+                [-1.0000, -0.6000],
+                [-0.6000, -0.6000],
+                [-0.2000, -0.6000],
+                [0.2000, -0.6000],
+                [0.6000, -0.6000],
+                [1.0000, -0.6000]
+            ],
+            [
+                [-1.0000, -0.2000],
+                [-0.6000, -0.2000],
+                [-0.2000, -0.2000],
+                [0.2000, -0.2000],
+                [0.6000, -0.2000],
+                [1.0000, -0.2000]
+            ],
+            [
+                [-1.0000, 0.2000],
+                [-0.6000, 0.2000],
+                [-0.2000, 0.2000],
+                [0.2000, 0.2000],
+                [0.6000, 0.2000],
+                [1.0000, 0.2000]
+            ],
+            [
+                [-1.0000, 0.6000],
+                [-0.6000, 0.6000],
+                [-0.2000, 0.6000],
+                [0.2000, 0.6000],
+                [0.6000, 0.6000],
+                [1.0000, 0.6000]
+            ],
+            [
+                [-1.0000, 1.0000],
+                [-0.6000, 1.0000],
+                [-0.2000, 1.0000],
+                [0.2000, 1.0000],
+                [0.6000, 1.0000],
+                [1.0000, 1.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
+Y = np.array(
+    [
+        [
+            [
+                [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
+                [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
+                [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
+                [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
+                [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
+                [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+expect(node, inputs=[X, Grid], outputs=[Y],
+       name='test_gridsample')
+```
+
+</details>
+<details>
+<summary>gridsample_mode_aligncorners</summary>
+
+```python
+# X shape, [N, C, H, W] - [1, 1, 3, 2]
+X = np.array(
+    [
+        [
+            [
+                [0., 1.],
+                [2., 3.],
+                [4., 5.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+Grid = np.array(
+    [
+        [
+            [
+                [-1.0000, -1.0000],
+                [-0.5000, -0.5000],
+                [-0.2000, -0.2000],
+                [0.0000, 0.0000]
+            ],
+
+            [
+                [0.0000, 0.0000],
+                [-0.2000, -0.2000],
+                [0.5000, 0.5000],
+                [1.0000, 1.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+# setting mode = 'bilinear', default align_corners = 0
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bilinear',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_bilinear = np.array(
+    [
+        [
+            [
+                [0.0000, 0.5000, 1.7000, 2.5000],
+                [2.5000, 1.7000, 4.5000, 1.2500]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_bilinear],
+       name='test_gridsample_bilinear')
+
+# setting mode = 'bilinear', align_corners = 1
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bilinear',
+    align_corners=1,
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_align_corners = np.array(
+    [
+        [
+            [
+                [0.0000, 1.2500, 2.0000, 2.5000],
+                [2.5000, 2.0000, 3.7500, 5.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_align_corners],
+       name='test_gridsample_aligncorners_true')
+
+# setting mode = 'nearest'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='nearest',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_nearest = np.array(
+    [
+        [
+            [
+                [0., 0., 2., 2.],
+                [2., 2., 5., 0.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_nearest],
+       name='test_gridsample_nearest')
+
+# setting mode = 'bicubic'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    mode='bicubic',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_bicubic = np.array(
+    [
+        [
+            [
+                [-0.1406, 0.3828, 1.7556, 2.9688],
+                [2.9688, 1.7556, 5.1445, 1.3906]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_bicubic],
+       name='test_gridsample_bicubic')
+```
+
+</details>
+<details>
+<summary>gridsample_paddingmode</summary>
+
+```python
+# X shape, [N, C, H, W] - [1, 1, 3, 2]
+X = np.array(
+    [
+        [
+            [
+                [0., 1.],
+                [2., 3.],
+                [4., 5.]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+Grid = np.array(
+    [
+        [
+            [
+                [-10.0000, -10.0000],
+                [-5.0000, -5.0000],
+                [-0.2000, -0.2000],
+                [10.0000, 10.0000]
+            ],
+
+            [
+                [10.0000, 10.0000],
+                [-0.2000, -0.2000],
+                [5.0000, 5.0000],
+                [10.0000, 10.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+# setting padding_mode = 'zeros'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    padding_mode='zeros',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_zeros = np.array(
+    [
+        [
+            [
+                [0.0000, 0.0000, 1.7000, 0.0000],
+                [0.0000, 1.7000, 0.0000, 0.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_zeros],
+       name='test_gridsample_zeros_padding')
+
+# setting padding_mode = 'border'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    padding_mode='border',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_border = np.array(
+    [
+        [
+            [
+                [0.0000, 0.0000, 1.7000, 5.0000],
+                [5.0000, 1.7000, 5.0000, 5.0000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_border],
+       name='test_gridsample_border_padding')
+
+# setting padding_mode = 'reflection'
+node = onnx.helper.make_node(
+    'GridSample',
+    inputs=['X', 'Grid'],
+    outputs=['Y'],
+    padding_mode='reflection',
+)
+# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+Y_reflection = np.array(
+    [
+        [
+            [
+                [2.5000, 0.0000, 1.7000, 2.5000],
+                [2.5000, 1.7000, 5.0000, 2.5000]
+            ]
+        ]
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, Grid], outputs=[Y_reflection],
+       name='test_gridsample_reflection_padding')
+```
+
+</details>
+
+
+### HardSigmoid
+There are 2 test cases, listed as following:
+<details>
+<summary>hardsigmoid</summary>
+
+```python
+node = onnx.helper.make_node(
+    'HardSigmoid',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=0.5,
+    beta=0.6
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardsigmoid_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x * 0.5 + 0.6, 0, 1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardsigmoid')
+```
+
+</details>
+<details>
+<summary>hardsigmoid_default</summary>
+
+```python
+default_alpha = 0.2
+default_beta = 0.5
+node = onnx.helper.make_node(
+    'HardSigmoid',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x * default_alpha + default_beta, 0, 1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardsigmoid_default')
+```
+
+</details>
+
+
+### HardSwish
+There are 1 test cases, listed as following:
+<details>
+<summary>hardswish</summary>
+
+```python
+node = onnx.helper.make_node(
+    'HardSwish',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = hardswish(x)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardswish')
+```
+
+</details>
+
+
+### Hardmax
+There are 2 test cases, listed as following:
+<details>
+<summary>hardmax</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2],
+              [0, 1, 2, 3]]).astype(np.float32)
+# expect result:
+# [[1. 0. 0. 0.]
+# [0. 1. 0. 0.]
+# [0. 0. 1. 0.]
+# [0. 0. 0. 1.]]
+y = hardmax(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_example')
+
+# For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
+x = np.array([[3, 3, 3, 1]]).astype(np.float32)
+# expect result:
+# [[1, 0, 0, 0]]
+y = hardmax(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_one_hot')
+```
+
+</details>
+<details>
+<summary>hardmax_axis</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=0,
+)
+y = hardmax(x, axis=0)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_axis_0')
+
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=1,
+)
+y = hardmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_axis_1')
+
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=2,
+)
+y = hardmax(x, axis=2)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_axis_2')
+
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=-1,
+)
+y = hardmax(x, axis=-1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_negative_axis')
+
+# default axis is -1
+node = onnx.helper.make_node(
+    'Hardmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_hardmax_default_axis')
+```
+
+</details>
+
+
+### Identity
+There are 3 test cases, listed as following:
+<details>
+<summary>identity</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Identity',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+expect(node, inputs=[data], outputs=[data],
+       name='test_identity')
+```
+
+</details>
+<details>
+<summary>identity_opt</summary>
+
+```python
+ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['opt_in'],
+    outputs=['opt_out']
+)
+
+x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+
+expect(identity_node, inputs=[x], outputs=[x], name='test_identity_opt',
+       opset_imports=[onnx.helper.make_opsetid("", 16)],
+       input_type_protos=[opt_in_tp],
+       output_type_protos=[opt_in_tp])
+```
+
+</details>
+<details>
+<summary>sequence</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Identity',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+data = [
+    np.array([[[
+        [1, 2],
+        [3, 4],
+    ]]], dtype=np.float32),
+    np.array([[[
+        [2, 3],
+        [1, 5],
+    ]]], dtype=np.float32)]
+
+expect(node, inputs=[data], outputs=[data], name='test_identity_sequence')
+```
+
+</details>
+
+
+### If
+There are 3 test cases, listed as following:
+<details>
+<summary>if</summary>
+
+```python
+# Given a bool scalar input cond.
+# return constant tensor x if cond is True, otherwise return constant tensor y.
+
+then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [5])
+else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [5])
+
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
+
+then_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['then_out'],
+    value=onnx.numpy_helper.from_array(x)
+)
+
+else_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['else_out'],
+    value=onnx.numpy_helper.from_array(y)
+)
+
+then_body = onnx.helper.make_graph(
+    [then_const_node],
+    'then_body',
+    [],
+    [then_out]
+)
+
+else_body = onnx.helper.make_graph(
+    [else_const_node],
+    'else_body',
+    [],
+    [else_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['cond'],
+    outputs=['res'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+cond = np.array(1).astype(bool)
+res = x if cond else y
+expect(if_node, inputs=[cond], outputs=[res], name='test_if',
+       opset_imports=[onnx.helper.make_opsetid("", 11)])
+```
+
+</details>
+<details>
+<summary>if_optional</summary>
+
+```python
+# Given a bool scalar input cond, return an empty optional sequence of
+# tensor if True, return an optional sequence with value x
+# (the input optional sequence) otherwise.
+
+ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+
+then_out_tensor_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp)
+then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp)
+then_out = onnx.helper.make_value_info('optional_empty', then_out_opt_tp)
+
+else_out_tensor_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp)
+else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp)
+else_out = onnx.helper.make_value_info('else_opt', else_out_opt_tp)
+
+x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+cond = np.array(0).astype(bool)
+res = compute_if_outputs(x, cond)
+
+opt_empty_in = onnx.helper.make_node(
+    'Optional',
+    inputs=[],
+    outputs=['optional_empty'],
+    type=seq_in_tp
+)
+
+then_body = onnx.helper.make_graph(
+    [opt_empty_in],
+    'then_body',
+    [],
+    [then_out]
+)
+
+else_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.numpy_helper.from_array(x[0])
+)
+
+else_seq_node = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['x'],
+    outputs=['else_seq']
+)
+
+else_optional_seq_node = onnx.helper.make_node(
+    'Optional',
+    inputs=['else_seq'],
+    outputs=['else_opt']
+)
+
+else_body = onnx.helper.make_graph(
+    [else_const_node, else_seq_node, else_optional_seq_node],
+    'else_body',
+    [],
+    [else_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['cond'],
+    outputs=['sequence'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+expect(if_node, inputs=[cond], outputs=[res], name='test_if_opt',
+       output_type_protos=[else_out_opt_tp],
+       opset_imports=[onnx.helper.make_opsetid("", 16)])
+```
+
+</details>
+<details>
+<summary>if_seq</summary>
+
+```python
+# Given a bool scalar input cond.
+# return constant sequence x if cond is True, otherwise return constant sequence y.
+
+then_out = onnx.helper.make_tensor_sequence_value_info('then_out', onnx.TensorProto.FLOAT, shape=[5])
+else_out = onnx.helper.make_tensor_sequence_value_info('else_out', onnx.TensorProto.FLOAT, shape=[5])
+
+x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)]
+
+then_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.numpy_helper.from_array(x[0])
+)
+
+then_seq_node = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['x'],
+    outputs=['then_out']
+)
+
+else_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['y'],
+    value=onnx.numpy_helper.from_array(y[0])
+)
+
+else_seq_node = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['y'],
+    outputs=['else_out']
+)
+
+then_body = onnx.helper.make_graph(
+    [then_const_node, then_seq_node],
+    'then_body',
+    [],
+    [then_out]
+)
+
+else_body = onnx.helper.make_graph(
+    [else_const_node, else_seq_node],
+    'else_body',
+    [],
+    [else_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['cond'],
+    outputs=['res'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+cond = np.array(1).astype(bool)
+res = x if cond else y
+expect(if_node, inputs=[cond], outputs=[res], name='test_if_seq',
+       opset_imports=[onnx.helper.make_opsetid("", 13)])
+```
+
+</details>
+
+
+### InstanceNormalization
+There are 1 test cases, listed as following:
+<details>
+<summary>instancenormalization</summary>
+
+```python
+def _instancenorm_test_mode(x, s, bias, epsilon=1e-5):  # type: ignore
+    dims_x = len(x.shape)
+    axis = tuple(range(2, dims_x))
+    mean = np.mean(x, axis=axis, keepdims=True)
+    var = np.var(x, axis=axis, keepdims=True)
+    dim_ones = (1,) * (dims_x - 2)
+    s = s.reshape(-1, *dim_ones)
+    bias = bias.reshape(-1, *dim_ones)
+    return s * (x - mean) / np.sqrt(var + epsilon) + bias
+
+# input size: (1, 2, 1, 3)
+x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
+s = np.array([1.0, 1.5]).astype(np.float32)
+bias = np.array([0, 1]).astype(np.float32)
+y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'InstanceNormalization',
+    inputs=['x', 's', 'bias'],
+    outputs=['y'],
+)
+
+# output size: (1, 2, 1, 3)
+expect(node, inputs=[x, s, bias], outputs=[y],
+       name='test_instancenorm_example')
+
+# input size: (2, 3, 4, 5)
+x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+s = np.random.randn(3).astype(np.float32)
+bias = np.random.randn(3).astype(np.float32)
+epsilon = 1e-2
+y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'InstanceNormalization',
+    inputs=['x', 's', 'bias'],
+    outputs=['y'],
+    epsilon=epsilon,
+)
+
+# output size: (2, 3, 4, 5)
+expect(node, inputs=[x, s, bias], outputs=[y],
+       name='test_instancenorm_epsilon')
+```
+
+</details>
+
+
+### IsInf
+There are 3 test cases, listed as following:
+<details>
+<summary>infinity</summary>
+
+```python
+node = onnx.helper.make_node('IsInf',
+                             inputs=['x'],
+                             outputs=['y'],
+                             )
+
+x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
+             dtype=np.float32)
+y = np.isinf(x)
+expect(node, inputs=[x], outputs=[y], name='test_isinf')
+```
+
+</details>
+<details>
+<summary>negative_infinity_only</summary>
+
+```python
+node = onnx.helper.make_node('IsInf',
+                             inputs=['x'],
+                             outputs=['y'],
+                             detect_positive=0
+                             )
+
+x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
+             dtype=np.float32)
+y = np.isneginf(x)
+expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')
+```
+
+</details>
+<details>
+<summary>positive_infinity_only</summary>
+
+```python
+node = onnx.helper.make_node('IsInf',
+                             inputs=['x'],
+                             outputs=['y'],
+                             detect_negative=0
+                             )
+
+x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
+             dtype=np.float32)
+y = np.isposinf(x)
+expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')
+```
+
+</details>
+
+
+### IsNaN
+There are 1 test cases, listed as following:
+<details>
+<summary>isnan</summary>
+
+```python
+node = onnx.helper.make_node(
+    'IsNaN',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([3.0, np.nan, 4.0, np.nan], dtype=np.float32)
+y = np.isnan(x)
+expect(node, inputs=[x], outputs=[y], name='test_isnan')
+```
+
+</details>
+
+
+### LRN
+There are 2 test cases, listed as following:
+<details>
+<summary>default</summary>
+
+```python
+alpha = 0.0001
+beta = 0.75
+bias = 1.0
+nsize = 3
+node = onnx.helper.make_node(
+    'LRN',
+    inputs=['x'],
+    outputs=['y'],
+    size=3
+)
+x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+for n, c, h, w in np.ndindex(x.shape):
+    square_sum[n, c, h, w] = sum(x[n,
+                                   max(0, c - int(math.floor((nsize - 1) / 2))):min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
+                                   h,
+                                   w] ** 2)
+y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+expect(node, inputs=[x], outputs=[y],
+       name='test_lrn_default')
+```
+
+</details>
+<details>
+<summary>lrn</summary>
+
+```python
+alpha = 0.0002
+beta = 0.5
+bias = 2.0
+nsize = 3
+node = onnx.helper.make_node(
+    'LRN',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=alpha,
+    beta=beta,
+    bias=bias,
+    size=nsize
+)
+x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+for n, c, h, w in np.ndindex(x.shape):
+    square_sum[n, c, h, w] = sum(x[n,
+                                   max(0, c - int(math.floor((nsize - 1) / 2))):min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
+                                   h,
+                                   w] ** 2)
+y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+expect(node, inputs=[x], outputs=[y],
+       name='test_lrn')
+```
+
+</details>
+
+
+### LSTM
+There are 4 test cases, listed as following:
+<details>
+<summary>batchwise</summary>
+
+```python
+input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 7
+weight_scale = 0.3
+number_of_gates = 4
+layout = 1
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R'],
+    outputs=['Y', 'Y_h'],
+    hidden_size=hidden_size,
+    layout=layout
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+lstm = LSTM_Helper(X=input, W=W, R=R, layout=layout)
+Y, Y_h = lstm.step()
+expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_lstm_batchwise')
+```
+
+</details>
+<details>
+<summary>defaults</summary>
+
+```python
+input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 3
+weight_scale = 0.1
+number_of_gates = 4
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+lstm = LSTM_Helper(X=input, W=W, R=R)
+_, Y_h = lstm.step()
+expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_lstm_defaults')
+```
+
+</details>
+<details>
+<summary>initial_bias</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 4
+weight_scale = 0.1
+custom_bias = 0.1
+number_of_gates = 4
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+# Adding custom bias
+W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(np.float32)
+R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+B = np.concatenate((W_B, R_B), 1)
+
+lstm = LSTM_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = lstm.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_lstm_with_initial_bias')
+```
+
+</details>
+<details>
+<summary>peepholes</summary>
+
+```python
+input = np.array([[[1., 2., 3., 4.], [5., 6., 7., 8.]]]).astype(np.float32)
+
+input_size = 4
+hidden_size = 3
+weight_scale = 0.1
+number_of_gates = 4
+number_of_peepholes = 3
+
+node = onnx.helper.make_node(
+    'LSTM',
+    inputs=['X', 'W', 'R', 'B', 'sequence_lens', 'initial_h', 'initial_c', 'P'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+# Initializing Inputs
+W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32)
+seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32)
+init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype(np.float32)
+
+lstm = LSTM_Helper(X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h)
+_, Y_h = lstm.step()
+expect(node, inputs=[input, W, R, B, seq_lens, init_h, init_c, P], outputs=[Y_h.astype(np.float32)],
+       name='test_lstm_with_peepholes')
+```
+
+</details>
+
+
+### LayerNormalization
+There are 4 test cases, listed as following:
+<details>
+<summary>d</summary>
+
+```python
+X = np.random.randn(3, 4).astype(np.float32)
+
+def case(axis: int) -> None:
+    normalized_shape = calculate_normalized_shape(X.shape, axis)
+    W = np.random.randn(*normalized_shape).astype(np.float32)
+    B = np.random.randn(*normalized_shape).astype(np.float32)
+    Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
+
+    node = onnx.helper.make_node(
+        'LayerNormalization',
+        inputs=['X', 'W', 'B'],
+        outputs=['Y', 'Mean', 'InvStdDev'],
+        axis=axis,
+    )
+
+    if axis < 0:
+        name = f'test_layer_normalization_2d_axis_negative_{-axis}'
+    else:
+        name = f'test_layer_normalization_2d_axis{axis}'
+
+    expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+           name=name)
+
+for i in range(len(X.shape)):
+    case(i)
+    case(i - len(X.shape))
+```
+
+</details>
+<details>
+<summary>d_epsilon</summary>
+
+```python
+X = np.random.randn(2, 3, 5).astype(np.float32)
+
+def case(axis: int) -> None:
+    normalized_shape = calculate_normalized_shape(X.shape, axis)
+    W = np.random.randn(*normalized_shape).astype(np.float32)
+    B = np.random.randn(*normalized_shape).astype(np.float32)
+    Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+    node = onnx.helper.make_node(
+        'LayerNormalization',
+        inputs=['X', 'W', 'B'],
+        outputs=['Y', 'Mean', 'InvStdDev'],
+        axis=axis,
+        epsilon=1e-1
+    )
+
+    if axis < 0:
+        name = f'test_layer_normalization_3d_axis_negative_{-axis}_epsilon'
+    else:
+        name = f'test_layer_normalization_3d_axis{axis}_epsilon'
+
+    expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+           name=name)
+
+for i in range(len(X.shape)):
+    case(i)
+    case(i - len(X.shape))
+```
+
+</details>
+<details>
+<summary>default_axis</summary>
+
+```python
+X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+# Default axis in LayerNormalization is -1.
+normalized_shape = calculate_normalized_shape(X.shape, -1)
+W = np.random.randn(*normalized_shape).astype(np.float32)
+B = np.random.randn(*normalized_shape).astype(np.float32)
+# Axis is default to -1 in the reference implementation.
+Y, mean, inv_std_dev = _layer_normalization(X, W, B)
+
+# Not specifying axis attribute means -1.
+node = onnx.helper.make_node(
+    'LayerNormalization',
+    inputs=['X', 'W', 'B'],
+    outputs=['Y', 'Mean', 'InvStdDev']
+)
+
+expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+        name='test_layer_normalization_default_axis')
+```
+
+</details>
+<details>
+<summary>layernormalization</summary>
+
+```python
+X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+def case(axis: int) -> None:
+    normalized_shape = calculate_normalized_shape(X.shape, axis)
+    W = np.random.randn(*normalized_shape).astype(np.float32)
+    B = np.random.randn(*normalized_shape).astype(np.float32)
+    Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+
+    node = onnx.helper.make_node(
+        'LayerNormalization',
+        inputs=['X', 'W', 'B'],
+        outputs=['Y', 'Mean', 'InvStdDev'],
+        axis=axis,
+    )
+
+    if axis < 0:
+        name = f'test_layer_normalization_4d_axis_negative_{-axis}'
+    else:
+        name = f'test_layer_normalization_4d_axis{axis}'
+
+    expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+           name=name)
+
+for i in range(len(X.shape)):
+    case(i)
+    case(i - len(X.shape))
+```
+
+</details>
+
+
+### LeakyRelu
+There are 2 test cases, listed as following:
+<details>
+<summary>leakyrelu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LeakyRelu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=0.1
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+# expected output [-0.1, 0., 1.]
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+expect(node, inputs=[x], outputs=[y],
+       name='test_leakyrelu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+expect(node, inputs=[x], outputs=[y],
+       name='test_leakyrelu')
+```
+
+</details>
+<details>
+<summary>leakyrelu_default</summary>
+
+```python
+default_alpha = 0.01
+node = onnx.helper.make_node(
+    'LeakyRelu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha
+expect(node, inputs=[x], outputs=[y],
+       name='test_leakyrelu_default')
+```
+
+</details>
+
+
+### Less
+There are 4 test cases, listed as following:
+<details>
+<summary>less</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Less',
+    inputs=['x', 'y'],
+    outputs=['less'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.less(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less')
+```
+
+</details>
+<details>
+<summary>less</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LessOrEqual',
+    inputs=['x', 'y'],
+    outputs=['less_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = np.less_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less_equal')
+```
+
+</details>
+<details>
+<summary>less_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Less',
+    inputs=['x', 'y'],
+    outputs=['less'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.less(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less_bcast')
+```
+
+</details>
+<details>
+<summary>less_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LessOrEqual',
+    inputs=['x', 'y'],
+    outputs=['less_equal'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = np.less_equal(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_less_equal_bcast')
+```
+
+</details>
+
+
+### Log
+There are 1 test cases, listed as following:
+<details>
+<summary>log</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Log',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([1, 10]).astype(np.float32)
+y = np.log(x)  # expected output [0., 2.30258512]
+expect(node, inputs=[x], outputs=[y],
+       name='test_log_example')
+
+x = np.exp(np.random.randn(3, 4, 5).astype(np.float32))
+y = np.log(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_log')
+```
+
+</details>
+
+
+### LogSoftmax
+There are 2 test cases, listed as following:
+<details>
+<summary>logsoftmax</summary>
+
+```python
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[-1, 0, 1]]).astype(np.float32)
+# expected output
+# [[-2.4076061 -1.407606  -0.407606 ]]
+y = logsoftmax(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_example_1')
+```
+
+</details>
+<details>
+<summary>logsoftmax_axis</summary>
+
+```python
+x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]
+             ).astype(np.float32)
+# expected output
+# [[-3.4401896  -2.4401896  -1.4401896  -0.44018966]
+# [-3.4401896  -2.4401896  -1.4401896  -0.44018966]]
+y = logsoftmax(x)
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_large_number')
+
+x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=0,
+)
+y = logsoftmax(x, axis=0)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_axis_0')
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=1,
+)
+y = logsoftmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_axis_1')
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=2,
+)
+y = logsoftmax(x, axis=2)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_axis_2')
+
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=-1,
+)
+y = logsoftmax(x, axis=-1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_negative_axis')
+
+# default axis is -1
+node = onnx.helper.make_node(
+    'LogSoftmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_logsoftmax_default_axis')
+```
+
+</details>
+
+
+### Loop
+There are 3 test cases, listed as following:
+<details>
+<summary>loop_11</summary>
+
+```python
+# Given a tensor x of values [x1, ..., xN], and initial tensor y
+# sum up its elements using a scan
+# returning the final state (y+x1+x2+...+xN) as well the scan_output
+# [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
+
+y_in = onnx.helper.make_tensor_value_info('y_in', onnx.TensorProto.FLOAT, [1])
+y_out = onnx.helper.make_tensor_value_info('y_out', onnx.TensorProto.FLOAT, [1])
+scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [1])
+cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+y = np.array([-2]).astype(np.float32)
+
+x_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_x',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=x.shape,
+        vals=x.flatten().astype(float),
+    )
+)
+
+one_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['one'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_one',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[1]
+    )
+)
+
+i_add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['iter_count', 'one'],
+    outputs=['end']
+)
+
+start_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['iter_count'],
+    outputs=['slice_start'],
+    axes=[0]
+)
+
+end_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['end'],
+    outputs=['slice_end'],
+    axes=[0]
+)
+
+slice_node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'slice_start', 'slice_end'],
+    outputs=['slice_out']
+)
+
+y_add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['y_in', 'slice_out'],
+    outputs=['y_out']
+)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['cond_in'],
+    outputs=['cond_out']
+)
+
+scan_identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['y_out'],
+    outputs=['scan_out']
+)
+
+loop_body = onnx.helper.make_graph(
+    [identity_node, x_const_node, one_const_node, i_add_node,
+     start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node,
+     scan_identity_node],
+    'loop_body',
+    [iter_count, cond_in, y_in],
+    [cond_out, y_out, scan_out]
+)
+
+node = onnx.helper.make_node(
+    'Loop',
+    inputs=['trip_count', 'cond', 'y'],
+    outputs=['res_y', 'res_scan'],
+    body=loop_body
+)
+
+trip_count = np.array(5).astype(np.int64)
+res_y = np.array([13]).astype(np.float32)
+cond = np.array(1).astype(bool)
+res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
+expect(node, inputs=[trip_count, cond, y], outputs=[res_y, res_scan],
+       name='test_loop11', opset_imports=[onnx.helper.make_opsetid("", 11)])
+```
+
+</details>
+<details>
+<summary>loop_13</summary>
+
+```python
+# Given a tensor x of values [x1, ..., xN],
+# Return a sequence of tensors of
+#   [[x1], [x1, x2], ..., [x1, ..., xN]]
+
+seq_in = onnx.helper.make_tensor_sequence_value_info('seq_in', onnx.TensorProto.FLOAT, None)
+seq_out = onnx.helper.make_tensor_sequence_value_info('seq_out', onnx.TensorProto.FLOAT, None)
+cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+x_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_x',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=x.shape,
+        vals=x.flatten().astype(float),
+    )
+)
+
+one_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['one'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_one',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[1]
+    )
+)
+
+zero_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['slice_start'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_zero',
+        data_type=onnx.TensorProto.INT64,
+        dims=(1,),
+        vals=[0]
+    )
+)
+
+axes_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['axes'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_axes',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[0]
+    )
+)
+
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['iter_count', 'one'],
+    outputs=['end']
+)
+
+end_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['end', 'axes'],
+    outputs=['slice_end']
+)
+
+slice_node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'slice_start', 'slice_end'],
+    outputs=['slice_out']
+)
+
+insert_node = onnx.helper.make_node(
+    'SequenceInsert',
+    inputs=['seq_in', 'slice_out'],
+    outputs=['seq_out']
+)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['cond_in'],
+    outputs=['cond_out']
+)
+
+loop_body = onnx.helper.make_graph(
+    [identity_node, x_const_node, one_const_node, zero_const_node, add_node,
+     axes_node, end_unsqueeze_node, slice_node, insert_node],
+    'loop_body',
+    [iter_count, cond_in, seq_in],
+    [cond_out, seq_out]
+)
+
+node = onnx.helper.make_node(
+    'Loop',
+    inputs=['trip_count', 'cond', 'seq_empty'],
+    outputs=['seq_res'],
+    body=loop_body
+)
+
+trip_count = np.array(5).astype(np.int64)
+seq_empty: List[Any] = []
+seq_res = [x[:int(i)] for i in x]
+cond = np.array(1).astype(bool)
+expect(node, inputs=[trip_count, cond, seq_empty], outputs=[seq_res],
+       name='test_loop13_seq', opset_imports=[onnx.helper.make_opsetid("", 13)],
+       input_type_protos=[onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, trip_count.shape),
+                          onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                          onnx.helper.make_sequence_type_proto(
+                              onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []))])
+```
+
+</details>
+<details>
+<summary>loop_16_none</summary>
+
+```python
+# Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
+# Return a concatenated sequence of tensors of
+#   [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
+
+ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
+seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+opt_in = onnx.helper.make_value_info('opt_seq_in', opt_in_tp)
+seq_out = onnx.helper.make_tensor_sequence_value_info('seq_out', onnx.TensorProto.FLOAT, [])
+cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+x0 = np.array(0).astype(np.float32)
+x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+optional_has_elem_node = onnx.helper.make_node(
+    'OptionalHasElement',
+    inputs=['opt_seq_in'],
+    outputs=['optional_has_elem']
+)
+
+optional_is_none = onnx.helper.make_node(
+    'Not',
+    inputs=['optional_has_elem'],
+    outputs=['optional_is_none']
+)
+
+optional_get_elem = onnx.helper.make_node(
+    'OptionalGetElement',
+    inputs=['opt_seq_in'],
+    outputs=['seq_in']
+)
+
+constant_in = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['constant_in'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=(),
+        vals=[0]
+    )
+)
+
+seq_const_in = onnx.helper.make_node(
+    'SequenceConstruct',
+    inputs=['constant_in'],
+    outputs=['init_seq_in']
+)
+
+then_seq_out = onnx.helper.make_tensor_sequence_value_info('init_seq_in', onnx.TensorProto.FLOAT, [])
+then_body = onnx.helper.make_graph(
+    [constant_in, seq_const_in],
+    'then_body',
+    [],
+    [then_seq_out]
+)
+
+else_seq_out = onnx.helper.make_tensor_sequence_value_info('seq_in', onnx.TensorProto.FLOAT, [])
+else_body = onnx.helper.make_graph(
+    [optional_get_elem],
+    'else_body',
+    [],
+    [else_seq_out]
+)
+
+if_node = onnx.helper.make_node(
+    'If',
+    inputs=['optional_is_none'],
+    outputs=['sequence'],
+    then_branch=then_body,
+    else_branch=else_body
+)
+
+x_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['x'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_x',
+        data_type=onnx.TensorProto.FLOAT,
+        dims=x.shape,
+        vals=x.flatten().astype(float),
+    )
+)
+
+one_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['one'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_one',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[1]
+    )
+)
+
+zero_const_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['slice_start'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_zero',
+        data_type=onnx.TensorProto.INT64,
+        dims=(1,),
+        vals=[0]
+    )
+)
+
+axes_node = onnx.helper.make_node(
+    'Constant',
+    inputs=[],
+    outputs=['axes'],
+    value=onnx.helper.make_tensor(
+        name='const_tensor_axes',
+        data_type=onnx.TensorProto.INT64,
+        dims=(),
+        vals=[0]
+    )
+)
+
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['iter_count', 'one'],
+    outputs=['end']
+)
+
+end_unsqueeze_node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['end', 'axes'],
+    outputs=['slice_end']
+)
+
+slice_node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'slice_start', 'slice_end'],
+    outputs=['slice_out']
+)
+
+insert_node = onnx.helper.make_node(
+    'SequenceInsert',
+    inputs=['sequence', 'slice_out'],
+    outputs=['seq_out']
+)
+
+identity_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['cond_in'],
+    outputs=['cond_out']
+)
+
+loop_body = onnx.helper.make_graph(
+    [identity_node, optional_has_elem_node, optional_is_none, if_node, x_const_node, one_const_node,
+     zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node],
+    'loop_body',
+    [iter_count, cond_in, opt_in],
+    [cond_out, seq_out]
+)
+
+node = onnx.helper.make_node(
+    'Loop',
+    inputs=['trip_count', 'cond', 'opt_seq'],
+    outputs=['seq_res'],
+    body=loop_body
+)
+
+trip_count = np.array(5).astype(np.int64)
+cond = np.array(1).astype(bool)
+seq_res = compute_loop_outputs(x, [x0], trip_count)
+opt_seq_in: List[Any] = [x0]
+expect(node, inputs=[trip_count, cond, opt_seq_in], outputs=[seq_res],
+       name='test_loop16_seq_none', opset_imports=[onnx.helper.make_opsetid("", 16)],
+       input_type_protos=[onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, trip_count.shape),
+                          onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                          opt_in_tp])
+```
+
+</details>
+
+
+### MatMul
+There are 1 test cases, listed as following:
+<details>
+<summary>matmul</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MatMul',
+    inputs=['a', 'b'],
+    outputs=['c'],
+)
+
+# 2d
+a = np.random.randn(3, 4).astype(np.float32)
+b = np.random.randn(4, 3).astype(np.float32)
+c = np.matmul(a, b)
+expect(node, inputs=[a, b], outputs=[c],
+       name='test_matmul_2d')
+
+# 3d
+a = np.random.randn(2, 3, 4).astype(np.float32)
+b = np.random.randn(2, 4, 3).astype(np.float32)
+c = np.matmul(a, b)
+expect(node, inputs=[a, b], outputs=[c],
+       name='test_matmul_3d')
+
+# 4d
+a = np.random.randn(1, 2, 3, 4).astype(np.float32)
+b = np.random.randn(1, 2, 4, 3).astype(np.float32)
+c = np.matmul(a, b)
+expect(node, inputs=[a, b], outputs=[c],
+       name='test_matmul_4d')
+```
+
+</details>
+
+
+### MatMulInteger
+There are 1 test cases, listed as following:
+<details>
+<summary>matmulinteger</summary>
+
+```python
+node = onnx.helper.make_node('MatMulInteger',
+    inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
+    outputs=['Y'],)
+
+A = np.array([[11, 7, 3],
+    [10, 6, 2],
+    [9, 5, 1],
+    [8, 4, 0], ], dtype=np.uint8)
+
+a_zero_point = np.array([12], dtype=np.uint8)
+
+B = np.array([[1, 4],
+    [2, 5],
+    [3, 6], ], dtype=np.uint8)
+
+b_zero_point = np.array([0], dtype=np.uint8)
+
+output = np.array([[-38, -83],
+    [-44, -98],
+    [-50, -113],
+    [-56, -128], ], dtype=np.int32)
+
+expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
+       name='test_matmulinteger')
+```
+
+</details>
+
+
+### Max
+There are 2 test cases, listed as following:
+<details>
+<summary>max</summary>
+
+```python
+data_0 = np.array([3, 2, 1]).astype(np.float32)
+data_1 = np.array([1, 4, 4]).astype(np.float32)
+data_2 = np.array([2, 5, 3]).astype(np.float32)
+result = np.array([3, 5, 4]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Max',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_max_example')
+
+node = onnx.helper.make_node(
+    'Max',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_max_one_input')
+
+result = np.maximum(data_0, data_1)
+node = onnx.helper.make_node(
+    'Max',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_max_two_inputs')
+```
+
+</details>
+<details>
+<summary>max_all_numeric_types</summary>
+
+```python
+for op_dtype in all_numeric_dtypes:
+    data_0 = np.array([3, 2, 1]).astype(op_dtype)
+    data_1 = np.array([1, 4, 4]).astype(op_dtype)
+    result = np.array([3, 4, 4]).astype(op_dtype)
+    node = onnx.helper.make_node(
+        'Max',
+        inputs=['data_0', 'data_1'],
+        outputs=['result'],
+    )
+    expect(node, inputs=[data_0, data_1], outputs=[result],
+           name='test_max_{0}'.format(np.dtype(op_dtype).name))
+```
+
+</details>
+
+
+### MaxPool
+There are 15 test cases, listed as following:
+<details>
+<summary>maxpool_1d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32]
+output_shape: [1, 3, 31]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2],
+)
+x = np.random.randn(1, 3, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2]
+strides = [1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_1d_default')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_ceil</summary>
+
+```python
+"""
+input_shape: [1, 1, 4, 4]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    ceil_mode=True
+)
+x = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]]).astype(np.float32)
+y = np.array([[[
+    [11, 12],
+    [15, 16]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_ceil')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_default')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_dilations</summary>
+
+```python
+"""
+input_shape: [1, 1, 4, 4]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[1, 1],
+    dilations=[2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]]).astype(np.float32)
+y = np.array([[[
+    [11, 12],
+    [15, 16]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_dilations')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_pads</summary>
+
+```python
+"""
+input_shape: [1, 3, 28, 28]
+output_shape: [1, 3, 30, 30]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    pads=[2, 2, 2, 2]
+)
+x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (3, 3)
+strides = (1, 1)
+pad_bottom = pad_top = pad_right = pad_left = 2
+pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_pads')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_precomputed_pads</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[
+    [13, 14, 15, 15, 15],
+    [18, 19, 20, 20, 20],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_pads')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_precomputed_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 3, 3]
+pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[3, 3],
+    strides=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 9, 10],
+                [17, 19, 20],
+                [22, 24, 25]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_same_upper')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_precomputed_strides</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 9],
+                [17, 19]]]]).astype(np.float32)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_strides')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_same_lower</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_LOWER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_bottom = pad_shape[0] // 2
+pad_top = pad_shape[0] - pad_bottom
+pad_right = pad_shape[1] // 2
+pad_left = pad_shape[1] - pad_right
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_same_lower')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_same_upper</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 32, 32]
+pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    auto_pad='SAME_UPPER'
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (2, 2)
+strides = (1, 1)
+out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
+pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
+pad_top = pad_shape[0] // 2
+pad_bottom = pad_shape[0] - pad_top
+pad_left = pad_shape[1] // 2
+pad_right = pad_shape[1] - pad_left
+padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                constant_values=np.nan)
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_same_upper')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_strides</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32]
+output_shape: [1, 3, 10, 10]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    strides=[3, 3]
+)
+x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = (5, 5)
+strides = (3, 3)
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_strides')
+```
+
+</details>
+<details>
+<summary>maxpool_2d_uint8</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.uint8)
+y = np.array([[[
+    [13, 14, 15, 15, 15],
+    [18, 19, 20, 20, 20],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25]]]]).astype(np.uint8)
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_uint8')
+```
+
+</details>
+<details>
+<summary>maxpool_3d_default</summary>
+
+```python
+"""
+input_shape: [1, 3, 32, 32, 32]
+output_shape: [1, 3, 31, 31, 31]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y'],
+    kernel_shape=[2, 2, 2],
+)
+x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+x_shape = np.shape(x)
+kernel_shape = [2, 2, 2]
+strides = [1, 1, 1]
+out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+padded = x
+y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'MAX')
+
+expect(node, inputs=[x], outputs=[y], name='test_maxpool_3d_default')
+```
+
+</details>
+<details>
+<summary>maxpool_with_argmax_2d_precomputed_pads</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 5, 5]
+pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y', 'z'],
+    kernel_shape=[5, 5],
+    pads=[2, 2, 2, 2]
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[
+    [13, 14, 15, 15, 15],
+    [18, 19, 20, 20, 20],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25],
+    [23, 24, 25, 25, 25]]]]).astype(np.float32)
+z = np.array([[[
+    [12, 13, 14, 14, 14],
+    [17, 18, 19, 19, 19],
+    [22, 23, 24, 24, 24],
+    [22, 23, 24, 24, 24],
+    [22, 23, 24, 24, 24]]]]).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y, z], name='test_maxpool_with_argmax_2d_precomputed_pads')
+```
+
+</details>
+<details>
+<summary>maxpool_with_argmax_2d_precomputed_strides</summary>
+
+```python
+"""
+input_shape: [1, 1, 5, 5]
+output_shape: [1, 1, 2, 2]
+"""
+node = onnx.helper.make_node(
+    'MaxPool',
+    inputs=['x'],
+    outputs=['y', 'z'],
+    kernel_shape=[2, 2],
+    strides=[2, 2],
+    storage_order=1
+)
+x = np.array([[[
+    [1, 2, 3, 4, 5],
+    [6, 7, 8, 9, 10],
+    [11, 12, 13, 14, 15],
+    [16, 17, 18, 19, 20],
+    [21, 22, 23, 24, 25],
+]]]).astype(np.float32)
+y = np.array([[[[7, 9],
+                [17, 19]]]]).astype(np.float32)
+z = np.array([[[[6, 16],
+                [8, 18]]]]).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y, z], name='test_maxpool_with_argmax_2d_precomputed_strides')
+```
+
+</details>
+
+
+### MaxUnpool
+There are 2 test cases, listed as following:
+<details>
+<summary>with_output_shape</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MaxUnpool',
+    inputs=['xT', 'xI', 'output_shape'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+xT = np.array([[[[5, 6],
+                 [7, 8]]]], dtype=np.float32)
+xI = np.array([[[[5, 7],
+                 [13, 15]]]], dtype=np.int64)
+output_shape = np.array((1, 1, 5, 5), dtype=np.int64)
+y = np.array([[[[0, 0, 0, 0, 0],
+                [0, 5, 0, 6, 0],
+                [0, 0, 0, 0, 0],
+                [0, 7, 0, 8, 0],
+                [0, 0, 0, 0, 0]]]], dtype=np.float32)
+expect(node, inputs=[xT, xI, output_shape], outputs=[y], name='test_maxunpool_export_with_output_shape')
+```
+
+</details>
+<details>
+<summary>without_output_shape</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MaxUnpool',
+    inputs=['xT', 'xI'],
+    outputs=['y'],
+    kernel_shape=[2, 2],
+    strides=[2, 2]
+)
+xT = np.array([[[[1, 2],
+                 [3, 4]]]], dtype=np.float32)
+xI = np.array([[[[5, 7],
+                 [13, 15]]]], dtype=np.int64)
+y = np.array([[[[0, 0, 0, 0],
+                [0, 1, 0, 2],
+                [0, 0, 0, 0],
+                [0, 3, 0, 4]]]], dtype=np.float32)
+expect(node, inputs=[xT, xI], outputs=[y], name='test_maxunpool_export_without_output_shape')
+```
+
+</details>
+
+
+### Mean
+There are 1 test cases, listed as following:
+<details>
+<summary>mean</summary>
+
+```python
+data_0 = np.array([3, 0, 2]).astype(np.float32)
+data_1 = np.array([1, 3, 4]).astype(np.float32)
+data_2 = np.array([2, 6, 6]).astype(np.float32)
+result = np.array([2, 3, 4]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Mean',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_mean_example')
+
+node = onnx.helper.make_node(
+    'Mean',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_mean_one_input')
+
+result = np.divide(np.add(data_0, data_1), 2.)
+node = onnx.helper.make_node(
+    'Mean',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_mean_two_inputs')
+```
+
+</details>
+
+
+### MeanVarianceNormalization
+There are 1 test cases, listed as following:
+<details>
+<summary>meanvariancenormalization</summary>
+
+```python
+node = onnx.helper.make_node(
+    'MeanVarianceNormalization',
+    inputs=['X'],
+    outputs=['Y']
+)
+
+input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+    [[0.02916367], [0.12964272], [0.5060197]],
+    [[0.79538304], [0.9411346], [0.9546573]]],
+    [[[0.17730942], [0.46192095], [0.26480448]],
+    [[0.6746842], [0.01665257], [0.62473077]],
+    [[0.9240844], [0.9722341], [0.11965699]]],
+    [[[0.41356155], [0.9129373], [0.59330076]],
+    [[0.81929934], [0.7862604], [0.11799799]],
+    [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+# Calculate expected output data
+data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
+data_mean_squared = np.power(data_mean, 2)
+data_squared = np.power(input_data, 2)
+data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
+std = np.sqrt(data_squared_mean - data_mean_squared)
+expected_output = (input_data - data_mean) / (std + 1e-9)
+
+expect(node, inputs=[input_data], outputs=[expected_output],
+       name='test_mvn')
+```
+
+</details>
+
+
+### Min
+There are 2 test cases, listed as following:
+<details>
+<summary>min</summary>
+
+```python
+data_0 = np.array([3, 2, 1]).astype(np.float32)
+data_1 = np.array([1, 4, 4]).astype(np.float32)
+data_2 = np.array([2, 5, 0]).astype(np.float32)
+result = np.array([1, 2, 0]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Min',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_min_example')
+
+node = onnx.helper.make_node(
+    'Min',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_min_one_input')
+
+result = np.minimum(data_0, data_1)
+node = onnx.helper.make_node(
+    'Min',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_min_two_inputs')
+```
+
+</details>
+<details>
+<summary>min_all_numeric_types</summary>
+
+```python
+for op_dtype in all_numeric_dtypes:
+    data_0 = np.array([3, 2, 1]).astype(op_dtype)
+    data_1 = np.array([1, 4, 4]).astype(op_dtype)
+    result = np.array([1, 2, 1]).astype(op_dtype)
+    node = onnx.helper.make_node(
+        'Min',
+        inputs=['data_0', 'data_1'],
+        outputs=['result'],
+    )
+    expect(node, inputs=[data_0, data_1], outputs=[result],
+           name='test_min_{0}'.format(np.dtype(op_dtype).name))
+```
+
+</details>
+
+
+### Mod
+There are 13 test cases, listed as following:
+<details>
+<summary>mod_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32)
+y = np.array([7]).astype(np.int32)
+z = np.mod(x, y)
+#   array([[[0, 1, 2, 3, 4],
+#     [5, 6, 0, 1, 2]],
+
+#    [[3, 4, 5, 6, 0],
+#     [1, 2, 3, 4, 5]],
+
+#    [[6, 0, 1, 2, 3],
+#     [4, 5, 6, 0, 1]]], dtype=int32)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_broadcast')
+```
+
+</details>
+<details>
+<summary>mod_int64_fmod</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+z = np.fmod(x, y)  # expected output [ 0,  1,  5,  0, -1,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_int64_fmod')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_float16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16)
+y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16)
+z = np.fmod(x, y)  # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 ,  3.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_float16')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_float32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32)
+y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32)
+z = np.fmod(x, y)  # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_float32')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_float64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+    fmod=1
+)
+
+x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64)
+y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64)
+z = np.fmod(x, y)  # expected output [-0.1,  0.4,  5. ,  0.1, -0.4,  3.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_float64')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_int16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int16')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_int32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int32')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_int64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int64')
+```
+
+</details>
+<details>
+<summary>mod_mixed_sign_int8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8)
+y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8)
+z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_mixed_sign_int8')
+```
+
+</details>
+<details>
+<summary>mod_uint16</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint16)
+y = np.array([2, 3, 8]).astype(np.uint16)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint16')
+```
+
+</details>
+<details>
+<summary>mod_uint32</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint32)
+y = np.array([2, 3, 8]).astype(np.uint32)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint32')
+```
+
+</details>
+<details>
+<summary>mod_uint64</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint64)
+y = np.array([2, 3, 8]).astype(np.uint64)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint64')
+```
+
+</details>
+<details>
+<summary>mod_uint8</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mod',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([4, 7, 5]).astype(np.uint8)
+y = np.array([2, 3, 8]).astype(np.uint8)
+z = np.mod(x, y)  # expected output [0, 1, 5]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mod_uint8')
+```
+
+</details>
+
+
+### Momentum
+There are 3 test cases, listed as following:
+<details>
+<summary>momentum</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.1
+
+# Create operator.
+node = onnx.helper.make_node('Momentum',
+                             inputs=['R', 'T', 'X', 'G', 'V'],
+                             outputs=['X_new', 'V_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             mode='standard',
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.2, 2.8], dtype=np.float32)
+g = np.array([-0.94, -2.5], dtype=np.float32)
+v = np.array([1.7, 3.6], dtype=np.float32)
+
+# Compute expected outputs of Momentum.
+x_new, v_new = apply_momentum(r, t, x, g, v,
+                              norm_coefficient, alpha, beta)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, v],
+       outputs=[x_new, v_new], name='test_momentum',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+<details>
+<summary>momentum_multiple</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.001
+alpha = 0.95
+beta = 0.85
+
+node = onnx.helper.make_node('Momentum',
+                             inputs=['R', 'T', 'X1', 'X2',
+                                     'G1', 'G2', 'H1', 'H2'],
+                             outputs=['X1_new', 'X2_new',
+                                      'V1_new', 'V2_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             mode='standard',
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+
+x1 = np.array([1.0], dtype=np.float32)
+g1 = np.array([-1.0], dtype=np.float32)
+v1 = np.array([2.0], dtype=np.float32)
+
+x2 = np.array([1.0, 2.0], dtype=np.float32)
+g2 = np.array([-1.0, -3.0], dtype=np.float32)
+v2 = np.array([4.0, 1.0], dtype=np.float32)
+
+# Compute expected outputs of Momentum.
+x1_new, v1_new = apply_momentum(r, t, x1, g1, v1,
+                                norm_coefficient, alpha, beta)
+x2_new, v2_new = apply_momentum(r, t, x2, g2, v2,
+                                norm_coefficient, alpha, beta)
+
+# Check results.
+expect(node, inputs=[r, t, x1, x2, g1, g2, v1, v2],
+       outputs=[x1_new, x2_new, v1_new, v2_new], name='test_momentum_multiple',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+<details>
+<summary>nesterov_momentum</summary>
+
+```python
+# Define operator attributes.
+norm_coefficient = 0.01
+alpha = 0.95
+beta = 1.0
+
+# Create operator.
+node = onnx.helper.make_node('Momentum',
+                             inputs=['R', 'T', 'X', 'G', 'V'],
+                             outputs=['X_new', 'V_new'],
+                             norm_coefficient=norm_coefficient,
+                             alpha=alpha,
+                             beta=beta,
+                             mode='nesterov',
+                             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                             )
+
+# Define operator inputs.
+r = np.array(0.1, dtype=np.float32)  # scalar
+t = np.array(0, dtype=np.int64)  # scalar
+x = np.array([1.2, 2.8], dtype=np.float32)
+g = np.array([-0.94, -2.5], dtype=np.float32)
+v = np.array([1.7, 3.6], dtype=np.float32)
+
+# Compute expected outputs of Momentum.
+x_new, v_new = apply_nesterov(r, t, x, g, v,
+                              norm_coefficient, alpha, beta)
+
+# Check results.
+expect(node, inputs=[r, t, x, g, v],
+       outputs=[x_new, v_new], name='test_nesterov_momentum',
+       opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+```
+
+</details>
+
+
+### Mul
+There are 2 test cases, listed as following:
+<details>
+<summary>mul</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mul',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = x * y  # expected output [4., 10., 18.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = x * y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul')
+
+x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+z = x * y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul_uint8')
+```
+
+</details>
+<details>
+<summary>mul_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Mul',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = x * y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_mul_bcast')
+```
+
+</details>
+
+
+### Neg
+There are 1 test cases, listed as following:
+<details>
+<summary>neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Neg',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-4, 2]).astype(np.float32)
+y = np.negative(x)  # expected output [4., -2.],
+expect(node, inputs=[x], outputs=[y],
+       name='test_neg_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.negative(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_neg')
+```
+
+</details>
+
+
+### NegativeLogLikelihoodLoss
+There are 18 test cases, listed as following:
+<details>
+<summary>input_shape_is_NC</summary>
+
+```python
+reduction = 'none'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C = 3, 5
+np.random.seed(0)
+input = np.random.rand(N, C).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, )).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NC')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(1)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+target[0][0] = np.int64(1)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1_mean_weight_negative_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(-1)
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index)
+
+N, C, dim1 = 3, 5, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+target[0][0] = -1
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    weight=weight,
+                                                                    reduction=reduction,
+                                                                    ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_mean_weight_negative_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1_weight</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_weight')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1_weight_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(1)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, d1 = 3, 5, 2
+np.random.seed(0)
+input = np.random.rand(N, C, d1).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+target[0][0] = np.int64(1)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1_weight_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2</summary>
+
+```python
+reduction = 'none'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_no_weight_reduction_mean_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(1)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+target[0][0][0] = np.int64(1)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_no_weight_reduction_mean_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_reduction_mean</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_reduction_mean')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_reduction_sum</summary>
+
+```python
+reduction = 'sum'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2))
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_reduction_sum')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_with_weight</summary>
+
+```python
+reduction = 'none'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_with_weight_reduction_mean</summary>
+
+```python
+reduction = 'mean'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight_reduction_mean')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_with_weight_reduction_sum</summary>
+
+```python
+reduction = 'sum'
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight_reduction_sum')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2_with_weight_reduction_sum_ii</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(0)
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index
+)
+
+N, C, dim1, dim2 = 3, 5, 6, 6
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+target[0][0][0] = np.int64(0)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction, ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2_with_weight_reduction_sum_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3_none_no_weight_negative_ii</summary>
+
+```python
+reduction = 'none'
+ignore_index = np.int64(-5)
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index)
+
+N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+target[0][0][0][0] = -5
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    reduction=reduction,
+                                                                    ignore_index=ignore_index)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3_none_no_weight_negative_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3_sum_weight_high_ii</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(10)
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction,
+    ignore_index=ignore_index)
+
+N, C = 3, 5
+np.random.seed(0)
+input = np.random.rand(N, C).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N))
+target[0] = 10
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    weight=weight,
+                                                                    reduction=reduction,
+                                                                    ignore_index=ignore_index)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3_sum_weight_high_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_mean_weight</summary>
+
+```python
+reduction = 'mean'
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target', 'weight'],
+    outputs=['loss'],
+    reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    weight=weight,
+                                                                    reduction=reduction)
+
+expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3d4d5_mean_weight')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_none_no_weight</summary>
+
+```python
+reduction = 'none'
+
+node = onnx.helper.make_node(
+    'NegativeLogLikelihoodLoss',
+    inputs=['input', 'target'],
+    outputs=['loss'],
+    reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                    target,
+                                                                    reduction=reduction)
+
+expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+    name='test_nllloss_NCd1d2d3d4d5_none_no_weight')
+```
+
+</details>
+
+
+### NonMaxSuppression
+There are 9 test cases, listed as following:
+<details>
+<summary>nonmaxsuppression_center_point_box_format</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices'],
+    center_point_box=1
+)
+boxes = np.array([[
+    [0.5, 0.5, 1.0, 1.0],
+    [0.5, 0.6, 1.0, 1.0],
+    [0.5, 0.4, 1.0, 1.0],
+    [0.5, 10.5, 1.0, 1.0],
+    [0.5, 10.6, 1.0, 1.0],
+    [0.5, 100.5, 1.0, 1.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_center_point_box_format')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_flipped_coordinates</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [1.0, 1.0, 0.0, 0.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, 0.9, 1.0, -0.1],
+    [0.0, 10.0, 1.0, 11.0],
+    [1.0, 10.1, 0.0, 11.1],
+    [1.0, 101.0, 0.0, 100.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_flipped_coordinates')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_identical_boxes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.0, 1.0, 1.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_identical_boxes')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_limit_output_size</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([2]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_limit_output_size')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_single_box</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_single_box')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_suppress_by_IOU</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_suppress_by_IOU')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_suppress_by_IOU_and_scores</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([3]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.4]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_suppress_by_IOU_and_scores')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_two_batches</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[[0.0, 0.0, 1.0, 1.0],
+                   [0.0, 0.1, 1.0, 1.1],
+                   [0.0, -0.1, 1.0, 0.9],
+                   [0.0, 10.0, 1.0, 11.0],
+                   [0.0, 10.1, 1.0, 11.1],
+                   [0.0, 100.0, 1.0, 101.0]],
+                  [[0.0, 0.0, 1.0, 1.0],
+                   [0.0, 0.1, 1.0, 1.1],
+                   [0.0, -0.1, 1.0, 0.9],
+                   [0.0, 10.0, 1.0, 11.0],
+                   [0.0, 10.1, 1.0, 11.1],
+                   [0.0, 100.0, 1.0, 101.0]]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]],
+                   [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([2]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_two_batches')
+```
+
+</details>
+<details>
+<summary>nonmaxsuppression_two_classes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonMaxSuppression',
+    inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+    outputs=['selected_indices']
+)
+boxes = np.array([[
+    [0.0, 0.0, 1.0, 1.0],
+    [0.0, 0.1, 1.0, 1.1],
+    [0.0, -0.1, 1.0, 0.9],
+    [0.0, 10.0, 1.0, 11.0],
+    [0.0, 10.1, 1.0, 11.1],
+    [0.0, 100.0, 1.0, 101.0]
+]]).astype(np.float32)
+scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3],
+                    [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+max_output_boxes_per_class = np.array([2]).astype(np.int64)
+iou_threshold = np.array([0.5]).astype(np.float32)
+score_threshold = np.array([0.0]).astype(np.float32)
+selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]]).astype(np.int64)
+
+expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_two_classes')
+```
+
+</details>
+
+
+### NonZero
+There are 1 test cases, listed as following:
+<details>
+<summary>nonzero</summary>
+
+```python
+node = onnx.helper.make_node(
+    'NonZero',
+    inputs=['condition'],
+    outputs=['result'],
+)
+
+condition = np.array([[1, 0], [1, 1]], dtype=bool)
+result = np.array(np.nonzero(condition), dtype=np.int64)  # expected output [[0, 1, 1], [0, 0, 1]]
+expect(node, inputs=[condition], outputs=[result],
+       name='test_nonzero_example')
+```
+
+</details>
+
+
+### Not
+There are 1 test cases, listed as following:
+<details>
+<summary>not</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Not',
+    inputs=['x'],
+    outputs=['not'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+expect(node, inputs=[x], outputs=[np.logical_not(x)],
+       name='test_not_2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+expect(node, inputs=[x], outputs=[np.logical_not(x)],
+       name='test_not_3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+expect(node, inputs=[x], outputs=[np.logical_not(x)],
+       name='test_not_4d')
+```
+
+</details>
+
+
+### OneHot
+There are 4 test cases, listed as following:
+<details>
+<summary>with_axis</summary>
+
+```python
+axisValue = 1
+on_value = 3
+off_value = 1
+output_type = np.float32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y'],
+    axis=axisValue
+)
+indices = np.array([[1, 9],
+                    [2, 4]], dtype=np.float32)
+depth = np.float32(10)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_with_axis')
+```
+
+</details>
+<details>
+<summary>with_negative_axis</summary>
+
+```python
+axisValue = -2
+on_value = 3
+off_value = 1
+output_type = np.float32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y'],
+    axis=axisValue
+)
+indices = np.array([[1, 9],
+                    [2, 4]], dtype=np.float32)
+depth = np.float32(10)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_with_negative_axis')
+```
+
+</details>
+<details>
+<summary>with_negative_indices</summary>
+
+```python
+axisValue = 1
+on_value = 3
+off_value = 1
+output_type = np.float32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y'],
+    axis=axisValue
+)
+indices = np.array([0, -7, -8], dtype=np.int64)
+
+# print(y)
+# [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
+#  [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.]
+#  [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]]
+
+depth = np.float32(10)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_negative_indices')
+```
+
+</details>
+<details>
+<summary>without_axis</summary>
+
+```python
+on_value = 5
+off_value = 2
+output_type = np.int32
+node = onnx.helper.make_node(
+    'OneHot',
+    inputs=['indices', 'depth', 'values'],
+    outputs=['y']
+)
+indices = np.array([0, 7, 8], dtype=np.int64)
+depth = np.float32(12)
+values = np.array([off_value, on_value], dtype=output_type)
+y = one_hot(indices, depth, dtype=output_type)
+y = y * (on_value - off_value) + off_value
+expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_without_axis')
+```
+
+</details>
+
+
+### OptionalHasElement
+There are 4 test cases, listed as following:
+<details>
+<summary>empty</summary>
+
+```python
+optional = None
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.INT32, shape=[])
+input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+node = onnx.helper.make_node(
+    'OptionalHasElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_has_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_has_element_empty')
+```
+
+</details>
+<details>
+<summary>get_element_sequence</summary>
+
+```python
+optional = [np.array([1, 2, 3, 4]).astype(np.int32)]
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.INT32, shape=[4, ])
+seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
+input_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto)
+
+node = onnx.helper.make_node(
+    'OptionalGetElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_get_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_get_element_sequence')
+```
+
+</details>
+<details>
+<summary>get_element_tensor</summary>
+
+```python
+optional = np.array([1, 2, 3, 4]).astype(np.float32)
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.FLOAT, shape=[4, ])
+input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+
+node = onnx.helper.make_node(
+    'OptionalGetElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_get_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_get_element')
+```
+
+</details>
+<details>
+<summary>optionalhaselement</summary>
+
+```python
+optional = np.array([1, 2, 3, 4]).astype(np.float32)
+tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.FLOAT, shape=[4, ])
+input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+node = onnx.helper.make_node(
+    'OptionalHasElement',
+    inputs=['optional_input'],
+    outputs=['output']
+)
+output = optional_has_element_reference_implementation(optional)
+expect(node, inputs=[optional], outputs=[output],
+       input_type_protos=[input_type_proto],
+       name='test_optional_has_element')
+```
+
+</details>
+
+
+### Or
+There are 2 test cases, listed as following:
+<details>
+<summary>or</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Or',
+    inputs=['x', 'y'],
+    outputs=['or'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+y = (np.random.randn(3, 4) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or4d')
+```
+
+</details>
+<details>
+<summary>or_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Or',
+    inputs=['x', 'y'],
+    outputs=['or'],
+)
+
+# 3d vs 1d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(5) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast3v1d')
+
+# 3d vs 2d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(4, 5) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast3v2d')
+
+# 4d vs 2d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast4v2d')
+
+# 4d vs 3d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast4v3d')
+
+# 4d vs 4d
+x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+z = np.logical_or(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_or_bcast4v4d')
+```
+
+</details>
+
+
+### PRelu
+There are 2 test cases, listed as following:
+<details>
+<summary>prelu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'PRelu',
+    inputs=['x', 'slope'],
+    outputs=['y'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+slope = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+expect(node, inputs=[x, slope], outputs=[y],
+       name='test_prelu_example')
+```
+
+</details>
+<details>
+<summary>prelu_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'PRelu',
+    inputs=['x', 'slope'],
+    outputs=['y'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+slope = np.random.randn(5).astype(np.float32)
+y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+expect(node, inputs=[x, slope], outputs=[y],
+       name='test_prelu_broadcast')
+```
+
+</details>
+
+
+### Pad
+There are 2 test cases, listed as following:
+<details>
+<summary>constant_pad</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pad',
+    inputs=['x', 'pads', 'value'],
+    outputs=['y'],
+    mode='constant'
+)
+x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+value = np.float32(1.2)
+y = pad_impl(
+    x,
+    pads,
+    'constant',
+    1.2
+)
+
+expect(node, inputs=[x, pads, value], outputs=[y],
+       name='test_constant_pad')
+```
+
+</details>
+<details>
+<summary>reflection_and_edge_pad</summary>
+
+```python
+for mode in ['edge', 'reflect']:
+    node = onnx.helper.make_node(
+        'Pad',
+        inputs=['x', 'pads'],
+        outputs=['y'],
+        mode=mode
+    )
+    x = np.random.randn(1, 3, 4, 5).astype(np.int32)
+    pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+    y = pad_impl(
+        x,
+        pads,
+        mode
+    )
+
+    expect(node, inputs=[x, pads], outputs=[y],
+           name='test_{}_pad'.format(mode))
+```
+
+</details>
+
+
+### Pow
+There are 3 test cases, listed as following:
+<details>
+<summary>pow</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_example')
+
+x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = pow(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow')
+```
+
+</details>
+<details>
+<summary>pow_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array(2).astype(np.float32)
+z = pow(x, y)  # expected output [1., 4., 9.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_bcast_scalar')
+
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+y = np.array([1, 2, 3]).astype(np.float32)
+# expected output [[1, 4, 27], [4, 25, 216]]
+z = pow(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_bcast_array')
+```
+
+</details>
+<details>
+<summary>types</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Pow',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.int64)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_int64')
+
+x = np.array([1, 2, 3]).astype(np.int64)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int64_float32')
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.int32)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_int32')
+
+x = np.array([1, 2, 3]).astype(np.int32)
+y = np.array([4, 5, 6]).astype(np.float32)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int32_float32')
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.uint64)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_uint64')
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([4, 5, 6]).astype(np.uint32)
+z = pow(x, y)  # expected output [1., 32., 729.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_float32_uint32')
+
+x = np.array([1, 2, 3]).astype(np.int64)
+y = np.array([4, 5, 6]).astype(np.int64)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int64_int64')
+
+x = np.array([1, 2, 3]).astype(np.int32)
+y = np.array([4, 5, 6]).astype(np.int32)
+z = pow(x, y)  # expected output [1, 32, 729]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_pow_types_int32_int32')
+```
+
+</details>
+
+
+### QLinearConv
+There are 1 test cases, listed as following:
+<details>
+<summary>qlinearconv</summary>
+
+```python
+node = onnx.helper.make_node('QLinearConv',
+    inputs=['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+x = np.array([[255, 174, 162, 25, 203, 168, 58],
+    [15, 59, 237, 95, 129, 0, 64],
+    [56, 242, 153, 221, 168, 12, 166],
+    [232, 178, 186, 195, 237, 162, 237],
+    [188, 39, 124, 77, 80, 102, 43],
+    [127, 230, 21, 83, 41, 40, 134],
+    [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+x_scale = np.float32(0.00369204697)
+x_zero_point = np.uint8(132)
+
+w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+w_scale = np.array([0.00172794575], dtype=np.float32)
+w_zero_point = np.array([255], dtype=np.uint8)
+
+y_scale = np.float32(0.00162681262)
+y_zero_point = np.uint8(123)
+
+output = np.array([[0, 81, 93, 230, 52, 87, 197],
+    [240, 196, 18, 160, 126, 255, 191],
+    [199, 13, 102, 34, 87, 243, 89],
+    [23, 77, 69, 60, 18, 93, 18],
+    [67, 216, 131, 178, 175, 153, 212],
+    [128, 25, 234, 172, 214, 215, 121],
+    [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+expect(node, inputs=[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearconv')
+```
+
+</details>
+
+
+### QLinearMatMul
+There are 1 test cases, listed as following:
+<details>
+<summary>qlinearmatmul</summary>
+
+```python
+node = onnx.helper.make_node('QLinearMatMul',
+    inputs=['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+#2D
+a = np.array([[208, 236, 0, 238],
+    [3, 214, 255, 29], ], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[168, 115, 255],
+    [1, 66, 151], ], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_2D')
+
+#3D
+a = np.array([[[208, 236, 0, 238],
+    [3, 214, 255, 29]],
+    [[208, 236, 0, 238],
+    [3, 214, 255, 29]]], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]],
+    [[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[[168, 115, 255],
+    [1, 66, 151]],
+    [[168, 115, 255],
+    [1, 66, 151]]], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_3D')
+```
+
+</details>
+
+
+### QuantizeLinear
+There are 2 test cases, listed as following:
+<details>
+<summary>axis</summary>
+
+```python
+node = onnx.helper.make_node('QuantizeLinear',
+                             inputs=['x', 'y_scale', 'y_zero_point'],
+                             outputs=['y'],)
+
+x = np.array([[[[-162, 10],
+                [-100, 232],
+                [-20, -50]],
+
+               [[-76, 0],
+                [0, 252],
+                [32, -44]],
+
+               [[245, -485],
+                [-960, -270],
+                [-375, -470]], ], ], dtype=np.float32)
+y_scale = np.array([2, 4, 5], dtype=np.float32)
+y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(np.uint8)
+
+expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+       name='test_quantizelinear_axis')
+```
+
+</details>
+<details>
+<summary>quantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('QuantizeLinear',
+                             inputs=['x', 'y_scale', 'y_zero_point'],
+                             outputs=['y'],)
+
+x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+y_scale = np.float32(2)
+y_zero_point = np.uint8(128)
+y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+       name='test_quantizelinear')
+```
+
+</details>
+
+
+### RNN
+There are 4 test cases, listed as following:
+<details>
+<summary>batchwise</summary>
+
+```python
+input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 4
+weight_scale = 0.5
+layout = 1
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R'],
+    outputs=['Y', 'Y_h'],
+    hidden_size=hidden_size,
+    layout=layout
+)
+
+W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+rnn = RNN_Helper(X=input, W=W, R=R, layout=layout)
+Y, Y_h = rnn.step()
+expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_simple_rnn_batchwise')
+```
+
+</details>
+<details>
+<summary>defaults</summary>
+
+```python
+input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+input_size = 2
+hidden_size = 4
+weight_scale = 0.1
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+rnn = RNN_Helper(X=input, W=W, R=R)
+_, Y_h = rnn.step()
+expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_simple_rnn_defaults')
+```
+
+</details>
+<details>
+<summary>initial_bias</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 5
+custom_bias = 0.1
+weight_scale = 0.1
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+# Adding custom bias
+W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
+R_B = np.zeros((1, hidden_size)).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+rnn = RNN_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = rnn.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)],
+       name='test_simple_rnn_with_initial_bias')
+```
+
+</details>
+<details>
+<summary>seq_length</summary>
+
+```python
+input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
+                  [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)
+
+input_size = 3
+hidden_size = 5
+
+node = onnx.helper.make_node(
+    'RNN',
+    inputs=['X', 'W', 'R', 'B'],
+    outputs=['', 'Y_h'],
+    hidden_size=hidden_size
+)
+
+W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
+R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
+
+# Adding custom bias
+W_B = np.random.randn(1, hidden_size).astype(np.float32)
+R_B = np.random.randn(1, hidden_size).astype(np.float32)
+B = np.concatenate((W_B, R_B), axis=1)
+
+rnn = RNN_Helper(X=input, W=W, R=R, B=B)
+_, Y_h = rnn.step()
+expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_rnn_seq_length')
+```
+
+</details>
+
+
+### Range
+There are 2 test cases, listed as following:
+<details>
+<summary>range_float_type_positive_delta</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Range',
+    inputs=['start', 'limit', 'delta'],
+    outputs=['output'],
+)
+
+start = np.float32(1)
+limit = np.float32(5)
+delta = np.float32(2)
+
+output = np.arange(start, limit, delta, dtype=np.float32)  # expected output [1.0, 3.0]
+expect(node, inputs=[start, limit, delta], outputs=[output],
+       name='test_range_float_type_positive_delta')
+```
+
+</details>
+<details>
+<summary>range_int32_type_negative_delta</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Range',
+    inputs=['start', 'limit', 'delta'],
+    outputs=['output'],
+)
+
+start = np.int32(10)
+limit = np.int32(6)
+delta = np.int32(-3)
+
+output = np.arange(start, limit, delta, dtype=np.int32)  # expected output [10, 7]
+expect(node, inputs=[start, limit, delta], outputs=[output],
+       name='test_range_int32_type_negative_delta')
+```
+
+</details>
+
+
+### Reciprocal
+There are 1 test cases, listed as following:
+<details>
+<summary>reciprocal</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Reciprocal',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-4, 2]).astype(np.float32)
+y = np.reciprocal(x)  # expected output [-0.25, 0.5],
+expect(node, inputs=[x], outputs=[y],
+       name='test_reciprocal_example')
+
+x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5
+y = np.reciprocal(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_reciprocal')
+```
+
+</details>
+
+
+### ReduceL1
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[78.]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[3., 7.], [11., 15.], [19., 23.]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_keep_dims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL1',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+# print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_negative_axes_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l1_negative_axes_keep_dims_random')
+```
+
+</details>
+
+
+### ReduceL2
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=axes, keepdims=keepdims == 1))
+#print(reduced)
+#[[[25.49509757]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=axes, keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+#print(reduced)
+#[[2.23606798, 5.],
+# [7.81024968, 10.63014581],
+# [13.45362405, 16.2788206]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+#print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+#print(reduced)
+#[[[2.23606798], [5.]]
+# [[7.81024968], [10.63014581]]
+# [[13.45362405], [16.2788206 ]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_l2_keep_dims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceL2',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+# print(data)
+#[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+# print(reduced)
+#[[[2.23606798], [5.]]
+# [[7.81024968], [10.63014581]]
+# [[13.45362405], [16.2788206 ]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_negative_axes_keep_dims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sqrt(np.sum(
+    a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_l2_negative_axes_keep_dims_random')
+```
+
+</details>
+
+
+### ReduceLogSum
+There are 3 test cases, listed as following:
+<details>
+<summary>keepdims</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"]
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, keepdims=True))
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_default')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"],
+    axes=[-2]
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, axis=(-2), keepdims=True))
+# print(reduced)
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_negative_axes')
+```
+
+</details>
+<details>
+<summary>nokeepdims</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"],
+    axes=[2, 1],
+    keepdims=0
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, axis=(2, 1), keepdims=False))
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_desc_axes')
+
+node = onnx.helper.make_node(
+    'ReduceLogSum',
+    inputs=['data'],
+    outputs=["reduced"],
+    axes=[0, 1],
+    keepdims=0
+)
+data = np.random.ranf([3, 4, 5]).astype(np.float32)
+reduced = np.log(np.sum(data, axis=(0, 1), keepdims=False))
+expect(node, inputs=[data], outputs=[reduced],
+       name='test_reduce_log_sum_asc_axes')
+```
+
+</details>
+
+
+### ReduceLogSumExp
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=axes,
+                        keepdims=keepdims == 1))
+# print(reduced)
+# [[[60.00671387]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=axes,
+                        keepdims=keepdims == 1))
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(
+    np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+# print(reduced)
+#[[20., 2.31326175]
+# [40.00004578, 2.31326175]
+# [60.00671387, 2.31326175]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(
+    np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+    name='test_reduce_log_sum_exp_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+# print(reduced)
+# [[[20., 2.31326175]]
+# [[40.00004578, 2.31326175]]
+# [[60.00671387, 2.31326175]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+node = onnx.helper.make_node(
+    'ReduceLogSumExp',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims
+)
+
+data = np.array(
+    [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+    dtype=np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+# print(reduced)
+# [[[20., 2.31326175]]
+# [[40.00004578, 2.31326175]]
+# [[60.00671387, 2.31326175]]]
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.double)
+reduced = np.log(np.sum(np.exp(data),
+                        axis=tuple(axes),
+                        keepdims=keepdims == 1))
+
+expect(node, inputs=[data], outputs=[reduced],
+      name='test_reduce_log_sum_exp_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### ReduceMax
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+[[[60.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_default_axes_keepdim_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[20., 2.]
+# [40., 2.]
+# [60., 2.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[20., 2.]]
+# [[40., 2.]]
+# [[60., 2.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMax',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[20., 2.]]
+# [[40., 2.]]
+# [[60., 2.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### ReduceMean
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[18.25]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[12.5, 1.5]
+# [35., 1.5]
+# [57.5, 1.5]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[12.5, 1.5]]
+# [[35., 1.5]]
+# [[57.5, 1.5]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMean',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+# [[[12.5, 1.5]]
+# [[35., 1.5]]
+# [[57.5, 1.5]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### ReduceMin
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMin',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[1.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceMin',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[5., 1.]
+# [30., 1.]
+# [55., 1.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMin', inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[5., 1.]]
+# [[30., 1.]]
+# [[55., 1.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceMin', inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[5., 1.]]
+# [[30., 1.]]
+# [[55., 1.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### ReduceProd
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[4.790016e+08]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[3., 8.]
+# [35., 48.]
+# [99., 120.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[3., 8.]]
+# [[35., 48.]]
+# [[99., 120.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceProd',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[3., 8.]]
+# [[35., 48.]]
+# [[99., 120.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### ReduceSum
+There are 5 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([], dtype=np.int64)
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
+#print(reduced)
+#[[[78.]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([1], dtype=np.int64)
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+#print(reduced)
+#[[4., 6.]
+# [12., 14.]
+# [20., 22.]]
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>empty_axes_input_noop</summary>
+
+```python
+shape = [3, 2, 2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims,
+    noop_with_empty_axes=True)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+axes = np.array([], dtype=np.int64)
+reduced = np.array(data)
+#print(reduced)
+#[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced],
+       name='test_reduce_sum_empty_axes_input_noop_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.array(data)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_negative_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([1], dtype=np.int64)
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+#print(reduced)
+#[[[4., 6.]]
+# [[12., 14.]]
+# [[20., 22.]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = np.array([-2], dtype=np.int64)
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSum',
+    inputs=['data', 'axes'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+# print(reduced)
+#[[[4., 6.]]
+# [[12., 14.]]
+# [[20., 22.]]]
+
+expect(node, inputs=[data, axes], outputs=[reduced],
+       name='test_reduce_sum_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(data, axis=tuple(
+    axes.tolist()), keepdims=keepdims == 1)
+
+expect(node, inputs=[data, axes], outputs=[reduced],
+       name='test_reduce_sum_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### ReduceSumSquare
+There are 4 test cases, listed as following:
+<details>
+<summary>default_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = None
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=axes, keepdims=keepdims == 1)
+#print(reduced)
+#[[[650.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_default_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=axes, keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_default_axes_keepdims_random')
+```
+
+</details>
+<details>
+<summary>do_not_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 0
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[10., 20.]
+# [74., 100.]
+# [202., 244.]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_do_not_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_do_not_keepdims_random')
+```
+
+</details>
+<details>
+<summary>keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [1]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+#print(reduced)
+#[[[10., 20.]]
+# [[74., 100.]]
+# [[202., 244.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_keepdims_random')
+```
+
+</details>
+<details>
+<summary>negative_axes_keepdims</summary>
+
+```python
+shape = [3, 2, 2]
+axes = [-2]
+keepdims = 1
+
+node = onnx.helper.make_node(
+    'ReduceSumSquare',
+    inputs=['data'],
+    outputs=['reduced'],
+    axes=axes,
+    keepdims=keepdims)
+
+data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+# print(reduced)
+#[[[10., 20.s]]
+# [[74., 100.]]
+# [[202., 244.]]]
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_negative_axes_keepdims_example')
+
+np.random.seed(0)
+data = np.random.uniform(-10, 10, shape).astype(np.float32)
+reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_negative_axes_keepdims_random')
+```
+
+</details>
+
+
+### Relu
+There are 1 test cases, listed as following:
+<details>
+<summary>relu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Relu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_relu')
+```
+
+</details>
+
+
+### Reshape
+There are 2 test cases, listed as following:
+<details>
+<summary>allowzero</summary>
+
+```python
+original_shape = [0, 3, 4]
+test_cases = {
+    'allowzero_reordered': np.array([3, 4, 0], dtype=np.int64),
+}
+data = np.random.random_sample(original_shape).astype(np.float32)
+
+for test_name, shape in test_cases.items():
+    node = onnx.helper.make_node(
+        'Reshape',
+        inputs=['data', 'shape'],
+        outputs=['reshaped'],
+        allowzero=1,  # if allowzero=1, final shape = (3, 4, 0)
+                      # if allowzero=0, final shape = (3, 4, 4)
+    )
+
+    reshaped = reshape_reference_implementation(data, shape, allowzero=1)
+
+    expect(node, inputs=[data, shape], outputs=[reshaped],
+           name='test_reshape_' + test_name)
+```
+
+</details>
+<details>
+<summary>reshape</summary>
+
+```python
+original_shape = [2, 3, 4]
+test_cases = {
+    'reordered_all_dims': np.array([4, 2, 3], dtype=np.int64),
+    'reordered_last_dims': np.array([2, 4, 3], dtype=np.int64),
+    'reduced_dims': np.array([2, 12], dtype=np.int64),
+    'extended_dims': np.array([2, 3, 2, 2], dtype=np.int64),
+    'one_dim': np.array([24], dtype=np.int64),
+    'negative_dim': np.array([2, -1, 2], dtype=np.int64),
+    'negative_extended_dims': np.array([-1, 2, 3, 4], dtype=np.int64),
+    'zero_dim': np.array([2, 0, 4, 1], dtype=np.int64),
+    'zero_and_negative_dim': np.array([2, 0, 1, -1], dtype=np.int64),
+}
+data = np.random.random_sample(original_shape).astype(np.float32)
+
+for test_name, shape in test_cases.items():
+    node = onnx.helper.make_node(
+        'Reshape',
+        inputs=['data', 'shape'],
+        outputs=['reshaped'],
+    )
+
+    reshaped = reshape_reference_implementation(data, shape)
+
+    expect(node, inputs=[data, shape], outputs=[reshaped],
+           name='test_reshape_' + test_name)
+```
+
+</details>
+
+
+### Resize
+There are 23 test cases, listed as following:
+<details>
+<summary>resize_downsample_scales_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+# [[[[ 1.47119141  2.78125     4.08251953]
+#    [ 6.71142578  8.02148438  9.32275391]
+#    [11.91650391 13.2265625  14.52783203]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_cubic')
+```
+
+</details>
+<details>
+<summary>resize_downsample_scales_cubic_A_n0p5_exclude_outside</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    cubic_coeff_a=-0.5,
+    exclude_outside=True
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+# [[[[ 1.36812675  2.6695014   4.0133367 ]
+#    [ 6.57362535  7.875       9.2188353 ]
+#    [11.94896657 13.25034122 14.59417652]]]]
+output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.5), scale_factors=scales,
+                        exclude_outside=True).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_cubic_A_n0p5_exclude_outside')
+```
+
+</details>
+<details>
+<summary>resize_downsample_scales_cubic_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+# [[[[ 1.          2.39519159  3.79038317]
+#    [ 6.58076634  7.97595793  9.37114951]
+#    [12.16153268 13.55672427 14.95191585]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_cubic_align_corners')
+```
+
+</details>
+<details>
+<summary>resize_downsample_scales_linear</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+# [[[[2.6666665 4.3333331]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_linear')
+```
+
+</details>
+<details>
+<summary>resize_downsample_scales_linear_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+# [[[[1.       3.142857]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_linear_align_corners')
+```
+
+</details>
+<details>
+<summary>resize_downsample_scales_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+# [[[[1. 3.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_downsample_scales_nearest')
+```
+
+</details>
+<details>
+<summary>resize_downsample_sizes_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+# [[[[ 1.63078704  3.00462963  4.37847222]
+#    [ 7.12615741  8.5         9.87384259]
+#    [12.62152778 13.99537037 15.36921296]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_downsample_sizes_cubic')
+```
+
+</details>
+<details>
+<summary>resize_downsample_sizes_linear_pytorch_half_pixel</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='pytorch_half_pixel'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 3, 1], dtype=np.int64)
+
+# [[[[ 1.6666666]
+#    [ 7.       ]
+#    [12.333333 ]]]]
+output = interpolate_nd(
+    data, linear_coeffs, output_size=sizes, coordinate_transformation_mode='pytorch_half_pixel').astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_downsample_sizes_linear_pytorch_half_pixel')
+```
+
+</details>
+<details>
+<summary>resize_downsample_sizes_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 1, 3], dtype=np.int64)
+
+# [[[[1. 3.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_downsample_sizes_nearest')
+```
+
+</details>
+<details>
+<summary>resize_tf_crop_and_resize</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', 'roi', '', 'sizes'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='tf_crop_and_resize'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+# Note: for some rois, the result may be different with that of TF for inaccurate floating point
+roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
+sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+# [[[[ 7.6000004  7.9        8.2      ]
+#    [ 8.8        9.1        9.400001 ]
+#    [10.        10.3       10.6      ]]]]
+output = interpolate_nd(data, linear_coeffs, output_size=sizes, roi=roi,
+                        coordinate_transformation_mode='tf_crop_and_resize').astype(np.float32)
+
+expect(node, inputs=[data, roi, sizes], outputs=[output],
+       name='test_resize_tf_crop_and_resize')
+```
+
+</details>
+<details>
+<summary>resize_tf_crop_and_resize_extrapolation_value</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', 'roi', '', 'sizes'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='tf_crop_and_resize',
+    extrapolation_value=10.0
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+# Note: for some rois, the result may be different with that of TF for inaccurate floating point
+roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
+sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+# [[[[ 7.6000004 10.        10.       ]
+#    [12.400001  10.        10.       ]
+#    [10.        10.        10.       ]]]]
+output = interpolate_nd(data, linear_coeffs, output_size=sizes, roi=roi,
+                        coordinate_transformation_mode='tf_crop_and_resize', extrapolation_value=10.0).astype(np.float32)
+
+expect(node, inputs=[data, roi, sizes], outputs=[output],
+       name='test_resize_tf_crop_and_resize')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 0.47265625  0.76953125  1.24609375  1.875       2.28125
+#      2.91015625  3.38671875  3.68359375]
+#    [ 1.66015625  1.95703125  2.43359375  3.0625      3.46875
+#      4.09765625  4.57421875  4.87109375]
+#    [ 3.56640625  3.86328125  4.33984375  4.96875     5.375
+#      6.00390625  6.48046875  6.77734375]
+#    [ 6.08203125  6.37890625  6.85546875  7.484375    7.890625
+#      8.51953125  8.99609375  9.29296875]
+#    [ 7.70703125  8.00390625  8.48046875  9.109375    9.515625
+#     10.14453125 10.62109375 10.91796875]
+#    [10.22265625 10.51953125 10.99609375 11.625      12.03125
+#     12.66015625 13.13671875 13.43359375]
+#    [12.12890625 12.42578125 12.90234375 13.53125    13.9375
+#     14.56640625 15.04296875 15.33984375]
+#    [13.31640625 13.61328125 14.08984375 14.71875    15.125
+#     15.75390625 16.23046875 16.52734375]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_cubic_A_n0p5_exclude_outside</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    cubic_coeff_a=-0.5,
+    exclude_outside=True
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 0.55882353  0.81494204  1.35698249  1.89705882  2.39705882
+#      2.93713516  3.47917561  3.73529412]
+#    [ 1.58329755  1.83941606  2.38145651  2.92153285  3.42153285
+#      3.96160918  4.50364964  4.75976814]
+#    [ 3.75145936  4.00757787  4.54961832  5.08969466  5.58969466
+#      6.12977099  6.67181144  6.92792995]
+#    [ 5.91176471  6.16788321  6.70992366  7.25        7.75
+#      8.29007634  8.83211679  9.08823529]
+#    [ 7.91176471  8.16788321  8.70992366  9.25        9.75
+#     10.29007634 10.83211679 11.08823529]
+#    [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534
+#     12.45038168 12.99242213 13.24854064]
+#    [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715
+#     14.61854349 15.16058394 15.41670245]
+#    [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118
+#     15.64301751 16.18505796 16.44117647]]]]
+output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.5), scale_factors=scales,
+                        exclude_outside=True).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic_A_n0p5_exclude_outside')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_cubic_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 1.          1.34110787  1.80029155  2.32944606  2.67055394
+#      3.19970845  3.65889213  4.        ]
+#    [ 2.36443149  2.70553936  3.16472303  3.69387755  4.03498542
+#      4.56413994  5.02332362  5.36443149]
+#    [ 4.20116618  4.54227405  5.00145773  5.53061224  5.87172012
+#      6.40087464  6.86005831  7.20116618]
+#    [ 6.31778426  6.65889213  7.1180758   7.64723032  7.98833819
+#      8.51749271  8.97667638  9.31778426]
+#    [ 7.68221574  8.02332362  8.48250729  9.01166181  9.35276968
+#      9.8819242  10.34110787 10.68221574]
+#    [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776
+#     11.99854227 12.45772595 12.79883382]
+#    [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245
+#     13.83527697 14.29446064 14.63556851]
+#    [13.         13.34110787 13.80029155 14.32944606 14.67055394
+#     15.19970845 15.65889213 16.        ]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic_align_corners')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_cubic_asymmetric</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='cubic',
+    coordinate_transformation_mode='asymmetric'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[ 1.       1.40625  2.       2.5      3.       3.59375  4.
+#      4.09375]
+#    [ 2.625    3.03125  3.625    4.125    4.625    5.21875  5.625
+#      5.71875]
+#    [ 5.       5.40625  6.       6.5      7.       7.59375  8.
+#      8.09375]
+#    [ 7.       7.40625  8.       8.5      9.       9.59375 10.
+#     10.09375]
+#    [ 9.       9.40625 10.      10.5     11.      11.59375 12.
+#     12.09375]
+#    [11.375   11.78125 12.375   12.875   13.375   13.96875 14.375
+#     14.46875]
+#    [13.      13.40625 14.      14.5     15.      15.59375 16.
+#     16.09375]
+#    [13.375   13.78125 14.375   14.875   15.375   15.96875 16.375
+#     16.46875]]]]
+output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.75), scale_factors=scales,
+                        coordinate_transformation_mode='asymmetric').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_cubic_asymmetric')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_linear</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[1.   1.25 1.75 2.  ]
+#    [1.5  1.75 2.25 2.5 ]
+#    [2.5  2.75 3.25 3.5 ]
+#    [3.   3.25 3.75 4.  ]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_linear')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_linear_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='linear',
+    coordinate_transformation_mode='align_corners'
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+# [[[[1.         1.33333333 1.66666667 2.        ]
+#    [1.66666667 2.         2.33333333 2.66666667]
+#    [2.33333333 2.66666667 3.         3.33333333]
+#    [3.         3.33333333 3.66666667 4.        ]]]]
+output = interpolate_nd(
+    data, linear_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_linear_align_corners')
+```
+
+</details>
+<details>
+<summary>resize_upsample_scales_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', 'scales'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+# [[[[1. 1. 1. 2. 2. 2.]
+#    [1. 1. 1. 2. 2. 2.]
+#    [3. 3. 3. 4. 4. 4.]
+#    [3. 3. 3. 4. 4. 4.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, scale_factors=scales).astype(np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_resize_upsample_scales_nearest')
+```
+
+</details>
+<details>
+<summary>resize_upsample_sizes_cubic</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='cubic',
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 9, 10], dtype=np.int64)
+
+# [[[[ 0.45507922  0.64057922  0.97157922  1.42257922  1.90732922
+#      2.22332922  2.70807922  3.15907922  3.49007922  3.67557922]
+#    [ 1.39437963  1.57987963  1.91087963  2.36187963  2.84662963
+#      3.16262963  3.64737963  4.09837963  4.42937963  4.61487963]
+#    [ 2.95130693  3.13680693  3.46780693  3.91880693  4.40355693
+#      4.71955693  5.20430693  5.65530693  5.98630693  6.17180693]
+#    [ 5.20525069  5.39075069  5.72175069  6.17275069  6.65750069
+#      6.97350069  7.45825069  7.90925069  8.24025069  8.42575069]
+#    [ 6.88975     7.07525     7.40625     7.85725     8.342
+#      8.658       9.14275     9.59375     9.92475    10.11025   ]
+#    [ 8.57424931  8.75974931  9.09074931  9.54174931 10.02649931
+#     10.34249931 10.82724931 11.27824931 11.60924931 11.79474931]
+#    [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307
+#     12.59644307 13.08119307 13.53219307 13.86319307 14.04869307]
+#    [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037
+#     14.15337037 14.63812037 15.08912037 15.42012037 15.60562037]
+#    [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078
+#     15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]]
+output = interpolate_nd(
+    data, cubic_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_cubic')
+```
+
+</details>
+<details>
+<summary>resize_upsample_sizes_nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 7, 8], dtype=np.int64)
+
+# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+#    [1. 1. 1. 1. 2. 2. 2. 2.]
+#    [1. 1. 1. 1. 2. 2. 2. 2.]
+#    [1. 1. 1. 1. 2. 2. 2. 2.]
+#    [3. 3. 3. 3. 4. 4. 4. 4.]
+#    [3. 3. 3. 3. 4. 4. 4. 4.]
+#    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+output = interpolate_nd(
+    data, nearest_coeffs, output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest')
+```
+
+</details>
+<details>
+<summary>resize_upsample_sizes_nearest_ceil_half_pixel</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+    coordinate_transformation_mode='half_pixel',
+    nearest_mode='ceil'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+# [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+output = interpolate_nd(
+    data, lambda x: nearest_coeffs(x, mode='ceil'), output_size=sizes).astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest_ceil_half_pixel')
+```
+
+</details>
+<details>
+<summary>resize_upsample_sizes_nearest_floor_align_corners</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+    coordinate_transformation_mode='align_corners',
+    nearest_mode='floor'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+# [[[[ 1.  1.  1.  2.  2.  3.  3.  4.]
+#    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+#    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+#    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+#    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+#    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+#    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+#    [13. 13. 13. 14. 14. 15. 15. 16.]]]]
+output = interpolate_nd(
+    data, lambda x: nearest_coeffs(x, mode='floor'), output_size=sizes, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest_floor_align_corners')
+```
+
+</details>
+<details>
+<summary>resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Resize',
+    inputs=['X', '', '', 'sizes'],
+    outputs=['Y'],
+    mode='nearest',
+    coordinate_transformation_mode='asymmetric',
+    nearest_mode='round_prefer_ceil'
+)
+
+data = np.array([[[
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]]], dtype=np.float32)
+
+sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+# [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]
+#    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+output = interpolate_nd(
+    data, lambda x: nearest_coeffs(x, mode='round_prefer_ceil'),
+    output_size=sizes, coordinate_transformation_mode='asymmetric').astype(np.float32)
+
+expect(node, inputs=[data, sizes], outputs=[output],
+       name='test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric')
+```
+
+</details>
+
+
+### ReverseSequence
+There are 2 test cases, listed as following:
+<details>
+<summary>reversesequence_batch</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReverseSequence',
+    inputs=['x', 'sequence_lens'],
+    outputs=['y'],
+    time_axis=1,
+    batch_axis=0,
+)
+x = np.array([[0.0, 1.0, 2.0, 3.0],
+              [4.0, 5.0, 6.0, 7.0],
+              [8.0, 9.0, 10.0, 11.0],
+              [12.0, 13.0, 14.0, 15.0]], dtype=np.float32)
+sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64)
+
+y = np.array([[0.0, 1.0, 2.0, 3.0],
+              [5.0, 4.0, 6.0, 7.0],
+              [10.0, 9.0, 8.0, 11.0],
+              [15.0, 14.0, 13.0, 12.0]], dtype=np.float32)
+
+expect(node, inputs=[x, sequence_lens], outputs=[y],
+       name='test_reversesequence_batch')
+```
+
+</details>
+<details>
+<summary>reversesequence_time</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ReverseSequence',
+    inputs=['x', 'sequence_lens'],
+    outputs=['y'],
+    time_axis=0,
+    batch_axis=1,
+)
+x = np.array([[0.0, 4.0, 8.0, 12.0],
+              [1.0, 5.0, 9.0, 13.0],
+              [2.0, 6.0, 10.0, 14.0],
+              [3.0, 7.0, 11.0, 15.0]], dtype=np.float32)
+sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)
+
+y = np.array([[3.0, 6.0, 9.0, 12.0],
+              [2.0, 5.0, 8.0, 13.0],
+              [1.0, 4.0, 10.0, 14.0],
+              [0.0, 7.0, 11.0, 15.0]], dtype=np.float32)
+
+expect(node, inputs=[x, sequence_lens], outputs=[y],
+       name='test_reversesequence_time')
+```
+
+</details>
+
+
+### RoiAlign
+There are 2 test cases, listed as following:
+<details>
+<summary>roialign_aligned_false</summary>
+
+```python
+node = onnx.helper.make_node(
+    "RoiAlign",
+    inputs=["X", "rois", "batch_indices"],
+    outputs=["Y"],
+    spatial_scale=1.0,
+    output_height=5,
+    output_width=5,
+    sampling_ratio=2,
+    coordinate_transformation_mode="output_half_pixel",
+)
+
+X, batch_indices, rois = get_roi_align_input_values()
+# (num_rois, C, output_height, output_width)
+Y = np.array(
+    [
+        [
+            [
+                [0.4664, 0.4466, 0.3405, 0.5688, 0.6068],
+                [0.3714, 0.4296, 0.3835, 0.5562, 0.3510],
+                [0.2768, 0.4883, 0.5222, 0.5528, 0.4171],
+                [0.4713, 0.4844, 0.6904, 0.4920, 0.8774],
+                [0.6239, 0.7125, 0.6289, 0.3355, 0.3495],
+            ]
+        ],
+        [
+            [
+                [0.3022, 0.4305, 0.4696, 0.3978, 0.5423],
+                [0.3656, 0.7050, 0.5165, 0.3172, 0.7015],
+                [0.2912, 0.5059, 0.6476, 0.6235, 0.8299],
+                [0.5916, 0.7389, 0.7048, 0.8372, 0.8893],
+                [0.6227, 0.6153, 0.7097, 0.6154, 0.4585],
+            ]
+        ],
+        [
+            [
+                [0.2384, 0.3379, 0.3717, 0.6100, 0.7601],
+                [0.3767, 0.3785, 0.7147, 0.9243, 0.9727],
+                [0.5749, 0.5826, 0.5709, 0.7619, 0.8770],
+                [0.5355, 0.2566, 0.2141, 0.2796, 0.3600],
+                [0.4365, 0.3504, 0.2887, 0.3661, 0.2349],
+            ]
+        ],
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_false")
+```
+
+</details>
+<details>
+<summary>roialign_aligned_true</summary>
+
+```python
+node = onnx.helper.make_node(
+    "RoiAlign",
+    inputs=["X", "rois", "batch_indices"],
+    outputs=["Y"],
+    spatial_scale=1.0,
+    output_height=5,
+    output_width=5,
+    sampling_ratio=2,
+    coordinate_transformation_mode="half_pixel",
+)
+
+X, batch_indices, rois = get_roi_align_input_values()
+# (num_rois, C, output_height, output_width)
+Y = np.array(
+    [
+        [
+            [
+                [0.5178, 0.3434, 0.3229, 0.4474, 0.6344],
+                [0.4031, 0.5366, 0.4428, 0.4861, 0.4023],
+                [0.2512, 0.4002, 0.5155, 0.6954, 0.3465],
+                [0.3350, 0.4601, 0.5881, 0.3439, 0.6849],
+                [0.4932, 0.7141, 0.8217, 0.4719, 0.4039],
+            ]
+        ],
+        [
+            [
+                [0.3070, 0.2187, 0.3337, 0.4880, 0.4870],
+                [0.1871, 0.4914, 0.5561, 0.4192, 0.3686],
+                [0.1433, 0.4608, 0.5971, 0.5310, 0.4982],
+                [0.2788, 0.4386, 0.6022, 0.7000, 0.7524],
+                [0.5774, 0.7024, 0.7251, 0.7338, 0.8163],
+            ]
+        ],
+        [
+            [
+                [0.2393, 0.4075, 0.3379, 0.2525, 0.4743],
+                [0.3671, 0.2702, 0.4105, 0.6419, 0.8308],
+                [0.5556, 0.4543, 0.5564, 0.7502, 0.9300],
+                [0.6626, 0.5617, 0.4813, 0.4954, 0.6663],
+                [0.6636, 0.3721, 0.2056, 0.1928, 0.2478],
+            ]
+        ],
+    ],
+    dtype=np.float32,
+)
+
+expect(node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_true")
+```
+
+</details>
+
+
+### Round
+There are 1 test cases, listed as following:
+<details>
+<summary>round</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Round',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([0.1, 0.5, 0.9, 1.2, 1.5,
+            1.8, 2.3, 2.5, 2.7, -1.1,
+            -1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float32)
+y = np.array([0., 0., 1., 1., 2.,
+            2., 2., 2., 3., -1.,
+            -2., -2., -2., -2., -3.]).astype(np.float32)  # expected output
+expect(node, inputs=[x], outputs=[y],
+       name='test_round')
+```
+
+</details>
+
+
+### Scan
+There are 2 test cases, listed as following:
+<details>
+<summary>scan_8</summary>
+
+```python
+# Given an input sequence [x1, ..., xN], sum up its elements using a scan
+# returning the final state (x1+x2+...+xN) as well the scan_output
+# [x1, x1+x2, ..., x1+x2+...+xN]
+#
+# create graph to represent scan body
+sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+next = onnx.helper.make_tensor_value_info('next', onnx.TensorProto.FLOAT, [2])
+sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['sum_in', 'next'],
+    outputs=['sum_out']
+)
+id_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['sum_out'],
+    outputs=['scan_out']
+)
+scan_body = onnx.helper.make_graph(
+    [add_node, id_node],
+    'scan_body',
+    [sum_in, next],
+    [sum_out, scan_out]
+)
+# create scan op node
+no_sequence_lens = ''   # optional input, not supplied
+node = onnx.helper.make_node(
+    'Scan',
+    inputs=[no_sequence_lens, 'initial', 'x'],
+    outputs=['y', 'z'],
+    num_scan_inputs=1,
+    body=scan_body
+)
+# create inputs for batch-size 1, sequence-length 3, inner dimension 2
+initial = np.array([0, 0]).astype(np.float32).reshape((1, 2))
+x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2))
+# final state computed = [1 + 3 + 5, 2 + 4 + 6]
+y = np.array([9, 12]).astype(np.float32).reshape((1, 2))
+# scan-output computed
+z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2))
+
+expect(node, inputs=[initial, x], outputs=[y, z],
+       name='test_scan_sum', opset_imports=[onnx.helper.make_opsetid("", 8)])
+```
+
+</details>
+<details>
+<summary>scan_9</summary>
+
+```python
+# Given an input sequence [x1, ..., xN], sum up its elements using a scan
+# returning the final state (x1+x2+...+xN) as well the scan_output
+# [x1, x1+x2, ..., x1+x2+...+xN]
+#
+# create graph to represent scan body
+sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+next = onnx.helper.make_tensor_value_info('next', onnx.TensorProto.FLOAT, [2])
+sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+add_node = onnx.helper.make_node(
+    'Add',
+    inputs=['sum_in', 'next'],
+    outputs=['sum_out']
+)
+id_node = onnx.helper.make_node(
+    'Identity',
+    inputs=['sum_out'],
+    outputs=['scan_out']
+)
+scan_body = onnx.helper.make_graph(
+    [add_node, id_node],
+    'scan_body',
+    [sum_in, next],
+    [sum_out, scan_out]
+)
+# create scan op node
+node = onnx.helper.make_node(
+    'Scan',
+    inputs=['initial', 'x'],
+    outputs=['y', 'z'],
+    num_scan_inputs=1,
+    body=scan_body
+)
+# create inputs for sequence-length 3, inner dimension 2
+initial = np.array([0, 0]).astype(np.float32).reshape((2,))
+x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
+# final state computed = [1 + 3 + 5, 2 + 4 + 6]
+y = np.array([9, 12]).astype(np.float32).reshape((2,))
+# scan-output computed
+z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
+
+expect(node, inputs=[initial, x], outputs=[y, z],
+       name='test_scan9_sum', opset_imports=[onnx.helper.make_opsetid("", 9)])
+```
+
+</details>
+
+
+### Scatter
+There are 2 test cases, listed as following:
+<details>
+<summary>scatter_with_axis</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'Scatter',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, 3]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter(data, indices, updates, axis=axis)
+# print(y) produces
+# [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_with_axis', opset_imports=[helper.make_opsetid("", 10)])
+```
+
+</details>
+<details>
+<summary>scatter_without_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Scatter',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+)
+data = np.zeros((3, 3), dtype=np.float32)
+indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+y = scatter(data, indices, updates)
+# print(y) produces
+# [[2.0, 1.1, 0.0],
+#  [1.0, 0.0, 2.2],
+#  [0.0, 2.1, 1.2]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_without_axis', opset_imports=[helper.make_opsetid("", 10)])
+```
+
+</details>
+
+
+### ScatterElements
+There are 4 test cases, listed as following:
+<details>
+<summary>scatter_elements_with_axis</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, 3]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates, axis)
+# print(y) produces
+# [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_elements_with_axis')
+```
+
+</details>
+<details>
+<summary>scatter_elements_with_duplicate_indices</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+    reduction='add',
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, 1]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates, axis, reduction='add')
+# print(y) produces
+# [[1.0, 5.2, 3.0, 4.0, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+        name='test_scatter_elements_with_duplicate_indices')
+```
+
+</details>
+<details>
+<summary>scatter_elements_with_negative_indices</summary>
+
+```python
+axis = 1
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    axis=axis,
+)
+data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+indices = np.array([[1, -3]], dtype=np.int64)
+updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates, axis)
+# print(y) produces
+# [[1.0, 1.1, 2.1, 4.0, 5.0]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_elements_with_negative_indices')
+```
+
+</details>
+<details>
+<summary>scatter_elements_without_axis</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterElements',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+)
+data = np.zeros((3, 3), dtype=np.float32)
+indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+y = scatter_elements(data, indices, updates)
+# print(y) produces
+# [[2.0, 1.1, 0.0],
+#  [1.0, 0.0, 2.2],
+#  [0.0, 2.1, 1.2]]
+
+expect(node, inputs=[data, indices, updates], outputs=[y],
+       name='test_scatter_elements_without_axis')
+```
+
+</details>
+
+
+### ScatterND
+There are 3 test cases, listed as following:
+<details>
+<summary>scatternd</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterND',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+)
+data = np.array(
+    [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+indices = np.array([[0], [2]], dtype=np.int64)
+updates = np.array(
+    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+# Expecting output as np.array(
+#    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+#     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+#     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+#     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+output = scatter_nd_impl(data, indices, updates)
+expect(node, inputs=[data, indices, updates], outputs=[output],
+       name='test_scatternd')
+```
+
+</details>
+<details>
+<summary>scatternd_add</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterND',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    reduction='add',
+)
+data = np.array(
+    [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+indices = np.array([[0], [0]], dtype=np.int64)
+updates = np.array(
+    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+        [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+# Expecting output as np.array(
+#    [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]],
+#     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+#     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+#     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+output = scatter_nd_impl(data, indices, updates, reduction='add')
+expect(node, inputs=[data, indices, updates], outputs=[output],
+       name='test_scatternd_add')
+```
+
+</details>
+<details>
+<summary>scatternd_multiply</summary>
+
+```python
+node = onnx.helper.make_node(
+    'ScatterND',
+    inputs=['data', 'indices', 'updates'],
+    outputs=['y'],
+    reduction='mul',
+)
+data = np.array(
+    [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+        [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+indices = np.array([[0], [0]], dtype=np.int64)
+updates = np.array(
+    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+        [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+# Expecting output as np.array(
+#    [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]],
+#     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+#     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+#     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+output = scatter_nd_impl(data, indices, updates, reduction='mul')
+expect(node, inputs=[data, indices, updates], outputs=[output],
+       name='test_scatternd_multiply')
+```
+
+</details>
+
+
+### Selu
+There are 2 test cases, listed as following:
+<details>
+<summary>selu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Selu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=2.0,
+    gamma=3.0
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+# expected output [-3.79272318, 0., 3.]
+y = np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_selu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+expect(node, inputs=[x], outputs=[y],
+       name='test_selu')
+```
+
+</details>
+<details>
+<summary>selu_default</summary>
+
+```python
+default_alpha = 1.67326319217681884765625
+default_gamma = 1.05070102214813232421875
+node = onnx.helper.make_node(
+    'Selu',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, 0, np.inf) * default_gamma + \
+    (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma
+expect(node, inputs=[x], outputs=[y],
+       name='test_selu_default')
+```
+
+</details>
+
+
+### SequenceInsert
+There are 1 test cases, listed as following:
+<details>
+<summary>sequenceinsert</summary>
+
+```python
+test_cases = {
+    'at_back': [np.array([10, 11, 12]).astype(np.int64)],
+    'at_front': [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)]
+}
+sequence = [np.array([1, 2, 3, 4]).astype(np.int64), np.array([5, 6, 7]).astype(np.int64), np.array([8, 9]).astype(np.int64)]
+
+for test_name, test_inputs in test_cases.items():
+    tensor = test_inputs[0].astype(np.int64)
+
+    if len(test_inputs) > 1:
+        node = onnx.helper.make_node(
+            'SequenceInsert',
+            inputs=['sequence', 'tensor', 'position'],
+            outputs=['output_sequence']
+        )
+        position = test_inputs[1]
+        inserted = sequence_insert_reference_implementation(sequence, tensor, position)
+        expect(node, inputs=[sequence, tensor, position], outputs=[inserted],
+               name='test_sequence_insert_' + test_name)
+    else:
+        node = onnx.helper.make_node(
+            'SequenceInsert',
+            inputs=['sequence', 'tensor'],
+            outputs=['output_sequence']
+        )
+        inserted = sequence_insert_reference_implementation(sequence, tensor)
+        expect(node, inputs=[sequence, tensor], outputs=[inserted],
+               name='test_sequence_insert_' + test_name)
+```
+
+</details>
+
+
+### SequenceMap
+There are 6 test cases, listed as following:
+<details>
+<summary>sequence_map_add_1_sequence_1_tensor</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Add', ['in0', 'in1'], ['out0'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['N'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['N'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0'],
+    body=body
+)
+
+x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
+x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+y0 = [x0[i] + x1 for i in range(3)]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N']),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_add_1_sequence_1_tensor')
+```
+
+</details>
+<details>
+<summary>sequence_map_add_2_sequences</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Add', ['in0', 'in1'], ['out0'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['N'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['N'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0'],
+    body=body
+)
+
+N = [np.random.randint(1, 10) for _ in range(3)]
+x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32)
+      for k in range(3)]
+x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32)
+      for k in range(3)]
+y0 = [x0[k] + x1[k] for k in range(3)]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_add_2_sequences')
+```
+
+</details>
+<details>
+<summary>sequence_map_extract_shapes</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Shape', ['x'], ['shape'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, ['H', 'W', 'C'])],
+    [onnx.helper.make_tensor_value_info('shape', onnx.TensorProto.INT64, [3])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['in_seq'],
+    outputs=['shapes'],
+    body=body
+)
+
+shapes = [
+    np.array([40, 30, 3], dtype=np.int64),
+    np.array([20, 10, 3], dtype=np.int64),
+    np.array([10, 5, 3], dtype=np.int64),
+]
+x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['H', 'W', 'C'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])),
+]
+expect(node, inputs=[x0], outputs=[shapes],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_extract_shapes')
+```
+
+</details>
+<details>
+<summary>sequence_map_identity_1_sequence</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Identity', ['in0'], ['out0'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info(
+        'in0', onnx.TensorProto.FLOAT, ['N'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['M'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x'],
+    outputs=['y'],
+    body=body
+)
+
+x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+     for _ in range(3)]
+y = x
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+]
+expect(node, inputs=[x], outputs=[y],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_identity_1_sequence')
+```
+
+</details>
+<details>
+<summary>sequence_map_identity_1_sequence_1_tensor</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Identity', ['in0'], ['out0']),
+     onnx.helper.make_node('Identity', ['in1'], ['out1'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['M'])],
+    [onnx.helper.make_tensor_value_info(
+        'out0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info(
+        'out1', onnx.TensorProto.FLOAT, ['M'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0', 'y1'],
+    body=body
+)
+
+x0 = [np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32) for _ in range(3)]
+x1 = np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32)
+y0 = x0
+y1 = [x1 for _ in range(3)]
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M']),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0, y1],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_identity_1_sequence_1_tensor')
+```
+
+</details>
+<details>
+<summary>sequence_map_identity_2_sequences</summary>
+
+```python
+body = onnx.helper.make_graph(
+    [onnx.helper.make_node('Identity', ['in0'], ['out0']),
+     onnx.helper.make_node('Identity', ['in1'], ['out1'])],
+    'seq_map_body',
+    [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['M'])],
+    [onnx.helper.make_tensor_value_info('out0', onnx.TensorProto.FLOAT, ['N']),
+     onnx.helper.make_tensor_value_info('out1', onnx.TensorProto.FLOAT, ['M'])]
+)
+
+node = onnx.helper.make_node(
+    'SequenceMap',
+    inputs=['x0', 'x1'],
+    outputs=['y0', 'y1'],
+    body=body
+)
+
+x0 = [np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32) for _ in range(3)]
+x1 = [np.random.uniform(0.0, 1.0, np.random.randint(
+    1, 10)).astype(np.float32) for _ in range(3)]
+y0 = x0
+y1 = x1
+input_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+]
+output_type_protos = [
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+    onnx.helper.make_sequence_type_proto(
+        onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+]
+expect(node, inputs=[x0, x1], outputs=[y0, y1],
+       input_type_protos=input_type_protos,
+       output_type_protos=output_type_protos,
+       name='test_sequence_map_identity_2_sequences')
+```
+
+</details>
+
+
+### Shape
+There are 1 test cases, listed as following:
+<details>
+<summary>shape</summary>
+
+```python
+x = np.array([
+    [1, 2, 3],
+    [4, 5, 6],
+]).astype(np.float32)
+test_shape('_example', x)  # preserve names of original test cases
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+
+test_shape('', x)  # preserve names of original test cases
+
+test_shape('_start_1', x, start=1)
+
+test_shape('_end_1', x, end=1)
+
+test_shape('_start_negative_1', x, start=-1)
+
+test_shape('_end_negative_1', x, end=-1)
+
+test_shape('_start_1_end_negative_1', x, start=1, end=-1)
+
+test_shape('_start_1_end_2', x, start=1, end=2)
+
+test_shape('_clip_start', x, start=-10)
+
+test_shape('_clip_end', x, end=10)
+```
+
+</details>
+
+
+### Shrink
+There are 2 test cases, listed as following:
+<details>
+<summary>hard_shrink</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Shrink',
+    inputs=['x'],
+    outputs=['y'],
+    lambd=1.5,
+)
+X = np.arange(-2.0, 2.1, dtype=np.float32)
+Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
+expect(node, inputs=[X], outputs=[Y],
+       name='test_shrink_hard')
+```
+
+</details>
+<details>
+<summary>soft_shrink</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Shrink',
+    inputs=['x'],
+    outputs=['y'],
+    lambd=1.5,
+    bias=1.5,
+)
+X = np.arange(-2.0, 2.1, dtype=np.float32)
+Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
+expect(node, inputs=[X], outputs=[Y],
+       name='test_shrink_soft')
+```
+
+</details>
+
+
+### Sigmoid
+There are 1 test cases, listed as following:
+<details>
+<summary>sigmoid</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sigmoid',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = 1.0 / (1.0 + np.exp(np.negative(x)))  # expected output [0.26894143, 0.5, 0.7310586]
+expect(node, inputs=[x], outputs=[y],
+       name='test_sigmoid_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = 1.0 / (1.0 + np.exp(np.negative(x)))
+expect(node, inputs=[x], outputs=[y],
+       name='test_sigmoid')
+```
+
+</details>
+
+
+### Sign
+There are 1 test cases, listed as following:
+<details>
+<summary>sign</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sign',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array(range(-5, 6)).astype(np.float32)
+y = np.sign(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sign')
+```
+
+</details>
+
+
+### Sin
+There are 1 test cases, listed as following:
+<details>
+<summary>sin</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sin',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.sin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sin_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.sin(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sin')
+```
+
+</details>
+
+
+### Sinh
+There are 1 test cases, listed as following:
+<details>
+<summary>sinh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sinh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.sinh(x)  # expected output [-1.17520118,  0.,  1.17520118]
+expect(node, inputs=[x], outputs=[y],
+       name='test_sinh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.sinh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sinh')
+```
+
+</details>
+
+
+### Size
+There are 1 test cases, listed as following:
+<details>
+<summary>size</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Size',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([
+    [1, 2, 3],
+    [4, 5, 6],
+]).astype(np.float32)
+y = np.array(6).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_size_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.array(x.size).astype(np.int64)
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_size')
+```
+
+</details>
+
+
+### Slice
+There are 8 test cases, listed as following:
+<details>
+<summary>slice</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+y = x[0:3, 0:10]
+starts = np.array([0, 0], dtype=np.int64)
+ends = np.array([3, 10], dtype=np.int64)
+axes = np.array([0, 1], dtype=np.int64)
+steps = np.array([1, 1], dtype=np.int64)
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice')
+```
+
+</details>
+<details>
+<summary>slice_default_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0, 0, 3], dtype=np.int64)
+ends = np.array([20, 10, 4], dtype=np.int64)
+y = x[:, :, 3:4]
+
+expect(node, inputs=[x, starts, ends], outputs=[y],
+       name='test_slice_default_axes')
+```
+
+</details>
+<details>
+<summary>slice_default_steps</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0, 0, 3], dtype=np.int64)
+ends = np.array([20, 10, 4], dtype=np.int64)
+axes = np.array([0, 1, 2], dtype=np.int64)
+y = x[:, :, 3:4]
+
+expect(node, inputs=[x, starts, ends, axes], outputs=[y],
+       name='test_slice_default_steps')
+```
+
+</details>
+<details>
+<summary>slice_end_out_of_bounds</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([1], dtype=np.int64)
+ends = np.array([1000], dtype=np.int64)
+axes = np.array([1], dtype=np.int64)
+steps = np.array([1], dtype=np.int64)
+y = x[:, 1:1000]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_end_out_of_bounds')
+```
+
+</details>
+<details>
+<summary>slice_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0], dtype=np.int64)
+ends = np.array([-1], dtype=np.int64)
+axes = np.array([1], dtype=np.int64)
+steps = np.array([1], dtype=np.int64)
+y = x[:, 0:-1]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_neg')
+```
+
+</details>
+<details>
+<summary>slice_neg_steps</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([20, 10, 4], dtype=np.int64)
+ends = np.array([0, 0, 1], dtype=np.int64)
+axes = np.array([0, 1, 2], dtype=np.int64)
+steps = np.array([-1, -3, -2]).astype(np.int64)
+y = x[20:0:-1, 10:0:-3, 4:1:-2]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_neg_steps')
+```
+
+</details>
+<details>
+<summary>slice_negative_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([0, 0, 3], dtype=np.int64)
+ends = np.array([20, 10, 4], dtype=np.int64)
+axes = np.array([0, -2, -1], dtype=np.int64)
+y = x[:, :, 3:4]
+
+expect(node, inputs=[x, starts, ends, axes], outputs=[y],
+       name='test_slice_negative_axes')
+```
+
+</details>
+<details>
+<summary>slice_start_out_of_bounds</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Slice',
+    inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+    outputs=['y'],
+)
+
+x = np.random.randn(20, 10, 5).astype(np.float32)
+starts = np.array([1000], dtype=np.int64)
+ends = np.array([1000], dtype=np.int64)
+axes = np.array([1], dtype=np.int64)
+steps = np.array([1], dtype=np.int64)
+y = x[:, 1000:1000]
+
+expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+       name='test_slice_start_out_of_bounds')
+```
+
+</details>
+
+
+### Softmax
+There are 2 test cases, listed as following:
+<details>
+<summary>softmax</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+x = np.array([[-1, 0, 1]]).astype(np.float32)
+# expected output [[0.09003058, 0.24472848, 0.66524094]]
+y = softmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_example')
+```
+
+</details>
+<details>
+<summary>softmax_axis</summary>
+
+```python
+x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]
+             ).astype(np.float32)
+# expected output
+# [[0.032058604 0.08714432  0.23688284  0.6439143  ]
+# [0.032058604 0.08714432  0.23688284  0.6439143  ]]
+y = softmax(x)
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_large_number')
+
+x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=0,
+)
+y = softmax(x, axis=0)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_axis_0')
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=1,
+)
+y = softmax(x, axis=1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_axis_1')
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=2,
+)
+y = softmax(x, axis=2)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_axis_2')
+
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+    axis=-1,
+)
+y = softmax(x, axis=-1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_negative_axis')
+
+# default axis is -1
+node = onnx.helper.make_node(
+    'Softmax',
+    inputs=['x'],
+    outputs=['y'],
+)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softmax_default_axis')
+```
+
+</details>
+
+
+### SoftmaxCrossEntropyLoss
+There are 34 test cases, listed as following:
+<details>
+<summary>input_shape_is_NCd1_mean_weight_negative_ii</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(-1)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1 = 3, 5, 6
+np.random.seed(0)
+x = np.random.rand(N, C, dim1).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+labels[0][0] = -1
+weight = np.random.rand(C).astype(np.float32)
+
+sce = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index)
+
+expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1_mean_weight_negative_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1_mean_weight_negative_ii_log_prob</summary>
+
+```python
+reduction = 'mean'
+ignore_index = np.int64(-1)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1 = 3, 5, 6
+np.random.seed(0)
+x = np.random.rand(N, C, dim1).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+labels[0][0] = -1
+weight = np.random.rand(C).astype(np.float32)
+
+loss, log_prob = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index,
+                          get_log_prob=True)
+
+expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1_mean_weight_negative_ii_log_prob')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3_none_no_weight_negative_ii</summary>
+
+```python
+reduction = 'none'
+ignore_index = np.int64(-5)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+labels[0][0][0][0] = -5
+
+sce = softmaxcrossentropy(x,
+                          labels,
+                          reduction=reduction,
+                          ignore_index=ignore_index)
+
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_NCd1d2d3_none_no_weight_negative_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob</summary>
+
+```python
+reduction = 'none'
+ignore_index = np.int64(-5)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+labels[0][0][0][0] = -5
+
+loss, log_prob = softmaxcrossentropy(x,
+                          labels,
+                          reduction=reduction,
+                          ignore_index=ignore_index,
+                          get_log_prob=True)
+
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3_sum_weight_high_ii</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(10)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C = 3, 5
+np.random.seed(0)
+x = np.random.rand(N, C).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+labels[0] = 10
+weight = np.random.rand(C).astype(np.float32)
+
+sce = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index)
+
+expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1d2d3_sum_weight_high_ii')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob</summary>
+
+```python
+reduction = 'sum'
+ignore_index = np.int64(10)
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+N, C = 3, 5
+np.random.seed(0)
+x = np.random.rand(N, C).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+labels[0] = 10
+weight = np.random.rand(C).astype(np.float32)
+
+loss, log_prob = softmaxcrossentropy(x,
+                          labels,
+                          weight=weight,
+                          reduction=reduction,
+                          ignore_index=ignore_index,
+                          get_log_prob=True)
+
+expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1d2d3_sum_weight_high_ii_log_prob')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_mean_weight</summary>
+
+```python
+reduction = 'mean'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+sce = softmaxcrossentropy(x,
+                        labels,
+                        weight=weight,
+                        reduction=reduction)
+
+expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1d2d3d4d5_mean_weight')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob</summary>
+
+```python
+reduction = 'mean'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+weight = np.random.rand(C).astype(np.float32)
+
+loss, log_prob = softmaxcrossentropy(x,
+                        labels,
+                        weight=weight,
+                        reduction=reduction,
+                        get_log_prob=True)
+
+expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1d2d3d4d5_mean_weight_log_prob')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_none_no_weight</summary>
+
+```python
+reduction = 'none'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+sce = softmaxcrossentropy(x,
+                        labels,
+                        reduction=reduction)
+
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_NCd1d2d3d4d5_none_no_weight')
+```
+
+</details>
+<details>
+<summary>input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob</summary>
+
+```python
+reduction = 'none'
+
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+np.random.seed(0)
+x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+loss, log_prob = softmaxcrossentropy(x,
+                        labels,
+                        reduction=reduction,
+                        get_log_prob=True)
+
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_NCd1d2d3d4d5_none_no_weight_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_3d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, y)
+
+# Check results
+expect(node, inputs=[x, y], outputs=[sce], name='test_sce_mean_3d')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_3d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, y], outputs=[loss, log_prob], name='test_sce_mean_3d_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_3d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii_3d')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_3d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_3d_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_4d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction=reduction, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii_4d')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_4d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_4d_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_no_weights_ii_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(2)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(0)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(0)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_3d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(1)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(1)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii_3d')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_3d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(1)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+labels[0][0] = np.int64(1)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_3d_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_4d</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii_4d')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_4d_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(2)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                            inputs=['x', 'y', 'w'],
+                            outputs=['z', 'log_prob'],
+                            reduction=reduction,
+                            ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+labels[0][0][0] = np.int64(2)
+weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_4d_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_ii_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+ignore_index = np.int64(0)
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction,
+                             ignore_index=ignore_index)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+labels[0] = np.int64(0)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_mean_weights_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'mean'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_none</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction='none')
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_none')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_none_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction='none', get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_none_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_none_weights</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, weight=weights, reduction='none')
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_none_weights')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_none_weights_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'none'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y', 'w'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, reduction='none', get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_none_weights_log_prob')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_sum</summary>
+
+```python
+# Define operator attributes.
+reduction = 'sum'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+sce = softmaxcrossentropy(x, labels, reduction='sum')
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_sum')
+```
+
+</details>
+<details>
+<summary>softmaxcrossentropy_sum_log_prob</summary>
+
+```python
+# Define operator attributes.
+reduction = 'sum'
+
+# Create operator.
+node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                             inputs=['x', 'y'],
+                             outputs=['z', 'log_prob'],
+                             reduction=reduction)
+
+# Define operator inputs.
+np.random.seed(0)
+x = np.random.rand(3, 5).astype(np.float32)
+labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+# Compute SoftmaxCrossEntropyLoss
+loss, log_prob = softmaxcrossentropy(x, labels, reduction='sum', get_log_prob=True)
+
+# Check results
+expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_sum_log_prob')
+```
+
+</details>
+
+
+### Softplus
+There are 1 test cases, listed as following:
+<details>
+<summary>softplus</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Softplus',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.log(np.exp(x) + 1)  # expected output [0.31326166, 0.69314718, 1.31326163]
+expect(node, inputs=[x], outputs=[y],
+       name='test_softplus_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.log(np.exp(x) + 1)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softplus')
+```
+
+</details>
+
+
+### Softsign
+There are 1 test cases, listed as following:
+<details>
+<summary>softsign</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Softsign',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.array([-0.5, 0, 0.5]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_softsign_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = x / (1 + np.abs(x))
+expect(node, inputs=[x], outputs=[y],
+       name='test_softsign')
+```
+
+</details>
+
+
+### SpaceToDepth
+There are 2 test cases, listed as following:
+<details>
+<summary>example</summary>
+
+```python
+node = onnx.helper.make_node(
+    'SpaceToDepth',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=2,
+)
+
+# (1, 1, 4, 6) input tensor
+x = np.array([[[[0, 6, 1, 7, 2, 8],
+                [12, 18, 13, 19, 14, 20],
+                [3, 9, 4, 10, 5, 11],
+                [15, 21, 16, 22, 17, 23]]]]).astype(np.float32)
+
+# (1, 4, 2, 3) output tensor
+y = np.array([[[[0, 1, 2],
+                [3, 4, 5]],
+               [[6, 7, 8],
+                [9, 10, 11]],
+               [[12, 13, 14],
+                [15, 16, 17]],
+               [[18, 19, 20],
+                [21, 22, 23]]]]).astype(np.float32)
+expect(node, inputs=[x], outputs=[y],
+       name='test_spacetodepth_example')
+```
+
+</details>
+<details>
+<summary>spacetodepth</summary>
+
+```python
+b, c, h, w = shape = (2, 2, 6, 6)
+blocksize = 2
+node = onnx.helper.make_node(
+    'SpaceToDepth',
+    inputs=['x'],
+    outputs=['y'],
+    blocksize=blocksize,
+)
+x = np.random.random_sample(shape).astype(np.float32)
+tmp = np.reshape(x, [b, c,
+                     h // blocksize, blocksize,
+                     w // blocksize, blocksize])
+tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
+y = np.reshape(tmp, [b, c * (blocksize**2),
+                     h // blocksize,
+                     w // blocksize])
+expect(node, inputs=[x], outputs=[y],
+       name='test_spacetodepth')
+```
+
+</details>
+
+
+### Split
+There are 4 test cases, listed as following:
+<details>
+<summary>1d</summary>
+
+```python
+input = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input'],
+    outputs=['output_1', 'output_2', 'output_3'],
+    axis=0
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4.]).astype(np.float32), np.array([5., 6.]).astype(np.float32)]
+expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_1d')
+
+split = np.array([2, 4]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2'],
+    axis=0,
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4., 5., 6.]).astype(np.float32)]
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_1d')
+```
+
+</details>
+<details>
+<summary>2d</summary>
+
+```python
+input = np.array([[1., 2., 3., 4., 5., 6.],
+                  [7., 8., 9., 10., 11., 12.]]).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input'],
+    outputs=['output_1', 'output_2'],
+    axis=1
+)
+
+expected_outputs = [np.array([[1., 2., 3.], [7., 8., 9.]]).astype(np.float32),
+                    np.array([[4., 5., 6.], [10., 11., 12.]]).astype(np.float32)]
+
+expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_2d')
+
+split = np.array([2, 4]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2'],
+    axis=1,
+)
+
+expected_outputs = [np.array([[1., 2.], [7., 8.]]).astype(np.float32),
+                    np.array([[3., 4., 5., 6.], [9., 10., 11., 12.]]).astype(np.float32)]
+
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_2d')
+```
+
+</details>
+<details>
+<summary>default_values</summary>
+
+```python
+input = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float32)
+
+# If axis is not specified, split is applied on default axis 0
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input'],
+    outputs=['output_1', 'output_2', 'output_3']
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4.]).astype(np.float32), np.array([5., 6.]).astype(np.float32)]
+expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_default_axis')
+
+split = np.array([2, 4]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2']
+)
+
+expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4., 5., 6.]).astype(np.float32)]
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_default_axis')
+```
+
+</details>
+<details>
+<summary>zero_size_splits</summary>
+
+```python
+input = np.array([]).astype(np.float32)
+
+# Split emtpy tensor to tensors of size zero
+split = np.array([0, 0, 0]).astype(np.int64)
+node = onnx.helper.make_node(
+    'Split',
+    inputs=['input', 'split'],
+    outputs=['output_1', 'output_2', 'output_3']
+)
+
+expected_outputs = [np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32)]
+expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_zero_size_splits')
+```
+
+</details>
+
+
+### Sqrt
+There are 1 test cases, listed as following:
+<details>
+<summary>sqrt</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sqrt',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([1, 4, 9]).astype(np.float32)
+y = np.sqrt(x)  # expected output [1., 2., 3.]
+expect(node, inputs=[x], outputs=[y],
+       name='test_sqrt_example')
+
+x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+y = np.sqrt(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_sqrt')
+```
+
+</details>
+
+
+### Squeeze
+There are 2 test cases, listed as following:
+<details>
+<summary>squeeze</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Squeeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+axes = np.array([0], dtype=np.int64)
+y = np.squeeze(x, axis=0)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_squeeze')
+```
+
+</details>
+<details>
+<summary>squeeze_negative_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Squeeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+axes = np.array([-2], dtype=np.int64)
+y = np.squeeze(x, axis=-2)
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_squeeze_negative_axes')
+```
+
+</details>
+
+
+### StringNormalizer
+There are 6 test cases, listed as following:
+<details>
+<summary>monday_casesensintive_lower</summary>
+
+```python
+input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='LOWER',
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_lower')
+```
+
+</details>
+<details>
+<summary>monday_casesensintive_nochangecase</summary>
+
+```python
+input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_nochangecase')
+```
+
+</details>
+<details>
+<summary>monday_casesensintive_upper</summary>
+
+```python
+input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+output = np.array([u'TUESDAY', u'WEDNESDAY', u'THURSDAY']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='UPPER',
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_upper')
+```
+
+</details>
+<details>
+<summary>monday_empty_output</summary>
+
+```python
+input = np.array([u'monday', u'monday']).astype(object)
+output = np.array([u'']).astype(object)
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='UPPER',
+    is_case_sensitive=1,
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_empty_output')
+```
+
+</details>
+<details>
+<summary>monday_insensintive_upper_twodim</summary>
+
+```python
+input = np.array([u'Monday', u'tuesday', u'wednesday', u'Monday', u'tuesday', u'wednesday']).astype(object).reshape([1, 6])
+
+# It does upper case cecedille, accented E
+# and german umlaut but fails
+# with german eszett
+output = np.array([u'TUESDAY', u'WEDNESDAY', u'TUESDAY', u'WEDNESDAY']).astype(object).reshape([1, 4])
+stopwords = [u'monday']
+
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    case_change_action='UPPER',
+    stopwords=stopwords
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_insensintive_upper_twodim')
+```
+
+</details>
+<details>
+<summary>nostopwords_nochangecase</summary>
+
+```python
+input = np.array([u'monday', u'tuesday']).astype(object)
+output = input
+
+# No stopwords. This is a NOOP
+node = onnx.helper.make_node(
+    'StringNormalizer',
+    inputs=['x'],
+    outputs=['y'],
+    is_case_sensitive=1,
+)
+expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_nostopwords_nochangecase')
+```
+
+</details>
+
+
+### Sub
+There are 2 test cases, listed as following:
+<details>
+<summary>sub</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sub',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.array([1, 2, 3]).astype(np.float32)
+y = np.array([3, 2, 1]).astype(np.float32)
+z = x - y  # expected output [-2., 0., 2.]
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(3, 4, 5).astype(np.float32)
+z = x - y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub')
+
+x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8)
+y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8)
+z = x - y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub_uint8')
+```
+
+</details>
+<details>
+<summary>sub_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Sub',
+    inputs=['x', 'y'],
+    outputs=['z'],
+)
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.random.randn(5).astype(np.float32)
+z = x - y
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_sub_bcast')
+```
+
+</details>
+
+
+### Sum
+There are 1 test cases, listed as following:
+<details>
+<summary>sum</summary>
+
+```python
+data_0 = np.array([3, 0, 2]).astype(np.float32)
+data_1 = np.array([1, 3, 4]).astype(np.float32)
+data_2 = np.array([2, 6, 6]).astype(np.float32)
+result = np.array([6, 9, 12]).astype(np.float32)
+node = onnx.helper.make_node(
+    'Sum',
+    inputs=['data_0', 'data_1', 'data_2'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+       name='test_sum_example')
+
+node = onnx.helper.make_node(
+    'Sum',
+    inputs=['data_0'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0], outputs=[data_0],
+       name='test_sum_one_input')
+
+result = np.add(data_0, data_1)
+node = onnx.helper.make_node(
+    'Sum',
+    inputs=['data_0', 'data_1'],
+    outputs=['result'],
+)
+expect(node, inputs=[data_0, data_1], outputs=[result],
+       name='test_sum_two_inputs')
+```
+
+</details>
+
+
+### Tan
+There are 1 test cases, listed as following:
+<details>
+<summary>tan</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tan',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.tan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_tan_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.tan(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_tan')
+```
+
+</details>
+
+
+### Tanh
+There are 1 test cases, listed as following:
+<details>
+<summary>tanh</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tanh',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.array([-1, 0, 1]).astype(np.float32)
+y = np.tanh(x)  # expected output [-0.76159418, 0., 0.76159418]
+expect(node, inputs=[x], outputs=[y],
+       name='test_tanh_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.tanh(x)
+expect(node, inputs=[x], outputs=[y],
+       name='test_tanh')
+```
+
+</details>
+
+
+### TfIdfVectorizer
+There are 7 test cases, listed as following:
+<details>
+<summary>tf_batch_onlybigrams_skip0</summary>
+
+```python
+input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+output = np.array([[0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 1.]]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=0,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_onlybigrams_skip0')
+```
+
+</details>
+<details>
+<summary>tf_batch_onlybigrams_skip5</summary>
+
+```python
+input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+output = np.array([[0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 1., 1.]]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_onlybigrams_skip5')
+```
+
+</details>
+<details>
+<summary>tf_batch_uniandbigrams_skip5</summary>
+
+```python
+input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+output = np.array([[0., 3., 0., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 1., 1.]]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=1,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_uniandbigrams_skip5')
+```
+
+</details>
+<details>
+<summary>tf_only_bigrams_skip0</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([0., 0., 0., 0., 1., 1., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=0,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_only_bigrams_skip0')
+```
+
+</details>
+<details>
+<summary>tf_onlybigrams_levelempty</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([1., 1., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 0]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2]).astype(np.int64)
+pool_int64s = np.array([    # unigrams none
+                       5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=0,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_onlybigrams_levelempty')
+```
+
+</details>
+<details>
+<summary>tf_onlybigrams_skip5</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([0., 0., 0., 0., 1., 3., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=2,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_onlybigrams_skip5')
+```
+
+</details>
+<details>
+<summary>tf_uniandbigrams_skip5</summary>
+
+```python
+input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+output = np.array([0., 3., 1., 0., 1., 3., 1.]).astype(np.float32)
+
+ngram_counts = np.array([0, 4]).astype(np.int64)
+ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                        5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+helper = TfIdfVectorizerHelper(
+    mode='TF',
+    min_gram_length=1,
+    max_gram_length=2,
+    max_skip_count=5,
+    ngram_counts=ngram_counts,
+    ngram_indexes=ngram_indexes,
+    pool_int64s=pool_int64s
+)
+node = helper.make_node_noweights()
+expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_uniandbigrams_skip5')
+```
+
+</details>
+
+
+### ThresholdedRelu
+There are 2 test cases, listed as following:
+<details>
+<summary>default</summary>
+
+```python
+default_alpha = 1.0
+node = onnx.helper.make_node(
+    'ThresholdedRelu',
+    inputs=['x'],
+    outputs=['y']
+)
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, default_alpha, np.inf)
+y[y == default_alpha] = 0
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_thresholdedrelu_default')
+```
+
+</details>
+<details>
+<summary>thresholdedrelu</summary>
+
+```python
+alpha = 2.0
+node = onnx.helper.make_node(
+    'ThresholdedRelu',
+    inputs=['x'],
+    outputs=['y'],
+    alpha=alpha
+)
+
+x = np.array([-1.5, 0., 1.2, 2.0, 2.2]).astype(np.float32)
+y = np.clip(x, alpha, np.inf)  # expected output [0., 0., 0., 0., 2.2]
+y[y == alpha] = 0
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_thresholdedrelu_example')
+
+x = np.random.randn(3, 4, 5).astype(np.float32)
+y = np.clip(x, alpha, np.inf)
+y[y == alpha] = 0
+
+expect(node, inputs=[x], outputs=[y],
+       name='test_thresholdedrelu')
+```
+
+</details>
+
+
+### Tile
+There are 2 test cases, listed as following:
+<details>
+<summary>tile</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tile',
+    inputs=['x', 'y'],
+    outputs=['z']
+)
+
+x = np.random.rand(2, 3, 4, 5).astype(np.float32)
+
+repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
+
+z = np.tile(x, repeats)
+
+expect(node,
+       inputs=[x, repeats],
+       outputs=[z],
+       name='test_tile')
+```
+
+</details>
+<details>
+<summary>tile_precomputed</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Tile',
+    inputs=['x', 'y'],
+    outputs=['z']
+)
+
+x = np.array([
+    [0, 1],
+    [2, 3]
+], dtype=np.float32)
+
+repeats = np.array([2, 2], dtype=np.int64)
+
+z = np.array([
+    [0, 1, 0, 1],
+    [2, 3, 2, 3],
+    [0, 1, 0, 1],
+    [2, 3, 2, 3]
+], dtype=np.float32)
+
+expect(node,
+       inputs=[x, repeats],
+       outputs=[z],
+       name='test_tile_precomputed')
+```
+
+</details>
+
+
+### TopK
+There are 3 test cases, listed as following:
+<details>
+<summary>top_k</summary>
+
+```python
+axis = 1
+largest = 1
+
+k = 3
+node = onnx.helper.make_node(
+    'TopK',
+    inputs=['x', 'k'],
+    outputs=['values', 'indices'],
+    axis=axis
+)
+X = np.array([
+    [0, 1, 2, 3],
+    [4, 5, 6, 7],
+    [8, 9, 10, 11],
+], dtype=np.float32)
+K = np.array([k], dtype=np.int64)
+values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+#print(values_ref)
+#[[ 3.  2.  1.]
+# [ 7.  6.  5.]
+# [11. 10.  9.]]
+#print(indices_ref)
+#[[3 2 1]
+# [3 2 1]
+# [3 2 1]]
+
+expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+       name='test_top_k')
+```
+
+</details>
+<details>
+<summary>top_k_negative_axis</summary>
+
+```python
+axis = -1
+largest = 1
+
+k = 3
+node = onnx.helper.make_node(
+    'TopK',
+    inputs=['x', 'k'],
+    outputs=['values', 'indices'],
+    axis=axis
+)
+X = np.array([
+    [0, 1, 2, 3],
+    [4, 5, 6, 7],
+    [8, 9, 10, 11],
+], dtype=np.float32)
+K = np.array([k], dtype=np.int64)
+values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+# print(values_ref)
+#[[ 3.  2.  1.]
+# [ 7.  6.  5.]
+# [11. 10.  9.]]
+# print(indices_ref)
+#[[3 2 1]
+# [3 2 1]
+# [3 2 1]]
+
+expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+       name='test_top_k_negative_axis')
+```
+
+</details>
+<details>
+<summary>top_k_smallest</summary>
+
+```python
+axis = 1
+largest = 0
+sorted = 1
+k = 3
+
+node = onnx.helper.make_node(
+    'TopK',
+    inputs=['x', 'k'],
+    outputs=['values', 'indices'],
+    axis=axis,
+    largest=largest,
+    sorted=sorted
+)
+
+X = np.array([
+    [0, 1, 2, 3],
+    [4, 5, 6, 7],
+    [11, 10, 9, 8],
+], dtype=np.float32)
+K = np.array([k], dtype=np.int64)
+values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+#print(values_ref)
+#[[ 0.  1.  2.]
+# [ 4.  5.  6.]
+# [ 8.  9. 10.]]
+#print(indices_ref)
+#[[0 1 2]
+# [0 1 2]
+# [3 2 1]]
+
+expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+       name='test_top_k_smallest')
+```
+
+</details>
+
+
+### Transpose
+There are 2 test cases, listed as following:
+<details>
+<summary>all_permutations</summary>
+
+```python
+shape = (2, 3, 4)
+data = np.random.random_sample(shape).astype(np.float32)
+permutations = list(itertools.permutations(np.arange(len(shape))))
+
+for i in range(len(permutations)):
+    node = onnx.helper.make_node(
+        'Transpose',
+        inputs=['data'],
+        outputs=['transposed'],
+        perm=permutations[i]
+    )
+    transposed = np.transpose(data, permutations[i])
+    expect(node, inputs=[data], outputs=[transposed],
+           name='test_transpose_all_permutations_' + str(i))
+```
+
+</details>
+<details>
+<summary>default</summary>
+
+```python
+shape = (2, 3, 4)
+data = np.random.random_sample(shape).astype(np.float32)
+
+node = onnx.helper.make_node(
+    'Transpose',
+    inputs=['data'],
+    outputs=['transposed']
+)
+
+transposed = np.transpose(data)
+expect(node, inputs=[data], outputs=[transposed],
+       name='test_transpose_default')
+```
+
+</details>
+
+
+### Trilu
+There are 18 test cases, listed as following:
+<details>
+<summary>tril</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 0, 0, 0, 0],
+#   [1, 2, 0, 0, 0],
+#   [9, 4, 1, 0, 0],
+#   [4, 3, 4, 2, 0]]
+y = tril_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_tril')
+```
+
+</details>
+<details>
+<summary>tril_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 0, 0, 0],
+#   [1, 0, 0, 0, 0],
+#   [9, 4, 0, 0, 0],
+#   [4, 3, 4, 0, 0]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_neg')
+```
+
+</details>
+<details>
+<summary>tril_one_row</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+# X:
+# [[[6, 2, 4, 1, 6]],
+#
+#  [[8, 3, 8, 7, 0]],
+#
+#  [[2, 2, 9, 5, 9]]]
+# expect result:
+# [[[6, 0, 0, 0, 0]],
+#
+#  [[8, 0, 0, 0, 0]],
+#
+#  [[2, 0, 0, 0, 0]]]
+y = tril_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_tril_one_row_neg')
+```
+
+</details>
+<details>
+<summary>tril_out_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-7).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_neg')
+```
+
+</details>
+<details>
+<summary>tril_out_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_pos')
+```
+
+</details>
+<details>
+<summary>tril_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(2).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 0, 0],
+#   [1, 2, 8, 6, 0],
+#   [9, 4, 1, 8, 7],
+#   [4, 3, 4, 2, 4]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_pos')
+```
+
+</details>
+<details>
+<summary>tril_square</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+# X:
+# [[[0, 4, 3],
+#   [2, 0, 9],
+#   [8, 2, 5]],
+#
+#  [[2, 7, 2],
+#   [2, 6, 0],
+#   [2, 6, 5]]]
+# expect result:
+# [[[0, 0, 0],
+#   [2, 0, 0],
+#   [8, 2, 5]],
+#
+#  [[2, 0, 0],
+#   [2, 6, 0],
+#   [2, 6, 5]]]
+y = tril_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_tril_square')
+```
+
+</details>
+<details>
+<summary>tril_square_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+# [[[0, 4, 3],
+#   [2, 0, 9],
+#   [8, 2, 5]],
+#
+#  [[2, 7, 2],
+#   [2, 6, 0],
+#   [2, 6, 5]]]
+# expect result:
+# [[[0, 0, 0],
+#   [2, 0, 0],
+#   [8, 2, 0]],
+#
+#  [[0, 0, 0],
+#   [2, 0, 0],
+#   [2, 6, 0]]]
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_square_neg')
+```
+
+</details>
+<details>
+<summary>tril_zero</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+    upper=0,
+)
+
+x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+# []
+# expect result:
+# []
+y = tril_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_tril_zero')
+```
+
+</details>
+<details>
+<summary>triu</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [0, 2, 8, 6, 9],
+#   [0, 0, 0, 8, 7],
+#   [0, 0, 0, 2, 4]]
+y = triu_reference_implementation(x)
+expect(node, inputs=[x], outputs=[y], name='test_triu')
+```
+
+</details>
+<details>
+<summary>triu_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [0, 4, 0, 8, 7],
+#   [0, 0, 4, 2, 4]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_neg')
+```
+
+</details>
+<details>
+<summary>triu_one_row</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+k = np.array(1).astype(np.int64)
+# X:
+# [[[1, 4, 9, 7, 1]],
+#
+#  [[9, 2, 8, 8, 4]],
+#
+#  [[3, 9, 7, 4, 2]]]
+# expect result:
+# [[[0, 4, 9, 7, 1]],
+#
+#  [[0, 2, 8, 8, 4]],
+#
+#  [[0, 9, 7, 4, 2]]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_one_row')
+```
+
+</details>
+<details>
+<summary>triu_out_neg_out</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(-7).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_neg_out')
+```
+
+</details>
+<details>
+<summary>triu_out_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0],
+#   [0, 0, 0, 0, 0]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_pos')
+```
+
+</details>
+<details>
+<summary>triu_pos</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+k = np.array(2).astype(np.int64)
+# X:
+#  [[4, 7, 3, 7, 9],
+#   [1, 2, 8, 6, 9],
+#   [9, 4, 0, 8, 7],
+#   [4, 3, 4, 2, 4]]
+# expect result:
+#  [[0, 0, 3, 7, 9],
+#   [0, 0, 0, 6, 9],
+#   [0, 0, 0, 0, 7],
+#   [0, 0, 0, 0, 0]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_pos')
+```
+
+</details>
+<details>
+<summary>triu_square</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+y = triu_reference_implementation(x)
+# X:
+# [[[4, 6, 9],
+#   [7, 5, 4],
+#   [8, 1, 2]],
+#
+#  [[1, 4, 9],
+#   [9, 6, 3],
+#   [8, 9, 8]]]
+# expect result:
+# [[[4, 6, 9],
+#   [0, 5, 4],
+#   [0, 0, 2]],
+#
+#  [[1, 4, 9],
+#   [0, 6, 3],
+#   [0, 0, 8]]]
+expect(node, inputs=[x], outputs=[y], name='test_triu_square')
+```
+
+</details>
+<details>
+<summary>triu_square_neg</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+k = np.array(-1).astype(np.int64)
+# X:
+# [[[4, 6, 9],
+#   [7, 5, 4],
+#   [8, 1, 2]],
+#
+#  [[1, 4, 9],
+#   [9, 6, 3],
+#   [8, 9, 8]]]
+# expect result:
+# [[[4, 6, 9],
+#   [7, 5, 4],
+#   [0, 1, 2]],
+#
+#  [[1, 4, 9],
+#   [9, 6, 3],
+#   [0, 9, 8]]]
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_square_neg')
+```
+
+</details>
+<details>
+<summary>triu_zero</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Trilu',
+    inputs=['x', 'k'],
+    outputs=['y'],
+)
+
+x = np.random.randint(10, size=(0, 5)).astype(np.int64)
+k = np.array(6).astype(np.int64)
+# X:
+# []
+# expect result:
+# []
+y = triu_reference_implementation(x, int(k))
+expect(node, inputs=[x, k], outputs=[y], name='test_triu_zero')
+```
+
+</details>
+
+
+### Unique
+There are 5 test cases, listed as following:
+<details>
+<summary>not_sorted_without_axis</summary>
+
+```python
+node_not_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=0
+)
+# numpy unique does not retain original order (it sorts the output unique values)
+# https://github.com/numpy/numpy/issues/8621
+# we need to recover unsorted output and indices
+x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+
+# prepare index mapping from sorted to unsorted
+argsorted_indices = np.argsort(indices)
+inverse_indices_map = {i: si for i, si in zip(argsorted_indices, np.arange(len(argsorted_indices)))}
+
+indices = indices[argsorted_indices]
+y = np.take(x, indices, axis=0)
+inverse_indices = np.asarray([inverse_indices_map[i] for i in inverse_indices], dtype=np.int64)
+counts = counts[argsorted_indices]
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [2.0, 1.0, 3.0, 4.0]
+# print(indices)
+# [0 1 3 4]
+# print(inverse_indices)
+# [0, 1, 1, 2, 3, 2]
+# print(counts)
+# [1, 2, 2, 1]
+
+expect(node_not_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_not_sorted_without_axis')
+```
+
+</details>
+<details>
+<summary>sorted_with_axis</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=1,
+    axis=0
+)
+
+x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [[1. 0. 0.]
+#  [2. 3. 4.]]
+# print(indices)
+# [0 2]
+# print(inverse_indices)
+# [0 0 1]
+# print(counts)
+# [2 1]
+
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_axis')
+```
+
+</details>
+<details>
+<summary>sorted_with_axis_3d</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=1,
+    axis=1
+)
+
+x = np.array([[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+              [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [[[0. 1.]
+#  [1. 1.]
+#  [2. 1.]]
+# [[0. 1.]
+#  [1. 1.]
+#  [2. 1.]]]
+# print(indices)
+# [1 0 2]
+# print(inverse_indices)
+# [1 0 2 0]
+# print(counts)
+# [2 1 1]
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_axis_3d')
+```
+
+</details>
+<details>
+<summary>sorted_with_negative_axis</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+    sorted=1,
+    axis=-1
+)
+
+x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+# print(y)
+# [[0. 1.]
+#  [0. 1.]
+#  [3. 2.]]
+# print(indices)
+# [1 0]
+# print(inverse_indices)
+# [1 0 0]
+# print(counts)
+# [2 1]
+
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_negative_axis')
+```
+
+</details>
+<details>
+<summary>sorted_without_axis</summary>
+
+```python
+node_sorted = onnx.helper.make_node(
+    'Unique',
+    inputs=['X'],
+    outputs=['Y', 'indices', 'inverse_indices', 'counts']
+)
+
+x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_without_axis')
+```
+
+</details>
+
+
+### Unsqueeze
+There are 5 test cases, listed as following:
+<details>
+<summary>unsqueeze_negative_axes</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+axes = np.array([-2]).astype(np.int64)
+y = np.expand_dims(x, axis=-2)
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_negative_axes')
+```
+
+</details>
+<details>
+<summary>unsqueeze_one_axis</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+
+for i in range(x.ndim):
+    axes = np.array([i]).astype(np.int64)
+    node = onnx.helper.make_node(
+        'Unsqueeze',
+        inputs=['x', 'axes'],
+        outputs=['y'],
+    )
+    y = np.expand_dims(x, axis=i)
+
+    expect(node, inputs=[x, axes], outputs=[y],
+           name='test_unsqueeze_axis_' + str(i))
+```
+
+</details>
+<details>
+<summary>unsqueeze_three_axes</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+axes = np.array([2, 4, 5]).astype(np.int64)
+
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+y = np.expand_dims(x, axis=2)
+y = np.expand_dims(y, axis=4)
+y = np.expand_dims(y, axis=5)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_three_axes')
+```
+
+</details>
+<details>
+<summary>unsqueeze_two_axes</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+axes = np.array([1, 4]).astype(np.int64)
+
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+y = np.expand_dims(x, axis=1)
+y = np.expand_dims(y, axis=4)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_two_axes')
+```
+
+</details>
+<details>
+<summary>unsqueeze_unsorted_axes</summary>
+
+```python
+x = np.random.randn(3, 4, 5).astype(np.float32)
+axes = np.array([5, 4, 2]).astype(np.int64)
+
+node = onnx.helper.make_node(
+    'Unsqueeze',
+    inputs=['x', 'axes'],
+    outputs=['y'],
+)
+y = np.expand_dims(x, axis=2)
+y = np.expand_dims(y, axis=4)
+y = np.expand_dims(y, axis=5)
+
+expect(node, inputs=[x, axes], outputs=[y],
+       name='test_unsqueeze_unsorted_axes')
+```
+
+</details>
+
+
+### Upsample
+There are 1 test cases, listed as following:
+<details>
+<summary>nearest</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Upsample',
+    inputs=['X', 'scales'],
+    outputs=['Y'],
+    mode='nearest',
+)
+
+data = np.array([[[
+    [1, 2],
+    [3, 4],
+]]], dtype=np.float32)
+
+scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+output = np.array([[[
+    [1, 1, 1, 2, 2, 2],
+    [1, 1, 1, 2, 2, 2],
+    [3, 3, 3, 4, 4, 4],
+    [3, 3, 3, 4, 4, 4],
+]]], dtype=np.float32)
+
+expect(node, inputs=[data, scales], outputs=[output],
+       name='test_upsample_nearest', opset_imports=[helper.make_opsetid("", 9)])
+```
+
+</details>
+
+
+### Where
+There are 2 test cases, listed as following:
+<details>
+<summary>long</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Where',
+    inputs=['condition', 'x', 'y'],
+    outputs=['z'],
+)
+
+condition = np.array([[1, 0], [1, 1]], dtype=bool)
+x = np.array([[1, 2], [3, 4]], dtype=np.int64)
+y = np.array([[9, 8], [7, 6]], dtype=np.int64)
+z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+expect(node, inputs=[condition, x, y], outputs=[z],
+       name='test_where_long_example')
+```
+
+</details>
+<details>
+<summary>where</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Where',
+    inputs=['condition', 'x', 'y'],
+    outputs=['z'],
+)
+
+condition = np.array([[1, 0], [1, 1]], dtype=bool)
+x = np.array([[1, 2], [3, 4]], dtype=np.float32)
+y = np.array([[9, 8], [7, 6]], dtype=np.float32)
+z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+expect(node, inputs=[condition, x, y], outputs=[z],
+       name='test_where_example')
+```
+
+</details>
+
+
+### Xor
+There are 2 test cases, listed as following:
+<details>
+<summary>xor</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Xor',
+    inputs=['x', 'y'],
+    outputs=['xor'],
+)
+
+# 2d
+x = (np.random.randn(3, 4) > 0).astype(bool)
+y = (np.random.randn(3, 4) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor2d')
+
+# 3d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor3d')
+
+# 4d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor4d')
+```
+
+</details>
+<details>
+<summary>xor_broadcast</summary>
+
+```python
+node = onnx.helper.make_node(
+    'Xor',
+    inputs=['x', 'y'],
+    outputs=['xor'],
+)
+
+# 3d vs 1d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(5) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast3v1d')
+
+# 3d vs 2d
+x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+y = (np.random.randn(4, 5) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast3v2d')
+
+# 4d vs 2d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast4v2d')
+
+# 4d vs 3d
+x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast4v3d')
+
+# 4d vs 4d
+x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+z = np.logical_xor(x, y)
+expect(node, inputs=[x, y], outputs=[z],
+       name='test_xor_bcast4v4d')
+```
+
+</details>
+
+
+<br/>
+
+## &#x1F494;No Cover Common Operators
+### ConcatFromSequence (call for test cases)
+
+
+### GlobalLpPool (call for test cases)
+
+
+### GreaterOrEqual (call for test cases)
+
+
+### LessOrEqual (call for test cases)
+
+
+### LpNormalization (call for test cases)
+
+
+### LpPool (call for test cases)
+
+
+### MaxRoiPool (call for test cases)
+
+
+### Multinomial (random generator operator)
+
+
+### Optional (call for test cases)
+
+
+### OptionalGetElement (call for test cases)
+
+
+### RandomNormal (random generator operator)
+
+
+### RandomNormalLike (random generator operator)
+
+
+### RandomUniform (random generator operator)
+
+
+### RandomUniformLike (random generator operator)
+
+
+### SequenceAt (call for test cases)
+
+
+### SequenceConstruct (call for test cases)
+
+
+### SequenceEmpty (call for test cases)
+
+
+### SequenceErase (call for test cases)
+
+
+### SequenceLength (call for test cases)
+
+
+### SplitToSequence (call for test cases)
+
+
+<br/>
+
+## &#x1F49A;Covered Experimental Operators
+<br/>
+
+## &#x1F494;No Cover Experimental Operators
+<br/>
+
+# Model Test Coverage
+## bvlc_alexnet
+
+bvlc_alexnet has 24 nodes. Of these, 24 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 1
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 2
+storage_order: 0
+strides: 1
+</details>
+</details>
+
+
+## densenet121
+
+densenet121 has 910 nodes. Of these, 910 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 1
+pads: 1
+strides: 1
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 1
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 1
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 3
+storage_order: 0
+strides: 1
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## inception_v1
+
+inception_v1 has 144 nodes. Of these, 144 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 2
+pads: 2
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 1
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 1
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## inception_v2
+
+inception_v2 has 509 nodes. Of these, 509 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 1
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 1
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## resnet50
+
+resnet50 has 176 nodes. Of these, 176 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 2
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 1
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## shufflenet
+
+shufflenet has 203 nodes. Of these, 203 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 2
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 6
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Transpose: 1 out of 1 attributes covered</summary>
+
+perm: 1
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## squeezenet_old
+
+squeezenet_old has 66 nodes. Of these, 66 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 2
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 6
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 1
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Transpose: 1 out of 1 attributes covered</summary>
+
+perm: 1
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## vgg19
+
+vgg19 has 46 nodes. Of these, 46 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 2
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 6
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 1
+beta: 1
+bias: 1
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 2
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Transpose: 1 out of 1 attributes covered</summary>
+
+perm: 1
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+## zfnet512
+
+zfnet512 has 22 nodes. Of these, 22 are covered by node tests (100.0%)
+
+
+<details>
+<summary>nodes</summary>
+
+<details>
+<summary>AveragePool: 3 out of 6 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+count_include_pad: 0
+kernel_shape: 3
+pads: 3
+strides: 2
+</details>
+<details>
+<summary>BatchNormalization: 1 out of 3 attributes covered</summary>
+
+epsilon: 2
+momentum: 0
+training_mode: 0
+</details>
+<details>
+<summary>Concat: 1 out of 1 attributes covered</summary>
+
+axis: 1
+</details>
+<details>
+<summary>Conv: 4 out of 6 attributes covered</summary>
+
+auto_pad: 0
+dilations: 0
+group: 6
+kernel_shape: 5
+pads: 4
+strides: 3
+</details>
+<details>
+<summary>Dropout: 1 out of 1 attributes covered</summary>
+
+seed: 0
+</details>
+<details>
+<summary>Gemm: 1 out of 4 attributes covered</summary>
+
+alpha: 0
+beta: 0
+transA: 0
+transB: 1
+</details>
+<details>
+<summary>LRN: 4 out of 4 attributes covered</summary>
+
+alpha: 2
+beta: 1
+bias: 2
+size: 1
+</details>
+<details>
+<summary>MaxPool: 3 out of 7 attributes covered</summary>
+
+auto_pad: 0
+ceil_mode: 0
+dilations: 0
+kernel_shape: 2
+pads: 3
+storage_order: 0
+strides: 2
+</details>
+<details>
+<summary>Transpose: 1 out of 1 attributes covered</summary>
+
+perm: 1
+</details>
+<details>
+<summary>Unsqueeze: 1 out of 0 attributes covered</summary>
+
+</details>
+</details>
+
+
+# Overall Test Coverage
+## To be filled.
diff --git a/third_party/onnx/docs/TypeAnnotations.md b/third_party/onnx/docs/TypeAnnotations.md
new file mode 100644
index 0000000000..27102ffc81
--- /dev/null
+++ b/third_party/onnx/docs/TypeAnnotations.md
@@ -0,0 +1,65 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Type annotations for ONNX
+
+At Facebook, we work with community best practices to ensure high code quality, readability and reliability. In line with this, we just added type annotations to our python code to help ONNX developers more easily contribute to the project.
+
+These type annotations are used by [mypy](https://github.com/python/mypy) within the ONNX CI systems to ensure a continuously high code quality standard.
+We also have type annotations for our APIs, which means your tools built on top of the ONNX APIs can use static analysis tools like mypy to ensure they are using the APIs correctly.
+
+We expect this to have a positive impact on the reliability of ONNX and dependent projects like ONNX converters - we've also found and fixed a few bugs thanks to the addition of type annotations.
+
+## What is mypy?
+Mypy is an opt-in type checker. Annotations are optional and, if present, will be used for static type checking. While it is not advisable to rely too much on mypy to find all errors (for programmers used to a static type system like in C++, it's often confusing that mypy doesn't find some very obvious typing errors), it is still very helpful given the errors it does find.
+
+## Using the type annotations in dependent projects
+Say, for example, you're building a converter for ONNX, which converts between ONNX models and the models of some machine learning framework. If you use mypy for your project, it will automatically detect the type hints in the ONNX code and use them to check that you read from and wrote to the ONNX model correctly. Doing this will notify you about identifier typos or wrong data types when accessing attributes of the ONNX protocol buffer or other ONNX features.
+
+## For ONNX contributors
+If you're contributing to the ONNX repository, you need to add type annotations to your project or CI will fail. Here is how to install it:
+
+#### Python 3
+If you're using Python 3, you can simply install mypy as an extra dependency of onnx.
+
+Call from the top level directory of your onnx repository:
+
+    ~/onnx $ pip install -e .[lint]
+
+And then run the type checks:
+
+    ~/onnx $ python setup.py typecheck
+
+#### Python 2
+The mypy type checker needs python 3 to run (even though it can check python 2 code), so if your onnx development environment is set up with python 2, you need to install mypy into your system python 3 packages (i.e. not into your python 2 virtualenv):
+
+    $ pip3 install mypy
+
+Running then works as it does above for python 3.
+
+    ~/onnx $ python setup.py typecheck
+
+Since you've installed mypy manually, you won't get updates for it when we change the version of the mypy dependency in setup.py, so if you're seeing a CI error and can't reproduce it locally, check your mypy version.
+
+### What should I annotate?
+
+Type annotations are (usually) only needed for function arguments and return types, mypy will infer local variable types automatically. "Usually" means there are a few exceptions, but mypy will report these if you hit them.
+
+### What do type annotations look like?
+We can't use PEP484 annotations, because we have to support python 2. So we're writing our type annotations in comments. Typed code looks like this:
+
+    def myfunc(arg):  # type: (int) -> bool
+      return arg == 2
+
+    class MyClass(object):
+      def __init__(self):  # type: () -> None
+        pass
+
+      def myfunc(self, arg):  # type: (int) -> bool
+        return arg == 2
+
+      # Alternative notation for many parameters
+      def myfunc(
+        arg1,  # type: int
+        arg2,  # type: int
+      ):  # type: (...) -> bool
+        return arg1 == arg2
diff --git a/third_party/onnx/docs/TypeDenotation.md b/third_party/onnx/docs/TypeDenotation.md
new file mode 100644
index 0000000000..0929b4f549
--- /dev/null
+++ b/third_party/onnx/docs/TypeDenotation.md
@@ -0,0 +1,56 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Type Denotation
+
+Type Denotation is used to describe semantic information around what the inputs and outputs are.    It is stored on the TypeProto message.
+
+## Motivation
+
+The motivation of such a mechanism can be illustrated via a simple example. In the the neural network SqueezeNet, it takes in an NCHW image input float[1,3,244,244] and produces a output float[1,1000,1,1]:
+
+```
+input_in_NCHW -> data_0 -> SqueezeNet() -> output_softmaxout_1
+```
+
+In order to run this model the user needs a lot of information.    In this case the user needs to know:
+* the input is an image
+* the image is in the format of NCHW
+* the color channels are in the order of bgr
+* the pixel data is 8 bit
+* the pixel data is normalized as values 0-255
+
+This proposal consists of three key components to provide all of this information:
+* Type Denotation,
+* [Dimension Denotation](DimensionDenotation.md),
+* [Model Metadata](MetadataProps.md).
+
+## Type Denotation Definition
+
+To begin with, we define a set of semantic types that define what models generally consume as inputs and produce as outputs.
+
+Specifically, in our first proposal we define the following set of standard denotations:
+
+0. `TENSOR` describes that a type holds a generic tensor using the standard TypeProto message.
+1. `IMAGE` describes that a type holds an image.  You can use dimension denotation to learn more about the layout of the image, and also the optional model metadata_props.
+2. `AUDIO` describes that a type holds an audio clip.
+3. `TEXT` describes that a type holds a block of text.
+
+Model authors SHOULD add type denotation to inputs and outputs for the model as appropriate.
+
+## An Example with input IMAGE
+
+Let's use the same SqueezeNet example from above and show everything to properly annotate the model:
+
+* First set the TypeProto.denotation =`IMAGE` for the ValueInfoProto `data_0`
+* Because it's an image, the model consumer now knows to go look for image metadata on the model
+* Then include 3 metadata strings on ModelProto.metadata_props
+	* `Image.BitmapPixelFormat` = `Bgr8`
+	* `Image.ColorSpaceGamma` = `SRGB`
+	* `Image.NominalPixelRange` = `NominalRange_0_255`
+* For that same ValueInfoProto, make sure to also use Dimension Denotations to denote NCHW
+	* TensorShapeProto.Dimension[0].denotation = `DATA_BATCH`
+	* TensorShapeProto.Dimension[1].denotation = `DATA_CHANNEL`
+	* TensorShapeProto.Dimension[2].denotation = `DATA_FEATURE`
+	* TensorShapeProto.Dimension[3].denotation = `DATA_FEATURE`
+
+Now there is enough information in the model to know everything about how to pass a correct image into the model.
diff --git a/third_party/onnx/docs/VersionConverter.md b/third_party/onnx/docs/VersionConverter.md
new file mode 100644
index 0000000000..4bb6acd772
--- /dev/null
+++ b/third_party/onnx/docs/VersionConverter.md
@@ -0,0 +1,51 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Version Converter
+
+ONNX provides a library for converting ONNX models between different
+opset versions. The primary motivation is to improve backwards compatibility of ONNX
+models without having to strengthen the spec for ONNX backends.  This
+allows backend developers to offer support for a particular opset version
+and for users to write or export models to a particular opset version but
+run in an environment with a different opset version. Implementation wise, the library leverages the in-memory representation that is much more convenient to manipulate than the raw protobuf structs, and converters to and from the protobuf format which were developed for the ONNX Optimizer.
+
+You may be interested in invoking the provided op-specific adapters, or in
+implementing new ones (or both). Default adapters only work in the default
+domain, but can be generalized to work cross-domain or utilizing new
+conversion methods, dependent on the nature of relevant breaking changes.
+
+## Invoking The Version Converter
+
+The version converter may be invoked either via C++ or Python.
+
+The Python API
+is described, with example,
+[here](PythonAPIOverview.md#converting-version-of-an-onnx-model-within-default-domain-aionnx).
+
+The C++ API consists of a single function
+
+```
+ModelProto ConvertVersion(
+    const ModelProto& mp_in,
+    const OpSetID& initial_version,
+    const OpSetID& target_version);
+```
+
+which accepts an input `ModelProto`, the initial opset version of the model,
+and the target opset version, and which returns a new `ModelProto` which
+is the result of apply all relevant adapters between initial_version and
+target_version. For a list of available passes, see
+[convert.h](/onnx/version_converter/convert.h).
+
+## Implementing Adapters
+
+You can implement a new adapter by subclassing `Adapter`, and registering
+your new adapter with `VersionConverter::registerAdapter()`. Adapters operate
+on an in-memory graph representation defined in [ir.h](/onnx/common/ir.h).
+There are a number of examples in the [adapters](/onnx/version_converter/adapters)
+directory.  Please ensure that all adapters convert from opset version i to i + 1
+or i - 1, i.e. from Version 6 to Version 5 or vice versa, even if the 2 versions
+being converted between are Version 1 and Version 6.
+
+If your adapter applies in the default domain, please consider adding it
+to the core ONNX repository
diff --git a/third_party/onnx/docs/Versioning.md b/third_party/onnx/docs/Versioning.md
new file mode 100644
index 0000000000..4be57480db
--- /dev/null
+++ b/third_party/onnx/docs/Versioning.md
@@ -0,0 +1,185 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Versioning
+
+This document describes the rules for versioning ONNX. MUST, SHOULD et al are used consistent with [RFC2119](https://tools.ietf.org/html/rfc2119).
+
+## Versioning Principles
+
+ONNX defines the versioning policy and mechanism for three classes of entities:
+
+* The [intermediate representation (IR) specification](IR.md), which is the abstract model for graphs and operators and the concrete format that represents them. These are always versioned atomically and are referred to as the *IR version*.
+* Operator specifications that may be referenced by a given ONNX graph. We refer to this as the *operator version*.
+* A defined/trained model that defines a specific graph in terms of specific operators. We refer to this as the *model version*.
+
+The versioning of all three of these entity types is distinct and largely independent. The IR specification evolves at a different (generally slower) rate than the operator specifications. Model versions are entirely independent of the other two versions.
+
+Specific policies for version management are mandated only for IR version and operator version. For model versioning, they are merely recommendations. For model versioning, ONNX users and systems MAY follow whichever local customs make sense; however, to facilitate easily managing shared collections of ONNX models, they SHOULD adhere to the policies described under model versioning.
+
+New IR and operator versions are released as part of ONNX _releases_, which have their own versioning scheme. The release versioning scheme is not described as part of the standard itself. It is discussed in the [ONNX release management document](../RELEASE-MANAGEMENT.md).
+
+### Semantic Versioning or Simple Numbers?
+
+The ONNX versioning system allows for simple monotonically increasing numbers or [semantic versioning (SemVer)](https://semver.org/). For IR and operator sets, versioning is based on simple numbers. For models, ONNX does not require any scheme, but recommends a set of shared conventions.
+
+Which versioning scheme is in use by a model is made clear by inspecting the most significant four bytes, which MUST be non-zero when using semantic versioning and MUST be zero when using simple numbers. In other words, when using SemVer, at least one of the MAJOR or MINOR numbers must be non-zero.
+
+### SemVer, Files and Consumers
+
+For model and release versioning, ONNX builds on the principles and syntax defined by [SemVer 2.0.0](http://semver.org/spec/v2.0.0.html). Throughout this document, we use the terms *breaking change*, *non-breaking change*, and *patch* consistent with SemVer 2.0.0.
+
+Because ONNX models are serialized files (not APIs), it's worth making clear the relationship between a serialized model and a piece of software that consumes that model. As a rough approximation, the serialized model plays the role of an API's *callee*, while the consumer of the serialized model plays the role of the API's *caller*.
+
+The ONNX versioning principles are based on the [robustness principle](https://en.wikipedia.org/wiki/Robustness_principle): "be conservative in what you do, be liberal in what you accept from others".
+
+1. A producer of a given ONNX model (and the ONNX specification itself) MUST strictly adhere to the rules for breaking vs. non-breaking changes defined in this specification.
+2. A consumer of a given ONNX model SHOULD consume an updated ONNX file, provided there are no breaking changes in the new ONNX file's IR version, referenced operator versions, or model version (meaning the MAJOR version numbers have not changed between the two ONNX files).
+3. A consumer of a given ONNX model MAY consume an updated ONNX file, provided there are one or more breaking changes in the new ONNX file's IR version, referenced operator versions, or model version.
+
+### Serializing SemVer version numbers in protobuf
+
+For efficiency, ONNX serializes the MAJOR, MINOR, and PATCH values as a bit-packed 64-bit integer; the two most significant bytes are the MAJOR component, the next two most significant bytes are the MINOR component, and the least significant four bytes are the PATCH component.
+
+For example, *1.2.345* is represented as *0x0001000200000159*.
+
+Pre-release and build metadata are not stored in the model.
+
+## IR versioning
+
+The IR format is versioned using simple numbers, which MUST be monotonically increasing. Breaking changes to the format or semantics of the ONNX specification require an increment of the version. Non-breaking changes to the IR format do not require changing the version number.
+
+NOTE: breaking changes include those that do not alter the serialized binary format, but still break software using libraries that write or read it. For example, changing the spelling of a message property will cause code accessing the property break.
+
+The IR format adheres to the versioning guidelines defined in the [Updating a Message Type](https://developers.google.com/protocol-buffers/docs/proto3#updating) section of the proto3 specification.
+
+As a general principle, implementations SHOULD be robust in the face of missing fields. However, to ensure basic interoperation, a subset of message fields will be marked as required for a given IR version and all producers MUST set these fields correctly. Required fields MUST always be marked with the comment:
+
+    // This field MUST be present for this version of the IR.
+
+For example, the `ModelProto.ir_version` property MUST be present in every model. The ONNX checker (`onnx/checker.py`) will enforce these rules.
+
+Because the protocol buffer message definitions (.proto / .proto3 files) are expected to be consumed by multiple independent developers, changes to those definitions SHOULD NOT break code that depends on generated language bindings (e.g., changing the type of an existing field).
+
+## Operator versioning
+
+The IR can evolve independently from the set of operators. Operators represent both the signature and semantics of a given operation. Operators are abstract interfaces in that they do not imply a specific implementation; rather, they are simply the contract between a model author and the implementations that model may execute on.
+
+A given operator is identified by a three-tuple: `(domain, op_type, since_version)`, written as `domain.op_type:since_version` in prose (e.g., `com.acme.FastConv:3`). `since_version` is the version of the operator set that introduced the operator. Breaking operator changes include:
+
+* Adding/removing/renaming an attribute. This even includes the case of adding a new optional attribute, where omitting the attribute would imply a default value yielding semantics identical to the previous operator version.
+
+* Adding/removing/reordering inputs or outputs.
+
+* Adding/removing types supported by inputs and outputs, and changing types used by attributes.
+
+* Supporting new behavior even when the existing parameter signature is otherwise identical (e.g. implicitly supporting tensor broadcasting in the Mean operator).
+
+The following are not breaking:
+
+* Clarifications of specification ambiguities to match prevailing
+  implementation practice.
+
+Changes to the semantics of an operator or function MUST be introduced in a new operator, which MUST be introduced in a new [operator set](#operator-sets).
+
+> In practice, this means that BC-breaking changes in the ONNX
+> repository require contributors to follow these steps:
+>
+> 1. Increment the maximum version in `DomainToVersionRange`.
+> 2. Copy the old operator schema to an `old.cc` file.
+> 3. Update the `SinceVersion` signifier to the new max version from
+>    step (1).
+> 4. Register the new operator in the corresponding `operator_sets`
+>    header file.
+> 5. Add a version adapter to `convert.h` so that the version 
+>    converter can upgrade the old version of the operator to the new 
+>    one. This can be a `CompatibleAdapter` in case operators following
+>    the old schema are still valid under the new one (which is usually 
+>    true).
+> 6. A version adapter to downgrade the new operator to the older version
+>    can also be added to `convert.h` but it's not mandatory.
+
+How nodes bind to operator declarations is strictly defined, and are designed to increase model compatibility across ONNX implementations, in the spirit of the conservative clause of the robustness principle.
+
+How ONNX implementations bind an operator declaration to a specific implementation is outside the scope of this specification. Implementations of ONNX MAY elect to introduce more sophisticated operator declaration/implementation binding modes, in the spirit of the liberal clause of the robustness principle.
+
+### Operator sets
+
+ONNX uses operator sets to group together immutable operator specifications. An operator set represents a specific version of a domain, indicated by a pair (domain, version). This represents the set of all operators belonging to the specified domain with the specified version (referred to as the `opset_version`). When the inventory of a given operator set changes either by addition, removal, or a change in semantics of a contained operator, its version MUST increase.
+
+Models declare which operator sets they require as a list of `(domain, opset_version)` pairs in `ModelProto.opset_import`. The empty string ("") domain indicates the operators defined as part of the ONNX specification; other domains correspond to operator sets of other vendors (meaning they can be used to provide vendor-specific extensions to ONNX). The union of the operator sets specified by a given model MUST have a compatible operator declaration for each node in the model's graph.
+
+### Example
+
+This section is not normative and informational only.
+
+Given the following operator sets:
+
+OpSet|Operators|Comments
+-|-|-
+1|{A}           | A introduced
+2|{A, B}        | B introduced
+3|{A', B, C}    | A updated (to A'), C introduced
+4|{B, C'}       | A removed, C updated (to C')
+
+The operators for a given operator set will have the following `since_version` values:
+
+Operator|OpSet 1|OpSet 2|OpSet 3|OpSet 4
+-|-|-|-|-
+A|**1** |1      |**3**  |**-**
+B|-     |**2**  |2      |2
+C|-     |-      |**3**  |**4**
+
+Notes:
+- Values that are new or updated from a previous OpSet version are in **bold**.
+
+## Model versioning
+
+This section of the specification is not normative. It simply outlines a set of recommended practices.
+
+Model authors and applications/systems MAY elect to ignore the model versioning mechanism and policy rules. For models that will be shared across developers, teams, or organizations, model authors and applications/systems SHOULD adhere to the following version policies:
+
+### Signature Changes
+
+1. Breaking changes to the ModelProto.graph.GraphProto.input or .output MUST increment the MAJOR version of `ModelProto.model_version`. Breaking changes include:
+
+    * Breaking changes to the semantics of an input or output (e.g., changing the required contents of an input tensor from a color image to a black and white image).
+    * Changing the declared type of an input or output to an incompatible type (e.g., `tensor(int)->tensor(string)`).
+    * Adding a new input for which there is no meaningful or specified default value. Recall that default values for inputs are specified in the initializer list.
+    * Removing an existing output for which there is no meaningful or specified default value.
+
+2. Non-breaking changes to the ModelProto.graph.GraphProto.input or .output MUST increment the MINOR version of `ModelProto.model_version`. Non-breaking changes include:
+
+    * Changing the declared type of an input or output to a compatible/widening type (e.g., `tensor(int32)->tensor(int64)`, `tensor(float16)->tensor(float32)`).
+    * Adding a new input for which there is a meaningful or specified default value.
+    * Adding new behavior that is only triggered in the presence of inputs that were not
+    possible in prior versions of the graph (typically by the presence of a new input
+    or allowing a previously invalid input value).
+
+### Accuracy or performance changes
+
+Changes that impact accuracy or performance significantly but do not change the model's inputs or outputs SHOULD increment the PATCH version of `ModelProto.model_version`.
+
+## Released Versions
+
+ONNX version|IR version|Opset version ai.onnx|Opset version ai.onnx.ml|Opset version ai.onnx.training
+------------|-------------------|---------------------|------------------------|------------------------------
+1.0|3|1|1|-
+1.1|3|5|1|-
+1.1.2|3|6|1|-
+1.2|3|7|1|-
+1.3|3|8|1|-
+1.4.1|4|9|1|-
+1.5.0|5|10|1|-
+1.6.0|6|11|2|-
+1.7.0|7|12|2|1
+1.8.0|7|13|2|1
+1.8.1|7|13|2|1
+1.9.0|7|14|2|1
+1.10.0|8|15|2|1
+1.10.1|8|15|2|1
+1.10.2|8|15|2|1
+1.11.0|8|16|3|1
+
+A programmatically accessible version of the above table is available [here](../onnx/helper.py). Limited version number
+information is also maintained in [version.h](../onnx/common/version.h) and [schema.h](../onnx/defs/schema.h).
+Please update all whenever a new version of ONNX is released.
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diff --git a/third_party/onnx/docs/proposals/ArchiveFileFormatProposal.md b/third_party/onnx/docs/proposals/ArchiveFileFormatProposal.md
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index 0000000000..1a731b85d2
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+++ b/third_party/onnx/docs/proposals/ArchiveFileFormatProposal.md
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+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX File Format Proposal
+
+## Summary
+
+We propose a new file format for ONNX models that is a specific application of the [zip](https://en.wikipedia.org/wiki/Zip_(file_format)) file format. We would like to address issues with capacity limits as well as (de)serialization inefficiencies[0][1]. We aim to design a file format that is simple, widely applicable, and efficient. By storing Tensor values (i.e. values typically contained in `TensorProto` messages) as files within a zip archive, we avoid these size limitations and—with special constraints—allow for direct memory-mapping of an ONNX file such that weights can be used directly from the memory-mapped region. Using zip as our base file format allows us to create a design that is conceptually simple as well as well-supported on various platforms.
+
+## Design
+
+We propose to treat a .zip file as a key-value store, mapping string keys (filenames) to binary data files. For ONNX model serialization, we will have the following entries:
+
+
+* Data files - Files mapping a unique string identifier to a raw binary data file. These files shall be referenced from the appropriate fields within the base `ModelProto`
+* `__MODEL_PROTO` - File that contains the `ModelProto` describing the file
+
+
+Note that the order is significant here. We place the model definition file at the end of the archive to allow for the common case of net manipulations while keeping the weights invariant. This way, tools that manipulate the archive do not need to repack or realign all weights when only touching the model file.
+
+Within the ONNX protobuf definition, we propose the following changes:
+
+
+* Add `optional string external_data` to `TensorProto`. This can be treated as a data field similar to `float_data`, `int_data`, etc in that there must be exactly one of those fields specified. If a `TensorProto` specifies `external_data`, the implementation shall resolve this reference by string key in the containing zip archive. All values of `external_data` must be unique (under down-casing) and conform to the C identifier specification.
+
+
+Raw data files referenced by `TensorProto`s shall conform to the following specification:
+
+
+* The data shall be equivalent to that stored within the `raw_data` field in `TensorProto`.
+* Raw data files within the zip archive shall reside on an alignment boundary of 64 bytes. That is, the byte offset within the file of the first byte of a raw data tensor must be divisible by 64. This requirement can be fulfilled by packing bytes into the `extra` field of each local file record in the zip archive. (example: [2]). This constraint facilitates the direct memory-mapping of data files within the archive, and allows for architectures with both strict alignment requirements (e.g. SIMD instructions on aligned data) to operate and give architectures that operate more efficiently on cache line-aligned data to take full advantage.
+
+## File Extension
+
+In keeping with other domain-specific zip applications, we propose to use a custom file extension rather than the `.zip` extension. A custom file extension makes it clear to the user that this is not a general zip file, but rather a file that should be emitted by ONNX tools to conform to the spec.
+
+
+## Future-Proofing Considerations
+
+This file format represents a generic key-value store that is scalable to many entries as well as large values. Further improvements to the format may come in the form of supporting different or multiple model definitions within the same model, or modifying the way in which weight files are stored. Building off of a proven archival format allows us the reliability as well as flexibility of zip.
+
+
+
+[0] https://github.com/onnx/onnx/issues/251
+[1] https://stackoverflow.com/questions/34128872/google-protobuf-maximum-size
+[2] https://developer.android.com/studio/command-line/zipalign.html implementation https://github.com/aosp-mirror/platform_build/blob/master/tools/zipalign/ZipAlign.cpp
diff --git a/third_party/onnx/docs/proposals/FunctionsProposal.md b/third_party/onnx/docs/proposals/FunctionsProposal.md
new file mode 100644
index 0000000000..0556565090
--- /dev/null
+++ b/third_party/onnx/docs/proposals/FunctionsProposal.md
@@ -0,0 +1,21 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+## Proposal Adding Function into ONNX
+
+Motivation:
+1.  Reduce number of primitive operators in ONNX
+To make it easier for hardware vendors to follow ONNX, we want to make it possible to define composite operators in terms of more primitive operators, reducing the number of kernels which must be directly implemented. For example, FC should be declared to be a composition MatMul and Add.
+
+2. Expose customize function capability for graph optimization.
+To provide a mechanism of doing graph optimization, say, kernel fusion (merge a subgraph into one node with generated efficient kernel codes). This will in turn help HW acceleration, since common-patterns of kernel fusion may be pre-defined as common functions in ONNX and no sub-graph (function) finding needed for kernel fusion anymore. For example, subgraph having "Add", "Sigmoid", "Tanh", "Mul" nodes could be merged into one fusion node with generated cuda kernel containing "+", "sigmoidf", "tanhf", "*".
+
+3. Provide a flexible RNN implementation.
+To define a library of RNN cells and allow the user to write a custom one.
+
+MAJOR CHANGES:
+1.	FunctionProto added to represent a function.
+2.	FunctionSetProto added to represent a function set.
+3.	AttributeProto updated to support function attribute type and allow attribute reference.
+4.	ModelProto updated to contain customized function set.
+
+Prototype details can be found [here](https://github.com/linkerzhang/onnx/blob/kezhan/add_function_private/onnx/onnx.in.proto)
diff --git a/third_party/onnx/docs/proposals/NLPinONNXproposal.md b/third_party/onnx/docs/proposals/NLPinONNXproposal.md
new file mode 100644
index 0000000000..663aa5a96d
--- /dev/null
+++ b/third_party/onnx/docs/proposals/NLPinONNXproposal.md
@@ -0,0 +1,69 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+## Background
+
+Modern NLP (Natural Language Processing) is an important domain in which deep learning is applied. In addition, modern NLP networks are often non-trivial to implement and even more difficult to transfer between frameworks. These networks are handled fairly non-uniformly across the landscape of frameworks. The ability for ONNX to interchange these networks can be a very compelling feature.
+
+NLP networks, including recurrent networks, are often built on dynamic control structures. Standardizing the handling of these structures can lead to better collaboration with backends to expose network semantics and achieve better performance. A tradition has developed within the Computer Vision field for optimizing hardware backends for canonical vision models, such as ResNet-50. There is not really such as tradition in the NLP field, however. Through standardizing the representation of NLP networks, we can give vendors a common representation and push forward the performance of NLP models.
+
+## Ultimate Goal and Challenges
+
+We should work toward being able to represent major classes of NLP model architectures. One example of such an architecture is the seq2seq with attention model (e.g. https://arxiv.org/abs/1409.0473). This architecture is used for many use cases, including neural machine translation, speech processing, summarization, dialog systems, image captioning, and syntax parsing, among many others. At the same time, seq2seq with attention is sufficiently complex that supporting it will push forward the state of the art in ONNX, but not so complex that we'd need to define a full programming language.
+
+seq2seq with attention can roughly be broken down into these constituent parts:
+
+* An Encoder network
+    * This network takes a sequence of tokens and yields a sequence of embeddings representing the context found at each time-step
+    * Major classes of encoders: recurrent network (e.g. LSTM[1]), convolutional[2], attention[3].
+    * Requirements from an ONNX representation
+        * Recurrent network - general recurrent network structures preserving outputs at every timestep. Handling of padding and hidden states for batches with different sequence lengths).
+        * Convolutional - 1d convolution, position embeddings
+        * Attention - sinusoid position encodings, layer normalization
+* A Decoder network
+    * This network generates a sequence token by token, parameterized by the context provided from the encoder.
+    * Yields a probability distribution over possible tokens given previous context and encoder context.
+    * Major classes of decoders: recurrent network (e.g. LSTM), convolutional (causal, temporal for generation), attention.
+    * Generation requires dynamic control flow. Often, this is done as a beam search, so this is distinct from regular recurrent networks.
+    * Model-specific requirements
+        * Recurrent network - Recurrent network cell that can be used within the context of beam search
+        * Convolutional - 1d causal convolution (only see previous timesteps)
+        * Attention - sinusoid position encodings, masking along diagonal
+* An Attention mechanism
+    * This network weights the Encoder contexts based on the Decoder's generation state, and provides a focused Encoder context to the decoder. The Decoder “focuses� on a certain part of the input sequence at each timestep via this mechanism.
+    * Many classes of attention mechanism: some examples are here https://arxiv.org/pdf/1508.04025.pdf
+
+
+Vanilla seq2seq with attention and non-backtracking beam search does NOT include things such as auxiliary data-structures (e.g. stacks), thus it does not require us to implement the full semantics of a programming language. It is an architecture that we can break down into incremental improvements to ONNX without compromising ONNX's fundamental goal.
+
+[1] https://arxiv.org/abs/1409.0473
+[2] https://arxiv.org/abs/1705.03122
+[3] https://arxiv.org/abs/1706.03762
+
+## Standard Recurrent Network Constructs
+
+Standard recurrent network architectures such as LSTM or GRU are very common, and we can get very far supporting these. We already have the [LSTM](/docs/Operators.md#LSTM) and [GRU](/docs/Operators.md#GRU) operators, which execute the standard LSTM and GRU[4] operations over a sequence of inputs. These high-level operators are great, since they give backends a semantic view of the computation to be performed, and thus backends can make informed decisions about optimization. Many NLP use cases can get away with using just these operators.
+
+[4] http://colah.github.io/posts/2015-08-Understanding-LSTMs/
+
+## Generic Control Flow
+
+Once we move beyond the domain of standard LSTM and GRU operations, we need a more generic abstraction onto which we can map NLP architectures. A simple example is how one can implement Multiplicative Integration LSTM (https://arxiv.org/pdf/1606.06630.pdf) in ONNX. We can expose a standard LSTMCell via the proposed Function abstraction (https://github.com/onnx/onnx/issues/481). Building on top of this, we can construct a MI-LSTM by applying the required second-order transformations to the inputs to the LSTMCell. Once we have this aggregated implementation, we can use the generic control flow operators (https://github.com/onnx/onnx/pull/436) to apply this “composite� MI-LSTM cell over a sequence.
+
+Of course, the dynamic control flow constructs can be used for more general use cases. For example, consider the [beam search](https://en.wikipedia.org/wiki/Beam_search) used often in NLP for sequence generation. This algorithm has several tricky aspects: a (potentially) dynamic stopping condition, a desired maximum trip count (so we don't fall into an infinite loop), loop-carried dependencies, and the desire to preserve the outputs at every time-step, not just the final time-step. Inherently, this is an imperative algorithm that operates on mutable state. The proposed control flow operators in ONNX, however, fulfill all of these requirements, and thus we can represent many instances of sequence generation in ONNX graphs.
+
+Note that there are more general forms of beam search, such as those including backtracking, but we are not considering these forms for this focused proposal.
+
+
+## End-to-end Example : seq2seq with attention
+
+We should endeavor to have full support for seq2seq with attention models in ONNX. Facebook is currently working on this internally and creating a pytorch→ONNX→caffe2 pathway. An example of such a model we'd like to represent in ONNX is [fairseq-py](https://github.com/facebookresearch/fairseq-py). We would love to engage with the community and collaborate on anything that will help make this a reality. Additionally, if the community has any other suggestions for prominent NLP models we should be able to represent, we would love to hear your ideas.
+
+## Further Challenges
+
+Beyond the constructs used in seq2seq with attention, there are NLP models that exist today that contain more non-trivial features, such as mutable data structures that are manipulated at runtime. Examples of this include back-tracking beam search and parser models such as RNNG (https://arxiv.org/abs/1602.07776). These will present further challenges for ONNX, and the representation of these models will likely remain tied to application code for the time being. We may want to revisit this class of models in the future.
+
+Another thing we should consider is how to handle preprocessing and postprocessing routines for NLP models. For example, do we defer tokenization, normalization, and index lookup to application code? And how do we, for example, distribute dictionaries that map tokens to indices. Initially this will probably remain out of the scope of ONNX unless there is a good story for standardizing text processing.
+
+## Conclusion
+
+We have presented a proposal for a strategy for representing NLP models in ONNX, using seq2seq with attention as a canonical example that covers many use cases. We would like to hear your thoughts about this proposal and to explore opportunities for collaboration with the ONNX community for making ONNX a pleasure to use for NLP. Please feel free to voice your opinions!
diff --git a/third_party/onnx/docs/proposals/ONNXIFIproposal.md b/third_party/onnx/docs/proposals/ONNXIFIproposal.md
new file mode 100644
index 0000000000..6980f854e2
--- /dev/null
+++ b/third_party/onnx/docs/proposals/ONNXIFIproposal.md
@@ -0,0 +1,116 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Interface for Framework Integration: API Proposal
+
+## Background
+
+Leading hardware and systems vendors offer highly optimized software to run neural network graphs. These software can deliver order-of-magnitude speedups compared to generic implementations, but their integration with deep learning frameworks and applications is complicated by large variety in vendor-specific interfaces, and subtle incompatibilities with the software stack of high-level applications.
+
+So far, ONNX format targets the problem of offline conversion of neural network models between different high-level frameworks and vendor-specific libraries through offline translation. In this proposal, we suggest that ONNX ecosystem could be enriched to enable runtime discovery and selection of high-performance graph execution backends, and online (in runtime) conversion of ONNX graph to internal representations of these implementations.
+
+## Ultimate Goal
+
+We should strive for consensus on a library API to interface with optimized backends and offload parts of ONNX graphs to these high-performance hardware and software implementation. The API should enable wide interoperability between high-level deep learning frameworks, software implementations of optimized graph runtimes, and existing and upcoming neural network acceleration hardware.
+
+The standardized API should reduce friction in deploying neural network models for all involved parties:
+- Applications would be able to ship only one version of a neural network model (either in ONNX format, or in the format of their deep learning framework, and convert it on the fly to ONNX).
+- Deep learning frameworks would be able to integrate with many hardware vendors by using only a single interface.
+- Hardware vendors would be able to implement only one interface and get integration with many deep learning frameworks.
+
+## Design Choices
+
+- Interface must use only highly portable aspects of C ABI.
+- Neural network graphs are passed as serialized ONNX ModelProto messages. To avoid serialization overhead, weights can be passed as raw memory blobs.
+- Input and output tensors are allocated by the caller and use NCHW layout.
+- Intermediate tensors are allocated by the vendor implementation, and can use any layout.
+- Backends (software implementations and hardware accelerators) are discovered, selected, and initialized on-demand in run-time. Multiple backends can be used in the same application simultaneously.
+- There is no minimal set of ONNX operators to implement. The implementer and the user (a deep learning framework) of the API decide which operators can and will be offloaded in runtime.
+- The proposal includes the minimal functionality to let deep learning frameworks and vendor libraries work together. Several extension mechanisms can be used for more efficient vendor- or platform-specific functionality.
+
+## Proposed Interface
+
+We propose a small C-based API, which includes the following functionality:
+
+* Discover (`onnxGetNumBackends`) and query information (`onnxGetBackendInfo`) about high-performance backends
+* Initialize (`onnxInitBackend`) and deinitialize (`onnxReleaseBackend`) high-performance backends
+* Query if a backend supports an ONNX operator with particular parameters and input shapes (`onnxGetBackendCompatibility`)
+* Convert an ONNX graph to opaque vendor-specific representation of a backend (`onnxInitGraph`)
+* Specify memory locations and metadata about graph inputs and outputs (`onnxSetGraphIO`)
+* Run an ONNX graph, converted to vendor-specific representation (`onnxRunGraph`)
+* Release the vendor-specific representation of a graph and associated resources (`onnxReleaseGraph`)
+
+## General Use Pattern for Deep Learning Frameworks
+
+1. The user (deep learning framework) iterates operators in a model graph one-by-one, convert them to ONNX, and calls `onnxGetBackendCompatibility` to check which of the operators can be offloaded to the backend.
+2. The user constructs connected subgraphs of operators that can be offloaded to the backend.
+3. (Optional) For each subgraph, the user estimates if it is beneficial to offload it to the optimized backend:
+
+    a. The user queries the backend about it high-level performance characteristics using `ONNX_BACKEND_MACS_*` and `ONNX_BACKEND_MEMORY_BANDWIDTH` information queries. These data let the user build a simple roofline model of backend performance.
+
+    b. For every subgraph the user estimates time to do inference using the roofline model.
+
+    c. The user additionally estimates time to transfer subgraph inputs to the backend using `ONNX_BACKEND_CPU_MEMORY_READ_BANDWIDTH` information query and to transfer subgraph outputs from the backend using `ONNX_BACKEND_CPU_MEMORY_WRITE_BANDWIDTH`.
+
+    d. If predicted time to transfer inputs to the backend, do inference, and transfer outputs from the backend exceeds predicted time to do the inference on default engine (e.g. CPU), the user falls back to a different ONNX backend, or to the default engine.
+
+
+4. The user initialized the backend, and offloads the subgraph execution to the ONNX backend by calling `onnxInitGraph`, `onnxSetGraphIO` and `onnxRunGraph`
+
+## Implementation Notes
+
+### Backend object
+
+Backend is a combination of software library and hardware device. The same device (e.g. "NVIDIA Tesla P100 on CUDA index #0" accessed though different software libraries would be seen as different backends. A single software library can expose multiple backends, one per device  (e.g. each CUDA GPU in a system is exposed as a separate backend, or CPU, GPU, and DSP on a mobile chipset are exposed as three different backends).
+
+We recommend that vendors make the backend object reference-counted, and use `uint32_t magic` as the first data field of the object:
+
+```c
+struct MyBackend {
+  uint32_t magic;
+  uint64_t referenceCount;
+  ...
+};
+
+/* This line won't compile, but gives you an idea of relation between MyBackend structure and onnxBackend type. */
+typedef MyBackend* onnxBackend;
+```
+
+Magic is an arbitrary 32-bit integer unique for a library implementing the API. It should be used to verify that the backend object passed to `onnxInitGraph` was created by `onnxInitBackend` in the same library.
+
+### Graph object
+
+Graph object is a vendor-specific representation of ONNX ModelProto message. Graph is logically related to the backend used to create it, and a typical implementation of a graph object would hold a reference to its backend object.
+
+We recommend that vendors use `uint32_t magic` as the first data field of the graph object:
+
+```c
+struct MyGraph {
+  uint32_t magic;
+  struct MyBackend* backend;
+  ...
+};
+
+/* This line won't compile, but gives you an idea of relation between MyGraph structure and onnxGraph type. */
+typedef MyGraph* onnxGraph;
+```
+
+Magic is an arbitrary 32-bit integer unique for a library implementing the API. It should be used to verify that the backend object passed to `onnxInitGraph` was created by `onnxInitBackend` in the same library. Magic for a graph object should be different from magic of a backend object of the same library.
+
+### Library initialization
+
+During one-time library initialization, the implementation of the API would detect `n` supported devices and map them to backend indices in `0...(n-1)` range. The implementation of device discovery and checking required device characteristics is highly vendor- and platform-specific, e.g.:
+- A CPU implementation may always expose 1 device.
+- A CUDA-based implementation may call `cudaGetDeviceCount` to get the number of CUDA-enabled devices, then
+ call `cudaGetDeviceProperties` for each device, and map CUDA devices which satisfy the minimum required functionality, such as compute capability, to backend indices.
+- An OpenCL-based implementation for a mobile GPU would try to load OpenCL library, call `clGetPlatformIDs` and `clGetPlatformInfo` to find a supported platform, then call `clGetDeviceIDs` and `clGetDeviceInfo` to find a supported GPU device, and map it to the only exposed backend if such device exists, or expose 0 devices otherwise.
+- An implementation for hardware neural network accelerators would call vendor-specific driver API to discover accelerator devices installed in the system and map them to backend indices.
+
+We recommend that library initialization is triggered on the first call to `onnxGetNumBackends`, `onnxGetBackendInfo`, or `onnxInitBackend`. Using a global static C++ object for initialization may hurt portability if library initialization involves loading other shared libraries (DLLs): on Windows `LoadLibrary` function can't be used in initializers of global static objects.
+
+### onnxGetNumBackends
+
+Implementation would [initialize the library](#library-initialization), if it wasn't initialized already, and return the number `n` of available backends.
+
+### onnxGetBackendInfo
+
+Implementation would [initialize the library](#library-initialization), if it wasn't initialized already, and query information about the backend using vendor- or platform-specific API (e.g. `cudaGetDeviceProperties`, `clGetDeviceInfo`, CPUID instruction). Implementation can cache this information when it is first queried or during initialization, and return the cached value.
diff --git a/third_party/onnx/docs/proposals/SymbolicShapeInfProposal.md b/third_party/onnx/docs/proposals/SymbolicShapeInfProposal.md
new file mode 100644
index 0000000000..b669cd6fb9
--- /dev/null
+++ b/third_party/onnx/docs/proposals/SymbolicShapeInfProposal.md
@@ -0,0 +1,163 @@
+# Proposal - Symbolic Shape Inference And Partial Data Propagation  
+
+*Note: This proposal was accepted and implemented in ONNX 1.10. Following PRs implemented this proposal: 3518, 3551, 3593, 3580*
+
+## Introduction 
+ONNX provides an implementation of shape inference on ONNX graphs. Shape inference is computed using the operator level shape inference functions. The inferred shape of an operator is used to get the shape information without having to launch the model in a session. Such static shape inference can be used to catch obvious errors before runtime, eliminate run-time checks which are otherwise guaranteed to pass, improve static memory planning and improve model visualization experience. For pytorch exporter and compiler-based execution providers like Nuphar, shape inference is required (rank inference is minimum requirement), and they cannot work with unknown shapes. 
+
+This document explains the limitations of shape inference and lays out a proposal for addressing these limitations.  
+
+## Current onnx shape inference limitations (Pre ONNX 1.10) 
+Today, ONNX shape inference is not guaranteed to be complete. Wherever possible we fall back to rank inference however, there are scenarios when rank inference is not possible either. Here are the various limitations which block the completion of shape inference: 
+
+1. Some dynamic behaviors block the flow of shape inference, and the shape inference stops. For example, reshape to a dynamically computed shape.
+
+2. Shape inference works only with constants and simple variables. It does not support arithmetic expressions containing variables nor does it support symbol generation. For example, concatenation on tensors of shapes (5, 2) and (7, 2) can be inferred to produce a result of shape (12, 2), but concatenation on tensors of shapes (5, 2) and (N, 2) will simply produce (?, 2), where “?� represents a dimension with neither dim value nor dim param, rather than containing a representation of N+5 or generating a new symbol (M, 2). In such scenarios shape propagation stops. 
+
+3. All operators are not required to have a shape inference implementation. When such an op is encountered the shape inference stops. There are also cases when rank inference is not done as a fallback mechanism. (Note: We are working on an ongoing basis to identify and fix such issues. The current document does not focus on this limitation)  
+  
+
+## Goals and Non-Goals 
+Our **goal** is to fix the shape inference gap in scenarios where: 
+
+* Shape computations are done in branches (refer to limitation 1) 
+
+* Symbolic dimensions are present (refer to limitation 2) 
+
+By fixing these gaps we aim to:   
+
+* Unblock pytorch exporter from exporting models when exporting stops because of absence of shape information.  
+
+* Improve static memory planning in the runtimes. 
+
+* Enable pre-allocating output buffers outside of the runtimes so that its lifetime can be managed by the caller itself.  
+  
+
+### Non-goals  
+* Add symbolic expressions to ONNX standard: This is not necessary for accomplishing our goals. There are advantages to having this capability, for example this can significantly reduce the number of symbols introduced and it can also provide more deterministic shape calculations in certain special cases. However, the tradeoff is the added complexity. So, at this point we are not considering it. This can be considered in future iterations. 
+
+* Enable data computation and propagation for older operator sets. (details in the proposal section) 
+
+Note: This work will benefit Nuphar as well but right now there is no plan to move Nuphar to use this solution.  
+  
+
+## Terminology 
+Shape inference can be broken into 2 parts:  
+
+* Node level shape inference: This refers to operator specific shape inference functions. They are defined with the operator schema itself.  
+
+* Graph-level shape inference: This refers to the higher-level logic which walks through the entire graph, gets the inferred shape from node level shape inference functions and then makes decisions on merging these inferred shapes with existing shapes so that they are available for downstream nodes.  
+ 
+
+## Proposal 
+Extend current shape inference to allow:  
+* Symbol generation and propagation 
+
+* Partial data computation and propagation 
+
+* Extend shape op to generate slice of the shape to facilitate simplifying shape computations.  
+
+
+## Extend shape inference  
+
+### Symbol generation and propagation 
+Extend graph level shape inference to maintain a graph level view of symbols and generate new symbols where necessary. This will enable us to continue the shape inference of the downstream nodes. 
+
+Example: 
+
+For an op like “Concat� if its inputs have shapes “[M]� and “[N]� current shape-inference returns “[?]� where “?� is to indicate a dimension with neither dim-value nor dim-param set. Now, suppose the output X of “Concat� is input to a unary-op Op1() whose output Y is then input to another unary-op Op2() whose output is Z, etc. The shape “[?]� is propagated further. We infer that Y and Z have shape “[?]�. However, we do not infer that X, Y, and Z have the same shape because two “?� cannot be considered equal.  
+
+Per the current proposal, “[?]� in inferred shapes will be replaced by a new unique symbol by the graph level shape inference so the downstream nodes can use the symbolic shapes to carry out shape inference. In the current example, “Concat� will produce “[?]� as the shape which will then be replaced by “[K]�, then subsequent shape inference will infer that X, Y, and Z all have the same shape “[K]�. Runtimes can use this information to reuse memory for these tensors.  
+
+
+### Partial data computation and propagation 
+When shape inputs are computed dynamically, shape inference post a reshape node stops. This can be prevented by making this data available to the reshape node during shape inference. We propose computation and propagation of data for operators which are used in shape computation. 
+
+It is called “partial� data computation and propagation because this will only be done for shape computations. It is not meant to be a full-fledged kernel for the operator. For the same reasons data computations will be implemented for a limited set of operators. While we will increase the coverage in the future iterations it is important to note that for some operators like LSTM, convolution ops, pooling ops etc. data propagation function will never be added because such ops are not used in shape computations. 
+
+The following operators will be picked in the first phase. (These operators are generally used for shape computations.) 
+
+| Ops     | 
+| --------| 
+| Add     | 
+| Sub     |
+| Mul     |
+| Cast    | 
+| Concat  |
+| Gather  |
+| Reshape |
+| Shape   |
+| Slice   |
+| Size    |
+| Squeeze |
+| UnSqueeze |
+
+The OpSchema class will be extended to include an optional “PartialDataPropagationFunction� like the existing TypeAndShapeInferenceFunction. This function will provide data computation for the operators which will then be propagated to the downstream operators by the graph level shape inference. PartialDataPropagationFunction will be called by the graph level shape inference after TypeAndShapeInference runs for the node because the output shape is required for partial data computation.  
+
+A new interface "DataPropagationContext� will be added to allow  PartialDataPropagationFunction to access all the information required to propagate shape data for the given node and allow writing of the computed data. 
+
+Example: 
+
+```
+using DataPropagationFunction = std::function<void(DataPropagationContext&)>  
+
+class OpSchema final {  
+
+ public:  
+  .  
+  .  
+  .  
+
+  OpSchema& PartialDataPropagationFunction(DataPropagationFunction dataPropagationFunction)  {  
+    partial_data_propagation_function_ = std::move(dataPropagationFunction);  
+    return *this;  
+  }  
+
+  DataPropagationFunction GetDataPropagationFunction() const {
+    return partial_data_propagation_function_ ? partial_data_propagation_function_ : dummyDataPropogator;  
+  }  
+} 
+
+// Operator schema example 
+ONNX_OPERATOR_SET_SCHEMA(  
+    Shape,  
+    13,  
+    OpSchema()  
+        .SetDoc(“�)  
+        .Input(0, "data", "An input tensor.", "T", . . .)  
+        .Output(0, "shape", "Shape of the input tensor", "T1", . . .)  
+        .TypeConstraint("T", OpSchema::all_tensor_types())  
+        .TypeConstraint("T1", {"tensor(int64)"})  
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {  
+        . . .  
+        })  
+
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {  
+          TensorShapeProto tp; 
+          // compute output data for shape operator 
+          // add computed data to DataPropagationContext for propagating it downstream  
+          ctx.addOutputData(0, std::move(tp));  
+        })); 
+```
+
+The symbol generation will happen at the graph level shape inference, therefore all the models (older opsets as well as the latest opset versions) can benefit from this enhancement. However, the data computation and propagation are tied to the OpScehma and will happen at node level. To begin with these functions will only be added to the latest op schemas. Older schemas can be extended to support data computation later, on a case by case basis to support some high priority scenarios. What this means is that older opset models will not benefit from shape inference improvements because of this enhancement.  
+  
+
+## Special Cases 
+This section considers some edge cases and proposes a solution to handle them.  
+
+
+### Broadcasting with symbolic dims 
+If we have a broadcast between two unknown dimensions “M� and “N� we cannot infer that both M and N should have the same value. The runtime semantics allows for one of the two symbols to have the value 1 and the other to have a value different from 1. So, merging M and N and treating them as the same value is potentially unsound. In this case, a new symbol will be generated for the output shape and the shape inference will continue.  
+
+
+### Inferred shape does not match output shape 
+Inferred and existing shapes can be mismatched. Although failing shape inference in such cases seems like the correct approach it may not always be practical. By default, shape inference will fail when such a case is encountered however callers will have an option to override existing types with inferred types. When this option is enabled, shape inference will continue with the inferred type.  
+
+
+### Handling symbolic dimensions with data propagation 
+When the shape contains symbolic dimensions, we try and propagate them downstream, however in cases where some arithmetic operations are performed on these symbolic dims we create new symbols and propagate them instead.   
+
+
+### Output shape is dependent on input data 
+There are certain nodes like NonZero where the output shape depends on the input data. In this case it is not possible to infer the shape completely hence a new symbolic shape will be created using the inferred rank and shape inference will continue.  
diff --git a/third_party/onnx/onnx/__init__.py b/third_party/onnx/onnx/__init__.py
new file mode 100644
index 0000000000..0dea1afa8a
--- /dev/null
+++ b/third_party/onnx/onnx/__init__.py
@@ -0,0 +1,217 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+from .onnx_cpp2py_export import ONNX_ML
+from onnx.external_data_helper import load_external_data_for_model, write_external_data_tensors, convert_model_to_external_data
+from .onnx_pb import *  # noqa
+from .onnx_operators_pb import * # noqa
+from .onnx_data_pb import * # noqa
+from .version import version as __version__  # noqa
+
+# Import common subpackages so they're available when you 'import onnx'
+import onnx.checker  # noqa
+import onnx.defs  # noqa
+import onnx.helper  # noqa
+import onnx.utils  # noqa
+import onnx.compose  # noqa
+
+import google.protobuf.message
+
+from typing import Union, Text, IO, Optional, cast, TypeVar, Any
+
+
+# f should be either readable or a file path
+def _load_bytes(f: Union[IO[bytes], Text]) -> bytes:
+    if hasattr(f, 'read') and callable(cast(IO[bytes], f).read):
+        s = cast(IO[bytes], f).read()
+    else:
+        with open(cast(Text, f), 'rb') as readable:
+            s = readable.read()
+    return s
+
+
+# str should be bytes,
+# f should be either writable or a file path
+def _save_bytes(str: bytes, f: Union[IO[bytes], Text]) -> None:
+    if hasattr(f, 'write') and callable(cast(IO[bytes], f).write):
+        cast(IO[bytes], f).write(str)
+    else:
+        with open(cast(Text, f), 'wb') as writable:
+            writable.write(str)
+
+
+# f should be either a readable file or a file path
+def _get_file_path(f: Union[IO[bytes], Text]) -> Optional[Text]:
+    if isinstance(f, str):
+        return os.path.abspath(f)
+    if hasattr(f, 'name'):
+        return os.path.abspath(f.name)
+    return None
+
+
+def _serialize(proto: Union[bytes, google.protobuf.message.Message]) -> bytes:
+    '''
+    Serialize a in-memory proto to bytes
+
+    Arguments:
+        proto: a in-memory proto, such as a ModelProto, TensorProto, etc
+
+    Returns:
+        Serialized proto in bytes
+    '''
+    if isinstance(proto, bytes):
+        return proto
+    elif hasattr(proto, 'SerializeToString') and callable(proto.SerializeToString):
+        result = proto.SerializeToString()
+        return result
+    else:
+        raise TypeError('No SerializeToString method is detected. '
+                         'neither proto is a str.\ntype is {}'.format(type(proto)))
+
+
+_Proto = TypeVar('_Proto', bound=google.protobuf.message.Message)
+
+
+def _deserialize(s: bytes, proto: _Proto) -> _Proto:
+    '''
+    Parse bytes into a in-memory proto
+
+    Arguments:
+        s: bytes containing serialized proto
+        proto: a in-memory proto object
+
+    Returns:
+        The proto instance filled in by s
+    '''
+    if not isinstance(s, bytes):
+        raise ValueError('Parameter s must be bytes, but got type: {}'.format(type(s)))
+
+    if not (hasattr(proto, 'ParseFromString') and callable(proto.ParseFromString)):
+        raise ValueError('No ParseFromString method is detected. '
+                         '\ntype is {}'.format(type(proto)))
+
+    decoded = cast(Optional[int], proto.ParseFromString(s))
+    if decoded is not None and decoded != len(s):
+        raise google.protobuf.message.DecodeError(
+            "Protobuf decoding consumed too few bytes: {} out of {}".format(
+                decoded, len(s)))
+    return proto
+
+
+def load_model(f: Union[IO[bytes], Text], format: Optional[Any] = None, load_external_data: bool = True) -> ModelProto:
+    '''
+    Loads a serialized ModelProto into memory
+    load_external_data is true if the external data under the same directory of the model and load the external data
+    If not, users need to call load_external_data_for_model with directory to load
+
+    Arguments:
+        f: can be a file-like object (has "read" function) or a string containing a file name
+        format: for future use
+
+    Returns:
+        Loaded in-memory ModelProto
+    '''
+    s = _load_bytes(f)
+    model = load_model_from_string(s, format=format)
+
+    if load_external_data:
+        model_filepath = _get_file_path(f)
+        if model_filepath:
+            base_dir = os.path.dirname(model_filepath)
+            load_external_data_for_model(model, base_dir)
+
+    return model
+
+
+def load_tensor(f: Union[IO[bytes], Text], format: Optional[Any] = None) -> TensorProto:
+    '''
+    Loads a serialized TensorProto into memory
+
+    Arguments:
+        f: can be a file-like object (has "read" function) or a string containing a file name
+        format: for future use
+
+    Returns:
+        Loaded in-memory TensorProto
+    '''
+    s = _load_bytes(f)
+    return load_tensor_from_string(s, format=format)
+
+
+def load_model_from_string(s: bytes, format: Optional[Any] = None) -> ModelProto:
+    '''
+    Loads a binary string (bytes) that contains serialized ModelProto
+
+    Arguments:
+        s: a string, which contains serialized ModelProto
+        format: for future use
+
+    Returns:
+        Loaded in-memory ModelProto
+    '''
+    return _deserialize(s, ModelProto())
+
+
+def load_tensor_from_string(s: bytes, format: Optional[Any] = None) -> TensorProto:
+    '''
+    Loads a binary string (bytes) that contains serialized TensorProto
+
+    Arguments:
+        s: a string, which contains serialized TensorProto
+        format: for future use
+
+    Returns:
+        Loaded in-memory TensorProto
+    '''
+    return _deserialize(s, TensorProto())
+
+
+def save_model(proto: Union[ModelProto, bytes], f: Union[IO[bytes], Text], format: Optional[Any] = None, save_as_external_data: bool = False, all_tensors_to_one_file: bool = True, location: Optional[Text] = None, size_threshold: int = 1024, convert_attribute: bool = False) -> None:
+    '''
+    Saves the ModelProto to the specified path and optionally, serialize tensors with raw data as external data before saving.
+
+    Arguments:
+        proto: should be a in-memory ModelProto
+        f: can be a file-like object (has "write" function) or a string containing a file name format for future use
+        all_tensors_to_one_file: If true, save all tensors to one external file specified by location.
+            If false, save each tensor to a file named with the tensor name.
+        location: specify the external file that all tensors to save to.
+            If not specified, will use the model name.
+        size_threshold: Threshold for size of data. Only when tensor's data is >= the size_threshold it will be converted
+            to external data. To convert every tensor with raw data to external data set size_threshold=0.
+        convert_attribute: If true, convert all tensors to external data
+            If false, convert only non-attribute tensors to external data
+    '''
+    if isinstance(proto, bytes):
+        proto = _deserialize(proto, ModelProto())
+
+    if save_as_external_data:
+        convert_model_to_external_data(proto, all_tensors_to_one_file, location, size_threshold, convert_attribute)
+
+    model_filepath = _get_file_path(f)
+    if model_filepath:
+        basepath = os.path.dirname(model_filepath)
+        proto = write_external_data_tensors(proto, basepath)
+
+    s = _serialize(proto)
+    _save_bytes(s, f)
+
+
+def save_tensor(proto: TensorProto, f: Union[IO[bytes], Text]) -> None:
+    '''
+    Saves the TensorProto to the specified path.
+
+    Arguments:
+        proto: should be a in-memory TensorProto
+        f: can be a file-like object (has "write" function) or a string containing a file name
+        format: for future use
+    '''
+    s = _serialize(proto)
+    _save_bytes(s, f)
+
+
+# For backward compatibility
+load = load_model
+load_from_string = load_model_from_string
+save = save_model
diff --git a/third_party/onnx/onnx/backend/__init__.py b/third_party/onnx/onnx/backend/__init__.py
new file mode 100644
index 0000000000..9881313609
--- /dev/null
+++ b/third_party/onnx/onnx/backend/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/third_party/onnx/onnx/backend/base.py b/third_party/onnx/onnx/backend/base.py
new file mode 100644
index 0000000000..af1fd41479
--- /dev/null
+++ b/third_party/onnx/onnx/backend/base.py
@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from collections import namedtuple
+from typing import Text, Sequence, Any, Type, Tuple, NewType, Optional, Dict
+
+import numpy  # type: ignore
+
+import onnx.checker
+import onnx.onnx_cpp2py_export.checker as c_checker
+from onnx import ModelProto, NodeProto, IR_VERSION
+
+
+class DeviceType(object):
+    _Type = NewType('_Type', int)
+    CPU: _Type = _Type(0)
+    CUDA: _Type = _Type(1)
+
+
+class Device(object):
+    '''
+    Describes device type and device id
+    syntax: device_type:device_id(optional)
+    example: 'CPU', 'CUDA', 'CUDA:1'
+    '''
+
+    def __init__(self, device: Text) -> None:
+        options = device.split(':')
+        self.type = getattr(DeviceType, options[0])
+        self.device_id = 0
+        if len(options) > 1:
+            self.device_id = int(options[1])
+
+
+def namedtupledict(typename: Text, field_names: Sequence[Text], *args: Any, **kwargs: Any) -> Type[Tuple[Any, ...]]:
+    field_names_map = {n: i for i, n in enumerate(field_names)}
+    # Some output names are invalid python identifier, e.g. "0"
+    kwargs.setdefault(str('rename'), True)
+    data = namedtuple(typename, field_names, *args, **kwargs)  # type: ignore
+
+    def getitem(self: Any, key: Any) -> Any:
+        if isinstance(key, str):
+            key = field_names_map[key]
+        return super(type(self), self).__getitem__(key)  # type: ignore
+    setattr(data, "__getitem__", getitem)
+    return data
+
+
+class BackendRep(object):
+    def run(self, inputs: Any, **kwargs: Any) -> Tuple[Any, ...]:
+        pass
+
+
+class Backend(object):
+    @classmethod
+    def is_compatible(cls,
+                      model: ModelProto,
+                      device: Text = 'CPU',
+                      **kwargs: Any
+                      ) -> bool:
+        # Return whether the model is compatible with the backend.
+        return True
+
+    @classmethod
+    def prepare(cls,
+                model: ModelProto,
+                device: Text = 'CPU',
+                **kwargs: Any
+                ) -> Optional[BackendRep]:
+        # TODO Remove Optional from return type
+        onnx.checker.check_model(model)
+        return None
+
+    @classmethod
+    def run_model(cls,
+                  model: ModelProto,
+                  inputs: Any,
+                  device: Text = 'CPU',
+                  **kwargs: Any
+                  ) -> Tuple[Any, ...]:
+        backend = cls.prepare(model, device, **kwargs)
+        assert backend is not None
+        return backend.run(inputs)
+
+    @classmethod
+    def run_node(cls,
+                 node: NodeProto,
+                 inputs: Any,
+                 device: Text = 'CPU',
+                 outputs_info: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] = None,
+                 **kwargs: Dict[Text, Any]
+                 ) -> Optional[Tuple[Any, ...]]:
+        '''Simple run one operator and return the results.
+        Args:
+            outputs_info: a list of tuples, which contains the element type and
+            shape of each output. First element of the tuple is the dtype, and
+            the second element is the shape. More use case can be found in
+            https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py
+        '''
+        # TODO Remove Optional from return type
+        if 'opset_version' in kwargs:
+            special_context = c_checker.CheckerContext()
+            special_context.ir_version = IR_VERSION
+            special_context.opset_imports = {'': kwargs['opset_version']}  # type: ignore
+            onnx.checker.check_node(node, special_context)
+        else:
+            onnx.checker.check_node(node)
+        return None
+
+    @classmethod
+    def supports_device(cls, device: Text) -> bool:
+        """
+        Checks whether the backend is compiled with particular device support.
+        In particular it's used in the testing suite.
+        """
+        return True
diff --git a/third_party/onnx/onnx/backend/sample/__init__.py b/third_party/onnx/onnx/backend/sample/__init__.py
new file mode 100644
index 0000000000..9881313609
--- /dev/null
+++ b/third_party/onnx/onnx/backend/sample/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/third_party/onnx/onnx/backend/sample/ops/__init__.py b/third_party/onnx/onnx/backend/sample/ops/__init__.py
new file mode 100644
index 0000000000..55e70e7b7b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/sample/ops/__init__.py
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import importlib
+import inspect
+import sys
+import pkgutil
+from typing import Dict, Text
+from types import ModuleType
+
+
+def collect_sample_implementations() -> Dict[Text, Text]:
+    dict: Dict[Text, Text] = {}
+    _recursive_scan(sys.modules[__name__], dict)
+    return dict
+
+
+def _recursive_scan(package: ModuleType, dict: Dict[Text, Text]) -> None:
+    pkg_dir = package.__path__  # type: ignore
+    module_location = package.__name__
+    for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir):  # type: ignore
+        module_name = "{}.{}".format(module_location, name)  # Module/package
+        module = importlib.import_module(module_name)
+        dict[name] = inspect.getsource(module)
+        if ispkg:
+            _recursive_scan(module, dict)
diff --git a/third_party/onnx/onnx/backend/sample/ops/abs.py b/third_party/onnx/onnx/backend/sample/ops/abs.py
new file mode 100644
index 0000000000..89fe75b792
--- /dev/null
+++ b/third_party/onnx/onnx/backend/sample/ops/abs.py
@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+
+def abs(input: np.ndarray) -> np.ndarray:
+    return np.abs(input)
diff --git a/third_party/onnx/onnx/backend/test/__init__.py b/third_party/onnx/onnx/backend/test/__init__.py
new file mode 100644
index 0000000000..4110663c22
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/__init__.py
@@ -0,0 +1,4 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# for backward compatibility
+from .runner import Runner as BackendTest # noqa
diff --git a/third_party/onnx/onnx/backend/test/case/__init__.py b/third_party/onnx/onnx/backend/test/case/__init__.py
new file mode 100644
index 0000000000..0286825087
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+
+from .base import Snippets
+from .utils import import_recursive
+from typing import Dict, Text, List, Tuple
+
+
+def collect_snippets() -> Dict[Text, List[Tuple[Text, Text]]]:
+    import_recursive(sys.modules[__name__])
+    return Snippets
diff --git a/third_party/onnx/onnx/backend/test/case/base.py b/third_party/onnx/onnx/backend/test/case/base.py
new file mode 100644
index 0000000000..79cb180337
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/base.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from collections import defaultdict
+import inspect
+from textwrap import dedent
+from typing import Dict, Text, List, Tuple, Type, Sequence, Any
+
+import numpy as np  # type: ignore
+
+
+def process_snippet(op_name: Text, name: Text, export: Any) -> Tuple[Text, Text]:
+    snippet_name = name[len('export_'):] or op_name.lower()
+    source_code = dedent(inspect.getsource(export))
+    # remove the function signature line
+    lines = source_code.splitlines()
+    assert lines[0] == '@staticmethod'
+    assert lines[1].startswith('def export')
+    return snippet_name, dedent("\n".join(lines[2:]))
+
+
+Snippets: Dict[Text, List[Tuple[Text, Text]]] = defaultdict(list)
+
+
+class _Exporter(type):
+    exports: Dict[Text, List[Tuple[Text, Text]]] = defaultdict(list)
+
+    def __init__(cls, name: str, bases: Tuple[Type[Any], ...], dct: Dict[str, Any]) -> None:
+        for k, v in dct.items():
+            if k.startswith('export'):
+                if not isinstance(v, staticmethod):
+                    raise ValueError(
+                        'Only staticmethods could be named as export.*')
+                export = getattr(cls, k)
+                Snippets[name].append(process_snippet(name, k, export))
+                # export functions should call expect and so populate
+                # TestCases
+                np.random.seed(seed=0)
+                export()
+        super(_Exporter, cls).__init__(name, bases, dct)
+
+
+class Base(object, metaclass=_Exporter):
+    pass
diff --git a/third_party/onnx/onnx/backend/test/case/model/__init__.py b/third_party/onnx/onnx/backend/test/case/model/__init__.py
new file mode 100644
index 0000000000..c5978f804e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/__init__.py
@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+
+import onnx.defs
+import numpy as np  # type: ignore
+from onnx import ModelProto
+from typing import List, Optional, Text, Sequence
+from ..utils import import_recursive
+from ..test_case import TestCase
+
+_SimpleModelTestCases = []
+
+
+def expect(model: ModelProto,
+           inputs: Sequence[np.ndarray],
+           outputs: Sequence[np.ndarray],
+           name: Optional[Text] = None,
+           ) -> None:
+    name = name or model.graph.name
+    _SimpleModelTestCases.append(
+        TestCase(
+            name=name,
+            model_name=model.graph.name,
+            url=None,
+            model_dir=None,
+            model=model,
+            data_sets=[(inputs, outputs)],
+            kind='simple',
+            rtol=1e-3,
+            atol=1e-7,
+        ))
+
+
+base_model_opset_version = 10
+BASE_URL = 'https://s3.amazonaws.com/download.onnx/models/opset_{}'.format(
+    base_model_opset_version)
+
+
+def collect_testcases() -> List[TestCase]:
+    '''Collect model test cases defined in python/numpy code and in model zoo.
+    '''
+
+    real_model_testcases = []
+
+    model_tests = [
+        ('test_bvlc_alexnet', 'bvlc_alexnet', 1e-3, 1e-7),
+        ('test_densenet121', 'densenet121', 2e-3, 1e-7),
+        ('test_inception_v1', 'inception_v1', 1e-3, 1e-7),
+        ('test_inception_v2', 'inception_v2', 1e-3, 1e-7),
+        ('test_resnet50', 'resnet50', 1e-3, 1e-7),
+        ('test_shufflenet', 'shufflenet', 1e-3, 1e-7),
+        ('test_squeezenet', 'squeezenet', 1e-3, 1e-7),
+        ('test_vgg19', 'vgg19', 1e-3, 1e-7),
+        ('test_zfnet512', 'zfnet512', 1e-3, 1e-7),
+    ]
+
+    for test_name, model_name, rtol, atol in model_tests:
+        url = '{}/{}.tar.gz'.format(BASE_URL, model_name)
+        real_model_testcases.append(TestCase(
+            name=test_name,
+            model_name=model_name,
+            url=url,
+            model_dir=None,
+            model=None,
+            data_sets=None,
+            kind='real',
+            rtol=rtol,
+            atol=atol,
+        ))
+
+    import_recursive(sys.modules[__name__])
+
+    return real_model_testcases + _SimpleModelTestCases
diff --git a/third_party/onnx/onnx/backend/test/case/model/expand.py b/third_party/onnx/onnx/backend/test/case/model/expand.py
new file mode 100644
index 0000000000..8333ce82b7
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/expand.py
@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from typing import Sequence
+
+
+class ExpandDynamicShape(Base):
+
+    @staticmethod
+    def export() -> None:
+
+        def make_graph(node: onnx.helper.NodeProto, input_shape: Sequence[int], shape_shape: Sequence[int], output_shape: Sequence[int]) -> onnx.helper.GraphProto:
+            graph = onnx.helper.make_graph(
+                nodes=[node],
+                name='Expand',
+                inputs=[onnx.helper.make_tensor_value_info('X',
+                                                             onnx.TensorProto.FLOAT,
+                                                             input_shape),
+                          onnx.helper.make_tensor_value_info('shape',
+                                                             onnx.TensorProto.INT64,
+                                                             shape_shape)],
+                outputs=[onnx.helper.make_tensor_value_info('Y',
+                                                              onnx.TensorProto.FLOAT,
+                                                              output_shape)])
+            return graph
+
+        node = onnx.helper.make_node(
+            'Expand', ['X', 'shape'], ['Y'], name='test')
+        input_shape = [1, 3, 1]
+        x = np.ones(input_shape, dtype=np.float32)
+
+        #1st testcase
+        shape = np.array([3, 1], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model1")
+
+        #2nd testcase
+        shape = np.array([1, 3], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model2")
+
+        #3rd testcase
+        shape = np.array([3, 1, 3], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model3")
+
+        #4th testcase
+        shape = np.array([3, 3, 1, 3], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model4")
diff --git a/third_party/onnx/onnx/backend/test/case/model/gradient.py b/third_party/onnx/onnx/backend/test/case/model/gradient.py
new file mode 100644
index 0000000000..e083faaec3
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/gradient.py
@@ -0,0 +1,101 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx.defs import ONNX_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN
+from ..base import Base
+from . import expect
+
+
+class Gradient(Base):
+
+    @staticmethod
+    def export_gradient_scalar_add() -> None:
+        add_node = onnx.helper.make_node('Add',
+                                         ['a', 'b'], ['c'], name='my_add')
+        gradient_node = onnx.helper.make_node(
+            'Gradient', ['a', 'b'],
+            ['dc_da', 'dc_db'], name='my_gradient',
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+            xs=['a', 'b'], y='c')
+
+        a = np.array(1.0).astype(np.float32)
+        b = np.array(2.0).astype(np.float32)
+        c = a + b
+        # dc / da = d(a+b) / da = 1
+        dc_da = np.array(1).astype(np.float32)
+        # db / db = d(a+b) / db = 1
+        dc_db = np.array(1).astype(np.float32)
+
+        graph = onnx.helper.make_graph(
+            nodes=[add_node, gradient_node],
+            name='GradientOfAdd',
+            inputs=[
+                onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
+                                                   []),
+                onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
+                                                   [])],
+            outputs=[
+                onnx.helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT,
+                                                   []),
+                onnx.helper.make_tensor_value_info('dc_da',
+                                                   onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info('dc_db',
+                                                   onnx.TensorProto.FLOAT, [])])
+        opsets = [
+            onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+            onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]
+        model = onnx.helper.make_model(
+            graph,
+            producer_name='backend-test',
+            opset_imports=opsets)
+        expect(model, inputs=[a, b], outputs=[c, dc_da, dc_db],
+               name='test_gradient_of_add')
+
+    @staticmethod
+    def export_gradient_scalar_add_and_mul() -> None:
+        add_node = onnx.helper.make_node('Add',
+                                         ['a', 'b'], ['c'], name='my_add')
+        mul_node = onnx.helper.make_node('Mul',
+                                         ['c', 'a'], ['d'], name='my_mul')
+        gradient_node = onnx.helper.make_node(
+            'Gradient', ['a', 'b'],
+            ['dd_da', 'dd_db'], name='my_gradient',
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+            xs=['a', 'b'], y='d')
+
+        a = np.array(1.0).astype(np.float32)
+        b = np.array(2.0).astype(np.float32)
+        c = a + b
+        # d = a * c = a * (a + b)
+        d = a * c
+        # dd / da = d(a*a+a*b) / da = 2 * a + b
+        dd_da = (2 * a + b).astype(np.float32)
+        # dd / db = d(a*a+a*b) / db = a
+        dd_db = a
+
+        graph = onnx.helper.make_graph(
+            nodes=[add_node, mul_node, gradient_node],
+            name='GradientOfTwoOperators',
+            inputs=[
+                onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
+                                                   []),
+                onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
+                                                   [])],
+            outputs=[
+                onnx.helper.make_tensor_value_info('d', onnx.TensorProto.FLOAT,
+                                                   []),
+                onnx.helper.make_tensor_value_info('dd_da',
+                                                   onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info('dd_db',
+                                                   onnx.TensorProto.FLOAT, [])])
+
+        opsets = [
+            onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+            onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]
+        model = onnx.helper.make_model(graph,
+            producer_name='backend-test',
+            opset_imports=opsets)
+        expect(model, inputs=[a, b], outputs=[d, dd_da, dd_db],
+               name='test_gradient_of_add_and_mul')
diff --git a/third_party/onnx/onnx/backend/test/case/model/sequence.py b/third_party/onnx/onnx/backend/test/case/model/sequence.py
new file mode 100644
index 0000000000..8def139ee1
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/sequence.py
@@ -0,0 +1,355 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import numpy as np  # type: ignore
+
+import onnx
+import typing
+from ..base import Base
+from . import expect
+from onnx import TensorProto
+from typing import List, Optional, Text, Union
+
+
+def SequenceEmptyImpl() -> List[Optional[np.ndarray]]:
+    return []
+
+
+def SequenceConstructImpl(*tensors: np.ndarray) -> List[np.ndarray]:
+    return list(tensors)
+
+
+def SequenceInsertImpl(sequence: List[np.ndarray], tensor: np.ndarray, position: Optional[int] = None) -> List[np.ndarray]:
+    if position is None:
+        position = len(sequence)
+    sequence.insert(position, tensor)
+    return sequence
+
+
+def SequenceAtImpl(sequence: List[np.ndarray], position: int) -> np.ndarray:
+    return sequence[position]
+
+
+def SequenceEraseImpl(sequence: List[np.ndarray], position: Optional[int] = None) -> List[Optional[np.ndarray]]:
+    if position is None:
+        position = -1
+    del sequence[position]
+    return sequence
+
+
+def SequenceLengthImpl(sequence: List[np.ndarray]) -> np.int64:
+    return np.int64(len(sequence))
+
+
+def SplitToSequenceImpl(tensor: np.ndarray, split: Optional[Union[int, List[int]]] = None, axis: int = 0, keepdims: int = 1) -> List[np.ndarray]:
+    dim_size = tensor.shape[axis]
+    if split is None:
+        split = 1
+        split_indices = [i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size]
+        if not keepdims:
+            results = np.array_split(tensor, split_indices, axis)
+            return [np.squeeze(res, axis) for res in results]
+    if np.isscalar(split):
+        split_indices = [i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size]  # type: ignore
+    else:
+        split_indices = np.cumsum(split) + 1
+    return np.array_split(tensor, split_indices, axis)  # type: ignore
+
+
+def ConcatFromSequenceImpl(sequence: List[np.ndarray], axis: int, new_axis: Optional[int] = 0) -> np.ndarray:
+    if not new_axis:
+        return np.concatenate(sequence, axis)
+    else:
+        return np.stack(sequence, axis)
+
+
+class Sequence(Base):
+
+    @staticmethod
+    def export() -> None:
+
+        def make_graph(
+                nodes: List[onnx.helper.NodeProto],
+                input_shapes: List[Optional[typing.Sequence[Union[Text, int]]]],
+                output_shapes: List[Optional[typing.Sequence[Union[Text, int]]]],
+                input_names: List[Text],
+                output_names: List[Text],
+                input_types: List[TensorProto.DataType],
+                output_types: List[TensorProto.DataType],
+                initializers: Optional[List[TensorProto]] = None
+        ) -> onnx.helper.GraphProto:
+            graph = onnx.helper.make_graph(
+                nodes=nodes,
+                name='Sequence',
+                inputs=[
+                    onnx.helper.make_tensor_value_info(
+                        name,
+                        input_type,
+                        input_shape)
+                    for name, input_type, input_shape in zip(input_names, input_types, input_shapes)],
+                outputs=[
+                    onnx.helper.make_tensor_value_info(
+                        name,
+                        output_type,
+                        output_shape)
+                    for name, output_type, output_shape in zip(output_names, output_types, output_shapes)],
+                initializer=initializers)
+            return graph
+
+        #1st testcase - insert and at.
+        # 1. SequenceEmpty:         -> []
+        # 2. SequenceInsert(x):     -> [x]
+        # 3. SequenceInsert(y):     -> [x, y]
+        # 4. SequenceInsert(z, 1):  -> [x, z, y]
+        # 5. SequenceAt(2):         -> y
+        seq_empty_node = onnx.helper.make_node('SequenceEmpty', [], ['Seq_empty'])
+        seq_insert_node = onnx.helper.make_node('SequenceInsert', ['Seq_empty', 'X'], ['Seq_1'])
+        seq_insert_node2 = onnx.helper.make_node('SequenceInsert', ['Seq_1', 'Y'], ['Seq_2'])
+        seq_insert_node3 = onnx.helper.make_node('SequenceInsert', ['Seq_2', 'Z', 'pos'], ['Seq_3'])
+        seq_at_node = onnx.helper.make_node('SequenceAt', ['Seq_3', 'pos_at'], ['out'])
+
+        x_shape = [2, 3, 4]
+        y_shape = [1, 3, 4]
+        z_shape = [3, 3, 4]
+        out_shape = [None, 3, 4]
+
+        x = np.ones(x_shape, dtype=np.float32)
+        y = np.zeros(y_shape, dtype=np.float32)
+        z = np.ones(z_shape, dtype=np.float32) * 2
+        pos_val = 1
+        pos_at_val = 2
+
+        out = SequenceEmptyImpl()
+        out = SequenceInsertImpl(out, x)
+        out = SequenceInsertImpl(out, y)
+        out = SequenceInsertImpl(out, z, pos_val)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, y)
+
+        pos = onnx.helper.make_tensor('pos', TensorProto.INT64, (), (pos_val, ))
+        pos_at = onnx.helper.make_tensor('pos_at', TensorProto.INT64, (), (pos_at_val, ))
+
+        graph = make_graph(
+            [seq_empty_node, seq_insert_node, seq_insert_node2, seq_insert_node3, seq_at_node],
+            [x_shape, y_shape, z_shape, [], []],  # type: ignore
+            [out_shape],  # type: ignore
+            ['X', 'Y', 'Z', 'pos', 'pos_at'],
+            ['out'],
+            [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2,  # type: ignore
+            [onnx.TensorProto.FLOAT],
+            [pos, pos_at])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model1")
+
+        #2nd testcase - erase and at.
+        # 1. SequenceConstruct(x, y, z):    -> [x, y, z]
+        # 2. SequenceErase(1):              -> [x, z]
+        # 3. SequenceAt(1):                 -> z
+        seq_construct_node = onnx.helper.make_node('SequenceConstruct', ['X', 'Y', 'Z'], ['seq_1'])
+        seq_erase_node = onnx.helper.make_node('SequenceErase', ['seq_1', 'pos_erase'], ['seq_2'])
+        seq_at_node = onnx.helper.make_node('SequenceAt', ['seq_2', 'pos_at'], ['out'])
+
+        tensor_shape = [2, 3, 4]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        pos_erase_val = 1
+        pos_at_val = 1
+
+        out = SequenceConstructImpl(x, y, z)
+        out = SequenceEraseImpl(out, pos_erase_val)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, z)
+
+        pos_erase = onnx.helper.make_tensor('pos_erase', TensorProto.INT64, (), (pos_erase_val, ))
+        pos_at = onnx.helper.make_tensor('pos_at', TensorProto.INT64, (), (pos_at_val, ))
+
+        graph = make_graph(
+            [seq_construct_node, seq_erase_node, seq_at_node],
+            [tensor_shape, tensor_shape, tensor_shape, [], []],  # type: ignore
+            [tensor_shape],  # type: ignore
+            ['X', 'Y', 'Z', 'pos_erase', 'pos_at'],
+            ['out'],
+            [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2,  # type: ignore
+            [onnx.TensorProto.FLOAT],
+            [pos_erase, pos_at])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model2")
+
+        #3rd testcase - erase, insert and at, with negative index value.
+        # 1. SequenceConstruct(x, y, z):    -> [x, y, z]
+        # 2. SequenceErase(-3):             -> [y, z]
+        # 3. SequenceInsert(x, -1):         -> [y, x, z]
+        # 4. SequenceAt(-1):                -> z
+        seq_construct_node = onnx.helper.make_node('SequenceConstruct', ['X', 'Y', 'Z'], ['seq_1'])
+        seq_erase_node = onnx.helper.make_node('SequenceErase', ['seq_1', 'pos_erase'], ['seq_2'])
+        seq_insert_node = onnx.helper.make_node('SequenceInsert', ['seq_2', 'X', 'pos_insert'], ['seq_3'])
+        seq_at_node = onnx.helper.make_node('SequenceAt', ['seq_3', 'pos_at'], ['out'])
+
+        tensor_shape = [2, 3, 4]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        pos_erase_val = -3
+        pos_insert_val = -1
+        pos_at_val = -1
+        out = SequenceConstructImpl(x, y, z)
+        out = SequenceEraseImpl(out, pos_erase_val)
+        out = SequenceInsertImpl(out, x, pos_insert_val)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, z)
+
+        pos_erase = onnx.helper.make_tensor('pos_erase', TensorProto.INT64, (), (pos_erase_val, ))
+        pos_insert = onnx.helper.make_tensor('pos_insert', TensorProto.INT64, (), (pos_insert_val, ))
+        pos_at = onnx.helper.make_tensor('pos_at', TensorProto.INT64, (), (pos_at_val, ))
+
+        graph = make_graph(
+            [seq_construct_node, seq_erase_node, seq_insert_node, seq_at_node],
+            [tensor_shape, tensor_shape, tensor_shape, [], [], []],  # type: ignore
+            [tensor_shape],  # type: ignore
+            ['X', 'Y', 'Z', 'pos_erase', 'pos_insert', 'pos_at'],
+            ['out'],
+            [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 3,  # type: ignore
+            [onnx.TensorProto.FLOAT],
+            [pos_erase, pos_insert, pos_at])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model3")
+
+        #4th testcase - concat
+        seq_construct_node = onnx.helper.make_node('SequenceConstruct', ['X', 'Y', 'Z'], ['seq_1'])
+        seq_concat_node = onnx.helper.make_node('ConcatFromSequence', ['seq_1'], ['out'], axis=1)
+
+        tensor_shape = [2, 3, 4]
+        concat_out_shape = [2, None, 4]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        out = SequenceConstructImpl(x, y, z)
+        concat_out = ConcatFromSequenceImpl(out, 1)
+
+        graph = make_graph(
+            [seq_construct_node, seq_concat_node],
+            [tensor_shape] * 3,  # type: ignore
+            [concat_out_shape],  # type: ignore
+            ['X', 'Y', 'Z'],
+            ['out'],
+            [onnx.TensorProto.FLOAT] * 3,  # type: ignore
+            [onnx.TensorProto.FLOAT])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model4")
+
+        #5th testcase - concat with new_axis = 1
+        seq_construct_node = onnx.helper.make_node('SequenceConstruct', ['X', 'Y', 'Z'], ['seq_1'])
+        seq_concat_node = onnx.helper.make_node('ConcatFromSequence', ['seq_1'], ['out'], axis=-1, new_axis=1)
+
+        tensor_shape = [2, 3, 4]
+        concat_out_shape = [2, 3, 4, 3]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        out = SequenceConstructImpl(x, y, z)
+        concat_out = ConcatFromSequenceImpl(out, -1, 1)
+
+        graph = make_graph(
+            [seq_construct_node, seq_concat_node],
+            [tensor_shape] * 3,  # type: ignore
+            [concat_out_shape],  # type: ignore
+            ['X', 'Y', 'Z'],
+            ['out'],
+            [onnx.TensorProto.FLOAT] * 3,  # type: ignore
+            [onnx.TensorProto.FLOAT],)
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model5")
+
+        #6th testcase - split and len
+        seq_split_node = onnx.helper.make_node('SplitToSequence', ['X'], ['seq_1'], axis=-1)
+        seq_len_node = onnx.helper.make_node('SequenceLength', ['seq_1'], ['len'])
+
+        tensor_shape = [2, 3, 4]
+        len_shape = []  # type: ignore
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        out = SplitToSequenceImpl(x, axis=-1)
+        out = SequenceLengthImpl(out)
+        assert np.array_equal(out, np.int64(4))
+
+        graph = onnx.helper.make_graph(
+            nodes=[seq_split_node, seq_len_node],
+            name='Sequence',
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    'X',
+                    onnx.TensorProto.FLOAT,
+                    tensor_shape)],
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    'len',
+                    onnx.TensorProto.INT64,
+                    len_shape)])  # type: ignore
+
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[out], name="test_sequence_model6")
+
+        #7th testcase - split with keepdims=0, and SequenceAt
+        seq_split_node = onnx.helper.make_node('SplitToSequence', ['X'], ['seq_1'], axis=0, keepdims=0)
+        seq_at_node = onnx.helper.make_node('SequenceAt', ['seq_1', 'pos_at'], ['out'])
+
+        tensor_shape = [2, 3, 4]
+        out_shape = [3, 4]
+
+        x = np.random.rand(*tensor_shape)
+        pos_at_val = 1
+        out = SplitToSequenceImpl(x, axis=0, keepdims=0)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, x[pos_at_val])
+
+        pos_at = onnx.helper.make_tensor('pos_at', TensorProto.INT64, (), (pos_at_val, ))
+
+        graph = make_graph(
+            [seq_split_node, seq_at_node],
+            [tensor_shape, []],  # type: ignore
+            [out_shape],  # type: ignore
+            ['X', 'pos_at'],
+            ['out'],
+            [onnx.TensorProto.DOUBLE, onnx.TensorProto.INT64],
+            [onnx.TensorProto.DOUBLE],
+            [pos_at])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[out], name="test_sequence_model7")
+
+        #8th testcase - split zero length
+        seq_split_node = onnx.helper.make_node('SplitToSequence', ['X', 'Splits'], ['seq_1'])
+        seq_len_node = onnx.helper.make_node('SequenceLength', ['seq_1'], ['len'])
+
+        tensor_shape = ['n']  # type: ignore
+        splits_shape = [3]  # type: ignore
+
+        x = np.array([]).astype(np.float32)
+        splits = np.array([0, 0, 0]).astype(np.int64)
+        out_len = np.int64(3)
+
+        graph = onnx.helper.make_graph(
+            nodes=[seq_split_node, seq_len_node],
+            name='Sequence',
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    'X',
+                    onnx.TensorProto.FLOAT,
+                    tensor_shape),  # type: ignore
+                onnx.helper.make_tensor_value_info(
+                    'Splits',
+                    onnx.TensorProto.INT64,
+                    splits_shape)],  # type: ignore
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    'len',
+                    onnx.TensorProto.INT64,
+                    len_shape)])  # type: ignore
+
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x, splits], outputs=[out_len], name="test_sequence_model8")
diff --git a/third_party/onnx/onnx/backend/test/case/model/shrink.py b/third_party/onnx/onnx/backend/test/case/model/shrink.py
new file mode 100644
index 0000000000..0fbd25e177
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/shrink.py
@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ShrinkTest(Base):
+
+    @staticmethod
+    def export() -> None:
+
+        node = onnx.helper.make_node(
+            'Shrink', ['x'], ['y'], lambd=1.5, bias=1.5,)
+        graph = onnx.helper.make_graph(
+            nodes=[node],
+            name='Shrink',
+            inputs=[onnx.helper.make_tensor_value_info(
+                'x', onnx.TensorProto.FLOAT, [5])],
+            outputs=[onnx.helper.make_tensor_value_info(
+                'y', onnx.TensorProto.FLOAT, [5])])
+        model = onnx.helper.make_model(graph,
+                                       producer_name='backend-test')
+
+        x = np.array([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=np.float32)
+        y = np.array([-0.5, 0.0, 0.0, 0.0, 0.5], dtype=np.float32)
+
+        expect(model, inputs=[x], outputs=[y],
+               name='test_shrink')
diff --git a/third_party/onnx/onnx/backend/test/case/model/sign.py b/third_party/onnx/onnx/backend/test/case/model/sign.py
new file mode 100644
index 0000000000..c3265f3043
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/sign.py
@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from typing import Sequence
+
+
+class SingleSign(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sign', ['x'], ['y'], name='test')
+
+        x = np.array([-1.0, 4.5, -4.5, 3.1, 0.0, 2.4, -5.5]).astype(np.float32)
+        y = np.array([-1.0, 1.0, -1.0, 1.0, 0.0, 1.0, -1.0]).astype(np.float32)
+
+        graph = onnx.helper.make_graph(
+            nodes=[node],
+            name='SingleSign',
+            inputs=[onnx.helper.make_tensor_value_info('x',
+                                                       onnx.TensorProto.FLOAT,
+                                                       [7])],
+            outputs=[onnx.helper.make_tensor_value_info('y',
+                                                        onnx.TensorProto.FLOAT,
+                                                        [7])])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y],
+               name='test_sign_model')
diff --git a/third_party/onnx/onnx/backend/test/case/model/single-relu.py b/third_party/onnx/onnx/backend/test/case/model/single-relu.py
new file mode 100644
index 0000000000..73c981c2e5
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/single-relu.py
@@ -0,0 +1,30 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class SingleRelu(Base):
+
+    @staticmethod
+    def export() -> None:
+
+        node = onnx.helper.make_node(
+            'Relu', ['x'], ['y'], name='test')
+        graph = onnx.helper.make_graph(
+            nodes=[node],
+            name='SingleRelu',
+            inputs=[onnx.helper.make_tensor_value_info(
+                'x', onnx.TensorProto.FLOAT, [1, 2])],
+            outputs=[onnx.helper.make_tensor_value_info(
+                'y', onnx.TensorProto.FLOAT, [1, 2])])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+
+        x = np.random.randn(1, 2).astype(np.float32)
+        y = np.maximum(x, 0)
+
+        expect(model, inputs=[x], outputs=[y],
+               name='test_single_relu_model')
diff --git a/third_party/onnx/onnx/backend/test/case/model/stringnormalizer.py b/third_party/onnx/onnx/backend/test/case/model/stringnormalizer.py
new file mode 100644
index 0000000000..27be340a1b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/model/stringnormalizer.py
@@ -0,0 +1,132 @@
+# SPDX-License-Identifier: Apache-2.0
+# coding: utf-8
+
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from typing import Sequence
+
+
+class NormalizeStrings(Base):
+
+    @staticmethod
+    def export() -> None:
+        def make_graph(node: onnx.helper.NodeProto, input_shape: Sequence[int], output_shape: Sequence[int]) -> onnx.helper.GraphProto:
+            graph = onnx.helper.make_graph(
+                nodes=[node],
+                name='StringNormalizer',
+                inputs=[onnx.helper.make_tensor_value_info('x',
+                                                            onnx.TensorProto.STRING,
+                                                            input_shape)],
+                outputs=[onnx.helper.make_tensor_value_info('y',
+                                                             onnx.TensorProto.STRING,
+                                                             output_shape)])
+            return graph
+
+        #1st model_monday_casesensintive_nochangecase
+        stopwords = [u'monday']
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+
+        x = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+        y = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+
+        graph = make_graph(node, [4], [3])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_casesensintive_nochangecase")
+
+        #2nd model_nostopwords_nochangecase
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            is_case_sensitive=1
+        )
+
+        x = np.array([u'monday', u'tuesday']).astype(object)
+        y = x
+
+        graph = make_graph(node, [2], [2])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y], name="test_strnorm_model_nostopwords_nochangecase")
+
+        # 3rd model_monday_casesensintive_lower
+        stopwords = [u'monday']
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='LOWER',
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+
+        x = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+        y = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+
+        graph = make_graph(node, [4], [3])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_casesensintive_lower")
+
+        #4 model_monday_casesensintive_upper
+        stopwords = [u'monday']
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='UPPER',
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+
+        x = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+        y = np.array([u'TUESDAY', u'WEDNESDAY', u'THURSDAY']).astype(object)
+
+        graph = make_graph(node, [4], [3])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_casesensintive_upper")
+
+        #5 monday_insensintive_upper_twodim
+        stopwords = [u'monday']
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='UPPER',
+            stopwords=stopwords
+        )
+
+        input_shape = [1, 6]
+        output_shape = [1, 4]
+        x = np.array([u'Monday', u'tuesday', u'wednesday', u'Monday', u'tuesday', u'wednesday']).astype(object).reshape(input_shape)
+        y = np.array([u'TUESDAY', u'WEDNESDAY', u'TUESDAY', u'WEDNESDAY']).astype(object).reshape(output_shape)
+
+        graph = make_graph(node, input_shape, output_shape)
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_insensintive_upper_twodim")
+
+        #6 monday_empty_output
+        stopwords = [u'monday']
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='UPPER',
+            is_case_sensitive=0,
+            stopwords=stopwords
+        )
+
+        x = np.array([u'monday', u'monday']).astype(object)
+        y = np.array([u'']).astype(object)
+
+        graph = make_graph(node, [2], [1])
+        model = onnx.helper.make_model(graph, producer_name='backend-test')
+        expect(model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_empty_output")
diff --git a/third_party/onnx/onnx/backend/test/case/node/__init__.py b/third_party/onnx/onnx/backend/test/case/node/__init__.py
new file mode 100644
index 0000000000..363dedaeec
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/__init__.py
@@ -0,0 +1,252 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+import re
+
+from typing import Callable, List, Text, Sequence, Any, Union, Optional, Dict
+import numpy as np  # type: ignore
+
+import onnx
+import onnx.mapping
+import onnx.helper
+
+from ..utils import import_recursive
+from ..test_case import TestCase
+
+
+_NodeTestCases = []
+_TargetOpType = ""
+
+from onnx.onnx_pb import NodeProto, AttributeProto, TypeProto, FunctionProto, GraphProto
+
+
+def _rename_edges_helper(internal_node: NodeProto,
+                         rename_helper: Callable[[Text], Text],
+                         attribute_map: Dict[Text, AttributeProto],
+                         prefix: Text) -> NodeProto:
+    new_node = NodeProto()
+    new_node.CopyFrom(internal_node)
+    new_node.ClearField("input")
+    new_node.ClearField("output")
+    new_node.ClearField("attribute")
+    for internal_name in internal_node.input:
+        new_node.input.append(rename_helper(internal_name))
+    for internal_name in internal_node.output:
+        new_node.output.append(rename_helper(internal_name))
+    for attr in internal_node.attribute:
+        if attr.HasField("ref_attr_name"):
+            if attr.ref_attr_name in attribute_map:
+                new_attr = AttributeProto()
+                new_attr.CopyFrom(attribute_map[attr.ref_attr_name])  # type: ignore
+                new_attr.name = attr.name
+                new_node.attribute.extend([new_attr])
+        else:
+            new_attr = AttributeProto()
+            new_attr.CopyFrom(attr)
+            if attr.type == AttributeProto.GRAPH:
+                new_graph = new_attr.g
+                sg_rename = {}
+                for in_desc in new_graph.input:
+                    sg_rename[in_desc.name] = in_desc.name = prefix + in_desc.name
+                for out_desc in new_graph.output:
+                    sg_rename[out_desc.name] = out_desc.name = prefix + out_desc.name
+                for init_desc in new_graph.initializer:
+                    sg_rename[init_desc.name] = init_desc.name = prefix + init_desc.name
+                for sparse_init_desc in new_graph.sparse_initializer:
+                    sg_rename[sparse_init_desc.values.name] = sparse_init_desc.values.name = prefix + \
+                        sparse_init_desc.values.name
+                for sparse_init_desc in new_graph.sparse_initializer:
+                    sg_rename[sparse_init_desc.indices.name] = sparse_init_desc.indices.name = prefix + \
+                        sparse_init_desc.indices.name
+
+                def subgraph_rename_helper(name: Text) -> Any:
+                    if name in sg_rename:
+                        return sg_rename[name]
+                    else:
+                        return rename_helper(name)
+                new_nodes = [
+                    _rename_edges_helper(node_desc, subgraph_rename_helper, attribute_map, prefix)
+                    for node_desc in new_graph.node
+                ]
+                new_graph.ClearField("node")
+                new_graph.node.extend(new_nodes)
+            new_node.attribute.extend([new_attr])
+    return new_node
+
+
+# FIXME(TMVector): Any reason we can't get rid of this and use the C++ helper directly?
+def function_expand_helper(node: NodeProto,
+                           function_proto: FunctionProto,
+                           op_prefix: Text
+                           ) -> List[NodeProto]:
+    io_names_map = dict()
+    attribute_map = dict((a.name, a) for a in node.attribute)
+
+    for idx in range(len(function_proto.input)):
+        io_names_map[function_proto.input[idx]] = node.input[idx] \
+            if idx in range(len(node.input)) else ""
+
+    for idx in range(len(function_proto.output)):
+        # Even if the node has been created with optional outputs missing, we
+        # can't assume that the function body handles this correctly, such as in
+        # the case that output is also an intermediate value.
+        # So we only add a name mapping if the output is present. An internal
+        # name will be generated if the missing output is used, the same as any
+        # other internal tensor.
+        if idx in range(len(node.output)) and node.output[idx] != "":
+            io_names_map[function_proto.output[idx]] = node.output[idx]
+
+    def rename_helper(internal_name: Text) -> Any:
+        if internal_name in io_names_map:
+            return io_names_map[internal_name]
+        elif internal_name == '':
+            return ''
+        else:
+            return op_prefix + internal_name
+
+    new_node_list = [
+        _rename_edges_helper(internal_node, rename_helper, attribute_map, op_prefix)
+        for internal_node in function_proto.node
+    ]
+    return new_node_list
+
+
+def function_testcase_helper(node: NodeProto, input_types: List[TypeProto], name: Text) -> List[NodeProto]:
+    test_op = node.op_type
+    op_prefix = test_op + "_" + name + "_expanded_function_"
+    schema = onnx.defs.get_schema(test_op, node.domain)
+
+    if schema.has_function:    # type: ignore
+        function_proto = schema.function_body  # type: ignore
+    elif schema.has_context_dependent_function:    # type: ignore
+        function_proto_str = schema.get_context_dependent_function(node.SerializeToString(), [t.SerializeToString() for t in input_types])  # type: ignore
+        function_proto = FunctionProto()
+        function_proto.ParseFromString(function_proto_str)
+    else:
+        return []
+
+    for attr in schema.attributes:
+        if attr in [a.name for a in node.attribute]:
+            continue
+        if schema.attributes[attr].default_value:
+            node.attribute.extend([schema.attributes[attr].default_value])
+
+    # function_proto.attributes
+    node_list = function_expand_helper(node, function_proto, op_prefix)
+    return node_list
+
+
+def _extract_value_info(input: Union[List[Any], np.ndarray, None], name: Text, type_proto: Optional[TypeProto] = None) -> onnx.ValueInfoProto:
+    if type_proto is None:
+        if input is None:
+            raise NotImplementedError("_extract_value_info: both input and type_proto arguments cannot be None.")
+        elif isinstance(input, list):
+            elem_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[input[0].dtype]
+            shape = None
+            tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
+            type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
+        else:
+            elem_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[input.dtype]
+            shape = input.shape
+            type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
+
+    return onnx.helper.make_value_info(name, type_proto)
+
+
+# In the case of ops with optional inputs and outputs, node.input and node.output indicate
+# which inputs/outputs are present and which are omitted. However, the parameter inputs
+# and outputs of this function include values only for inputs/outputs that are present.
+# E.g., for an op with 3 inputs, if the second parameter is optional and we wish to omit it,
+# node.inputs would look like ["Param1", "", "Param3"], while inputs would look like
+# [input-1-value, input-3-value]
+def expect(node: onnx.NodeProto,
+           inputs: Sequence[np.ndarray],
+           outputs: Sequence[np.ndarray],
+           name: Text,
+           **kwargs: Any
+           ) -> None:
+    # skip if the node's op_type is not same as the given one
+    if _TargetOpType and node.op_type != _TargetOpType:
+        return
+    present_inputs = [x for x in node.input if (x != '')]
+    present_outputs = [x for x in node.output if (x != '')]
+    input_type_protos = [None] * len(inputs)
+    if 'input_type_protos' in kwargs:
+        input_type_protos = kwargs[str('input_type_protos')]
+        del kwargs[str('input_type_protos')]
+    output_type_protos = [None] * len(outputs)
+    if 'output_type_protos' in kwargs:
+        output_type_protos = kwargs[str('output_type_protos')]
+        del kwargs[str('output_type_protos')]
+    inputs_vi = [_extract_value_info(arr, arr_name, input_type)
+                 for arr, arr_name, input_type in zip(inputs, present_inputs, input_type_protos)]
+    outputs_vi = [_extract_value_info(arr, arr_name, output_type)
+                  for arr, arr_name, output_type in zip(outputs, present_outputs, output_type_protos)]
+    graph = onnx.helper.make_graph(
+        nodes=[node],
+        name=name,
+        inputs=inputs_vi,
+        outputs=outputs_vi)
+    kwargs[str('producer_name')] = 'backend-test'
+    model = onnx.helper.make_model(graph, **kwargs)
+
+    _NodeTestCases.append(TestCase(
+        name=name,
+        model_name=name,
+        url=None,
+        model_dir=None,
+        model=model,
+        data_sets=[(inputs, outputs)],
+        kind='node',
+        rtol=1e-3,
+        atol=1e-7,
+    ))
+
+    # Create list of types for node.input, filling a default TypeProto for missing inputs:
+    # E.g. merge(["x", "", "y"], [x-value-info, y-value-info]) will return [x-type, default-type, y-type]
+    def merge(node_inputs: List[Text], present_value_info: List[onnx.ValueInfoProto]) -> List[TypeProto]:
+        if (node_inputs):
+            if (node_inputs[0] != ''):
+                return [present_value_info[0].type] + merge(node_inputs[1:], present_value_info[1:])
+            else:
+                return [TypeProto()] + merge(node_inputs[1:], present_value_info)
+        return []
+    merged_types = merge(list(node.input), inputs_vi)
+    expanded_function_nodes = function_testcase_helper(node, merged_types, name)
+    if expanded_function_nodes:
+        function_test_name = name + '_expanded'
+        graph = onnx.helper.make_graph(
+            nodes=expanded_function_nodes,
+            name=function_test_name,
+            inputs=inputs_vi,
+            outputs=outputs_vi)
+        kwargs[str('producer_name')] = 'backend-test'
+        model = onnx.helper.make_model(graph, **kwargs)
+        _NodeTestCases.append(TestCase(
+            name=function_test_name,
+            model_name=function_test_name,
+            url=None,
+            model_dir=None,
+            model=model,
+            data_sets=[(inputs, outputs)],
+            kind='node',
+            rtol=1e-3,
+            atol=1e-7,
+        ))
+
+
+def collect_testcases() -> List[TestCase]:
+    '''Collect node test cases defined in python/numpy code.
+    '''
+    import_recursive(sys.modules[__name__])
+    return _NodeTestCases
+
+
+def collect_testcases_by_operator(op_type: Text) -> List[TestCase]:
+    '''Collect node test cases which include specific operator
+    '''
+    # only keep those tests related to this operator
+    global _TargetOpType
+    _TargetOpType = op_type
+    import_recursive(sys.modules[__name__])
+    return _NodeTestCases
diff --git a/third_party/onnx/onnx/backend/test/case/node/abs.py b/third_party/onnx/onnx/backend/test/case/node/abs.py
new file mode 100644
index 0000000000..5f00283a9f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/abs.py
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+from onnx.backend.sample.ops.abs import abs
+
+
+class Abs(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Abs',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = abs(x)
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_abs')
diff --git a/third_party/onnx/onnx/backend/test/case/node/acos.py b/third_party/onnx/onnx/backend/test/case/node/acos.py
new file mode 100644
index 0000000000..6dd4af8015
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/acos.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Acos(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Acos',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        y = np.arccos(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_acos_example')
+
+        x = np.random.rand(3, 4, 5).astype(np.float32)
+        y = np.arccos(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_acos')
diff --git a/third_party/onnx/onnx/backend/test/case/node/acosh.py b/third_party/onnx/onnx/backend/test/case/node/acosh.py
new file mode 100644
index 0000000000..820ebd18ad
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/acosh.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Acosh(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Acosh',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([10, np.e, 1]).astype(np.float32)
+        y = np.arccosh(x)  # expected output [2.99322295,  1.65745449,  0.]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_acosh_example')
+
+        x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32)
+        y = np.arccosh(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_acosh')
diff --git a/third_party/onnx/onnx/backend/test/case/node/adagrad.py b/third_party/onnx/onnx/backend/test/case/node/adagrad.py
new file mode 100644
index 0000000000..c98eb85793
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/adagrad.py
@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx.defs import ONNX_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN
+from ..base import Base
+from . import expect
+
+
+def apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor):  # type: ignore
+    # Compute adjusted learning-rate.
+    r_ = r / (1 + t * decay_factor)
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Update squared accumulated gradient.
+    h_new = h + g_regularized * g_regularized
+    # Compute ADAGRAD's gradient scaling factors
+    h_sqrt = np.sqrt(h_new) + epsilon
+    # Apply ADAGRAD update rule.
+    x_new = x - r_ * g_regularized / h_sqrt
+    return (x_new, h_new)
+
+
+class Adagrad(Base):
+
+    @staticmethod
+    def export_adagrad() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        epsilon = 1e-5
+        decay_factor = 0.1
+
+        # Create operator.
+        node = onnx.helper.make_node('Adagrad',
+                                     inputs=['R', 'T', 'X', 'G', 'H'],
+                                     outputs=['X_new', 'H_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     epsilon=epsilon,
+                                     decay_factor=decay_factor,
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.0], dtype=np.float32)
+        g = np.array([-1.0], dtype=np.float32)
+        h = np.array([2.0], dtype=np.float32)
+
+        # Compute expected outputs of Adagrad.
+        x_new, h_new = apply_adagrad(r, t, x, g, h,
+                                     norm_coefficient, epsilon, decay_factor)
+
+        # Check results.
+        expect(node, inputs=[r, t, x, g, h],
+               outputs=[x_new, h_new], name='test_adagrad',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    @staticmethod
+    def export_adagrad_multiple() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        epsilon = 1e-5
+        decay_factor = 0.1
+
+        node = onnx.helper.make_node('Adagrad',
+                                     inputs=['R', 'T', 'X1', 'X2',
+                                             'G1', 'G2', 'H1', 'H2'],
+                                     outputs=['X1_new', 'X2_new',
+                                              'H1_new', 'H2_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     epsilon=epsilon,
+                                     decay_factor=decay_factor,
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+
+        x1 = np.array([1.0], dtype=np.float32)
+        g1 = np.array([-1.0], dtype=np.float32)
+        h1 = np.array([2.0], dtype=np.float32)
+
+        x2 = np.array([1.0, 2.0], dtype=np.float32)
+        g2 = np.array([-1.0, -3.0], dtype=np.float32)
+        h2 = np.array([4.0, 1.0], dtype=np.float32)
+
+        # Compute expected outputs of Adagrad.
+        x1_new, h1_new = apply_adagrad(r, t, x1, g1, h1,
+                                       norm_coefficient, epsilon, decay_factor)
+        x2_new, h2_new = apply_adagrad(r, t, x2, g2, h2,
+                                       norm_coefficient, epsilon, decay_factor)
+
+        # Check results.
+        expect(node, inputs=[r, t, x1, x2, g1, g2, h1, h2],
+               outputs=[x1_new, x2_new, h1_new, h2_new], name='test_adagrad_multiple',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/adam.py b/third_party/onnx/onnx/backend/test/case/node/adam.py
new file mode 100644
index 0000000000..ed947bf48e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/adam.py
@@ -0,0 +1,121 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx.defs import ONNX_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN
+from ..base import Base
+from . import expect
+
+
+def apply_adam(r, t, x, g, v, h, norm_coefficient, norm_coefficient_post, alpha, beta, epsilon):  # type: ignore
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Update momentum.
+    v_new = alpha * v + (1 - alpha) * g_regularized
+    # Update second-order momentum.
+    h_new = beta * h + (1 - beta) * (g_regularized * g_regularized)
+    # Compute element-wise square root.
+    h_sqrt = np.sqrt(h_new) + epsilon
+    # Adjust learning rate.
+    r_adjusted = None
+    if t > 0:
+        # Consider bias correction on momentums.
+        r_adjusted = r * np.sqrt(1 - beta**t) / (1 - alpha**t)
+    else:
+        # No bias correction on momentums.
+        r_adjusted = r
+    # Apply Adam update rule.
+    x_new = x - r_adjusted * (v_new / h_sqrt)
+    # It's possible to apply regularization in the end.
+    x_final = (1 - norm_coefficient_post) * x_new
+    return x_final, v_new, h_new
+
+
+class Adam(Base):
+
+    @staticmethod
+    def export_adam() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.1
+        epsilon = 1e-7
+
+        # Create operator.
+        node = onnx.helper.make_node('Adam',
+                                     inputs=['R', 'T', 'X', 'G', 'V', 'H'],
+                                     outputs=['X_new', 'V_new', 'H_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     alpha=alpha,
+                                     beta=beta,
+                                     epsilon=epsilon,
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.2, 2.8], dtype=np.float32)
+        g = np.array([-0.94, -2.5], dtype=np.float32)
+        v = np.array([1.7, 3.6], dtype=np.float32)
+        h = np.array([0.1, 0.1], dtype=np.float32)
+
+        # Compute expected outputs of Adam.
+        x_new, v_new, h_new = apply_adam(r, t, x, g, v, h,
+                                         norm_coefficient, 0.0, alpha, beta,
+                                         epsilon)
+
+        # Check results.
+        expect(node, inputs=[r, t, x, g, v, h],
+               outputs=[x_new, v_new, h_new], name='test_adam',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    @staticmethod
+    def export_adam_multiple() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.85
+        epsilon = 1e-2
+
+        node = onnx.helper.make_node('Adam',
+                                     inputs=['R', 'T', 'X1', 'X2',
+                                             'G1', 'G2', 'V1', 'V2',
+                                             'H1', 'H2'],
+                                     outputs=['X1_new', 'X2_new',
+                                              'V1_new', 'V2_new',
+                                              'H1_new', 'H2_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     alpha=alpha,
+                                     beta=beta,
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+
+        x1 = np.array([1.0], dtype=np.float32)
+        g1 = np.array([-1.0], dtype=np.float32)
+        v1 = np.array([2.0], dtype=np.float32)
+        h1 = np.array([0.5], dtype=np.float32)
+
+        x2 = np.array([1.0, 2.0], dtype=np.float32)
+        g2 = np.array([-1.0, -3.0], dtype=np.float32)
+        v2 = np.array([4.0, 1.0], dtype=np.float32)
+        h2 = np.array([1.0, 10.0], dtype=np.float32)
+
+        # Compute expected outputs of Adam.
+        x1_new, v1_new, h1_new = apply_adam(r, t, x1, g1, v1, h1,
+                                    norm_coefficient, 0.0, alpha, beta,
+                                    epsilon)
+        x2_new, v2_new, h2_new = apply_adam(r, t, x2, g2, v2, h2,
+                                    norm_coefficient, 0.0, alpha, beta,
+                                    epsilon)
+
+        # Check results.
+        expect(node, inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
+               outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
+               name='test_adam_multiple',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/add.py b/third_party/onnx/onnx/backend/test/case/node/add.py
new file mode 100644
index 0000000000..40c072d4de
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/add.py
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Add(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Add',
+            inputs=['x', 'y'],
+            outputs=['sum'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        expect(node, inputs=[x, y], outputs=[x + y],
+               name='test_add')
+
+    @staticmethod
+    def export_add_uint8() -> None:
+        node = onnx.helper.make_node(
+            'Add',
+            inputs=['x', 'y'],
+            outputs=['sum'],
+        )
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        expect(node, inputs=[x, y], outputs=[x + y],
+               name='test_add_uint8')
+
+    @staticmethod
+    def export_add_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Add',
+            inputs=['x', 'y'],
+            outputs=['sum'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        expect(node, inputs=[x, y], outputs=[x + y],
+               name='test_add_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/and.py b/third_party/onnx/onnx/backend/test/case/node/and.py
new file mode 100644
index 0000000000..993132b196
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/and.py
@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class And(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'And',
+            inputs=['x', 'y'],
+            outputs=['and'],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        y = (np.random.randn(3, 4) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and2d')
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and3d')
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and4d')
+
+    @staticmethod
+    def export_and_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'And',
+            inputs=['x', 'y'],
+            outputs=['and'],
+        )
+
+        # 3d vs 1d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(5) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and_bcast3v1d')
+
+        # 3d vs 2d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(4, 5) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and_bcast3v2d')
+
+        # 4d vs 2d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and_bcast4v2d')
+
+        # 4d vs 3d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and_bcast4v3d')
+
+        # 4d vs 4d
+        x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_and_bcast4v4d')
diff --git a/third_party/onnx/onnx/backend/test/case/node/argmax.py b/third_party/onnx/onnx/backend/test/case/node/argmax.py
new file mode 100644
index 0000000000..2876636add
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/argmax.py
@@ -0,0 +1,188 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def argmax_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> (np.ndarray):
+    result = np.argmax(data, axis=axis)
+    if (keepdims == 1):
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+def argmax_use_numpy_select_last_index(data: np.ndarray, axis: int = 0, keepdims: int = True) -> (np.ndarray):
+    data = np.flip(data, axis)
+    result = np.argmax(data, axis=axis)
+    result = data.shape[axis] - result - 1
+    if keepdims:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+class ArgMax(Base):
+
+    @staticmethod
+    def export_no_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims)
+        # result: [0, 1]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims)
+        # result: [[0], [1]]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            keepdims=keepdims)
+
+        # result: [[1, 1]]
+        result = argmax_use_numpy(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmax_use_numpy(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_random')
+
+    @staticmethod
+    def export_negative_axis_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims)
+        # result: [[0], [1]]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_random')
+
+    @staticmethod
+    def export_no_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True)
+        # result: [1, 1]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_no_keepdims_random_select_last_index')
+
+    @staticmethod
+    def export_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True)
+        # result: [[1], [1]]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_keepdims_random_select_last_index')
+
+    @staticmethod
+    def export_default_axes_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            keepdims=keepdims,
+            select_last_index=True)
+
+        # result: [[1, 1]]
+        result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_default_axis_random_select_last_index')
+
+    @staticmethod
+    def export_negative_axis_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMax',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True)
+        # result: [[1], [1]]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmax_negative_axis_keepdims_random_select_last_index')
diff --git a/third_party/onnx/onnx/backend/test/case/node/argmin.py b/third_party/onnx/onnx/backend/test/case/node/argmin.py
new file mode 100644
index 0000000000..b537a3b301
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/argmin.py
@@ -0,0 +1,188 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def argmin_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> (np.ndarray):
+    result = np.argmin(data, axis=axis)
+    if (keepdims == 1):
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+def argmin_use_numpy_select_last_index(data: np.ndarray, axis: int = 0, keepdims: int = True) -> (np.ndarray):
+    data = np.flip(data, axis)
+    result = np.argmin(data, axis=axis)
+    result = data.shape[axis] - result - 1
+    if keepdims:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+class ArgMin(Base):
+
+    @staticmethod
+    def export_no_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims)
+        # The content of result is : [[1, 0]]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims)
+        # The content of result is : [[1], [0]]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            keepdims=keepdims)
+
+        # The content of result is : [[0], [0]]
+        result = argmin_use_numpy(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmin_use_numpy(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_random')
+
+    @staticmethod
+    def export_negative_axis_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims)
+        # The content of result is : [[1], [0]]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_example')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_random')
+
+    @staticmethod
+    def export_no_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True)
+        # result: [[1, 0]]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_no_keepdims_random_select_last_index')
+
+    @staticmethod
+    def export_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True)
+        # result: [[1], [0]]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_keepdims_random_select_last_index')
+
+    @staticmethod
+    def export_default_axes_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            keepdims=keepdims,
+            select_last_index=True)
+
+        # result: [[0, 0]]
+        result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_default_axis_random_select_last_index')
+
+    @staticmethod
+    def export_negative_axis_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ArgMin',
+            inputs=['data'],
+            outputs=['result'],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True)
+        # result: [[1], [0]]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_example_select_last_index')
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(node, inputs=[data], outputs=[result], name='test_argmin_negative_axis_keepdims_random_select_last_index')
diff --git a/third_party/onnx/onnx/backend/test/case/node/asin.py b/third_party/onnx/onnx/backend/test/case/node/asin.py
new file mode 100644
index 0000000000..307952c136
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/asin.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Asin(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Asin',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        y = np.arcsin(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_asin_example')
+
+        x = np.random.rand(3, 4, 5).astype(np.float32)
+        y = np.arcsin(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_asin')
diff --git a/third_party/onnx/onnx/backend/test/case/node/asinh.py b/third_party/onnx/onnx/backend/test/case/node/asinh.py
new file mode 100644
index 0000000000..27e6056866
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/asinh.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Asinh(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Asinh',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.arcsinh(x)  # expected output [-0.88137358,  0.,  0.88137358]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_asinh_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.arcsinh(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_asinh')
diff --git a/third_party/onnx/onnx/backend/test/case/node/atan.py b/third_party/onnx/onnx/backend/test/case/node/atan.py
new file mode 100644
index 0000000000..f6b6b4b71d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/atan.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Atan(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Atan',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.arctan(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_atan_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.arctan(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_atan')
diff --git a/third_party/onnx/onnx/backend/test/case/node/atanh.py b/third_party/onnx/onnx/backend/test/case/node/atanh.py
new file mode 100644
index 0000000000..d9531b2788
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/atanh.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Atanh(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Atanh',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        y = np.arctanh(x)  # expected output [-0.54930615,  0.,  0.54930615]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_atanh_example')
+
+        x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32)
+        y = np.arctanh(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_atanh')
diff --git a/third_party/onnx/onnx/backend/test/case/node/averagepool.py b/third_party/onnx/onnx/backend/test/case/node/averagepool.py
new file mode 100644
index 0000000000..1b8066768c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/averagepool.py
@@ -0,0 +1,360 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from .pool_op_common import get_pad_shape, get_output_shape, pool
+
+
+class AveragePool(Base):
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_pads() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2]
+
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[7, 7.5, 8, 8.5, 9],
+                        [9.5, 10, 10.5, 11, 11.5],
+                        [12, 12.5, 13, 13.5, 14],
+                        [14.5, 15, 15.5, 16, 16.5],
+                        [17, 17.5, 18, 18.5, 19]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads')
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_pads_count_include_pad() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2],
+            count_include_pad=1
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
+                        [4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
+                        [7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
+                        [6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
+                        [6.1200, 8.4000, 10.8000, 8.8800, 6.8400]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads_count_include_pad')
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_strides() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            strides=[2, 2]
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[4, 6],
+                        [14, 16]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_strides')
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_same_upper() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 3, 3]
+        pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            auto_pad='SAME_UPPER'
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[4, 5.5, 7],
+                        [11.5, 13, 14.5],
+                        [19, 20.5, 22]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_same_upper')
+
+    @staticmethod
+    def export_averagepool_1d_default() -> None:
+        """
+        input_shape: [1, 3, 32]
+        output_shape: [1, 3, 31]
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2],
+        )
+        x = np.random.randn(1, 3, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = [2]
+        strides = [1]
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_1d_default')
+
+    @staticmethod
+    def export_averagepool_2d_default() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_default')
+
+    @staticmethod
+    def export_averagepool_3d_default() -> None:
+        """
+        input_shape: [1, 3, 32, 32, 32]
+        output_shape: [1, 3, 31, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = [2, 2, 2]
+        strides = [1, 1, 1]
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_3d_default')
+
+    @staticmethod
+    def export_averagepool_2d_same_upper() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            auto_pad='SAME_UPPER'
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
+        pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
+        pad_top = pad_shape[0] // 2
+        pad_bottom = pad_shape[0] - pad_top
+        pad_left = pad_shape[1] // 2
+        pad_right = pad_shape[1] - pad_left
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=np.nan)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_upper')
+
+    @staticmethod
+    def export_averagepool_2d_same_lower() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            auto_pad='SAME_LOWER'
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
+        pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
+        pad_bottom = pad_shape[0] // 2
+        pad_top = pad_shape[0] - pad_bottom
+        pad_right = pad_shape[1] // 2
+        pad_left = pad_shape[1] - pad_right
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=np.nan)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_lower')
+
+    @staticmethod
+    def export_averagepool_2d_pads() -> None:
+        """
+        input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2]
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = 2
+        pad_top = 2
+        pad_right = 2
+        pad_left = 2
+        pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+        out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=np.nan)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads')
+
+    @staticmethod
+    def export_averagepool_2d_pads_count_include_pad() -> None:
+        """
+        input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2],
+            count_include_pad=1,
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = 2
+        pad_top = 2
+        pad_right = 2
+        pad_left = 2
+        pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+        out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=0)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG', count_include_pad=1)
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads_count_include_pad')
+
+    @staticmethod
+    def export_averagepool_2d_strides() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 10, 10]
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[5, 5],
+            strides=[3, 3]
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (5, 5)
+        strides = (3, 3)
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_strides')
+
+    @staticmethod
+    def export_averagepool_2d_ceil() -> None:
+        """
+        input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            'AveragePool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            ceil_mode=True
+        )
+        x = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]]).astype(np.float32)
+        y = np.array([[[
+            [6, 7.5],
+            [12, 13.5]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_ceil')
diff --git a/third_party/onnx/onnx/backend/test/case/node/batchnorm.py b/third_party/onnx/onnx/backend/test/case/node/batchnorm.py
new file mode 100644
index 0000000000..a6831e7548
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/batchnorm.py
@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def _batchnorm_test_mode(x, s, bias, mean, var, epsilon=1e-5):  # type: ignore
+    dims_x = len(x.shape)
+    dim_ones = (1,) * (dims_x - 2)
+    s = s.reshape(-1, *dim_ones)
+    bias = bias.reshape(-1, *dim_ones)
+    mean = mean.reshape(-1, *dim_ones)
+    var = var.reshape(-1, *dim_ones)
+    return s * (x - mean) / np.sqrt(var + epsilon) + bias
+
+
+def _batchnorm_training_mode(x, s, bias, mean, var, momentum=0.9, epsilon=1e-5):  # type: ignore
+    axis = tuple(np.delete(np.arange(len(x.shape)), 1))
+    saved_mean = x.mean(axis=axis)
+    saved_var = x.var(axis=axis)
+    output_mean = mean * momentum + saved_mean * (1 - momentum)
+    output_var = var * momentum + saved_var * (1 - momentum)
+    y = _batchnorm_test_mode(x, s, bias, saved_mean, saved_var, epsilon=epsilon)
+    return y.astype(np.float32), output_mean, output_var
+
+
+class BatchNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'BatchNormalization',
+            inputs=['x', 's', 'bias', 'mean', 'var'],
+            outputs=['y'],
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
+               name='test_batchnorm_example')
+
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        epsilon = 1e-2
+        y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'BatchNormalization',
+            inputs=['x', 's', 'bias', 'mean', 'var'],
+            outputs=['y'],
+            epsilon=epsilon,
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
+               name='test_batchnorm_epsilon')
+
+    @staticmethod
+    def export_train() -> None:
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        # using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'"
+        # working around by using np.byte(1).astype(bool)
+        training_mode = 1
+        y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var)
+
+        node = onnx.helper.make_node(
+            'BatchNormalization',
+            inputs=['x', 's', 'bias', 'mean', 'var'],
+            outputs=['y', 'output_mean', 'output_var'],
+            training_mode=training_mode
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(node, inputs=[x, s, bias, mean, var],
+               outputs=[y, output_mean, output_var],
+               name='test_batchnorm_example_training_mode')
+
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        training_mode = 1
+        momentum = 0.9
+        epsilon = 1e-2
+        y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var, momentum,
+                                                              epsilon)
+
+        node = onnx.helper.make_node(
+            'BatchNormalization',
+            inputs=['x', 's', 'bias', 'mean', 'var'],
+            outputs=['y', 'output_mean', 'output_var'],
+            epsilon=epsilon,
+            training_mode=training_mode
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(node, inputs=[x, s, bias, mean, var],
+               outputs=[y, output_mean, output_var],
+               name='test_batchnorm_epsilon_training_mode')
diff --git a/third_party/onnx/onnx/backend/test/case/node/bernoulli.py b/third_party/onnx/onnx/backend/test/case/node/bernoulli.py
new file mode 100755
index 0000000000..a61195c70f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/bernoulli.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def bernoulli_reference_implementation(x, dtype):  # type: ignore
+    # binomial n = 1 equal bernoulli
+    # This example and test-case is for informational purpose. The generator operator is
+    # non-deterministic and may not produce the same values in different implementations
+    # even if a seed is specified.
+    return np.random.binomial(1, p=x).astype(dtype)
+
+
+class Bernoulli(Base):
+    @staticmethod
+    def export_bernoulli_without_dtype() -> None:
+        node = onnx.helper.make_node(
+            'Bernoulli',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.random.uniform(0.0, 1.0, 10).astype(np.float)
+        y = bernoulli_reference_implementation(x, np.float)
+        expect(node, inputs=[x], outputs=[y], name='test_bernoulli')
+
+    @staticmethod
+    def export_bernoulli_with_dtype() -> None:
+        node = onnx.helper.make_node(
+            'Bernoulli',
+            inputs=['x'],
+            outputs=['y'],
+            dtype=onnx.TensorProto.DOUBLE,
+        )
+
+        x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+        y = bernoulli_reference_implementation(x, np.float64)
+        expect(node, inputs=[x], outputs=[y], name='test_bernoulli_double')
+
+    @staticmethod
+    def export_bernoulli_with_seed() -> None:
+        seed = np.float(0)
+        node = onnx.helper.make_node(
+            'Bernoulli',
+            inputs=['x'],
+            outputs=['y'],
+            seed=seed,
+        )
+
+        x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+        y = bernoulli_reference_implementation(x, np.float32)
+        expect(node, inputs=[x], outputs=[y], name='test_bernoulli_seed')
diff --git a/third_party/onnx/onnx/backend/test/case/node/bitshift.py b/third_party/onnx/onnx/backend/test/case/node/bitshift.py
new file mode 100644
index 0000000000..b8dea55e17
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/bitshift.py
@@ -0,0 +1,130 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class BitShift(Base):
+
+    @staticmethod
+    def export_right_unit8() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint8)
+        y = np.array([1, 2, 3]).astype(np.uint8)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_right_uint8')
+
+    @staticmethod
+    def export_right_unit16() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint16)
+        y = np.array([1, 2, 3]).astype(np.uint16)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_right_uint16')
+
+    @staticmethod
+    def export_right_unit32() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint32)
+        y = np.array([1, 2, 3]).astype(np.uint32)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_right_uint32')
+
+    @staticmethod
+    def export_right_unit64() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint64)
+        y = np.array([1, 2, 3]).astype(np.uint64)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_right_uint64')
+
+    @staticmethod
+    def export_left_unit8() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint8)
+        y = np.array([1, 2, 3]).astype(np.uint8)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_left_uint8')
+
+    @staticmethod
+    def export_left_unit16() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint16)
+        y = np.array([1, 2, 3]).astype(np.uint16)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_left_uint16')
+
+    @staticmethod
+    def export_left_unit32() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint32)
+        y = np.array([1, 2, 3]).astype(np.uint32)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_left_uint32')
+
+    @staticmethod
+    def export_left_unit64() -> None:
+        node = onnx.helper.make_node(
+            'BitShift',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint64)
+        y = np.array([1, 2, 3]).astype(np.uint64)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_bitshift_left_uint64')
diff --git a/third_party/onnx/onnx/backend/test/case/node/cast.py b/third_party/onnx/onnx/backend/test/case/node/cast.py
new file mode 100644
index 0000000000..d3fb7c5d3d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/cast.py
@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+# coding: utf-8
+
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx import TensorProto
+from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
+
+from ..base import Base
+from . import expect
+import sys
+
+
+class Cast(Base):
+
+    @staticmethod
+    def export() -> None:
+        shape = (3, 4)
+        test_cases = [
+            ('FLOAT', 'FLOAT16'),
+            ('FLOAT', 'DOUBLE'),
+            ('FLOAT16', 'FLOAT'),
+            ('FLOAT16', 'DOUBLE'),
+            ('DOUBLE', 'FLOAT'),
+            ('DOUBLE', 'FLOAT16'),
+            ('FLOAT', 'STRING'),
+            ('STRING', 'FLOAT'),
+            ('FLOAT', 'BFLOAT16'),
+            ('BFLOAT16', 'FLOAT'),
+        ]
+
+        for from_type, to_type in test_cases:
+            input_type_proto = None
+            output_type_proto = None
+            if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type:
+                np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+                    u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+                    u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32)
+                little_endisan = sys.byteorder == 'little'
+                np_uint16_view = np_fp32.view(dtype=np.uint16)
+                np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
+                if 'BFLOAT16' == to_type:
+                    assert from_type == 'FLOAT'
+                    input = np_fp32.reshape([3, 4])
+                    output = np_bfp16.reshape([3, 4])
+                    input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), input.shape)
+                    output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), output.shape)
+                else:
+                    assert to_type == 'FLOAT'
+                    input = np_bfp16.reshape([3, 4])
+                    #convert bfloat to FLOAT
+                    np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
+                    if little_endisan:
+                        np_fp32_zeros[1::2] = np_bfp16
+                    else:
+                        np_fp32_zeros[0::2] = np_bfp16
+                    np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
+                    output = np_fp32_from_bfloat.reshape([3, 4])
+                    input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), input.shape)
+                    output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), output.shape)
+            elif 'STRING' != from_type:
+                input = np.random.random_sample(shape).astype(
+                    TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)])
+                if ('STRING' == to_type):
+                    # Converting input to str, then give it object dtype for generating script
+                    ss = []
+                    for i in input.flatten():
+                        s = str(i).encode('utf-8')
+                        su = s.decode('utf-8')
+                        ss.append(su)
+
+                    output = np.array(ss).astype(object).reshape([3, 4])
+                else:
+                    output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+            else:
+                input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+                    u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+                    u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(object)).reshape([3, 4])
+                output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+            node = onnx.helper.make_node(
+                'Cast',
+                inputs=['input'],
+                outputs=['output'],
+                to=getattr(TensorProto, to_type),
+            )
+            if input_type_proto and output_type_proto:
+                expect(node, inputs=[input], outputs=[output],
+                           name='test_cast_' + from_type + '_to_' + to_type,
+                           input_type_protos=[input_type_proto],
+                           output_type_protos=[output_type_proto])
+            else:
+                expect(node, inputs=[input], outputs=[output],
+                           name='test_cast_' + from_type + '_to_' + to_type)
diff --git a/third_party/onnx/onnx/backend/test/case/node/castlike.py b/third_party/onnx/onnx/backend/test/case/node/castlike.py
new file mode 100644
index 0000000000..ddd058691f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/castlike.py
@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+# coding: utf-8
+
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx import TensorProto
+from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
+
+from ..base import Base
+from . import expect
+import sys
+
+
+class CastLike(Base):
+
+    @staticmethod
+    def export() -> None:
+        shape = (3, 4)
+        test_cases = [
+            ('FLOAT', 'FLOAT16'),
+            ('FLOAT', 'DOUBLE'),
+            ('FLOAT16', 'FLOAT'),
+            ('FLOAT16', 'DOUBLE'),
+            ('DOUBLE', 'FLOAT'),
+            ('DOUBLE', 'FLOAT16'),
+            ('FLOAT', 'STRING'),
+            ('STRING', 'FLOAT'),
+            ('FLOAT', 'BFLOAT16'),
+            ('BFLOAT16', 'FLOAT'),
+        ]
+
+        for from_type, to_type in test_cases:
+            input_type_proto = None
+            output_type_proto = None
+            if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type:
+                np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+                    u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+                    u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32)
+                little_endisan = sys.byteorder == 'little'
+                np_uint16_view = np_fp32.view(dtype=np.uint16)
+                np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
+                if 'BFLOAT16' == to_type:
+                    assert from_type == 'FLOAT'
+                    input = np_fp32.reshape([3, 4])
+                    output = np_bfp16.reshape([3, 4])
+                    input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), input.shape)
+                    output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), output.shape)
+                else:
+                    assert to_type == 'FLOAT'
+                    input = np_bfp16.reshape([3, 4])
+                    #convert bfloat to FLOAT
+                    np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
+                    if little_endisan:
+                        np_fp32_zeros[1::2] = np_bfp16
+                    else:
+                        np_fp32_zeros[0::2] = np_bfp16
+                    np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
+                    output = np_fp32_from_bfloat.reshape([3, 4])
+                    input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), input.shape)
+                    output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), output.shape)
+            elif 'STRING' != from_type:
+                input = np.random.random_sample(shape).astype(
+                    TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)])
+                if ('STRING' == to_type):
+                    # Converting input to str, then give it np.object dtype for generating script
+                    ss = []
+                    for i in input.flatten():
+                        s = str(i).encode('utf-8')
+                        su = s.decode('utf-8')
+                        ss.append(su)
+
+                    output = np.array(ss).astype(np.object).reshape([3, 4])
+                else:
+                    output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+            else:
+                input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545',
+                    u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038',
+                    u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(np.object)).reshape([3, 4])
+                output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)])
+            like = output.flatten()[0:1]
+            node = onnx.helper.make_node(
+                'CastLike',
+                inputs=['input', 'like'],
+                outputs=['output'],
+            )
+            if input_type_proto and output_type_proto:
+                expect(node, inputs=[input, like], outputs=[output],
+                           name='test_castlike_' + from_type + '_to_' + to_type,
+                           input_type_protos=[input_type_proto, output_type_proto],
+                           output_type_protos=[output_type_proto])
+            else:
+                expect(node, inputs=[input, like], outputs=[output],
+                           name='test_castlike_' + from_type + '_to_' + to_type)
diff --git a/third_party/onnx/onnx/backend/test/case/node/ceil.py b/third_party/onnx/onnx/backend/test/case/node/ceil.py
new file mode 100644
index 0000000000..3e77d83a8f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/ceil.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Ceil(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Ceil',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1.5, 1.2]).astype(np.float32)
+        y = np.ceil(x)  # expected output [-1., 2.]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_ceil_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.ceil(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_ceil')
diff --git a/third_party/onnx/onnx/backend/test/case/node/celu.py b/third_party/onnx/onnx/backend/test/case/node/celu.py
new file mode 100644
index 0000000000..c64f15a40b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/celu.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Celu(Base):
+
+    @staticmethod
+    def export() -> None:
+        alpha = 2.0
+        node = onnx.helper.make_node(
+            'Celu',
+            inputs=['X'],
+            outputs=['Y'],
+            alpha=alpha,
+        )
+
+        input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+            [[0.02916367], [0.12964272], [0.5060197]],
+            [[0.79538304], [0.9411346], [0.9546573]]],
+            [[[0.17730942], [0.46192095], [0.26480448]],
+            [[0.6746842], [0.01665257], [0.62473077]],
+            [[0.9240844], [0.9722341], [0.11965699]]],
+            [[[0.41356155], [0.9129373], [0.59330076]],
+            [[0.81929934], [0.7862604], [0.11799799]],
+            [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+        # Calculate expected output data
+        positive_input = np.maximum(0, input_data)
+        negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
+        expected_output = positive_input + negative_input
+
+        expect(node, inputs=[input_data], outputs=[expected_output],
+               name='test_celu')
diff --git a/third_party/onnx/onnx/backend/test/case/node/clip.py b/third_party/onnx/onnx/backend/test/case/node/clip.py
new file mode 100644
index 0000000000..41abc4f9e5
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/clip.py
@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Clip(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', 'min', 'max'],
+            outputs=['y'],
+        )
+
+        x = np.array([-2, 0, 2]).astype(np.float32)
+        min_val = np.float32(-1)
+        max_val = np.float32(1)
+        y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
+               name='test_clip_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, min_val, max_val)
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
+               name='test_clip')
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', 'min', 'max'],
+            outputs=['y'],
+        )
+
+        min_val = np.float32(-5)
+        max_val = np.float32(5)
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.array([-1, 0, 1]).astype(np.float32)
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
+               name='test_clip_inbounds')
+
+        x = np.array([-6, 0, 6]).astype(np.float32)
+        y = np.array([-5, 0, 5]).astype(np.float32)
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
+               name='test_clip_outbounds')
+
+        x = np.array([-1, 0, 6]).astype(np.float32)
+        y = np.array([-1, 0, 5]).astype(np.float32)
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
+               name='test_clip_splitbounds')
+
+    @staticmethod
+    def export_clip_default() -> None:
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', 'min'],
+            outputs=['y'],
+        )
+        min_val = np.float32(0)
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, min_val, np.inf)
+        expect(node, inputs=[x, min_val], outputs=[y],
+               name='test_clip_default_min')
+
+        no_min = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', no_min, 'max'],
+            outputs=['y'],
+        )
+        max_val = np.float32(0)
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, -np.inf, max_val)
+        expect(node, inputs=[x, max_val], outputs=[y],
+               name='test_clip_default_max')
+
+        no_max = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', no_min, no_max],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.array([-1, 0, 1]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_clip_default_inbounds')
+
+    @staticmethod
+    def export_clip_default_int8() -> None:
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', 'min'],
+            outputs=['y'],
+        )
+        min_val = np.int8(0)
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.clip(x, min_val, np.iinfo(np.int8).max)
+        expect(node, inputs=[x, min_val], outputs=[y],
+               name='test_clip_default_int8_min')
+
+        no_min = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', no_min, 'max'],
+            outputs=['y'],
+        )
+        max_val = np.int8(0)
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.clip(x, np.iinfo(np.int8).min, max_val)
+        expect(node, inputs=[x, max_val], outputs=[y],
+               name='test_clip_default_int8_max')
+
+        no_max = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            'Clip',
+            inputs=['x', no_min, no_max],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.int8)
+        y = np.array([-1, 0, 1]).astype(np.int8)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_clip_default_int8_inbounds')
diff --git a/third_party/onnx/onnx/backend/test/case/node/compress.py b/third_party/onnx/onnx/backend/test/case/node/compress.py
new file mode 100644
index 0000000000..e951a13474
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/compress.py
@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Compress(Base):
+
+    @staticmethod
+    def export_compress_0() -> None:
+        node = onnx.helper.make_node(
+            'Compress',
+            inputs=['input', 'condition'],
+            outputs=['output'],
+            axis=0,
+        )
+        input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+        condition = np.array([0, 1, 1])
+        output = np.compress(condition, input, axis=0)
+        #print(output)
+        #[[ 3.  4.]
+        # [ 5.  6.]]
+
+        expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+               name='test_compress_0')
+
+    @staticmethod
+    def export_compress_1() -> None:
+        node = onnx.helper.make_node(
+            'Compress',
+            inputs=['input', 'condition'],
+            outputs=['output'],
+            axis=1,
+        )
+        input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+        condition = np.array([0, 1])
+        output = np.compress(condition, input, axis=1)
+        #print(output)
+        #[[ 2.]
+        # [ 4.]
+        # [ 6.]]
+
+        expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+               name='test_compress_1')
+
+    @staticmethod
+    def export_compress_default_axis() -> None:
+        node = onnx.helper.make_node(
+            'Compress',
+            inputs=['input', 'condition'],
+            outputs=['output'],
+        )
+        input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+        condition = np.array([0, 1, 0, 0, 1])
+        output = np.compress(condition, input)
+        #print(output)
+        #[ 2., 5.]
+
+        expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+               name='test_compress_default_axis')
+
+    @staticmethod
+    def export_compress_negative_axis() -> None:
+        node = onnx.helper.make_node(
+            'Compress',
+            inputs=['input', 'condition'],
+            outputs=['output'],
+            axis=-1,
+        )
+        input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
+        condition = np.array([0, 1])
+        output = np.compress(condition, input, axis=-1)
+        # print(output)
+        #[[ 2.]
+        # [ 4.]
+        # [ 6.]]
+        expect(node, inputs=[input, condition.astype(bool)], outputs=[output],
+               name='test_compress_negative_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/concat.py b/third_party/onnx/onnx/backend/test/case/node/concat.py
new file mode 100644
index 0000000000..1705cd2f0a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/concat.py
@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from typing import Dict, Sequence, Text, Any
+
+
+class Concat(Base):
+
+    @staticmethod
+    def export() -> None:
+        test_cases: Dict[Text, Sequence[Any]] = {
+            '1d': ([1, 2],
+                   [3, 4]),
+            '2d': ([[1, 2], [3, 4]],
+                   [[5, 6], [7, 8]]),
+            '3d': ([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
+                   [[[9, 10], [11, 12]], [[13, 14], [15, 16]]])
+        }
+
+        for test_case, values_ in test_cases.items():
+            values = [np.asarray(v, dtype=np.float32) for v in values_]
+            for i in range(len(values[0].shape)):
+                in_args = ['value' + str(k) for k in range(len(values))]
+                node = onnx.helper.make_node(
+                    'Concat',
+                    inputs=[s for s in in_args],
+                    outputs=['output'],
+                    axis=i
+                )
+                output = np.concatenate(values, i)
+                expect(node, inputs=[v for v in values], outputs=[output],
+                       name='test_concat_' + test_case + '_axis_' + str(i))
+
+            for i in range(-len(values[0].shape), 0):
+                in_args = ['value' + str(k) for k in range(len(values))]
+                node = onnx.helper.make_node(
+                    'Concat',
+                    inputs=[s for s in in_args],
+                    outputs=['output'],
+                    axis=i
+                )
+                output = np.concatenate(values, i)
+                expect(node, inputs=[v for v in values], outputs=[output],
+                       name='test_concat_' + test_case + '_axis_negative_' + str(abs(i)))
diff --git a/third_party/onnx/onnx/backend/test/case/node/constant.py b/third_party/onnx/onnx/backend/test/case/node/constant.py
new file mode 100644
index 0000000000..f69ac4bc94
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/constant.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Constant(Base):
+
+    @staticmethod
+    def export() -> None:
+        values = np.random.randn(5, 5).astype(np.float32)
+        node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['values'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=values.shape,
+                vals=values.flatten().astype(float),
+            ),
+        )
+
+        expect(node, inputs=[], outputs=[values],
+               name='test_constant')
diff --git a/third_party/onnx/onnx/backend/test/case/node/constantofshape.py b/third_party/onnx/onnx/backend/test/case/node/constantofshape.py
new file mode 100644
index 0000000000..ca0d725307
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/constantofshape.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ConstantOfShape(Base):
+
+    @staticmethod
+    def export_float_ones() -> None:
+        x = np.array([4, 3, 2]).astype(np.int64)
+        tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.FLOAT,
+                                               [1], [1])
+        node = onnx.helper.make_node(
+            'ConstantOfShape',
+            inputs=['x'],
+            outputs=['y'],
+            value=tensor_value,
+        )
+
+        y = np.ones(x, dtype=np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_constantofshape_float_ones')
+
+    @staticmethod
+    def export_int32_zeros() -> None:
+        x = np.array([10, 6]).astype(np.int64)
+        tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.INT32,
+                                               [1], [0])
+        node = onnx.helper.make_node(
+            'ConstantOfShape',
+            inputs=['x'],
+            outputs=['y'],
+            value=tensor_value,
+        )
+        y = np.zeros(x, dtype=np.int32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_constantofshape_int_zeros')
+
+    @staticmethod
+    def export_int32_shape_zero() -> None:
+        x = np.array([0, ]).astype(np.int64)
+        tensor_value = onnx.helper.make_tensor("value", onnx.TensorProto.INT32,
+                                               [1], [0])
+        node = onnx.helper.make_node(
+            'ConstantOfShape',
+            inputs=['x'],
+            outputs=['y'],
+            value=tensor_value,
+        )
+        y = np.zeros(x, dtype=np.int32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_constantofshape_int_shape_zero')
diff --git a/third_party/onnx/onnx/backend/test/case/node/conv.py b/third_party/onnx/onnx/backend/test/case/node/conv.py
new file mode 100644
index 0000000000..e406cb9b2f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/conv.py
@@ -0,0 +1,142 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Conv(Base):
+
+    @staticmethod
+    def export() -> None:
+
+        x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 5, 5) input tensor
+                        [5., 6., 7., 8., 9.],
+                        [10., 11., 12., 13., 14.],
+                        [15., 16., 17., 18., 19.],
+                        [20., 21., 22., 23., 24.]]]]).astype(np.float32)
+        W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        # Convolution with padding
+        node_with_padding = onnx.helper.make_node(
+            'Conv',
+            inputs=['x', 'W'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
+            pads=[1, 1, 1, 1],
+        )
+        y_with_padding = np.array([[[[12., 21., 27., 33., 24.],  # (1, 1, 5, 5) output tensor
+                                     [33., 54., 63., 72., 51.],
+                                     [63., 99., 108., 117., 81.],
+                                     [93., 144., 153., 162., 111.],
+                                     [72., 111., 117., 123., 84.]]]]).astype(np.float32)
+        expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
+               name='test_basic_conv_with_padding')
+
+        # Convolution without padding
+        node_without_padding = onnx.helper.make_node(
+            'Conv',
+            inputs=['x', 'W'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
+            pads=[0, 0, 0, 0],
+        )
+        y_without_padding = np.array([[[[54., 63., 72.],  # (1, 1, 3, 3) output tensor
+                                        [99., 108., 117.],
+                                        [144., 153., 162.]]]]).astype(np.float32)
+        expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
+               name='test_basic_conv_without_padding')
+
+    @staticmethod
+    def export_conv_with_strides() -> None:
+
+        x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 7, 5) input tensor
+                        [5., 6., 7., 8., 9.],
+                        [10., 11., 12., 13., 14.],
+                        [15., 16., 17., 18., 19.],
+                        [20., 21., 22., 23., 24.],
+                        [25., 26., 27., 28., 29.],
+                        [30., 31., 32., 33., 34.]]]]).astype(np.float32)
+        W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        # Convolution with strides=2 and padding
+        node_with_padding = onnx.helper.make_node(
+            'Conv',
+            inputs=['x', 'W'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            pads=[1, 1, 1, 1],
+            strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+        )
+        y_with_padding = np.array([[[[12., 27., 24.],  # (1, 1, 4, 3) output tensor
+                                     [63., 108., 81.],
+                                     [123., 198., 141.],
+                                     [112., 177., 124.]]]]).astype(np.float32)
+        expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
+               name='test_conv_with_strides_padding')
+
+        # Convolution with strides=2 and no padding
+        node_without_padding = onnx.helper.make_node(
+            'Conv',
+            inputs=['x', 'W'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            pads=[0, 0, 0, 0],
+            strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+        )
+        y_without_padding = np.array([[[[54., 72.],  # (1, 1, 3, 2) output tensor
+                                        [144., 162.],
+                                        [234., 252.]]]]).astype(np.float32)
+        expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
+               name='test_conv_with_strides_no_padding')
+
+        # Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
+        node_with_asymmetric_padding = onnx.helper.make_node(
+            'Conv',
+            inputs=['x', 'W'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            pads=[1, 0, 1, 0],
+            strides=[2, 2],  # Default values for other attributes: dilations=[1, 1], groups=1
+        )
+        y_with_asymmetric_padding = np.array([[[[21., 33.],  # (1, 1, 4, 2) output tensor
+                                                [99., 117.],
+                                                [189., 207.],
+                                                [171., 183.]]]]).astype(np.float32)
+        expect(node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding],
+               name='test_conv_with_strides_and_asymmetric_padding')
+
+    @staticmethod
+    def export_conv_with_autopad_same() -> None:
+
+        x = np.array([[[[0., 1., 2., 3., 4.],  # (1, 1, 5, 5) input tensor
+                        [5., 6., 7., 8., 9.],
+                        [10., 11., 12., 13., 14.],
+                        [15., 16., 17., 18., 19.],
+                        [20., 21., 22., 23., 24.]]]]).astype(np.float32)
+        W = np.array([[[[1., 1., 1.],  # (1, 1, 3, 3) tensor for convolution weights
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        # Convolution with auto_pad='SAME_LOWER' and strides=2
+        node = onnx.helper.make_node(
+            'Conv',
+            inputs=['x', 'W'],
+            outputs=['y'],
+            auto_pad='SAME_LOWER',
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+        )
+        y = np.array([[[[12., 27., 24.],
+                     [63., 108., 81.],
+                     [72., 117., 84.]]]]).astype(np.float32)
+        expect(node, inputs=[x, W], outputs=[y],
+               name='test_conv_with_autopad_same')
diff --git a/third_party/onnx/onnx/backend/test/case/node/convinteger.py b/third_party/onnx/onnx/backend/test/case/node/convinteger.py
new file mode 100644
index 0000000000..c8b8a56013
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/convinteger.py
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ConvInteger(Base):
+
+    @staticmethod
+    def export_without_padding() -> None:
+
+        x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+        x_zero_point = np.uint8(1)
+        w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+        y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
+
+        # ConvInteger without padding
+        convinteger_node = onnx.helper.make_node('ConvInteger',
+            inputs=['x', 'w', 'x_zero_point'],
+            outputs=['y'])
+
+        expect(convinteger_node, inputs=[x, w, x_zero_point], outputs=[y],
+               name='test_convinteger_without_padding')
+
+    @staticmethod
+    def export_with_padding() -> None:
+
+        x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+        x_zero_point = np.uint8(1)
+        w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+        y = np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9]).astype(np.int32).reshape((1, 1, 4, 4))
+
+        # ConvInteger with padding
+        convinteger_node_with_padding = onnx.helper.make_node('ConvInteger',
+            inputs=['x', 'w', 'x_zero_point'],
+            outputs=['y'],
+            pads=[1, 1, 1, 1],)
+
+        expect(convinteger_node_with_padding, inputs=[x, w, x_zero_point], outputs=[y],
+               name='test_convinteger_with_padding')
diff --git a/third_party/onnx/onnx/backend/test/case/node/convtranspose.py b/third_party/onnx/onnx/backend/test/case/node/convtranspose.py
new file mode 100644
index 0000000000..a77ef1d3fb
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/convtranspose.py
@@ -0,0 +1,301 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ConvTranspose(Base):
+
+    @staticmethod
+    def export() -> None:
+        x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                        [3., 4., 5.],
+                        [6., 7., 8.]]]]).astype(np.float32)
+
+        W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                        [1., 1., 1.],
+                        [1., 1., 1.]],
+                       [[1., 1., 1.],
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+        y = np.array([[[[0., 1., 3., 3., 2.],  # (1, 2, 5, 5)
+                        [3., 8., 15., 12., 7.],
+                        [9., 21., 36., 27., 15.],
+                        [9., 20., 33., 24., 13.],
+                        [6., 13., 21., 15., 8.]],
+
+                       [[0., 1., 3., 3., 2.],
+                        [3., 8., 15., 12., 7.],
+                        [9., 21., 36., 27., 15.],
+                        [9., 20., 33., 24., 13.],
+                        [6., 13., 21., 15., 8.]]]]).astype(np.float32)
+
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose')
+
+    @staticmethod
+    def export_convtranspose_1d() -> None:
+        x = np.array([[[0., 1., 2.]]]).astype(np.float32)  # (1, 1, 3)
+
+        W = np.array([[[1., 1., 1.],  # (1, 2, 3)
+                       [1., 1., 1.]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+        y = np.array([[[0., 1., 3., 3., 2.],  # (1, 2, 5)
+                       [0., 1., 3., 3., 2.]]]).astype(np.float32)
+
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_1d')
+
+    @staticmethod
+    def export_convtranspose_3d() -> None:
+        x = np.array([[[[[0., 1., 2., 3., 4.],  # (1, 1, 3, 4, 5)
+                         [5., 6., 7., 8., 9.],
+                         [10., 11., 12., 13., 14.],
+                         [15., 16., 17., 18., 19.]],
+                        [[20., 21., 22., 23., 24.],
+                         [25., 26., 27., 28., 29.],
+                         [30., 31., 32., 33., 34.],
+                         [35., 36., 37., 38., 39.]],
+                        [[40., 41., 42., 43., 44.],
+                         [45., 46., 47., 48., 49.],
+                         [50., 51., 52., 53., 54.],
+                         [55., 56., 57., 58., 59.]]]]]).astype(np.float32)
+
+        W = np.array([[[[[1., 1., 1.],  # (1, 2, 3, 3, 3)
+                         [1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.],
+                         [1., 1., 1.]]],
+                       [[[1., 1., 1.],
+                         [1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.],
+                         [1., 1., 1.]],
+                        [[1., 1., 1.],
+                         [1., 1., 1.],
+                         [1., 1., 1.]]]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
+
+        y = np.array([[[[[0., 1., 3., 6., 9., 7., 4.],  # (1, 2, 5, 6, 7)
+                         [5., 12., 21., 27., 33., 24., 13.],
+                         [15., 33., 54., 63., 72., 51., 27.],
+                         [30., 63., 99., 108., 117., 81., 42.],
+                         [25., 52., 81., 87., 93., 64., 33.],
+                         [15., 31., 48., 51., 54., 37., 19.]],
+
+                        [[20., 42., 66., 72., 78., 54., 28.],
+                         [50., 104., 162., 174., 186., 128., 66.],
+                         [90., 186., 288., 306., 324., 222., 114.],
+                         [120., 246., 378., 396., 414., 282., 144.],
+                         [90., 184., 282., 294., 306., 208., 106.],
+                         [50., 102., 156., 162., 168., 114., 58.]],
+
+                        [[60., 123., 189., 198., 207., 141., 72.],
+                         [135., 276., 423., 441., 459., 312., 159.],
+                         [225., 459., 702., 729., 756., 513., 261.],
+                         [270., 549., 837., 864., 891., 603., 306.],
+                         [195., 396., 603., 621., 639., 432., 219.],
+                         [105., 213., 324., 333., 342., 231., 117.]],
+
+                        [[60., 122., 186., 192., 198., 134., 68.],
+                         [130., 264., 402., 414., 426., 288., 146.],
+                         [210., 426., 648., 666., 684., 462., 234.],
+                         [240., 486., 738., 756., 774., 522., 264.],
+                         [170., 344., 522., 534., 546., 368., 186.],
+                         [90., 182., 276., 282., 288., 194., 98.]],
+
+                        [[40., 81., 123., 126., 129., 87., 44.],
+                         [85., 172., 261., 267., 273., 184., 93.],
+                         [135., 273., 414., 423., 432., 291., 147.],
+                         [150., 303., 459., 468., 477., 321., 162.],
+                         [105., 212., 321., 327., 333., 224., 113.],
+                         [55., 111., 168., 171., 174., 117., 59.]]],
+
+                       [[[0., 1., 3., 6., 9., 7., 4.],
+                         [5., 12., 21., 27., 33., 24., 13.],
+                         [15., 33., 54., 63., 72., 51., 27.],
+                         [30., 63., 99., 108., 117., 81., 42.],
+                         [25., 52., 81., 87., 93., 64., 33.],
+                         [15., 31., 48., 51., 54., 37., 19.]],
+
+                        [[20., 42., 66., 72., 78., 54., 28.],
+                         [50., 104., 162., 174., 186., 128., 66.],
+                         [90., 186., 288., 306., 324., 222., 114.],
+                         [120., 246., 378., 396., 414., 282., 144.],
+                         [90., 184., 282., 294., 306., 208., 106.],
+                         [50., 102., 156., 162., 168., 114., 58.]],
+
+                        [[60., 123., 189., 198., 207., 141., 72.],
+                         [135., 276., 423., 441., 459., 312., 159.],
+                         [225., 459., 702., 729., 756., 513., 261.],
+                         [270., 549., 837., 864., 891., 603., 306.],
+                         [195., 396., 603., 621., 639., 432., 219.],
+                         [105., 213., 324., 333., 342., 231., 117.]],
+
+                        [[60., 122., 186., 192., 198., 134., 68.],
+                         [130., 264., 402., 414., 426., 288., 146.],
+                         [210., 426., 648., 666., 684., 462., 234.],
+                         [240., 486., 738., 756., 774., 522., 264.],
+                         [170., 344., 522., 534., 546., 368., 186.],
+                         [90., 182., 276., 282., 288., 194., 98.]],
+
+                        [[40., 81., 123., 126., 129., 87., 44.],
+                         [85., 172., 261., 267., 273., 184., 93.],
+                         [135., 273., 414., 423., 432., 291., 147.],
+                         [150., 303., 459., 468., 477., 321., 162.],
+                         [105., 212., 321., 327., 333., 224., 113.],
+                         [55., 111., 168., 171., 174., 117., 59.]]]]]).astype(np.float32)
+
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_3d')
+
+    @staticmethod
+    def export_convtranspose_attributes() -> None:
+        x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                        [3., 4., 5.],
+                        [6., 7., 8.]]]]).astype(np.float32)
+
+        W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                        [1., 1., 1.],
+                        [1., 1., 1.]],
+                       [[1., 1., 1.],
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        y = np.array([[[[0., 0., 1., 1., 3., 2., 2., 0.],  # (1, 2, 10, 8)
+                        [0., 0., 1., 1., 3., 2., 2., 0.],
+                        [0., 0., 1., 1., 3., 2., 2., 0.],
+                        [3., 3., 7., 4., 9., 5., 5., 0.],
+                        [3., 3., 7., 4., 9., 5., 5., 0.],
+                        [3., 3., 7., 4., 9., 5., 5., 0.],
+                        [6., 6., 13., 7., 15., 8., 8., 0.],
+                        [6., 6., 13., 7., 15., 8., 8., 0.],
+                        [6., 6., 13., 7., 15., 8., 8., 0.],
+                        [0., 0., 0., 0., 0., 0., 0., 0.]],
+
+                       [[0., 0., 1., 1., 3., 2., 2., 0.],
+                        [0., 0., 1., 1., 3., 2., 2., 0.],
+                        [0., 0., 1., 1., 3., 2., 2., 0.],
+                        [3., 3., 7., 4., 9., 5., 5., 0.],
+                        [3., 3., 7., 4., 9., 5., 5., 0.],
+                        [3., 3., 7., 4., 9., 5., 5., 0.],
+                        [6., 6., 13., 7., 15., 8., 8., 0.],
+                        [6., 6., 13., 7., 15., 8., 8., 0.],
+                        [6., 6., 13., 7., 15., 8., 8., 0.],
+                        [0., 0., 0., 0., 0., 0., 0., 0.]]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                                     strides=[3, 2],
+                                     output_shape=[10, 8])
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_output_shape')
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                                     strides=[3, 2],
+                                     output_padding=[1, 1])
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_pad')
+
+        node = onnx.helper.make_node(
+            'ConvTranspose', ['X', 'W'], ['Y'],
+            name='test',
+            strides=[3, 2],
+            output_shape=[10, 8],
+            kernel_shape=[3, 3],
+            output_padding=[1, 1]
+        )
+        expect(node, inputs=[x, W], outputs=[y],
+               name='test_convtranspose_kernel_shape')
+
+    @staticmethod
+    def export_convtranspose_pads() -> None:
+        x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                        [3., 4., 5.],
+                        [6., 7., 8.]]]]).astype(np.float32)
+
+        W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                        [1., 1., 1.],
+                        [1., 1., 1.]],
+                       [[1., 1., 1.],
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"],
+                                     strides=[3, 2],
+                                     pads=[1, 2, 1, 2])
+
+        y = np.array([[[[1., 1., 3.],  # (1, 2, 7, 3)
+                        [1., 1., 3.],
+                        [7., 4., 9.],
+                        [7., 4., 9.],
+                        [7., 4., 9.],
+                        [13., 7., 15.],
+                        [13., 7., 15.]],
+
+                       [[1., 1., 3.],
+                        [1., 1., 3.],
+                        [7., 4., 9.],
+                        [7., 4., 9.],
+                        [7., 4., 9.],
+                        [13., 7., 15.],
+                        [13., 7., 15.]]]]).astype(np.float32)
+
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_pads')
+
+    @staticmethod
+    def export_convtranspose_dilations() -> None:
+        x = np.array([[[[3., 8., 1.],  # (1, 1, 3, 3)
+                        [9., 5., 7.],
+                        [3., 2., 6.]]]]).astype(np.float32)
+        W = np.array([[[[7., 2.],  # (1, 1, 2, 2)
+                        [1., 9.]]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2])
+
+        y = np.array([[[[21., 56., 13., 16., 2.],  # [1, 1, 5, 5]
+                        [63., 35., 67., 10., 14.],
+                        [24., 22., 76., 76., 21.],
+                        [9., 5., 88., 45., 63.],
+                        [3., 2., 33., 18., 54.]]]]).astype(np.float32)
+
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_dilations')
+
+    @staticmethod
+    def export_convtranspose_autopad_same() -> None:
+        x = np.array([[[[0., 1., 2.],  # (1, 1, 3, 3)
+                        [3., 4., 5.],
+                        [6., 7., 8.]]]]).astype(np.float32)
+
+        W = np.array([[[[1., 1., 1.],  # (1, 2, 3, 3)
+                        [1., 1., 1.],
+                        [1., 1., 1.]],
+                       [[1., 1., 1.],
+                        [1., 1., 1.],
+                        [1., 1., 1.]]]]).astype(np.float32)
+
+        node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2])
+
+        y = np.array([[[[0., 0., 1., 1., 3., 2.],
+                        [0., 0., 1., 1., 3., 2.],
+                        [3., 3., 8., 5., 12., 7.],
+                        [3., 3., 7., 4., 9., 5.],
+                        [9., 9., 20., 11., 24., 13.],
+                        [6., 6., 13., 7., 15., 8.]],
+
+                       [[0., 0., 1., 1., 3., 2.],
+                        [0., 0., 1., 1., 3., 2.],
+                        [3., 3., 8., 5., 12., 7.],
+                        [3., 3., 7., 4., 9., 5.],
+                        [9., 9., 20., 11., 24., 13.],
+                        [6., 6., 13., 7., 15., 8.]]]]).astype(np.float32)
+
+        expect(node, inputs=[x, W], outputs=[y], name='test_convtranspose_autopad_same')
diff --git a/third_party/onnx/onnx/backend/test/case/node/cos.py b/third_party/onnx/onnx/backend/test/case/node/cos.py
new file mode 100644
index 0000000000..ca46614fd0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/cos.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Cos(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Cos',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.cos(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_cos_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.cos(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_cos')
diff --git a/third_party/onnx/onnx/backend/test/case/node/cosh.py b/third_party/onnx/onnx/backend/test/case/node/cosh.py
new file mode 100644
index 0000000000..9a855251fb
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/cosh.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Cosh(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Cosh',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.cosh(x)  # expected output [1.54308069,  1.,  1.54308069]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_cosh_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.cosh(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_cosh')
diff --git a/third_party/onnx/onnx/backend/test/case/node/cumsum.py b/third_party/onnx/onnx/backend/test/case/node/cumsum.py
new file mode 100644
index 0000000000..3abad2a6cb
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/cumsum.py
@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class CumSum(Base):
+
+    @staticmethod
+    def export_cumsum_1d() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y']
+        )
+        x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+        axis = np.int32(0)
+        y = np.array([1., 3., 6., 10., 15.]).astype(np.float64)
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_1d')
+
+    @staticmethod
+    def export_cumsum_1d_exclusive() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y'],
+            exclusive=1
+        )
+        x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+        axis = np.int32(0)
+        y = np.array([0., 1., 3., 6., 10.]).astype(np.float64)
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_1d_exclusive')
+
+    @staticmethod
+    def export_cumsum_1d_reverse() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y'],
+            reverse=1
+        )
+        x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+        axis = np.int32(0)
+        y = np.array([15., 14., 12., 9., 5.]).astype(np.float64)
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_1d_reverse')
+
+    @staticmethod
+    def export_cumsum_1d_reverse_exclusive() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y'],
+            reverse=1,
+            exclusive=1
+        )
+        x = np.array([1., 2., 3., 4., 5.]).astype(np.float64)
+        axis = np.int32(0)
+        y = np.array([14., 12., 9., 5., 0.]).astype(np.float64)
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_1d_reverse_exclusive')
+
+    @staticmethod
+    def export_cumsum_2d_axis_0() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y'],
+        )
+        x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+        axis = np.int32(0)
+        y = np.array([1., 2., 3., 5., 7., 9.]).astype(np.float64).reshape((2, 3))
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_2d_axis_0')
+
+    @staticmethod
+    def export_cumsum_2d_axis_1() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y'],
+        )
+        x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+        axis = np.int32(1)
+        y = np.array([1., 3., 6., 4., 9., 15.]).astype(np.float64).reshape((2, 3))
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_2d_axis_1')
+
+    @staticmethod
+    def export_cumsum_2d_negative_axis() -> None:
+        node = onnx.helper.make_node(
+            'CumSum',
+            inputs=['x', 'axis'],
+            outputs=['y'],
+        )
+        x = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float64).reshape((2, 3))
+        axis = np.int32(-1)
+        y = np.array([1., 3., 6., 4., 9., 15.]).astype(np.float64).reshape((2, 3))
+        expect(node, inputs=[x, axis], outputs=[y],
+               name='test_cumsum_2d_negative_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/depthtospace.py b/third_party/onnx/onnx/backend/test/case/node/depthtospace.py
new file mode 100755
index 0000000000..798f744237
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/depthtospace.py
@@ -0,0 +1,90 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class DepthToSpace(Base):
+
+    @staticmethod
+    def export_default_mode_example() -> None:
+        node = onnx.helper.make_node(
+            'DepthToSpace',
+            inputs=['x'],
+            outputs=['y'],
+            blocksize=2,
+            mode='DCR'
+        )
+
+        # (1, 8, 2, 3) input tensor
+        x = np.array([[[[0., 1., 2.],
+                        [3., 4., 5.]],
+                       [[9., 10., 11.],
+                        [12., 13., 14.]],
+                       [[18., 19., 20.],
+                        [21., 22., 23.]],
+                       [[27., 28., 29.],
+                        [30., 31., 32.]],
+                       [[36., 37., 38.],
+                        [39., 40., 41.]],
+                       [[45., 46., 47.],
+                        [48., 49., 50.]],
+                       [[54., 55., 56.],
+                        [57., 58., 59.]],
+                       [[63., 64., 65.],
+                        [66., 67., 68.]]]]).astype(np.float32)
+
+        # (1, 2, 4, 6) output tensor
+        y = np.array([[[[0., 18., 1., 19., 2., 20.],
+                        [36., 54., 37., 55., 38., 56.],
+                        [3., 21., 4., 22., 5., 23.],
+                        [39., 57., 40., 58., 41., 59.]],
+                       [[9., 27., 10., 28., 11., 29.],
+                        [45., 63., 46., 64., 47., 65.],
+                        [12., 30., 13., 31., 14., 32.],
+                        [48., 66., 49., 67., 50., 68.]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_depthtospace_example')
+
+    @staticmethod
+    def export_crd_mode_example() -> None:
+        node = onnx.helper.make_node(
+            'DepthToSpace',
+            inputs=['x'],
+            outputs=['y'],
+            blocksize=2,
+            mode='CRD'
+        )
+
+        # (1, 8, 2, 3) input tensor
+        x = np.array([[[[0., 1., 2.],
+                        [3., 4., 5.]],
+                       [[9., 10., 11.],
+                        [12., 13., 14.]],
+                       [[18., 19., 20.],
+                        [21., 22., 23.]],
+                       [[27., 28., 29.],
+                        [30., 31., 32.]],
+                       [[36., 37., 38.],
+                        [39., 40., 41.]],
+                       [[45., 46., 47.],
+                        [48., 49., 50.]],
+                       [[54., 55., 56.],
+                        [57., 58., 59.]],
+                       [[63., 64., 65.],
+                        [66., 67., 68.]]]]).astype(np.float32)
+
+        # (1, 2, 4, 6) output tensor
+        y = np.array([[[[0., 9., 1., 10., 2., 11.],
+                        [18., 27., 19., 28., 20., 29.],
+                        [3., 12., 4., 13., 5., 14.],
+                        [21., 30., 22., 31., 23., 32.]],
+                       [[36., 45., 37., 46., 38., 47.],
+                        [54., 63., 55., 64., 56., 65.],
+                        [39., 48., 40., 49., 41., 50.],
+                        [57., 66., 58., 67., 59., 68.]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_depthtospace_crd_mode_example')
diff --git a/third_party/onnx/onnx/backend/test/case/node/dequantizelinear.py b/third_party/onnx/onnx/backend/test/case/node/dequantizelinear.py
new file mode 100644
index 0000000000..3a9cbc79ca
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/dequantizelinear.py
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class DequantizeLinear(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node('DequantizeLinear',
+                                     inputs=['x', 'x_scale', 'x_zero_point'],
+                                     outputs=['y'],)
+
+        # scalar zero point and scale
+        x = np.array([0, 3, 128, 255]).astype(np.uint8)
+        x_scale = np.float32(2)
+        x_zero_point = np.uint8(128)
+        y = np.array([-256, -250, 0, 254], dtype=np.float32)
+
+        expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+               name='test_dequantizelinear')
+
+    @staticmethod
+    def export_axis() -> None:
+        node = onnx.helper.make_node('DequantizeLinear',
+                                     inputs=['x', 'x_scale', 'x_zero_point'],
+                                     outputs=['y'],)
+
+        # 1-D tensor zero point and scale of size equal to axis 1 of the input tensor
+        x = np.array([[[[3, 89],
+                        [34, 200],
+                        [74, 59]],
+
+                       [[5, 24],
+                        [24, 87],
+                        [32, 13]],
+
+                       [[245, 99],
+                        [4, 142],
+                        [121, 102]], ], ], dtype=np.uint8)
+        x_scale = np.array([2, 4, 5], dtype=np.float32)
+        x_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+        y = (x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32)) * x_scale.reshape(1, 3, 1, 1)
+
+        expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+               name='test_dequantizelinear_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/det.py b/third_party/onnx/onnx/backend/test/case/node/det.py
new file mode 100644
index 0000000000..2f52fe14c6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/det.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Det(Base):
+
+    @staticmethod
+    def export_2d() -> None:
+        node = onnx.helper.make_node(
+            'Det',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.arange(4).reshape(2, 2).astype(np.float32)
+        y = np.linalg.det(x)  # expect -2
+        expect(node, inputs=[x], outputs=[y],
+               name='test_det_2d')
+
+    @staticmethod
+    def export_nd() -> None:
+        node = onnx.helper.make_node(
+            'Det',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype(np.float32)
+        y = np.linalg.det(x)  # expect array([-2., -3., -8.])
+        expect(node, inputs=[x], outputs=[y],
+               name='test_det_nd')
diff --git a/third_party/onnx/onnx/backend/test/case/node/div.py b/third_party/onnx/onnx/backend/test/case/node/div.py
new file mode 100644
index 0000000000..d20734ee2e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/div.py
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Div(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Div',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([3, 4]).astype(np.float32)
+        y = np.array([1, 2]).astype(np.float32)
+        z = x / y  # expected output [3., 2.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_div_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
+        z = x / y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_div')
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
+        z = x // y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_div_uint8')
+
+    @staticmethod
+    def export_div_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Div',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.rand(5).astype(np.float32) + 1.0
+        z = x / y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_div_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/dropout.py b/third_party/onnx/onnx/backend/test/case/node/dropout.py
new file mode 100644
index 0000000000..4331423762
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/dropout.py
@@ -0,0 +1,214 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import random
+
+import onnx
+from ..base import Base
+from . import expect
+from onnx import helper
+
+
+def dropout(X, drop_probability=0.5, seed=0, training_mode=False, return_mask=False):  # type: ignore
+    if drop_probability == 0 or training_mode is False:
+        if return_mask is True:
+            return X, np.ones(X.shape, dtype=bool)
+        else:
+            return X
+
+    np.random.seed(seed)
+    mask = np.random.uniform(0, 1.0, X.shape) >= drop_probability
+    scale = (1 / (1 - drop_probability))
+    if return_mask is True:
+        return mask * X * scale, mask.astype(bool)
+    else:
+        return mask * X * scale
+
+
+class Dropout(Base):
+
+    # Inferencing tests.
+    @staticmethod
+    def export_default() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x'],
+            outputs=['y'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = dropout(x)
+        expect(node, inputs=[x], outputs=[y], name='test_dropout_default')
+
+    @staticmethod
+    def export_default_ratio() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r'],
+            outputs=['y'],
+            seed=seed
+        )
+
+        r = np.float32(0.1)
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = dropout(x, r)
+        expect(node, inputs=[x, r], outputs=[y], name='test_dropout_default_ratio')
+
+    @staticmethod
+    def export_default_mask() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x'],
+            outputs=['y', 'z'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y, z = dropout(x, return_mask=True)
+        expect(node, inputs=[x], outputs=[y, z], name='test_dropout_default_mask')
+
+    @staticmethod
+    def export_default_mask_ratio() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r'],
+            outputs=['y', 'z'],
+            seed=seed
+        )
+
+        r = np.float32(0.1)
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y, z = dropout(x, r, return_mask=True)
+        expect(node, inputs=[x, r], outputs=[y, z], name='test_dropout_default_mask_ratio')
+
+    # Training tests.
+
+    @staticmethod
+    def export_training_default() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r', 't'],
+            outputs=['y'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        r = np.float32(0.5)
+        t = np.bool_(True)
+        y = dropout(x, r, training_mode=t)
+        expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout_default')
+
+    @staticmethod
+    def export_training_default_ratio_mask() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r', 't'],
+            outputs=['y', 'z'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        r = np.float32(0.5)
+        t = np.bool_(True)
+        y, z = dropout(x, r, training_mode=t, return_mask=True)
+        expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_default_mask')
+
+    @staticmethod
+    def export_training() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r', 't'],
+            outputs=['y'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        r = np.float32(0.75)
+        t = np.bool_(True)
+        y = dropout(x, r, training_mode=t)
+        expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout')
+
+    @staticmethod
+    def export_training_ratio_mask() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r', 't'],
+            outputs=['y', 'z'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        r = np.float32(0.75)
+        t = np.bool_(True)
+        y, z = dropout(x, r, training_mode=t, return_mask=True)
+        expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_mask')
+
+    @staticmethod
+    def export_training_default_zero_ratio() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r', 't'],
+            outputs=['y'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        r = np.float32(0.0)
+        t = np.bool_(True)
+        y = dropout(x, r, training_mode=t)
+        expect(node, inputs=[x, r, t], outputs=[y], name='test_training_dropout_zero_ratio')
+
+    @staticmethod
+    def export_training_default_zero_ratio_mask() -> None:
+        seed = np.int64(0)
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x', 'r', 't'],
+            outputs=['y', 'z'],
+            seed=seed
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        r = np.float32(0.0)
+        t = np.bool_(True)
+        y, z = dropout(x, r, training_mode=t, return_mask=True)
+        expect(node, inputs=[x, r, t], outputs=[y, z], name='test_training_dropout_zero_ratio_mask')
+
+    # Old dropout tests
+
+    @staticmethod
+    def export_default_old() -> None:
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = x
+        expect(node, inputs=[x], outputs=[y],
+               name='test_dropout_default_old', opset_imports=[helper.make_opsetid("", 11)])
+
+    @staticmethod
+    def export_random_old() -> None:
+        node = onnx.helper.make_node(
+            'Dropout',
+            inputs=['x'],
+            outputs=['y'],
+            ratio=.2,
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = x
+        expect(node, inputs=[x], outputs=[y],
+               name='test_dropout_random_old', opset_imports=[helper.make_opsetid("", 11)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/dynamicquantizelinear.py b/third_party/onnx/onnx/backend/test/case/node/dynamicquantizelinear.py
new file mode 100644
index 0000000000..bfa472e071
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/dynamicquantizelinear.py
@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx import TensorProto
+from ..base import Base
+from . import expect
+
+
+class DynamicQuantizeLinear(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node('DynamicQuantizeLinear',
+            inputs=['x'],
+            outputs=['y', 'y_scale', 'y_zero_point'],
+        )
+
+        # expected scale 0.0196078438 and zero point 153
+        X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32)
+        x_min = np.minimum(0, np.min(X))
+        x_max = np.maximum(0, np.max(X))
+        Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+        Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+        Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+        expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+               name='test_dynamicquantizelinear')
+
+        # expected scale 0.0156862754 and zero point 255
+        X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32)
+        x_min = np.minimum(0, np.min(X))
+        x_max = np.maximum(0, np.max(X))
+        Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+        Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+        Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+        expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+               name='test_dynamicquantizelinear_max_adjusted')
+
+        X = np.array([1, 2.1, 1.3, 2.5,
+                      3.34, 4.0, 1.5, 2.6,
+                      3.9, 4.0, 3.0, 2.345]).astype(np.float32).reshape((3, 4))
+
+        # expected scale 0.0156862754 and zero point 0
+        x_min = np.minimum(0, np.min(X))
+        x_max = np.maximum(0, np.max(X))
+        Y_Scale = np.float32((x_max - x_min) / (255 - 0))  # uint8 -> [0, 255]
+        Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
+        Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
+
+        expect(node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint],
+               name='test_dynamicquantizelinear_min_adjusted')
diff --git a/third_party/onnx/onnx/backend/test/case/node/einsum.py b/third_party/onnx/onnx/backend/test/case/node/einsum.py
new file mode 100644
index 0000000000..d5d915c6d7
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/einsum.py
@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from typing import Tuple, Text
+
+
+def einsum_reference_implementation(Eqn: Text, Operands: Tuple[np.ndarray, ...]) -> np.ndarray:
+    Z = np.einsum(Eqn, *Operands)
+    return Z
+
+
+class Einsum(Base):
+
+    @staticmethod
+    def export_einsum_transpose() -> None:
+        Eqn = 'ij->ji'
+        node = onnx.helper.make_node(
+            'Einsum',
+            inputs=['x'],
+            outputs=['y'],
+            equation=Eqn
+        )
+
+        X = np.random.randn(3, 4)
+        Y = einsum_reference_implementation(Eqn, (X,))
+
+        expect(node, inputs=[X], outputs=[Y], name='test_einsum_transpose')
+
+    @staticmethod
+    def export_einsum_sum() -> None:
+        Eqn = 'ij->i'
+        node = onnx.helper.make_node(
+            'Einsum',
+            inputs=['x'],
+            outputs=['y'],
+            equation=Eqn
+        )
+
+        X = np.random.randn(3, 4)
+        Z = einsum_reference_implementation(Eqn, (X,))
+
+        expect(node, inputs=[X], outputs=[Z], name='test_einsum_sum')
+
+    @staticmethod
+    def export_einsum_batch_diagonal() -> None:
+        Eqn = '...ii ->...i'
+        node = onnx.helper.make_node(
+            'Einsum',
+            inputs=['x'],
+            outputs=['y'],
+            equation=Eqn
+        )
+
+        X = np.random.randn(3, 5, 5)
+        Z = einsum_reference_implementation(Eqn, (X,))
+
+        expect(node, inputs=[X], outputs=[Z], name='test_einsum_batch_diagonal')
+
+    @staticmethod
+    def export_einsum_inner_prod() -> None:
+        Eqn = 'i,i'
+        node = onnx.helper.make_node(
+            'Einsum',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            equation=Eqn
+        )
+
+        X = np.random.randn(5)
+        Y = np.random.randn(5)
+        Z = einsum_reference_implementation(Eqn, (X, Y))
+
+        expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_inner_prod')
+
+    @staticmethod
+    def export_einsum_batch_matmul() -> None:
+        Eqn = 'bij, bjk -> bik'
+        node = onnx.helper.make_node(
+            'Einsum',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            equation=Eqn
+        )
+
+        X = np.random.randn(5, 2, 3)
+        Y = np.random.randn(5, 3, 4)
+        Z = einsum_reference_implementation(Eqn, (X, Y))
+
+        expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_batch_matmul')
diff --git a/third_party/onnx/onnx/backend/test/case/node/elu.py b/third_party/onnx/onnx/backend/test/case/node/elu.py
new file mode 100644
index 0000000000..a06871c5cd
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/elu.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Elu(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Elu',
+            inputs=['x'],
+            outputs=['y'],
+            alpha=2.0
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-1.2642411, 0., 1.]
+        y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+        expect(node, inputs=[x], outputs=[y],
+               name='test_elu_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+        expect(node, inputs=[x], outputs=[y],
+               name='test_elu')
+
+    @staticmethod
+    def export_elu_default() -> None:
+        default_alpha = 1.0
+        node = onnx.helper.make_node(
+            'Elu',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
+        expect(node, inputs=[x], outputs=[y],
+               name='test_elu_default')
diff --git a/third_party/onnx/onnx/backend/test/case/node/equal.py b/third_party/onnx/onnx/backend/test/case/node/equal.py
new file mode 100644
index 0000000000..37be9de848
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/equal.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Equal(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Equal',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+        y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_equal')
+
+    @staticmethod
+    def export_equal_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Equal',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+        y = (np.random.randn(5) * 10).astype(np.int32)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_equal_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/erf.py b/third_party/onnx/onnx/backend/test/case/node/erf.py
new file mode 100644
index 0000000000..29432e89e6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/erf.py
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Erf(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Erf',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        y = np.vectorize(math.erf)(x).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_erf')
diff --git a/third_party/onnx/onnx/backend/test/case/node/exp.py b/third_party/onnx/onnx/backend/test/case/node/exp.py
new file mode 100644
index 0000000000..0fd3a3205e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/exp.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Exp(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Exp',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.exp(x)  # expected output [0.36787945, 1., 2.71828175]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_exp_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.exp(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_exp')
diff --git a/third_party/onnx/onnx/backend/test/case/node/expand.py b/third_party/onnx/onnx/backend/test/case/node/expand.py
new file mode 100644
index 0000000000..341f059379
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/expand.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Expand(Base):
+
+    @staticmethod
+    def export_dim_changed() -> None:
+        node = onnx.helper.make_node(
+            'Expand',
+            inputs=['data', 'new_shape'],
+            outputs=['expanded'],
+        )
+        shape = [3, 1]
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[1.], [2.], [3.]]
+        new_shape = [2, 1, 6]
+        expanded = data * np.ones(new_shape, dtype=np.float32)
+        #print(expanded)
+        #[[[1., 1., 1., 1., 1., 1.],
+        #  [2., 2., 2., 2., 2., 2.],
+        #  [3., 3., 3., 3., 3., 3.]],
+        #
+        # [[1., 1., 1., 1., 1., 1.],
+        #  [2., 2., 2., 2., 2., 2.],
+        #  [3., 3., 3., 3., 3., 3.]]]
+        new_shape = np.array(new_shape, dtype=np.int64)
+        expect(node, inputs=[data, new_shape], outputs=[expanded],
+               name='test_expand_dim_changed')
+
+    @staticmethod
+    def export_dim_unchanged() -> None:
+        node = onnx.helper.make_node(
+            'Expand',
+            inputs=['data', 'new_shape'],
+            outputs=['expanded'],
+        )
+        shape = [3, 1]
+        new_shape = [3, 4]
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[1.], [2.], [3.]]
+        expanded = np.tile(data, 4)
+        #print(expanded)
+        #[[1., 1., 1., 1.],
+        # [2., 2., 2., 2.],
+        # [3., 3., 3., 3.]]
+        new_shape = np.array(new_shape, dtype=np.int64)
+        expect(node, inputs=[data, new_shape], outputs=[expanded],
+               name='test_expand_dim_unchanged')
diff --git a/third_party/onnx/onnx/backend/test/case/node/eyelike.py b/third_party/onnx/onnx/backend/test/case/node/eyelike.py
new file mode 100644
index 0000000000..c9ef58c325
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/eyelike.py
@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class EyeLike(Base):
+
+    @staticmethod
+    def export_without_dtype() -> None:
+        shape = (4, 4)
+        node = onnx.helper.make_node(
+            'EyeLike',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+        y = np.eye(shape[0], shape[1], dtype=np.int32)
+        expect(node, inputs=[x], outputs=[y], name='test_eyelike_without_dtype')
+
+    @staticmethod
+    def export_with_dtype() -> None:
+        shape = (3, 4)
+        node = onnx.helper.make_node(
+            'EyeLike',
+            inputs=['x'],
+            outputs=['y'],
+            dtype=onnx.TensorProto.DOUBLE,
+        )
+
+        x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+        y = np.eye(shape[0], shape[1], dtype=np.float64)
+        expect(node, inputs=[x], outputs=[y], name='test_eyelike_with_dtype')
+
+    @staticmethod
+    def export_populate_off_main_diagonal() -> None:
+        shape = (4, 5)
+        off_diagonal_offset = 1
+        node = onnx.helper.make_node(
+            'EyeLike',
+            inputs=['x'],
+            outputs=['y'],
+            k=off_diagonal_offset,
+            dtype=onnx.TensorProto.FLOAT,
+        )
+
+        x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+        y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
+        expect(node, inputs=[x], outputs=[y], name='test_eyelike_populate_off_main_diagonal')
diff --git a/third_party/onnx/onnx/backend/test/case/node/flatten.py b/third_party/onnx/onnx/backend/test/case/node/flatten.py
new file mode 100644
index 0000000000..0da7cc9eae
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/flatten.py
@@ -0,0 +1,61 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Flatten(Base):
+
+    @staticmethod
+    def export() -> None:
+        shape = (2, 3, 4, 5)
+        a = np.random.random_sample(shape).astype(np.float32)
+
+        for i in range(len(shape)):
+            node = onnx.helper.make_node(
+                'Flatten',
+                inputs=['a'],
+                outputs=['b'],
+                axis=i,
+            )
+
+            new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
+            b = np.reshape(a, new_shape)
+            expect(node, inputs=[a], outputs=[b],
+                   name='test_flatten_axis' + str(i))
+
+    @staticmethod
+    def export_flatten_with_default_axis() -> None:
+        node = onnx.helper.make_node(
+            'Flatten',
+            inputs=['a'],
+            outputs=['b'],  # Default value for axis: axis=1
+        )
+
+        shape = (5, 4, 3, 2)
+        a = np.random.random_sample(shape).astype(np.float32)
+        new_shape = (5, 24)
+        b = np.reshape(a, new_shape)
+        expect(node, inputs=[a], outputs=[b],
+               name='test_flatten_default_axis')
+
+    @staticmethod
+    def export_flatten_negative_axis() -> None:
+        shape = (2, 3, 4, 5)
+        a = np.random.random_sample(shape).astype(np.float32)
+
+        for i in range(-len(shape), 0):
+            node = onnx.helper.make_node(
+                'Flatten',
+                inputs=['a'],
+                outputs=['b'],
+                axis=i,
+            )
+
+            new_shape = (np.prod(shape[0:i]).astype(int), -1)
+            b = np.reshape(a, new_shape)
+            expect(node, inputs=[a], outputs=[b],
+                   name='test_flatten_negative_axis' + str(abs(i)))
diff --git a/third_party/onnx/onnx/backend/test/case/node/floor.py b/third_party/onnx/onnx/backend/test/case/node/floor.py
new file mode 100644
index 0000000000..2b922c5c68
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/floor.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Floor(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Floor',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1.5, 1.2, 2]).astype(np.float32)
+        y = np.floor(x)  # expected output [-2., 1., 2.]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_floor_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.floor(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_floor')
diff --git a/third_party/onnx/onnx/backend/test/case/node/gather.py b/third_party/onnx/onnx/backend/test/case/node/gather.py
new file mode 100644
index 0000000000..a5150ab0d0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/gather.py
@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Gather(Base):
+
+    @staticmethod
+    def export_gather_0() -> None:
+        node = onnx.helper.make_node(
+            'Gather',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=0,
+        )
+        data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+        indices = np.array([0, 1, 3])
+        y = np.take(data, indices, axis=0)
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_0')
+
+    @staticmethod
+    def export_gather_1() -> None:
+        node = onnx.helper.make_node(
+            'Gather',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=1,
+        )
+        data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+        indices = np.array([0, 1, 3])
+        y = np.take(data, indices, axis=1)
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_1')
+
+    @staticmethod
+    def export_gather_2d_indices() -> None:
+        node = onnx.helper.make_node(
+            'Gather',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=1,
+        )
+        data = np.random.randn(3, 3).astype(np.float32)
+        indices = np.array([[0, 2]])
+        y = np.take(data, indices, axis=1)
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_2d_indices')
+
+    @staticmethod
+    def export_gather_negative_indices() -> None:
+        node = onnx.helper.make_node(
+            'Gather',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=0,
+        )
+        data = np.arange(10).astype(np.float32)
+        indices = np.array([0, -9, -10])
+        y = np.take(data, indices, axis=0)
+
+        # print(y)
+        # [0. 1. 0.]
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_negative_indices')
diff --git a/third_party/onnx/onnx/backend/test/case/node/gatherelements.py b/third_party/onnx/onnx/backend/test/case/node/gatherelements.py
new file mode 100644
index 0000000000..ce313fbf13
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/gatherelements.py
@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+# The below GatherElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
+def gather_elements(data, indices, axis=0):  # type: ignore
+    data_swaped = np.swapaxes(data, 0, axis)
+    index_swaped = np.swapaxes(indices, 0, axis)
+    gathered = np.choose(index_swaped, data_swaped, mode='wrap')
+    y = np.swapaxes(gathered, 0, axis)
+    return y
+
+
+class GatherElements(Base):
+
+    @staticmethod
+    def export_gather_elements_0() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            'GatherElements',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=axis,
+        )
+        data = np.array([[1, 2],
+                         [3, 4]], dtype=np.float32)
+        indices = np.array([[0, 0],
+                            [1, 0]], dtype=np.int32)
+
+        y = gather_elements(data, indices, axis)
+        # print(y) produces
+        # [[1, 1],
+        #  [4, 3]]
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_elements_0')
+
+    @staticmethod
+    def export_gather_elements_1() -> None:
+        axis = 0
+        node = onnx.helper.make_node(
+            'GatherElements',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=axis,
+        )
+        data = np.array([[1, 2, 3],
+                         [4, 5, 6],
+                         [7, 8, 9]], dtype=np.float32)
+        indices = np.array([[1, 2, 0],
+                            [2, 0, 0]], dtype=np.int32)
+
+        y = gather_elements(data, indices, axis)
+        # print(y) produces
+        # [[4, 8, 3],
+        #  [7, 2, 3]]
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_elements_1')
+
+    @staticmethod
+    def export_gather_elements_negative_indices() -> None:
+        axis = 0
+        node = onnx.helper.make_node(
+            'GatherElements',
+            inputs=['data', 'indices'],
+            outputs=['y'],
+            axis=axis,
+        )
+        data = np.array([[1, 2, 3],
+                         [4, 5, 6],
+                         [7, 8, 9]], dtype=np.float32)
+        indices = np.array([[-1, -2, 0],
+                            [-2, 0, 0]], dtype=np.int32)
+
+        y = gather_elements(data, indices, axis)
+        # print(y) produces
+        # [[7, 5, 3],
+        #  [4, 2, 3]]
+
+        expect(node, inputs=[data, indices.astype(np.int64)], outputs=[y],
+               name='test_gather_elements_negative_indices')
diff --git a/third_party/onnx/onnx/backend/test/case/node/gathernd.py b/third_party/onnx/onnx/backend/test/case/node/gathernd.py
new file mode 100644
index 0000000000..e81aeee188
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/gathernd.py
@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def gather_nd_impl(data: np.ndarray, indices: np.ndarray, batch_dims: int) -> np.ndarray:
+    # Note the data rank - will be reused multiple times later
+    data_rank = len(data.shape)
+
+    # Check input tensors' shape/rank condition
+    assert indices.shape[-1] <= data_rank
+
+    #The list of data/indice shape of batch_dims
+    batch_dims_shape = []
+
+    #The number of elements in the batch_dims for data/indice array
+    batch_dims_size = 1
+
+    # Check the shape of indice and data are identicial for batch dims.
+    for i in range(batch_dims):
+        batch_dims_shape.append(indices.shape[i])
+        batch_dims_size *= indices.shape[i]
+
+    # Compute output of the op as below
+
+    # Compute shape of output array
+    output_shape = batch_dims_shape + list(indices.shape)[batch_dims:-1] if (indices.shape[-1] == data_rank - batch_dims) \
+     else batch_dims_shape + list(indices.shape)[batch_dims:-1] + list(data.shape)[batch_dims + indices.shape[-1]:]
+
+    # Placeholder for output data
+    output_data_buffer = []
+
+    # Flatten 'indices' to 2D array
+    reshaped_indices = indices.reshape(batch_dims_size, -1, indices.shape[-1])
+
+    # Flatten 'data' to array of shape (batch_dim_size, data.shape[batch_dimes:])
+    reshaped_data = data.reshape((batch_dims_size, ) + data.shape[batch_dims:])
+
+    # gather each scalar value from 'data'
+    for batch_dim in range(reshaped_indices.shape[0]):
+        for outer_dim in range(reshaped_indices.shape[1]):
+            gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
+            output_data_buffer.append(reshaped_data[(batch_dim,) + gather_index])
+    return np.asarray(output_data_buffer, dtype=data.dtype).reshape(output_shape)
+
+
+class GatherND(Base):
+
+    @staticmethod
+    def export_int32() -> None:
+        node = onnx.helper.make_node(
+            'GatherND',
+            inputs=['data', 'indices'],
+            outputs=['output'],
+        )
+
+        data = np.array([[0, 1], [2, 3]], dtype=np.int32)
+        indices = np.array([[0, 0], [1, 1]], dtype=np.int64)
+        output = gather_nd_impl(data, indices, 0)
+        expected_output = np.array([0, 3], dtype=np.int32)
+        assert (np.array_equal(output, expected_output))
+        expect(node, inputs=[data, indices], outputs=[output],
+               name='test_gathernd_example_int32')
+
+    @staticmethod
+    def export_float32() -> None:
+        node = onnx.helper.make_node(
+            'GatherND',
+            inputs=['data', 'indices'],
+            outputs=['output'],
+        )
+
+        data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32)
+        indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64)
+        output = gather_nd_impl(data, indices, 0)
+        expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32)
+        assert (np.array_equal(output, expected_output))
+        expect(node, inputs=[data, indices], outputs=[output],
+               name='test_gathernd_example_float32')
+
+    @staticmethod
+    def export_int32_batchdim_1() -> None:
+        node = onnx.helper.make_node(
+            'GatherND',
+            inputs=['data', 'indices'],
+            outputs=['output'],
+            batch_dims=1,
+        )
+
+        data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32)
+        indices = np.array([[1], [0]], dtype=np.int64)
+        output = gather_nd_impl(data, indices, 1)
+        expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32)
+        assert (np.array_equal(output, expected_output))
+        expect(node, inputs=[data, indices], outputs=[output],
+               name='test_gathernd_example_int32_batch_dim1')
diff --git a/third_party/onnx/onnx/backend/test/case/node/gemm.py b/third_party/onnx/onnx/backend/test/case/node/gemm.py
new file mode 100644
index 0000000000..2f025fa0b1
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/gemm.py
@@ -0,0 +1,183 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Optional
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def gemm_reference_implementation(A: np.ndarray, B: np.ndarray, C: Optional[np.ndarray] = None, alpha: float = 1., beta: float = 1., transA: int = 0,
+                                  transB: int = 0) -> np.ndarray:
+    A = A if transA == 0 else A.T
+    B = B if transB == 0 else B.T
+    C = C if C is not None else np.array(0)
+
+    Y = alpha * np.dot(A, B) + beta * C
+
+    return Y
+
+
+class Gemm(Base):
+
+    @staticmethod
+    def export_default_zero_bias() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y']
+        )
+        a = np.random.ranf([3, 5]).astype(np.float32)
+        b = np.random.ranf([5, 4]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_default_zero_bias')
+
+    @staticmethod
+    def export_default_no_bias() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b'],
+            outputs=['y']
+        )
+        a = np.random.ranf([2, 10]).astype(np.float32)
+        b = np.random.ranf([10, 3]).astype(np.float32)
+        y = gemm_reference_implementation(a, b)
+        expect(node, inputs=[a, b], outputs=[y],
+               name='test_gemm_default_no_bias')
+
+    @staticmethod
+    def export_default_scalar_bias() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y']
+        )
+        a = np.random.ranf([2, 3]).astype(np.float32)
+        b = np.random.ranf([3, 4]).astype(np.float32)
+        c = np.array(3.14).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_default_scalar_bias')
+
+    @staticmethod
+    def export_default_single_elem_vector_bias() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y']
+        )
+        a = np.random.ranf([3, 7]).astype(np.float32)
+        b = np.random.ranf([7, 3]).astype(np.float32)
+        c = np.random.ranf([1]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_default_single_elem_vector_bias')
+
+    @staticmethod
+    def export_default_vector_bias() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y']
+        )
+        a = np.random.ranf([2, 7]).astype(np.float32)
+        b = np.random.ranf([7, 4]).astype(np.float32)
+        c = np.random.ranf([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_default_vector_bias')
+
+    @staticmethod
+    def export_default_matrix_bias() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y']
+        )
+        a = np.random.ranf([3, 6]).astype(np.float32)
+        b = np.random.ranf([6, 4]).astype(np.float32)
+        c = np.random.ranf([3, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_default_matrix_bias')
+
+    @staticmethod
+    def export_transposeA() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y'],
+            transA=1
+        )
+        a = np.random.ranf([6, 3]).astype(np.float32)
+        b = np.random.ranf([6, 4]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, transA=1)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_transposeA')
+
+    @staticmethod
+    def export_transposeB() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y'],
+            transB=1
+        )
+        a = np.random.ranf([3, 6]).astype(np.float32)
+        b = np.random.ranf([4, 6]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, transB=1)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_transposeB')
+
+    @staticmethod
+    def export_alpha() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y'],
+            alpha=0.5
+        )
+        a = np.random.ranf([3, 5]).astype(np.float32)
+        b = np.random.ranf([5, 4]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, alpha=0.5)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_alpha')
+
+    @staticmethod
+    def export_beta() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y'],
+            beta=0.5
+        )
+        a = np.random.ranf([2, 7]).astype(np.float32)
+        b = np.random.ranf([7, 4]).astype(np.float32)
+        c = np.random.ranf([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, beta=0.5)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_beta')
+
+    @staticmethod
+    def export_all_attributes() -> None:
+        node = onnx.helper.make_node(
+            'Gemm',
+            inputs=['a', 'b', 'c'],
+            outputs=['y'],
+            alpha=0.25,
+            beta=0.35,
+            transA=1,
+            transB=1
+        )
+        a = np.random.ranf([4, 3]).astype(np.float32)
+        b = np.random.ranf([5, 4]).astype(np.float32)
+        c = np.random.ranf([1, 5]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35)
+        expect(node, inputs=[a, b, c], outputs=[y],
+               name='test_gemm_all_attributes')
diff --git a/third_party/onnx/onnx/backend/test/case/node/globalaveragepool.py b/third_party/onnx/onnx/backend/test/case/node/globalaveragepool.py
new file mode 100644
index 0000000000..b15407c2b1
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/globalaveragepool.py
@@ -0,0 +1,37 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class GlobalAveragePool(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'GlobalAveragePool',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+        y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+        expect(node, inputs=[x], outputs=[y], name='test_globalaveragepool')
+
+    @staticmethod
+    def export_globalaveragepool_precomputed() -> None:
+
+        node = onnx.helper.make_node(
+            'GlobalAveragePool',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.array([[[
+            [1, 2, 3],
+            [4, 5, 6],
+            [7, 8, 9],
+        ]]]).astype(np.float32)
+        y = np.array([[[[5]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name='test_globalaveragepool_precomputed')
diff --git a/third_party/onnx/onnx/backend/test/case/node/globalmaxpool.py b/third_party/onnx/onnx/backend/test/case/node/globalmaxpool.py
new file mode 100644
index 0000000000..888fdba96f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/globalmaxpool.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class GlobalMaxPool(Base):
+
+    @staticmethod
+    def export() -> None:
+
+        node = onnx.helper.make_node(
+            'GlobalMaxPool',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+        y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+        expect(node, inputs=[x], outputs=[y], name='test_globalmaxpool')
+
+    @staticmethod
+    def export_globalmaxpool_precomputed() -> None:
+
+        node = onnx.helper.make_node(
+            'GlobalMaxPool',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.array([[[
+            [1, 2, 3],
+            [4, 5, 6],
+            [7, 8, 9],
+        ]]]).astype(np.float32)
+        y = np.array([[[[9]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name='test_globalmaxpool_precomputed')
diff --git a/third_party/onnx/onnx/backend/test/case/node/greater.py b/third_party/onnx/onnx/backend/test/case/node/greater.py
new file mode 100644
index 0000000000..2a282efbab
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/greater.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Greater(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Greater',
+            inputs=['x', 'y'],
+            outputs=['greater'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_greater')
+
+    @staticmethod
+    def export_greater_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Greater',
+            inputs=['x', 'y'],
+            outputs=['greater'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_greater_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/greater_equal.py b/third_party/onnx/onnx/backend/test/case/node/greater_equal.py
new file mode 100644
index 0000000000..25133430f0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/greater_equal.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Greater(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'GreaterOrEqual',
+            inputs=['x', 'y'],
+            outputs=['greater_equal'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_greater_equal')
+
+    @staticmethod
+    def export_greater_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'GreaterOrEqual',
+            inputs=['x', 'y'],
+            outputs=['greater_equal'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_greater_equal_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/gridsample.py b/third_party/onnx/onnx/backend/test/case/node/gridsample.py
new file mode 100644
index 0000000000..18e8785166
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/gridsample.py
@@ -0,0 +1,374 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class GridSample(Base):
+
+    @staticmethod
+    def export_gridsample() -> None:
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='bilinear',
+            padding_mode='zeros',
+            align_corners=0,
+        )
+        # X shape, [N, C, H, W] - [1, 1, 4, 4]
+        X = np.array(
+            [
+                [
+                    [
+                        [0., 1., 2., 3.],
+                        [4., 5., 6., 7.],
+                        [8., 9., 10., 11.],
+                        [12., 13., 14., 15.]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
+        Grid = np.array(
+            [
+                [
+                    [
+                        [-1.0000, -1.0000],
+                        [-0.6000, -1.0000],
+                        [-0.2000, -1.0000],
+                        [0.2000, -1.0000],
+                        [0.6000, -1.0000],
+                        [1.0000, -1.0000]
+                    ],
+                    [
+                        [-1.0000, -0.6000],
+                        [-0.6000, -0.6000],
+                        [-0.2000, -0.6000],
+                        [0.2000, -0.6000],
+                        [0.6000, -0.6000],
+                        [1.0000, -0.6000]
+                    ],
+                    [
+                        [-1.0000, -0.2000],
+                        [-0.6000, -0.2000],
+                        [-0.2000, -0.2000],
+                        [0.2000, -0.2000],
+                        [0.6000, -0.2000],
+                        [1.0000, -0.2000]
+                    ],
+                    [
+                        [-1.0000, 0.2000],
+                        [-0.6000, 0.2000],
+                        [-0.2000, 0.2000],
+                        [0.2000, 0.2000],
+                        [0.6000, 0.2000],
+                        [1.0000, 0.2000]
+                    ],
+                    [
+                        [-1.0000, 0.6000],
+                        [-0.6000, 0.6000],
+                        [-0.2000, 0.6000],
+                        [0.2000, 0.6000],
+                        [0.6000, 0.6000],
+                        [1.0000, 0.6000]
+                    ],
+                    [
+                        [-1.0000, 1.0000],
+                        [-0.6000, 1.0000],
+                        [-0.2000, 1.0000],
+                        [0.2000, 1.0000],
+                        [0.6000, 1.0000],
+                        [1.0000, 1.0000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
+                        [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
+                        [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
+                        [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
+                        [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
+                        [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        expect(node, inputs=[X, Grid], outputs=[Y],
+               name='test_gridsample')
+
+    @staticmethod
+    def export_gridsample_paddingmode() -> None:
+        # X shape, [N, C, H, W] - [1, 1, 3, 2]
+        X = np.array(
+            [
+                [
+                    [
+                        [0., 1.],
+                        [2., 3.],
+                        [4., 5.]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+        Grid = np.array(
+            [
+                [
+                    [
+                        [-10.0000, -10.0000],
+                        [-5.0000, -5.0000],
+                        [-0.2000, -0.2000],
+                        [10.0000, 10.0000]
+                    ],
+
+                    [
+                        [10.0000, 10.0000],
+                        [-0.2000, -0.2000],
+                        [5.0000, 5.0000],
+                        [10.0000, 10.0000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # setting padding_mode = 'zeros'
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            padding_mode='zeros',
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_zeros = np.array(
+            [
+                [
+                    [
+                        [0.0000, 0.0000, 1.7000, 0.0000],
+                        [0.0000, 1.7000, 0.0000, 0.0000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_zeros],
+               name='test_gridsample_zeros_padding')
+
+        # setting padding_mode = 'border'
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            padding_mode='border',
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_border = np.array(
+            [
+                [
+                    [
+                        [0.0000, 0.0000, 1.7000, 5.0000],
+                        [5.0000, 1.7000, 5.0000, 5.0000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_border],
+               name='test_gridsample_border_padding')
+
+        # setting padding_mode = 'reflection'
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            padding_mode='reflection',
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_reflection = np.array(
+            [
+                [
+                    [
+                        [2.5000, 0.0000, 1.7000, 2.5000],
+                        [2.5000, 1.7000, 5.0000, 2.5000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_reflection],
+               name='test_gridsample_reflection_padding')
+
+    @staticmethod
+    def export_gridsample_mode_aligncorners() -> None:
+        # X shape, [N, C, H, W] - [1, 1, 3, 2]
+        X = np.array(
+            [
+                [
+                    [
+                        [0., 1.],
+                        [2., 3.],
+                        [4., 5.]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+        Grid = np.array(
+            [
+                [
+                    [
+                        [-1.0000, -1.0000],
+                        [-0.5000, -0.5000],
+                        [-0.2000, -0.2000],
+                        [0.0000, 0.0000]
+                    ],
+
+                    [
+                        [0.0000, 0.0000],
+                        [-0.2000, -0.2000],
+                        [0.5000, 0.5000],
+                        [1.0000, 1.0000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # setting mode = 'bilinear', default align_corners = 0
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='bilinear',
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bilinear = np.array(
+            [
+                [
+                    [
+                        [0.0000, 0.5000, 1.7000, 2.5000],
+                        [2.5000, 1.7000, 4.5000, 1.2500]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_bilinear],
+               name='test_gridsample_bilinear')
+
+        # setting mode = 'bilinear', align_corners = 1
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='bilinear',
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_align_corners = np.array(
+            [
+                [
+                    [
+                        [0.0000, 1.2500, 2.0000, 2.5000],
+                        [2.5000, 2.0000, 3.7500, 5.0000]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_align_corners],
+               name='test_gridsample_aligncorners_true')
+
+        # setting mode = 'nearest'
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='nearest',
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_nearest = np.array(
+            [
+                [
+                    [
+                        [0., 0., 2., 2.],
+                        [2., 2., 5., 0.]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_nearest],
+               name='test_gridsample_nearest')
+
+        # setting mode = 'bicubic'
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='bicubic',
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bicubic = np.array(
+            [
+                [
+                    [
+                        [-0.1406, 0.3828, 1.7556, 2.9688],
+                        [2.9688, 1.7556, 5.1445, 1.3906]
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, Grid], outputs=[Y_bicubic],
+               name='test_gridsample_bicubic')
+
+    '''
+    For someone who want to test by script. Comment it cause github ONNX CI
+    do not have the torch python package.
+    @staticmethod
+    def export_gridsample_torch():  # type: () -> None
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='bilinear',
+            padding_mode='zeros',
+            align_corners=0,
+        )
+
+        # X shape, [N, C, H, W] - [1, 1, 4, 4]
+        # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
+        import torch
+        X = torch.arange(3 * 3).view(1, 1, 3, 3).float()
+        d = torch.linspace(-1, 1, 6)
+        meshx, meshy = torch.meshgrid((d, d))
+        grid = torch.stack((meshy, meshx), 2)
+        Grid = grid.unsqueeze(0)
+        Y = torch.nn.functional.grid_sample(X, Grid, mode='bilinear',
+                                            padding_mode='zeros', align_corners=False)
+        expect(node, inputs=[X.numpy(), Grid.numpy()], outputs=[Y.numpy()],
+               name='test_gridsample_torch')
+    '''
diff --git a/third_party/onnx/onnx/backend/test/case/node/gru.py b/third_party/onnx/onnx/backend/test/case/node/gru.py
new file mode 100644
index 0000000000..feafc1cd4f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/gru.py
@@ -0,0 +1,207 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Any, Tuple
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class GRU_Helper():
+    def __init__(self, **params: Any) -> None:
+        # GRU Input Names
+        X = str('X')
+        W = str('W')
+        R = str('R')
+        B = str('B')
+        H_0 = str('initial_h')
+        LBR = str('linear_before_reset')
+        LAYOUT = str('layout')
+        number_of_gates = 3
+
+        required_inputs = [X, W, R]
+        for i in required_inputs:
+            assert i in params, "Missing Required Input: {0}".format(i)
+
+        self.num_directions = params[W].shape[0]
+
+        if self.num_directions == 1:
+            for k in params.keys():
+                if k != X:
+                    params[k] = np.squeeze(params[k], axis=0)
+
+            hidden_size = params[R].shape[-1]
+            batch_size = params[X].shape[1]
+
+            layout = params[LAYOUT] if LAYOUT in params else 0
+            x = params[X]
+            x = x if layout == 0 else np.swapaxes(x, 0, 1)
+            b = params[B] if B in params else np.zeros(2 * number_of_gates * hidden_size)
+            h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size))
+            lbr = params[LBR] if LBR in params else 0
+
+            self.X = x
+            self.W = params[W]
+            self.R = params[R]
+            self.B = b
+            self.H_0 = h_0
+            self.LBR = lbr
+            self.LAYOUT = layout
+
+        else:
+            raise NotImplementedError()
+
+    def f(self, x: np.ndarray) -> np.ndarray:
+        return 1 / (1 + np.exp(-x))
+
+    def g(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def step(self) -> Tuple[np.ndarray, np.ndarray]:
+        seq_length = self.X.shape[0]
+        hidden_size = self.H_0.shape[-1]
+        batch_size = self.X.shape[1]
+
+        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
+        h_list = []
+
+        [w_z, w_r, w_h] = np.split(self.W, 3)
+        [r_z, r_r, r_h] = np.split(self.R, 3)
+        [w_bz, w_br, w_bh, r_bz, r_br, r_bh] = np.split(self.B, 6)
+        gates_w = np.transpose(np.concatenate((w_z, w_r)))
+        gates_r = np.transpose(np.concatenate((r_z, r_r)))
+        gates_b = np.add(np.concatenate((w_bz, w_br)), np.concatenate((r_bz, r_br)))
+
+        H_t = self.H_0
+        for x in np.split(self.X, self.X.shape[0], axis=0):
+            gates = np.dot(x, gates_w) + np.dot(H_t, gates_r) + gates_b
+            z, r = np.split(gates, 2, -1)
+            z = self.f(z)
+            r = self.f(r)
+            h_default = self.g(np.dot(x, np.transpose(w_h)) + np.dot(r * H_t, np.transpose(r_h)) + w_bh + r_bh)
+            h_linear = self.g(np.dot(x, np.transpose(w_h)) + r * (np.dot(H_t, np.transpose(r_h)) + r_bh) + w_bh)
+            h = h_linear if self.LBR else h_default
+            H = (1 - z) * h + z * H_t
+            h_list.append(H)
+            H_t = H
+
+        concatenated = np.concatenate(h_list)
+        if self.num_directions == 1:
+            Y[:, 0, :, :] = concatenated
+
+        if self.LAYOUT == 0:
+            Y_h = Y[-1]
+        else:
+            Y = np.transpose(Y, [2, 0, 1, 3])
+            Y_h = Y[:, :, -1, :]
+
+        return Y, Y_h
+
+
+class GRU(Base):
+
+    @staticmethod
+    def export_defaults() -> None:
+        input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 5
+        weight_scale = 0.1
+        number_of_gates = 3
+
+        node = onnx.helper.make_node(
+            'GRU',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+        gru = GRU_Helper(X=input, W=W, R=R)
+        _, Y_h = gru.step()
+        expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_gru_defaults')
+
+    @staticmethod
+    def export_initial_bias() -> None:
+        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 3
+        weight_scale = 0.1
+        custom_bias = 0.1
+        number_of_gates = 3
+
+        node = onnx.helper.make_node(
+            'GRU',
+            inputs=['X', 'W', 'R', 'B'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+        # Adding custom bias
+        W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(np.float32)
+        R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        gru = GRU_Helper(X=input, W=W, R=R, B=B)
+        _, Y_h = gru.step()
+        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_gru_with_initial_bias')
+
+    @staticmethod
+    def export_seq_length() -> None:
+        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
+                          [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 5
+        number_of_gates = 3
+
+        node = onnx.helper.make_node(
+            'GRU',
+            inputs=['X', 'W', 'R', 'B'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype(np.float32)
+        R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(np.float32)
+
+        # Adding custom bias
+        W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+        R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        gru = GRU_Helper(X=input, W=W, R=R, B=B)
+        _, Y_h = gru.step()
+        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_gru_seq_length')
+
+    @staticmethod
+    def export_batchwise() -> None:
+        input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 6
+        number_of_gates = 3
+        weight_scale = 0.2
+        layout = 1
+
+        node = onnx.helper.make_node(
+            'GRU',
+            inputs=['X', 'W', 'R'],
+            outputs=['Y', 'Y_h'],
+            hidden_size=hidden_size,
+            layout=layout
+        )
+
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+        gru = GRU_Helper(X=input, W=W, R=R, layout=layout)
+        Y, Y_h = gru.step()
+        expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_gru_batchwise')
diff --git a/third_party/onnx/onnx/backend/test/case/node/hardmax.py b/third_party/onnx/onnx/backend/test/case/node/hardmax.py
new file mode 100644
index 0000000000..8f60376a12
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/hardmax.py
@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def hardmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_argmax = np.argmax(x, axis=axis)
+    y = np.zeros_like(x)
+    np.put_along_axis(y, np.expand_dims(x_argmax, axis=axis), 1, axis=axis)
+    return y
+
+
+class Hardmax(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Hardmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2],
+                      [0, 1, 2, 3]]).astype(np.float32)
+        # expect result:
+        # [[1. 0. 0. 0.]
+        # [0. 1. 0. 0.]
+        # [0. 0. 1. 0.]
+        # [0. 0. 0. 1.]]
+        y = hardmax(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_example')
+
+        # For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
+        x = np.array([[3, 3, 3, 1]]).astype(np.float32)
+        # expect result:
+        # [[1, 0, 0, 0]]
+        y = hardmax(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_one_hot')
+
+    @staticmethod
+    def export_hardmax_axis() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        node = onnx.helper.make_node(
+            'Hardmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=0,
+        )
+        y = hardmax(x, axis=0)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_axis_0')
+
+        node = onnx.helper.make_node(
+            'Hardmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=1,
+        )
+        y = hardmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_axis_1')
+
+        node = onnx.helper.make_node(
+            'Hardmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=2,
+        )
+        y = hardmax(x, axis=2)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_axis_2')
+
+        node = onnx.helper.make_node(
+            'Hardmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=-1,
+        )
+        y = hardmax(x, axis=-1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_negative_axis')
+
+        # default axis is -1
+        node = onnx.helper.make_node(
+            'Hardmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardmax_default_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/hardsigmoid.py b/third_party/onnx/onnx/backend/test/case/node/hardsigmoid.py
new file mode 100644
index 0000000000..13dd637a46
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/hardsigmoid.py
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class HardSigmoid(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'HardSigmoid',
+            inputs=['x'],
+            outputs=['y'],
+            alpha=0.5,
+            beta=0.6
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardsigmoid_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x * 0.5 + 0.6, 0, 1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardsigmoid')
+
+    @staticmethod
+    def export_hardsigmoid_default() -> None:
+        default_alpha = 0.2
+        default_beta = 0.5
+        node = onnx.helper.make_node(
+            'HardSigmoid',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x * default_alpha + default_beta, 0, 1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardsigmoid_default')
diff --git a/third_party/onnx/onnx/backend/test/case/node/hardswish.py b/third_party/onnx/onnx/backend/test/case/node/hardswish.py
new file mode 100644
index 0000000000..c13b7e27af
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/hardswish.py
@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def hardswish(x: np.ndarray) -> np.ndarray:
+    alfa = float(1 / 6)
+    beta = 0.5
+    return x * np.maximum(0, np.minimum(1, alfa * x + beta))
+
+
+class HardSwish(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'HardSwish',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = hardswish(x)
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_hardswish')
diff --git a/third_party/onnx/onnx/backend/test/case/node/identity.py b/third_party/onnx/onnx/backend/test/case/node/identity.py
new file mode 100644
index 0000000000..81277224c8
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/identity.py
@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Identity(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Identity',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        data = np.array([[[
+            [1, 2],
+            [3, 4],
+        ]]], dtype=np.float32)
+
+        expect(node, inputs=[data], outputs=[data],
+               name='test_identity')
+
+    @staticmethod
+    def export_sequence() -> None:
+        node = onnx.helper.make_node(
+            'Identity',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        data = [
+            np.array([[[
+                [1, 2],
+                [3, 4],
+            ]]], dtype=np.float32),
+            np.array([[[
+                [2, 3],
+                [1, 5],
+            ]]], dtype=np.float32)]
+
+        expect(node, inputs=[data], outputs=[data], name='test_identity_sequence')
+
+    @staticmethod
+    def export_identity_opt() -> None:
+        ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+        seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+        opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+
+        identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['opt_in'],
+            outputs=['opt_out']
+        )
+
+        x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+
+        expect(identity_node, inputs=[x], outputs=[x], name='test_identity_opt',
+               opset_imports=[onnx.helper.make_opsetid("", 16)],
+               input_type_protos=[opt_in_tp],
+               output_type_protos=[opt_in_tp])
diff --git a/third_party/onnx/onnx/backend/test/case/node/if.py b/third_party/onnx/onnx/backend/test/case/node/if.py
new file mode 100644
index 0000000000..61610f0700
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/if.py
@@ -0,0 +1,207 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def compute_if_outputs(x, cond):  # type: ignore
+    if cond:
+        return []
+    else:
+        return x
+
+
+class If(Base):
+    @staticmethod
+    def export_if() -> None:
+        # Given a bool scalar input cond.
+        # return constant tensor x if cond is True, otherwise return constant tensor y.
+
+        then_out = onnx.helper.make_tensor_value_info('then_out', onnx.TensorProto.FLOAT, [5])
+        else_out = onnx.helper.make_tensor_value_info('else_out', onnx.TensorProto.FLOAT, [5])
+
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+        y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
+
+        then_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['then_out'],
+            value=onnx.numpy_helper.from_array(x)
+        )
+
+        else_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['else_out'],
+            value=onnx.numpy_helper.from_array(y)
+        )
+
+        then_body = onnx.helper.make_graph(
+            [then_const_node],
+            'then_body',
+            [],
+            [then_out]
+        )
+
+        else_body = onnx.helper.make_graph(
+            [else_const_node],
+            'else_body',
+            [],
+            [else_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            'If',
+            inputs=['cond'],
+            outputs=['res'],
+            then_branch=then_body,
+            else_branch=else_body
+        )
+
+        cond = np.array(1).astype(bool)
+        res = x if cond else y
+        expect(if_node, inputs=[cond], outputs=[res], name='test_if',
+               opset_imports=[onnx.helper.make_opsetid("", 11)])
+
+    @staticmethod
+    def export_if_seq() -> None:
+        # Given a bool scalar input cond.
+        # return constant sequence x if cond is True, otherwise return constant sequence y.
+
+        then_out = onnx.helper.make_tensor_sequence_value_info('then_out', onnx.TensorProto.FLOAT, shape=[5])
+        else_out = onnx.helper.make_tensor_sequence_value_info('else_out', onnx.TensorProto.FLOAT, shape=[5])
+
+        x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+        y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)]
+
+        then_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['x'],
+            value=onnx.numpy_helper.from_array(x[0])
+        )
+
+        then_seq_node = onnx.helper.make_node(
+            'SequenceConstruct',
+            inputs=['x'],
+            outputs=['then_out']
+        )
+
+        else_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['y'],
+            value=onnx.numpy_helper.from_array(y[0])
+        )
+
+        else_seq_node = onnx.helper.make_node(
+            'SequenceConstruct',
+            inputs=['y'],
+            outputs=['else_out']
+        )
+
+        then_body = onnx.helper.make_graph(
+            [then_const_node, then_seq_node],
+            'then_body',
+            [],
+            [then_out]
+        )
+
+        else_body = onnx.helper.make_graph(
+            [else_const_node, else_seq_node],
+            'else_body',
+            [],
+            [else_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            'If',
+            inputs=['cond'],
+            outputs=['res'],
+            then_branch=then_body,
+            else_branch=else_body
+        )
+
+        cond = np.array(1).astype(bool)
+        res = x if cond else y
+        expect(if_node, inputs=[cond], outputs=[res], name='test_if_seq',
+               opset_imports=[onnx.helper.make_opsetid("", 13)])
+
+    @staticmethod
+    def export_if_optional() -> None:
+        # Given a bool scalar input cond, return an empty optional sequence of
+        # tensor if True, return an optional sequence with value x
+        # (the input optional sequence) otherwise.
+
+        ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+        seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+
+        then_out_tensor_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+        then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp)
+        then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp)
+        then_out = onnx.helper.make_value_info('optional_empty', then_out_opt_tp)
+
+        else_out_tensor_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, shape=[5])
+        else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp)
+        else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp)
+        else_out = onnx.helper.make_value_info('else_opt', else_out_opt_tp)
+
+        x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+        cond = np.array(0).astype(bool)
+        res = compute_if_outputs(x, cond)
+
+        opt_empty_in = onnx.helper.make_node(
+            'Optional',
+            inputs=[],
+            outputs=['optional_empty'],
+            type=seq_in_tp
+        )
+
+        then_body = onnx.helper.make_graph(
+            [opt_empty_in],
+            'then_body',
+            [],
+            [then_out]
+        )
+
+        else_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['x'],
+            value=onnx.numpy_helper.from_array(x[0])
+        )
+
+        else_seq_node = onnx.helper.make_node(
+            'SequenceConstruct',
+            inputs=['x'],
+            outputs=['else_seq']
+        )
+
+        else_optional_seq_node = onnx.helper.make_node(
+            'Optional',
+            inputs=['else_seq'],
+            outputs=['else_opt']
+        )
+
+        else_body = onnx.helper.make_graph(
+            [else_const_node, else_seq_node, else_optional_seq_node],
+            'else_body',
+            [],
+            [else_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            'If',
+            inputs=['cond'],
+            outputs=['sequence'],
+            then_branch=then_body,
+            else_branch=else_body
+        )
+
+        expect(if_node, inputs=[cond], outputs=[res], name='test_if_opt',
+               output_type_protos=[else_out_opt_tp],
+               opset_imports=[onnx.helper.make_opsetid("", 16)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/instancenorm.py b/third_party/onnx/onnx/backend/test/case/node/instancenorm.py
new file mode 100644
index 0000000000..10689016ed
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/instancenorm.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class InstanceNormalization(Base):
+
+    @staticmethod
+    def export() -> None:
+        def _instancenorm_test_mode(x, s, bias, epsilon=1e-5):  # type: ignore
+            dims_x = len(x.shape)
+            axis = tuple(range(2, dims_x))
+            mean = np.mean(x, axis=axis, keepdims=True)
+            var = np.var(x, axis=axis, keepdims=True)
+            dim_ones = (1,) * (dims_x - 2)
+            s = s.reshape(-1, *dim_ones)
+            bias = bias.reshape(-1, *dim_ones)
+            return s * (x - mean) / np.sqrt(var + epsilon) + bias
+
+        # input size: (1, 2, 1, 3)
+        x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
+        s = np.array([1.0, 1.5]).astype(np.float32)
+        bias = np.array([0, 1]).astype(np.float32)
+        y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'InstanceNormalization',
+            inputs=['x', 's', 'bias'],
+            outputs=['y'],
+        )
+
+        # output size: (1, 2, 1, 3)
+        expect(node, inputs=[x, s, bias], outputs=[y],
+               name='test_instancenorm_example')
+
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        epsilon = 1e-2
+        y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'InstanceNormalization',
+            inputs=['x', 's', 'bias'],
+            outputs=['y'],
+            epsilon=epsilon,
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(node, inputs=[x, s, bias], outputs=[y],
+               name='test_instancenorm_epsilon')
diff --git a/third_party/onnx/onnx/backend/test/case/node/isinf.py b/third_party/onnx/onnx/backend/test/case/node/isinf.py
new file mode 100644
index 0000000000..21680ee412
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/isinf.py
@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class IsInf(Base):
+
+    @staticmethod
+    def export_infinity() -> None:
+        node = onnx.helper.make_node('IsInf',
+                                     inputs=['x'],
+                                     outputs=['y'],
+                                     )
+
+        x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
+                     dtype=np.float32)
+        y = np.isinf(x)
+        expect(node, inputs=[x], outputs=[y], name='test_isinf')
+
+    @staticmethod
+    def export_positive_infinity_only() -> None:
+        node = onnx.helper.make_node('IsInf',
+                                     inputs=['x'],
+                                     outputs=['y'],
+                                     detect_negative=0
+                                     )
+
+        x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
+                     dtype=np.float32)
+        y = np.isposinf(x)
+        expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')
+
+    @staticmethod
+    def export_negative_infinity_only() -> None:
+        node = onnx.helper.make_node('IsInf',
+                                     inputs=['x'],
+                                     outputs=['y'],
+                                     detect_positive=0
+                                     )
+
+        x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
+                     dtype=np.float32)
+        y = np.isneginf(x)
+        expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')
diff --git a/third_party/onnx/onnx/backend/test/case/node/isnan.py b/third_party/onnx/onnx/backend/test/case/node/isnan.py
new file mode 100644
index 0000000000..0c7104f61f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/isnan.py
@@ -0,0 +1,22 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class IsNaN(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'IsNaN',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([3.0, np.nan, 4.0, np.nan], dtype=np.float32)
+        y = np.isnan(x)
+        expect(node, inputs=[x], outputs=[y], name='test_isnan')
diff --git a/third_party/onnx/onnx/backend/test/case/node/layernormalization.py b/third_party/onnx/onnx/backend/test/case/node/layernormalization.py
new file mode 100644
index 0000000000..0bafa7a10e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/layernormalization.py
@@ -0,0 +1,173 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+# Layer normalization's reference implementation
+def _layer_normalization(X, W, B, axis=-1, epsilon=1e-5):  # type: ignore
+    X_shape = X.shape
+    X_rank = len(X_shape)
+    if axis < 0:
+        # If axis = -1 and rank of X is 4,
+        # the axis is changed to -1 + 4 = 3,
+        # which means the last axis.
+        axis = axis + X_rank
+    unsqueezed_rank = X_rank - axis
+    reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank
+
+    # Parameter used to convert N-D tensor layer
+    # normalization to equivalent 2-D matirx operations.
+    row_number = 1
+    col_number = 1
+    for i in range(X_rank):
+        if i < axis:
+            row_number *= X_shape[i]
+        else:
+            col_number *= X_shape[i]
+
+    # After reshaping input tensor X into a matrix,
+    # layer normalization is equivalent to conducting
+    # standardization on each column vector (s.t. each
+    # column has zero mean and unit variance).
+    x_mat = np.reshape(X, (row_number, col_number))
+    # This computes mean for every x_mat's column.
+    x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number
+    x_diff = x_mat - x_mean
+    x_squared_diff = x_diff * x_diff
+    # This computes variance for every x_mat's column.
+    variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number
+    variance_eps = variance + epsilon
+    std_dev = np.sqrt(variance_eps)
+    inv_std_dev = np.reciprocal(std_dev)
+    # Standardization step. y_mat is zero-mean and unit-variance.
+    y_mat = x_diff * inv_std_dev
+    # Apply affine transform on normalization outcome.
+    # W is linear coefficient while B is bias.
+    Y = np.reshape(y_mat, X_shape) * W + B
+    # Matrix-level operations' outputs should be reshaped
+    # to compensate the initial tensor-to-matrix reshape.
+    X_mean = np.reshape(x_mean, reduction_shape)
+    X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape)
+
+    return Y, X_mean, X_inv_std_dev
+
+
+def calculate_normalized_shape(X_shape, axis):  # type: ignore
+    X_rank = len(X_shape)
+    if axis < 0:
+        axis = axis + X_rank
+    return X_shape[axis:]
+
+
+class LayerNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+
+            node = onnx.helper.make_node(
+                'LayerNormalization',
+                inputs=['X', 'W', 'B'],
+                outputs=['Y', 'Mean', 'InvStdDev'],
+                axis=axis,
+            )
+
+            if axis < 0:
+                name = f'test_layer_normalization_4d_axis_negative_{-axis}'
+            else:
+                name = f'test_layer_normalization_4d_axis{axis}'
+
+            expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+                   name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export_default_axis() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        # Default axis in LayerNormalization is -1.
+        normalized_shape = calculate_normalized_shape(X.shape, -1)
+        W = np.random.randn(*normalized_shape).astype(np.float32)
+        B = np.random.randn(*normalized_shape).astype(np.float32)
+        # Axis is default to -1 in the reference implementation.
+        Y, mean, inv_std_dev = _layer_normalization(X, W, B)
+
+        # Not specifying axis attribute means -1.
+        node = onnx.helper.make_node(
+            'LayerNormalization',
+            inputs=['X', 'W', 'B'],
+            outputs=['Y', 'Mean', 'InvStdDev']
+        )
+
+        expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+                name='test_layer_normalization_default_axis')
+
+    @staticmethod
+    def export2d() -> None:
+        X = np.random.randn(3, 4).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
+
+            node = onnx.helper.make_node(
+                'LayerNormalization',
+                inputs=['X', 'W', 'B'],
+                outputs=['Y', 'Mean', 'InvStdDev'],
+                axis=axis,
+            )
+
+            if axis < 0:
+                name = f'test_layer_normalization_2d_axis_negative_{-axis}'
+            else:
+                name = f'test_layer_normalization_2d_axis{axis}'
+
+            expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+                   name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export3d_epsilon() -> None:
+        X = np.random.randn(2, 3, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+            node = onnx.helper.make_node(
+                'LayerNormalization',
+                inputs=['X', 'W', 'B'],
+                outputs=['Y', 'Mean', 'InvStdDev'],
+                axis=axis,
+                epsilon=1e-1
+            )
+
+            if axis < 0:
+                name = f'test_layer_normalization_3d_axis_negative_{-axis}_epsilon'
+            else:
+                name = f'test_layer_normalization_3d_axis{axis}_epsilon'
+
+            expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev],
+                   name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
diff --git a/third_party/onnx/onnx/backend/test/case/node/leakyrelu.py b/third_party/onnx/onnx/backend/test/case/node/leakyrelu.py
new file mode 100644
index 0000000000..b8e448e1a4
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/leakyrelu.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class LeakyRelu(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'LeakyRelu',
+            inputs=['x'],
+            outputs=['y'],
+            alpha=0.1
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-0.1, 0., 1.]
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+        expect(node, inputs=[x], outputs=[y],
+               name='test_leakyrelu_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+        expect(node, inputs=[x], outputs=[y],
+               name='test_leakyrelu')
+
+    @staticmethod
+    def export_leakyrelu_default() -> None:
+        default_alpha = 0.01
+        node = onnx.helper.make_node(
+            'LeakyRelu',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha
+        expect(node, inputs=[x], outputs=[y],
+               name='test_leakyrelu_default')
diff --git a/third_party/onnx/onnx/backend/test/case/node/less.py b/third_party/onnx/onnx/backend/test/case/node/less.py
new file mode 100644
index 0000000000..8560c99d6b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/less.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Less(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Less',
+            inputs=['x', 'y'],
+            outputs=['less'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_less')
+
+    @staticmethod
+    def export_less_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Less',
+            inputs=['x', 'y'],
+            outputs=['less'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_less_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/less_equal.py b/third_party/onnx/onnx/backend/test/case/node/less_equal.py
new file mode 100644
index 0000000000..4cf9ea72b2
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/less_equal.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Less(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'LessOrEqual',
+            inputs=['x', 'y'],
+            outputs=['less_equal'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_less_equal')
+
+    @staticmethod
+    def export_less_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'LessOrEqual',
+            inputs=['x', 'y'],
+            outputs=['less_equal'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_less_equal_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/log.py b/third_party/onnx/onnx/backend/test/case/node/log.py
new file mode 100644
index 0000000000..69e030f094
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/log.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Log(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Log',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([1, 10]).astype(np.float32)
+        y = np.log(x)  # expected output [0., 2.30258512]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_log_example')
+
+        x = np.exp(np.random.randn(3, 4, 5).astype(np.float32))
+        y = np.log(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_log')
diff --git a/third_party/onnx/onnx/backend/test/case/node/logsoftmax.py b/third_party/onnx/onnx/backend/test/case/node/logsoftmax.py
new file mode 100644
index 0000000000..c1f7f3b756
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/logsoftmax.py
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def logsoftmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_max = np.max(x, axis=axis, keepdims=True)
+    tmp = np.exp(x - x_max)
+    s = np.sum(tmp, axis=axis, keepdims=True)
+    return (x - x_max) - np.log(s)
+
+
+class LogSoftmax(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.array([[-1, 0, 1]]).astype(np.float32)
+        # expected output
+        # [[-2.4076061 -1.407606  -0.407606 ]]
+        y = logsoftmax(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_example_1')
+
+    @staticmethod
+    def export_logsoftmax_axis() -> None:
+        x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]
+                     ).astype(np.float32)
+        # expected output
+        # [[-3.4401896  -2.4401896  -1.4401896  -0.44018966]
+        # [-3.4401896  -2.4401896  -1.4401896  -0.44018966]]
+        y = logsoftmax(x)
+
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_large_number')
+
+        x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=0,
+        )
+        y = logsoftmax(x, axis=0)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_axis_0')
+
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=1,
+        )
+        y = logsoftmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_axis_1')
+
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=2,
+        )
+        y = logsoftmax(x, axis=2)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_axis_2')
+
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=-1,
+        )
+        y = logsoftmax(x, axis=-1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_negative_axis')
+
+        # default axis is -1
+        node = onnx.helper.make_node(
+            'LogSoftmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        expect(node, inputs=[x], outputs=[y],
+               name='test_logsoftmax_default_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/loop.py b/third_party/onnx/onnx/backend/test/case/node/loop.py
new file mode 100644
index 0000000000..c67b23fb33
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/loop.py
@@ -0,0 +1,426 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import List, Any
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def compute_loop_outputs(x, seq, trip_count):  # type: ignore
+    for i in range(trip_count):
+        if seq is None:
+            seq = []
+        seq += [x[:int(i + 1)]]
+    return seq
+
+
+class Loop(Base):
+
+    @staticmethod
+    def export_loop_11() -> None:
+        # Given a tensor x of values [x1, ..., xN], and initial tensor y
+        # sum up its elements using a scan
+        # returning the final state (y+x1+x2+...+xN) as well the scan_output
+        # [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
+
+        y_in = onnx.helper.make_tensor_value_info('y_in', onnx.TensorProto.FLOAT, [1])
+        y_out = onnx.helper.make_tensor_value_info('y_out', onnx.TensorProto.FLOAT, [1])
+        scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [1])
+        cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+        cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+        iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+        y = np.array([-2]).astype(np.float32)
+
+        x_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['x'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_x',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=x.shape,
+                vals=x.flatten().astype(float),
+            )
+        )
+
+        one_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['one'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_one',
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1]
+            )
+        )
+
+        i_add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['iter_count', 'one'],
+            outputs=['end']
+        )
+
+        start_unsqueeze_node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['iter_count'],
+            outputs=['slice_start'],
+            axes=[0]
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['end'],
+            outputs=['slice_end'],
+            axes=[0]
+        )
+
+        slice_node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'slice_start', 'slice_end'],
+            outputs=['slice_out']
+        )
+
+        y_add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['y_in', 'slice_out'],
+            outputs=['y_out']
+        )
+
+        identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['cond_in'],
+            outputs=['cond_out']
+        )
+
+        scan_identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['y_out'],
+            outputs=['scan_out']
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [identity_node, x_const_node, one_const_node, i_add_node,
+             start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node,
+             scan_identity_node],
+            'loop_body',
+            [iter_count, cond_in, y_in],
+            [cond_out, y_out, scan_out]
+        )
+
+        node = onnx.helper.make_node(
+            'Loop',
+            inputs=['trip_count', 'cond', 'y'],
+            outputs=['res_y', 'res_scan'],
+            body=loop_body
+        )
+
+        trip_count = np.array(5).astype(np.int64)
+        res_y = np.array([13]).astype(np.float32)
+        cond = np.array(1).astype(bool)
+        res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
+        expect(node, inputs=[trip_count, cond, y], outputs=[res_y, res_scan],
+               name='test_loop11', opset_imports=[onnx.helper.make_opsetid("", 11)])
+
+    @staticmethod
+    def export_loop_13() -> None:
+        # Given a tensor x of values [x1, ..., xN],
+        # Return a sequence of tensors of
+        #   [[x1], [x1, x2], ..., [x1, ..., xN]]
+
+        seq_in = onnx.helper.make_tensor_sequence_value_info('seq_in', onnx.TensorProto.FLOAT, None)
+        seq_out = onnx.helper.make_tensor_sequence_value_info('seq_out', onnx.TensorProto.FLOAT, None)
+        cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+        cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+        iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+        x_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['x'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_x',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=x.shape,
+                vals=x.flatten().astype(float),
+            )
+        )
+
+        one_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['one'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_one',
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1]
+            )
+        )
+
+        zero_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['slice_start'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_zero',
+                data_type=onnx.TensorProto.INT64,
+                dims=(1,),
+                vals=[0]
+            )
+        )
+
+        axes_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['axes'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_axes',
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[0]
+            )
+        )
+
+        add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['iter_count', 'one'],
+            outputs=['end']
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['end', 'axes'],
+            outputs=['slice_end']
+        )
+
+        slice_node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'slice_start', 'slice_end'],
+            outputs=['slice_out']
+        )
+
+        insert_node = onnx.helper.make_node(
+            'SequenceInsert',
+            inputs=['seq_in', 'slice_out'],
+            outputs=['seq_out']
+        )
+
+        identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['cond_in'],
+            outputs=['cond_out']
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [identity_node, x_const_node, one_const_node, zero_const_node, add_node,
+             axes_node, end_unsqueeze_node, slice_node, insert_node],
+            'loop_body',
+            [iter_count, cond_in, seq_in],
+            [cond_out, seq_out]
+        )
+
+        node = onnx.helper.make_node(
+            'Loop',
+            inputs=['trip_count', 'cond', 'seq_empty'],
+            outputs=['seq_res'],
+            body=loop_body
+        )
+
+        trip_count = np.array(5).astype(np.int64)
+        seq_empty: List[Any] = []
+        seq_res = [x[:int(i)] for i in x]
+        cond = np.array(1).astype(bool)
+        expect(node, inputs=[trip_count, cond, seq_empty], outputs=[seq_res],
+               name='test_loop13_seq', opset_imports=[onnx.helper.make_opsetid("", 13)],
+               input_type_protos=[onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, trip_count.shape),
+                                  onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                                  onnx.helper.make_sequence_type_proto(
+                                      onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []))])
+
+    @staticmethod
+    def export_loop_16_none() -> None:
+        # Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
+        # Return a concatenated sequence of tensors of
+        #   [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
+
+        ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
+        seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+        opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+        opt_in = onnx.helper.make_value_info('opt_seq_in', opt_in_tp)
+        seq_out = onnx.helper.make_tensor_sequence_value_info('seq_out', onnx.TensorProto.FLOAT, [])
+        cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+        cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+        iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+        x0 = np.array(0).astype(np.float32)
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+        optional_has_elem_node = onnx.helper.make_node(
+            'OptionalHasElement',
+            inputs=['opt_seq_in'],
+            outputs=['optional_has_elem']
+        )
+
+        optional_is_none = onnx.helper.make_node(
+            'Not',
+            inputs=['optional_has_elem'],
+            outputs=['optional_is_none']
+        )
+
+        optional_get_elem = onnx.helper.make_node(
+            'OptionalGetElement',
+            inputs=['opt_seq_in'],
+            outputs=['seq_in']
+        )
+
+        constant_in = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['constant_in'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=(),
+                vals=[0]
+            )
+        )
+
+        seq_const_in = onnx.helper.make_node(
+            'SequenceConstruct',
+            inputs=['constant_in'],
+            outputs=['init_seq_in']
+        )
+
+        then_seq_out = onnx.helper.make_tensor_sequence_value_info('init_seq_in', onnx.TensorProto.FLOAT, [])
+        then_body = onnx.helper.make_graph(
+            [constant_in, seq_const_in],
+            'then_body',
+            [],
+            [then_seq_out]
+        )
+
+        else_seq_out = onnx.helper.make_tensor_sequence_value_info('seq_in', onnx.TensorProto.FLOAT, [])
+        else_body = onnx.helper.make_graph(
+            [optional_get_elem],
+            'else_body',
+            [],
+            [else_seq_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            'If',
+            inputs=['optional_is_none'],
+            outputs=['sequence'],
+            then_branch=then_body,
+            else_branch=else_body
+        )
+
+        x_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['x'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_x',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=x.shape,
+                vals=x.flatten().astype(float),
+            )
+        )
+
+        one_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['one'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_one',
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1]
+            )
+        )
+
+        zero_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['slice_start'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_zero',
+                data_type=onnx.TensorProto.INT64,
+                dims=(1,),
+                vals=[0]
+            )
+        )
+
+        axes_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['axes'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_axes',
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[0]
+            )
+        )
+
+        add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['iter_count', 'one'],
+            outputs=['end']
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['end', 'axes'],
+            outputs=['slice_end']
+        )
+
+        slice_node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'slice_start', 'slice_end'],
+            outputs=['slice_out']
+        )
+
+        insert_node = onnx.helper.make_node(
+            'SequenceInsert',
+            inputs=['sequence', 'slice_out'],
+            outputs=['seq_out']
+        )
+
+        identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['cond_in'],
+            outputs=['cond_out']
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [identity_node, optional_has_elem_node, optional_is_none, if_node, x_const_node, one_const_node,
+             zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node],
+            'loop_body',
+            [iter_count, cond_in, opt_in],
+            [cond_out, seq_out]
+        )
+
+        node = onnx.helper.make_node(
+            'Loop',
+            inputs=['trip_count', 'cond', 'opt_seq'],
+            outputs=['seq_res'],
+            body=loop_body
+        )
+
+        trip_count = np.array(5).astype(np.int64)
+        cond = np.array(1).astype(bool)
+        seq_res = compute_loop_outputs(x, [x0], trip_count)
+        opt_seq_in: List[Any] = [x0]
+        expect(node, inputs=[trip_count, cond, opt_seq_in], outputs=[seq_res],
+               name='test_loop16_seq_none', opset_imports=[onnx.helper.make_opsetid("", 16)],
+               input_type_protos=[onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, trip_count.shape),
+                                  onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                                  opt_in_tp])
diff --git a/third_party/onnx/onnx/backend/test/case/node/lrn.py b/third_party/onnx/onnx/backend/test/case/node/lrn.py
new file mode 100644
index 0000000000..3931810822
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/lrn.py
@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class LRN(Base):
+
+    @staticmethod
+    def export() -> None:
+        alpha = 0.0002
+        beta = 0.5
+        bias = 2.0
+        nsize = 3
+        node = onnx.helper.make_node(
+            'LRN',
+            inputs=['x'],
+            outputs=['y'],
+            alpha=alpha,
+            beta=beta,
+            bias=bias,
+            size=nsize
+        )
+        x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+        square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+        for n, c, h, w in np.ndindex(x.shape):
+            square_sum[n, c, h, w] = sum(x[n,
+                                           max(0, c - int(math.floor((nsize - 1) / 2))):min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
+                                           h,
+                                           w] ** 2)
+        y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_lrn')
+
+    @staticmethod
+    def export_default() -> None:
+        alpha = 0.0001
+        beta = 0.75
+        bias = 1.0
+        nsize = 3
+        node = onnx.helper.make_node(
+            'LRN',
+            inputs=['x'],
+            outputs=['y'],
+            size=3
+        )
+        x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+        square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+        for n, c, h, w in np.ndindex(x.shape):
+            square_sum[n, c, h, w] = sum(x[n,
+                                           max(0, c - int(math.floor((nsize - 1) / 2))):min(5, c + int(math.ceil((nsize - 1) / 2)) + 1),
+                                           h,
+                                           w] ** 2)
+        y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_lrn_default')
diff --git a/third_party/onnx/onnx/backend/test/case/node/lstm.py b/third_party/onnx/onnx/backend/test/case/node/lstm.py
new file mode 100644
index 0000000000..5cc68667a9
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/lstm.py
@@ -0,0 +1,214 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Any, Tuple
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class LSTM_Helper():
+    def __init__(self, **params: Any) -> None:
+        # LSTM Input Names
+        X = str('X')
+        W = str('W')
+        R = str('R')
+        B = str('B')
+        H_0 = str('initial_h')
+        C_0 = str('initial_c')
+        P = str('P')
+        LAYOUT = str('layout')
+        number_of_gates = 4
+        number_of_peepholes = 3
+
+        required_inputs = [X, W, R]
+        for i in required_inputs:
+            assert i in params, "Missing Required Input: {0}".format(i)
+
+        self.num_directions = params[W].shape[0]
+
+        if self.num_directions == 1:
+            for k in params.keys():
+                if k != X:
+                    params[k] = np.squeeze(params[k], axis=0)
+
+            hidden_size = params[R].shape[-1]
+            batch_size = params[X].shape[1]
+
+            layout = params[LAYOUT] if LAYOUT in params else 0
+            x = params[X]
+            x = x if layout == 0 else np.swapaxes(x, 0, 1)
+            b = params[B] if B in params else np.zeros(2 * number_of_gates * hidden_size, dtype=np.float32)
+            p = params[P] if P in params else np.zeros(number_of_peepholes * hidden_size, dtype=np.float32)
+            h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32)
+            c_0 = params[C_0] if C_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32)
+
+            self.X = x
+            self.W = params[W]
+            self.R = params[R]
+            self.B = b
+            self.P = p
+            self.H_0 = h_0
+            self.C_0 = c_0
+            self.LAYOUT = layout
+
+        else:
+            raise NotImplementedError()
+
+    def f(self, x: np.ndarray) -> np.ndarray:
+        return 1 / (1 + np.exp(-x))
+
+    def g(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def h(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def step(self) -> Tuple[np.ndarray, np.ndarray]:
+        seq_length = self.X.shape[0]
+        hidden_size = self.H_0.shape[-1]
+        batch_size = self.X.shape[1]
+
+        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
+        h_list = []
+
+        [p_i, p_o, p_f] = np.split(self.P, 3)
+        H_t = self.H_0
+        C_t = self.C_0
+        for x in np.split(self.X, self.X.shape[0], axis=0):
+            gates = np.dot(x, np.transpose(self.W)) + np.dot(H_t, np.transpose(self.R)) + np.add(
+                *np.split(self.B, 2))
+            i, o, f, c = np.split(gates, 4, -1)
+            i = self.f(i + p_i * C_t)
+            f = self.f(f + p_f * C_t)
+            c = self.g(c)
+            C = f * C_t + i * c
+            o = self.f(o + p_o * C)
+            H = o * self.h(C)
+            h_list.append(H)
+            H_t = H
+            C_t = C
+
+        concatenated = np.concatenate(h_list)
+        if self.num_directions == 1:
+            Y[:, 0, :, :] = concatenated
+
+        if self.LAYOUT == 0:
+            Y_h = Y[-1]
+        else:
+            Y = np.transpose(Y, [2, 0, 1, 3])
+            Y_h = Y[:, :, -1, :]
+
+        return Y, Y_h
+
+
+class LSTM(Base):
+
+    @staticmethod
+    def export_defaults() -> None:
+        input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 3
+        weight_scale = 0.1
+        number_of_gates = 4
+
+        node = onnx.helper.make_node(
+            'LSTM',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+        lstm = LSTM_Helper(X=input, W=W, R=R)
+        _, Y_h = lstm.step()
+        expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_lstm_defaults')
+
+    @staticmethod
+    def export_initial_bias() -> None:
+        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 4
+        weight_scale = 0.1
+        custom_bias = 0.1
+        number_of_gates = 4
+
+        node = onnx.helper.make_node(
+            'LSTM',
+            inputs=['X', 'W', 'R', 'B'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+        # Adding custom bias
+        W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(np.float32)
+        R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+        B = np.concatenate((W_B, R_B), 1)
+
+        lstm = LSTM_Helper(X=input, W=W, R=R, B=B)
+        _, Y_h = lstm.step()
+        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_lstm_with_initial_bias')
+
+    @staticmethod
+    def export_peepholes() -> None:
+        input = np.array([[[1., 2., 3., 4.], [5., 6., 7., 8.]]]).astype(np.float32)
+
+        input_size = 4
+        hidden_size = 3
+        weight_scale = 0.1
+        number_of_gates = 4
+        number_of_peepholes = 3
+
+        node = onnx.helper.make_node(
+            'LSTM',
+            inputs=['X', 'W', 'R', 'B', 'sequence_lens', 'initial_h', 'initial_c', 'P'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        # Initializing Inputs
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+        B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32)
+        seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32)
+        init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+        init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+        P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype(np.float32)
+
+        lstm = LSTM_Helper(X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h)
+        _, Y_h = lstm.step()
+        expect(node, inputs=[input, W, R, B, seq_lens, init_h, init_c, P], outputs=[Y_h.astype(np.float32)],
+               name='test_lstm_with_peepholes')
+
+    @staticmethod
+    def export_batchwise() -> None:
+        input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 7
+        weight_scale = 0.3
+        number_of_gates = 4
+        layout = 1
+
+        node = onnx.helper.make_node(
+            'LSTM',
+            inputs=['X', 'W', 'R'],
+            outputs=['Y', 'Y_h'],
+            hidden_size=hidden_size,
+            layout=layout
+        )
+
+        W = weight_scale * np.ones((1, number_of_gates * hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
+
+        lstm = LSTM_Helper(X=input, W=W, R=R, layout=layout)
+        Y, Y_h = lstm.step()
+        expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_lstm_batchwise')
diff --git a/third_party/onnx/onnx/backend/test/case/node/matmul.py b/third_party/onnx/onnx/backend/test/case/node/matmul.py
new file mode 100644
index 0000000000..e735f098c0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/matmul.py
@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class MatMul(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'MatMul',
+            inputs=['a', 'b'],
+            outputs=['c'],
+        )
+
+        # 2d
+        a = np.random.randn(3, 4).astype(np.float32)
+        b = np.random.randn(4, 3).astype(np.float32)
+        c = np.matmul(a, b)
+        expect(node, inputs=[a, b], outputs=[c],
+               name='test_matmul_2d')
+
+        # 3d
+        a = np.random.randn(2, 3, 4).astype(np.float32)
+        b = np.random.randn(2, 4, 3).astype(np.float32)
+        c = np.matmul(a, b)
+        expect(node, inputs=[a, b], outputs=[c],
+               name='test_matmul_3d')
+
+        # 4d
+        a = np.random.randn(1, 2, 3, 4).astype(np.float32)
+        b = np.random.randn(1, 2, 4, 3).astype(np.float32)
+        c = np.matmul(a, b)
+        expect(node, inputs=[a, b], outputs=[c],
+               name='test_matmul_4d')
diff --git a/third_party/onnx/onnx/backend/test/case/node/matmulinteger.py b/third_party/onnx/onnx/backend/test/case/node/matmulinteger.py
new file mode 100644
index 0000000000..14b0c5991b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/matmulinteger.py
@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class MatMulInteger(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node('MatMulInteger',
+            inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
+            outputs=['Y'],)
+
+        A = np.array([[11, 7, 3],
+            [10, 6, 2],
+            [9, 5, 1],
+            [8, 4, 0], ], dtype=np.uint8)
+
+        a_zero_point = np.array([12], dtype=np.uint8)
+
+        B = np.array([[1, 4],
+            [2, 5],
+            [3, 6], ], dtype=np.uint8)
+
+        b_zero_point = np.array([0], dtype=np.uint8)
+
+        output = np.array([[-38, -83],
+            [-44, -98],
+            [-50, -113],
+            [-56, -128], ], dtype=np.int32)
+
+        expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
+               name='test_matmulinteger')
diff --git a/third_party/onnx/onnx/backend/test/case/node/max.py b/third_party/onnx/onnx/backend/test/case/node/max.py
new file mode 100644
index 0000000000..7b2fb3674a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/max.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from ..utils import all_numeric_dtypes
+
+
+class Max(Base):
+
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 2, 1]).astype(np.float32)
+        data_1 = np.array([1, 4, 4]).astype(np.float32)
+        data_2 = np.array([2, 5, 3]).astype(np.float32)
+        result = np.array([3, 5, 4]).astype(np.float32)
+        node = onnx.helper.make_node(
+            'Max',
+            inputs=['data_0', 'data_1', 'data_2'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+               name='test_max_example')
+
+        node = onnx.helper.make_node(
+            'Max',
+            inputs=['data_0'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0],
+               name='test_max_one_input')
+
+        result = np.maximum(data_0, data_1)
+        node = onnx.helper.make_node(
+            'Max',
+            inputs=['data_0', 'data_1'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1], outputs=[result],
+               name='test_max_two_inputs')
+
+    @staticmethod
+    def export_max_all_numeric_types() -> None:
+        for op_dtype in all_numeric_dtypes:
+            data_0 = np.array([3, 2, 1]).astype(op_dtype)
+            data_1 = np.array([1, 4, 4]).astype(op_dtype)
+            result = np.array([3, 4, 4]).astype(op_dtype)
+            node = onnx.helper.make_node(
+                'Max',
+                inputs=['data_0', 'data_1'],
+                outputs=['result'],
+            )
+            expect(node, inputs=[data_0, data_1], outputs=[result],
+                   name='test_max_{0}'.format(np.dtype(op_dtype).name))
diff --git a/third_party/onnx/onnx/backend/test/case/node/maxpool.py b/third_party/onnx/onnx/backend/test/case/node/maxpool.py
new file mode 100644
index 0000000000..62ec760c61
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/maxpool.py
@@ -0,0 +1,417 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from .pool_op_common import get_output_shape, get_pad_shape, pool
+
+
+class MaxPool(Base):
+
+    @staticmethod
+    def export_maxpool_2d_uint8() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2]
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.uint8)
+        y = np.array([[[
+            [13, 14, 15, 15, 15],
+            [18, 19, 20, 20, 20],
+            [23, 24, 25, 25, 25],
+            [23, 24, 25, 25, 25],
+            [23, 24, 25, 25, 25]]]]).astype(np.uint8)
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_uint8')
+
+    @staticmethod
+    def export_maxpool_2d_precomputed_pads() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2]
+
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[
+            [13, 14, 15, 15, 15],
+            [18, 19, 20, 20, 20],
+            [23, 24, 25, 25, 25],
+            [23, 24, 25, 25, 25],
+            [23, 24, 25, 25, 25]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_pads')
+
+    @staticmethod
+    def export_maxpool_with_argmax_2d_precomputed_pads() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y', 'z'],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2]
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[
+            [13, 14, 15, 15, 15],
+            [18, 19, 20, 20, 20],
+            [23, 24, 25, 25, 25],
+            [23, 24, 25, 25, 25],
+            [23, 24, 25, 25, 25]]]]).astype(np.float32)
+        z = np.array([[[
+            [12, 13, 14, 14, 14],
+            [17, 18, 19, 19, 19],
+            [22, 23, 24, 24, 24],
+            [22, 23, 24, 24, 24],
+            [22, 23, 24, 24, 24]]]]).astype(np.int64)
+
+        expect(node, inputs=[x], outputs=[y, z], name='test_maxpool_with_argmax_2d_precomputed_pads')
+
+    @staticmethod
+    def export_maxpool_2d_precomputed_strides() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            strides=[2, 2]
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[7, 9],
+                        [17, 19]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_strides')
+
+    @staticmethod
+    def export_maxpool_with_argmax_2d_precomputed_strides() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y', 'z'],
+            kernel_shape=[2, 2],
+            strides=[2, 2],
+            storage_order=1
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[7, 9],
+                        [17, 19]]]]).astype(np.float32)
+        z = np.array([[[[6, 16],
+                        [8, 18]]]]).astype(np.int64)
+
+        expect(node, inputs=[x], outputs=[y, z], name='test_maxpool_with_argmax_2d_precomputed_strides')
+
+    @staticmethod
+    def export_maxpool_2d_precomputed_same_upper() -> None:
+        """
+        input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 3, 3]
+        pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            auto_pad='SAME_UPPER'
+        )
+        x = np.array([[[
+            [1, 2, 3, 4, 5],
+            [6, 7, 8, 9, 10],
+            [11, 12, 13, 14, 15],
+            [16, 17, 18, 19, 20],
+            [21, 22, 23, 24, 25],
+        ]]]).astype(np.float32)
+        y = np.array([[[[7, 9, 10],
+                        [17, 19, 20],
+                        [22, 24, 25]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_precomputed_same_upper')
+
+    @staticmethod
+    def export_maxpool_1d_default() -> None:
+        """
+        input_shape: [1, 3, 32]
+        output_shape: [1, 3, 31]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2],
+        )
+        x = np.random.randn(1, 3, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = [2]
+        strides = [1]
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_1d_default')
+
+    @staticmethod
+    def export_maxpool_2d_default() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_default')
+
+    @staticmethod
+    def export_maxpool_3d_default() -> None:
+        """
+        input_shape: [1, 3, 32, 32, 32]
+        output_shape: [1, 3, 31, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = [2, 2, 2]
+        strides = [1, 1, 1]
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_3d_default')
+
+    @staticmethod
+    def export_maxpool_2d_same_upper() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            auto_pad='SAME_UPPER'
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
+        pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
+        pad_top = pad_shape[0] // 2
+        pad_bottom = pad_shape[0] - pad_top
+        pad_left = pad_shape[1] // 2
+        pad_right = pad_shape[1] - pad_left
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=np.nan)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_same_upper')
+
+    @staticmethod
+    def export_maxpool_2d_same_lower() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            auto_pad='SAME_LOWER'
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
+        pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
+        pad_bottom = pad_shape[0] // 2
+        pad_top = pad_shape[0] - pad_bottom
+        pad_right = pad_shape[1] // 2
+        pad_left = pad_shape[1] - pad_right
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=np.nan)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_same_lower')
+
+    @staticmethod
+    def export_maxpool_2d_pads() -> None:
+        """
+        input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2]
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = pad_top = pad_right = pad_left = 2
+        pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
+        out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
+        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
+                        constant_values=np.nan)
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_pads')
+
+    @staticmethod
+    def export_maxpool_2d_strides() -> None:
+        """
+        input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 10, 10]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[5, 5],
+            strides=[3, 3]
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (5, 5)
+        strides = (3, 3)
+        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'MAX')
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_strides')
+
+    @staticmethod
+    def export_maxpool_2d_ceil() -> None:
+        """
+        input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            ceil_mode=True
+        )
+        x = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]]).astype(np.float32)
+        y = np.array([[[
+            [11, 12],
+            [15, 16]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_ceil')
+
+    @staticmethod
+    def export_maxpool_2d_dilations() -> None:
+        """
+        input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            'MaxPool',
+            inputs=['x'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            strides=[1, 1],
+            dilations=[2, 2]
+        )
+        x = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]]).astype(np.float32)
+        y = np.array([[[
+            [11, 12],
+            [15, 16]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_dilations')
diff --git a/third_party/onnx/onnx/backend/test/case/node/maxunpool.py b/third_party/onnx/onnx/backend/test/case/node/maxunpool.py
new file mode 100644
index 0000000000..a3177e3662
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/maxunpool.py
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class MaxUnpool(Base):
+
+    @staticmethod
+    def export_without_output_shape() -> None:
+        node = onnx.helper.make_node(
+            'MaxUnpool',
+            inputs=['xT', 'xI'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            strides=[2, 2]
+        )
+        xT = np.array([[[[1, 2],
+                         [3, 4]]]], dtype=np.float32)
+        xI = np.array([[[[5, 7],
+                         [13, 15]]]], dtype=np.int64)
+        y = np.array([[[[0, 0, 0, 0],
+                        [0, 1, 0, 2],
+                        [0, 0, 0, 0],
+                        [0, 3, 0, 4]]]], dtype=np.float32)
+        expect(node, inputs=[xT, xI], outputs=[y], name='test_maxunpool_export_without_output_shape')
+
+    @staticmethod
+    def export_with_output_shape() -> None:
+        node = onnx.helper.make_node(
+            'MaxUnpool',
+            inputs=['xT', 'xI', 'output_shape'],
+            outputs=['y'],
+            kernel_shape=[2, 2],
+            strides=[2, 2]
+        )
+        xT = np.array([[[[5, 6],
+                         [7, 8]]]], dtype=np.float32)
+        xI = np.array([[[[5, 7],
+                         [13, 15]]]], dtype=np.int64)
+        output_shape = np.array((1, 1, 5, 5), dtype=np.int64)
+        y = np.array([[[[0, 0, 0, 0, 0],
+                        [0, 5, 0, 6, 0],
+                        [0, 0, 0, 0, 0],
+                        [0, 7, 0, 8, 0],
+                        [0, 0, 0, 0, 0]]]], dtype=np.float32)
+        expect(node, inputs=[xT, xI, output_shape], outputs=[y], name='test_maxunpool_export_with_output_shape')
diff --git a/third_party/onnx/onnx/backend/test/case/node/mean.py b/third_party/onnx/onnx/backend/test/case/node/mean.py
new file mode 100644
index 0000000000..f74f43fe29
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/mean.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Mean(Base):
+
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 0, 2]).astype(np.float32)
+        data_1 = np.array([1, 3, 4]).astype(np.float32)
+        data_2 = np.array([2, 6, 6]).astype(np.float32)
+        result = np.array([2, 3, 4]).astype(np.float32)
+        node = onnx.helper.make_node(
+            'Mean',
+            inputs=['data_0', 'data_1', 'data_2'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+               name='test_mean_example')
+
+        node = onnx.helper.make_node(
+            'Mean',
+            inputs=['data_0'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0],
+               name='test_mean_one_input')
+
+        result = np.divide(np.add(data_0, data_1), 2.)
+        node = onnx.helper.make_node(
+            'Mean',
+            inputs=['data_0', 'data_1'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1], outputs=[result],
+               name='test_mean_two_inputs')
diff --git a/third_party/onnx/onnx/backend/test/case/node/meanvariancenormalization.py b/third_party/onnx/onnx/backend/test/case/node/meanvariancenormalization.py
new file mode 100644
index 0000000000..34ef811049
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/meanvariancenormalization.py
@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class MeanVarianceNormalization(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'MeanVarianceNormalization',
+            inputs=['X'],
+            outputs=['Y']
+        )
+
+        input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+            [[0.02916367], [0.12964272], [0.5060197]],
+            [[0.79538304], [0.9411346], [0.9546573]]],
+            [[[0.17730942], [0.46192095], [0.26480448]],
+            [[0.6746842], [0.01665257], [0.62473077]],
+            [[0.9240844], [0.9722341], [0.11965699]]],
+            [[[0.41356155], [0.9129373], [0.59330076]],
+            [[0.81929934], [0.7862604], [0.11799799]],
+            [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+        # Calculate expected output data
+        data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
+        data_mean_squared = np.power(data_mean, 2)
+        data_squared = np.power(input_data, 2)
+        data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
+        std = np.sqrt(data_squared_mean - data_mean_squared)
+        expected_output = (input_data - data_mean) / (std + 1e-9)
+
+        expect(node, inputs=[input_data], outputs=[expected_output],
+               name='test_mvn')
diff --git a/third_party/onnx/onnx/backend/test/case/node/min.py b/third_party/onnx/onnx/backend/test/case/node/min.py
new file mode 100644
index 0000000000..ec121cc665
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/min.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from ..utils import all_numeric_dtypes
+
+
+class Min(Base):
+
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 2, 1]).astype(np.float32)
+        data_1 = np.array([1, 4, 4]).astype(np.float32)
+        data_2 = np.array([2, 5, 0]).astype(np.float32)
+        result = np.array([1, 2, 0]).astype(np.float32)
+        node = onnx.helper.make_node(
+            'Min',
+            inputs=['data_0', 'data_1', 'data_2'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+               name='test_min_example')
+
+        node = onnx.helper.make_node(
+            'Min',
+            inputs=['data_0'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0],
+               name='test_min_one_input')
+
+        result = np.minimum(data_0, data_1)
+        node = onnx.helper.make_node(
+            'Min',
+            inputs=['data_0', 'data_1'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1], outputs=[result],
+               name='test_min_two_inputs')
+
+    @staticmethod
+    def export_min_all_numeric_types() -> None:
+        for op_dtype in all_numeric_dtypes:
+            data_0 = np.array([3, 2, 1]).astype(op_dtype)
+            data_1 = np.array([1, 4, 4]).astype(op_dtype)
+            result = np.array([1, 2, 1]).astype(op_dtype)
+            node = onnx.helper.make_node(
+                'Min',
+                inputs=['data_0', 'data_1'],
+                outputs=['result'],
+            )
+            expect(node, inputs=[data_0, data_1], outputs=[result],
+                   name='test_min_{0}'.format(np.dtype(op_dtype).name))
diff --git a/third_party/onnx/onnx/backend/test/case/node/mod.py b/third_party/onnx/onnx/backend/test/case/node/mod.py
new file mode 100644
index 0000000000..39895b1c38
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/mod.py
@@ -0,0 +1,204 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Mod(Base):
+
+    @staticmethod
+    def export_mod_mixed_sign_float64() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            fmod=1
+        )
+
+        x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64)
+        y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64)
+        z = np.fmod(x, y)  # expected output [-0.1,  0.4,  5. ,  0.1, -0.4,  3.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_float64')
+
+    @staticmethod
+    def export_mod_mixed_sign_float32() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            fmod=1
+        )
+
+        x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32)
+        y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32)
+        z = np.fmod(x, y)  # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_float32')
+
+    @staticmethod
+    def export_mod_mixed_sign_float16() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            fmod=1
+        )
+
+        x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16)
+        y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16)
+        z = np.fmod(x, y)  # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 ,  3.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_float16')
+
+    @staticmethod
+    def export_mod_mixed_sign_int64() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_int64')
+
+    @staticmethod
+    def export_mod_mixed_sign_int32() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_int32')
+
+    @staticmethod
+    def export_mod_mixed_sign_int16() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_int16')
+
+    @staticmethod
+    def export_mod_mixed_sign_int8() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_mixed_sign_int8')
+
+    @staticmethod
+    def export_mod_uint8() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint8)
+        y = np.array([2, 3, 8]).astype(np.uint8)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_uint8')
+
+    @staticmethod
+    def export_mod_uint16() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint16)
+        y = np.array([2, 3, 8]).astype(np.uint16)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_uint16')
+
+    @staticmethod
+    def export_mod_uint32() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint32)
+        y = np.array([2, 3, 8]).astype(np.uint32)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_uint32')
+
+    @staticmethod
+    def export_mod_uint64() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint64)
+        y = np.array([2, 3, 8]).astype(np.uint64)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_uint64')
+
+    @staticmethod
+    def export_mod_int64_fmod() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+            fmod=1
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+        z = np.fmod(x, y)  # expected output [ 0,  1,  5,  0, -1,  3]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_int64_fmod')
+
+    @staticmethod
+    def export_mod_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Mod',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32)
+        y = np.array([7]).astype(np.int32)
+        z = np.mod(x, y)
+        #   array([[[0, 1, 2, 3, 4],
+        #     [5, 6, 0, 1, 2]],
+
+        #    [[3, 4, 5, 6, 0],
+        #     [1, 2, 3, 4, 5]],
+
+        #    [[6, 0, 1, 2, 3],
+        #     [4, 5, 6, 0, 1]]], dtype=int32)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mod_broadcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/momentum.py b/third_party/onnx/onnx/backend/test/case/node/momentum.py
new file mode 100644
index 0000000000..f91aa36030
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/momentum.py
@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx.defs import ONNX_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN
+from ..base import Base
+from . import expect
+
+
+def apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta):  # type: ignore
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Coefficient of gradient should be 1 at the first iteration.
+    beta_adjusted = beta if t > 0 else 1
+    # Update momentum.
+    v_new = alpha * v + beta_adjusted * g_regularized
+    # Apply SG with momentum update rule.
+    x_new = x - r * v_new
+    return x_new, v_new
+
+
+def apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta):  # type: ignore
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Coefficient of gradient should be 1 at the first iteration.
+    beta_adjusted = beta if t > 0 else 1
+    # Update momentum.
+    v_new = alpha * v + beta_adjusted * g_regularized
+    # Apply Nesterov with momentum update rule.
+    x_new = x - r * (g_regularized + alpha * v_new)
+    return x_new, v_new
+
+
+class Momentum(Base):
+
+    @staticmethod
+    def export_momentum() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.1
+
+        # Create operator.
+        node = onnx.helper.make_node('Momentum',
+                                     inputs=['R', 'T', 'X', 'G', 'V'],
+                                     outputs=['X_new', 'V_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     alpha=alpha,
+                                     beta=beta,
+                                     mode='standard',
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.2, 2.8], dtype=np.float32)
+        g = np.array([-0.94, -2.5], dtype=np.float32)
+        v = np.array([1.7, 3.6], dtype=np.float32)
+
+        # Compute expected outputs of Momentum.
+        x_new, v_new = apply_momentum(r, t, x, g, v,
+                                      norm_coefficient, alpha, beta)
+
+        # Check results.
+        expect(node, inputs=[r, t, x, g, v],
+               outputs=[x_new, v_new], name='test_momentum',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    @staticmethod
+    def export_nesterov_momentum() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.01
+        alpha = 0.95
+        beta = 1.0
+
+        # Create operator.
+        node = onnx.helper.make_node('Momentum',
+                                     inputs=['R', 'T', 'X', 'G', 'V'],
+                                     outputs=['X_new', 'V_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     alpha=alpha,
+                                     beta=beta,
+                                     mode='nesterov',
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.2, 2.8], dtype=np.float32)
+        g = np.array([-0.94, -2.5], dtype=np.float32)
+        v = np.array([1.7, 3.6], dtype=np.float32)
+
+        # Compute expected outputs of Momentum.
+        x_new, v_new = apply_nesterov(r, t, x, g, v,
+                                      norm_coefficient, alpha, beta)
+
+        # Check results.
+        expect(node, inputs=[r, t, x, g, v],
+               outputs=[x_new, v_new], name='test_nesterov_momentum',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    @staticmethod
+    def export_momentum_multiple() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.85
+
+        node = onnx.helper.make_node('Momentum',
+                                     inputs=['R', 'T', 'X1', 'X2',
+                                             'G1', 'G2', 'H1', 'H2'],
+                                     outputs=['X1_new', 'X2_new',
+                                              'V1_new', 'V2_new'],
+                                     norm_coefficient=norm_coefficient,
+                                     alpha=alpha,
+                                     beta=beta,
+                                     mode='standard',
+                                     domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN
+                                     )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+
+        x1 = np.array([1.0], dtype=np.float32)
+        g1 = np.array([-1.0], dtype=np.float32)
+        v1 = np.array([2.0], dtype=np.float32)
+
+        x2 = np.array([1.0, 2.0], dtype=np.float32)
+        g2 = np.array([-1.0, -3.0], dtype=np.float32)
+        v2 = np.array([4.0, 1.0], dtype=np.float32)
+
+        # Compute expected outputs of Momentum.
+        x1_new, v1_new = apply_momentum(r, t, x1, g1, v1,
+                                        norm_coefficient, alpha, beta)
+        x2_new, v2_new = apply_momentum(r, t, x2, g2, v2,
+                                        norm_coefficient, alpha, beta)
+
+        # Check results.
+        expect(node, inputs=[r, t, x1, x2, g1, g2, v1, v2],
+               outputs=[x1_new, x2_new, v1_new, v2_new], name='test_momentum_multiple',
+               opset_imports=[onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/mul.py b/third_party/onnx/onnx/backend/test/case/node/mul.py
new file mode 100644
index 0000000000..778fc432dd
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/mul.py
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Mul(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Mul',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = x * y  # expected output [4., 10., 18.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mul_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mul')
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mul_uint8')
+
+    @staticmethod
+    def export_mul_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Mul',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_mul_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/neg.py b/third_party/onnx/onnx/backend/test/case/node/neg.py
new file mode 100644
index 0000000000..f44f07c316
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/neg.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Neg(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Neg',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-4, 2]).astype(np.float32)
+        y = np.negative(x)  # expected output [4., -2.],
+        expect(node, inputs=[x], outputs=[y],
+               name='test_neg_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.negative(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_neg')
diff --git a/third_party/onnx/onnx/backend/test/case/node/negativeloglikelihoodloss.py b/third_party/onnx/onnx/backend/test/case/node/negativeloglikelihoodloss.py
new file mode 100644
index 0000000000..5894b7b6e1
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/negativeloglikelihoodloss.py
@@ -0,0 +1,477 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def compute_negative_log_likelihood_loss(input, target, weight=None, reduction='mean', ignore_index=None):  # type: ignore
+    input_shape = input.shape
+    if len(input_shape) == 1:
+        raise RuntimeError("Unsupported shape")
+
+    target_shape = target.shape
+    N = input_shape[0]
+    C = input_shape[1]
+
+    # initialize the positional weights when required
+    gather_weight = None
+    if weight is not None:
+        # setting mode='clip' to deal with ignore_index > C or < 0 cases.
+        # when the target value is > C or < 0, it doesn't matter which value we are
+        # taking in gather_weight, since it will be set to 0 in the following if-block
+        # use np.int32 to make it compatible with x86 machines
+        gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode='clip')
+        # set `ignore_index`'s loss weight to 0.
+        # The loss tensor will be multiplied by this weight tensor,
+        # so `ingore_index`'s loss value will be eliminated.
+        if ignore_index is not None:
+            gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(dtype=np.float32)
+    elif ignore_index is not None:
+        gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32)
+
+    # if input is 4-d and above, make it 3-d
+    if len(input_shape) != 3:
+        input = input.reshape((N, C, -1))
+        target = target.reshape((N, -1))
+
+    # Get a dimension from the reshaped input.
+    # If the original input shape is [N, C, H, W],
+    # the D here should be H * W because we reshape
+    # [N, C, H, W] to [N, C, H * W].
+    D = input.shape[2]
+    neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
+    for i in range(N):
+        for d in range(D):
+            if target[i][d] != ignore_index:
+                neg_gather_element_input[i][d] = -input[i][target[i][d]][d]
+
+    loss = neg_gather_element_input
+
+    # if the input was 4-d or above reshape to the right shape
+    if len(input_shape) != 3:
+        loss = loss.reshape(target_shape)
+
+    # apply the weights when required
+    if gather_weight is not None:
+        loss = gather_weight * loss
+        if reduction == 'mean':
+            loss = loss.sum() / gather_weight.sum()
+            return loss
+
+    if reduction == 'mean':
+        loss = np.mean(loss)
+    elif reduction == 'sum':
+        loss = np.sum(loss)
+    return loss
+
+
+class NegativeLogLikelihoodLoss(Base):
+
+    @staticmethod
+    def export_input_shape_is_NC() -> None:
+        reduction = 'none'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C = 3, 5
+        np.random.seed(0)
+        input = np.random.rand(N, C).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, )).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NC')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2() -> None:
+        reduction = 'none'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_reduction_mean() -> None:
+        reduction = 'mean'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_reduction_mean')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_reduction_sum() -> None:
+        reduction = 'sum'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2))
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_reduction_sum')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight() -> None:
+        reduction = 'none'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_with_weight')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight_reduction_mean() -> None:
+        reduction = 'mean'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_with_weight_reduction_mean')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight_reduction_sum() -> None:
+        reduction = 'sum'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_with_weight_reduction_sum')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight_reduction_sum_ii() -> None:
+        reduction = 'sum'
+        ignore_index = np.int64(0)
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        target[0][0][0] = np.int64(0)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction, ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_with_weight_reduction_sum_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_no_weight_reduction_mean_ii() -> None:
+        reduction = 'mean'
+        ignore_index = np.int64(1)
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        target[0][0][0] = np.int64(1)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, reduction=reduction, ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2_no_weight_reduction_mean_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1() -> None:
+        reduction = 'mean'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1')
+
+    @staticmethod
+    def export_input_shape_is_NCd1_weight() -> None:
+        reduction = 'mean'
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1_weight')
+
+    @staticmethod
+    def export_input_shape_is_NCd1_ii() -> None:
+        reduction = 'mean'
+        ignore_index = np.int64(1)
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+        target[0][0] = np.int64(1)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=None, reduction=reduction, ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1_weight_ii() -> None:
+        reduction = 'mean'
+        ignore_index = np.int64(1)
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+        target[0][0] = np.int64(1)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input, target, weight=weight, reduction=reduction, ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1_weight_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None:
+        reduction = 'mean'
+
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction)
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                            target,
+                                                                            weight=weight,
+                                                                            reduction=reduction)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2d3d4d5_mean_weight')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None:
+        reduction = 'none'
+
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction)
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                            target,
+                                                                            reduction=reduction)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2d3d4d5_none_no_weight')
+
+    @staticmethod
+    def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None:
+        reduction = 'mean'
+        ignore_index = np.int64(-1)
+
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index)
+
+        N, C, dim1 = 3, 5, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+        target[0][0] = -1
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                            target,
+                                                                            weight=weight,
+                                                                            reduction=reduction,
+                                                                            ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1_mean_weight_negative_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None:
+        reduction = 'none'
+        ignore_index = np.int64(-5)
+
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index)
+
+        N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+        target[0][0][0][0] = -5
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                            target,
+                                                                            reduction=reduction,
+                                                                            ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2d3_none_no_weight_negative_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None:
+        reduction = 'sum'
+        ignore_index = np.int64(10)
+
+        node = onnx.helper.make_node(
+            'NegativeLogLikelihoodLoss',
+            inputs=['input', 'target', 'weight'],
+            outputs=['loss'],
+            reduction=reduction,
+            ignore_index=ignore_index)
+
+        N, C = 3, 5
+        np.random.seed(0)
+        input = np.random.rand(N, C).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N))
+        target[0] = 10
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(input,
+                                                                            target,
+                                                                            weight=weight,
+                                                                            reduction=reduction,
+                                                                            ignore_index=ignore_index)
+
+        expect(node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss],
+            name='test_nllloss_NCd1d2d3_sum_weight_high_ii')
diff --git a/third_party/onnx/onnx/backend/test/case/node/nonmaxsuppression.py b/third_party/onnx/onnx/backend/test/case/node/nonmaxsuppression.py
new file mode 100644
index 0000000000..c9026dbfe6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/nonmaxsuppression.py
@@ -0,0 +1,224 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class NonMaxSuppression(Base):
+
+    @staticmethod
+    def export_nonmaxsuppression_suppress_by_IOU() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.1, 1.0, 1.1],
+            [0.0, -0.1, 1.0, 0.9],
+            [0.0, 10.0, 1.0, 11.0],
+            [0.0, 10.1, 1.0, 11.1],
+            [0.0, 100.0, 1.0, 101.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_suppress_by_IOU')
+
+    @staticmethod
+    def export_nonmaxsuppression_suppress_by_IOU_and_scores() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.1, 1.0, 1.1],
+            [0.0, -0.1, 1.0, 0.9],
+            [0.0, 10.0, 1.0, 11.0],
+            [0.0, 10.1, 1.0, 11.1],
+            [0.0, 100.0, 1.0, 101.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.4]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_suppress_by_IOU_and_scores')
+
+    @staticmethod
+    def export_nonmaxsuppression_flipped_coordinates() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [1.0, 1.0, 0.0, 0.0],
+            [0.0, 0.1, 1.0, 1.1],
+            [0.0, 0.9, 1.0, -0.1],
+            [0.0, 10.0, 1.0, 11.0],
+            [1.0, 10.1, 0.0, 11.1],
+            [1.0, 101.0, 0.0, 100.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_flipped_coordinates')
+
+    @staticmethod
+    def export_nonmaxsuppression_limit_output_size() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.1, 1.0, 1.1],
+            [0.0, -0.1, 1.0, 0.9],
+            [0.0, 10.0, 1.0, 11.0],
+            [0.0, 10.1, 1.0, 11.1],
+            [0.0, 100.0, 1.0, 101.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([2]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_limit_output_size')
+
+    @staticmethod
+    def export_nonmaxsuppression_single_box() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [0.0, 0.0, 1.0, 1.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_single_box')
+
+    @staticmethod
+    def export_nonmaxsuppression_identical_boxes() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_identical_boxes')
+
+    @staticmethod
+    def export_nonmaxsuppression_center_point_box_format() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices'],
+            center_point_box=1
+        )
+        boxes = np.array([[
+            [0.5, 0.5, 1.0, 1.0],
+            [0.5, 0.6, 1.0, 1.0],
+            [0.5, 0.4, 1.0, 1.0],
+            [0.5, 10.5, 1.0, 1.0],
+            [0.5, 10.6, 1.0, 1.0],
+            [0.5, 100.5, 1.0, 1.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_center_point_box_format')
+
+    @staticmethod
+    def export_nonmaxsuppression_two_classes() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.1, 1.0, 1.1],
+            [0.0, -0.1, 1.0, 0.9],
+            [0.0, 10.0, 1.0, 11.0],
+            [0.0, 10.1, 1.0, 11.1],
+            [0.0, 100.0, 1.0, 101.0]
+        ]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3],
+                            [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([2]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_two_classes')
+
+    @staticmethod
+    def export_nonmaxsuppression_two_batches() -> None:
+        node = onnx.helper.make_node(
+            'NonMaxSuppression',
+            inputs=['boxes', 'scores', 'max_output_boxes_per_class', 'iou_threshold', 'score_threshold'],
+            outputs=['selected_indices']
+        )
+        boxes = np.array([[[0.0, 0.0, 1.0, 1.0],
+                           [0.0, 0.1, 1.0, 1.1],
+                           [0.0, -0.1, 1.0, 0.9],
+                           [0.0, 10.0, 1.0, 11.0],
+                           [0.0, 10.1, 1.0, 11.1],
+                           [0.0, 100.0, 1.0, 101.0]],
+                          [[0.0, 0.0, 1.0, 1.0],
+                           [0.0, 0.1, 1.0, 1.1],
+                           [0.0, -0.1, 1.0, 0.9],
+                           [0.0, 10.0, 1.0, 11.0],
+                           [0.0, 10.1, 1.0, 11.1],
+                           [0.0, 100.0, 1.0, 101.0]]]).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]],
+                           [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([2]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]]).astype(np.int64)
+
+        expect(node, inputs=[boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold], outputs=[selected_indices], name='test_nonmaxsuppression_two_batches')
diff --git a/third_party/onnx/onnx/backend/test/case/node/nonzero.py b/third_party/onnx/onnx/backend/test/case/node/nonzero.py
new file mode 100644
index 0000000000..2bf21e206c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/nonzero.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class NonZero(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'NonZero',
+            inputs=['condition'],
+            outputs=['result'],
+        )
+
+        condition = np.array([[1, 0], [1, 1]], dtype=bool)
+        result = np.array(np.nonzero(condition), dtype=np.int64)  # expected output [[0, 1, 1], [0, 0, 1]]
+        expect(node, inputs=[condition], outputs=[result],
+               name='test_nonzero_example')
diff --git a/third_party/onnx/onnx/backend/test/case/node/not.py b/third_party/onnx/onnx/backend/test/case/node/not.py
new file mode 100644
index 0000000000..ea4067bc12
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/not.py
@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Not(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Not',
+            inputs=['x'],
+            outputs=['not'],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        expect(node, inputs=[x], outputs=[np.logical_not(x)],
+               name='test_not_2d')
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        expect(node, inputs=[x], outputs=[np.logical_not(x)],
+               name='test_not_3d')
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        expect(node, inputs=[x], outputs=[np.logical_not(x)],
+               name='test_not_4d')
diff --git a/third_party/onnx/onnx/backend/test/case/node/onehot.py b/third_party/onnx/onnx/backend/test/case/node/onehot.py
new file mode 100644
index 0000000000..5567460729
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/onehot.py
@@ -0,0 +1,106 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def one_hot(indices, depth, axis=-1, dtype=np.float32):  # type: ignore
+    ''' Compute one hot from indices at a specific axis '''
+    values = np.asarray(indices)
+    rank = len(values.shape)
+    depth_range = np.arange(depth)
+    if axis < 0:
+        axis += (rank + 1)
+    ls = values.shape[0:axis]
+    rs = values.shape[axis:rank]
+    targets = np.reshape(depth_range, (1,) * len(ls) + depth_range.shape + (1,) * len(rs))
+    values = np.reshape(np.mod(values, depth), ls + (1,) + rs)
+    return np.asarray(targets == values, dtype=dtype)
+
+
+class OneHot(Base):
+
+    @staticmethod
+    def export_without_axis() -> None:
+        on_value = 5
+        off_value = 2
+        output_type = np.int32
+        node = onnx.helper.make_node(
+            'OneHot',
+            inputs=['indices', 'depth', 'values'],
+            outputs=['y']
+        )
+        indices = np.array([0, 7, 8], dtype=np.int64)
+        depth = np.float32(12)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_without_axis')
+
+    @staticmethod
+    def export_with_axis() -> None:
+        axisValue = 1
+        on_value = 3
+        off_value = 1
+        output_type = np.float32
+        node = onnx.helper.make_node(
+            'OneHot',
+            inputs=['indices', 'depth', 'values'],
+            outputs=['y'],
+            axis=axisValue
+        )
+        indices = np.array([[1, 9],
+                            [2, 4]], dtype=np.float32)
+        depth = np.float32(10)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_with_axis')
+
+    @staticmethod
+    def export_with_negative_indices() -> None:
+        axisValue = 1
+        on_value = 3
+        off_value = 1
+        output_type = np.float32
+        node = onnx.helper.make_node(
+            'OneHot',
+            inputs=['indices', 'depth', 'values'],
+            outputs=['y'],
+            axis=axisValue
+        )
+        indices = np.array([0, -7, -8], dtype=np.int64)
+
+        # print(y)
+        # [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
+        #  [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.]
+        #  [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]]
+
+        depth = np.float32(10)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_negative_indices')
+
+    @staticmethod
+    def export_with_negative_axis() -> None:
+        axisValue = -2
+        on_value = 3
+        off_value = 1
+        output_type = np.float32
+        node = onnx.helper.make_node(
+            'OneHot',
+            inputs=['indices', 'depth', 'values'],
+            outputs=['y'],
+            axis=axisValue
+        )
+        indices = np.array([[1, 9],
+                            [2, 4]], dtype=np.float32)
+        depth = np.float32(10)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(node, inputs=[indices, depth, values], outputs=[y], name='test_onehot_with_negative_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/optionalgetelement.py b/third_party/onnx/onnx/backend/test/case/node/optionalgetelement.py
new file mode 100644
index 0000000000..ba124fda83
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/optionalgetelement.py
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Optional, Any
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def optional_get_element_reference_implementation(optional: Optional[Any]) -> Any:
+    assert optional is not None
+    return optional
+
+
+class OptionalHasElement(Base):
+
+    @staticmethod
+    def export_get_element_tensor() -> None:
+        optional = np.array([1, 2, 3, 4]).astype(np.float32)
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.FLOAT, shape=[4, ])
+        input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+
+        node = onnx.helper.make_node(
+            'OptionalGetElement',
+            inputs=['optional_input'],
+            outputs=['output']
+        )
+        output = optional_get_element_reference_implementation(optional)
+        expect(node, inputs=[optional], outputs=[output],
+               input_type_protos=[input_type_proto],
+               name='test_optional_get_element')
+
+    @staticmethod
+    def export_get_element_sequence() -> None:
+        optional = [np.array([1, 2, 3, 4]).astype(np.int32)]
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.INT32, shape=[4, ])
+        seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
+        input_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto)
+
+        node = onnx.helper.make_node(
+            'OptionalGetElement',
+            inputs=['optional_input'],
+            outputs=['output']
+        )
+        output = optional_get_element_reference_implementation(optional)
+        expect(node, inputs=[optional], outputs=[output],
+               input_type_protos=[input_type_proto],
+               name='test_optional_get_element_sequence')
diff --git a/third_party/onnx/onnx/backend/test/case/node/optionalhaselement.py b/third_party/onnx/onnx/backend/test/case/node/optionalhaselement.py
new file mode 100644
index 0000000000..2966cd1127
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/optionalhaselement.py
@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Optional
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def optional_has_element_reference_implementation(optional: Optional[np.ndarray]) -> np.ndarray:
+    if optional is None:
+        return np.array(False)
+    else:
+        return np.array(True)
+
+
+class OptionalHasElement(Base):
+
+    @staticmethod
+    def export() -> None:
+        optional = np.array([1, 2, 3, 4]).astype(np.float32)
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.FLOAT, shape=[4, ])
+        input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+        node = onnx.helper.make_node(
+            'OptionalHasElement',
+            inputs=['optional_input'],
+            outputs=['output']
+        )
+        output = optional_has_element_reference_implementation(optional)
+        expect(node, inputs=[optional], outputs=[output],
+               input_type_protos=[input_type_proto],
+               name='test_optional_has_element')
+
+    @staticmethod
+    def export_empty() -> None:
+        optional = None
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=onnx.TensorProto.INT32, shape=[])
+        input_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+        node = onnx.helper.make_node(
+            'OptionalHasElement',
+            inputs=['optional_input'],
+            outputs=['output']
+        )
+        output = optional_has_element_reference_implementation(optional)
+        expect(node, inputs=[optional], outputs=[output],
+               input_type_protos=[input_type_proto],
+               name='test_optional_has_element_empty')
diff --git a/third_party/onnx/onnx/backend/test/case/node/or.py b/third_party/onnx/onnx/backend/test/case/node/or.py
new file mode 100644
index 0000000000..b75fd77c40
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/or.py
@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Or(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Or',
+            inputs=['x', 'y'],
+            outputs=['or'],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        y = (np.random.randn(3, 4) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or2d')
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or3d')
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or4d')
+
+    @staticmethod
+    def export_or_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Or',
+            inputs=['x', 'y'],
+            outputs=['or'],
+        )
+
+        # 3d vs 1d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(5) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or_bcast3v1d')
+
+        # 3d vs 2d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(4, 5) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or_bcast3v2d')
+
+        # 4d vs 2d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or_bcast4v2d')
+
+        # 4d vs 3d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or_bcast4v3d')
+
+        # 4d vs 4d
+        x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_or_bcast4v4d')
diff --git a/third_party/onnx/onnx/backend/test/case/node/pad.py b/third_party/onnx/onnx/backend/test/case/node/pad.py
new file mode 100644
index 0000000000..99c3ffce91
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/pad.py
@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def pad_impl(data, raw_pads, mode, constant_values=0.0):  # type: ignore
+
+    input_rank = data.ndim
+    if input_rank * 2 != raw_pads.size:
+        raise Exception('The number of elements in raw_pads should be 2 * data_rank')
+
+    # re-order to np.pad accepted order ((x1_begin, x1_end), (x2_begin, x2_end), ...)
+    pad_width = ()
+    for i in range(int(raw_pads.size / 2)):
+        pad_width += ((raw_pads[i], raw_pads[i + input_rank])),  # type: ignore
+
+    if mode == 'constant':
+        y = np.pad(
+            data,
+            pad_width=pad_width,
+            mode=mode,
+            constant_values=constant_values,
+        )
+        return y
+
+    y = np.pad(
+        data,
+        pad_width=pad_width,
+        mode=mode,
+    )
+
+    return y
+
+
+class Pad(Base):
+
+    @staticmethod
+    def export_constant_pad() -> None:
+        node = onnx.helper.make_node(
+            'Pad',
+            inputs=['x', 'pads', 'value'],
+            outputs=['y'],
+            mode='constant'
+        )
+        x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+        pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+        value = np.float32(1.2)
+        y = pad_impl(
+            x,
+            pads,
+            'constant',
+            1.2
+        )
+
+        expect(node, inputs=[x, pads, value], outputs=[y],
+               name='test_constant_pad')
+
+    @staticmethod
+    def export_reflection_and_edge_pad() -> None:
+        for mode in ['edge', 'reflect']:
+            node = onnx.helper.make_node(
+                'Pad',
+                inputs=['x', 'pads'],
+                outputs=['y'],
+                mode=mode
+            )
+            x = np.random.randn(1, 3, 4, 5).astype(np.int32)
+            pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(np.int64)  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+            y = pad_impl(
+                x,
+                pads,
+                mode
+            )
+
+            expect(node, inputs=[x, pads], outputs=[y],
+                   name='test_{}_pad'.format(mode))
diff --git a/third_party/onnx/onnx/backend/test/case/node/pool_op_common.py b/third_party/onnx/onnx/backend/test/case/node/pool_op_common.py
new file mode 100644
index 0000000000..217ef525e8
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/pool_op_common.py
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np  # type: ignore
+import itertools
+from typing import Text, Sequence
+
+
+def get_pad_shape(auto_pad: Text,
+                  input_spatial_shape: Sequence[int],
+                  kernel_spatial_shape: Sequence[int],
+                  strides_spatial: Sequence[int],
+                  output_spatial_shape: Sequence[int]
+                  ) -> Sequence[int]:
+    pad_shape = [0] * len(input_spatial_shape)
+    if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
+        for i in range(len(input_spatial_shape)):
+            pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial[i] + \
+                kernel_spatial_shape[i] - input_spatial_shape[i]
+    elif auto_pad == 'VALID':
+        pass
+    return pad_shape
+
+
+def get_output_shape(auto_pad: Text,
+                     input_spatial_shape: Sequence[int],
+                     kernel_spatial_shape: Sequence[int],
+                     strides_spatial: Sequence[int]
+                     ) -> Sequence[int]:
+    out_shape = [0] * len(input_spatial_shape)
+    if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
+        for i in range(len(input_spatial_shape)):
+            out_shape[i] = int(
+                np.ceil(
+                    float(
+                        input_spatial_shape[i])
+                    / float(
+                        strides_spatial[i])))
+    elif auto_pad == 'VALID':
+        for i in range(len(input_spatial_shape)):
+            out_shape[i] = int(np.ceil(float(input_spatial_shape[i] - (kernel_spatial_shape[i] - 1)) / float(strides_spatial[i])))
+    return out_shape
+
+
+def pool(padded: np.ndarray,
+         x_shape: Sequence[int],
+         kernel_shape: Sequence[int],
+         strides_shape: Sequence[int],
+         out_shape: Sequence[int],
+         pad_shape: Sequence[int],
+         pooling_type: Text,
+         count_include_pad: int = 0
+         ) -> np.ndarray:
+    spatial_size = len(x_shape) - 2
+    y = np.zeros([x_shape[0], x_shape[1]] + list(out_shape))
+
+    for shape in itertools.product(range(x_shape[0]), range(x_shape[1]), *[range(int(
+            (x_shape[i + 2] + pad_shape[i] - kernel_shape[i]) / strides_shape[i] + 1)) for i in range(spatial_size)]):
+        window = padded[shape[0], shape[1]]
+        window_vals = np.array([window[i] for i in list(
+            itertools.product(
+                *[range(strides_shape[i] * shape[i + 2], strides_shape[i] * shape[i + 2] + kernel_shape[i]) for i in
+                  range(spatial_size)])
+        )])
+        if pooling_type == 'AVG':
+            f = np.average
+        elif pooling_type == 'MAX':
+            f = np.max
+        else:
+            raise NotImplementedError(
+                'Pooling type {} does not support. Should be AVG, MAX'.format(pooling_type))
+
+        if count_include_pad == 1 and pooling_type == 'AVG':
+            y[shape] = f(window_vals)
+        else:
+            y[shape] = f(window_vals[np.where(~np.isnan(window_vals))])
+    return y.astype(np.float32)
diff --git a/third_party/onnx/onnx/backend/test/case/node/pow.py b/third_party/onnx/onnx/backend/test/case/node/pow.py
new file mode 100644
index 0000000000..dcffbdfc95
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/pow.py
@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def pow(x, y):  # type: ignore
+    z = np.power(x, y).astype(x.dtype)
+    return z
+
+
+class Pow(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Pow',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_example')
+
+        x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = pow(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow')
+
+    @staticmethod
+    def export_pow_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Pow',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array(2).astype(np.float32)
+        z = pow(x, y)  # expected output [1., 4., 9.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_bcast_scalar')
+
+        node = onnx.helper.make_node(
+            'Pow',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+        x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+        y = np.array([1, 2, 3]).astype(np.float32)
+        # expected output [[1, 4, 27], [4, 25, 216]]
+        z = pow(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_bcast_array')
+
+    @staticmethod
+    def export_types() -> None:
+        node = onnx.helper.make_node(
+            'Pow',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.int64)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_float32_int64')
+
+        x = np.array([1, 2, 3]).astype(np.int64)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_int64_float32')
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.int32)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_float32_int32')
+
+        x = np.array([1, 2, 3]).astype(np.int32)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_int32_float32')
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.uint64)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_float32_uint64')
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.uint32)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_float32_uint32')
+
+        x = np.array([1, 2, 3]).astype(np.int64)
+        y = np.array([4, 5, 6]).astype(np.int64)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_int64_int64')
+
+        x = np.array([1, 2, 3]).astype(np.int32)
+        y = np.array([4, 5, 6]).astype(np.int32)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_pow_types_int32_int32')
diff --git a/third_party/onnx/onnx/backend/test/case/node/prelu.py b/third_party/onnx/onnx/backend/test/case/node/prelu.py
new file mode 100644
index 0000000000..0a650afdf1
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/prelu.py
@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class PRelu(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'PRelu',
+            inputs=['x', 'slope'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        slope = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+        expect(node, inputs=[x, slope], outputs=[y],
+               name='test_prelu_example')
+
+    @staticmethod
+    def export_prelu_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'PRelu',
+            inputs=['x', 'slope'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        slope = np.random.randn(5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+        expect(node, inputs=[x, slope], outputs=[y],
+               name='test_prelu_broadcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/qlinearconv.py b/third_party/onnx/onnx/backend/test/case/node/qlinearconv.py
new file mode 100644
index 0000000000..20f2b26036
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/qlinearconv.py
@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class QLinearConv(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node('QLinearConv',
+            inputs=['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'],
+            outputs=['y'],)
+
+        x = np.array([[255, 174, 162, 25, 203, 168, 58],
+            [15, 59, 237, 95, 129, 0, 64],
+            [56, 242, 153, 221, 168, 12, 166],
+            [232, 178, 186, 195, 237, 162, 237],
+            [188, 39, 124, 77, 80, 102, 43],
+            [127, 230, 21, 83, 41, 40, 134],
+            [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+        x_scale = np.float32(0.00369204697)
+        x_zero_point = np.uint8(132)
+
+        w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+        w_scale = np.array([0.00172794575], dtype=np.float32)
+        w_zero_point = np.array([255], dtype=np.uint8)
+
+        y_scale = np.float32(0.00162681262)
+        y_zero_point = np.uint8(123)
+
+        output = np.array([[0, 81, 93, 230, 52, 87, 197],
+            [240, 196, 18, 160, 126, 255, 191],
+            [199, 13, 102, 34, 87, 243, 89],
+            [23, 77, 69, 60, 18, 93, 18],
+            [67, 216, 131, 178, 175, 153, 212],
+            [128, 25, 234, 172, 214, 215, 121],
+            [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+        expect(node, inputs=[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point], outputs=[output],
+               name='test_qlinearconv')
diff --git a/third_party/onnx/onnx/backend/test/case/node/qlinearmatmul.py b/third_party/onnx/onnx/backend/test/case/node/qlinearmatmul.py
new file mode 100644
index 0000000000..e70366ec6b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/qlinearmatmul.py
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class QLinearMatMul(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node('QLinearMatMul',
+            inputs=['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'],
+            outputs=['y'],)
+
+        #2D
+        a = np.array([[208, 236, 0, 238],
+            [3, 214, 255, 29], ], dtype=np.uint8)
+
+        a_scale = np.array([0.0066], dtype=np.float32)
+        a_zero_point = np.array([113], dtype=np.uint8)
+
+        b = np.array([[152, 51, 244],
+            [60, 26, 255],
+            [0, 127, 246],
+            [127, 254, 247]], dtype=np.uint8)
+
+        b_scale = np.array([0.00705], dtype=np.float32)
+        b_zero_point = np.array([114], dtype=np.uint8)
+
+        y_scale = np.array([0.0107], dtype=np.float32)
+        y_zero_point = np.array([118], dtype=np.uint8)
+
+        output = np.array([[168, 115, 255],
+            [1, 66, 151], ], dtype=np.uint8)
+
+        expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+               name='test_qlinearmatmul_2D')
+
+        #3D
+        a = np.array([[[208, 236, 0, 238],
+            [3, 214, 255, 29]],
+            [[208, 236, 0, 238],
+            [3, 214, 255, 29]]], dtype=np.uint8)
+
+        a_scale = np.array([0.0066], dtype=np.float32)
+        a_zero_point = np.array([113], dtype=np.uint8)
+
+        b = np.array([[[152, 51, 244],
+            [60, 26, 255],
+            [0, 127, 246],
+            [127, 254, 247]],
+            [[152, 51, 244],
+            [60, 26, 255],
+            [0, 127, 246],
+            [127, 254, 247]]], dtype=np.uint8)
+
+        b_scale = np.array([0.00705], dtype=np.float32)
+        b_zero_point = np.array([114], dtype=np.uint8)
+
+        y_scale = np.array([0.0107], dtype=np.float32)
+        y_zero_point = np.array([118], dtype=np.uint8)
+
+        output = np.array([[[168, 115, 255],
+            [1, 66, 151]],
+            [[168, 115, 255],
+            [1, 66, 151]]], dtype=np.uint8)
+
+        expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+               name='test_qlinearmatmul_3D')
diff --git a/third_party/onnx/onnx/backend/test/case/node/quantizelinear.py b/third_party/onnx/onnx/backend/test/case/node/quantizelinear.py
new file mode 100644
index 0000000000..205a3564c2
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/quantizelinear.py
@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class QuantizeLinear(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node('QuantizeLinear',
+                                     inputs=['x', 'y_scale', 'y_zero_point'],
+                                     outputs=['y'],)
+
+        x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+        y_scale = np.float32(2)
+        y_zero_point = np.uint8(128)
+        y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+        expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+               name='test_quantizelinear')
+
+    @staticmethod
+    def export_axis() -> None:
+        node = onnx.helper.make_node('QuantizeLinear',
+                                     inputs=['x', 'y_scale', 'y_zero_point'],
+                                     outputs=['y'],)
+
+        x = np.array([[[[-162, 10],
+                        [-100, 232],
+                        [-20, -50]],
+
+                       [[-76, 0],
+                        [0, 252],
+                        [32, -44]],
+
+                       [[245, -485],
+                        [-960, -270],
+                        [-375, -470]], ], ], dtype=np.float32)
+        y_scale = np.array([2, 4, 5], dtype=np.float32)
+        y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+        y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(np.uint8)
+
+        expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+               name='test_quantizelinear_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/rangeop.py b/third_party/onnx/onnx/backend/test/case/node/rangeop.py
new file mode 100644
index 0000000000..0a0e5ffd47
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/rangeop.py
@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Range(Base):
+
+    @staticmethod
+    def export_range_float_type_positive_delta() -> None:
+        node = onnx.helper.make_node(
+            'Range',
+            inputs=['start', 'limit', 'delta'],
+            outputs=['output'],
+        )
+
+        start = np.float32(1)
+        limit = np.float32(5)
+        delta = np.float32(2)
+
+        output = np.arange(start, limit, delta, dtype=np.float32)  # expected output [1.0, 3.0]
+        expect(node, inputs=[start, limit, delta], outputs=[output],
+               name='test_range_float_type_positive_delta')
+
+    @staticmethod
+    def export_range_int32_type_negative_delta() -> None:
+        node = onnx.helper.make_node(
+            'Range',
+            inputs=['start', 'limit', 'delta'],
+            outputs=['output'],
+        )
+
+        start = np.int32(10)
+        limit = np.int32(6)
+        delta = np.int32(-3)
+
+        output = np.arange(start, limit, delta, dtype=np.int32)  # expected output [10, 7]
+        expect(node, inputs=[start, limit, delta], outputs=[output],
+               name='test_range_int32_type_negative_delta')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reciprocal.py b/third_party/onnx/onnx/backend/test/case/node/reciprocal.py
new file mode 100644
index 0000000000..441fc0df6c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reciprocal.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Reciprocal(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Reciprocal',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-4, 2]).astype(np.float32)
+        y = np.reciprocal(x)  # expected output [-0.25, 0.5],
+        expect(node, inputs=[x], outputs=[y],
+               name='test_reciprocal_example')
+
+        x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5
+        y = np.reciprocal(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_reciprocal')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reduce_log_sum.py b/third_party/onnx/onnx/backend/test/case/node/reduce_log_sum.py
new file mode 100644
index 0000000000..a4720d515a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reduce_log_sum.py
@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceLogSum(Base):
+
+    @staticmethod
+    def export_nokeepdims() -> None:
+        node = onnx.helper.make_node(
+            'ReduceLogSum',
+            inputs=['data'],
+            outputs=["reduced"],
+            axes=[2, 1],
+            keepdims=0
+        )
+        data = np.random.ranf([3, 4, 5]).astype(np.float32)
+        reduced = np.log(np.sum(data, axis=(2, 1), keepdims=False))
+        expect(node, inputs=[data], outputs=[reduced],
+               name='test_reduce_log_sum_desc_axes')
+
+        node = onnx.helper.make_node(
+            'ReduceLogSum',
+            inputs=['data'],
+            outputs=["reduced"],
+            axes=[0, 1],
+            keepdims=0
+        )
+        data = np.random.ranf([3, 4, 5]).astype(np.float32)
+        reduced = np.log(np.sum(data, axis=(0, 1), keepdims=False))
+        expect(node, inputs=[data], outputs=[reduced],
+               name='test_reduce_log_sum_asc_axes')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        node = onnx.helper.make_node(
+            'ReduceLogSum',
+            inputs=['data'],
+            outputs=["reduced"]
+        )
+        data = np.random.ranf([3, 4, 5]).astype(np.float32)
+        reduced = np.log(np.sum(data, keepdims=True))
+        expect(node, inputs=[data], outputs=[reduced],
+               name='test_reduce_log_sum_default')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        node = onnx.helper.make_node(
+            'ReduceLogSum',
+            inputs=['data'],
+            outputs=["reduced"],
+            axes=[-2]
+        )
+        data = np.random.ranf([3, 4, 5]).astype(np.float32)
+        reduced = np.log(np.sum(data, axis=(-2), keepdims=True))
+        # print(reduced)
+        expect(node, inputs=[data], outputs=[reduced],
+               name='test_reduce_log_sum_negative_axes')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reduce_log_sum_exp.py b/third_party/onnx/onnx/backend/test/case/node/reduce_log_sum_exp.py
new file mode 100644
index 0000000000..695f797b9c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reduce_log_sum_exp.py
@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceLogSumExp(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.double)
+        reduced = np.log(np.sum(
+            np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+        # print(reduced)
+        #[[20., 2.31326175]
+        # [40.00004578, 2.31326175]
+        # [60.00671387, 2.31326175]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(
+            np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_log_sum_exp_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.double)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=tuple(axes),
+                                keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[20., 2.31326175]]
+        # [[40.00004578, 2.31326175]]
+        # [[60.00671387, 2.31326175]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=tuple(axes),
+                                keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.double)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=axes,
+                                keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[60.00671387]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=axes,
+                                keepdims=keepdims == 1))
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-2]
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.double)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=tuple(axes),
+                                keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[20., 2.31326175]]
+        # [[40.00004578, 2.31326175]]
+        # [[60.00671387, 2.31326175]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=tuple(axes),
+                                keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducel1.py b/third_party/onnx/onnx/backend/test/case/node/reducel1.py
new file mode 100644
index 0000000000..70b31d79ab
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducel1.py
@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceL1(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[3., 7.], [11., 15.], [19., 23.]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_keep_dims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_keep_dims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[78.]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        #[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_negative_axes_keep_dims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_negative_axes_keep_dims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducel2.py b/third_party/onnx/onnx/backend/test/case/node/reducel2.py
new file mode 100644
index 0000000000..6dd61b269f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducel2.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceL2(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+        #print(reduced)
+        #[[2.23606798, 5.],
+        # [7.81024968, 10.63014581],
+        # [13.45362405, 16.2788206]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+        #print(reduced)
+        #[[[2.23606798], [5.]]
+        # [[7.81024968], [10.63014581]]
+        # [[13.45362405], [16.2788206 ]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_keep_dims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_l2_keep_dims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=axes, keepdims=keepdims == 1))
+        #print(reduced)
+        #[[[25.49509757]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=axes, keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+        # print(reduced)
+        #[[[2.23606798], [5.]]
+        # [[7.81024968], [10.63014581]]
+        # [[13.45362405], [16.2788206 ]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_negative_axes_keep_dims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_negative_axes_keep_dims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducemax.py b/third_party/onnx/onnx/backend/test/case/node/reducemax.py
new file mode 100644
index 0000000000..c187b5b67d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducemax.py
@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceMax(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceMax',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[20., 2.]
+        # [40., 2.]
+        # [60., 2.]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMax',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[20., 2.]]
+        # [[40., 2.]]
+        # [[60., 2.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ReduceMax',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        [[[60.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_default_axes_keepdim_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMax',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        #[[[20., 2.]]
+        # [[40., 2.]]
+        # [[60., 2.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_max_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducemean.py b/third_party/onnx/onnx/backend/test/case/node/reducemean.py
new file mode 100644
index 0000000000..d23924d259
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducemean.py
@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceMean(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceMean',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[12.5, 1.5]
+        # [35., 1.5]
+        # [57.5, 1.5]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMean',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[12.5, 1.5]]
+        # [[35., 1.5]]
+        # [[57.5, 1.5]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMean',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.mean(data, axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[18.25]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.mean(data, axis=axes, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMean',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        # [[[12.5, 1.5]]
+        # [[35., 1.5]]
+        # [[57.5, 1.5]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_mean_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducemin.py b/third_party/onnx/onnx/backend/test/case/node/reducemin.py
new file mode 100644
index 0000000000..8cd2f72aa3
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducemin.py
@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceMin(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceMin',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[5., 1.]
+        # [30., 1.]
+        # [55., 1.]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMin', inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[5., 1.]]
+        # [[30., 1.]]
+        # [[55., 1.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMin',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[1.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceMin', inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
+        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        #[[[5., 1.]]
+        # [[30., 1.]]
+        # [[55., 1.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reduceprod.py b/third_party/onnx/onnx/backend/test/case/node/reduceprod.py
new file mode 100644
index 0000000000..dbd651d5fa
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reduceprod.py
@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceProd(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceProd',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[3., 8.]
+        # [35., 48.]
+        # [99., 120.]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceProd',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[3., 8.]]
+        # [[35., 48.]]
+        # [[99., 120.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceProd',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[4.790016e+08]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceProd',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        #[[[3., 8.]]
+        # [[35., 48.]]
+        # [[99., 120.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_prod_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducesum.py b/third_party/onnx/onnx/backend/test/case/node/reducesum.py
new file mode 100644
index 0000000000..6ec666ceb9
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducesum.py
@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceSum(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([1], dtype=np.int64)
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceSum',
+            inputs=['data', 'axes'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[4., 6.]
+        # [12., 14.]
+        # [20., 22.]]
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([1], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSum',
+            inputs=['data', 'axes'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[4., 6.]]
+        # [[12., 14.]]
+        # [[20., 22.]]]
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSum',
+            inputs=['data', 'axes'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[78.]]]
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([-2], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSum',
+            inputs=['data', 'axes'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+        # print(reduced)
+        #[[[4., 6.]]
+        # [[12., 14.]]
+        # [[20., 22.]]]
+
+        expect(node, inputs=[data, axes], outputs=[reduced],
+               name='test_reduce_sum_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(data, axis=tuple(
+            axes.tolist()), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data, axes], outputs=[reduced],
+               name='test_reduce_sum_negative_axes_keepdims_random')
+
+    @staticmethod
+    def export_empty_axes_input_noop() -> None:
+        shape = [3, 2, 2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSum',
+            inputs=['data', 'axes'],
+            outputs=['reduced'],
+            keepdims=keepdims,
+            noop_with_empty_axes=True)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        axes = np.array([], dtype=np.int64)
+        reduced = np.array(data)
+        #print(reduced)
+        #[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
+
+        expect(node, inputs=[data, axes], outputs=[reduced],
+               name='test_reduce_sum_empty_axes_input_noop_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.array(data)
+
+        expect(node, inputs=[data, axes], outputs=[reduced], name='test_reduce_sum_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reducesumsquare.py b/third_party/onnx/onnx/backend/test/case/node/reducesumsquare.py
new file mode 100644
index 0000000000..20fc1a4643
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reducesumsquare.py
@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceSumSquare(Base):
+
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceSumSquare',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[10., 20.]
+        # [74., 100.]
+        # [202., 244.]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSumSquare',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[10., 20.]]
+        # [[74., 100.]]
+        # [[202., 244.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSumSquare',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(np.square(data), axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[650.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(np.square(data), axis=axes, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_default_axes_keepdims_random')
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = [-2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceSumSquare',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims)
+
+        data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32)
+        reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        #[[[10., 20.s]]
+        # [[74., 100.]]
+        # [[202., 244.]]]
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_negative_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_sum_square_negative_axes_keepdims_random')
diff --git a/third_party/onnx/onnx/backend/test/case/node/relu.py b/third_party/onnx/onnx/backend/test/case/node/relu.py
new file mode 100644
index 0000000000..26d2a71524
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/relu.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Relu(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Relu',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf)
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_relu')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reshape.py b/third_party/onnx/onnx/backend/test/case/node/reshape.py
new file mode 100644
index 0000000000..a435166e51
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reshape.py
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def reshape_reference_implementation(data: np.ndarray, shape: np.ndarray, allowzero: int = 0) -> np.ndarray:
+    # replace zeros with corresponding dim size
+    # we need to do this because np.reshape doesn't support 0 by default unless 'allowzero' is set
+    new_shape = np.copy(shape)
+    if allowzero == 0:
+        zeros_index = np.where(shape == 0)
+        new_shape[zeros_index] = np.array(data.shape)[zeros_index]
+    reshaped = np.reshape(data, new_shape)
+    return reshaped
+
+
+class Reshape(Base):
+
+    @staticmethod
+    def export_reshape() -> None:
+        original_shape = [2, 3, 4]
+        test_cases = {
+            'reordered_all_dims': np.array([4, 2, 3], dtype=np.int64),
+            'reordered_last_dims': np.array([2, 4, 3], dtype=np.int64),
+            'reduced_dims': np.array([2, 12], dtype=np.int64),
+            'extended_dims': np.array([2, 3, 2, 2], dtype=np.int64),
+            'one_dim': np.array([24], dtype=np.int64),
+            'negative_dim': np.array([2, -1, 2], dtype=np.int64),
+            'negative_extended_dims': np.array([-1, 2, 3, 4], dtype=np.int64),
+            'zero_dim': np.array([2, 0, 4, 1], dtype=np.int64),
+            'zero_and_negative_dim': np.array([2, 0, 1, -1], dtype=np.int64),
+        }
+        data = np.random.random_sample(original_shape).astype(np.float32)
+
+        for test_name, shape in test_cases.items():
+            node = onnx.helper.make_node(
+                'Reshape',
+                inputs=['data', 'shape'],
+                outputs=['reshaped'],
+            )
+
+            reshaped = reshape_reference_implementation(data, shape)
+
+            expect(node, inputs=[data, shape], outputs=[reshaped],
+                   name='test_reshape_' + test_name)
+
+    @staticmethod
+    def export_allowzero() -> None:
+        original_shape = [0, 3, 4]
+        test_cases = {
+            'allowzero_reordered': np.array([3, 4, 0], dtype=np.int64),
+        }
+        data = np.random.random_sample(original_shape).astype(np.float32)
+
+        for test_name, shape in test_cases.items():
+            node = onnx.helper.make_node(
+                'Reshape',
+                inputs=['data', 'shape'],
+                outputs=['reshaped'],
+                allowzero=1,  # if allowzero=1, final shape = (3, 4, 0)
+                              # if allowzero=0, final shape = (3, 4, 4)
+            )
+
+            reshaped = reshape_reference_implementation(data, shape, allowzero=1)
+
+            expect(node, inputs=[data, shape], outputs=[reshaped],
+                   name='test_reshape_' + test_name)
diff --git a/third_party/onnx/onnx/backend/test/case/node/resize.py b/third_party/onnx/onnx/backend/test/case/node/resize.py
new file mode 100644
index 0000000000..f220e6cb7d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/resize.py
@@ -0,0 +1,939 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from typing import Any, List, Callable, Union, Optional, Text
+
+
+def cartesian(arrays: List[np.ndarray], out: np.ndarray = None) -> np.ndarray:
+    """
+    From https://stackoverflow.com/a/1235363
+    Generate a cartesian product of input arrays.
+    Parameters
+    ----------
+    arrays : list of array-like
+        1-D arrays to form the cartesian product of.
+    out : ndarray
+        Array to place the cartesian product in.
+    Returns
+    -------
+    out : ndarray
+        2-D array of shape (M, len(arrays)) containing cartesian products
+        formed of input arrays.
+    Examples
+    --------
+    >>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
+    array([[1, 4, 6],
+           [1, 4, 7],
+           [1, 5, 6],
+           [1, 5, 7],
+           [2, 4, 6],
+           [2, 4, 7],
+           [2, 5, 6],
+           [2, 5, 7],
+           [3, 4, 6],
+           [3, 4, 7],
+           [3, 5, 6],
+           [3, 5, 7]])
+    """
+
+    arrays = [np.asarray(x) for x in arrays]
+    dtype = arrays[0].dtype
+
+    n = np.prod([x.size for x in arrays])
+    if out is None:
+        out = np.zeros([n, len(arrays)], dtype=dtype)
+
+    m = n // arrays[0].size
+    out[:, 0] = np.repeat(arrays[0], m)
+    if arrays[1:]:
+        cartesian(arrays[1:], out=out[0:m, 1:])
+        for j in range(1, arrays[0].size):
+            out[j * m:(j + 1) * m, 1:] = out[0:m, 1:]
+    return out
+
+
+def interpolate_1d_with_x(data: np.ndarray,
+                          scale_factor: float,
+                          x: float,
+                          get_coeffs: Callable[[float], np.ndarray],
+                          roi: np.ndarray = None,
+                          extrapolation_value: float = 0.0,
+                          coordinate_transformation_mode: Text = 'half_pixel',
+                          exclude_outside: bool = False,
+                          ) -> np.ndarray:
+    def get_neighbor_idxes(x: float, n: int, limit: int) -> np.ndarray:
+        """
+        Return the n nearest indexes to x among [0, limit), prefer the indexes smaller than x.
+        As a result, the ratio must be in (0, 1]
+        Examples:
+        get_neighbor_idxes(4, 2, 10) == [3, 4]
+        get_neighbor_idxes(4, 3, 10) == [3, 4, 5]
+        get_neighbor_idxes(4.4, 3, 10) == [3, 4, 5]
+        get_neighbor_idxes(4.5, 3, 10) == [3, 4, 5]
+        get_neighbor_idxes(4.6, 3, 10) == [4, 5, 6]
+        get_neighbor_idxes(4.4, 1, 10) == [4]
+        get_neighbor_idxes(4.6, 1, 10) == [5]
+        :param x:
+        :param n: the number of the wanted indexes
+        :param limit: the maximum value of index
+        :return: An np.array containing n nearest indexes in ascending order
+        """
+        idxes = sorted(range(limit), key=lambda idx: (abs(x - idx), idx))[:n]
+        idxes = sorted(idxes)
+        return np.array(idxes)
+
+    def get_neighbor(x: float, n: int, data: np.ndarray) -> np.ndarray:
+        """
+        Pad `data` in 'edge' mode, and get n nearest elements in the padded array and their indexes in the original
+        array
+        :param x: center index (in the unpadded coordinate system) of the found nearest elements.
+        :param n: the number of neighbors.
+        :param data: the array
+        :return: A tuple containing the indexes of neighbor elements (the index can be smaller than 0 or higher than
+        len(data)) and the value of these elements
+        """
+        pad_width = np.ceil(n / 2).astype(int)
+        padded = np.pad(data, pad_width, mode='edge')
+        x += pad_width
+
+        idxes = get_neighbor_idxes(x, n, len(padded))
+        ret = padded[idxes]
+        return idxes - pad_width, ret
+
+    input_width = len(data)
+    output_width = scale_factor * input_width
+    if coordinate_transformation_mode == 'align_corners':
+        if output_width == 1:
+            x_ori = 0.
+        else:
+            x_ori = x * (input_width - 1) / (output_width - 1)
+    elif coordinate_transformation_mode == 'asymmetric':
+        x_ori = x / scale_factor
+    elif coordinate_transformation_mode == 'tf_crop_and_resize':
+        if output_width == 1:
+            x_ori = (roi[1] - roi[0]) * (input_width - 1) / 2
+        else:
+            x_ori = x * (roi[1] - roi[0]) * \
+                (input_width - 1) / (output_width - 1)
+        x_ori += (roi[0] * (input_width - 1))
+        # Return extrapolation_value directly as what TF CropAndResize does
+        if x_ori < 0 or x_ori > input_width - 1:
+            return extrapolation_value
+    elif coordinate_transformation_mode == 'pytorch_half_pixel':
+        if output_width == 1:
+            x_ori = -0.5
+        else:
+            x_ori = (x + 0.5) / scale_factor - 0.5
+    elif coordinate_transformation_mode == 'half_pixel':
+        x_ori = (x + 0.5) / scale_factor - 0.5
+    else:
+        raise ValueError(f'invalid coordinate_transformation_mode: {coordinate_transformation_mode}')
+    x_ori_int = np.floor(x_ori).astype(int).item()
+
+    # ratio must be in (0, 1] since we prefer the pixel on the left of `x_ori`
+    if x_ori.is_integer():
+        ratio = 1
+    else:
+        ratio = x_ori - x_ori_int
+
+    coeffs = get_coeffs(ratio)
+    n = len(coeffs)
+
+    idxes, points = get_neighbor(x_ori, n, data)
+
+    if exclude_outside:
+        for i, idx in enumerate(idxes):
+            if idx < 0 or idx >= input_width:
+                coeffs[i] = 0
+        coeffs /= sum(coeffs)
+
+    return np.dot(coeffs, points).item()
+
+
+def interpolate_nd_with_x(data: np.ndarray,
+                          n: int,
+                          scale_factors: List[float],
+                          x: List[float],
+                          get_coeffs: Callable[[float], np.ndarray],
+                          roi: np.ndarray = None,
+                          **kwargs: Any
+                          ) -> np.ndarray:
+    if n == 1:
+        return interpolate_1d_with_x(data, scale_factors[0], x[0], get_coeffs, roi=roi,
+                                     **kwargs)
+    return interpolate_1d_with_x(
+        [interpolate_nd_with_x(data[i], n - 1, scale_factors[1:], x[1:], get_coeffs,
+                               roi=None if roi is None else np.concatenate(
+                                   [roi[1:n], roi[n + 1:]]),
+                               **kwargs)
+         for i in range(data.shape[0])], scale_factors[0], x[0], get_coeffs,
+        roi=None if roi is None else [roi[0], roi[n]], **kwargs)
+
+
+def interpolate_nd(data: np.ndarray,
+                   get_coeffs: Callable[[float], np.ndarray],
+                   output_size: Optional[List[int]] = None,
+                   scale_factors: Optional[List[float]] = None,
+                   roi: np.ndarray = None,
+                   **kwargs: Any
+                   ) -> np.ndarray:
+    def get_all_coords(data: np.ndarray) -> np.ndarray:
+        return cartesian([list(range(data.shape[i])) for i in range(len(data.shape))])
+
+    assert output_size is not None or scale_factors is not None
+    if output_size is not None:
+        scale_factors = np.array(output_size) / np.array(data.shape)
+    else:
+        output_size = (scale_factors * np.array(data.shape)).astype(int)
+    assert scale_factors is not None
+
+    ret = np.zeros(output_size)
+    for x in get_all_coords(ret):
+        ret[tuple(x)] = interpolate_nd_with_x(data, len(data.shape), scale_factors, x, get_coeffs, roi=roi,
+                                              **kwargs)
+    return ret
+
+
+def cubic_coeffs(ratio: float, A: float = -0.75) -> np.ndarray:
+    coeffs = [((A * (ratio + 1) - 5 * A) * (ratio + 1) + 8 * A) * (ratio + 1) - 4 * A,
+              ((A + 2) * ratio - (A + 3)) * ratio * ratio + 1,
+              ((A + 2) * (1 - ratio) - (A + 3)) * (1 - ratio) * (1 - ratio) + 1,
+              ((A * ((1 - ratio) + 1) - 5 * A) * ((1 - ratio) + 1) + 8 * A) * ((1 - ratio) + 1) - 4 * A]
+
+    return np.array(coeffs)
+
+
+def linear_coeffs(ratio: float) -> np.ndarray:
+    return np.array([1 - ratio, ratio])
+
+
+def nearest_coeffs(ratio: float, mode: Text = 'round_prefer_floor') -> np.ndarray:
+    if type(ratio) == int or ratio.is_integer():
+        return np.array([0, 1])
+    elif mode == 'round_prefer_floor':
+        return np.array([ratio <= 0.5, ratio > 0.5])
+    elif mode == 'round_prefer_ceil':
+        return np.array([ratio < 0.5, ratio >= 0.5])
+    elif mode == 'floor':
+        return np.array([1, 0])
+    elif mode == 'ceil':
+        return np.array([0, 1])
+
+
+class Resize(Base):
+
+    @staticmethod
+    def export_resize_upsample_scales_nearest() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='nearest',
+        )
+
+        data = np.array([[[
+            [1, 2],
+            [3, 4],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+        # [[[[1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 2. 2. 2.]
+        #    [3. 3. 3. 4. 4. 4.]
+        #    [3. 3. 3. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, nearest_coeffs, scale_factors=scales).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_nearest')
+
+    @staticmethod
+    def export_resize_downsample_scales_nearest() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='nearest',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[1. 3.]]]]
+        output = interpolate_nd(
+            data, nearest_coeffs, scale_factors=scales).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_downsample_scales_nearest')
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='nearest',
+        )
+
+        data = np.array([[[
+            [1, 2],
+            [3, 4],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 7, 8], dtype=np.int64)
+
+        # [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, nearest_coeffs, output_size=sizes).astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_upsample_sizes_nearest')
+
+    @staticmethod
+    def export_resize_downsample_sizes_nearest() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='nearest',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 1, 3], dtype=np.int64)
+
+        # [[[[1. 3.]]]]
+        output = interpolate_nd(
+            data, nearest_coeffs, output_size=sizes).astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_downsample_sizes_nearest')
+
+    @staticmethod
+    def export_resize_upsample_scales_linear() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='linear',
+        )
+
+        data = np.array([[[
+            [1, 2],
+            [3, 4],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[1.   1.25 1.75 2.  ]
+        #    [1.5  1.75 2.25 2.5 ]
+        #    [2.5  2.75 3.25 3.5 ]
+        #    [3.   3.25 3.75 4.  ]]]]
+        output = interpolate_nd(
+            data, linear_coeffs, scale_factors=scales).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_linear')
+
+    @staticmethod
+    def export_resize_upsample_scales_linear_align_corners() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='linear',
+            coordinate_transformation_mode='align_corners'
+        )
+
+        data = np.array([[[
+            [1, 2],
+            [3, 4],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[1.         1.33333333 1.66666667 2.        ]
+        #    [1.66666667 2.         2.33333333 2.66666667]
+        #    [2.33333333 2.66666667 3.         3.33333333]
+        #    [3.         3.33333333 3.66666667 4.        ]]]]
+        output = interpolate_nd(
+            data, linear_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_linear_align_corners')
+
+    @staticmethod
+    def export_resize_downsample_scales_linear() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='linear',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[2.6666665 4.3333331]]]]
+        output = interpolate_nd(
+            data, linear_coeffs, scale_factors=scales).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_downsample_scales_linear')
+
+    @staticmethod
+    def export_resize_downsample_scales_linear_align_corners() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='linear',
+            coordinate_transformation_mode='align_corners'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[1.       3.142857]]]]
+        output = interpolate_nd(
+            data, linear_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_downsample_scales_linear_align_corners')
+
+    @staticmethod
+    def export_resize_upsample_scales_cubic() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 0.47265625  0.76953125  1.24609375  1.875       2.28125
+        #      2.91015625  3.38671875  3.68359375]
+        #    [ 1.66015625  1.95703125  2.43359375  3.0625      3.46875
+        #      4.09765625  4.57421875  4.87109375]
+        #    [ 3.56640625  3.86328125  4.33984375  4.96875     5.375
+        #      6.00390625  6.48046875  6.77734375]
+        #    [ 6.08203125  6.37890625  6.85546875  7.484375    7.890625
+        #      8.51953125  8.99609375  9.29296875]
+        #    [ 7.70703125  8.00390625  8.48046875  9.109375    9.515625
+        #     10.14453125 10.62109375 10.91796875]
+        #    [10.22265625 10.51953125 10.99609375 11.625      12.03125
+        #     12.66015625 13.13671875 13.43359375]
+        #    [12.12890625 12.42578125 12.90234375 13.53125    13.9375
+        #     14.56640625 15.04296875 15.33984375]
+        #    [13.31640625 13.61328125 14.08984375 14.71875    15.125
+        #     15.75390625 16.23046875 16.52734375]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs, scale_factors=scales).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_cubic')
+
+    @staticmethod
+    def export_resize_upsample_scales_cubic_align_corners() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+            coordinate_transformation_mode='align_corners'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 1.          1.34110787  1.80029155  2.32944606  2.67055394
+        #      3.19970845  3.65889213  4.        ]
+        #    [ 2.36443149  2.70553936  3.16472303  3.69387755  4.03498542
+        #      4.56413994  5.02332362  5.36443149]
+        #    [ 4.20116618  4.54227405  5.00145773  5.53061224  5.87172012
+        #      6.40087464  6.86005831  7.20116618]
+        #    [ 6.31778426  6.65889213  7.1180758   7.64723032  7.98833819
+        #      8.51749271  8.97667638  9.31778426]
+        #    [ 7.68221574  8.02332362  8.48250729  9.01166181  9.35276968
+        #      9.8819242  10.34110787 10.68221574]
+        #    [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776
+        #     11.99854227 12.45772595 12.79883382]
+        #    [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245
+        #     13.83527697 14.29446064 14.63556851]
+        #    [13.         13.34110787 13.80029155 14.32944606 14.67055394
+        #     15.19970845 15.65889213 16.        ]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_cubic_align_corners')
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        # [[[[ 1.47119141  2.78125     4.08251953]
+        #    [ 6.71142578  8.02148438  9.32275391]
+        #    [11.91650391 13.2265625  14.52783203]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs, scale_factors=scales).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_downsample_scales_cubic')
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic_align_corners() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+            coordinate_transformation_mode='align_corners'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        # [[[[ 1.          2.39519159  3.79038317]
+        #    [ 6.58076634  7.97595793  9.37114951]
+        #    [12.16153268 13.55672427 14.95191585]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs, scale_factors=scales, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_downsample_scales_cubic_align_corners')
+
+    @staticmethod
+    def export_resize_upsample_sizes_cubic() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='cubic',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 9, 10], dtype=np.int64)
+
+        # [[[[ 0.45507922  0.64057922  0.97157922  1.42257922  1.90732922
+        #      2.22332922  2.70807922  3.15907922  3.49007922  3.67557922]
+        #    [ 1.39437963  1.57987963  1.91087963  2.36187963  2.84662963
+        #      3.16262963  3.64737963  4.09837963  4.42937963  4.61487963]
+        #    [ 2.95130693  3.13680693  3.46780693  3.91880693  4.40355693
+        #      4.71955693  5.20430693  5.65530693  5.98630693  6.17180693]
+        #    [ 5.20525069  5.39075069  5.72175069  6.17275069  6.65750069
+        #      6.97350069  7.45825069  7.90925069  8.24025069  8.42575069]
+        #    [ 6.88975     7.07525     7.40625     7.85725     8.342
+        #      8.658       9.14275     9.59375     9.92475    10.11025   ]
+        #    [ 8.57424931  8.75974931  9.09074931  9.54174931 10.02649931
+        #     10.34249931 10.82724931 11.27824931 11.60924931 11.79474931]
+        #    [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307
+        #     12.59644307 13.08119307 13.53219307 13.86319307 14.04869307]
+        #    [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037
+        #     14.15337037 14.63812037 15.08912037 15.42012037 15.60562037]
+        #    [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078
+        #     15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs, output_size=sizes).astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_upsample_sizes_cubic')
+
+    @staticmethod
+    def export_resize_downsample_sizes_cubic() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='cubic',
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 1.63078704  3.00462963  4.37847222]
+        #    [ 7.12615741  8.5         9.87384259]
+        #    [12.62152778 13.99537037 15.36921296]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs, output_size=sizes).astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_downsample_sizes_cubic')
+
+    # TensorFlow v1 bicubic with half_pixel_centers=True
+    @staticmethod
+    def export_resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+            cubic_coeff_a=-0.5,
+            exclude_outside=True
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 0.55882353  0.81494204  1.35698249  1.89705882  2.39705882
+        #      2.93713516  3.47917561  3.73529412]
+        #    [ 1.58329755  1.83941606  2.38145651  2.92153285  3.42153285
+        #      3.96160918  4.50364964  4.75976814]
+        #    [ 3.75145936  4.00757787  4.54961832  5.08969466  5.58969466
+        #      6.12977099  6.67181144  6.92792995]
+        #    [ 5.91176471  6.16788321  6.70992366  7.25        7.75
+        #      8.29007634  8.83211679  9.08823529]
+        #    [ 7.91176471  8.16788321  8.70992366  9.25        9.75
+        #     10.29007634 10.83211679 11.08823529]
+        #    [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534
+        #     12.45038168 12.99242213 13.24854064]
+        #    [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715
+        #     14.61854349 15.16058394 15.41670245]
+        #    [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118
+        #     15.64301751 16.18505796 16.44117647]]]]
+        output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.5), scale_factors=scales,
+                                exclude_outside=True).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_cubic_A_n0p5_exclude_outside')
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+            cubic_coeff_a=-0.5,
+            exclude_outside=True
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        # [[[[ 1.36812675  2.6695014   4.0133367 ]
+        #    [ 6.57362535  7.875       9.2188353 ]
+        #    [11.94896657 13.25034122 14.59417652]]]]
+        output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.5), scale_factors=scales,
+                                exclude_outside=True).astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_downsample_scales_cubic_A_n0p5_exclude_outside')
+
+    # TensorFlow v1 bicubic with half_pixel_centers=False
+    @staticmethod
+    def export_resize_upsample_scales_cubic_asymmetric() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', 'scales'],
+            outputs=['Y'],
+            mode='cubic',
+            coordinate_transformation_mode='asymmetric'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 1.       1.40625  2.       2.5      3.       3.59375  4.
+        #      4.09375]
+        #    [ 2.625    3.03125  3.625    4.125    4.625    5.21875  5.625
+        #      5.71875]
+        #    [ 5.       5.40625  6.       6.5      7.       7.59375  8.
+        #      8.09375]
+        #    [ 7.       7.40625  8.       8.5      9.       9.59375 10.
+        #     10.09375]
+        #    [ 9.       9.40625 10.      10.5     11.      11.59375 12.
+        #     12.09375]
+        #    [11.375   11.78125 12.375   12.875   13.375   13.96875 14.375
+        #     14.46875]
+        #    [13.      13.40625 14.      14.5     15.      15.59375 16.
+        #     16.09375]
+        #    [13.375   13.78125 14.375   14.875   15.375   15.96875 16.375
+        #     16.46875]]]]
+        output = interpolate_nd(data, lambda x: cubic_coeffs(x, A=-0.75), scale_factors=scales,
+                                coordinate_transformation_mode='asymmetric').astype(np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_resize_upsample_scales_cubic_asymmetric')
+
+    @staticmethod
+    def export_resize_tf_crop_and_resize() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', 'roi', '', 'sizes'],
+            outputs=['Y'],
+            mode='linear',
+            coordinate_transformation_mode='tf_crop_and_resize'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        # Note: for some rois, the result may be different with that of TF for inaccurate floating point
+        roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 7.6000004  7.9        8.2      ]
+        #    [ 8.8        9.1        9.400001 ]
+        #    [10.        10.3       10.6      ]]]]
+        output = interpolate_nd(data, linear_coeffs, output_size=sizes, roi=roi,
+                                coordinate_transformation_mode='tf_crop_and_resize').astype(np.float32)
+
+        expect(node, inputs=[data, roi, sizes], outputs=[output],
+               name='test_resize_tf_crop_and_resize')
+
+    @staticmethod
+    def export_resize_tf_crop_and_resize_extrapolation_value() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', 'roi', '', 'sizes'],
+            outputs=['Y'],
+            mode='linear',
+            coordinate_transformation_mode='tf_crop_and_resize',
+            extrapolation_value=10.0
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        # Note: for some rois, the result may be different with that of TF for inaccurate floating point
+        roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 7.6000004 10.        10.       ]
+        #    [12.400001  10.        10.       ]
+        #    [10.        10.        10.       ]]]]
+        output = interpolate_nd(data, linear_coeffs, output_size=sizes, roi=roi,
+                                coordinate_transformation_mode='tf_crop_and_resize', extrapolation_value=10.0).astype(np.float32)
+
+        expect(node, inputs=[data, roi, sizes], outputs=[output],
+               name='test_resize_tf_crop_and_resize')
+
+    @staticmethod
+    def export_resize_downsample_sizes_linear_pytorch_half_pixel() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='linear',
+            coordinate_transformation_mode='pytorch_half_pixel'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 3, 1], dtype=np.int64)
+
+        # [[[[ 1.6666666]
+        #    [ 7.       ]
+        #    [12.333333 ]]]]
+        output = interpolate_nd(
+            data, linear_coeffs, output_size=sizes, coordinate_transformation_mode='pytorch_half_pixel').astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_downsample_sizes_linear_pytorch_half_pixel')
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_floor_align_corners() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='nearest',
+            coordinate_transformation_mode='align_corners',
+            nearest_mode='floor'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        # [[[[ 1.  1.  1.  2.  2.  3.  3.  4.]
+        #    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+        #    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+        #    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+        #    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+        #    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+        #    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+        #    [13. 13. 13. 14. 14. 15. 15. 16.]]]]
+        output = interpolate_nd(
+            data, lambda x: nearest_coeffs(x, mode='floor'), output_size=sizes, coordinate_transformation_mode='align_corners').astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_upsample_sizes_nearest_floor_align_corners')
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='nearest',
+            coordinate_transformation_mode='asymmetric',
+            nearest_mode='round_prefer_ceil'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        # [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+        output = interpolate_nd(
+            data, lambda x: nearest_coeffs(x, mode='round_prefer_ceil'),
+            output_size=sizes, coordinate_transformation_mode='asymmetric').astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric')
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_ceil_half_pixel() -> None:
+        node = onnx.helper.make_node(
+            'Resize',
+            inputs=['X', '', '', 'sizes'],
+            outputs=['Y'],
+            mode='nearest',
+            coordinate_transformation_mode='half_pixel',
+            nearest_mode='ceil'
+        )
+
+        data = np.array([[[
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]]], dtype=np.float32)
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        # [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+        output = interpolate_nd(
+            data, lambda x: nearest_coeffs(x, mode='ceil'), output_size=sizes).astype(np.float32)
+
+        expect(node, inputs=[data, sizes], outputs=[output],
+               name='test_resize_upsample_sizes_nearest_ceil_half_pixel')
diff --git a/third_party/onnx/onnx/backend/test/case/node/reversesequence.py b/third_party/onnx/onnx/backend/test/case/node/reversesequence.py
new file mode 100644
index 0000000000..d102a61242
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/reversesequence.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReverseSequence(Base):
+
+    @staticmethod
+    def export_reversesequence_time() -> None:
+        node = onnx.helper.make_node(
+            'ReverseSequence',
+            inputs=['x', 'sequence_lens'],
+            outputs=['y'],
+            time_axis=0,
+            batch_axis=1,
+        )
+        x = np.array([[0.0, 4.0, 8.0, 12.0],
+                      [1.0, 5.0, 9.0, 13.0],
+                      [2.0, 6.0, 10.0, 14.0],
+                      [3.0, 7.0, 11.0, 15.0]], dtype=np.float32)
+        sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)
+
+        y = np.array([[3.0, 6.0, 9.0, 12.0],
+                      [2.0, 5.0, 8.0, 13.0],
+                      [1.0, 4.0, 10.0, 14.0],
+                      [0.0, 7.0, 11.0, 15.0]], dtype=np.float32)
+
+        expect(node, inputs=[x, sequence_lens], outputs=[y],
+               name='test_reversesequence_time')
+
+    @staticmethod
+    def export_reversesequence_batch() -> None:
+        node = onnx.helper.make_node(
+            'ReverseSequence',
+            inputs=['x', 'sequence_lens'],
+            outputs=['y'],
+            time_axis=1,
+            batch_axis=0,
+        )
+        x = np.array([[0.0, 1.0, 2.0, 3.0],
+                      [4.0, 5.0, 6.0, 7.0],
+                      [8.0, 9.0, 10.0, 11.0],
+                      [12.0, 13.0, 14.0, 15.0]], dtype=np.float32)
+        sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64)
+
+        y = np.array([[0.0, 1.0, 2.0, 3.0],
+                      [5.0, 4.0, 6.0, 7.0],
+                      [10.0, 9.0, 8.0, 11.0],
+                      [15.0, 14.0, 13.0, 12.0]], dtype=np.float32)
+
+        expect(node, inputs=[x, sequence_lens], outputs=[y],
+               name='test_reversesequence_batch')
diff --git a/third_party/onnx/onnx/backend/test/case/node/rnn.py b/third_party/onnx/onnx/backend/test/case/node/rnn.py
new file mode 100644
index 0000000000..14cb6f8705
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/rnn.py
@@ -0,0 +1,184 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Any, Tuple
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class RNN_Helper():
+    def __init__(self, **params: Any) -> None:
+        # RNN Input Names
+        X = str('X')
+        W = str('W')
+        R = str('R')
+        B = str('B')
+        H_0 = str('initial_h')
+        LAYOUT = str('layout')
+
+        required_inputs = [X, W, R]
+        for i in required_inputs:
+            assert i in params, "Missing Required Input: {0}".format(i)
+
+        self.num_directions = params[str(W)].shape[0]
+
+        if self.num_directions == 1:
+            for k in params.keys():
+                if k != X:
+                    params[k] = np.squeeze(params[k], axis=0)
+
+            hidden_size = params[R].shape[-1]
+            batch_size = params[X].shape[1]
+
+            layout = params[LAYOUT] if LAYOUT in params else 0
+            x = params[X]
+            x = x if layout == 0 else np.swapaxes(x, 0, 1)
+            b = params[B] if B in params else np.zeros(2 * hidden_size, dtype=np.float32)
+            h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32)
+
+            self.X = x
+            self.W = params[W]
+            self.R = params[R]
+            self.B = b
+            self.H_0 = h_0
+            self.LAYOUT = layout
+
+        else:
+            raise NotImplementedError()
+
+    def f(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def step(self) -> Tuple[np.ndarray, np.ndarray]:
+        seq_length = self.X.shape[0]
+        hidden_size = self.H_0.shape[-1]
+        batch_size = self.X.shape[1]
+
+        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
+        h_list = []
+
+        H_t = self.H_0
+        for x in np.split(self.X, self.X.shape[0], axis=0):
+            H = self.f(np.dot(x, np.transpose(self.W)) + np.dot(H_t, np.transpose(self.R)) + np.add(
+                *np.split(self.B, 2)))
+            h_list.append(H)
+            H_t = H
+
+        concatenated = np.concatenate(h_list)
+        if self.num_directions == 1:
+            Y[:, 0, :, :] = concatenated
+
+        if self.LAYOUT == 0:
+            Y_h = Y[-1]
+        else:
+            Y = np.transpose(Y, [2, 0, 1, 3])
+            Y_h = Y[:, :, -1, :]
+
+        return Y, Y_h
+
+
+class RNN(Base):
+
+    @staticmethod
+    def export_defaults() -> None:
+        input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 4
+        weight_scale = 0.1
+
+        node = onnx.helper.make_node(
+            'RNN',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+        rnn = RNN_Helper(X=input, W=W, R=R)
+        _, Y_h = rnn.step()
+        expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_simple_rnn_defaults')
+
+    @staticmethod
+    def export_initial_bias() -> None:
+        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 5
+        custom_bias = 0.1
+        weight_scale = 0.1
+
+        node = onnx.helper.make_node(
+            'RNN',
+            inputs=['X', 'W', 'R', 'B'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+        # Adding custom bias
+        W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
+        R_B = np.zeros((1, hidden_size)).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        rnn = RNN_Helper(X=input, W=W, R=R, B=B)
+        _, Y_h = rnn.step()
+        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)],
+               name='test_simple_rnn_with_initial_bias')
+
+    @staticmethod
+    def export_seq_length() -> None:
+        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],
+                          [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 5
+
+        node = onnx.helper.make_node(
+            'RNN',
+            inputs=['X', 'W', 'R', 'B'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )
+
+        W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
+        R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
+
+        # Adding custom bias
+        W_B = np.random.randn(1, hidden_size).astype(np.float32)
+        R_B = np.random.randn(1, hidden_size).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        rnn = RNN_Helper(X=input, W=W, R=R, B=B)
+        _, Y_h = rnn.step()
+        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_rnn_seq_length')
+
+    @staticmethod
+    def export_batchwise() -> None:
+        input = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 4
+        weight_scale = 0.5
+        layout = 1
+
+        node = onnx.helper.make_node(
+            'RNN',
+            inputs=['X', 'W', 'R'],
+            outputs=['Y', 'Y_h'],
+            hidden_size=hidden_size,
+            layout=layout
+        )
+
+        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+        rnn = RNN_Helper(X=input, W=W, R=R, layout=layout)
+        Y, Y_h = rnn.step()
+        expect(node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name='test_simple_rnn_batchwise')
diff --git a/third_party/onnx/onnx/backend/test/case/node/roialign.py b/third_party/onnx/onnx/backend/test/case/node/roialign.py
new file mode 100644
index 0000000000..882d5ebc82
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/roialign.py
@@ -0,0 +1,244 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def get_roi_align_input_values():  # type: ignore
+    X = np.array(
+        [
+            [
+                [
+                    [
+                        0.2764,
+                        0.7150,
+                        0.1958,
+                        0.3416,
+                        0.4638,
+                        0.0259,
+                        0.2963,
+                        0.6518,
+                        0.4856,
+                        0.7250,
+                    ],
+                    [
+                        0.9637,
+                        0.0895,
+                        0.2919,
+                        0.6753,
+                        0.0234,
+                        0.6132,
+                        0.8085,
+                        0.5324,
+                        0.8992,
+                        0.4467,
+                    ],
+                    [
+                        0.3265,
+                        0.8479,
+                        0.9698,
+                        0.2471,
+                        0.9336,
+                        0.1878,
+                        0.4766,
+                        0.4308,
+                        0.3400,
+                        0.2162,
+                    ],
+                    [
+                        0.0206,
+                        0.1720,
+                        0.2155,
+                        0.4394,
+                        0.0653,
+                        0.3406,
+                        0.7724,
+                        0.3921,
+                        0.2541,
+                        0.5799,
+                    ],
+                    [
+                        0.4062,
+                        0.2194,
+                        0.4473,
+                        0.4687,
+                        0.7109,
+                        0.9327,
+                        0.9815,
+                        0.6320,
+                        0.1728,
+                        0.6119,
+                    ],
+                    [
+                        0.3097,
+                        0.1283,
+                        0.4984,
+                        0.5068,
+                        0.4279,
+                        0.0173,
+                        0.4388,
+                        0.0430,
+                        0.4671,
+                        0.7119,
+                    ],
+                    [
+                        0.1011,
+                        0.8477,
+                        0.4726,
+                        0.1777,
+                        0.9923,
+                        0.4042,
+                        0.1869,
+                        0.7795,
+                        0.9946,
+                        0.9689,
+                    ],
+                    [
+                        0.1366,
+                        0.3671,
+                        0.7011,
+                        0.6234,
+                        0.9867,
+                        0.5585,
+                        0.6985,
+                        0.5609,
+                        0.8788,
+                        0.9928,
+                    ],
+                    [
+                        0.5697,
+                        0.8511,
+                        0.6711,
+                        0.9406,
+                        0.8751,
+                        0.7496,
+                        0.1650,
+                        0.1049,
+                        0.1559,
+                        0.2514,
+                    ],
+                    [
+                        0.7012,
+                        0.4056,
+                        0.7879,
+                        0.3461,
+                        0.0415,
+                        0.2998,
+                        0.5094,
+                        0.3727,
+                        0.5482,
+                        0.0502,
+                    ],
+                ]
+            ]
+        ],
+        dtype=np.float32,
+    )
+    batch_indices = np.array([0, 0, 0], dtype=np.int64)
+    rois = np.array([[0, 0, 9, 9], [0, 5, 4, 9], [5, 5, 9, 9]], dtype=np.float32)
+    return X, batch_indices, rois
+
+
+class RoiAlign(Base):
+    @staticmethod
+    def export_roialign_aligned_false() -> None:
+        node = onnx.helper.make_node(
+            "RoiAlign",
+            inputs=["X", "rois", "batch_indices"],
+            outputs=["Y"],
+            spatial_scale=1.0,
+            output_height=5,
+            output_width=5,
+            sampling_ratio=2,
+            coordinate_transformation_mode="output_half_pixel",
+        )
+
+        X, batch_indices, rois = get_roi_align_input_values()
+        # (num_rois, C, output_height, output_width)
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.4664, 0.4466, 0.3405, 0.5688, 0.6068],
+                        [0.3714, 0.4296, 0.3835, 0.5562, 0.3510],
+                        [0.2768, 0.4883, 0.5222, 0.5528, 0.4171],
+                        [0.4713, 0.4844, 0.6904, 0.4920, 0.8774],
+                        [0.6239, 0.7125, 0.6289, 0.3355, 0.3495],
+                    ]
+                ],
+                [
+                    [
+                        [0.3022, 0.4305, 0.4696, 0.3978, 0.5423],
+                        [0.3656, 0.7050, 0.5165, 0.3172, 0.7015],
+                        [0.2912, 0.5059, 0.6476, 0.6235, 0.8299],
+                        [0.5916, 0.7389, 0.7048, 0.8372, 0.8893],
+                        [0.6227, 0.6153, 0.7097, 0.6154, 0.4585],
+                    ]
+                ],
+                [
+                    [
+                        [0.2384, 0.3379, 0.3717, 0.6100, 0.7601],
+                        [0.3767, 0.3785, 0.7147, 0.9243, 0.9727],
+                        [0.5749, 0.5826, 0.5709, 0.7619, 0.8770],
+                        [0.5355, 0.2566, 0.2141, 0.2796, 0.3600],
+                        [0.4365, 0.3504, 0.2887, 0.3661, 0.2349],
+                    ]
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_false")
+
+    @staticmethod
+    def export_roialign_aligned_true() -> None:
+        node = onnx.helper.make_node(
+            "RoiAlign",
+            inputs=["X", "rois", "batch_indices"],
+            outputs=["Y"],
+            spatial_scale=1.0,
+            output_height=5,
+            output_width=5,
+            sampling_ratio=2,
+            coordinate_transformation_mode="half_pixel",
+        )
+
+        X, batch_indices, rois = get_roi_align_input_values()
+        # (num_rois, C, output_height, output_width)
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.5178, 0.3434, 0.3229, 0.4474, 0.6344],
+                        [0.4031, 0.5366, 0.4428, 0.4861, 0.4023],
+                        [0.2512, 0.4002, 0.5155, 0.6954, 0.3465],
+                        [0.3350, 0.4601, 0.5881, 0.3439, 0.6849],
+                        [0.4932, 0.7141, 0.8217, 0.4719, 0.4039],
+                    ]
+                ],
+                [
+                    [
+                        [0.3070, 0.2187, 0.3337, 0.4880, 0.4870],
+                        [0.1871, 0.4914, 0.5561, 0.4192, 0.3686],
+                        [0.1433, 0.4608, 0.5971, 0.5310, 0.4982],
+                        [0.2788, 0.4386, 0.6022, 0.7000, 0.7524],
+                        [0.5774, 0.7024, 0.7251, 0.7338, 0.8163],
+                    ]
+                ],
+                [
+                    [
+                        [0.2393, 0.4075, 0.3379, 0.2525, 0.4743],
+                        [0.3671, 0.2702, 0.4105, 0.6419, 0.8308],
+                        [0.5556, 0.4543, 0.5564, 0.7502, 0.9300],
+                        [0.6626, 0.5617, 0.4813, 0.4954, 0.6663],
+                        [0.6636, 0.3721, 0.2056, 0.1928, 0.2478],
+                    ]
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_true")
diff --git a/third_party/onnx/onnx/backend/test/case/node/round.py b/third_party/onnx/onnx/backend/test/case/node/round.py
new file mode 100644
index 0000000000..017d9cf049
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/round.py
@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Round(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Round',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([0.1, 0.5, 0.9, 1.2, 1.5,
+                    1.8, 2.3, 2.5, 2.7, -1.1,
+                    -1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float32)
+        y = np.array([0., 0., 1., 1., 2.,
+                    2., 2., 2., 3., -1.,
+                    -2., -2., -2., -2., -3.]).astype(np.float32)  # expected output
+        expect(node, inputs=[x], outputs=[y],
+               name='test_round')
diff --git a/third_party/onnx/onnx/backend/test/case/node/scan.py b/third_party/onnx/onnx/backend/test/case/node/scan.py
new file mode 100644
index 0000000000..6fd7ff7f27
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/scan.py
@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Scan(Base):
+
+    @staticmethod
+    def export_scan_8() -> None:
+        # Given an input sequence [x1, ..., xN], sum up its elements using a scan
+        # returning the final state (x1+x2+...+xN) as well the scan_output
+        # [x1, x1+x2, ..., x1+x2+...+xN]
+        #
+        # create graph to represent scan body
+        sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+        next = onnx.helper.make_tensor_value_info('next', onnx.TensorProto.FLOAT, [2])
+        sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+        scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+        add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['sum_in', 'next'],
+            outputs=['sum_out']
+        )
+        id_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['sum_out'],
+            outputs=['scan_out']
+        )
+        scan_body = onnx.helper.make_graph(
+            [add_node, id_node],
+            'scan_body',
+            [sum_in, next],
+            [sum_out, scan_out]
+        )
+        # create scan op node
+        no_sequence_lens = ''   # optional input, not supplied
+        node = onnx.helper.make_node(
+            'Scan',
+            inputs=[no_sequence_lens, 'initial', 'x'],
+            outputs=['y', 'z'],
+            num_scan_inputs=1,
+            body=scan_body
+        )
+        # create inputs for batch-size 1, sequence-length 3, inner dimension 2
+        initial = np.array([0, 0]).astype(np.float32).reshape((1, 2))
+        x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2))
+        # final state computed = [1 + 3 + 5, 2 + 4 + 6]
+        y = np.array([9, 12]).astype(np.float32).reshape((1, 2))
+        # scan-output computed
+        z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2))
+
+        expect(node, inputs=[initial, x], outputs=[y, z],
+               name='test_scan_sum', opset_imports=[onnx.helper.make_opsetid("", 8)])
+
+    @staticmethod
+    def export_scan_9() -> None:
+        # Given an input sequence [x1, ..., xN], sum up its elements using a scan
+        # returning the final state (x1+x2+...+xN) as well the scan_output
+        # [x1, x1+x2, ..., x1+x2+...+xN]
+        #
+        # create graph to represent scan body
+        sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+        next = onnx.helper.make_tensor_value_info('next', onnx.TensorProto.FLOAT, [2])
+        sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+        scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+        add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['sum_in', 'next'],
+            outputs=['sum_out']
+        )
+        id_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['sum_out'],
+            outputs=['scan_out']
+        )
+        scan_body = onnx.helper.make_graph(
+            [add_node, id_node],
+            'scan_body',
+            [sum_in, next],
+            [sum_out, scan_out]
+        )
+        # create scan op node
+        node = onnx.helper.make_node(
+            'Scan',
+            inputs=['initial', 'x'],
+            outputs=['y', 'z'],
+            num_scan_inputs=1,
+            body=scan_body
+        )
+        # create inputs for sequence-length 3, inner dimension 2
+        initial = np.array([0, 0]).astype(np.float32).reshape((2,))
+        x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
+        # final state computed = [1 + 3 + 5, 2 + 4 + 6]
+        y = np.array([9, 12]).astype(np.float32).reshape((2,))
+        # scan-output computed
+        z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
+
+        expect(node, inputs=[initial, x], outputs=[y, z],
+               name='test_scan9_sum', opset_imports=[onnx.helper.make_opsetid("", 9)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/scatter.py b/third_party/onnx/onnx/backend/test/case/node/scatter.py
new file mode 100644
index 0000000000..c587d46c69
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/scatter.py
@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from onnx import helper
+
+
+# The below Scatter's numpy implementation is from https://stackoverflow.com/a/46204790/11767360
+def scatter(data, indices, updates, axis=0):  # type: ignore
+    if axis < 0:
+        axis = data.ndim + axis
+
+    idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1:]
+
+    def make_slice(arr, axis, i):  # type: ignore
+        slc = [slice(None)] * arr.ndim
+        slc[axis] = i
+        return slc
+
+    def unpack(packed):  # type: ignore
+        unpacked = packed[0]
+        for i in range(1, len(packed)):
+            unpacked = unpacked, packed[i]
+        return unpacked
+
+    # We use indices and axis parameters to create idx
+    # idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data
+    idx = [[unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
+            indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0]] for i in range(indices.shape[axis])]
+    idx = list(np.concatenate(idx, axis=1))
+    idx.insert(axis, idx.pop())
+
+    # updates_idx is a NumPy advanced indices for indexing of elements in the updates
+    updates_idx = list(idx)
+    updates_idx.pop(axis)
+    updates_idx.insert(axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape)))
+
+    scattered = np.copy(data)
+    scattered[tuple(idx)] = updates[tuple(updates_idx)]
+    return scattered
+
+
+class Scatter(Base):
+
+    @staticmethod
+    def export_scatter_without_axis() -> None:
+        node = onnx.helper.make_node(
+            'Scatter',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+        )
+        data = np.zeros((3, 3), dtype=np.float32)
+        indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+        updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+        y = scatter(data, indices, updates)
+        # print(y) produces
+        # [[2.0, 1.1, 0.0],
+        #  [1.0, 0.0, 2.2],
+        #  [0.0, 2.1, 1.2]]
+
+        expect(node, inputs=[data, indices, updates], outputs=[y],
+               name='test_scatter_without_axis', opset_imports=[helper.make_opsetid("", 10)])
+
+    @staticmethod
+    def export_scatter_with_axis() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            'Scatter',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+            axis=axis,
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 3]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter(data, indices, updates, axis=axis)
+        # print(y) produces
+        # [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+        expect(node, inputs=[data, indices, updates], outputs=[y],
+               name='test_scatter_with_axis', opset_imports=[helper.make_opsetid("", 10)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/scatterelements.py b/third_party/onnx/onnx/backend/test/case/node/scatterelements.py
new file mode 100644
index 0000000000..f134eb5133
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/scatterelements.py
@@ -0,0 +1,140 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+# The below ScatterElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
+def scatter_elements(data, indices, updates, axis=0, reduction='none'):  # type: ignore
+    if axis < 0:
+        axis = data.ndim + axis
+
+    idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1:]
+
+    def make_slice(arr, axis, i):  # type: ignore
+        slc = [slice(None)] * arr.ndim
+        slc[axis] = i
+        return slc
+
+    def unpack(packed):  # type: ignore
+        unpacked = packed[0]
+        for i in range(1, len(packed)):
+            unpacked = unpacked, packed[i]
+        return unpacked
+
+    def make_indices_for_duplicate(idx):  # type: ignore
+        final_idx = list()
+        for i in range(len(idx[0])):
+            final_idx.append(tuple(idx_element[i] for idx_element in idx))
+        return list(final_idx)
+
+    # We use indices and axis parameters to create idx
+    # idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data
+    idx = [[unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
+            indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0]] for i in range(indices.shape[axis])]
+    idx = list(np.concatenate(idx, axis=1))
+    idx.insert(axis, idx.pop())
+
+    # updates_idx is a NumPy advanced indices for indexing of elements in the updates
+    updates_idx = list(idx)
+    updates_idx.pop(axis)
+    updates_idx.insert(axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape)))
+
+    scattered = np.copy(data)
+    if reduction == 'none':
+        scattered[tuple(idx)] = updates[tuple(updates_idx)]
+    else:
+        idx, updates_idx = make_indices_for_duplicate(idx), make_indices_for_duplicate(updates_idx)
+        for iter, idx_set in enumerate(idx):
+            if reduction == 'add':
+                scattered[idx_set] += updates[updates_idx[iter]]
+            elif reduction == 'mul':
+                scattered[idx_set] *= updates[updates_idx[iter]]
+    return scattered
+
+
+class ScatterElements(Base):
+
+    @staticmethod
+    def export_scatter_elements_without_axis() -> None:
+        node = onnx.helper.make_node(
+            'ScatterElements',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+        )
+        data = np.zeros((3, 3), dtype=np.float32)
+        indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+        updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates)
+        # print(y) produces
+        # [[2.0, 1.1, 0.0],
+        #  [1.0, 0.0, 2.2],
+        #  [0.0, 2.1, 1.2]]
+
+        expect(node, inputs=[data, indices, updates], outputs=[y],
+               name='test_scatter_elements_without_axis')
+
+    @staticmethod
+    def export_scatter_elements_with_axis() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            'ScatterElements',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+            axis=axis,
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 3]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis)
+        # print(y) produces
+        # [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+        expect(node, inputs=[data, indices, updates], outputs=[y],
+               name='test_scatter_elements_with_axis')
+
+    @staticmethod
+    def export_scatter_elements_with_negative_indices() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            'ScatterElements',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+            axis=axis,
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, -3]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis)
+        # print(y) produces
+        # [[1.0, 1.1, 2.1, 4.0, 5.0]]
+
+        expect(node, inputs=[data, indices, updates], outputs=[y],
+               name='test_scatter_elements_with_negative_indices')
+
+    @staticmethod
+    def export_scatter_elements_with_duplicate_indices() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            'ScatterElements',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+            axis=axis,
+            reduction='add',
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 1]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis, reduction='add')
+        # print(y) produces
+        # [[1.0, 5.2, 3.0, 4.0, 5.0]]
+
+        expect(node, inputs=[data, indices, updates], outputs=[y],
+                name='test_scatter_elements_with_duplicate_indices')
diff --git a/third_party/onnx/onnx/backend/test/case/node/scatternd.py b/third_party/onnx/onnx/backend/test/case/node/scatternd.py
new file mode 100644
index 0000000000..72f91597db
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/scatternd.py
@@ -0,0 +1,106 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def scatter_nd_impl(data, indices, updates, reduction='none'):  # type: ignore
+
+    # Check tensor shapes
+    assert indices.shape[-1] <= len(data.shape)
+    assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1]:]
+
+    # Compute output
+    output = np.copy(data)
+    for i in np.ndindex(indices.shape[:-1]):
+        # NOTE: The order of iteration in this loop is not specified.
+        if reduction == 'add':
+            output[indices[i]] += updates[i]
+        elif reduction == 'mul':
+            output[indices[i]] *= updates[i]
+        else:
+            output[indices[i]] = updates[i]
+    return output
+
+
+class ScatterND(Base):
+
+    @staticmethod
+    def export_scatternd() -> None:
+        node = onnx.helper.make_node(
+            'ScatterND',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+        )
+        data = np.array(
+            [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        indices = np.array([[0], [2]], dtype=np.int64)
+        updates = np.array(
+            [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+        # Expecting output as np.array(
+        #    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates)
+        expect(node, inputs=[data, indices, updates], outputs=[output],
+               name='test_scatternd')
+
+    @staticmethod
+    def export_scatternd_add() -> None:
+        node = onnx.helper.make_node(
+            'ScatterND',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+            reduction='add',
+        )
+        data = np.array(
+            [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        indices = np.array([[0], [0]], dtype=np.int64)
+        updates = np.array(
+            [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+        # Expecting output as np.array(
+        #    [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates, reduction='add')
+        expect(node, inputs=[data, indices, updates], outputs=[output],
+               name='test_scatternd_add')
+
+    @staticmethod
+    def export_scatternd_multiply() -> None:
+        node = onnx.helper.make_node(
+            'ScatterND',
+            inputs=['data', 'indices', 'updates'],
+            outputs=['y'],
+            reduction='mul',
+        )
+        data = np.array(
+            [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        indices = np.array([[0], [0]], dtype=np.int64)
+        updates = np.array(
+            [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
+        # Expecting output as np.array(
+        #    [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates, reduction='mul')
+        expect(node, inputs=[data, indices, updates], outputs=[output],
+               name='test_scatternd_multiply')
diff --git a/third_party/onnx/onnx/backend/test/case/node/selu.py b/third_party/onnx/onnx/backend/test/case/node/selu.py
new file mode 100644
index 0000000000..2b2f49e3be
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/selu.py
@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Selu(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Selu',
+            inputs=['x'],
+            outputs=['y'],
+            alpha=2.0,
+            gamma=3.0
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-3.79272318, 0., 3.]
+        y = np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+        expect(node, inputs=[x], outputs=[y],
+               name='test_selu_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+        expect(node, inputs=[x], outputs=[y],
+               name='test_selu')
+
+    @staticmethod
+    def export_selu_default() -> None:
+        default_alpha = 1.67326319217681884765625
+        default_gamma = 1.05070102214813232421875
+        node = onnx.helper.make_node(
+            'Selu',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) * default_gamma + \
+            (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma
+        expect(node, inputs=[x], outputs=[y],
+               name='test_selu_default')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sequence_map.py b/third_party/onnx/onnx/backend/test/case/node/sequence_map.py
new file mode 100755
index 0000000000..b8ad3f0531
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sequence_map.py
@@ -0,0 +1,242 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class SequenceMap(Base):
+    @staticmethod
+    def export_sequence_map_identity_1_sequence():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node('Identity', ['in0'], ['out0'])],
+            'seq_map_body',
+            [onnx.helper.make_tensor_value_info(
+                'in0', onnx.TensorProto.FLOAT, ['N'])],
+            [onnx.helper.make_tensor_value_info(
+                'out0', onnx.TensorProto.FLOAT, ['M'])]
+        )
+
+        node = onnx.helper.make_node(
+            'SequenceMap',
+            inputs=['x'],
+            outputs=['y'],
+            body=body
+        )
+
+        x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+             for _ in range(3)]
+        y = x
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+        ]
+        expect(node, inputs=[x], outputs=[y],
+               input_type_protos=input_type_protos,
+               output_type_protos=output_type_protos,
+               name='test_sequence_map_identity_1_sequence')
+
+    @staticmethod
+    def export_sequence_map_identity_2_sequences():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node('Identity', ['in0'], ['out0']),
+             onnx.helper.make_node('Identity', ['in1'], ['out1'])],
+            'seq_map_body',
+            [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+             onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['M'])],
+            [onnx.helper.make_tensor_value_info('out0', onnx.TensorProto.FLOAT, ['N']),
+             onnx.helper.make_tensor_value_info('out1', onnx.TensorProto.FLOAT, ['M'])]
+        )
+
+        node = onnx.helper.make_node(
+            'SequenceMap',
+            inputs=['x0', 'x1'],
+            outputs=['y0', 'y1'],
+            body=body
+        )
+
+        x0 = [np.random.uniform(0.0, 1.0, np.random.randint(
+            1, 10)).astype(np.float32) for _ in range(3)]
+        x1 = [np.random.uniform(0.0, 1.0, np.random.randint(
+            1, 10)).astype(np.float32) for _ in range(3)]
+        y0 = x0
+        y1 = x1
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+        ]
+        expect(node, inputs=[x0, x1], outputs=[y0, y1],
+               input_type_protos=input_type_protos,
+               output_type_protos=output_type_protos,
+               name='test_sequence_map_identity_2_sequences')
+
+    @staticmethod
+    def export_sequence_map_identity_1_sequence_1_tensor():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node('Identity', ['in0'], ['out0']),
+             onnx.helper.make_node('Identity', ['in1'], ['out1'])],
+            'seq_map_body',
+            [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+             onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['M'])],
+            [onnx.helper.make_tensor_value_info(
+                'out0', onnx.TensorProto.FLOAT, ['N']),
+             onnx.helper.make_tensor_value_info(
+                'out1', onnx.TensorProto.FLOAT, ['M'])]
+        )
+
+        node = onnx.helper.make_node(
+            'SequenceMap',
+            inputs=['x0', 'x1'],
+            outputs=['y0', 'y1'],
+            body=body
+        )
+
+        x0 = [np.random.uniform(0.0, 1.0, np.random.randint(
+            1, 10)).astype(np.float32) for _ in range(3)]
+        x1 = np.random.uniform(0.0, 1.0, np.random.randint(
+            1, 10)).astype(np.float32)
+        y0 = x0
+        y1 = [x1 for _ in range(3)]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+            onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M']),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['M'])),
+        ]
+        expect(node, inputs=[x0, x1], outputs=[y0, y1],
+               input_type_protos=input_type_protos,
+               output_type_protos=output_type_protos,
+               name='test_sequence_map_identity_1_sequence_1_tensor')
+
+    @staticmethod
+    def export_sequence_map_add_2_sequences():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node('Add', ['in0', 'in1'], ['out0'])],
+            'seq_map_body',
+            [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+             onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['N'])],
+            [onnx.helper.make_tensor_value_info(
+                'out0', onnx.TensorProto.FLOAT, ['N'])]
+        )
+
+        node = onnx.helper.make_node(
+            'SequenceMap',
+            inputs=['x0', 'x1'],
+            outputs=['y0'],
+            body=body
+        )
+
+        N = [np.random.randint(1, 10) for _ in range(3)]
+        x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32)
+              for k in range(3)]
+        x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32)
+              for k in range(3)]
+        y0 = [x0[k] + x1[k] for k in range(3)]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+        ]
+        expect(node, inputs=[x0, x1], outputs=[y0],
+               input_type_protos=input_type_protos,
+               output_type_protos=output_type_protos,
+               name='test_sequence_map_add_2_sequences')
+
+    @staticmethod
+    def export_sequence_map_add_1_sequence_1_tensor():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node('Add', ['in0', 'in1'], ['out0'])],
+            'seq_map_body',
+            [onnx.helper.make_tensor_value_info('in0', onnx.TensorProto.FLOAT, ['N']),
+             onnx.helper.make_tensor_value_info('in1', onnx.TensorProto.FLOAT, ['N'])],
+            [onnx.helper.make_tensor_value_info(
+                'out0', onnx.TensorProto.FLOAT, ['N'])]
+        )
+
+        node = onnx.helper.make_node(
+            'SequenceMap',
+            inputs=['x0', 'x1'],
+            outputs=['y0'],
+            body=body
+        )
+
+        x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
+        x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+        y0 = [x0[i] + x1 for i in range(3)]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+            onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N']),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['N'])),
+        ]
+        expect(node, inputs=[x0, x1], outputs=[y0],
+               input_type_protos=input_type_protos,
+               output_type_protos=output_type_protos,
+               name='test_sequence_map_add_1_sequence_1_tensor')
+
+    @staticmethod
+    def export_sequence_map_extract_shapes():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node('Shape', ['x'], ['shape'])],
+            'seq_map_body',
+            [onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, ['H', 'W', 'C'])],
+            [onnx.helper.make_tensor_value_info('shape', onnx.TensorProto.INT64, [3])]
+        )
+
+        node = onnx.helper.make_node(
+            'SequenceMap',
+            inputs=['in_seq'],
+            outputs=['shapes'],
+            body=body
+        )
+
+        shapes = [
+            np.array([40, 30, 3], dtype=np.int64),
+            np.array([20, 10, 3], dtype=np.int64),
+            np.array([10, 5, 3], dtype=np.int64),
+        ]
+        x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ['H', 'W', 'C'])),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])),
+        ]
+        expect(node, inputs=[x0], outputs=[shapes],
+               input_type_protos=input_type_protos,
+               output_type_protos=output_type_protos,
+               name='test_sequence_map_extract_shapes')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sequenceinsert.py b/third_party/onnx/onnx/backend/test/case/node/sequenceinsert.py
new file mode 100644
index 0000000000..d5ca6633e6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sequenceinsert.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import List, Any
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def sequence_insert_reference_implementation(sequence: List[Any], tensor: np.ndarray, position: np.ndarray = None) -> List[Any]:
+    # make a copy of input sequence
+    seq = list(sequence)
+    if position is not None:
+        # In these cases, insert_position will be between [-len(sequence), len(sequence)]
+        # The position argument will be in the format np.array([pos_index])
+        insert_position = position[0]
+        seq.insert(insert_position, tensor)
+    else:
+        # Default position of insertion is at the end of the sequence.
+        seq.append(tensor)
+    return seq
+
+
+class SequenceInsert(Base):
+
+    @staticmethod
+    def export() -> None:
+        test_cases = {
+            'at_back': [np.array([10, 11, 12]).astype(np.int64)],
+            'at_front': [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)]
+        }
+        sequence = [np.array([1, 2, 3, 4]).astype(np.int64), np.array([5, 6, 7]).astype(np.int64), np.array([8, 9]).astype(np.int64)]
+
+        for test_name, test_inputs in test_cases.items():
+            tensor = test_inputs[0].astype(np.int64)
+
+            if len(test_inputs) > 1:
+                node = onnx.helper.make_node(
+                    'SequenceInsert',
+                    inputs=['sequence', 'tensor', 'position'],
+                    outputs=['output_sequence']
+                )
+                position = test_inputs[1]
+                inserted = sequence_insert_reference_implementation(sequence, tensor, position)
+                expect(node, inputs=[sequence, tensor, position], outputs=[inserted],
+                       name='test_sequence_insert_' + test_name)
+            else:
+                node = onnx.helper.make_node(
+                    'SequenceInsert',
+                    inputs=['sequence', 'tensor'],
+                    outputs=['output_sequence']
+                )
+                inserted = sequence_insert_reference_implementation(sequence, tensor)
+                expect(node, inputs=[sequence, tensor], outputs=[inserted],
+                       name='test_sequence_insert_' + test_name)
diff --git a/third_party/onnx/onnx/backend/test/case/node/shape.py b/third_party/onnx/onnx/backend/test/case/node/shape.py
new file mode 100644
index 0000000000..eaed93059c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/shape.py
@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+# Reference implementation of shape op
+def shape_reference_impl(x, start=None, end=None):  # type: ignore
+    dims = x.shape[start:end]
+    return np.array(dims).astype(np.int64)
+
+
+def test_shape(testname, xval, start=None, end=None):  # type: ignore
+    node = onnx.helper.make_node(
+        'Shape',
+        inputs=['x'],
+        outputs=['y'],
+        start=start,
+        end=end
+    )
+
+    yval = shape_reference_impl(xval, start, end)
+
+    expect(node, inputs=[xval], outputs=[yval], name='test_shape' + testname)
+
+
+class Shape(Base):
+
+    @staticmethod
+    def export() -> None:
+        x = np.array([
+            [1, 2, 3],
+            [4, 5, 6],
+        ]).astype(np.float32)
+        test_shape('_example', x)  # preserve names of original test cases
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+
+        test_shape('', x)  # preserve names of original test cases
+
+        test_shape('_start_1', x, start=1)
+
+        test_shape('_end_1', x, end=1)
+
+        test_shape('_start_negative_1', x, start=-1)
+
+        test_shape('_end_negative_1', x, end=-1)
+
+        test_shape('_start_1_end_negative_1', x, start=1, end=-1)
+
+        test_shape('_start_1_end_2', x, start=1, end=2)
+
+        test_shape('_clip_start', x, start=-10)
+
+        test_shape('_clip_end', x, end=10)
diff --git a/third_party/onnx/onnx/backend/test/case/node/shrink.py b/third_party/onnx/onnx/backend/test/case/node/shrink.py
new file mode 100644
index 0000000000..5e4de81b4a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/shrink.py
@@ -0,0 +1,37 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Shrink(Base):
+
+    @staticmethod
+    def export_hard_shrink() -> None:
+        node = onnx.helper.make_node(
+            'Shrink',
+            inputs=['x'],
+            outputs=['y'],
+            lambd=1.5,
+        )
+        X = np.arange(-2.0, 2.1, dtype=np.float32)
+        Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
+        expect(node, inputs=[X], outputs=[Y],
+               name='test_shrink_hard')
+
+    @staticmethod
+    def export_soft_shrink() -> None:
+        node = onnx.helper.make_node(
+            'Shrink',
+            inputs=['x'],
+            outputs=['y'],
+            lambd=1.5,
+            bias=1.5,
+        )
+        X = np.arange(-2.0, 2.1, dtype=np.float32)
+        Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
+        expect(node, inputs=[X], outputs=[Y],
+               name='test_shrink_soft')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sigmoid.py b/third_party/onnx/onnx/backend/test/case/node/sigmoid.py
new file mode 100644
index 0000000000..7fd34b5b0a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sigmoid.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sigmoid(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sigmoid',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = 1.0 / (1.0 + np.exp(np.negative(x)))  # expected output [0.26894143, 0.5, 0.7310586]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sigmoid_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = 1.0 / (1.0 + np.exp(np.negative(x)))
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sigmoid')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sign.py b/third_party/onnx/onnx/backend/test/case/node/sign.py
new file mode 100644
index 0000000000..72e926067a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sign.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sign(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sign',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array(range(-5, 6)).astype(np.float32)
+        y = np.sign(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sign')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sin.py b/third_party/onnx/onnx/backend/test/case/node/sin.py
new file mode 100644
index 0000000000..e3e74d3690
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sin.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sin(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sin',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.sin(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sin_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.sin(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sin')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sinh.py b/third_party/onnx/onnx/backend/test/case/node/sinh.py
new file mode 100644
index 0000000000..898d9818c5
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sinh.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sinh(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sinh',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.sinh(x)  # expected output [-1.17520118,  0.,  1.17520118]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sinh_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.sinh(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sinh')
diff --git a/third_party/onnx/onnx/backend/test/case/node/size.py b/third_party/onnx/onnx/backend/test/case/node/size.py
new file mode 100644
index 0000000000..2efbddff8a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/size.py
@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Size(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Size',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([
+            [1, 2, 3],
+            [4, 5, 6],
+        ]).astype(np.float32)
+        y = np.array(6).astype(np.int64)
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_size_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.array(x.size).astype(np.int64)
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_size')
diff --git a/third_party/onnx/onnx/backend/test/case/node/slice.py b/third_party/onnx/onnx/backend/test/case/node/slice.py
new file mode 100644
index 0000000000..af6586a1be
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/slice.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Slice(Base):
+
+    @staticmethod
+    def export_slice() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        y = x[0:3, 0:10]
+        starts = np.array([0, 0], dtype=np.int64)
+        ends = np.array([3, 10], dtype=np.int64)
+        axes = np.array([0, 1], dtype=np.int64)
+        steps = np.array([1, 1], dtype=np.int64)
+
+        expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+               name='test_slice')
+
+    @staticmethod
+    def export_slice_neg() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0], dtype=np.int64)
+        ends = np.array([-1], dtype=np.int64)
+        axes = np.array([1], dtype=np.int64)
+        steps = np.array([1], dtype=np.int64)
+        y = x[:, 0:-1]
+
+        expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+               name='test_slice_neg')
+
+    @staticmethod
+    def export_slice_start_out_of_bounds() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([1000], dtype=np.int64)
+        ends = np.array([1000], dtype=np.int64)
+        axes = np.array([1], dtype=np.int64)
+        steps = np.array([1], dtype=np.int64)
+        y = x[:, 1000:1000]
+
+        expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+               name='test_slice_start_out_of_bounds')
+
+    @staticmethod
+    def export_slice_end_out_of_bounds() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([1], dtype=np.int64)
+        ends = np.array([1000], dtype=np.int64)
+        axes = np.array([1], dtype=np.int64)
+        steps = np.array([1], dtype=np.int64)
+        y = x[:, 1:1000]
+
+        expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+               name='test_slice_end_out_of_bounds')
+
+    @staticmethod
+    def export_slice_default_axes() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0, 0, 3], dtype=np.int64)
+        ends = np.array([20, 10, 4], dtype=np.int64)
+        y = x[:, :, 3:4]
+
+        expect(node, inputs=[x, starts, ends], outputs=[y],
+               name='test_slice_default_axes')
+
+    @staticmethod
+    def export_slice_default_steps() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0, 0, 3], dtype=np.int64)
+        ends = np.array([20, 10, 4], dtype=np.int64)
+        axes = np.array([0, 1, 2], dtype=np.int64)
+        y = x[:, :, 3:4]
+
+        expect(node, inputs=[x, starts, ends, axes], outputs=[y],
+               name='test_slice_default_steps')
+
+    @staticmethod
+    def export_slice_neg_steps() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes', 'steps'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([20, 10, 4], dtype=np.int64)
+        ends = np.array([0, 0, 1], dtype=np.int64)
+        axes = np.array([0, 1, 2], dtype=np.int64)
+        steps = np.array([-1, -3, -2]).astype(np.int64)
+        y = x[20:0:-1, 10:0:-3, 4:1:-2]
+
+        expect(node, inputs=[x, starts, ends, axes, steps], outputs=[y],
+               name='test_slice_neg_steps')
+
+    @staticmethod
+    def export_slice_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'starts', 'ends', 'axes'],
+            outputs=['y'],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0, 0, 3], dtype=np.int64)
+        ends = np.array([20, 10, 4], dtype=np.int64)
+        axes = np.array([0, -2, -1], dtype=np.int64)
+        y = x[:, :, 3:4]
+
+        expect(node, inputs=[x, starts, ends, axes], outputs=[y],
+               name='test_slice_negative_axes')
diff --git a/third_party/onnx/onnx/backend/test/case/node/softmax.py b/third_party/onnx/onnx/backend/test/case/node/softmax.py
new file mode 100644
index 0000000000..b02827166b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/softmax.py
@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_max = np.max(x, axis=axis, keepdims=True)
+    tmp = np.exp(x - x_max)
+    s = np.sum(tmp, axis=axis, keepdims=True)
+    return tmp / s
+
+
+class Softmax(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        x = np.array([[-1, 0, 1]]).astype(np.float32)
+        # expected output [[0.09003058, 0.24472848, 0.66524094]]
+        y = softmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_example')
+
+    @staticmethod
+    def export_softmax_axis() -> None:
+        x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]
+                     ).astype(np.float32)
+        # expected output
+        # [[0.032058604 0.08714432  0.23688284  0.6439143  ]
+        # [0.032058604 0.08714432  0.23688284  0.6439143  ]]
+        y = softmax(x)
+
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_large_number')
+
+        x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=0,
+        )
+        y = softmax(x, axis=0)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_axis_0')
+
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=1,
+        )
+        y = softmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_axis_1')
+
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=2,
+        )
+        y = softmax(x, axis=2)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_axis_2')
+
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+            axis=-1,
+        )
+        y = softmax(x, axis=-1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_negative_axis')
+
+        # default axis is -1
+        node = onnx.helper.make_node(
+            'Softmax',
+            inputs=['x'],
+            outputs=['y'],
+        )
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softmax_default_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/softmaxcrossentropy.py b/third_party/onnx/onnx/backend/test/case/node/softmaxcrossentropy.py
new file mode 100644
index 0000000000..fa3059b328
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/softmaxcrossentropy.py
@@ -0,0 +1,902 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def softmaxcrossentropy(x, target, weight=None, reduction='mean', ignore_index=None, get_log_prob=None):  # type: ignore
+    input_shape = x.shape
+    if len(input_shape) == 1:
+        raise RuntimeError("Unsupported shape")
+
+    target_shape = target.shape
+    N = input_shape[0]
+    C = input_shape[1]
+
+    # compute log_softmax
+    max_x = np.max(x, axis=1, keepdims=True)
+    exp_x = np.exp(x - max_x)
+    p = exp_x / np.sum(exp_x, axis=1, keepdims=True)
+    inp = np.log(p)
+    log_prob = None
+    if get_log_prob is True:
+        log_prob = np.copy(inp)
+
+    # initialize the positional weights when required
+    gather_weight = None
+    if weight is not None:
+        # setting mode='clip' to deal with ignore_index > C or < 0 cases.
+        # when the target value is > C or < 0, it doesn't matter which value we are
+        # taking in gather_weight, since it will be set to 0 in the following if-block
+        # use np.int32 to make it compatible with x86 machines
+        gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode='clip')
+        # set `ignore_index`'s loss weight to 0.
+        # The loss tensor will be multiplied by this weight tensor,
+        # so `ingore_index`'s loss value will be eliminated.
+        if ignore_index is not None:
+            gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(dtype=np.float32)
+    elif ignore_index is not None:
+        gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32)
+
+    # if input is 4-d and above, make it 3-d
+    if len(input_shape) != 3:
+        inp = inp.reshape((N, C, -1))
+        target = target.reshape((N, -1))
+
+    # Get a dimension from the reshaped input.
+    # If the original input shape is [N, C, H, W],
+    # the D here should be H * W because we reshape
+    # [N, C, H, W] to [N, C, H * W].
+    D = inp.shape[2]
+    neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
+    for i in range(N):
+        for d in range(D):
+            if target[i][d] != ignore_index:
+                neg_gather_element_input[i][d] = -inp[i][target[i][d]][d]
+
+    loss = neg_gather_element_input
+
+    # if the input was 4-d or above reshape to the right shape
+    if len(input_shape) != 3:
+        loss = loss.reshape(target_shape)
+
+    # apply the weights when required
+    if gather_weight is not None:
+        loss = gather_weight * loss
+        if reduction == 'mean':
+            loss = loss.sum() / gather_weight.sum()
+            if get_log_prob is True:
+                return loss, log_prob
+            else:
+                return loss
+
+    if reduction == 'mean':
+        loss = np.mean(loss)
+    elif reduction == 'sum':
+        loss = np.sum(loss)
+
+    if get_log_prob is True:
+        return loss, log_prob
+    else:
+        return loss
+
+
+class SoftmaxCrossEntropyLoss(Base):
+
+    @staticmethod
+    def export_softmaxcrossentropy_none() -> None:
+        # Define operator attributes.
+        reduction = 'none'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, reduction='none')
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_none')
+
+    @staticmethod
+    def export_softmaxcrossentropy_none_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'none'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, reduction='none', get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_none_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_none_weights() -> None:
+        # Define operator attributes.
+        reduction = 'none'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights, reduction='none')
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_none_weights')
+
+    @staticmethod
+    def export_softmaxcrossentropy_none_weights_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'none'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, reduction='none', get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_none_weights_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_sum() -> None:
+        # Define operator attributes.
+        reduction = 'sum'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, reduction='sum')
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_sum')
+
+    @staticmethod
+    def export_softmaxcrossentropy_sum_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'sum'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, reduction='sum', get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_sum_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_3d() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, y)
+
+        # Check results
+        expect(node, inputs=[x, y], outputs=[sce], name='test_sce_mean_3d')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_3d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, y], outputs=[loss, log_prob], name='test_sce_mean_3d_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(0)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        labels[0] = np.int64(0)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(0)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        labels[0] = np.int64(0)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y'],
+                                    outputs=['z'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        labels[0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y'],
+                                    outputs=['z', 'log_prob'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, )).astype(np.int64)
+        labels[0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, ignore_index=ignore_index, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_3d() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(1)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y', 'w'],
+                                    outputs=['z'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(1)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii_3d')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_3d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(1)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y', 'w'],
+                                    outputs=['z', 'log_prob'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(1)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_3d_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_3d() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y'],
+                                    outputs=['z'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii_3d')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_3d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y'],
+                                    outputs=['z', 'log_prob'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, ignore_index=ignore_index, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_3d_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_4d() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y', 'w'],
+                                    outputs=['z'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[sce], name='test_sce_mean_weight_ii_4d')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_4d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y', 'w'],
+                                    outputs=['z', 'log_prob'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels, weights], outputs=[loss, log_prob], name='test_sce_mean_weight_ii_4d_log_prob')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_4d() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y'],
+                                    outputs=['z'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, reduction=reduction, ignore_index=ignore_index)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_mean_no_weight_ii_4d')
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_4d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = 'mean'
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                    inputs=['x', 'y'],
+                                    outputs=['z', 'log_prob'],
+                                    reduction=reduction,
+                                    ignore_index=ignore_index)
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_mean_no_weight_ii_4d_log_prob')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None:
+        reduction = 'mean'
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        sce = softmaxcrossentropy(x,
+                                labels,
+                                weight=weight,
+                                reduction=reduction)
+
+        expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1d2d3d4d5_mean_weight')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob() -> None:
+        reduction = 'mean'
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        loss, log_prob = softmaxcrossentropy(x,
+                                labels,
+                                weight=weight,
+                                reduction=reduction,
+                                get_log_prob=True)
+
+        expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1d2d3d4d5_mean_weight_log_prob')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None:
+        reduction = 'none'
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z'],
+                                     reduction=reduction)
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+        sce = softmaxcrossentropy(x,
+                                labels,
+                                reduction=reduction)
+
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_NCd1d2d3d4d5_none_no_weight')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob() -> None:
+        reduction = 'none'
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction)
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)).astype(np.int64)
+
+        loss, log_prob = softmaxcrossentropy(x,
+                                labels,
+                                reduction=reduction,
+                                get_log_prob=True)
+
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_NCd1d2d3d4d5_none_no_weight_log_prob')
+
+    @staticmethod
+    def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None:
+        reduction = 'mean'
+        ignore_index = np.int64(-1)
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        N, C, dim1 = 3, 5, 6
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+        labels[0][0] = -1
+        weight = np.random.rand(C).astype(np.float32)
+
+        sce = softmaxcrossentropy(x,
+                                  labels,
+                                  weight=weight,
+                                  reduction=reduction,
+                                  ignore_index=ignore_index)
+
+        expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1_mean_weight_negative_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1_mean_weight_negative_ii_log_prob() -> None:
+        reduction = 'mean'
+        ignore_index = np.int64(-1)
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        N, C, dim1 = 3, 5, 6
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+        labels[0][0] = -1
+        weight = np.random.rand(C).astype(np.float32)
+
+        loss, log_prob = softmaxcrossentropy(x,
+                                  labels,
+                                  weight=weight,
+                                  reduction=reduction,
+                                  ignore_index=ignore_index,
+                                  get_log_prob=True)
+
+        expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1_mean_weight_negative_ii_log_prob')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None:
+        reduction = 'none'
+        ignore_index = np.int64(-5)
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+        labels[0][0][0][0] = -5
+
+        sce = softmaxcrossentropy(x,
+                                  labels,
+                                  reduction=reduction,
+                                  ignore_index=ignore_index)
+
+        expect(node, inputs=[x, labels], outputs=[sce], name='test_sce_NCd1d2d3_none_no_weight_negative_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob() -> None:
+        reduction = 'none'
+        ignore_index = np.int64(-5)
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(np.int64)
+        labels[0][0][0][0] = -5
+
+        loss, log_prob = softmaxcrossentropy(x,
+                                  labels,
+                                  reduction=reduction,
+                                  ignore_index=ignore_index,
+                                  get_log_prob=True)
+
+        expect(node, inputs=[x, labels], outputs=[loss, log_prob], name='test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None:
+        reduction = 'sum'
+        ignore_index = np.int64(10)
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        N, C = 3, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+        labels[0] = 10
+        weight = np.random.rand(C).astype(np.float32)
+
+        sce = softmaxcrossentropy(x,
+                                  labels,
+                                  weight=weight,
+                                  reduction=reduction,
+                                  ignore_index=ignore_index)
+
+        expect(node, inputs=[x, labels, weight], outputs=[sce], name='test_sce_NCd1d2d3_sum_weight_high_ii')
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob() -> None:
+        reduction = 'sum'
+        ignore_index = np.int64(10)
+
+        node = onnx.helper.make_node('SoftmaxCrossEntropyLoss',
+                                     inputs=['x', 'y', 'w'],
+                                     outputs=['z', 'log_prob'],
+                                     reduction=reduction,
+                                     ignore_index=ignore_index)
+
+        N, C = 3, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+        labels[0] = 10
+        weight = np.random.rand(C).astype(np.float32)
+
+        loss, log_prob = softmaxcrossentropy(x,
+                                  labels,
+                                  weight=weight,
+                                  reduction=reduction,
+                                  ignore_index=ignore_index,
+                                  get_log_prob=True)
+
+        expect(node, inputs=[x, labels, weight], outputs=[loss, log_prob], name='test_sce_NCd1d2d3_sum_weight_high_ii_log_prob')
diff --git a/third_party/onnx/onnx/backend/test/case/node/softplus.py b/third_party/onnx/onnx/backend/test/case/node/softplus.py
new file mode 100644
index 0000000000..fbcc83ebc3
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/softplus.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Softplus(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Softplus',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.log(np.exp(x) + 1)  # expected output [0.31326166, 0.69314718, 1.31326163]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softplus_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.log(np.exp(x) + 1)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softplus')
diff --git a/third_party/onnx/onnx/backend/test/case/node/softsign.py b/third_party/onnx/onnx/backend/test/case/node/softsign.py
new file mode 100644
index 0000000000..c0715ce559
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/softsign.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Softsign(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Softsign',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softsign_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = x / (1 + np.abs(x))
+        expect(node, inputs=[x], outputs=[y],
+               name='test_softsign')
diff --git a/third_party/onnx/onnx/backend/test/case/node/spacetodepth.py b/third_party/onnx/onnx/backend/test/case/node/spacetodepth.py
new file mode 100644
index 0000000000..0278ad3d87
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/spacetodepth.py
@@ -0,0 +1,57 @@
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class SpaceToDepth(Base):
+
+    @staticmethod
+    def export() -> None:
+        b, c, h, w = shape = (2, 2, 6, 6)
+        blocksize = 2
+        node = onnx.helper.make_node(
+            'SpaceToDepth',
+            inputs=['x'],
+            outputs=['y'],
+            blocksize=blocksize,
+        )
+        x = np.random.random_sample(shape).astype(np.float32)
+        tmp = np.reshape(x, [b, c,
+                             h // blocksize, blocksize,
+                             w // blocksize, blocksize])
+        tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
+        y = np.reshape(tmp, [b, c * (blocksize**2),
+                             h // blocksize,
+                             w // blocksize])
+        expect(node, inputs=[x], outputs=[y],
+               name='test_spacetodepth')
+
+    @staticmethod
+    def export_example() -> None:
+        node = onnx.helper.make_node(
+            'SpaceToDepth',
+            inputs=['x'],
+            outputs=['y'],
+            blocksize=2,
+        )
+
+        # (1, 1, 4, 6) input tensor
+        x = np.array([[[[0, 6, 1, 7, 2, 8],
+                        [12, 18, 13, 19, 14, 20],
+                        [3, 9, 4, 10, 5, 11],
+                        [15, 21, 16, 22, 17, 23]]]]).astype(np.float32)
+
+        # (1, 4, 2, 3) output tensor
+        y = np.array([[[[0, 1, 2],
+                        [3, 4, 5]],
+                       [[6, 7, 8],
+                        [9, 10, 11]],
+                       [[12, 13, 14],
+                        [15, 16, 17]],
+                       [[18, 19, 20],
+                        [21, 22, 23]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_spacetodepth_example')
diff --git a/third_party/onnx/onnx/backend/test/case/node/split.py b/third_party/onnx/onnx/backend/test/case/node/split.py
new file mode 100644
index 0000000000..dcc1ae5eee
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/split.py
@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Split(Base):
+
+    @staticmethod
+    def export_1d() -> None:
+        input = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input'],
+            outputs=['output_1', 'output_2', 'output_3'],
+            axis=0
+        )
+
+        expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4.]).astype(np.float32), np.array([5., 6.]).astype(np.float32)]
+        expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_1d')
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input', 'split'],
+            outputs=['output_1', 'output_2'],
+            axis=0,
+        )
+
+        expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4., 5., 6.]).astype(np.float32)]
+        expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_1d')
+
+    @staticmethod
+    def export_2d() -> None:
+        input = np.array([[1., 2., 3., 4., 5., 6.],
+                          [7., 8., 9., 10., 11., 12.]]).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input'],
+            outputs=['output_1', 'output_2'],
+            axis=1
+        )
+
+        expected_outputs = [np.array([[1., 2., 3.], [7., 8., 9.]]).astype(np.float32),
+                            np.array([[4., 5., 6.], [10., 11., 12.]]).astype(np.float32)]
+
+        expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_2d')
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input', 'split'],
+            outputs=['output_1', 'output_2'],
+            axis=1,
+        )
+
+        expected_outputs = [np.array([[1., 2.], [7., 8.]]).astype(np.float32),
+                            np.array([[3., 4., 5., 6.], [9., 10., 11., 12.]]).astype(np.float32)]
+
+        expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_2d')
+
+    @staticmethod
+    def export_default_values() -> None:
+        input = np.array([1., 2., 3., 4., 5., 6.]).astype(np.float32)
+
+        # If axis is not specified, split is applied on default axis 0
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input'],
+            outputs=['output_1', 'output_2', 'output_3']
+        )
+
+        expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4.]).astype(np.float32), np.array([5., 6.]).astype(np.float32)]
+        expect(node, inputs=[input], outputs=[y for y in expected_outputs], name='test_split_equal_parts_default_axis')
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input', 'split'],
+            outputs=['output_1', 'output_2']
+        )
+
+        expected_outputs = [np.array([1., 2.]).astype(np.float32), np.array([3., 4., 5., 6.]).astype(np.float32)]
+        expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_variable_parts_default_axis')
+
+    @staticmethod
+    def export_zero_size_splits() -> None:
+        input = np.array([]).astype(np.float32)
+
+        # Split emtpy tensor to tensors of size zero
+        split = np.array([0, 0, 0]).astype(np.int64)
+        node = onnx.helper.make_node(
+            'Split',
+            inputs=['input', 'split'],
+            outputs=['output_1', 'output_2', 'output_3']
+        )
+
+        expected_outputs = [np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32)]
+        expect(node, inputs=[input, split], outputs=[y for y in expected_outputs], name='test_split_zero_size_splits')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sqrt.py b/third_party/onnx/onnx/backend/test/case/node/sqrt.py
new file mode 100644
index 0000000000..4647d7c6ef
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sqrt.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sqrt(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sqrt',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([1, 4, 9]).astype(np.float32)
+        y = np.sqrt(x)  # expected output [1., 2., 3.]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sqrt_example')
+
+        x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+        y = np.sqrt(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_sqrt')
diff --git a/third_party/onnx/onnx/backend/test/case/node/squeeze.py b/third_party/onnx/onnx/backend/test/case/node/squeeze.py
new file mode 100644
index 0000000000..bed1bd787e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/squeeze.py
@@ -0,0 +1,37 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Squeeze(Base):
+
+    @staticmethod
+    def export_squeeze() -> None:
+        node = onnx.helper.make_node(
+            'Squeeze',
+            inputs=['x', 'axes'],
+            outputs=['y'],
+        )
+        x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+        axes = np.array([0], dtype=np.int64)
+        y = np.squeeze(x, axis=0)
+
+        expect(node, inputs=[x, axes], outputs=[y],
+               name='test_squeeze')
+
+    @staticmethod
+    def export_squeeze_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            'Squeeze',
+            inputs=['x', 'axes'],
+            outputs=['y'],
+        )
+        x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+        axes = np.array([-2], dtype=np.int64)
+        y = np.squeeze(x, axis=-2)
+        expect(node, inputs=[x, axes], outputs=[y],
+               name='test_squeeze_negative_axes')
diff --git a/third_party/onnx/onnx/backend/test/case/node/stringnormalizer.py b/third_party/onnx/onnx/backend/test/case/node/stringnormalizer.py
new file mode 100644
index 0000000000..ccf09bca62
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/stringnormalizer.py
@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: Apache-2.0
+# coding: utf-8
+
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class StringNormalizer(Base):
+
+    @staticmethod
+    def export_nostopwords_nochangecase() -> None:
+        input = np.array([u'monday', u'tuesday']).astype(object)
+        output = input
+
+        # No stopwords. This is a NOOP
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            is_case_sensitive=1,
+        )
+        expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_nostopwords_nochangecase')
+
+    @staticmethod
+    def export_monday_casesensintive_nochangecase() -> None:
+        input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+        output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+        stopwords = [u'monday']
+
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+        expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_nochangecase')
+
+    @staticmethod
+    def export_monday_casesensintive_lower() -> None:
+        input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+        output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
+        stopwords = [u'monday']
+
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='LOWER',
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+        expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_lower')
+
+    @staticmethod
+    def export_monday_casesensintive_upper() -> None:
+        input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
+        output = np.array([u'TUESDAY', u'WEDNESDAY', u'THURSDAY']).astype(object)
+        stopwords = [u'monday']
+
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='UPPER',
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+        expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_upper')
+
+    @staticmethod
+    def export_monday_empty_output() -> None:
+        input = np.array([u'monday', u'monday']).astype(object)
+        output = np.array([u'']).astype(object)
+        stopwords = [u'monday']
+
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='UPPER',
+            is_case_sensitive=1,
+            stopwords=stopwords
+        )
+        expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_empty_output')
+
+    @staticmethod
+    def export_monday_insensintive_upper_twodim() -> None:
+        input = np.array([u'Monday', u'tuesday', u'wednesday', u'Monday', u'tuesday', u'wednesday']).astype(object).reshape([1, 6])
+
+        # It does upper case cecedille, accented E
+        # and german umlaut but fails
+        # with german eszett
+        output = np.array([u'TUESDAY', u'WEDNESDAY', u'TUESDAY', u'WEDNESDAY']).astype(object).reshape([1, 4])
+        stopwords = [u'monday']
+
+        node = onnx.helper.make_node(
+            'StringNormalizer',
+            inputs=['x'],
+            outputs=['y'],
+            case_change_action='UPPER',
+            stopwords=stopwords
+        )
+        expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_insensintive_upper_twodim')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sub.py b/third_party/onnx/onnx/backend/test/case/node/sub.py
new file mode 100644
index 0000000000..856ade897b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sub.py
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sub(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Sub',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([3, 2, 1]).astype(np.float32)
+        z = x - y  # expected output [-2., 0., 2.]
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_sub_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = x - y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_sub')
+
+        x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8)
+        z = x - y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_sub_uint8')
+
+    @staticmethod
+    def export_sub_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Sub',
+            inputs=['x', 'y'],
+            outputs=['z'],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = x - y
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_sub_bcast')
diff --git a/third_party/onnx/onnx/backend/test/case/node/sum.py b/third_party/onnx/onnx/backend/test/case/node/sum.py
new file mode 100644
index 0000000000..2806ab26c4
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/sum.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Sum(Base):
+
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 0, 2]).astype(np.float32)
+        data_1 = np.array([1, 3, 4]).astype(np.float32)
+        data_2 = np.array([2, 6, 6]).astype(np.float32)
+        result = np.array([6, 9, 12]).astype(np.float32)
+        node = onnx.helper.make_node(
+            'Sum',
+            inputs=['data_0', 'data_1', 'data_2'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
+               name='test_sum_example')
+
+        node = onnx.helper.make_node(
+            'Sum',
+            inputs=['data_0'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0],
+               name='test_sum_one_input')
+
+        result = np.add(data_0, data_1)
+        node = onnx.helper.make_node(
+            'Sum',
+            inputs=['data_0', 'data_1'],
+            outputs=['result'],
+        )
+        expect(node, inputs=[data_0, data_1], outputs=[result],
+               name='test_sum_two_inputs')
diff --git a/third_party/onnx/onnx/backend/test/case/node/tan.py b/third_party/onnx/onnx/backend/test/case/node/tan.py
new file mode 100644
index 0000000000..d03f8fbc61
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/tan.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Tan(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Tan',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.tan(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_tan_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.tan(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_tan')
diff --git a/third_party/onnx/onnx/backend/test/case/node/tanh.py b/third_party/onnx/onnx/backend/test/case/node/tanh.py
new file mode 100644
index 0000000000..ae689239f6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/tanh.py
@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Tanh(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Tanh',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.tanh(x)  # expected output [-0.76159418, 0., 0.76159418]
+        expect(node, inputs=[x], outputs=[y],
+               name='test_tanh_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.tanh(x)
+        expect(node, inputs=[x], outputs=[y],
+               name='test_tanh')
diff --git a/third_party/onnx/onnx/backend/test/case/node/tfidfvectorizer.py b/third_party/onnx/onnx/backend/test/case/node/tfidfvectorizer.py
new file mode 100644
index 0000000000..260eb59ddf
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/tfidfvectorizer.py
@@ -0,0 +1,206 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+from typing import Any, Sequence
+
+import onnx
+from onnx import NodeProto
+from ..base import Base
+from . import expect
+
+
+class TfIdfVectorizerHelper():
+    def __init__(self, **params: Any) -> None:
+        # Attr names
+        mode = str('mode')
+        min_gram_length = str('min_gram_length')
+        max_gram_length = str('max_gram_length')
+        max_skip_count = str('max_skip_count')
+        ngram_counts = str('ngram_counts')
+        ngram_indexes = str('ngram_indexes')
+        pool_int64s = str('pool_int64s')
+
+        required_attr = [mode, min_gram_length, max_gram_length, max_skip_count,
+                         ngram_counts, ngram_indexes, pool_int64s]
+
+        for i in required_attr:
+            assert i in params, "Missing attribute: {0}".format(i)
+
+        self.mode = params[mode]
+        self.min_gram_length = params[min_gram_length]
+        self.max_gram_length = params[max_gram_length]
+        self.max_skip_count = params[max_skip_count]
+        self.ngram_counts = params[ngram_counts]
+        self.ngram_indexes = params[ngram_indexes]
+        self.pool_int64s = params[pool_int64s]
+
+    def make_node_noweights(self) -> NodeProto:
+        return onnx.helper.make_node(
+            'TfIdfVectorizer',
+            inputs=['X'],
+            outputs=['Y'],
+            mode=self.mode,
+            min_gram_length=self.min_gram_length,
+            max_gram_length=self.max_gram_length,
+            max_skip_count=self.max_skip_count,
+            ngram_counts=self.ngram_counts,
+            ngram_indexes=self.ngram_indexes,
+            pool_int64s=self.pool_int64s
+        )
+
+
+class TfIdfVectorizer(Base):
+
+    @staticmethod
+    def export_tf_only_bigrams_skip0() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([0., 0., 0., 0., 1., 1., 1.]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                                5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=0,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_only_bigrams_skip0')
+
+    @staticmethod
+    def export_tf_batch_onlybigrams_skip0() -> None:
+        input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+        output = np.array([[0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 1.]]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                                5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=0,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_onlybigrams_skip0')
+
+    @staticmethod
+    def export_tf_onlybigrams_levelempty() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([1., 1., 1.]).astype(np.float32)
+
+        ngram_counts = np.array([0, 0]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2]).astype(np.int64)
+        pool_int64s = np.array([    # unigrams none
+                               5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=0,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_onlybigrams_levelempty')
+
+    @staticmethod
+    def export_tf_onlybigrams_skip5() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([0., 0., 0., 0., 1., 3., 1.]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                                5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_onlybigrams_skip5')
+
+    @staticmethod
+    def export_tf_batch_onlybigrams_skip5() -> None:
+        input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+        output = np.array([[0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 1., 1.]]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                                5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_onlybigrams_skip5')
+
+    @staticmethod
+    def export_tf_uniandbigrams_skip5() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([0., 3., 1., 0., 1., 3., 1.]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                                5, 6, 7, 8, 6, 7]).astype(np.int64)    # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=1,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_uniandbigrams_skip5')
+
+    @staticmethod
+    def export_tf_batch_uniandbigrams_skip5() -> None:
+        input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+        output = np.array([[0., 3., 0., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 1., 1.]]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4,    # unigrams
+                                5, 6, 7, 8, 6, 7]).astype(np.int64)   # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode='TF',
+            min_gram_length=1,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s
+        )
+        node = helper.make_node_noweights()
+        expect(node, inputs=[input], outputs=[output], name='test_tfidfvectorizer_tf_batch_uniandbigrams_skip5')
diff --git a/third_party/onnx/onnx/backend/test/case/node/thresholdedrelu.py b/third_party/onnx/onnx/backend/test/case/node/thresholdedrelu.py
new file mode 100644
index 0000000000..e1132e0a6f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/thresholdedrelu.py
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ThresholdedRelu(Base):
+
+    @staticmethod
+    def export() -> None:
+        alpha = 2.0
+        node = onnx.helper.make_node(
+            'ThresholdedRelu',
+            inputs=['x'],
+            outputs=['y'],
+            alpha=alpha
+        )
+
+        x = np.array([-1.5, 0., 1.2, 2.0, 2.2]).astype(np.float32)
+        y = np.clip(x, alpha, np.inf)  # expected output [0., 0., 0., 0., 2.2]
+        y[y == alpha] = 0
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_thresholdedrelu_example')
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, alpha, np.inf)
+        y[y == alpha] = 0
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_thresholdedrelu')
+
+    @staticmethod
+    def export_default() -> None:
+        default_alpha = 1.0
+        node = onnx.helper.make_node(
+            'ThresholdedRelu',
+            inputs=['x'],
+            outputs=['y']
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, default_alpha, np.inf)
+        y[y == default_alpha] = 0
+
+        expect(node, inputs=[x], outputs=[y],
+               name='test_thresholdedrelu_default')
diff --git a/third_party/onnx/onnx/backend/test/case/node/tile.py b/third_party/onnx/onnx/backend/test/case/node/tile.py
new file mode 100644
index 0000000000..1002d2a6d0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/tile.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Tile(Base):
+
+    @staticmethod
+    def export_tile() -> None:
+        node = onnx.helper.make_node(
+            'Tile',
+            inputs=['x', 'y'],
+            outputs=['z']
+        )
+
+        x = np.random.rand(2, 3, 4, 5).astype(np.float32)
+
+        repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
+
+        z = np.tile(x, repeats)
+
+        expect(node,
+               inputs=[x, repeats],
+               outputs=[z],
+               name='test_tile')
+
+    @staticmethod
+    def export_tile_precomputed() -> None:
+        node = onnx.helper.make_node(
+            'Tile',
+            inputs=['x', 'y'],
+            outputs=['z']
+        )
+
+        x = np.array([
+            [0, 1],
+            [2, 3]
+        ], dtype=np.float32)
+
+        repeats = np.array([2, 2], dtype=np.int64)
+
+        z = np.array([
+            [0, 1, 0, 1],
+            [2, 3, 2, 3],
+            [0, 1, 0, 1],
+            [2, 3, 2, 3]
+        ], dtype=np.float32)
+
+        expect(node,
+               inputs=[x, repeats],
+               outputs=[z],
+               name='test_tile_precomputed')
diff --git a/third_party/onnx/onnx/backend/test/case/node/topk.py b/third_party/onnx/onnx/backend/test/case/node/topk.py
new file mode 100644
index 0000000000..c4f64e2983
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/topk.py
@@ -0,0 +1,121 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def topk_sorted_implementation(X, k, axis, largest):  # type: ignore
+    sorted_indices = np.argsort(X, axis=axis)
+    sorted_values = np.sort(X, axis=axis)
+    if largest:
+        sorted_indices = np.flip(sorted_indices, axis=axis)
+        sorted_values = np.flip(sorted_values, axis=axis)
+    topk_sorted_indices = np.take(sorted_indices, np.arange(k), axis=axis)
+    topk_sorted_values = np.take(sorted_values, np.arange(k), axis=axis)
+    return topk_sorted_values, np.array(topk_sorted_indices, dtype=np.int64)
+
+
+class TopK(Base):
+
+    @staticmethod
+    def export_top_k() -> None:
+        axis = 1
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            'TopK',
+            inputs=['x', 'k'],
+            outputs=['values', 'indices'],
+            axis=axis
+        )
+        X = np.array([
+            [0, 1, 2, 3],
+            [4, 5, 6, 7],
+            [8, 9, 10, 11],
+        ], dtype=np.float32)
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        #print(values_ref)
+        #[[ 3.  2.  1.]
+        # [ 7.  6.  5.]
+        # [11. 10.  9.]]
+        #print(indices_ref)
+        #[[3 2 1]
+        # [3 2 1]
+        # [3 2 1]]
+
+        expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+               name='test_top_k')
+
+    @staticmethod
+    def export_top_k_smallest() -> None:
+        axis = 1
+        largest = 0
+        sorted = 1
+        k = 3
+
+        node = onnx.helper.make_node(
+            'TopK',
+            inputs=['x', 'k'],
+            outputs=['values', 'indices'],
+            axis=axis,
+            largest=largest,
+            sorted=sorted
+        )
+
+        X = np.array([
+            [0, 1, 2, 3],
+            [4, 5, 6, 7],
+            [11, 10, 9, 8],
+        ], dtype=np.float32)
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        #print(values_ref)
+        #[[ 0.  1.  2.]
+        # [ 4.  5.  6.]
+        # [ 8.  9. 10.]]
+        #print(indices_ref)
+        #[[0 1 2]
+        # [0 1 2]
+        # [3 2 1]]
+
+        expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+               name='test_top_k_smallest')
+
+    @staticmethod
+    def export_top_k_negative_axis() -> None:
+        axis = -1
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            'TopK',
+            inputs=['x', 'k'],
+            outputs=['values', 'indices'],
+            axis=axis
+        )
+        X = np.array([
+            [0, 1, 2, 3],
+            [4, 5, 6, 7],
+            [8, 9, 10, 11],
+        ], dtype=np.float32)
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        #[[ 3.  2.  1.]
+        # [ 7.  6.  5.]
+        # [11. 10.  9.]]
+        # print(indices_ref)
+        #[[3 2 1]
+        # [3 2 1]
+        # [3 2 1]]
+
+        expect(node, inputs=[X, K], outputs=[values_ref, indices_ref],
+               name='test_top_k_negative_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/transpose.py b/third_party/onnx/onnx/backend/test/case/node/transpose.py
new file mode 100644
index 0000000000..334c449a5d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/transpose.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import itertools
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Transpose(Base):
+
+    @staticmethod
+    def export_default() -> None:
+        shape = (2, 3, 4)
+        data = np.random.random_sample(shape).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            'Transpose',
+            inputs=['data'],
+            outputs=['transposed']
+        )
+
+        transposed = np.transpose(data)
+        expect(node, inputs=[data], outputs=[transposed],
+               name='test_transpose_default')
+
+    @staticmethod
+    def export_all_permutations() -> None:
+        shape = (2, 3, 4)
+        data = np.random.random_sample(shape).astype(np.float32)
+        permutations = list(itertools.permutations(np.arange(len(shape))))
+
+        for i in range(len(permutations)):
+            node = onnx.helper.make_node(
+                'Transpose',
+                inputs=['data'],
+                outputs=['transposed'],
+                perm=permutations[i]
+            )
+            transposed = np.transpose(data, permutations[i])
+            expect(node, inputs=[data], outputs=[transposed],
+                   name='test_transpose_all_permutations_' + str(i))
diff --git a/third_party/onnx/onnx/backend/test/case/node/trilu.py b/third_party/onnx/onnx/backend/test/case/node/trilu.py
new file mode 100644
index 0000000000..7477f7a766
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/trilu.py
@@ -0,0 +1,450 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def triu_reference_implementation(x, k=0):  # type: ignore
+    return np.triu(x, k)
+
+
+def tril_reference_implementation(x, k=0):  # type: ignore
+    return np.tril(x, k)
+
+
+class Trilu(Base):
+    @staticmethod
+    def export_triu() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [0, 2, 8, 6, 9],
+        #   [0, 0, 0, 8, 7],
+        #   [0, 0, 0, 2, 4]]
+        y = triu_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name='test_triu')
+
+    @staticmethod
+    def export_triu_neg() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [0, 4, 0, 8, 7],
+        #   [0, 0, 4, 2, 4]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_neg')
+
+    @staticmethod
+    def export_triu_out_neg_out() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-7).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_neg_out')
+
+    @staticmethod
+    def export_triu_pos() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(2).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 3, 7, 9],
+        #   [0, 0, 0, 6, 9],
+        #   [0, 0, 0, 0, 7],
+        #   [0, 0, 0, 0, 0]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_pos')
+
+    @staticmethod
+    def export_triu_out_pos() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_pos')
+
+    @staticmethod
+    def export_triu_square() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        y = triu_reference_implementation(x)
+        # X:
+        # [[[4, 6, 9],
+        #   [7, 5, 4],
+        #   [8, 1, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [9, 6, 3],
+        #   [8, 9, 8]]]
+        # expect result:
+        # [[[4, 6, 9],
+        #   [0, 5, 4],
+        #   [0, 0, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [0, 6, 3],
+        #   [0, 0, 8]]]
+        expect(node, inputs=[x], outputs=[y], name='test_triu_square')
+
+    @staticmethod
+    def export_triu_square_neg() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        # [[[4, 6, 9],
+        #   [7, 5, 4],
+        #   [8, 1, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [9, 6, 3],
+        #   [8, 9, 8]]]
+        # expect result:
+        # [[[4, 6, 9],
+        #   [7, 5, 4],
+        #   [0, 1, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [9, 6, 3],
+        #   [0, 9, 8]]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_square_neg')
+
+    @staticmethod
+    def export_triu_one_row() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+        k = np.array(1).astype(np.int64)
+        # X:
+        # [[[1, 4, 9, 7, 1]],
+        #
+        #  [[9, 2, 8, 8, 4]],
+        #
+        #  [[3, 9, 7, 4, 2]]]
+        # expect result:
+        # [[[0, 4, 9, 7, 1]],
+        #
+        #  [[0, 2, 8, 8, 4]],
+        #
+        #  [[0, 9, 7, 4, 2]]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_one_row')
+
+    @staticmethod
+    def export_triu_zero() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+        )
+
+        x = np.random.randint(10, size=(0, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        # []
+        # expect result:
+        # []
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_triu_zero')
+
+    @staticmethod
+    def export_tril() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 0, 0, 0, 0],
+        #   [1, 2, 0, 0, 0],
+        #   [9, 4, 1, 0, 0],
+        #   [4, 3, 4, 2, 0]]
+        y = tril_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name='test_tril')
+
+    @staticmethod
+    def export_tril_neg() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 0, 0, 0],
+        #   [1, 0, 0, 0, 0],
+        #   [9, 4, 0, 0, 0],
+        #   [4, 3, 4, 0, 0]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_tril_neg')
+
+    @staticmethod
+    def export_tril_out_neg() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-7).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_neg')
+
+    @staticmethod
+    def export_tril_pos() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(2).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 0, 0],
+        #   [1, 2, 8, 6, 0],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_tril_pos')
+
+    @staticmethod
+    def export_tril_out_pos() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+            upper=0,
+        )
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_pos')
+
+    @staticmethod
+    def export_tril_square() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        # X:
+        # [[[0, 4, 3],
+        #   [2, 0, 9],
+        #   [8, 2, 5]],
+        #
+        #  [[2, 7, 2],
+        #   [2, 6, 0],
+        #   [2, 6, 5]]]
+        # expect result:
+        # [[[0, 0, 0],
+        #   [2, 0, 0],
+        #   [8, 2, 5]],
+        #
+        #  [[2, 0, 0],
+        #   [2, 6, 0],
+        #   [2, 6, 5]]]
+        y = tril_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name='test_tril_square')
+
+    @staticmethod
+    def export_tril_square_neg() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        # [[[0, 4, 3],
+        #   [2, 0, 9],
+        #   [8, 2, 5]],
+        #
+        #  [[2, 7, 2],
+        #   [2, 6, 0],
+        #   [2, 6, 5]]]
+        # expect result:
+        # [[[0, 0, 0],
+        #   [2, 0, 0],
+        #   [8, 2, 0]],
+        #
+        #  [[0, 0, 0],
+        #   [2, 0, 0],
+        #   [2, 6, 0]]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_tril_square_neg')
+
+    @staticmethod
+    def export_tril_one_row() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+        # X:
+        # [[[6, 2, 4, 1, 6]],
+        #
+        #  [[8, 3, 8, 7, 0]],
+        #
+        #  [[2, 2, 9, 5, 9]]]
+        # expect result:
+        # [[[6, 0, 0, 0, 0]],
+        #
+        #  [[8, 0, 0, 0, 0]],
+        #
+        #  [[2, 0, 0, 0, 0]]]
+        y = tril_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name='test_tril_one_row_neg')
+
+    @staticmethod
+    def export_tril_zero() -> None:
+        node = onnx.helper.make_node(
+            'Trilu',
+            inputs=['x', 'k'],
+            outputs=['y'],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        # []
+        # expect result:
+        # []
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name='test_tril_zero')
diff --git a/third_party/onnx/onnx/backend/test/case/node/unique.py b/third_party/onnx/onnx/backend/test/case/node/unique.py
new file mode 100644
index 0000000000..7f81feaa03
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/unique.py
@@ -0,0 +1,141 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+def specify_int64(indices, inverse_indices, counts):  # type: ignore
+    return np.array(indices, dtype=np.int64), np.array(inverse_indices, dtype=np.int64), np.array(counts, dtype=np.int64)
+
+
+class Unique(Base):
+
+    @staticmethod
+    def export_sorted_without_axis() -> None:
+        node_sorted = onnx.helper.make_node(
+            'Unique',
+            inputs=['X'],
+            outputs=['Y', 'indices', 'inverse_indices', 'counts']
+        )
+
+        x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+        indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+        expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_without_axis')
+
+    @staticmethod
+    def export_not_sorted_without_axis() -> None:
+        node_not_sorted = onnx.helper.make_node(
+            'Unique',
+            inputs=['X'],
+            outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+            sorted=0
+        )
+        # numpy unique does not retain original order (it sorts the output unique values)
+        # https://github.com/numpy/numpy/issues/8621
+        # we need to recover unsorted output and indices
+        x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+
+        # prepare index mapping from sorted to unsorted
+        argsorted_indices = np.argsort(indices)
+        inverse_indices_map = {i: si for i, si in zip(argsorted_indices, np.arange(len(argsorted_indices)))}
+
+        indices = indices[argsorted_indices]
+        y = np.take(x, indices, axis=0)
+        inverse_indices = np.asarray([inverse_indices_map[i] for i in inverse_indices], dtype=np.int64)
+        counts = counts[argsorted_indices]
+        indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+        # print(y)
+        # [2.0, 1.0, 3.0, 4.0]
+        # print(indices)
+        # [0 1 3 4]
+        # print(inverse_indices)
+        # [0, 1, 1, 2, 3, 2]
+        # print(counts)
+        # [1, 2, 2, 1]
+
+        expect(node_not_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_not_sorted_without_axis')
+
+    @staticmethod
+    def export_sorted_with_axis() -> None:
+        node_sorted = onnx.helper.make_node(
+            'Unique',
+            inputs=['X'],
+            outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+            sorted=1,
+            axis=0
+        )
+
+        x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0)
+        indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+        # print(y)
+        # [[1. 0. 0.]
+        #  [2. 3. 4.]]
+        # print(indices)
+        # [0 2]
+        # print(inverse_indices)
+        # [0 0 1]
+        # print(counts)
+        # [2 1]
+
+        expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_axis')
+
+    @staticmethod
+    def export_sorted_with_axis_3d() -> None:
+        node_sorted = onnx.helper.make_node(
+            'Unique',
+            inputs=['X'],
+            outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+            sorted=1,
+            axis=1
+        )
+
+        x = np.array([[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+                      [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1)
+        indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+        # print(y)
+        # [[[0. 1.]
+        #  [1. 1.]
+        #  [2. 1.]]
+        # [[0. 1.]
+        #  [1. 1.]
+        #  [2. 1.]]]
+        # print(indices)
+        # [1 0 2]
+        # print(inverse_indices)
+        # [1 0 2 0]
+        # print(counts)
+        # [2 1 1]
+        expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_axis_3d')
+
+    @staticmethod
+    def export_sorted_with_negative_axis() -> None:
+        node_sorted = onnx.helper.make_node(
+            'Unique',
+            inputs=['X'],
+            outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+            sorted=1,
+            axis=-1
+        )
+
+        x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1)
+        indices, inverse_indices, counts = specify_int64(indices, inverse_indices, counts)
+        # print(y)
+        # [[0. 1.]
+        #  [0. 1.]
+        #  [3. 2.]]
+        # print(indices)
+        # [1 0]
+        # print(inverse_indices)
+        # [1 0 0]
+        # print(counts)
+        # [2 1]
+
+        expect(node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name='test_unique_sorted_with_negative_axis')
diff --git a/third_party/onnx/onnx/backend/test/case/node/unsqueeze.py b/third_party/onnx/onnx/backend/test/case/node/unsqueeze.py
new file mode 100644
index 0000000000..21092225a1
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/unsqueeze.py
@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Unsqueeze(Base):
+
+    @staticmethod
+    def export_unsqueeze_one_axis() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+
+        for i in range(x.ndim):
+            axes = np.array([i]).astype(np.int64)
+            node = onnx.helper.make_node(
+                'Unsqueeze',
+                inputs=['x', 'axes'],
+                outputs=['y'],
+            )
+            y = np.expand_dims(x, axis=i)
+
+            expect(node, inputs=[x, axes], outputs=[y],
+                   name='test_unsqueeze_axis_' + str(i))
+
+    @staticmethod
+    def export_unsqueeze_two_axes() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        axes = np.array([1, 4]).astype(np.int64)
+
+        node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['x', 'axes'],
+            outputs=['y'],
+        )
+        y = np.expand_dims(x, axis=1)
+        y = np.expand_dims(y, axis=4)
+
+        expect(node, inputs=[x, axes], outputs=[y],
+               name='test_unsqueeze_two_axes')
+
+    @staticmethod
+    def export_unsqueeze_three_axes() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        axes = np.array([2, 4, 5]).astype(np.int64)
+
+        node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['x', 'axes'],
+            outputs=['y'],
+        )
+        y = np.expand_dims(x, axis=2)
+        y = np.expand_dims(y, axis=4)
+        y = np.expand_dims(y, axis=5)
+
+        expect(node, inputs=[x, axes], outputs=[y],
+               name='test_unsqueeze_three_axes')
+
+    @staticmethod
+    def export_unsqueeze_unsorted_axes() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        axes = np.array([5, 4, 2]).astype(np.int64)
+
+        node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['x', 'axes'],
+            outputs=['y'],
+        )
+        y = np.expand_dims(x, axis=2)
+        y = np.expand_dims(y, axis=4)
+        y = np.expand_dims(y, axis=5)
+
+        expect(node, inputs=[x, axes], outputs=[y],
+               name='test_unsqueeze_unsorted_axes')
+
+    @staticmethod
+    def export_unsqueeze_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['x', 'axes'],
+            outputs=['y'],
+        )
+        x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+        axes = np.array([-2]).astype(np.int64)
+        y = np.expand_dims(x, axis=-2)
+        expect(node, inputs=[x, axes], outputs=[y],
+               name='test_unsqueeze_negative_axes')
diff --git a/third_party/onnx/onnx/backend/test/case/node/upsample.py b/third_party/onnx/onnx/backend/test/case/node/upsample.py
new file mode 100644
index 0000000000..8828ca09b5
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/upsample.py
@@ -0,0 +1,37 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+from onnx import helper
+
+
+class Upsample(Base):
+
+    @staticmethod
+    def export_nearest() -> None:
+        node = onnx.helper.make_node(
+            'Upsample',
+            inputs=['X', 'scales'],
+            outputs=['Y'],
+            mode='nearest',
+        )
+
+        data = np.array([[[
+            [1, 2],
+            [3, 4],
+        ]]], dtype=np.float32)
+
+        scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+        output = np.array([[[
+            [1, 1, 1, 2, 2, 2],
+            [1, 1, 1, 2, 2, 2],
+            [3, 3, 3, 4, 4, 4],
+            [3, 3, 3, 4, 4, 4],
+        ]]], dtype=np.float32)
+
+        expect(node, inputs=[data, scales], outputs=[output],
+               name='test_upsample_nearest', opset_imports=[helper.make_opsetid("", 9)])
diff --git a/third_party/onnx/onnx/backend/test/case/node/where.py b/third_party/onnx/onnx/backend/test/case/node/where.py
new file mode 100644
index 0000000000..4ecd3d1d54
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/where.py
@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Where(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Where',
+            inputs=['condition', 'x', 'y'],
+            outputs=['z'],
+        )
+
+        condition = np.array([[1, 0], [1, 1]], dtype=bool)
+        x = np.array([[1, 2], [3, 4]], dtype=np.float32)
+        y = np.array([[9, 8], [7, 6]], dtype=np.float32)
+        z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+        expect(node, inputs=[condition, x, y], outputs=[z],
+               name='test_where_example')
+
+    @staticmethod
+    def export_long() -> None:
+        node = onnx.helper.make_node(
+            'Where',
+            inputs=['condition', 'x', 'y'],
+            outputs=['z'],
+        )
+
+        condition = np.array([[1, 0], [1, 1]], dtype=bool)
+        x = np.array([[1, 2], [3, 4]], dtype=np.int64)
+        y = np.array([[9, 8], [7, 6]], dtype=np.int64)
+        z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+        expect(node, inputs=[condition, x, y], outputs=[z],
+               name='test_where_long_example')
diff --git a/third_party/onnx/onnx/backend/test/case/node/xor.py b/third_party/onnx/onnx/backend/test/case/node/xor.py
new file mode 100644
index 0000000000..76bbf76d89
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/node/xor.py
@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class Xor(Base):
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            'Xor',
+            inputs=['x', 'y'],
+            outputs=['xor'],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        y = (np.random.randn(3, 4) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor2d')
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor3d')
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor4d')
+
+    @staticmethod
+    def export_xor_broadcast() -> None:
+        node = onnx.helper.make_node(
+            'Xor',
+            inputs=['x', 'y'],
+            outputs=['xor'],
+        )
+
+        # 3d vs 1d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(5) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor_bcast3v1d')
+
+        # 3d vs 2d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(4, 5) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor_bcast3v2d')
+
+        # 4d vs 2d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(5, 6) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor_bcast4v2d')
+
+        # 4d vs 3d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor_bcast4v3d')
+
+        # 4d vs 4d
+        x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+        z = np.logical_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z],
+               name='test_xor_bcast4v4d')
diff --git a/third_party/onnx/onnx/backend/test/case/test_case.py b/third_party/onnx/onnx/backend/test/case/test_case.py
new file mode 100644
index 0000000000..2a1c6fd1a4
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/test_case.py
@@ -0,0 +1,21 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass
+from typing import Optional, Sequence, Tuple
+import onnx
+import numpy as np  # type: ignore
+
+
+@dataclass
+class TestCase:
+    name: str
+    model_name: str
+    url: Optional[str]
+    model_dir: Optional[str]
+    model: Optional[onnx.ModelProto]
+    data_sets: Optional[Sequence[Tuple[Sequence[np.ndarray], Sequence[np.ndarray]]]]
+    kind: str
+    rtol: float
+    atol: float
+    # Tell PyTest this isn't a real test.
+    __test__: bool = False
diff --git a/third_party/onnx/onnx/backend/test/case/utils.py b/third_party/onnx/onnx/backend/test/case/utils.py
new file mode 100644
index 0000000000..8eb7d287e7
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/case/utils.py
@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import importlib
+import pkgutil
+from types import ModuleType
+from typing import Optional, List
+
+import numpy as np  # type: ignore
+
+
+all_numeric_dtypes = [
+    np.int8, np.int16, np.int32, np.int64,
+    np.uint8, np.uint16, np.uint32, np.uint64,
+    np.float16, np.float32, np.float64,
+]
+
+
+def import_recursive(package: ModuleType) -> None:
+    """
+    Takes a package and imports all modules underneath it
+    """
+    pkg_dir: Optional[List[str]] = None
+    pkg_dir = package.__path__  # type: ignore
+    module_location = package.__name__
+    for (_module_loader, name, ispkg) in pkgutil.iter_modules(pkg_dir):
+        module_name = "{}.{}".format(module_location, name)  # Module/package
+        module = importlib.import_module(module_name)
+        if ispkg:
+            import_recursive(module)
diff --git a/third_party/onnx/onnx/backend/test/cmd_tools.py b/third_party/onnx/onnx/backend/test/cmd_tools.py
new file mode 100644
index 0000000000..904fd8c50a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cmd_tools.py
@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import json
+import os
+import shutil
+
+import onnx.backend.test.case.node as node_test
+import onnx.backend.test.case.model as model_test
+from onnx import numpy_helper
+from typing import Text
+
+
+TOP_DIR = os.path.realpath(os.path.dirname(__file__))
+DATA_DIR = os.path.join(TOP_DIR, 'data')
+
+
+def generate_data(args: argparse.Namespace) -> None:
+
+    def prepare_dir(path: Text) -> None:
+        if os.path.exists(path):
+            shutil.rmtree(path)
+        os.makedirs(path)
+
+    cases = model_test.collect_testcases()
+    if args.op_type is None:
+        # include all of the testcases
+        cases += node_test.collect_testcases()
+    else:
+        # only include those testcases including the given operator
+        cases += node_test.collect_testcases_by_operator(args.op_type)
+    for case in cases:
+        output_dir = os.path.join(
+            args.output, case.kind, case.name)
+        prepare_dir(output_dir)
+        if case.kind == 'real':
+            with open(os.path.join(output_dir, 'data.json'), 'w') as fi:
+                json.dump({
+                    'url': case.url,
+                    'model_name': case.model_name,
+                    'rtol': case.rtol,
+                    'atol': case.atol,
+                }, fi, sort_keys=True)
+        else:
+            assert case.model
+            with open(os.path.join(output_dir, 'model.onnx'), 'wb') as f:
+                f.write(case.model.SerializeToString())
+            assert case.data_sets
+            for i, (inputs, outputs) in enumerate(case.data_sets):
+                data_set_dir = os.path.join(
+                    output_dir, 'test_data_set_{}'.format(i))
+                prepare_dir(data_set_dir)
+                for j, input in enumerate(inputs):
+                    with open(os.path.join(
+                            data_set_dir, 'input_{}.pb'.format(j)), 'wb') as f:
+                        if case.model.graph.input[j].type.HasField('map_type'):
+                            f.write(numpy_helper.from_dict(
+                                input, case.model.graph.input[j].name).SerializeToString())
+                        elif case.model.graph.input[j].type.HasField('sequence_type'):
+                            f.write(numpy_helper.from_list(
+                                input, case.model.graph.input[j].name).SerializeToString())
+                        elif case.model.graph.input[j].type.HasField('optional_type'):
+                            f.write(numpy_helper.from_optional(
+                                input, case.model.graph.input[j].name).SerializeToString())
+                        else:
+                            assert case.model.graph.input[j].type.HasField('tensor_type')
+                            f.write(numpy_helper.from_array(
+                                input, case.model.graph.input[j].name).SerializeToString())
+                for j, output in enumerate(outputs):
+                    with open(os.path.join(
+                            data_set_dir, 'output_{}.pb'.format(j)), 'wb') as f:
+                        if case.model.graph.output[j].type.HasField('map_type'):
+                            f.write(numpy_helper.from_dict(
+                                output, case.model.graph.output[j].name).SerializeToString())
+                        elif case.model.graph.output[j].type.HasField('sequence_type'):
+                            f.write(numpy_helper.from_list(
+                                output, case.model.graph.output[j].name).SerializeToString())
+                        elif case.model.graph.output[j].type.HasField('optional_type'):
+                            f.write(numpy_helper.from_optional(
+                                output, case.model.graph.output[j].name).SerializeToString())
+                        else:
+                            assert case.model.graph.output[j].type.HasField('tensor_type')
+                            f.write(numpy_helper.from_array(
+                                output, case.model.graph.output[j].name).SerializeToString())
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser('backend-test-tools')
+    subparsers = parser.add_subparsers()
+
+    subparser = subparsers.add_parser('generate-data', help='convert testcases to test data')
+    subparser.add_argument('-o', '--output', default=DATA_DIR,
+                           help='output directory (default: %(default)s)')
+    subparser.add_argument('-t', '--op_type', default=None,
+                           help='op_type for test case generation. (generates test data for the specified op_type only.)')
+    subparser.set_defaults(func=generate_data)
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    args.func(args)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/onnx/backend/test/cpp/driver/test_driver.cc b/third_party/onnx/onnx/backend/test/cpp/driver/test_driver.cc
new file mode 100644
index 0000000000..0abb406957
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cpp/driver/test_driver.cc
@@ -0,0 +1,273 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "test_driver.h"
+#include <cstdio>
+#include <cstdlib>
+#include <fstream>
+
+#include <errno.h>
+#include <fcntl.h>
+#include <sys/stat.h>
+#ifdef _WIN32
+#include <io.h>
+#include <windows.h>
+#else
+#include <dirent.h>
+#endif
+#include "onnx/common/common.h"
+
+namespace ONNX_NAMESPACE {
+namespace testing {
+bool FileExists(const std::string& filename) {
+#ifdef _WIN32
+  if (INVALID_FILE_ATTRIBUTES == GetFileAttributes(filename.c_str())) {
+    return false;
+  }
+#else
+  struct stat stats;
+  if (lstat(filename.c_str(), &stats) != 0 || !S_ISREG(stats.st_mode)) {
+    return false;
+  }
+#endif
+  return true;
+}
+void TestDriver::SetDefaultDir(const std::string& s) {
+  default_dir_ = s;
+}
+
+/**
+ *	It is possible that case_dir is not a dir.
+ *	But it does not affect the result.
+ */
+void TestDriver::FetchSingleTestCase(const std::string& case_dir, const std::string& test_case_name) {
+  std::string model_name = case_dir;
+  model_name += "model.onnx";
+  if (FileExists(model_name)) {
+    UnsolvedTestCase test_case;
+    test_case.test_case_name_ = test_case_name;
+    test_case.model_filename_ = model_name;
+    test_case.model_dirname_ = case_dir;
+    for (int case_count = 0;; case_count++) {
+      std::vector<std::string> input_filenames, output_filenames;
+      std::string input_name, output_name;
+      std::string case_dirname = case_dir;
+      case_dirname += "test_data_set_" + ONNX_NAMESPACE::to_string(case_count);
+      input_name = case_dirname + "/input_" + "0" + ".pb";
+      output_name = case_dirname + "/output_" + "0" + ".pb";
+      if (!FileExists(output_name) && !FileExists(input_name)) {
+        break;
+      }
+
+      for (int data_count = 0;; data_count++) {
+        input_name = case_dirname + "/input_" + ONNX_NAMESPACE::to_string(data_count) + ".pb";
+        output_name = case_dirname + "/output_" + ONNX_NAMESPACE::to_string(data_count) + ".pb";
+        const bool input_exists = FileExists(input_name);
+        const bool output_exists = FileExists(output_name);
+        if (!output_exists && !input_exists) {
+          break;
+        }
+        if (input_exists) {
+          input_filenames.emplace_back(std::move(input_name));
+        }
+        if (output_exists) {
+          output_filenames.emplace_back(std::move(output_name));
+        }
+      }
+      UnsolvedTestData test_data(input_filenames, output_filenames);
+      test_case.test_data_.emplace_back(std::move(test_data));
+    }
+    testcases_.emplace_back(std::move(test_case));
+  }
+}
+
+bool TestDriver::FetchAllTestCases(const std::string& target) {
+  std::string target_dir = target;
+  if (target_dir == "") {
+    target_dir = default_dir_;
+  }
+  if (target_dir[target_dir.size() - 1] == '/') {
+    target_dir.erase(target_dir.size() - 1, 1);
+  }
+#ifdef _WIN32
+  _finddata_t file;
+  intptr_t lf;
+  if ((lf = _findfirst(target_dir.c_str(), &file)) == -1) {
+    std::cerr << "Error: cannot open directory " << target_dir << " when fetching test data: " << strerror(errno)
+              << std::endl;
+    return false;
+  } else {
+    ONNX_TRY {
+      do {
+        std::string entry_dname = file.name;
+        std::string full_entry_dname;
+        if (entry_dname != "." && entry_dname != "..") {
+          full_entry_dname = target_dir + "/" + entry_dname + "/";
+          FetchSingleTestCase(full_entry_dname, entry_dname);
+        }
+      } while (_findnext(lf, &file) == 0);
+    }
+    ONNX_CATCH(const std::exception& e) {
+      ONNX_HANDLE_EXCEPTION([&]() {
+        _findclose(lf);
+        ONNX_THROW_EX(e);
+      });
+    }
+    _findclose(lf);
+  }
+#else
+  DIR* directory;
+  ONNX_TRY {
+    directory = opendir(target_dir.c_str());
+    if (directory == NULL) {
+      std::cerr << "Error: cannot open directory " << target_dir << " when fetching test data: " << strerror(errno)
+                << std::endl;
+      return false;
+    }
+    while (true) {
+      errno = 0;
+      struct dirent* entry = readdir(directory);
+      if (entry == NULL) {
+        if (errno != 0) {
+          std::cerr << "Error: cannot read directory " << target_dir << " when fetching test data: " << strerror(errno)
+                    << std::endl;
+          return -1;
+        } else {
+          break;
+        }
+      }
+      std::string full_entry_dname;
+      std::string entry_dname = entry->d_name;
+      if (entry_dname != "." && entry_dname != "..") {
+        full_entry_dname = target_dir + "/" + entry_dname + "/";
+        FetchSingleTestCase(full_entry_dname, entry_dname);
+      }
+    }
+  }
+  ONNX_CATCH(const std::exception& e) {
+    ONNX_HANDLE_EXCEPTION([&]() {
+      if (directory != NULL) {
+        closedir(directory);
+      }
+      ONNX_THROW_EX(e);
+    });
+  }
+  if (directory != NULL) {
+    if (closedir(directory) != 0) {
+      std::cerr << "Warning: failed to close directory " << target_dir
+                << " when fetching test data: " << strerror(errno) << std::endl;
+      return false;
+    }
+  }
+#endif
+  return true;
+}
+
+std::vector<UnsolvedTestCase> GetTestCase(const std::string& location) {
+  TestDriver test_driver;
+  test_driver.FetchAllTestCases(location);
+  return test_driver.testcases_;
+}
+
+void LoadSingleFile(const std::string& filename, std::string& filedata) {
+  FILE* fp;
+  if ((fp = fopen(filename.c_str(), "r")) != NULL) {
+    ONNX_TRY {
+      int fsize;
+      char buff[1024] = {0};
+      do {
+        fsize = fread(buff, sizeof(char), 1024, fp);
+        filedata += std::string(buff, buff + fsize);
+      } while (fsize == 1024);
+    }
+    ONNX_CATCH(const std::exception& e) {
+      ONNX_HANDLE_EXCEPTION([&]() {
+        fclose(fp);
+        ONNX_THROW_EX(e);
+      });
+    }
+    fclose(fp);
+  } else {
+    std::cerr << "Warning: failed to open file: " << filename << std::endl;
+  }
+}
+
+ResolvedTestCase LoadSingleTestCase(const UnsolvedTestCase& t) {
+  ResolvedTestCase st;
+  std::string raw_model;
+  LoadSingleFile(t.model_filename_, raw_model);
+  st.test_case_name_ = t.test_case_name_;
+  ONNX_NAMESPACE::ParseProtoFromBytes(&st.model_, raw_model.c_str(), raw_model.size());
+  int test_data_counter = 0;
+  for (auto& test_data : t.test_data_) {
+    ResolvedTestData proto_test_data;
+    for (auto& input_file : test_data.input_filenames_) {
+      std::string input_data;
+      ONNX_NAMESPACE::ValueInfoProto input_info;
+      LoadSingleFile(input_file, input_data);
+      input_info = st.model_.graph().input(test_data_counter);
+      if (input_info.type().has_tensor_type()) {
+        ONNX_NAMESPACE::TensorProto input_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&input_proto, input_data.c_str(), input_data.size());
+        proto_test_data.inputs_.emplace_back(std::move(input_proto));
+      } else if (input_info.type().has_sequence_type()) {
+        ONNX_NAMESPACE::SequenceProto input_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&input_proto, input_data.c_str(), input_data.size());
+        proto_test_data.seq_inputs_.emplace_back(std::move(input_proto));
+      } else if (input_info.type().has_map_type()) {
+        ONNX_NAMESPACE::MapProto input_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&input_proto, input_data.c_str(), input_data.size());
+        proto_test_data.map_inputs_.emplace_back(std::move(input_proto));
+      } else if (input_info.type().has_optional_type()) {
+        ONNX_NAMESPACE::OptionalProto input_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&input_proto, input_data.c_str(), input_data.size());
+        proto_test_data.optional_inputs_.emplace_back(std::move(input_proto));
+      }
+      test_data_counter++;
+    }
+    test_data_counter = 0;
+    for (auto& output_file : test_data.output_filenames_) {
+      std::string output_data;
+      ONNX_NAMESPACE::ValueInfoProto output_info;
+      output_info = st.model_.graph().output(test_data_counter);
+      LoadSingleFile(output_file, output_data);
+      if (output_info.type().has_tensor_type()) {
+        ONNX_NAMESPACE::TensorProto output_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&output_proto, output_data.c_str(), output_data.size());
+        proto_test_data.outputs_.emplace_back(std::move(output_proto));
+      } else if (output_info.type().has_sequence_type()) {
+        ONNX_NAMESPACE::SequenceProto output_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&output_proto, output_data.c_str(), output_data.size());
+        proto_test_data.seq_outputs_.emplace_back(std::move(output_proto));
+      } else if (output_info.type().has_map_type()) {
+        ONNX_NAMESPACE::MapProto output_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&output_proto, output_data.c_str(), output_data.size());
+        proto_test_data.map_outputs_.emplace_back(std::move(output_proto));
+      } else if (output_info.type().has_optional_type()) {
+        ONNX_NAMESPACE::OptionalProto output_proto;
+        ONNX_NAMESPACE::ParseProtoFromBytes(&output_proto, output_data.c_str(), output_data.size());
+        proto_test_data.optional_outputs_.emplace_back(std::move(output_proto));
+      }
+      test_data_counter++;
+    }
+    st.proto_test_data_.emplace_back(std::move(proto_test_data));
+  }
+  return st;
+}
+
+std::vector<ResolvedTestCase> LoadAllTestCases(const std::string& location) {
+  std::vector<UnsolvedTestCase> t = GetTestCase(location);
+  return LoadAllTestCases(t);
+}
+
+std::vector<ResolvedTestCase> LoadAllTestCases(const std::vector<UnsolvedTestCase>& t) {
+  std::vector<ResolvedTestCase> st;
+  for (const auto& i : t) {
+    st.push_back(LoadSingleTestCase(i));
+  }
+  return st;
+}
+
+} // namespace testing
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/backend/test/cpp/driver/test_driver.h b/third_party/onnx/onnx/backend/test/cpp/driver/test_driver.h
new file mode 100644
index 0000000000..c66d7dc6f3
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cpp/driver/test_driver.h
@@ -0,0 +1,142 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include <queue>
+#include <cstdlib>
+
+#include "onnx/onnxifi_loader.h"
+#include "onnx/onnxifi_utils.h"
+#include "onnx/string_utils.h"
+#include "onnx/onnx-data_pb.h"
+
+#include "gtest/gtest.h"
+namespace ONNX_NAMESPACE {
+namespace testing {
+/**
+ *	Store one chunk of test data files repos,
+ *	includng input and output.
+ *	No real data was loaded in this type of class,
+ *	but only data location.
+ *  Data loading is unsolved, this class CANNOT be treated as real data.
+ */
+struct UnsolvedTestData {
+  std::vector<std::string> input_filenames_;
+  std::vector<std::string> output_filenames_;
+  UnsolvedTestData() {}
+  UnsolvedTestData(
+      const std::vector<std::string>& input_filenames,
+      const std::vector<std::string>& output_filenames)
+      : input_filenames_(input_filenames),
+        output_filenames_(output_filenames) {}
+};
+
+/**
+ *	Store one model file repos,
+ *	including one model file and several chunks of test data
+ *	No real data was loaded in this type of class,
+ *	but only data location.
+ *  Data loading is unsolved, this class CANNOT be treated as real data.
+ */
+struct UnsolvedTestCase {
+  UnsolvedTestCase(
+      const std::string& model_filename,
+      const std::string& model_dirname,
+      const std::vector<UnsolvedTestData>& test_data)
+      : model_filename_(model_filename),
+        model_dirname_(model_dirname),
+        test_data_(test_data) {}
+
+  UnsolvedTestCase() {}
+  std::string test_case_name_;
+  std::string model_filename_;
+  std::string model_dirname_;
+  std::vector<UnsolvedTestData> test_data_;
+};
+
+/**
+ *	Store one chunk of test data,
+ *	including raw input/output and protos.
+ *	Real data was loaded in this type of class.
+ *	Data loading is resolved, this class may contains large chunk of data.
+ */
+struct ResolvedTestData {
+  std::vector<ONNX_NAMESPACE::TensorProto> inputs_;
+  std::vector<ONNX_NAMESPACE::SequenceProto> seq_inputs_;
+  std::vector<ONNX_NAMESPACE::MapProto> map_inputs_;
+  std::vector<ONNX_NAMESPACE::OptionalProto> optional_inputs_;
+
+  std::vector<ONNX_NAMESPACE::TensorProto> outputs_;
+  std::vector<ONNX_NAMESPACE::SequenceProto> seq_outputs_;
+  std::vector<ONNX_NAMESPACE::MapProto> map_outputs_;
+  std::vector<ONNX_NAMESPACE::OptionalProto> optional_outputs_;
+};
+
+/**
+ *	Store one test model,
+ *	including raw model, model proto and several chunks of test data.
+ *	Real data was loaded in this type of class.
+ *	Data loading is resolved, this class may contains large chunk of data.
+ */
+struct ResolvedTestCase {
+  ONNX_NAMESPACE::ModelProto model_;
+  std::vector<ResolvedTestData> proto_test_data_;
+  std::string test_case_name_;
+};
+
+/**
+ *	Store all unloaded test cases in one repo.
+ */
+class TestDriver {
+  std::string default_dir_;
+
+ public:
+  void SetDefaultDir(const std::string& s);
+  std::vector<UnsolvedTestCase> testcases_;
+  TestDriver(const std::string& default_dir = ".") {
+    default_dir_ = default_dir;
+  }
+  /**
+   *	Fetch all test cases in target.
+   *	Return true if success, and false if failed.
+   */
+  bool FetchAllTestCases(const std::string& target_dir);
+  /**
+   *	Fetch one test case from repo case_dir.
+   *	The structure of case_dir should include following contents.
+   *
+   *	Regular file: model.onnx, store the protobuf of the model.
+   *	Repo(s): test_data_set_X: store the input&output data.
+   *
+   *	Each repo in test_data_set_X should include following contents.
+   *
+   *	Regular file(s): input_X.pb, store one input tensor.
+   *	Regular file(s): output_X.pb, store one output tensor.
+   */
+  void FetchSingleTestCase(
+      const std::string& case_dir,
+      const std::string& test_case_name);
+};
+
+std::vector<UnsolvedTestCase> GetTestCase();
+
+/**
+ *	Load one proto file from filename as string to filedata.
+ */
+void LoadSingleFile(const std::string& filename, std::string& filedata);
+
+/**
+ *	Load one test case.
+ */
+ResolvedTestCase LoadSingleTestCase(const UnsolvedTestCase& t);
+
+/**
+ *	Load all test cases.
+ */
+std::vector<ResolvedTestCase> LoadAllTestCases(const std::string& location);
+std::vector<ResolvedTestCase> LoadAllTestCases(
+    const std::vector<UnsolvedTestCase>& t);
+
+} // namespace testing
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/backend/test/cpp/driver_test.cc b/third_party/onnx/onnx/backend/test/cpp/driver_test.cc
new file mode 100644
index 0000000000..1774aeb02b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cpp/driver_test.cc
@@ -0,0 +1,415 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "gtest_utils.h"
+
+#include "onnx/checker.h"
+#include "onnx/onnxifi.h"
+#include "onnx/onnxifi_ext.h"
+#include "onnx/onnxifi_loader.h"
+#include "onnx/string_utils.h"
+
+/**
+ *	In order to not test the backend itself
+ *	but only to test the funcionality of this test driver,
+ *	include IO and integrity checkers,
+ *	please set ONNXIFI_DUMMY_BACKEND to be true when compiling.
+ *	By default it is false.
+ */
+#ifndef ONNXIFI_DUMMY_BACKEND
+#define ONNXIFI_DUMMY_BACKEND false
+#endif
+
+#ifndef ONNXIFI_TESTDATA_EPS
+#define ONNXIFI_TESTDATA_EPS 1e-5
+#endif
+
+namespace ONNX_NAMESPACE {
+namespace testing {
+const float onnxifi_testdata_eps = static_cast<float>(ONNXIFI_TESTDATA_EPS);
+
+template <typename T>
+class CompareOnnxifiData {
+ public:
+  bool IsEqual(void* P, void* Q) {
+    T x = *((T*)P), y = *((T*)Q);
+    return ((x - y) > -onnxifi_testdata_eps) &&
+        ((x - y) < onnxifi_testdata_eps);
+  }
+};
+
+class ONNXCppDriverTest : public ::testing::TestWithParam<
+                              ONNX_NAMESPACE::testing::ResolvedTestCase> {
+ public:
+  struct PrintToStringParamName {
+    template <class T>
+    std::string operator()(const ::testing::TestParamInfo<T>& t) const {
+      auto test_case =
+          static_cast<ONNX_NAMESPACE::testing::ResolvedTestCase>(t.param);
+      /**
+       *  We pick folder name instead of model.graph().name as test case name
+       * here mostly for two reasons:
+       *  1. Name of proto graph can be empty or ruined.
+       *  2. Compares to the complex logic of protobuf, folder name is easier to
+       * parse, to modify and to use.
+       */
+      return test_case.test_case_name_;
+    }
+  };
+
+ protected:
+  std::vector<ONNX_NAMESPACE::testing::ResolvedTestData> protos_;
+  ONNX_NAMESPACE::ModelProto model_;
+
+  // A memory pool of shape information of onnxTensorDescriptorV1 used in this
+  // test driver.
+  std::vector<std::vector<uint64_t>> shape_pool;
+  // A memory pool of raw_data of backend output in onnxTensorDescriptorV1.
+  std::vector<std::vector<uint8_t>> data_pool;
+
+  void SetUp() override {
+    ONNX_NAMESPACE::testing::ResolvedTestCase t = GetParam();
+    protos_ = t.proto_test_data_;
+    model_ = t.model_;
+  }
+
+  uint64_t GetDescriptorSize(const onnxTensorDescriptorV1* t) {
+    uint64_t d_size = 1;
+    for (uint32_t i = 0; i < t->dimensions; i++) {
+      d_size *= t->shape[i];
+    }
+    return d_size;
+  }
+
+  bool IsGtestAssertMemorySafeSuccess(
+      onnxStatus x,
+      onnxGraph* graph_pointer,
+      onnxifi_library& lib) {
+    if (x != ONNXIFI_STATUS_SUCCESS) {
+      if (graph_pointer != NULL) {
+        lib.onnxReleaseGraph(*graph_pointer);
+      }
+    }
+    bool result = (x == ONNXIFI_STATUS_SUCCESS);
+    return result;
+  }
+
+  bool IsUnsupported(onnxStatus s, std::string& msg) {
+    if (s == ONNXIFI_STATUS_UNSUPPORTED_VERSION) {
+      msg = "Unsupported version of ONNX or operator.";
+      return true;
+    }
+
+    if (s == ONNXIFI_STATUS_UNSUPPORTED_OPERATOR) {
+      msg = "Unsupported operator.";
+      return true;
+    }
+
+    if (s == ONNXIFI_STATUS_UNSUPPORTED_ATTRIBUTE) {
+      msg = "Unsupported attribute.";
+      return true;
+    }
+
+    if (s == ONNXIFI_STATUS_UNSUPPORTED_SHAPE) {
+      msg = "Unsupported shape.";
+      return true;
+    }
+    if (s == ONNXIFI_STATUS_UNSUPPORTED_DATATYPE) {
+      msg = "Unsupported datatype";
+      return true;
+    }
+    return false;
+  }
+
+  bool IsDescriptorEqual(
+      const onnxTensorDescriptorV1& x,
+      const onnxTensorDescriptorV1& y) {
+    if (x.dataType != y.dataType || x.dimensions != y.dimensions) {
+      return false;
+    }
+    const int dims = x.dimensions;
+    for (int i = 0; i < dims; i++) {
+      if (x.shape[i] != y.shape[i]) {
+        return false;
+      }
+    }
+    const uint64_t d_size = GetDescriptorSize(&x);
+    void* p1 = (void*)x.buffer;
+    void* p2 = (void*)y.buffer;
+    bool is_equal = true;
+    for (uint64_t i = 0; i < d_size; i++) {
+      int offset = 1;
+      switch (x.dataType) {
+        case ONNXIFI_DATATYPE_UNDEFINED:
+        case ONNXIFI_DATATYPE_INT8:
+          CompareOnnxifiData<char> compare_int8;
+          is_equal &= compare_int8.IsEqual(p1, p2);
+          offset = sizeof(char);
+          break;
+        case ONNXIFI_DATATYPE_UINT8:
+          CompareOnnxifiData<unsigned char> compare_uint8;
+          is_equal &= compare_uint8.IsEqual(p1, p2);
+          offset = sizeof(unsigned char);
+          break;
+        case ONNXIFI_DATATYPE_FLOAT16:
+          // no support now
+          break;
+        case ONNXIFI_DATATYPE_INT16:
+          CompareOnnxifiData<short> compare_int16;
+          is_equal &= compare_int16.IsEqual(p1, p2);
+          offset = sizeof(short);
+          break;
+        case ONNXIFI_DATATYPE_UINT16:
+          CompareOnnxifiData<unsigned short> compare_uint16;
+          is_equal &= compare_uint16.IsEqual(p1, p2);
+          offset = sizeof(unsigned short);
+          break;
+        case ONNXIFI_DATATYPE_FLOAT32:
+          CompareOnnxifiData<float> compare_float32;
+          is_equal &= compare_float32.IsEqual(p1, p2);
+          offset = sizeof(float);
+          break;
+        case ONNXIFI_DATATYPE_INT32:
+          CompareOnnxifiData<int> compare_int32;
+          is_equal &= compare_int32.IsEqual(p1, p2);
+          offset = sizeof(int);
+          break;
+        case ONNXIFI_DATATYPE_UINT32:
+          CompareOnnxifiData<unsigned int> compare_uint32;
+          is_equal &= compare_uint32.IsEqual(p1, p2);
+          offset = sizeof(unsigned int);
+          break;
+        case ONNXIFI_DATATYPE_FLOAT64:
+          CompareOnnxifiData<long double> compare_float64;
+          is_equal &= compare_float64.IsEqual(p1, p2);
+          offset = sizeof(long double);
+          break;
+        case ONNXIFI_DATATYPE_INT64:
+          CompareOnnxifiData<long long> compare_int64;
+          is_equal &= compare_int64.IsEqual(p1, p2);
+          offset = sizeof(long long);
+          break;
+        case ONNXIFI_DATATYPE_UINT64:
+          CompareOnnxifiData<unsigned long long> compare_uint64;
+          is_equal &= compare_uint64.IsEqual(p1, p2);
+          offset = sizeof(unsigned long long);
+          break;
+        case ONNXIFI_DATATYPE_COMPLEX64:
+        case ONNXIFI_DATATYPE_COMPLEX128:
+          // no support now
+          break;
+      }
+      p1 = (char*)p1 + offset;
+      p2 = (char*)p2 + offset;
+      if (!is_equal) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  void RunAndVerify(
+      onnxifi_library& lib,
+      onnxBackend& backend,
+      onnxBackendID backendID) {
+    // Check Model
+    ONNX_NAMESPACE::checker::check_model(model_);
+    // Check Input & Output Data
+    ONNX_NAMESPACE::checker::CheckerContext ctx;
+    for (auto proto_test_data : protos_) {
+      for (auto input : proto_test_data.inputs_) {
+        ONNX_NAMESPACE::checker::check_tensor(input, ctx);
+      }
+      for (auto input : proto_test_data.seq_inputs_) {
+        ONNX_NAMESPACE::checker::check_sequence(input, ctx);
+      }
+      for (auto input : proto_test_data.map_inputs_) {
+        ONNX_NAMESPACE::checker::check_map(input, ctx);
+      }
+      for (auto input : proto_test_data.optional_inputs_) {
+        ONNX_NAMESPACE::checker::check_optional(input, ctx);
+      }
+      for (auto output : proto_test_data.outputs_) {
+        ONNX_NAMESPACE::checker::check_tensor(output, ctx);
+      }
+      for (auto output : proto_test_data.seq_outputs_) {
+        ONNX_NAMESPACE::checker::check_sequence(output, ctx);
+      }
+      for (auto output : proto_test_data.map_outputs_) {
+        ONNX_NAMESPACE::checker::check_map(output, ctx);
+      }
+      for (auto output : proto_test_data.optional_outputs_) {
+        ONNX_NAMESPACE::checker::check_optional(output, ctx);
+      }
+    }
+    if (!ONNXIFI_DUMMY_BACKEND) {
+      onnxGraph graph;
+      uint32_t weightCount = model_.graph().initializer_size();
+      onnxTensorDescriptorV1 weightDescriptors,
+          *weightDescriptors_pointer = NULL;
+      if (weightCount != 0) {
+        weightDescriptors =
+            ONNX_NAMESPACE::testing::ProtoToOnnxTensorDescriptor(
+                model_.graph().initializer(0), shape_pool);
+        weightDescriptors_pointer = &weightDescriptors;
+      }
+      std::string serialized_model;
+      model_.SerializeToString(&serialized_model);
+      auto is_compatible = lib.onnxGetBackendCompatibility(
+          backendID, serialized_model.size(), serialized_model.data());
+      std::string error_msg;
+      if (IsUnsupported(is_compatible, error_msg)) {
+        std::cout << "Warning: " << error_msg
+                  << " This test case will be skipped." << std::endl;
+        GTEST_SKIP();
+        return;
+      }
+      ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(is_compatible, NULL, lib));
+      ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+          lib.onnxInitGraph(
+              backend,
+              NULL,
+              serialized_model.size(),
+              serialized_model.data(),
+              weightCount,
+              weightDescriptors_pointer,
+              &graph),
+          NULL,
+          lib));
+      for (const auto& proto_test_data : protos_) {
+        std::vector<onnxTensorDescriptorV1> input_descriptor, output_descriptor,
+            result_descriptor;
+        for (const auto& input : proto_test_data.inputs_) {
+          input_descriptor.push_back(
+              ONNX_NAMESPACE::testing::ProtoToOnnxTensorDescriptor(
+                  input, shape_pool));
+        }
+        int output_count = 0;
+        for (auto& output : proto_test_data.outputs_) {
+          output_count++;
+          output_descriptor.push_back(
+              ONNX_NAMESPACE::testing::ProtoToOnnxTensorDescriptor(
+                  output, shape_pool));
+          onnxTensorDescriptorV1 result;
+          result.tag = ONNXIFI_TAG_TENSOR_DESCRIPTOR_V1;
+          std::string name_string = output_descriptor[output_count - 1].name;
+          result.name = name_string.c_str();
+          result.dataType = output.data_type();
+          result.memoryType = ONNXIFI_MEMORY_TYPE_CPU;
+          std::vector<uint64_t> shape_values(
+              output.dims().begin(), output.dims().end());
+          shape_pool.emplace_back(std::move(shape_values));
+          result.dimensions = shape_pool.back().size();
+          result.shape = shape_pool.back().data();
+          std::vector<uint8_t> raw_data(output.raw_data().size(), 0);
+          data_pool.emplace_back(std::move(raw_data));
+          result.buffer = (onnxPointer)data_pool.back().data();
+          result_descriptor.emplace_back(std::move(result));
+        }
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxSetGraphIO(
+                graph,
+                input_descriptor.size(),
+                input_descriptor.data(),
+                result_descriptor.size(),
+                result_descriptor.data()),
+            &graph,
+            lib));
+
+        onnxMemoryFenceV1 inputFence, outputFence;
+        inputFence.tag = ONNXIFI_TAG_MEMORY_FENCE_V1;
+        outputFence.tag = ONNXIFI_TAG_MEMORY_FENCE_V1;
+        inputFence.type = ONNXIFI_SYNCHRONIZATION_EVENT;
+        outputFence.type = ONNXIFI_SYNCHRONIZATION_EVENT;
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxInitEvent(backend, &inputFence.event), &graph, lib));
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxInitEvent(backend, &outputFence.event), &graph, lib));
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxRunGraph(graph, &inputFence, &outputFence), &graph, lib));
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxSignalEvent(inputFence.event), &graph, lib));
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxWaitEvent(outputFence.event), &graph, lib));
+        lib.onnxReleaseEvent(outputFence.event);
+        lib.onnxReleaseEvent(inputFence.event);
+        ASSERT_EQ(output_descriptor.size(), result_descriptor.size());
+        for (int i = 0; i < output_descriptor.size(); i++) {
+          auto output_size = GetDescriptorSize(&output_descriptor[i]);
+          auto result_size = GetDescriptorSize(&result_descriptor[i]);
+          ASSERT_EQ(output_size, result_size);
+          for (int j = 0; j < output_size; j++) {
+            EXPECT_EQ(
+                IsDescriptorEqual(output_descriptor[i], result_descriptor[i]),
+                true);
+          }
+        }
+/*
+ * Examine functions in onnxifi_ext
+ */
+#ifdef ONNXIFI_ENABLE_EXT
+        onnxExtensionFunctionPointer* f;
+        ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+            lib.onnxGetExtensionFunctionAddress(
+                backendID, "onnxGetExtensionFunctionAddress", f),
+            NULL,
+            lib));
+#endif
+      }
+      ASSERT_TRUE(IsGtestAssertMemorySafeSuccess(
+          lib.onnxReleaseGraph(graph), NULL, lib));
+    }
+  }
+};
+
+/**
+ *	When testing backend, we do not specify target backend here,
+ *	but always use the by-default backend of onnxifi_loader.
+ *	Check onnxifi_loader.c for more detail of how to indicate specific
+ *  backend.
+ */
+TEST_P(ONNXCppDriverTest, ONNXCppDriverUnitTest){
+  onnxifi_library lib;
+  onnxBackendID* backendIDs = NULL;
+  onnxBackend backend;
+  const size_t max_info_size = 50; // The maximum backend info size
+  if (!ONNXIFI_DUMMY_BACKEND) {
+    ASSERT_TRUE(onnxifi_load(1, NULL, &lib));
+    size_t numBackends = 0;
+    ASSERT_EQ(
+        lib.onnxGetBackendIDs(backendIDs, &numBackends),
+        ONNXIFI_STATUS_FALLBACK);
+    backendIDs = (void**)malloc(numBackends * sizeof(onnxBackendID));
+    ASSERT_EQ(
+        lib.onnxGetBackendIDs(backendIDs, &numBackends),
+        ONNXIFI_STATUS_SUCCESS);
+
+    for (int i = 0; i < numBackends; i++) {
+      const uint64_t backendProperties[] = {ONNXIFI_BACKEND_PROPERTY_NONE};
+      ASSERT_EQ(
+          lib.onnxInitBackend(backendIDs[i], backendProperties, &backend),
+          ONNXIFI_STATUS_SUCCESS);
+      char infovalue[max_info_size];
+      size_t infoValueSize = max_info_size;
+      ASSERT_EQ(
+          lib.onnxGetBackendInfo(
+              backendIDs[i], ONNXIFI_BACKEND_NAME, infovalue, &infoValueSize),
+          ONNXIFI_STATUS_SUCCESS);
+      RunAndVerify(lib, backend, backendIDs[i]);
+      lib.onnxReleaseBackend(backend);
+      lib.onnxReleaseBackendID(backendIDs[i]);
+    }
+    free(backendIDs);
+  } else {
+    RunAndVerify(lib, backend, NULL);
+  }
+}
+
+INSTANTIATE_TEST_CASE_P(
+    ONNXCppAllTest,
+    ONNXCppDriverTest,
+    ::testing::ValuesIn(GetTestCases()),
+    ONNXCppDriverTest::PrintToStringParamName());
+} // namespace testing
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/backend/test/cpp/gtest_utils.cc b/third_party/onnx/onnx/backend/test/cpp/gtest_utils.cc
new file mode 100644
index 0000000000..13fed6295a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cpp/gtest_utils.cc
@@ -0,0 +1,13 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "gtest_utils.h"
+namespace ONNX_NAMESPACE {
+namespace testing {
+std::vector<ONNX_NAMESPACE::testing::ResolvedTestCase>& GetTestCases() {
+  static std::vector<ONNX_NAMESPACE::testing::ResolvedTestCase> all_test_cases;
+  return all_test_cases;
+}
+} // namespace testing
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/backend/test/cpp/gtest_utils.h b/third_party/onnx/onnx/backend/test/cpp/gtest_utils.h
new file mode 100644
index 0000000000..d05c77b6fa
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cpp/gtest_utils.h
@@ -0,0 +1,11 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "gtest/gtest.h"
+#include "onnx/backend/test/cpp/driver/test_driver.h"
+namespace ONNX_NAMESPACE {
+namespace testing {
+std::vector<ONNX_NAMESPACE::testing::ResolvedTestCase>& GetTestCases();
+}
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/backend/test/cpp/test_main.cc b/third_party/onnx/onnx/backend/test/cpp/test_main.cc
new file mode 100644
index 0000000000..b67afdb3ff
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/cpp/test_main.cc
@@ -0,0 +1,19 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest_utils.h"
+
+GTEST_API_ int main(int argc, char** argv) {
+  if (argc < 2) {
+    std::cerr << "target directory must be given!" << std::endl;
+    return EXIT_FAILURE;
+  }
+  argc--;
+  std::string target_dir = argv[argc];
+  auto testcases = ONNX_NAMESPACE::testing::LoadAllTestCases(target_dir);
+  ONNX_NAMESPACE::testing::GetTestCases() = testcases;
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_abs/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_abs/model.onnx
new file mode 100644
index 0000000000..cbb52b8a83
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_abs/model.onnx
@@ -0,0 +1,13 @@
+backend-test:I
+
+
x
y"Abstest_absZ
+
x
+

+
+
+b
+
y
+

+
+
+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_abs/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_abs/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_abs/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_abs/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_abs/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..414f615a9f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_abs/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acos/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_acos/model.onnx
new file mode 100644
index 0000000000..874cf57053
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acos/model.onnx
@@ -0,0 +1,13 @@
+backend-test:K
+
+
x
y"Acos	test_acosZ
+
x
+

+
+
+b
+
y
+

+
+
+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acos/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acos/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..7a61135c2c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acos/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
?¦7?³N?w}?HéØ>QY%?nà><Kd?š²v?rRÄ>~®J?¨e?^k?çól?Z{‘=Ùp²=
¡¥<“&U?H5G?¹^?»†z?¨•L?Gì>ÃÐG?Ý9ò=ÝÑ#?4Ë>ÒÕq?Ú—?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N²?cï?y™q?Æ‹.?k¸>§Âß>õ—2?ü®v=9±*?ì®+?‡nW>A>Ó¡>L8º>jø?a�à>}?ßüÐ=ÊãU>R.%>2'?p¯�>IÀî>´Jz>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acos/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acos/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..15177ccd16
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acos_example/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_acos_example/model.onnx
new file mode 100644
index 0000000000..e2c32804a6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acos_example/model.onnx
@@ -0,0 +1,11 @@
+backend-test:C
+
+
x
y"Acostest_acos_exampleZ
+
x
+
+

+b
+
y
+
+

+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acos_example/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acos_example/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..7c32a2f1c9
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acos_example/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acos_example/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..598a49de8b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acos_example/test_data_set_0/output_0.pb
@@ -0,0 +1,3 @@
+
B
yJ’
+@ÛÉ?’
+†?
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acosh/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_acosh/model.onnx
new file mode 100644
index 0000000000..77301e9d5f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acosh/model.onnx
@@ -0,0 +1,14 @@
+backend-test:M
+
+
x
y"Acosh
+test_acoshZ
+
x
+

+
+
+b
+
y
+

+
+
+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acosh/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acosh/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..23af965f62
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acosh/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acosh/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..8fbcb45c56
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acosh/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
B
yJð
Îì@D{,@„#@‚‰@ë8@ÓÑ&@vç@‡ø8@[m=@@È22@*ï@
Ê@>;@“ö‰?ZT—?/?é
5@Á81@ÕŽ7@øP>@&º2@í¯@Àd1@\<­?šN&@}2¼?žG<@ñ6@¾@½Àñ?¾ï0@»@’Ó@·Û?j[$@çÜ#@sK$@=9<@oÑ)@âÕ@†Ö@+@VÅ?5”(@aæ(@ÕµÜ?(Õ³?�V
@Àk@™þ@\@¿ã>@—^¢?×Ü?OÓÅ?én'@éªí?<<@YZê?
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acosh_example/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_acosh_example/model.onnx
new file mode 100644
index 0000000000..868c12b59b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_acosh_example/model.onnx
@@ -0,0 +1,11 @@
+backend-test:E
+
+
x
y"Acoshtest_acosh_exampleZ
+
x
+
+

+b
+
y
+
+

+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_acosh_example/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_acosh_example/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..7c690ef0b1
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new file mode 100644
index 0000000000..1b7c7729e0
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/model.onnx
new file mode 100644
index 0000000000..324e40e38a
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..d0483cc61f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
RJÍÌÌ=
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_1.pb
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index 0000000000..54656de61d
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index 0000000000..038e9dcb5c
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_3.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_3.pb
new file mode 100644
index 0000000000..265567aa17
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_4.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/input_4.pb
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index 0000000000..82851b8509
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new file mode 100644
index 0000000000..075011c207
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+

BX_newJ—b‡?
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/output_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/output_1.pb
new file mode 100644
index 0000000000..47b03d2f37
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad/test_data_set_0/output_1.pb
@@ -0,0 +1 @@
+

BH_newJ@ß?@
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/model.onnx
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..d0483cc61f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
RJÍÌÌ=
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_1.pb
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index 0000000000..259b58bf7f
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new file mode 100644
index 0000000000..eb51654d45
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new file mode 100644
index 0000000000..ad3e95e23d
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_7.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/input_7.pb
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..f84f709d78
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+

BX1_newJ—b‡?
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_1.pb
new file mode 100644
index 0000000000..c8d6212c8d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_1.pb
@@ -0,0 +1 @@
+
BX2_newJ@¸…?7@
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_2.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_2.pb
new file mode 100644
index 0000000000..ac197e13eb
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_2.pb
@@ -0,0 +1 @@
+

BH1_newJ@ß?@
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_3.pb b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_3.pb
new file mode 100644
index 0000000000..987572fef4
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_adagrad_multiple/test_data_set_0/output_3.pb
@@ -0,0 +1 @@
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x
y"Coshtest_cosh_exampleZ
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,16 @@
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+
x
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y
z"Divtest_div_uint8Z
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x
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new file mode 100644
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new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
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\ No newline at end of file
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new file mode 100644
index 0000000000..ad67ee7f4d
--- /dev/null
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/model.onnx
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..ad67ee7f4d
--- /dev/null
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@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/test_data_set_0/output_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/test_data_set_0/output_1.pb
new file mode 100644
index 0000000000..de98527e0f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask/test_data_set_0/output_1.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/model.onnx
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..18a4deb5fe
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..ad67ee7f4d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/output_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/output_1.pb
new file mode 100644
index 0000000000..de98527e0f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_mask_ratio/test_data_set_0/output_1.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_old/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_old/model.onnx
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/model.onnx
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/model.onnx differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..18a4deb5fe
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..ad67ee7f4d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_default_ratio/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_random_old/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_dropout_random_old/model.onnx
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_dropout_random_old/model.onnx differ
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new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_dropout_random_old/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dropout_random_old/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dropout_random_old/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..ad67ee7f4d
--- /dev/null
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@@ -0,0 +1 @@
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dynamicquantizelinear/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_dynamicquantizelinear/model.onnx
new file mode 100644
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_dynamicquantizelinear/model.onnx differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_dynamicquantizelinear/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_dynamicquantizelinear/test_data_set_0/input_0.pb
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a
b"Flattentest_flatten_default_axisZ
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a
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a
b"Flatten*
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test_flatten_negative_axis1Z
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a
b"Flatten*
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test_flatten_negative_axis2Z
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test_flatten_negative_axis3Z
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new file mode 100644
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GlobalMaxPooltest_globalmaxpoolZ
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x
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GlobalMaxPooltest_globalmaxpool_precomputedZ
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ygreater"Greatertest_greaterZ
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new file mode 100644
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new file mode 100644
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_bcast/model.onnx
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+
+
x
+
ygreater"Greatertest_greater_bcastZ
+
x
+

+
+
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+
y
+
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+b
+greater
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+
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\ No newline at end of file
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new file mode 100644
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_bcast/test_data_set_0/input_0.pb
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\ No newline at end of file
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new file mode 100644
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_bcast/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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B
yJ^&,¿Z¸¾[*P¿ÔöÜ¿3¯5>
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,19 @@
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+%
+
x
+
y
greater_equal"GreaterOrEqualtest_greater_equalZ
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x
+

+
+
+Z
+
y
+

+
+
+b#
+
greater_equal
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+
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+B
\ No newline at end of file
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new file mode 100644
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--- /dev/null
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_greater_equal/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..1424d72971
--- /dev/null
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new file mode 100644
index 0000000000..6d0c362c27
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast/model.onnx
@@ -0,0 +1,18 @@
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+%
+
x
+
y
greater_equal"GreaterOrEqualtest_greater_equal_bcastZ
+
x
+

+
+
+Z
+
y
+
+

+b#
+
greater_equal
+	
+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast/test_data_set_0/input_0.pb
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new file mode 100644
index 0000000000..dcbe531c44
--- /dev/null
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new file mode 100644
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast/test_data_set_0/output_0.pb differ
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new file mode 100644
index 0000000000..31f63dec86
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast_expanded/model.onnx
@@ -0,0 +1,24 @@
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x
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y;GreaterOrEqual_test_greater_equal_bcast_expanded_functionO1"Greater
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x
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y;GreaterOrEqual_test_greater_equal_bcast_expanded_functionO2"Equal
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+;GreaterOrEqual_test_greater_equal_bcast_expanded_functionO1
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greater_equal"Or!test_greater_equal_bcast_expandedZ
+
x
+

+
+
+Z
+
y
+
+

+b#
+
greater_equal
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+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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new file mode 100644
index 0000000000..dcbe531c44
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast_expanded/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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new file mode 100644
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_bcast_expanded/test_data_set_0/output_0.pb differ
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new file mode 100644
index 0000000000..ebc0ab20b9
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_greater_equal_expanded/model.onnx
@@ -0,0 +1,25 @@
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+F
+
x
+
y5GreaterOrEqual_test_greater_equal_expanded_functionO1"Greater
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x
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y5GreaterOrEqual_test_greater_equal_expanded_functionO2"Equal
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greater_equal"Ortest_greater_equal_expandedZ
+
x
+

+
+
+Z
+
y
+

+
+
+b#
+
greater_equal
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\ No newline at end of file
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new file mode 100644
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@@ -0,0 +1 @@
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new file mode 100644
index 0000000000..0e739da48d
--- /dev/null
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@@ -0,0 +1,13 @@
+backend-test:^
+
+
x
y"Hardmaxtest_hardmax_default_axisZ
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x
+

+
+
+b
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y
+

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\ No newline at end of file
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new file mode 100644
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_default_axis/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardmax_default_axis/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_default_axis/test_data_set_0/output_0.pb
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new file mode 100644
index 0000000000..982284e768
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_example/model.onnx
@@ -0,0 +1,11 @@
+backend-test:Q
+
+
x
y"Hardmaxtest_hardmax_exampleZ
+
x
+

+
+b
+
y
+

+
+B
\ No newline at end of file
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new file mode 100644
index 0000000000..3672c16282
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_negative_axis/model.onnx
@@ -0,0 +1,14 @@
+backend-test:u
+%
+
x
y"Hardmax*
+axisÿÿÿÿÿÿÿÿÿ
 
test_hardmax_negative_axisZ
+
x
+

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+
+b
+
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+

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+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardmax_negative_axis/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_negative_axis/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_negative_axis/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
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xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardmax_negative_axis/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_negative_axis/test_data_set_0/output_0.pb
new file mode 100644
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new file mode 100644
index 0000000000..cd0b6a0e57
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_one_hot/model.onnx
@@ -0,0 +1,11 @@
+backend-test:Q
+
+
x
y"Hardmaxtest_hardmax_one_hotZ
+
x
+

+

+b
+
y
+

+

+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardmax_one_hot/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardmax_one_hot/test_data_set_0/input_0.pb
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new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid/test_data_set_0/output_0.pb
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new file mode 100644
index 0000000000..34a13728cb
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_default/model.onnx
@@ -0,0 +1,13 @@
+backend-test:a
+
+
x
y"HardSigmoidtest_hardsigmoid_defaultZ
+
x
+

+
+
+b
+
y
+

+
+
+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_default/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_default/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_default/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_default/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_default/test_data_set_0/output_0.pb
new file mode 100644
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_example/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_hardsigmoid_example/model.onnx
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new file mode 100644
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new file mode 100644
index 0000000000..e4ed18be12
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardswish/model.onnx
@@ -0,0 +1,13 @@
+backend-test:U
+
+
x
y"	HardSwishtest_hardswishZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardswish/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardswish/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardswish/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardswish/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardswish/test_data_set_0/output_0.pb
new file mode 100644
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/model.onnx
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/test_data_set_0/input_0.pb
new file mode 100644
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/test_data_set_0/output_0.pb
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_hardswish_expanded/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_identity/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_identity/model.onnx
new file mode 100644
index 0000000000..717d47bc13
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_identity/model.onnx
@@ -0,0 +1,15 @@
+backend-test:[
+
+
x
y"Identity
test_identityZ
+
x
+

+

+

+
+b
+
y
+

+

+

+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_identity/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_identity/test_data_set_0/input_0.pb
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new file mode 100644
index 0000000000..9e707e6884
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_identity_opt/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_identity_opt/model.onnx
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_identity_sequence/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_identity_sequence/model.onnx
new file mode 100644
index 0000000000..4deff66122
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_identity_sequence/model.onnx
@@ -0,0 +1,9 @@
+backend-test:H
+
+
x
y"Identitytest_identity_sequenceZ
+
x"
+
+
b
+
y"
+
+
B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_identity_sequence/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_identity_sequence/test_data_set_0/input_0.pb
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index 0000000000..edef126fea
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new file mode 100644
index 0000000000..55d9fdb730
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_leakyrelu_example/model.onnx
@@ -0,0 +1,12 @@
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+
x
y"	LeakyRelu*
+alphaÍÌÌ= 

test_leakyrelu_exampleZ
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@@ -0,0 +1,19 @@
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+
+
x
+
yless"Less	test_lessZ
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x
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+
+Z
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+

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+
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\ No newline at end of file
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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xJð
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new file mode 100644
index 0000000000..1424d72971
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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new file mode 100644
index 0000000000..0542f71601
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_bcast/model.onnx
@@ -0,0 +1,18 @@
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+
+
x
+
yless"Lesstest_less_bcastZ
+
x
+

+
+
+Z
+
y
+
+

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+less
+	
+
+
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\ No newline at end of file
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new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_bcast/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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xJð
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\ No newline at end of file
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new file mode 100644
index 0000000000..dcbe531c44
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_bcast/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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B
yJ^&,¿Z¸¾[*P¿ÔöÜ¿3¯5>
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new file mode 100644
index 0000000000..3ca38c6b54
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/model.onnx
@@ -0,0 +1,21 @@
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+
+
x
+
y
+less_equal"LessOrEqualtest_less_equalZ
+
x
+

+
+
+Z
+
y
+

+
+
+b 
+
+less_equal
+	
+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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B
xJð
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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..1424d72971
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
+
B
yJð
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f=¡#•¿‘œf?Okî>¢£Ä¿¾?¬ò?@â–?7>8¾l‰¿Fø†?5mξyœ?	FU>z?¨u¶>ûá4?G,<h—ä?>õ
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/output_0.pb
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_less_equal/test_data_set_0/output_0.pb differ
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new file mode 100644
index 0000000000..5bf56ea721
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/model.onnx
@@ -0,0 +1,20 @@
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+
+
x
+
y
+less_equal"LessOrEqualtest_less_equal_bcastZ
+
x
+

+
+
+Z
+
y
+
+

+b 
+
+less_equal
+	
+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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B
xJð
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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..dcbe531c44
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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B
yJ^&,¿Z¸¾[*P¿ÔöÜ¿3¯5>
\ No newline at end of file
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/model.onnx
new file mode 100644
index 0000000000..e74a07dfcf
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/model.onnx
@@ -0,0 +1,26 @@
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x
+
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x
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+less_equal"Ortest_less_equal_bcast_expandedZ
+
x
+

+
+
+Z
+
y
+
+

+b 
+
+less_equal
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+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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B
xJð
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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..dcbe531c44
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_bcast_expanded/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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B
yJ^&,¿Z¸¾[*P¿ÔöÜ¿3¯5>
\ No newline at end of file
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new file mode 100644
index 0000000000..a943e7fe98
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/model.onnx
@@ -0,0 +1,27 @@
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+=
+
x
+
y/LessOrEqual_test_less_equal_expanded_functionO1"Less
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x
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y/LessOrEqual_test_less_equal_expanded_functionO2"Equal
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+
x
+

+
+
+Z
+
y
+

+
+
+b 
+
+less_equal
+	
+
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+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..1424d72971
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
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new file mode 100644
index 0000000000..6c300263c6
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_less_equal_expanded/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_log/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_log/model.onnx
new file mode 100644
index 0000000000..6ab18d29f3
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_log/model.onnx
@@ -0,0 +1,13 @@
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+
+
x
y"Logtest_logZ
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x
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+
+
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+
y
+

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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_log/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_log/test_data_set_0/input_0.pb
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_log/test_data_set_0/input_0.pb differ
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index 0000000000..ffa52800d0
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_log_example/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_log_example/model.onnx
new file mode 100644
index 0000000000..9b3de4b959
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_log_example/model.onnx
@@ -0,0 +1,11 @@
+backend-test:A
+
+
x
y"Logtest_log_exampleZ
+
x
+
+

+b
+
y
+
+

+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_log_example/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_log_example/test_data_set_0/input_0.pb
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index 0000000000..e410a4851a
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new file mode 100644
index 0000000000..d6c12876bc
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/model.onnx
new file mode 100644
index 0000000000..1c7ccb961e
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/model.onnx differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..ae044b31c9
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/model.onnx
new file mode 100644
index 0000000000..9da991ad5a
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/model.onnx differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..ae044b31c9
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_0_expanded/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/model.onnx
new file mode 100644
index 0000000000..779045fc0f
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/model.onnx
@@ -0,0 +1,15 @@
+backend-test:k
+
+
x
y"
+LogSoftmax*
+axis
 
test_logsoftmax_axis_1Z
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..c9e960ab6b
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/model.onnx
new file mode 100644
index 0000000000..b85cb90158
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/model.onnx
@@ -0,0 +1,31 @@
+backend-test:ì
+X7LogSoftmax_test_logsoftmax_axis_1_expanded_functionaxes"Constant*
+value*
:

 

+l
+
x>LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_ReduceMax"	ReduceMax*
+axes@
 
*
+keepdims
 

+‚

+
x
+>LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_ReduceMax8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Sub"Sub
+y
+8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Sub8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Exp"Exp
+Ï

+8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Exp
+7LogSoftmax_test_logsoftmax_axis_1_expanded_functionaxes>LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_ReduceSum"	ReduceSum*
+keepdims
 

+
+>LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_ReduceSum8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Log"Log
+|
+8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Sub
+8LogSoftmax_test_logsoftmax_axis_1_expanded_functionX_Log
y"Subtest_logsoftmax_axis_1_expandedZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..c9e960ab6b
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_1_expanded/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/model.onnx
new file mode 100644
index 0000000000..5a4a79df3b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/model.onnx
@@ -0,0 +1,15 @@
+backend-test:k
+
+
x
y"
+LogSoftmax*
+axis 
test_logsoftmax_axis_2Z
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..bf0547d7e4
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/model.onnx
new file mode 100644
index 0000000000..332528f20a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/model.onnx
@@ -0,0 +1,31 @@
+backend-test:ì
+X7LogSoftmax_test_logsoftmax_axis_2_expanded_functionaxes"Constant*
+value*
:
 

+l
+
x>LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_ReduceMax"	ReduceMax*
+axes@ 
*
+keepdims
 

+‚

+
x
+>LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_ReduceMax8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Sub"Sub
+y
+8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Sub8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Exp"Exp
+Ï

+8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Exp
+7LogSoftmax_test_logsoftmax_axis_2_expanded_functionaxes>LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_ReduceSum"	ReduceSum*
+keepdims
 

+
+>LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_ReduceSum8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Log"Log
+|
+8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Sub
+8LogSoftmax_test_logsoftmax_axis_2_expanded_functionX_Log
y"Subtest_logsoftmax_axis_2_expandedZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..bf0547d7e4
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/model.onnx
new file mode 100644
index 0000000000..04284e55ce
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/model.onnx
@@ -0,0 +1,14 @@
+backend-test:d
+
+
x
y"
+LogSoftmaxtest_logsoftmax_default_axisZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..bf0547d7e4
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded/model.onnx
new file mode 100644
index 0000000000..a03c182328
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded/model.onnx
@@ -0,0 +1,32 @@
+backend-test:Õ
+g=LogSoftmax_test_logsoftmax_default_axis_expanded_functionaxes"Constant*
+value*
:
+ÿÿÿÿÿÿÿÿÿ
 

+{
+
xDLogSoftmax_test_logsoftmax_default_axis_expanded_functionX_ReduceMax"	ReduceMax*
+axes@ÿÿÿÿÿÿÿÿÿ
 
*
+keepdims
 

+Ž

+
x
+DLogSoftmax_test_logsoftmax_default_axis_expanded_functionX_ReduceMax>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Sub"Sub
+…

+>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Sub>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Exp"Exp
+á

+>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Exp
+=LogSoftmax_test_logsoftmax_default_axis_expanded_functionaxesDLogSoftmax_test_logsoftmax_default_axis_expanded_functionX_ReduceSum"	ReduceSum*
+keepdims
 

+‹

+DLogSoftmax_test_logsoftmax_default_axis_expanded_functionX_ReduceSum>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Log"Log
+ˆ

+>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Sub
+>LogSoftmax_test_logsoftmax_default_axis_expanded_functionX_Log
y"Sub%test_logsoftmax_default_axis_expandedZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
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y"MaxPool*
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,33 @@
+backend-test:ã
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+

+
b"
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--- /dev/null
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@@ -0,0 +1,35 @@
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iou_threshold
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+

+
b"
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--- /dev/null
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iou_threshold
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+

+
b"
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--- /dev/null
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+backend-test:Ú
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+Z
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Z(
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+
Z
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iou_threshold
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+
Z
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+

+
b"
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+
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--- /dev/null
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@@ -0,0 +1,33 @@
+backend-test:ß
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+
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iou_threshold
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+

+
b"
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--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_nonmaxsuppression_suppress_by_IOU_and_scores/model.onnx
@@ -0,0 +1,33 @@
+backend-test:ê
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iou_threshold
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+
+Z
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+

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+
Z
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iou_threshold
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+
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+

+
b"
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+
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*
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+

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+
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+backend-test:®

+A
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+backend-test:­

+A
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new file mode 100644
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diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/model.onnx
new file mode 100644
index 0000000000..6ae888a439
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/model.onnx differ
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new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..dd62065039
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
B
yJð
ÔÀŠ>óQJ>¨:�>/Ø
?¥º>eE >˜ž¹>\
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\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/model.onnx
new file mode 100644
index 0000000000..1092330ae7
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/model.onnx differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..dd62065039
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_0_expanded/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
B
yJð
ÔÀŠ>óQJ>¨:�>/Ø
?¥º>eE >˜ž¹>\
ƒ>/ýÒ=i¸Y>âw>°ñ?ò·¯>‹ÎÆ=‘XÇ>
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?KH>Õ‡>iQÜ>)é?Йz>Ð3O>«vÇ>Á~?…µ>Ô’>FÛÃ>/?)’Ÿ>ÀȆ>«¯>­??þ¿?ûJÂ>ôa¿=¸ñ:>B=û>‡õ>Nïî>õ¢2>a?rQ>bш>ÛN�>ꋃ> ¦¿>«ô=<�j>GGÌ>x.�>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/model.onnx
new file mode 100644
index 0000000000..6b0e9ec2cc
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/model.onnx
@@ -0,0 +1,14 @@
+backend-test:e
+
+
x
y"Softmax*
+axis
 
test_softmax_axis_1Z
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..d61d8aa11d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
B
yJð
¬F?uî=Çl½>	9?á`?è^v>­£N>e¡%>n‘®=‚Þ
>p*Ö=K«>÷]˜>âѱ=C>/q
>˜
²>É.>T×=òZJ>þò(?|¾z>P(‚>G0>\†?QCa>¬ˆ>*>>#p™>›…„>Kµu=_>>z;…>~)ð>ô´¬=‚
v=ã,ß> }¶>ç8Ã=·
¥=è5®>Ð>ü?“gÑ>�Êž>n9=>&°>æ)P>›ƒ•>¶ÿk>|{•>†!>|o>éâA>õ9D>Èc;>#×=”s
>o�à=�‰>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/model.onnx
new file mode 100644
index 0000000000..c1ae594724
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/model.onnx
@@ -0,0 +1,29 @@
+backend-test:«
+R1Softmax_test_softmax_axis_1_expanded_functionaxes"Constant*
+value*
:

 

+f
+
x8Softmax_test_softmax_axis_1_expanded_functionX_ReduceMax"	ReduceMax*
+axes@
 
*
+keepdims
 

+v
+
x
+8Softmax_test_softmax_axis_1_expanded_functionX_ReduceMax2Softmax_test_softmax_axis_1_expanded_functionX_Sub"Sub
+m
+2Softmax_test_softmax_axis_1_expanded_functionX_Sub2Softmax_test_softmax_axis_1_expanded_functionX_Exp"Exp
+½

+2Softmax_test_softmax_axis_1_expanded_functionX_Exp
+1Softmax_test_softmax_axis_1_expanded_functionaxes8Softmax_test_softmax_axis_1_expanded_functionX_ReduceSum"	ReduceSum*
+keepdims
 

+v
+2Softmax_test_softmax_axis_1_expanded_functionX_Exp
+8Softmax_test_softmax_axis_1_expanded_functionX_ReduceSum
y"Divtest_softmax_axis_1_expandedZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..d61d8aa11d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_1_expanded/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
B
yJð
¬F?uî=Çl½>	9?á`?è^v>­£N>e¡%>n‘®=‚Þ
>p*Ö=K«>÷]˜>âѱ=C>/q
>˜
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v=ã,ß> }¶>ç8Ã=·
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>o�à=�‰>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/model.onnx
new file mode 100644
index 0000000000..26164ecd24
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/model.onnx
@@ -0,0 +1,14 @@
+backend-test:e
+
+
x
y"Softmax*
+axis 
test_softmax_axis_2Z
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..26c97e6991
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/model.onnx
new file mode 100644
index 0000000000..dabe5ed518
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/model.onnx
@@ -0,0 +1,29 @@
+backend-test:«
+R1Softmax_test_softmax_axis_2_expanded_functionaxes"Constant*
+value*
:
 

+f
+
x8Softmax_test_softmax_axis_2_expanded_functionX_ReduceMax"	ReduceMax*
+axes@ 
*
+keepdims
 

+v
+
x
+8Softmax_test_softmax_axis_2_expanded_functionX_ReduceMax2Softmax_test_softmax_axis_2_expanded_functionX_Sub"Sub
+m
+2Softmax_test_softmax_axis_2_expanded_functionX_Sub2Softmax_test_softmax_axis_2_expanded_functionX_Exp"Exp
+½

+2Softmax_test_softmax_axis_2_expanded_functionX_Exp
+1Softmax_test_softmax_axis_2_expanded_functionaxes8Softmax_test_softmax_axis_2_expanded_functionX_ReduceSum"	ReduceSum*
+keepdims
 

+v
+2Softmax_test_softmax_axis_2_expanded_functionX_Exp
+8Softmax_test_softmax_axis_2_expanded_functionX_ReduceSum
y"Divtest_softmax_axis_2_expandedZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..26c97e6991
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/model.onnx
new file mode 100644
index 0000000000..28c0875985
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/model.onnx
@@ -0,0 +1,13 @@
+backend-test:^
+
+
x
y"Softmaxtest_softmax_default_axisZ
+
x
+

+
+
+b
+
y
+

+
+
+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..c3a1c952e0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z?ÿ8s?bý>hdÓ=ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R>iJ >¦Z?/d#@ŒS'?±K]?‡þ=?©C@¨(º?Hm;= ­?>2Ä?ó¼?Šª>…žÁ>íEc?½Šý?‹!²>ò >*z�?•ç™?³OÆ>mÇš>ü6†?&õ?gÚ?³ù?‘x?FKà>™[ ?œ	G?4”Î?—ØY>L=e?Æ> Ä?õ—?kÞæ<QNÛ>.:ˆ=™Ýš>ïb"?6¹¹>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..26c97e6991
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis_expanded/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis_expanded/model.onnx
new file mode 100644
index 0000000000..b742ad6d6c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_softmax_default_axis_expanded/model.onnx
@@ -0,0 +1,30 @@
+backend-test:‡
+a7Softmax_test_softmax_default_axis_expanded_functionaxes"Constant*
+value*
:
+ÿÿÿÿÿÿÿÿÿ
 

+u
+
x>Softmax_test_softmax_default_axis_expanded_functionX_ReduceMax"	ReduceMax*
+axes@ÿÿÿÿÿÿÿÿÿ
 
*
+keepdims
 

+‚

+
x
+>Softmax_test_softmax_default_axis_expanded_functionX_ReduceMax8Softmax_test_softmax_default_axis_expanded_functionX_Sub"Sub
+y
+8Softmax_test_softmax_default_axis_expanded_functionX_Sub8Softmax_test_softmax_default_axis_expanded_functionX_Exp"Exp
+Ï

+8Softmax_test_softmax_default_axis_expanded_functionX_Exp
+7Softmax_test_softmax_default_axis_expanded_functionaxes>Softmax_test_softmax_default_axis_expanded_functionX_ReduceSum"	ReduceSum*
+keepdims
 

+‚

+8Softmax_test_softmax_default_axis_expanded_functionX_Exp
+>Softmax_test_softmax_default_axis_expanded_functionX_ReduceSum
y"Div"test_softmax_default_axis_expandedZ
+
x
+

+
+
+b
+
y
+

+
+
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\ No newline at end of file
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,11 @@
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+
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@@ -0,0 +1,14 @@
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@@ -0,0 +1 @@
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+3
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x
+
kvaluesindices"TopK*
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test_top_k_negative_axisZ
+
x
+

+
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+
k
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+
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b
+values
+

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@@ -0,0 +1 @@
+
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@@ -0,0 +1 @@
+	B
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@@ -0,0 +1 @@
+	B
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@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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b
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xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_1/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_1/test_data_set_0/input_1.pb
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Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_1/test_data_set_0/input_1.pb differ
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new file mode 100644
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+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_1/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+

B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/model.onnx
new file mode 100644
index 0000000000..2a98581097
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/model.onnx
@@ -0,0 +1,19 @@
+backend-test:z
+
+
x
+axes
y"	Unsqueezetest_unsqueeze_axis_2Z
+
x
+

+
+
+Z
+axes
+
+
+
b
+
y
+

+
+
+

+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..e8256ea4de
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/input_1.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..4e331e4bdc
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_2/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+

B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/model.onnx
new file mode 100644
index 0000000000..13437cae7c
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/model.onnx
@@ -0,0 +1,15 @@
+backend-test:m
+
+
x
y"	Unsqueeze*
+axes@ 
test_unsqueeze_axis_3Z
+
x
+

+
+
+b
+
y
+

+
+
+
+
B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..5b5348d4f0
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_axis_3/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+

B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/model.onnx
new file mode 100644
index 0000000000..3ac7c8d948
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/model.onnx
@@ -0,0 +1,21 @@
+backend-test:‰

+
+
x
+axes
y"	Unsqueezetest_unsqueeze_negative_axesZ
+
x
+

+

+
+

+Z
+axes
+
+
+
b
+
y
+

+

+
+

+

+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..55c2bdcf0e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+


B
xJ<xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..2f4bbd39a8
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
+
BaxesJþÿÿÿÿÿÿÿ
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..4ef93c72b4
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_negative_axes/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+



B
yJ<xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/model.onnx
new file mode 100644
index 0000000000..4602f22464
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/model.onnx
@@ -0,0 +1,21 @@
+backend-test:†

+
+
x
+axes
y"	Unsqueezetest_unsqueeze_three_axesZ
+
x
+

+
+
+Z
+axes
+
+
+b#
+
y
+

+
+
+

+
+

+
B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..7fff38d737
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/input_1.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..6c8b8816a6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_three_axes/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+



B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/model.onnx
new file mode 100644
index 0000000000..9c9e4100e9
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/model.onnx
@@ -0,0 +1,20 @@
+backend-test:€

+
+
x
+axes
y"	Unsqueezetest_unsqueeze_two_axesZ
+
x
+

+
+
+Z
+axes
+
+
+b
+
y
+

+
+

+
+
+
B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..bcf4f4e739
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/input_1.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..0f34445df7
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_two_axes/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+


B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/model.onnx
new file mode 100644
index 0000000000..9b5f8bc12a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/model.onnx
@@ -0,0 +1,21 @@
+backend-test:‰

+
+
x
+axes
y"	Unsqueezetest_unsqueeze_unsorted_axesZ
+
x
+

+
+
+Z
+axes
+
+
+b#
+
y
+

+
+
+

+
+

+
B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..bae0ffd632
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
B
xJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/input_1.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/input_1.pb
new file mode 100644
index 0000000000..b22e7e7720
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/input_1.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..6c8b8816a6
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/node/test_unsqueeze_unsorted_axes/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+



B
yJð
xÌá?háÌ>“Žz?Ëj@$ï?â.z¿ÿ8s?bý¾hdÓ½ø9Ò>(€>¢%º?^ÓB?À0ù=Bã>]ת>ü=¿?R¾iJ >¦Z¿/d#ÀŒS'?±K]?‡þ=¿©C@¨(º¿Hm;= ­?¾2Ä?ó¼?Šª>…žÁ>íEc¿½Šý¿‹!²¾ò >*z�?•ç™?³OƾmÇš¾ü6†¿&õ¿gÚ¿³ù?‘x¿FKྙ[ ¿œ	G?4”ο—ØY¾L=e¿Æ> Ä¿õ—¿kÞæ¼QNÛ>.:ˆ=™Ýš>ïb"¿6¹¹¾
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/node/test_upsample_nearest/model.onnx b/third_party/onnx/onnx/backend/test/data/node/test_upsample_nearest/model.onnx
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+SingleReluZ
+
x
+

+

+b
+
y
+

+

+B	
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_single_relu_model/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_single_relu_model/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..e5097e0992
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_single_relu_model/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+

B
xJxÌá?háÌ>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_single_relu_model/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_single_relu_model/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..7038f87052
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_single_relu_model/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+

B
yJxÌá?háÌ>
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/model.onnx b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/model.onnx
new file mode 100644
index 0000000000..6cae372e51
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/model.onnx
@@ -0,0 +1,14 @@
+backend-test: 

+j
+
x
y"StringNormalizer*
+case_change_action"LOWER 
*
+is_case_sensitive
 
*
+	stopwordsJmonday 
StringNormalizerZ
+
x
+
+
+b
+
y
+
+
+B
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..179b510f58
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+2monday2tuesday2	wednesday2thursdayB
x
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..cdc27a745e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_lower/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+2tuesday2	wednesday2thursdayB
y
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/model.onnx b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/model.onnx
new file mode 100644
index 0000000000..ffe252972a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/model.onnx
@@ -0,0 +1,13 @@
+backend-test:€

+J
+
x
y"StringNormalizer*
+is_case_sensitive
 
*
+	stopwordsJmonday 
StringNormalizerZ
+
x
+
+
+b
+
y
+
+
+B
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..179b510f58
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+2monday2tuesday2	wednesday2thursdayB
x
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..cdc27a745e
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_nochangecase/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+2tuesday2	wednesday2thursdayB
y
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/model.onnx b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/model.onnx
new file mode 100644
index 0000000000..2703493519
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/model.onnx
@@ -0,0 +1,14 @@
+backend-test: 

+j
+
x
y"StringNormalizer*
+case_change_action"UPPER 
*
+is_case_sensitive
 
*
+	stopwordsJmonday 
StringNormalizerZ
+
x
+
+
+b
+
y
+
+
+B
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..179b510f58
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+2monday2tuesday2	wednesday2thursdayB
x
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..f99dcd833d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_casesensintive_upper/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+2TUESDAY2	WEDNESDAY2THURSDAYB
y
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/model.onnx b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/model.onnx
new file mode 100644
index 0000000000..81e3def54e
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/model.onnx differ
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..f8443f6f02
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+2monday2mondayB
x
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..fc66148d36
Binary files /dev/null and b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_empty_output/test_data_set_0/output_0.pb differ
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/model.onnx b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/model.onnx
new file mode 100644
index 0000000000..9ba1ce5112
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/model.onnx
@@ -0,0 +1,13 @@
+backend-test:Ž

+P
+
x
y"StringNormalizer*
+case_change_action"UPPER 
*
+	stopwordsJmonday 
StringNormalizerZ
+
x
+
+

+b
+
y
+
+

+B
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..0b3e995018
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+
2Monday2tuesday2	wednesday2Monday2tuesday2	wednesdayB
x
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..7db720dd9a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_monday_insensintive_upper_twodim/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+
2TUESDAY2	WEDNESDAY2TUESDAY2	WEDNESDAYB
y
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/model.onnx b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/model.onnx
new file mode 100644
index 0000000000..9b412e3076
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/model.onnx
@@ -0,0 +1,12 @@
+backend-test:h
+2
+
x
y"StringNormalizer*
+is_case_sensitive
 
StringNormalizerZ
+
x
+
+
+b
+
y
+
+
+B
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/test_data_set_0/input_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/test_data_set_0/input_0.pb
new file mode 100644
index 0000000000..a2b84057f5
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+2monday2tuesdayB
x
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/test_data_set_0/output_0.pb b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/test_data_set_0/output_0.pb
new file mode 100644
index 0000000000..bc7045a6d8
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/data/simple/test_strnorm_model_nostopwords_nochangecase/test_data_set_0/output_0.pb
@@ -0,0 +1 @@
+2monday2tuesdayB
y
\ No newline at end of file
diff --git a/third_party/onnx/onnx/backend/test/loader/__init__.py b/third_party/onnx/onnx/backend/test/loader/__init__.py
new file mode 100644
index 0000000000..8a6349511d
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/loader/__init__.py
@@ -0,0 +1,60 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import os
+
+from ..case.test_case import TestCase
+from typing import List, Text, Optional
+
+DATA_DIR = os.path.join(
+    os.path.dirname(os.path.realpath(os.path.dirname(__file__))),
+    'data')
+
+
+def load_model_tests(
+    data_dir: Text = DATA_DIR,
+    kind: Optional[Text] = None,
+) -> List[TestCase]:
+    '''Load model test cases from on-disk data files.
+    '''
+
+    supported_kinds = os.listdir(data_dir)
+    if kind not in supported_kinds:
+        raise ValueError("kind must be one of {}".format(supported_kinds))
+
+    testcases = []
+
+    kind_dir = os.path.join(data_dir, kind)
+    for test_name in os.listdir(kind_dir):
+        case_dir = os.path.join(kind_dir, test_name)
+        # skip the non-dir files, such as generated __init__.py.
+        rtol = 1e-3
+        atol = 1e-7
+        if not os.path.isdir(case_dir):
+            continue
+        if os.path.exists(os.path.join(case_dir, 'model.onnx')):
+            url = None
+            model_name = test_name[len('test_')]
+            model_dir: Optional[Text] = case_dir
+        else:
+            with open(os.path.join(case_dir, 'data.json')) as f:
+                data = json.load(f)
+                url = data['url']
+                model_name = data['model_name']
+                rtol = data.get('rtol', 1e-3)
+                atol = data.get('atol', 1e-7)
+                model_dir = None
+        testcases.append(
+            TestCase(
+                name=test_name,
+                url=url,
+                model_name=model_name,
+                model_dir=model_dir,
+                model=None,
+                data_sets=None,
+                kind=kind,
+                rtol=rtol,
+                atol=atol,
+            ))
+
+    return testcases
diff --git a/third_party/onnx/onnx/backend/test/report/__init__.py b/third_party/onnx/onnx/backend/test/report/__init__.py
new file mode 100644
index 0000000000..6c3a975745
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/report/__init__.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest  # type: ignore
+import _pytest  # type: ignore
+
+from .coverage import Coverage
+from typing import Dict, Text, Sequence, Any, List
+
+_coverage = Coverage()
+_marks: Dict[Text, Sequence[Any]] = {}
+
+
+def _add_mark(mark: Any, bucket: Text) -> None:
+    proto = mark.args[0]
+    if isinstance(proto, list):
+        assert len(proto) == 1
+        proto = proto[0]
+    if proto is not None:
+        _coverage.add_proto(proto, bucket, mark.args[1] == 'RealModel')
+
+
+def pytest_runtest_call(item: _pytest.nodes.Item) -> None:
+    mark = item.get_closest_marker('onnx_coverage')
+    if mark:
+        assert item.nodeid not in _marks
+        _marks[item.nodeid] = mark
+
+
+def pytest_runtest_logreport(report: Any) -> None:
+    if (report.when == 'call'
+        and report.outcome == 'passed'
+            and report.nodeid in _marks):
+        mark = _marks[report.nodeid]
+        _add_mark(mark, 'passed')
+
+
+@pytest.hookimpl(trylast=True)  # type: ignore
+def pytest_terminal_summary(terminalreporter: _pytest.terminal.TerminalReporter, exitstatus: int) -> None:
+    for mark in _marks.values():
+        _add_mark(mark, 'loaded')
+    _coverage.report_text(terminalreporter)
diff --git a/third_party/onnx/onnx/backend/test/report/base.py b/third_party/onnx/onnx/backend/test/report/base.py
new file mode 100644
index 0000000000..c43ec5ef3b
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/report/base.py
@@ -0,0 +1,5 @@
+# SPDX-License-Identifier: Apache-2.0
+
+
+class ReporterBase(object):
+    pass
diff --git a/third_party/onnx/onnx/backend/test/report/coverage.py b/third_party/onnx/onnx/backend/test/report/coverage.py
new file mode 100644
index 0000000000..6f5ff9e9d3
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/report/coverage.py
@@ -0,0 +1,250 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from collections import defaultdict, OrderedDict
+import os
+import csv
+import datetime
+
+from tabulate import tabulate  # type: ignore
+
+import onnx
+from onnx import defs, helper, GraphProto
+from typing import Optional, Text, Set, Dict, IO, List, Any
+
+_all_schemas = defs.get_all_schemas()
+
+
+class AttrCoverage(object):
+    def __init__(self) -> None:
+        self.name: Optional[Text] = None
+        self.values: Set[Text] = set()
+
+    def add(self, attr: onnx.AttributeProto) -> None:
+        assert self.name in [None, attr.name]
+        self.name = attr.name
+        value = helper.get_attribute_value(attr)
+        # Turn list into tuple so we can put it into set
+        # As value can be string, don't blindly turn `collections.Iterable`
+        # into tuple.
+        if isinstance(value, list):
+            value = tuple(value)
+        self.values.add(str(value))
+
+
+class NodeCoverage(object):
+    def __init__(self) -> None:
+        self.op_type: Optional[Text] = None
+        self.attr_coverages: Dict[Text, AttrCoverage] = defaultdict(AttrCoverage)
+
+    def add(self, node: onnx.NodeProto) -> None:
+        assert self.op_type in [None, node.op_type]
+
+        if self.op_type is None:
+            self.op_type = node.op_type
+            assert self.op_type is not None
+            self.schema = defs.get_schema(self.op_type, node.domain)
+
+        for attr in node.attribute:
+            self.attr_coverages[attr.name].add(attr)
+
+
+class ModelCoverage(object):
+    def __init__(self) -> None:
+        self.name: Optional[Text] = None
+        self.graph: Optional[GraphProto] = None
+        self.node_coverages: Dict[Text, NodeCoverage] = defaultdict(NodeCoverage)
+
+    def add(self, model: onnx.ModelProto) -> None:
+        assert self.name in [None, model.graph.name]
+
+        if self.name is None:
+            self.name = model.graph.name
+            assert self.name is not None
+            self.graph = model.graph
+
+        for node in model.graph.node:
+            self.node_coverages[node.op_type].add(node)
+
+
+class Coverage(object):
+    def __init__(self) -> None:
+        self.buckets: Dict[Text, Dict[Text, NodeCoverage]] = {
+            'loaded': defaultdict(NodeCoverage),
+            'passed': defaultdict(NodeCoverage),
+        }
+        self.models: Dict[Text, Dict[Text, ModelCoverage]] = {
+            'loaded': defaultdict(ModelCoverage),
+            'passed': defaultdict(ModelCoverage),
+        }
+
+    def add_node(self, node: onnx.NodeProto, bucket: Text) -> None:
+        self.buckets[bucket][node.op_type].add(node)
+
+    def add_graph(self, graph: onnx.GraphProto, bucket: Text) -> None:
+        for node in graph.node:
+            self.add_node(node, bucket)
+
+    def add_model(self, model: onnx.ModelProto, bucket: Text, is_model: bool) -> None:
+        self.add_graph(model.graph, bucket)
+        # Only add model if name does not start with test
+        if is_model:
+            self.models[bucket][model.graph.name].add(model)
+
+    def add_proto(self, proto: onnx.ModelProto, bucket: Text, is_model: bool) -> None:
+        assert isinstance(proto, onnx.ModelProto)
+        self.add_model(proto, bucket, is_model)
+
+    def report_text(self, writer: IO[Text]) -> None:
+        writer.write('---------- onnx coverage: ----------\n')
+        writer.write('Operators (passed/loaded/total): {}/{}/{}\n'.format(
+            len(self.buckets['passed']),
+            len(self.buckets['loaded']),
+            len(_all_schemas)))
+        writer.write('------------------------------------\n')
+
+        rows = []
+        passed = []
+        all_ops: List[Text] = []
+        experimental: List[Text] = []
+        for op_cov in self.buckets['passed'].values():
+            covered_attrs = [
+                '{}: {}'.format(attr_cov.name, len(attr_cov.values))
+                for attr_cov in op_cov.attr_coverages.values()]
+            uncovered_attrs = [
+                '{}: 0'.format(attr)
+                for attr in op_cov.schema.attributes
+                if attr not in op_cov.attr_coverages
+            ]
+            attrs = sorted(covered_attrs) + sorted(uncovered_attrs)
+            if attrs:
+                attrs_column = os.linesep.join(attrs)
+            else:
+                attrs_column = 'No attributes'
+            rows.append([op_cov.op_type, attrs_column])
+            passed.append(op_cov.op_type)
+        writer.write(tabulate(
+            rows,
+            headers=['Operator', 'Attributes\n(name: #values)'],
+            tablefmt='plain'))
+        if os.environ.get(str('CSVDIR')) is not None:
+            self.report_csv(all_ops, passed, experimental)
+
+    # This function writes the coverage report to a set of CSV files for
+    # the Backend Scoreboard (onnx.ai/backend-scoreboard). To enable this
+    # feature, set a CSVDIR environment variable locally with the directory
+    # where you would like the files to be written, relative to the
+    # directory from which you're running pytest.  The format of the CSV
+    # files is a column naming each op or model and columns for each
+    # backend with indications of whether the tests passed or failed for
+    # each row.
+    def report_csv(self, all_ops: List[Text], passed: List[Optional[Text]], experimental: List[Text]) -> None:
+        for schema in _all_schemas:
+            if schema.domain == '' or schema.domain == 'ai.onnx':
+                all_ops.append(schema.name)
+                if schema.support_level == defs.OpSchema.SupportType.EXPERIMENTAL:
+                    experimental.append(schema.name)
+        all_ops.sort()
+        nodes_path = os.path.join(str(os.environ.get('CSVDIR')),  # type: ignore
+                'nodes.csv')  # type: ignore
+        models_path = os.path.join(str(os.environ.get('CSVDIR')),  # type: ignore
+                'models.csv')  # type: ignore
+        existing_nodes: OrderedDict[Text, Dict[str, str]] = OrderedDict()
+        existing_models: OrderedDict[Text, Dict[str, str]] = OrderedDict()
+        frameworks: List[str] = []
+        if os.path.isfile(nodes_path):
+            with open(nodes_path, 'r') as nodes_file:
+                reader = csv.DictReader(nodes_file)
+                assert reader.fieldnames
+                frameworks = list(reader.fieldnames)
+                for row in reader:
+                    op = row[str('Op')]
+                    del row[str('Op')]
+                    existing_nodes[str(op)] = row
+        if os.path.isfile(models_path):
+            with open(models_path, 'r') as models_file:
+                reader = csv.DictReader(models_file)
+                for row in reader:
+                    model = row[str('Model')]
+                    del row[str('Model')]
+                    existing_models[str(model)] = row
+        backend = os.environ.get(str('BACKEND'))
+        other_frameworks = frameworks[1:]
+        with open(nodes_path, 'w') as nodes_file:
+            if str('Op') not in frameworks:
+                frameworks.append(str('Op'))
+            if backend not in frameworks:
+                frameworks.append(str(backend))
+            else:
+                other_frameworks.remove(str(backend))
+            node_writer = csv.DictWriter(nodes_file, fieldnames=frameworks)
+            node_writer.writeheader()
+            for node in all_ops:
+                node_name = node
+                if node in experimental:
+                    node_name = node + ' (Experimental)'
+                if node_name not in existing_nodes:
+                    # Also add Skipped for other nodes
+                    existing_nodes[node_name] = OrderedDict()
+                    for other_framework in other_frameworks:
+                        existing_nodes[node_name][other_framework] = str("Skipped!")
+                if node in passed:
+                    existing_nodes[node_name][str(backend)] = str("Passed!")
+                else:
+                    existing_nodes[node_name][str(backend)] = str("Failed!")
+            summaries: Dict[Any, Any] = dict()
+            if "Summary" in existing_nodes:
+                summaries = existing_nodes["Summary"]
+                del existing_nodes["Summary"]
+            summaries[str(backend)] = \
+                "{}/{} node tests passed".format(len(passed), len(all_ops))
+            summaries['Op'] = 'Summary'
+            for node in existing_nodes:
+                existing_nodes[node][str('Op')] = str(node)
+                node_writer.writerow(existing_nodes[node])
+            node_writer.writerow(summaries)
+        with open(models_path, 'w') as models_file:
+            frameworks[0] = str("Model")
+            model_writer = csv.DictWriter(models_file, fieldnames=frameworks)
+            model_writer.writeheader()
+            # Consider both buckets
+            num_models = 0
+            for bucket in self.models:
+                for model in self.models[bucket]:  # type: ignore
+                    # Both analyze and run the model on the backend
+                    num_covered = 0
+                    for node in self.models[bucket][model].node_coverages:
+                        if node in passed:
+                            num_covered += 1
+                    # TODO: Identify if there are models that are being
+                    # skipped/not loaded, but that are in other frameworks
+                    msg = "Passed!"
+                    if bucket == 'loaded':
+                        if model in self.models['passed']:
+                            continue
+                        msg = "Failed!"
+                    num_models += 1
+                    if model not in existing_models:
+                        # Also add Skipped for other models
+                        existing_models[model] = OrderedDict()
+                        for other_framework in other_frameworks:
+                            existing_models[model][other_framework] = str("Skipped!")
+                    existing_models[model][str(backend)] = str("{}/{} nodes covered: {}"
+                        .format(num_covered, len(self.models[bucket][model]
+                            .node_coverages), msg))
+            summaries.clear()
+            if "Summary" in existing_models:
+                summaries = existing_models["Summary"]
+                del existing_models["Summary"]
+            if str(backend) in summaries:
+                del summaries[str(backend)]
+            summaries[str(backend)] = "{}/{} model tests passed" \
+                .format(len(self.models['passed']), num_models)
+            summaries['Model'] = 'Summary'
+            for model in existing_models:  # type: ignore
+                existing_models[model][str('Model')] = model
+                model_writer.writerow(existing_models[model])
+            model_writer.writerow(summaries)
+        with open(os.path.join(str(os.environ.get('CSVDIR')),  # type: ignore
+                'metadata.csv'), 'w') as metadata_file:  # type: ignore
+            metadata_writer = csv.writer(metadata_file)
+            metadata_writer.writerow(["Latest Update", datetime.datetime.now().isoformat().replace('T', ' ')])
diff --git a/third_party/onnx/onnx/backend/test/runner/__init__.py b/third_party/onnx/onnx/backend/test/runner/__init__.py
new file mode 100644
index 0000000000..e9fbfb05ee
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/runner/__init__.py
@@ -0,0 +1,338 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections import defaultdict
+import functools
+import glob
+import os
+import re
+import shutil
+import sys
+import tarfile
+import tempfile
+import time
+import unittest
+
+import numpy as np  # type: ignore
+
+import onnx
+from onnx import helper, numpy_helper, NodeProto, ModelProto, TypeProto
+from onnx.backend.base import Backend
+from urllib.request import urlretrieve
+from ..loader import load_model_tests
+from ..case.test_case import TestCase
+from .item import TestItem
+from typing import Optional, Pattern, Set, Dict, Text, Type, Sequence, Any, Callable, Union, Iterable, List
+
+
+class BackendIsNotSupposedToImplementIt(unittest.SkipTest):
+    pass
+
+
+def retry_excute(times: int) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
+    assert times >= 1
+
+    def wrapper(func: Callable[..., Any]) -> Callable[..., Any]:
+        @functools.wraps(func)
+        def wrapped(*args: Any, **kwargs: Any) -> Any:
+            for i in range(1, times + 1):
+                try:
+                    return func(*args, **kwargs)
+                except Exception:
+                    print('{} times tried'.format(i))
+                    if i == times:
+                        raise
+                    time.sleep(5 * i)
+        return wrapped
+    return wrapper
+
+
+class Runner(object):
+
+    def __init__(self, backend: Type[Backend], parent_module: Optional[str] = None) -> None:
+        self.backend = backend
+        self._parent_module = parent_module
+        self._include_patterns: Set[Pattern[Text]] = set()
+        self._exclude_patterns: Set[Pattern[Text]] = set()
+        self._xfail_patterns: Set[Pattern[Text]] = set()
+
+        # This is the source of the truth of all test functions.
+        # Properties `test_cases`, `test_suite` and `tests` will be
+        # derived from it.
+        # {category: {name: func}}
+        self._test_items: Dict[Text, Dict[Text, TestItem]] = defaultdict(dict)
+
+        for rt in load_model_tests(kind='node'):
+            self._add_model_test(rt, 'Node')
+
+        for rt in load_model_tests(kind='real'):
+            self._add_model_test(rt, 'Real')
+
+        for rt in load_model_tests(kind='simple'):
+            self._add_model_test(rt, 'Simple')
+
+        for ct in load_model_tests(kind='pytorch-converted'):
+            self._add_model_test(ct, 'PyTorchConverted')
+
+        for ot in load_model_tests(kind='pytorch-operator'):
+            self._add_model_test(ot, 'PyTorchOperator')
+
+    def _get_test_case(self, name: Text) -> Type[unittest.TestCase]:
+        test_case = type(str(name), (unittest.TestCase,), {})
+        if self._parent_module:
+            test_case.__module__ = self._parent_module
+        return test_case
+
+    def include(self, pattern: Text) -> Runner:
+        self._include_patterns.add(re.compile(pattern))
+        return self
+
+    def exclude(self, pattern: Text) -> Runner:
+        self._exclude_patterns.add(re.compile(pattern))
+        return self
+
+    def xfail(self, pattern: Text) -> Runner:
+        self._xfail_patterns.add(re.compile(pattern))
+        return self
+
+    def enable_report(self) -> Runner:
+        import pytest  # type: ignore
+
+        for category, items_map in self._test_items.items():
+            for name, item in items_map.items():
+                item.func = pytest.mark.onnx_coverage(item.proto, category)(item.func)
+        return self
+
+    @property
+    def _filtered_test_items(self) -> Dict[Text, Dict[Text, TestItem]]:
+        filtered: Dict[Text, Dict[Text, TestItem]] = {}
+        for category, items_map in self._test_items.items():
+            filtered[category] = {}
+            for name, item in items_map.items():
+                if (self._include_patterns
+                    and (not any(include.search(name)
+                             for include in self._include_patterns))):
+                    item.func = unittest.skip(
+                        'no matched include pattern'
+                    )(item.func)
+                for exclude in self._exclude_patterns:
+                    if exclude.search(name):
+                        item.func = unittest.skip(
+                            'matched exclude pattern "{}"'.format(
+                                exclude.pattern)
+                        )(item.func)
+                for xfail in self._xfail_patterns:
+                    if xfail.search(name):
+                        item.func = unittest.expectedFailure(item.func)
+                filtered[category][name] = item
+        return filtered
+
+    @property
+    def test_cases(self) -> Dict[str, Type[unittest.TestCase]]:
+        '''
+        List of test cases to be applied on the parent scope
+        Example usage:
+            globals().update(BackendTest(backend).test_cases)
+        '''
+        test_cases = {}
+        for category, items_map in self._filtered_test_items.items():
+            test_case_name = str('OnnxBackend{}Test').format(category)
+            test_case = self._get_test_case(test_case_name)
+            for name, item in sorted(items_map.items()):
+                setattr(test_case, name, item.func)
+            test_cases[test_case_name] = test_case
+        return test_cases
+
+    @property
+    def test_suite(self) -> unittest.TestSuite:
+        '''
+        TestSuite that can be run by TestRunner
+        Example usage:
+            unittest.TextTestRunner().run(BackendTest(backend).test_suite)
+        '''
+        suite = unittest.TestSuite()
+        for case in sorted(self.test_cases.values()):
+            suite.addTests(unittest.defaultTestLoader.loadTestsFromTestCase(case))
+        return suite
+
+    # For backward compatibility (we used to expose `.tests`)
+    @property
+    def tests(self) -> Type[unittest.TestCase]:
+        '''
+        One single unittest.TestCase that hosts all the test functions
+        Example usage:
+            onnx_backend_tests = BackendTest(backend).tests
+        '''
+        tests = self._get_test_case('OnnxBackendTest')
+        for items_map in sorted(self._filtered_test_items.values()):
+            for name, item in sorted(items_map.items()):
+                setattr(tests, name, item.func)
+        return tests
+
+    @classmethod
+    def assert_similar_outputs(cls, ref_outputs: Sequence[Any], outputs: Sequence[Any], rtol: float, atol: float) -> None:
+        np.testing.assert_equal(len(outputs), len(ref_outputs))
+        for i in range(len(outputs)):
+            if isinstance(outputs[i], (list, tuple)):
+                for j in range(len(outputs[i])):
+                    cls.assert_similar_outputs(ref_outputs[i][j], outputs[i][j], rtol, atol)
+            else:
+                np.testing.assert_equal(outputs[i].dtype, ref_outputs[i].dtype)
+                if ref_outputs[i].dtype == np.object:
+                    np.testing.assert_array_equal(outputs[i], ref_outputs[i])
+                else:
+                    np.testing.assert_allclose(
+                        outputs[i],
+                        ref_outputs[i],
+                        rtol=rtol,
+                        atol=atol)
+
+    @classmethod
+    @retry_excute(3)
+    def download_model(cls, model_test: TestCase, model_dir: Text, models_dir: Text) -> None:
+        # On Windows, NamedTemporaryFile can not be opened for a
+        # second time
+        download_file = tempfile.NamedTemporaryFile(delete=False)
+        try:
+            download_file.close()
+            assert model_test.url
+            print('Start downloading model {} from {}'.format(
+                model_test.model_name,
+                model_test.url))
+            urlretrieve(model_test.url, download_file.name)
+            print('Done')
+            with tarfile.open(download_file.name) as t:
+                t.extractall(models_dir)
+        except Exception as e:
+            print('Failed to prepare data for model {}: {}'.format(
+                model_test.model_name, e))
+            raise
+        finally:
+            os.remove(download_file.name)
+
+    @classmethod
+    def prepare_model_data(cls, model_test: TestCase) -> Text:
+        onnx_home = os.path.expanduser(os.getenv('ONNX_HOME', os.path.join('~', '.onnx')))
+        models_dir = os.getenv('ONNX_MODELS',
+                               os.path.join(onnx_home, 'models'))
+        model_dir: Text = os.path.join(models_dir, model_test.model_name)
+        if not os.path.exists(os.path.join(model_dir, 'model.onnx')):
+            if os.path.exists(model_dir):
+                bi = 0
+                while True:
+                    dest = '{}.old.{}'.format(model_dir, bi)
+                    if os.path.exists(dest):
+                        bi += 1
+                        continue
+                    shutil.move(model_dir, dest)
+                    break
+            os.makedirs(model_dir)
+
+            cls.download_model(model_test=model_test, model_dir=model_dir, models_dir=models_dir)
+        return model_dir
+
+    def _add_test(self,
+                  category: Text,
+                  test_name: Text,
+                  test_func: Callable[..., Any],
+                  report_item: List[Optional[Union[ModelProto, NodeProto]]],
+                  devices: Iterable[Text] = ('CPU', 'CUDA'),
+                  ) -> None:
+        # We don't prepend the 'test_' prefix to improve greppability
+        if not test_name.startswith('test_'):
+            raise ValueError(
+                'Test name must start with test_: {}'.format(test_name))
+
+        def add_device_test(device: Text) -> None:
+            device_test_name = '{}_{}'.format(test_name, device.lower())
+            if device_test_name in self._test_items[category]:
+                raise ValueError(
+                    'Duplicated test name "{}" in category "{}"'.format(
+                        device_test_name, category))
+
+            @unittest.skipIf(  # type: ignore
+                not self.backend.supports_device(device),
+                "Backend doesn't support device {}".format(device))
+            @functools.wraps(test_func)
+            def device_test_func(*args: Any, **kwargs: Any) -> Any:
+                try:
+                    return test_func(*args, device=device, **kwargs)
+                except BackendIsNotSupposedToImplementIt as e:
+                    # hacky verbose reporting
+                    if '-v' in sys.argv or '--verbose' in sys.argv:
+                        print('Test {} is effectively skipped: {}'.format(
+                            device_test_name, e))
+
+            self._test_items[category][device_test_name] = TestItem(
+                device_test_func, report_item)
+
+        for device in devices:
+            add_device_test(device)
+
+    def _add_model_test(self, model_test: TestCase, kind: Text) -> None:
+        # model is loaded at runtime, note sometimes it could even
+        # never loaded if the test skipped
+        model_marker: List[Optional[Union[ModelProto, NodeProto]]] = [None]
+
+        def run(test_self: Any, device: Text) -> None:
+            if model_test.model_dir is None:
+                model_dir = self.prepare_model_data(model_test)
+            else:
+                model_dir = model_test.model_dir
+            model_pb_path = os.path.join(model_dir, 'model.onnx')
+            model = onnx.load(model_pb_path)
+            model_marker[0] = model
+            if hasattr(self.backend, 'is_compatible') \
+               and callable(self.backend.is_compatible) \
+               and not self.backend.is_compatible(model):
+                raise unittest.SkipTest('Not compatible with backend')
+            prepared_model = self.backend.prepare(model, device)
+            assert prepared_model is not None
+
+            # TODO after converting all npz files to protobuf, we can delete this.
+            for test_data_npz in glob.glob(
+                    os.path.join(model_dir, 'test_data_*.npz')):
+                test_data = np.load(test_data_npz, encoding='bytes')
+                inputs = list(test_data['inputs'])
+                outputs = list(prepared_model.run(inputs))
+                ref_outputs = test_data['outputs']
+                self.assert_similar_outputs(ref_outputs, outputs,
+                                            rtol=model_test.rtol,
+                                            atol=model_test.atol)
+            for test_data_dir in glob.glob(
+                    os.path.join(model_dir, "test_data_set*")):
+                inputs = []
+                inputs_num = len(glob.glob(os.path.join(test_data_dir, 'input_*.pb')))
+                for i in range(inputs_num):
+                    input_file = os.path.join(test_data_dir, 'input_{}.pb'.format(i))
+                    self._load_proto(input_file, inputs, model.graph.input[i].type)
+                ref_outputs = []
+                ref_outputs_num = len(glob.glob(os.path.join(test_data_dir, 'output_*.pb')))
+                for i in range(ref_outputs_num):
+                    output_file = os.path.join(test_data_dir, 'output_{}.pb'.format(i))
+                    self._load_proto(output_file, ref_outputs, model.graph.output[i].type)
+                outputs = list(prepared_model.run(inputs))
+                self.assert_similar_outputs(ref_outputs, outputs,
+                                            rtol=model_test.rtol,
+                                            atol=model_test.atol)
+
+        self._add_test(kind + 'Model', model_test.name, run, model_marker)
+
+    def _load_proto(self, proto_filename: Text, target_list: List[Union[np.ndarray, List[Any]]], model_type_proto: TypeProto) -> None:
+        with open(proto_filename, 'rb') as f:
+            protobuf_content = f.read()
+            if model_type_proto.HasField('sequence_type'):
+                sequence = onnx.SequenceProto()
+                sequence.ParseFromString(protobuf_content)
+                target_list.append(numpy_helper.to_list(sequence))
+            elif model_type_proto.HasField('tensor_type'):
+                tensor = onnx.TensorProto()
+                tensor.ParseFromString(protobuf_content)
+                target_list.append(numpy_helper.to_array(tensor))
+            elif model_type_proto.HasField('optional_type'):
+                optional = onnx.OptionalProto()
+                optional.ParseFromString(protobuf_content)
+                target_list.append(numpy_helper.to_optional(optional))
+            else:
+                print('Loading proto of that specific type (Map/Sparse Tensor) is currently not supported')
diff --git a/third_party/onnx/onnx/backend/test/runner/item.py b/third_party/onnx/onnx/backend/test/runner/item.py
new file mode 100644
index 0000000000..1694805c1a
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/runner/item.py
@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Callable, Any, Union, List, Optional
+from onnx import NodeProto, ModelProto
+
+
+# A container that hosts the test function and the associated
+# test item (ModelProto)
+
+
+class TestItem(object):
+    def __init__(self, func: Callable[..., Any], proto: List[Optional[Union[ModelProto, NodeProto]]]) -> None:
+        self.func = func
+        self.proto = proto
diff --git a/third_party/onnx/onnx/backend/test/stat_coverage.py b/third_party/onnx/onnx/backend/test/stat_coverage.py
new file mode 100644
index 0000000000..981615d6d4
--- /dev/null
+++ b/third_party/onnx/onnx/backend/test/stat_coverage.py
@@ -0,0 +1,249 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import io
+
+from onnx import defs, load, AttributeProto
+from onnx.backend.test.case import collect_snippets
+from onnx.backend.test.runner import Runner
+from onnx.backend.test.loader import load_model_tests
+from typing import Any, IO, Sequence, Text, Dict, List
+
+
+def is_ml(schemas: Sequence[defs.OpSchema]) -> bool:
+    for s in schemas:
+        if s.domain == 'ai.onnx.ml':
+            return True
+    return False
+
+
+def gen_outlines(f: IO[Any], ml: bool) -> None:
+    f.write('# Test Coverage Report')
+    if ml:
+        f.write(' (ONNX-ML Operators)\n')
+    else:
+        f.write(' (ONNX Core Operators)\n')
+    f.write('## Outlines\n')
+    f.write('* [Node Test Coverage](#node-test-coverage)\n')
+    f.write('* [Model Test Coverage](#model-test-coverage)\n')
+    f.write('* [Overall Test Coverage](#overall-test-coverage)\n')
+
+
+common_covered: Sequence[Text] = []
+experimental_covered: Sequence[Text] = []
+
+
+def gen_node_test_coverage(schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool) -> None:
+    global common_covered
+    global experimental_covered
+    generators = set({
+        'Multinomial',
+        'RandomNormal',
+        'RandomNormalLike',
+        'RandomUniform',
+        'RandomUniformLike',
+    })
+    node_tests = collect_snippets()
+    common_covered = sorted([s.name for s in schemas
+            if s.name in node_tests
+            and s.support_level == defs.OpSchema.SupportType.COMMON
+            and (s.domain == 'ai.onnx.ml') == ml])
+    common_no_cover = sorted([s.name for s in schemas
+            if s.name not in node_tests
+            and s.support_level == defs.OpSchema.SupportType.COMMON
+            and (s.domain == 'ai.onnx.ml') == ml])
+    common_generator = sorted([name for name in common_no_cover
+            if name in generators])
+    experimental_covered = sorted([s.name for s in schemas
+            if s.name in node_tests
+            and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
+            and (s.domain == 'ai.onnx.ml') == ml])
+    experimental_no_cover = sorted([s.name for s in schemas
+            if s.name not in node_tests
+            and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
+            and (s.domain == 'ai.onnx.ml') == ml])
+    experimental_generator = sorted([name for name in experimental_no_cover
+            if name in generators])
+    num_common = len(common_covered) + len(common_no_cover) \
+            - len(common_generator)
+    num_experimental = len(experimental_covered) + len(experimental_no_cover) \
+            - len(experimental_generator)
+    f.write('# Node Test Coverage\n')
+    f.write('## Summary\n')
+    if num_common:
+        f.write('Node tests have covered {}/{} ({:.2f}%, {} generators excluded) '
+                'common operators.\n\n'.format(
+                    len(common_covered), num_common,
+                    (len(common_covered) / float(num_common) * 100),
+                    len(common_generator)))
+    else:
+        f.write('Node tests have covered 0/0 (N/A) common operators. \n\n')
+    if num_experimental:
+        f.write('Node tests have covered {}/{} ({:.2f}%, {} generators excluded) '
+                'experimental operators.\n\n'.format(
+                    len(experimental_covered), num_experimental,
+                    (len(experimental_covered) / float(num_experimental) * 100),
+                    len(experimental_generator)))
+    else:
+        f.write('Node tests have covered 0/0 (N/A) experimental operators.\n\n')
+    titles = ['&#x1F49A;Covered Common Operators',
+              '&#x1F494;No Cover Common Operators',
+              '&#x1F49A;Covered Experimental Operators',
+              '&#x1F494;No Cover Experimental Operators',
+              ]
+    all_lists = [common_covered, common_no_cover,
+            experimental_covered, experimental_no_cover]
+    for t in titles:
+        f.write('* [{}](#{})\n'.format(t[9:], t[9:].lower().replace(' ', '-')))
+    f.write('\n')
+    for t, l in zip(titles, all_lists):
+        f.write('## {}\n'.format(t))
+        for s in l:
+            f.write('### {}'.format(s))
+            if s in node_tests:
+                f.write('\nThere are {} test cases, listed as following:\n'.format(
+                    len(node_tests[s])))
+                for summary, code in sorted(node_tests[s]):
+                    f.write('<details>\n')
+                    f.write('<summary>{}</summary>\n\n'.format(summary))
+                    f.write('```python\n{}\n```\n\n'.format(code))
+                    f.write('</details>\n')
+            else:
+                if s in generators:
+                    f.write(' (random generator operator)\n')
+                else:
+                    f.write(' (call for test cases)\n')
+            f.write('\n\n')
+        f.write('<br/>\n\n')
+
+
+def gen_model_test_coverage(schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool) -> None:
+    f.write('# Model Test Coverage\n')
+    # Process schemas
+    schema_dict = dict()
+    for schema in schemas:
+        schema_dict[schema.name] = schema
+    # Load models from each model test using Runner.prepare_model_data
+    # Need to grab associated nodes
+    attrs: Dict[Text, Dict[Text, List[Any]]] = dict()
+    model_paths: List[Any] = []
+    for rt in load_model_tests(kind='real'):
+        model_dir = Runner.prepare_model_data(rt)
+        model_paths.append(os.path.join(model_dir, 'model.onnx'))
+    model_paths.sort()
+    model_written = False
+    for model_pb_path in model_paths:
+        model = load(model_pb_path)
+        if ml:
+            ml_present = False
+            for opset in model.opset_import:
+                if opset.domain == 'ai.onnx.ml':
+                    ml_present = True
+            if not ml_present:
+                continue
+            else:
+                model_written = True
+        f.write('## {}\n'.format(model.graph.name))
+        # Deconstruct model
+        num_covered = 0
+        for node in model.graph.node:
+            if node.op_type in common_covered or node.op_type in experimental_covered:
+                num_covered += 1
+                # Add details of which nodes are/aren't covered
+                # Iterate through and store each node's attributes
+                for attr in node.attribute:
+                    if node.op_type not in attrs:
+                        attrs[node.op_type] = dict()
+                    if attr.name not in attrs[node.op_type]:
+                        attrs[node.op_type][attr.name] = []
+                    if attr.type == AttributeProto.FLOAT:
+                        if attr.f not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.f)
+                    elif attr.type == AttributeProto.INT:
+                        if attr.i not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.i)
+                    elif attr.type == AttributeProto.STRING:
+                        if attr.s not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.s)
+                    elif attr.type == AttributeProto.TENSOR:
+                        if attr.t not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.t)
+                    elif attr.type == AttributeProto.GRAPH:
+                        if attr.g not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.g)
+                    elif attr.type == AttributeProto.FLOATS:
+                        if attr.floats not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.floats)
+                    elif attr.type == AttributeProto.INTS:
+                        if attr.ints not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.ints)
+                    elif attr.type == AttributeProto.STRINGS:
+                        if attr.strings not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.strings)
+                    elif attr.type == AttributeProto.TENSORS:
+                        if attr.tensors not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.tensors)
+                    elif attr.type == AttributeProto.GRAPHS:
+                        if attr.graphs not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.graphs)
+        f.write('\n{} has {} nodes. Of these, {} are covered by node tests ({}%)\n\n\n'.format(
+            model.graph.name, num_covered, len(model.graph.node), 100.0 * float(
+                num_covered) / float(len(model.graph.node))))
+        # Iterate through attrs, print
+        f.write('<details>\n')
+        f.write('<summary>nodes</summary>\n\n')
+        for op in sorted(attrs):
+            f.write('<details>\n')
+            # Get total number of attributes for node schema
+            f.write('<summary>{}: {} out of {} attributes covered</summary>\n\n'
+                    .format(op, len(attrs[op].keys()), len(schema_dict[op]
+                        .attributes)))
+            for attribute in sorted(schema_dict[op].attributes):
+                if attribute in attrs[op]:
+                    f.write('{}: {}\n'.format(attribute, len(attrs[op][attribute])))
+                else:
+                    f.write('{}: 0\n'.format(attribute))
+            f.write('</details>\n')
+        f.write('</details>\n\n\n')
+    if not model_written and ml:
+        f.write('No model tests present for selected domain\n')
+
+
+def gen_overall_test_coverage(schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool) -> None:
+    f.write('# Overall Test Coverage\n')
+    f.write('## To be filled.\n')
+
+
+def gen_spdx(f: IO[Any]) -> None:
+    f.write('<!--- SPDX-License-Identifier: Apache-2.0 -->\n')
+
+
+def main() -> None:
+    base_dir = os.path.dirname(os.path.dirname(os.path.dirname(
+        os.path.dirname(os.path.realpath(__file__)))))
+    docs_dir = os.path.join(base_dir, 'docs')
+    schemas = defs.get_all_schemas()
+
+    has_ml = is_ml(schemas)
+    fname = os.path.join(docs_dir, 'TestCoverage.md')
+    with io.open(fname, 'w+', newline='', encoding="utf-8") as f:  # type: ignore
+        gen_spdx(f)
+        gen_outlines(f, False)
+        gen_node_test_coverage(schemas, f, False)
+        gen_model_test_coverage(schemas, f, False)
+        gen_overall_test_coverage(schemas, f, False)
+
+    if has_ml:
+        fname = os.path.join(docs_dir, 'TestCoverage-ml.md')
+        with io.open(fname, 'w+', newline='', encoding="utf-8") as f:  # type: ignore
+            gen_spdx(f)
+            gen_outlines(f, True)
+            gen_node_test_coverage(schemas, f, True)
+            gen_model_test_coverage(schemas, f, True)
+            gen_overall_test_coverage(schemas, f, True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/onnx/bin/__init__.py b/third_party/onnx/onnx/bin/__init__.py
new file mode 100644
index 0000000000..9881313609
--- /dev/null
+++ b/third_party/onnx/onnx/bin/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/third_party/onnx/onnx/bin/checker.py b/third_party/onnx/onnx/bin/checker.py
new file mode 100644
index 0000000000..aa7e975ae6
--- /dev/null
+++ b/third_party/onnx/onnx/bin/checker.py
@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+
+from onnx import load, checker, NodeProto
+
+
+def check_model() -> None:
+    parser = argparse.ArgumentParser('check-model')
+    parser.add_argument('model_pb', type=argparse.FileType('rb'))
+    args = parser.parse_args()
+
+    model = load(args.model_pb)
+    checker.check_model(model)
+
+
+def check_node() -> None:
+    parser = argparse.ArgumentParser('check-node')
+    parser.add_argument('node_pb', type=argparse.FileType('rb'))
+    args = parser.parse_args()
+
+    node = NodeProto()
+    node.ParseFromString(args.node_pb.read())
+    checker.check_node(node)
diff --git a/third_party/onnx/onnx/checker.cc b/third_party/onnx/onnx/checker.cc
new file mode 100644
index 0000000000..2d949e8949
--- /dev/null
+++ b/third_party/onnx/onnx/checker.cc
@@ -0,0 +1,957 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/checker.h"
+#include "onnx/common/file_utils.h"
+#include "onnx/common/path.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/tensor_proto_util.h"
+#include "onnx/proto_utils.h"
+#include "onnx/string_utils.h"
+
+#include <fstream>
+#include <iterator>
+#include <unordered_set>
+
+#ifdef _WIN32
+#include <direct.h>
+#else // POSIX
+#include <sys/stat.h>
+#endif
+
+namespace ONNX_NAMESPACE {
+namespace checker {
+
+#define enforce_has_field(proto, field)                                              \
+  do {                                                                               \
+    if (!proto.has_##field()) {                                                      \
+      fail_check("Field '", #field, "' of '", #proto, "' is required but missing."); \
+    }                                                                                \
+  } while (0)
+
+#define enforce_has_repeated_field(proto, field)                                              \
+  do {                                                                                        \
+    if (!proto.field##_size()) {                                                              \
+      fail_check("Repeated Field '", #field, "' of '", #proto, "' is required but missing."); \
+    }                                                                                         \
+  } while (0)
+
+#define enforce_non_empty_field(proto, field)                                            \
+  do {                                                                                   \
+    if (proto.field().empty()) {                                                         \
+      fail_check("Field '", #field, "' of '", #proto, "' is required to be non-empty."); \
+    }                                                                                    \
+  } while (0)
+
+void check_value_info(const ValueInfoProto& value_info, const CheckerContext& ctx) {
+  enforce_non_empty_field(value_info, name);
+  // Relax constraint for subgraph input/output.
+  if (!ctx.is_main_graph())
+    return;
+  enforce_has_field(value_info, type);
+  const auto value_case = value_info.type().value_case();
+  switch (value_case) {
+    case TypeProto::kTensorType: {
+      const auto& type = value_info.type().tensor_type();
+      enforce_has_field(type, elem_type);
+      enforce_has_field(type, shape);
+    } break;
+    case TypeProto::kOptionalType: {
+      const auto& type = value_info.type().optional_type();
+      enforce_has_field(type, elem_type);
+    } break;
+    case TypeProto::kSequenceType: {
+      const auto& type = value_info.type().sequence_type();
+      enforce_has_field(type, elem_type);
+    } break;
+    case TypeProto::kMapType: {
+      const auto& type = value_info.type().map_type();
+      enforce_has_field(type, key_type);
+      enforce_has_field(type, value_type);
+    } break;
+#ifdef ONNX_ML
+    case TypeProto::kOpaqueType:
+      break;
+#endif
+    case TypeProto::kSparseTensorType: {
+      const auto& type = value_info.type().sparse_tensor_type();
+      enforce_has_field(type, elem_type);
+      enforce_has_field(type, shape);
+    } break;
+
+    default:
+      fail_check("Unrecognized type value case (value_info name: ", value_info.name(), "): ", value_case);
+  }
+}
+
+void check_tensor(const TensorProto& tensor, const CheckerContext& ctx) {
+  enforce_has_field(tensor, data_type);
+  if (tensor.data_type() == TensorProto::UNDEFINED) {
+    fail_check("setting data_type field (tensor name: ", tensor.name(), ") to UNDEFINED is not allowed");
+  }
+
+  int num_value_fields = 0;
+
+  const char* value_field = nullptr;
+
+#define check_data_field(field)             \
+  bool has_##field = tensor.field().size(); \
+  if (has_##field) {                        \
+    ++num_value_fields;                     \
+    value_field = #field;                   \
+  }
+
+  check_data_field(float_data);
+  check_data_field(int32_data);
+  check_data_field(string_data);
+  check_data_field(int64_data);
+  check_data_field(raw_data);
+  check_data_field(double_data);
+  check_data_field(uint64_data);
+
+#undef check_data_field
+
+  bool stored_externally = tensor.has_data_location() && tensor.data_location() == TensorProto::EXTERNAL;
+  if (stored_externally) {
+    if (num_value_fields != 0) {
+      fail_check(
+          "Data of TensorProto ( tensor name: ",
+          tensor.name(),
+          ") is stored externally and should not have data field.",
+          value_field);
+    }
+
+    bool has_location = false;
+    for (const StringStringEntryProto& entry : tensor.external_data()) {
+      if (entry.has_key() && entry.has_value() && entry.key() == "location") {
+        has_location = true;
+        std::string data_path = path_join(ctx.get_model_dir(), entry.value());
+        // use stat to check whether the file exists
+        struct stat buffer;
+        if (stat((data_path).c_str(), &buffer) != 0) {
+          fail_check(
+              "Data of TensorProto ( tensor name: ",
+              tensor.name(),
+              ") should be stored in ",
+              data_path,
+              ", but it doesn't exist or is not accessible.");
+        }
+      }
+    }
+    if (!has_location) {
+      fail_check("TensorProto ( tensor name: ", tensor.name(), ") is stored externally but doesn't have a location.");
+    }
+    return;
+  }
+  int64_t nelem = 1;
+  for (auto x : tensor.dims()) {
+    nelem *= x;
+  }
+  if (nelem == 0 && num_value_fields != 0) {
+    fail_check("TensorProto (tensor name: ", tensor.name(), ") is 0-element but contains data!");
+  }
+  if (nelem != 0 && num_value_fields != 1) {
+    fail_check("TensorProto (tensor name: ", tensor.name(), ") should contain one and only one value field.");
+  }
+  if (has_raw_data) {
+    if (tensor.data_type() == TensorProto::STRING) {
+      fail_check("STRING data (tensor name: ", tensor.name(), ") should not be stored in raw_data field");
+    }
+    return;
+  } else {
+#define check_field(field)               \
+  if (nelem != 0 && !has_##field) {      \
+    fail_check(                          \
+        "values of data_type '",         \
+        tensor.data_type(),              \
+        "' should be stored in field '", \
+        #field,                          \
+        "' instead of '",                \
+        value_field,                     \
+        "'");                            \
+  }
+
+    switch (tensor.data_type()) {
+      case TensorProto::FLOAT:
+      case TensorProto::COMPLEX64:
+        check_field(float_data);
+        break;
+
+      case TensorProto::DOUBLE:
+      case TensorProto::COMPLEX128:
+        check_field(double_data);
+        break;
+
+      case TensorProto::INT32:
+      case TensorProto::UINT8:
+      case TensorProto::INT8:
+      case TensorProto::UINT16:
+      case TensorProto::INT16:
+      case TensorProto::BOOL:
+      case TensorProto::FLOAT16:
+      case TensorProto::BFLOAT16:
+        check_field(int32_data);
+        break;
+
+      case TensorProto::INT64:
+        check_field(int64_data);
+        break;
+
+      case TensorProto::UINT32:
+      case TensorProto::UINT64:
+        check_field(uint64_data);
+        break;
+
+      case TensorProto::STRING:
+        check_field(string_data);
+        break;
+
+      default:
+        fail_check("Unrecognized data_type (tensor name: ", tensor.name(), "): ", tensor.data_type());
+    }
+  }
+
+#undef check_field
+}
+
+void check_sequence(const SequenceProto& sequence, const CheckerContext& ctx) {
+  enforce_has_field(sequence, elem_type);
+  if (sequence.elem_type() == SequenceProto::TENSOR) {
+    for (const TensorProto& tensor : sequence.tensor_values()) {
+      check_tensor(tensor, ctx);
+    }
+  } else if (sequence.elem_type() == SequenceProto::SPARSE_TENSOR) {
+    for (const SparseTensorProto& sparse_tensor : sequence.sparse_tensor_values()) {
+      check_sparse_tensor(sparse_tensor, ctx);
+    }
+  } else if (sequence.elem_type() == SequenceProto::SEQUENCE) {
+    for (const SequenceProto& seq : sequence.sequence_values()) {
+      check_sequence(seq, ctx);
+    }
+  } else if (sequence.elem_type() == SequenceProto::MAP) {
+    for (const MapProto& map : sequence.map_values()) {
+      check_map(map, ctx);
+    }
+  } else {
+    fail_check(
+        "Sequence ( Structure name: ",
+        sequence.name(),
+        ", elem_type: ",
+        sequence.elem_type(),
+        ") is not have a valid element type.");
+  }
+}
+
+void check_optional(const OptionalProto& optional, const CheckerContext& ctx) {
+  enforce_has_field(optional, elem_type);
+  if (optional.elem_type() == OptionalProto::UNDEFINED) {
+    return;
+  } else if (optional.elem_type() == OptionalProto::TENSOR) {
+    if (optional.has_tensor_value())
+      check_tensor(optional.tensor_value(), ctx);
+  } else if (optional.elem_type() == OptionalProto::SPARSE_TENSOR) {
+    if (optional.has_sparse_tensor_value())
+      check_sparse_tensor(optional.sparse_tensor_value(), ctx);
+  } else if (optional.elem_type() == OptionalProto::SEQUENCE) {
+    if (optional.has_sequence_value())
+      check_sequence(optional.sequence_value(), ctx);
+  } else if (optional.elem_type() == OptionalProto::MAP) {
+    if (optional.has_map_value())
+      check_map(optional.map_value(), ctx);
+  } else {
+    fail_check(
+        "Optional ( Structure name: ",
+        optional.name(),
+        ", elem_type: ",
+        optional.elem_type(),
+        ") is not have a valid element type.");
+  }
+}
+
+void check_map(const MapProto& map, const CheckerContext& ctx) {
+  enforce_has_field(map, key_type);
+  if (map.key_type() == TensorProto::UNDEFINED) {
+    fail_check("setting key_type field (map name: ", map.name(), ") to UNDEFINED is not allowed");
+  }
+  // Check if key is a valid type, specifically INT8, INT16, INT32, INT64,
+  // UINT8, UINT16, UINT32, UINT64, or STRING.
+  if ((map.key_type() == TensorProto::FLOAT) || (map.key_type() == TensorProto::BOOL) ||
+      (map.key_type() == TensorProto::FLOAT16) || (map.key_type() == TensorProto::COMPLEX64) ||
+      (map.key_type() == TensorProto::COMPLEX128)) {
+    fail_check(
+        "setting key_type field (map name: ",
+        map.name(),
+        ") to invalid TensorProto key_type ",
+        map.key_type(),
+        " is not allowed");
+  }
+
+  // MapProto will use either keys or string_keys, so only one should be > 0.
+  if ((map.keys_size() > 0) && (map.string_keys_size() > 0)) {
+    fail_check("Map (name: ", map.name(), ") should not contain more than one keys field.");
+  }
+
+  int num_keys = map.keys_size() + map.string_keys_size();
+  int num_values = 0;
+
+  enforce_has_field(map, values);
+  check_sequence(map.values(), ctx);
+
+  if (map.values().elem_type() == SequenceProto::TENSOR) {
+    num_values = map.values().tensor_values_size();
+  } else if (map.values().elem_type() == SequenceProto::SPARSE_TENSOR) {
+    num_values = map.values().sparse_tensor_values_size();
+  } else if (map.values().elem_type() == SequenceProto::SEQUENCE) {
+    num_values = map.values().sequence_values_size();
+  } else if (map.values().elem_type() == SequenceProto::MAP) {
+    num_values = map.values().map_values_size();
+  }
+
+  if (num_keys != num_values) {
+    fail_check("Length of map keys and map values are not the same (map name: ", map.name(), ")");
+  }
+}
+
+// Check that the index data stored in a SparseTensorProto is valid.
+// indices: a 1-dimensional tensor; indices[i] represents the
+// linearized index value for the i-th nonzero value.
+void check_sparse_tensor_indices_1(
+    const TensorProto& indices,
+    const SparseTensorProto& sparse_tensor_proto,
+    size_t nnz) {
+  int dense_rank = sparse_tensor_proto.dims_size();
+  int64_t dense_size = 1;
+  for (int i = 0; i < dense_rank; ++i)
+    dense_size *= sparse_tensor_proto.dims(i);
+  if (static_cast<size_t>(indices.dims(0)) != nnz) {
+    fail_check("Sparse tensor indices (", indices.name(), ") has ", indices.dims(0), " values, but NNZ is ", nnz);
+  }
+
+  // Check if indices appear in ascending order, and if they have valid
+  // values. The i-th value in index_data is the linear index of the i-th
+  // non-zero value.
+  const std::vector<int64_t> index_data = ParseData<int64_t>(&indices);
+
+  int64_t prev_index = -1;
+  for (size_t i = 0; i < nnz; ++i) {
+    int64_t curr_index = index_data[i]; // linearized index of i-th value
+    if (curr_index < 0 || curr_index >= dense_size) {
+      fail_check(
+          "Sparse tensor (",
+          indices.name(),
+          ") index value at position [",
+          i,
+          "] out of range [0, ",
+          dense_size - 1,
+          "]");
+    }
+    if (curr_index <= prev_index) {
+      fail_check("Sparse tensor (", indices.name(), ") index value at position [", i, "] not in sorted order.");
+    }
+    prev_index = curr_index;
+  }
+}
+
+// Check that the index data stored in a SparseTensorProto is valid.
+// indices: a 2-dimensional tensor; indices[i,j] represents the j-th
+// index value for the i-th nonzero value.
+void check_sparse_tensor_indices_2(
+    const TensorProto& indices,
+    const SparseTensorProto& sparse_tensor_proto,
+    size_t nnz) {
+  int dense_rank = sparse_tensor_proto.dims_size();
+  if (static_cast<size_t>(indices.dims(0)) != nnz) {
+    fail_check("Sparse tensor indices (", indices.name(), ") first dimension size does not equal NNZ.");
+  }
+  if (indices.dims(1) != dense_rank) {
+    fail_check("Sparse tensor indices (", indices.name(), ") second dimension size does not match rank of tensor.");
+  }
+
+  // Check if indices appear in ascending order, and if they have valid
+  // values.
+  const std::vector<int64_t> index_data = ParseData<int64_t>(&indices);
+  int64_t prev_index = -1;
+  for (size_t i = 0; i < nnz; ++i) {
+    int64_t curr_index = 0; // linearized index of i-th value
+    for (int j = 0; j < dense_rank; ++j) {
+      auto index_ij = index_data[i * dense_rank + j];
+      if ((index_ij < 0) || (index_ij >= sparse_tensor_proto.dims(j))) {
+        fail_check("Sparse tensor (", indices.name(), ") index value at position [", i, ",", j, "] out of range.");
+      }
+      curr_index = curr_index * sparse_tensor_proto.dims(j) + index_ij;
+    }
+    if (curr_index <= prev_index) {
+      fail_check(
+          "Sparse tensor (", indices.name(), ") index value at position [", i, "] not in lexicographic sorted order.");
+    }
+    prev_index = curr_index;
+  }
+}
+
+void check_sparse_tensor(const SparseTensorProto& sparse_tensor_proto, const CheckerContext& ctx) {
+  enforce_has_field(sparse_tensor_proto, values);
+
+  const TensorProto& values = sparse_tensor_proto.values();
+  check_tensor(values, ctx);
+
+  // values must be a tensor of shape [NNZ]
+  // Currently we restrict the value associated with a particular index-tuple
+  // to be a single value. In the future, if there is a requirement,
+  // we may extend this to permit the value to be a "sub-tensor", in which
+  // case values will have dimension > 1.
+  if (values.dims_size() != 1) {
+    fail_check("Sparse tensor values (", values.name(), ") must have rank 1.");
+  }
+  size_t nnz = static_cast<size_t>(values.dims(0));
+  int dense_rank = sparse_tensor_proto.dims_size();
+  if (dense_rank == 0) {
+    fail_check("Sparse tensor (", values.name(), ") must have a dense-rank > 0");
+  }
+  for (int i = 0; i < dense_rank; ++i) {
+    if (sparse_tensor_proto.dims(i) <= 0) {
+      fail_check("Sparse tensor (", values.name(), ") dimensions are not positive.");
+    }
+  }
+
+  if (sparse_tensor_proto.has_indices()) {
+    const TensorProto& indices = sparse_tensor_proto.indices();
+    check_tensor(indices, ctx);
+    if (indices.data_type() != TensorProto::INT64) {
+      fail_check("Sparse tensor indices (", indices.name(), ") must have INT64 type.");
+    }
+    switch (indices.dims().size()) {
+      case 1:
+        // Indices in linearized format
+        check_sparse_tensor_indices_1(indices, sparse_tensor_proto, nnz);
+        return;
+      case 2:
+        // Check COO-style index. E.g., an index for a 3D tensor is a 3-tuple.
+        check_sparse_tensor_indices_2(indices, sparse_tensor_proto, nnz);
+        return;
+      default:
+        fail_check("Sparse tensor indices (", indices.name(), ") must have rank 1 or 2.");
+    }
+  } else if (nnz != 0) {
+    fail_check("Sparse tensor (", values.name(), ") has no index values.");
+  }
+}
+
+// NB: This is a generic "attribute well-formedness" check, it doesn't
+// actually test if an attribute is valid per a schema
+void check_attribute(const AttributeProto& attr, const CheckerContext& ctx, const LexicalScopeContext& lex_ctx) {
+  enforce_non_empty_field(attr, name);
+
+  if (ctx.get_ir_version() >= 0x00000002) {
+    enforce_has_field(attr, type);
+  }
+
+  int used_fields = 0;
+
+#define check_type(expected_type)                                                     \
+  if (attr.has_type() && attr.type() != expected_type) {                              \
+    fail_check("type field and data field mismatch in attribute ", attr.name(), "."); \
+  }
+
+#define check_singular_field(field, type) \
+  if (attr.has_##field()) {               \
+    ++used_fields;                        \
+    check_type(type);                     \
+  }
+
+#define check_repeated_field(field, type) \
+  if (attr.field##_size() > 0) {          \
+    ++used_fields;                        \
+    check_type(type);                     \
+  }
+
+  check_singular_field(f, AttributeProto::FLOAT);
+  check_singular_field(i, AttributeProto::INT);
+  check_singular_field(s, AttributeProto::STRING);
+  check_singular_field(t, AttributeProto::TENSOR);
+  check_singular_field(g, AttributeProto::GRAPH);
+  check_singular_field(tp, AttributeProto::TYPE_PROTO);
+  check_singular_field(sparse_tensor, AttributeProto::SPARSE_TENSOR);
+  check_repeated_field(floats, AttributeProto::FLOATS);
+  check_repeated_field(ints, AttributeProto::INTS);
+  check_repeated_field(strings, AttributeProto::STRINGS);
+  check_repeated_field(tensors, AttributeProto::TENSORS);
+  check_repeated_field(graphs, AttributeProto::GRAPHS);
+  check_repeated_field(sparse_tensors, AttributeProto::SPARSE_TENSORS);
+  check_repeated_field(type_protos, AttributeProto::TYPE_PROTOS);
+
+#undef check_type
+#undef check_singular_field
+#undef check_repeated_field
+
+  // Normally, used_fields is expected to be 1.
+  // In proto3, when the value to be set is type default value (say 0 for
+  // int), used_fields may be 0.
+  if (used_fields > 1) {
+    fail_check("Attribute (name: ", attr.name(), ") should not contain more than one value field.");
+  }
+
+  if (!ctx.is_main_graph()) {
+    // It's an attribute of a node in function body.
+    if (attr.has_ref_attr_name() && used_fields != 0) {
+      // The attribute proto is supposed to refer to data outside and does not
+      // have its own value field set.
+      fail_check("Attribute (name: ", attr.name(), ") should refer to attribute in parent node.");
+    }
+  }
+
+  if (attr.has_t()) {
+    check_tensor(attr.t(), ctx);
+  }
+
+  if (attr.has_sparse_tensor()) {
+    check_sparse_tensor(attr.sparse_tensor(), ctx);
+  }
+
+  if (attr.has_g()) {
+    CheckerContext subgraph_ctx(ctx);
+    subgraph_ctx.set_is_main_graph(false);
+    check_graph(attr.g(), subgraph_ctx, lex_ctx);
+  }
+
+  for (const auto& tensor : attr.tensors()) {
+    check_tensor(tensor, ctx);
+  }
+  for (const auto& sparse_tensor : attr.sparse_tensors()) {
+    check_sparse_tensor(sparse_tensor, ctx);
+  }
+  if (attr.graphs().size() > 0) {
+    CheckerContext subgraph_ctx(ctx);
+    subgraph_ctx.set_is_main_graph(false);
+    for (const auto& graph : attr.graphs()) {
+      check_graph(graph, subgraph_ctx, lex_ctx);
+    }
+  }
+}
+
+void check_node(const NodeProto& node, const CheckerContext& ctx, const LexicalScopeContext& lex_ctx) {
+  enforce_non_empty_field(node, op_type);
+
+  if (node.input().empty() && node.output().empty()) {
+    fail_check("NodeProto (name: ", node.name(), ", type: ", node.op_type(), ") has zero input and zero output.");
+  }
+
+  // If encounter experimental op, stop checking
+  if (check_is_experimental_op(node.op_type())) {
+    std::cerr << "Warning: Checker does not support models with experimental ops: " << node.op_type() << std::endl;
+    return;
+  }
+
+  // Resolve domain for node
+  const auto& opset_imports = ctx.get_opset_imports();
+  auto dit = opset_imports.find(node.domain());
+  if (dit == opset_imports.end()) {
+    fail_check("No opset import for domain '" + node.domain() + "'");
+  }
+  auto domain_version = dit->second;
+
+  for (const auto& attr : node.attribute()) {
+    check_attribute(attr, ctx, lex_ctx);
+  }
+
+  const auto* schema = ctx.get_schema_registry()->GetSchema(node.op_type(), domain_version, node.domain());
+  if (!schema) {
+    if (node.domain() == ONNX_DOMAIN || node.domain() == AI_ONNX_ML_DOMAIN || node.domain() == "ai.onnx" ||
+        node.domain() == AI_ONNX_TRAINING_DOMAIN) {
+      // fail the checker if op in built-in domains has no schema
+      fail_check(
+          "No Op registered for " + node.op_type() + " with domain_version of " +
+          ONNX_NAMESPACE::to_string(domain_version));
+    } else {
+      // TODO: expose the registration of the op schemas appropriately in
+      // python, so we can load and register operators in other domains
+      //
+      // before we complete the above todo, let's skip the schema check for
+      // now
+    }
+  } else if (schema->Deprecated()) {
+    fail_check(
+        "Op registered for " + node.op_type() + " is deprecated in domain_version of " +
+        ONNX_NAMESPACE::to_string(domain_version));
+  } else {
+    schema->Verify(node);
+  }
+}
+
+void check_graph(const GraphProto& graph, const CheckerContext& ctx, const LexicalScopeContext& parent_lex) {
+  enforce_non_empty_field(graph, name);
+
+  for (const auto& value_info : graph.input()) {
+    check_value_info(value_info, ctx);
+  }
+  for (const auto& value_info : graph.output()) {
+    check_value_info(value_info, ctx);
+  }
+
+  // Inherit values available in outer scope
+  // Note that we do not allow shadowing, so the presence of an already-defined
+  // name is always an error.
+  LexicalScopeContext lex_ctx{parent_lex};
+
+  for (const auto& value_info : graph.input()) {
+    // TODO: If shadowing isn't allowed, this should maybe use
+    // this_or_ancestor_graph_has
+    if (lex_ctx.this_graph_has(value_info.name())) {
+      fail_check(
+          "Graph must be in single static assignment (SSA) form, however '",
+          value_info.name(),
+          "' has been used as graph input names multiple times.");
+    }
+    lex_ctx.add(value_info.name());
+  }
+
+  std::unordered_set<std::reference_wrapper<const std::string>, std::hash<std::string>, std::equal_to<std::string>>
+      initializer_name_checker;
+
+  for (const auto& init : graph.initializer()) {
+    enforce_has_field(init, name);
+    const auto& name = init.name();
+    if (name.empty()) {
+      fail_check("Tensor initializers must have a non-empty name");
+    }
+
+    if (!initializer_name_checker.insert(std::cref(name)).second) {
+      fail_check(name + " initializer name is not unique");
+    }
+
+    check_tensor(init, ctx);
+
+    if (ctx.get_ir_version() <= 0x00000003) {
+      // Initializers are a subset of graph inputs for IR_VERSION <= 3
+      if (!lex_ctx.this_graph_has(name)) {
+        fail_check(name + " in initializer but not in graph input");
+      }
+    } else {
+      // An initializer is allowed to have the same name as an input,
+      // but is not required to (for IR_VERSION >= 4)
+      lex_ctx.add(name);
+    }
+  }
+
+  for (const auto& sparse_init : graph.sparse_initializer()) {
+    const auto& values = sparse_init.values();
+    enforce_has_field(values, name);
+    const auto& name = values.name();
+    if (name.empty()) {
+      fail_check("Sparse tensor initializers must have a non-empty name");
+    }
+    if (!initializer_name_checker.insert(std::cref(name)).second) {
+      fail_check(name + " sparse initializer name is not unique across initializers and sparse_initializers");
+    }
+    check_sparse_tensor(sparse_init, ctx);
+    lex_ctx.add(name);
+  }
+
+  for (const auto& node : graph.node()) {
+    // nodes must be in topologically sorted order
+    for (const auto& input : node.input()) {
+      // explicit optional input
+      if (input.empty()) {
+        continue;
+      }
+      if (!lex_ctx.this_or_ancestor_graph_has(input)) {
+        fail_check(
+            "Nodes in a graph must be topologically sorted, however input '",
+            input,
+            "' of node: \n",
+            "name: ",
+            node.name(),
+            " OpType: ",
+            node.op_type(),
+            "\n is not output of any previous nodes.");
+      }
+    }
+
+    // This needs to happen before SSA check since we don't want to recurse and
+    // find that outputs from control flow ops are colliding with names in the
+    // inner block
+
+    ONNX_TRY {
+      check_node(node, ctx, lex_ctx);
+    }
+    ONNX_CATCH(ValidationError & ex) {
+      ONNX_HANDLE_EXCEPTION([&]() {
+        ex.AppendContext("Bad node spec for node. Name: " + node.name() + " OpType: " + node.op_type());
+        ONNX_THROW_EX(ex);
+      });
+    }
+    // check for SSA form
+    for (const auto& output : node.output()) {
+      // optional output
+      if (output.empty()) {
+        continue;
+      }
+
+      if (lex_ctx.this_or_ancestor_graph_has(output)) {
+        fail_check(
+            "Graph must be in single static assignment (SSA) form, however '",
+            output,
+            "' has been used as output names multiple times.");
+      }
+      lex_ctx.add(output);
+    }
+  }
+}
+
+// Utilify function to get the imported version of domain from opset imports
+// Returns -1 if requested domain is not found in the opset_imports
+int get_version_for_domain(const std::string& domain, const std::unordered_map<std::string, int>& opset_imports) {
+  auto it = opset_imports.find(domain);
+  if (it == opset_imports.end()) {
+    return -1;
+  }
+
+  return it->second;
+}
+
+void check_opset_compatibility(
+    const NodeProto& node,
+    const CheckerContext& ctx,
+    const std::unordered_map<std::string, int>& func_opset_imports,
+    const std::unordered_map<std::string, int>& model_opset_imports) {
+  auto func_opset_version = get_version_for_domain(node.domain(), func_opset_imports);
+  auto model_opset_version = get_version_for_domain(node.domain(), model_opset_imports);
+
+  if (func_opset_version == -1) {
+    fail_check("No Opset registered for domain " + node.domain());
+  }
+
+  if (model_opset_version == -1) {
+    // model does not include opset import for a node present in function body.
+    // This is ok as along as the opset import is present in function level opset imports.
+    return;
+  }
+
+  if (func_opset_version == model_opset_version) {
+    // both versions are same, no need to verify schema.
+    return;
+  }
+
+  const auto* schema_for_model_import =
+      ctx.get_schema_registry()->GetSchema(node.op_type(), model_opset_version, node.domain());
+
+  const auto* schema_for_function_import =
+      ctx.get_schema_registry()->GetSchema(node.op_type(), func_opset_version, node.domain());
+
+  if (!schema_for_model_import && !schema_for_function_import) {
+    // the op belongs to a custom domain so we cannot verify schema
+    return;
+  }
+
+  // if schema is present for 1 but not other or the schema since versions do not match then raise an error
+  if (!schema_for_model_import || !schema_for_function_import ||
+      schema_for_function_import->since_version() != schema_for_model_import->since_version()) {
+    fail_check(
+        "Opset import for domain " + node.domain() + " in function op " + node.op_type() +
+        "is not compatible with the version imported by model. FunctionOp imports version " +
+        ONNX_NAMESPACE::to_string(func_opset_version) + "whereas model imports version " +
+        ONNX_NAMESPACE::to_string(model_opset_version));
+  }
+}
+
+void check_model_local_functions(
+    const ModelProto& model,
+    const CheckerContext& ctx,
+    const LexicalScopeContext& parent_lex) {
+    // make a copy of model opset imports to maintain a main copy of opset imports across the model and 
+    // all model local functions to verify opset compatibility
+    std::unordered_map<std::string, int> model_opset_imports(ctx.get_opset_imports());
+
+    // merge the opset imports from every function in model_opset_imports
+    // only add the opset import if an entry for it does not exist in model_opset_imports
+    // if there is an entry then the compatibility will be checked later on in check_opset_compatibility
+    // called by check_function.
+    for (const auto& function_proto : model.functions()) {
+      for (const auto& opset_import : function_proto.opset_import()) {
+        if (get_version_for_domain(opset_import.domain(), model_opset_imports) == -1) {
+          model_opset_imports[opset_import.domain()] = opset_import.version();
+        }
+      }
+    }
+
+    CheckerContext ctx_copy = ctx;
+    ctx_copy.set_opset_imports(model_opset_imports);
+
+    for (const auto& function_proto : model.functions()) {
+        check_function(function_proto, ctx_copy, parent_lex);
+    }
+}
+
+void check_function(const FunctionProto& function, const CheckerContext& ctx, const LexicalScopeContext& parent_lex) {
+  enforce_non_empty_field(function, name);
+
+  if (ctx.get_ir_version() >= 0x00000008) {
+    enforce_has_field(function, domain);
+  }
+
+  const auto& model_opset_imports = ctx.get_opset_imports();
+  CheckerContext ctx_copy = ctx;
+
+  std::unordered_map<std::string, int> func_opset_imports;
+  for (auto& relied_opset : function.opset_import()) {
+    func_opset_imports[relied_opset.domain()] = static_cast<int>(relied_opset.version());
+  }
+
+  ctx_copy.set_opset_imports(func_opset_imports);
+
+  LexicalScopeContext lex_ctx{parent_lex};
+
+  for (const auto& input : function.input()) {
+    // TODO: If shadowing isn't allowed, this should maybe use
+    // this_or_ancestor_graph_has
+    if (lex_ctx.this_graph_has(input)) {
+      fail_check(
+          "Graph must be in single static assignment (SSA) form, however '", input, "' has been used multiple times.");
+    }
+    lex_ctx.add(input);
+  }
+
+  std::unordered_set<std::string> outputs;
+  for (const auto& output : function.output()) {
+    auto result = outputs.insert(output);
+    if (!result.second) {
+      fail_check("function (", function.name(), ") should not have duplicate outputs specified.");
+    }
+  }
+
+  std::unordered_set<std::string> attrs;
+  for (const auto& attr : function.attribute()) {
+    auto result = attrs.insert(attr);
+    if (!result.second) {
+      fail_check("function (", function.name(), ") should not have duplicate attributes specified.");
+    }
+  }
+
+  for (const auto& node : function.node()) {
+    // nodes must be in topologically sorted order
+    for (const auto& input : node.input()) {
+      // explicit optional input
+      if (input.empty()) {
+        continue;
+      }
+      if (!lex_ctx.this_graph_has(input)) {
+        fail_check(
+            "Nodes in a function must be topologically sorted, however input '",
+            input,
+            "' of node: \n",
+            "Name: ",
+            node.name(),
+            " OpType: ",
+            node.op_type(),
+            "\n is neither output of any previous nodes nor input of the function.");
+      }
+    }
+
+    // check whether the opset version imported for a domain by function and model are
+    // compatible
+    check_opset_compatibility(node, ctx_copy, func_opset_imports, model_opset_imports);
+
+    check_node(node, ctx_copy, lex_ctx);
+
+    // check for SSA form
+    for (const auto& output : node.output()) {
+      // optional output
+      if (output.empty()) {
+        continue;
+      }
+      if (lex_ctx.this_or_ancestor_graph_has(output)) {
+        fail_check(
+            "Function must be in single static assignment (SSA) form, however '",
+            output,
+            "' has been used as output names multiple times.");
+      }
+      lex_ctx.add(output);
+    }
+  }
+}
+
+void check_model(const ModelProto& model, CheckerContext& ctx) {
+  if (!model.ir_version()) {
+    fail_check("The model does not have an ir_version set properly.");
+  }
+  if (model.ir_version() > IR_VERSION) {
+    fail_check("Your model ir_version is higher than the checker's.");
+  }
+  if (model.metadata_props_size() > 1) {
+    std::unordered_set<std::string> keys;
+    for (const StringStringEntryProto& entry : model.metadata_props()) {
+      auto i = keys.insert(entry.key());
+      if (!i.second) {
+        fail_check("Your model has duplicate keys in metadata_props.");
+      }
+    }
+  }
+  std::unordered_map<std::string, int> versions;
+  ctx.set_ir_version(static_cast<int>(model.ir_version()));
+  std::unordered_map<std::string, int> opset_imports;
+  for (const auto& opset_import : model.opset_import()) {
+    opset_imports[opset_import.domain()] = static_cast<int>(opset_import.version());
+  }
+  if (model.ir_version() >= 3) {
+    if (opset_imports.empty()) {
+      fail_check("model with IR version >= 3 must specify opset_import for ONNX");
+    }
+  } else {
+    if (opset_imports.empty())
+      opset_imports[ONNX_DOMAIN] = 1;
+    else {
+      fail_check("model with IR version < 3 cannot have opset_import specified");
+    }
+  }
+  ctx.set_opset_imports(opset_imports);
+  LexicalScopeContext lex_ctx;
+  check_graph(model.graph(), ctx, lex_ctx);
+
+  if (ctx.get_ir_version() >= 0x00000008) {
+    check_model_local_functions(model, ctx, lex_ctx);
+  }
+}
+
+void check_model(const std::string& model_path) {
+  ModelProto model;
+  LoadProtoFromPath(model_path, model);
+
+  CheckerContext ctx;
+  std::string model_dir;
+  size_t pos = model_path.find_last_of("\\/");
+  if (pos != std::string::npos) {
+    model_dir = model_path.substr(0, pos + 1);
+  }
+  ctx.set_model_dir(model_dir);
+  check_model(model, ctx);
+}
+
+void check_model(const ModelProto& model) {
+  CheckerContext ctx;
+  check_model(model, ctx);
+}
+
+std::set<std::string> experimental_ops = {
+    "ATen",
+    "Affine",
+    "ConstantFill",
+    "Crop",
+    "DynamicSlice",
+    "GRUUnit",
+    "GivenTensorFill",
+    "ImageScaler",
+    "ParametricSoftplus",
+    "Scale",
+    "ScaledTanh"};
+
+bool check_is_experimental_op(std::string node_op_type) {
+  return (experimental_ops.count(node_op_type)) ? true : false;
+}
+
+#undef fail_check
+#undef enforce_has_field
+#undef enforce_has_repeated_field
+#undef enforce_non_empty_field
+
+} // namespace checker
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/checker.h b/third_party/onnx/onnx/checker.h
new file mode 100644
index 0000000000..8200bf5d21
--- /dev/null
+++ b/third_party/onnx/onnx/checker.h
@@ -0,0 +1,151 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <stdexcept>
+#include <unordered_map>
+#include <unordered_set>
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+#include "onnx/onnx-data_pb.h"
+#include "onnx/onnx-operators_pb.h"
+#include "onnx/onnx_pb.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace checker {
+class ValidationError final : public std::runtime_error {
+ public:
+  using std::runtime_error::runtime_error;
+  const char* what() const noexcept override {
+    if (!expanded_message_.empty()) {
+      return expanded_message_.c_str();
+    }
+    return std::runtime_error::what();
+  }
+  void AppendContext(const std::string& context) {
+    expanded_message_ = ONNX_NAMESPACE::MakeString(std::runtime_error::what(), "\n\n==> Context: ", context);
+  }
+
+ private:
+  std::string expanded_message_;
+};
+
+#define fail_check(...) \
+  ONNX_THROW_EX(ONNX_NAMESPACE::checker::ValidationError(ONNX_NAMESPACE::MakeString(__VA_ARGS__)));
+
+class CheckerContext final {
+ public:
+  int get_ir_version() const {
+    return ir_version_;
+  }
+  void set_ir_version(int v) {
+    ir_version_ = v;
+  }
+  const std::unordered_map<std::string, int>& get_opset_imports() const {
+    return opset_imports_;
+  }
+  void set_opset_imports(std::unordered_map<std::string, int> imps) {
+    opset_imports_ = std::move(imps);
+  }
+  bool is_main_graph() const {
+    return is_main_graph_;
+  }
+  void set_is_main_graph(bool is_main_graph) {
+    is_main_graph_ = is_main_graph;
+  }
+
+  void set_schema_registry(const ISchemaRegistry* schema_registry) {
+    schema_registry_ = schema_registry;
+  }
+
+  const ISchemaRegistry* get_schema_registry() const {
+    return schema_registry_;
+  }
+
+  void set_model_dir(const std::string& model_dir) {
+    model_dir_ = model_dir;
+  }
+
+  std::string get_model_dir() const {
+    return model_dir_;
+  }
+
+  explicit CheckerContext() : ir_version_(-1) {}
+
+ private:
+  int ir_version_;
+  std::unordered_map<std::string, int> opset_imports_;
+  bool is_main_graph_ = true;
+  const ISchemaRegistry* schema_registry_ = OpSchemaRegistry::Instance();
+  std::string model_dir_;
+};
+
+class LexicalScopeContext final {
+ public:
+  LexicalScopeContext() = default;
+
+  // Construct an instance with the lexical scope from the parent graph to allow
+  // lookup of names from that scope via this_or_ancestor_graph_has.
+  // The caller must ensure parent_context remains valid for the entire lifetime
+  // of the new instance. Alternatively, if that cannot be guaranteed, create an
+  // instance with the default constructor and populate output_names with the
+  // values from the parent scope so the values are copied instead.
+  LexicalScopeContext(const LexicalScopeContext& parent_context) : parent_context_{&parent_context} {}
+
+  void add(const std::string& name) {
+    output_names.insert(name);
+  }
+
+  bool this_graph_has(const std::string& name) const {
+    return output_names.find(name) != output_names.cend();
+  }
+
+  bool this_or_ancestor_graph_has(const std::string& name) const {
+    return this_graph_has(name) || (parent_context_ && parent_context_->this_or_ancestor_graph_has(name));
+  }
+
+  // public for backwards compatibility. please prefer the public interface of
+  // this class over directly changing output_names
+  std::unordered_set<std::string> output_names;
+
+ private:
+  const LexicalScopeContext* parent_context_{nullptr};
+};
+
+using IR_VERSION_TYPE = decltype(Version::IR_VERSION);
+void check_value_info(const ValueInfoProto& value_info, const CheckerContext&);
+void check_tensor(const TensorProto& tensor, const CheckerContext&);
+void check_sparse_tensor(const SparseTensorProto& sparse_tensor, const CheckerContext&);
+void check_sequence(const SequenceProto& sequence, const CheckerContext&);
+void check_map(const MapProto& map, const CheckerContext&);
+void check_optional(const OptionalProto& opt, const CheckerContext&);
+void check_attribute(const AttributeProto& attr, const CheckerContext&, const LexicalScopeContext&);
+void check_node(const NodeProto& node, const CheckerContext&, const LexicalScopeContext&);
+void check_graph(const GraphProto& graph, const CheckerContext&, const LexicalScopeContext&);
+void check_function(const FunctionProto& function, const CheckerContext&, const LexicalScopeContext&);
+
+// Check schema compatibility for 2 opset versions for a given node.
+// Checks whether the schema for 2 versions is same, this is true when the opschema
+// does not change between versions.
+void check_opset_compatibility(
+    const NodeProto& node,
+    const CheckerContext& ctx,
+    const std::unordered_map<std::string, int>& func_opset_imports,
+    const std::unordered_map<std::string, int>& model_opset_imports);
+
+// Checks all model local functions present in ModelProto
+void check_model_local_functions(
+    const ModelProto& model,
+    const CheckerContext& ctx,
+    const LexicalScopeContext& parent_lex);
+
+void check_model(const ModelProto& model);
+void check_model(const std::string& model_path);
+
+bool check_is_experimental_op(std::string node_op_type);
+
+} // namespace checker
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/checker.py b/third_party/onnx/onnx/checker.py
new file mode 100644
index 0000000000..bf8e726513
--- /dev/null
+++ b/third_party/onnx/onnx/checker.py
@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+"""onnx checker
+
+This implements graphalities that allows us to check whether a serialized
+proto is legal.
+"""
+
+import functools
+
+from onnx import (ValueInfoProto,
+                  AttributeProto,
+                  TensorProto,
+                  SparseTensorProto,
+                  NodeProto,
+                  ModelProto,
+                  GraphProto,
+                  IR_VERSION)
+import onnx.onnx_cpp2py_export.checker as C
+import onnx.defs
+from google.protobuf.message import Message
+from typing import TypeVar, Callable, Any, Type, cast, Union, Text
+import onnx.shape_inference
+import sys
+
+
+# Limitation of single protobuf file is 2GB
+MAXIMUM_PROTOBUF = 2000000000
+
+# TODO: This thing where we reserialize the protobuf back into the
+# string, only to deserialize it at the call site, is really goofy.
+# Stop doing that.
+
+
+# NB: Please don't edit this context!
+DEFAULT_CONTEXT = C.CheckerContext()
+DEFAULT_CONTEXT.ir_version = IR_VERSION
+# TODO: Maybe ONNX-ML should also be defaulted?
+DEFAULT_CONTEXT.opset_imports = {'': onnx.defs.onnx_opset_version()}
+
+
+FuncType = TypeVar('FuncType', bound=Callable[..., Any])
+
+
+# TODO: This really doesn't seem worth the metaprogramming...
+def _create_checker(proto_type: Type[Message]) -> Callable[[FuncType], FuncType]:
+    def decorator(py_func: FuncType) -> FuncType:
+        @functools.wraps(py_func)
+        def checker(proto: Message, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> Any:
+            if not isinstance(proto, proto_type):
+                raise RuntimeError(
+                    'You cannot pass an object that is not of type {}'.format(
+                        proto_type.__name__))
+            return getattr(C, py_func.__name__)(
+                proto.SerializeToString(), ctx)
+        return cast(FuncType, checker)
+    return decorator
+
+
+@_create_checker(ValueInfoProto)
+def check_value_info(value_info: ValueInfoProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
+    pass
+
+
+@_create_checker(TensorProto)
+def check_tensor(tensor: TensorProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
+    pass
+
+
+@_create_checker(AttributeProto)
+def check_attribute(attr: AttributeProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
+    pass
+
+
+@_create_checker(NodeProto)
+def check_node(node: NodeProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
+    pass
+
+
+@_create_checker(GraphProto)
+def check_graph(graph: GraphProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
+    pass
+
+
+def check_sparse_tensor(sparse: SparseTensorProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
+    C.check_sparse_tensor(sparse.SerializeToString(), ctx)
+
+
+def check_model(model: Union[ModelProto, Text, bytes], full_check: bool = False) -> None:
+    """Check the consistency of a model. An exception is raised if the test fails.
+
+    Arguments:
+        model (ModelProto): model to check
+        full_check (bool): if True, the function checks shapes can be inferred
+    """
+    # If model is a path instead of ModelProto
+    if isinstance(model, str):
+        C.check_model_path(model)
+        if full_check:
+            onnx.shape_inference.infer_shapes_path(model, check_type=True, strict_mode=True)
+    else:
+        protobuf_string = model if isinstance(model, bytes) else model.SerializeToString()
+        # If the protobuf is larger than 2GB,
+        # remind users should use the model path to check
+        if sys.getsizeof(protobuf_string) > MAXIMUM_PROTOBUF:
+            raise ValueError('This protobuf of onnx model is too large (>2GB). Call check_model with model path instead.')
+        C.check_model(protobuf_string)
+        if full_check:
+            onnx.shape_inference.infer_shapes(model, check_type=True, strict_mode=True)
+
+
+ValidationError = C.ValidationError
diff --git a/third_party/onnx/onnx/common/array_ref.h b/third_party/onnx/onnx/common/array_ref.h
new file mode 100644
index 0000000000..4a74ece57f
--- /dev/null
+++ b/third_party/onnx/onnx/common/array_ref.h
@@ -0,0 +1,188 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+//===--- ArrayRef.h - Array Reference Wrapper -------------------*- C++ -*-===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// ONNX: modified from llvm::ArrayRef.
+// removed llvm-specific functionality
+// removed some implicit const -> non-const conversions that rely on
+// complicated std::enable_if meta-programming
+// removed a bunch of slice variants for simplicity...
+
+#pragma once
+#include <assert.h>
+#include <array>
+#include <vector>
+
+namespace ONNX_NAMESPACE {
+  /// ArrayRef - Represent a constant reference to an array (0 or more elements
+  /// consecutively in memory), i.e. a start pointer and a length.  It allows
+  /// various APIs to take consecutive elements easily and conveniently.
+  ///
+  /// This class does not own the underlying data, it is expected to be used in
+  /// situations where the data resides in some other buffer, whose lifetime
+  /// extends past that of the ArrayRef. For this reason, it is not in general
+  /// safe to store an ArrayRef.
+  ///
+  /// This is intended to be trivially copyable, so it should be passed by
+  /// value.
+  template<typename T>
+  class ArrayRef {
+  public:
+    typedef const T *iterator;
+    typedef const T *const_iterator;
+    typedef size_t size_type;
+
+    typedef std::reverse_iterator<iterator> reverse_iterator;
+
+  private:
+    /// The start of the array, in an external buffer.
+    const T *Data;
+
+    /// The number of elements.
+    size_type Length;
+
+  public:
+    /// @name Constructors
+    /// @{
+
+    /// Construct an empty ArrayRef.
+    /*implicit*/ ArrayRef() : Data(nullptr), Length(0) {}
+
+    /// Construct an ArrayRef from a single element.
+    /*implicit*/ ArrayRef(const T &OneElt)
+      : Data(&OneElt), Length(1) {}
+
+    /// Construct an ArrayRef from a pointer and length.
+    /*implicit*/ ArrayRef(const T *data, size_t length)
+      : Data(data), Length(length) {}
+
+    /// Construct an ArrayRef from a range.
+    ArrayRef(const T *begin, const T *end)
+      : Data(begin), Length(end - begin) {}
+
+    /// Construct an ArrayRef from a std::vector.
+    template<typename A>
+    /*implicit*/ ArrayRef(const std::vector<T, A> &Vec)
+      : Data(Vec.data()), Length(Vec.size()) {}
+
+    /// Construct an ArrayRef from a std::array
+    template <size_t N>
+    /*implicit*/ constexpr ArrayRef(const std::array<T, N> &Arr)
+        : Data(Arr.data()), Length(N) {}
+
+    /// Construct an ArrayRef from a C array.
+    template <size_t N>
+    /*implicit*/ constexpr ArrayRef(const T (&Arr)[N]) : Data(Arr), Length(N) {}
+
+    /// Construct an ArrayRef from a std::initializer_list.
+    /*implicit*/ ArrayRef(const std::initializer_list<T> &Vec)
+    : Data(Vec.begin() == Vec.end() ? (T*)nullptr : Vec.begin()),
+      Length(Vec.size()) {}
+
+    /// @}
+    /// @name Simple Operations
+    /// @{
+
+    iterator begin() const { return Data; }
+    iterator end() const { return Data + Length; }
+
+    reverse_iterator rbegin() const { return reverse_iterator(end()); }
+    reverse_iterator rend() const { return reverse_iterator(begin()); }
+
+    /// empty - Check if the array is empty.
+    bool empty() const { return Length == 0; }
+
+    const T *data() const { return Data; }
+
+    /// size - Get the array size.
+    size_t size() const { return Length; }
+
+    /// front - Get the first element.
+    const T &front() const {
+      assert(!empty());
+      return Data[0];
+    }
+
+    /// back - Get the last element.
+    const T &back() const {
+      assert(!empty());
+      return Data[Length-1];
+    }
+
+    /// equals - Check for element-wise equality.
+    bool equals(ArrayRef RHS) const {
+      if (Length != RHS.Length)
+        return false;
+      return std::equal(begin(), end(), RHS.begin());
+    }
+
+    /// slice(n, m) - Chop off the first N elements of the array, and keep M
+    /// elements in the array.
+    ArrayRef<T> slice(size_t N, size_t M) const {
+      assert(N+M <= size() && "Invalid specifier");
+      return ArrayRef<T>(data()+N, M);
+    }
+
+    /// slice(n) - Chop off the first N elements of the array.
+    ArrayRef<T> slice(size_t N) const { return slice(N, size() - N); }
+
+    /// @}
+    /// @name Operator Overloads
+    /// @{
+    const T &operator[](size_t Index) const {
+      assert(Index < Length && "Invalid index!");
+      return Data[Index];
+    }
+
+    /// Vector compatibility
+    const T &at(size_t Index) const {
+      assert(Index < Length && "Invalid index!");
+      return Data[Index];
+    }
+
+    /// Disallow accidental assignment from a temporary.
+    ///
+    /// The declaration here is extra complicated so that "arrayRef = {}"
+    /// continues to select the move assignment operator.
+    template <typename U>
+    typename std::enable_if<std::is_same<U, T>::value, ArrayRef<T>>::type &
+    operator=(U &&Temporary) = delete;
+
+    /// Disallow accidental assignment from a temporary.
+    ///
+    /// The declaration here is extra complicated so that "arrayRef = {}"
+    /// continues to select the move assignment operator.
+    template <typename U>
+    typename std::enable_if<std::is_same<U, T>::value, ArrayRef<T>>::type &
+    operator=(std::initializer_list<U>) = delete;
+
+    /// @}
+    /// @name Expensive Operations
+    /// @{
+    std::vector<T> vec() const {
+      return std::vector<T>(Data, Data+Length);
+    }
+
+    /// @}
+    /// @name Conversion operators
+    /// @{
+    operator std::vector<T>() const {
+      return std::vector<T>(Data, Data+Length);
+    }
+
+    /// @}
+  };
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/assertions.cc b/third_party/onnx/onnx/common/assertions.cc
new file mode 100644
index 0000000000..74cc5a33db
--- /dev/null
+++ b/third_party/onnx/onnx/common/assertions.cc
@@ -0,0 +1,34 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include <cstdarg>
+#include <cstdio>
+#include "onnx/common/common.h"
+#include "onnx/common/assertions.h"
+
+namespace ONNX_NAMESPACE {
+
+std::string barf(const char* fmt, ...) {
+  char msg[2048];
+  va_list args;
+
+  va_start(args, fmt);
+  vsnprintf(msg, 2048, fmt, args);
+  va_end(args);
+
+  return std::string(msg);
+}
+
+void throw_assert_error(std::string& msg) {
+  ONNX_THROW_EX(assert_error(msg));
+}
+
+void throw_tensor_error(std::string& msg) {
+  ONNX_THROW_EX(tensor_error(msg));
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/assertions.h b/third_party/onnx/onnx/common/assertions.h
new file mode 100644
index 0000000000..757702bf58
--- /dev/null
+++ b/third_party/onnx/onnx/common/assertions.h
@@ -0,0 +1,84 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include <stdexcept>
+#include <string>
+
+namespace ONNX_NAMESPACE {
+
+struct assert_error : public std::runtime_error {
+ public:
+  explicit assert_error(const std::string& msg) : runtime_error(msg) {}
+};
+
+struct tensor_error : public assert_error {
+ public:
+  explicit tensor_error(std::string msg) : assert_error(msg) {}
+};
+
+std::string barf(const char* fmt, ...);
+
+[[noreturn]] void throw_assert_error(std::string&);
+
+[[noreturn]] void throw_tensor_error(std::string&);
+
+} // namespace ONNX_NAMESPACE
+
+#if defined(__GNUC__) || defined(__ICL) || defined(__clang__)
+#define _ONNX_EXPECT(x, y) (__builtin_expect((x), (y)))
+#else
+#define _ONNX_EXPECT(x, y) (x)
+#endif
+
+#define ONNX_ASSERT(cond)                           \
+  if (_ONNX_EXPECT(!(cond), 0)) {                   \
+    std::string error_msg = ::ONNX_NAMESPACE::barf( \
+        "%s:%u: %s: Assertion `%s` failed.",        \
+        __FILE__,                                   \
+        __LINE__,                                   \
+        __func__,                                   \
+        #cond);                                     \
+    throw_assert_error(error_msg);                  \
+  }
+
+// The following is used to prevent MSVC from passing the whole __VA_ARGS__ list
+// as the first parameter value to a macro call.
+#define ONNX_EXPAND(x) x
+
+// Note: msg must be a string literal
+#define _ONNX_ASSERTM(cond, msg, ...)               \
+  if (_ONNX_EXPECT(!(cond), 0)) {                   \
+    std::string error_msg = ::ONNX_NAMESPACE::barf( \
+        "%s:%u: %s: Assertion `%s` failed: " msg,   \
+        __FILE__,                                   \
+        __LINE__,                                   \
+        __func__,                                   \
+        #cond,                                      \
+        __VA_ARGS__);                               \
+    throw_assert_error(error_msg);                  \
+  }
+
+// The trailing ' ' argument is a hack to deal with the extra comma when ... is empty.
+// Another way to solve this is ##__VA_ARGS__ in _ONNX_ASSERTM, but this is a non-portable
+// extension we shouldn't use.
+#define ONNX_ASSERTM(...) ONNX_EXPAND(_ONNX_ASSERTM(__VA_ARGS__, " "))
+
+#define _TENSOR_ASSERTM(cond, msg, ...)             \
+  if (_ONNX_EXPECT(!(cond), 0)) {                   \
+    std::string error_msg = ::ONNX_NAMESPACE::barf( \
+        "%s:%u: %s: Assertion `%s` failed: " msg,   \
+        __FILE__,                                   \
+        __LINE__,                                   \
+        __func__,                                   \
+        #cond,                                      \
+        __VA_ARGS__);                               \
+    throw_tensor_error(error_msg);                  \
+  }
+
+#define TENSOR_ASSERTM(...) ONNX_EXPAND(_TENSOR_ASSERTM(__VA_ARGS__, " "))
diff --git a/third_party/onnx/onnx/common/common.h b/third_party/onnx/onnx/common/common.h
new file mode 100644
index 0000000000..def414c8b9
--- /dev/null
+++ b/third_party/onnx/onnx/common/common.h
@@ -0,0 +1,34 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#define ONNX_UNUSED_PARAMETER(x) (void)(x)
+
+#ifdef ONNX_NO_EXCEPTIONS
+#include <iostream>
+#define ONNX_THROW(...)                                   \
+  do {                                                    \
+    std::cerr << ONNX_NAMESPACE::MakeString(__VA_ARGS__); \
+    abort();                                              \
+  } while(false)
+
+#define ONNX_THROW_EX(ex)                \
+  do {                                   \
+    std::cerr << ex.what() << std::endl; \
+    abort();                             \
+  } while (false)
+
+#define ONNX_TRY if (true)
+#define ONNX_CATCH(x) else if (false)
+#define ONNX_HANDLE_EXCEPTION(func)
+
+#else
+#define ONNX_THROW(...) throw std::runtime_error(ONNX_NAMESPACE::MakeString(__VA_ARGS__))
+#define ONNX_THROW_EX(ex) throw ex
+
+#define ONNX_TRY try
+#define ONNX_CATCH(x) catch (x)
+#define ONNX_HANDLE_EXCEPTION(func) func()
+#endif
diff --git a/third_party/onnx/onnx/common/constants.h b/third_party/onnx/onnx/common/constants.h
new file mode 100644
index 0000000000..33ae6f253c
--- /dev/null
+++ b/third_party/onnx/onnx/common/constants.h
@@ -0,0 +1,35 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include <string>
+
+namespace ONNX_NAMESPACE {
+// For ONNX op/function registration.
+
+// ONNX domains.
+constexpr const char* AI_ONNX_ML_DOMAIN = "ai.onnx.ml";
+constexpr const char* AI_ONNX_TRAINING_DOMAIN = "ai.onnx.training";
+constexpr const char* AI_ONNX_PREVIEW_TRAINING_DOMAIN = "ai.onnx.preview.training";
+constexpr const char* ONNX_DOMAIN = "";
+constexpr bool OPTIONAL_VALUE = false;
+
+// For dimension denotation.
+constexpr const char* DATA_BATCH = "DATA_BATCH";
+constexpr const char* DATA_CHANNEL = "DATA_CHANNEL";
+constexpr const char* DATA_TIME = "DATA_TIME";
+constexpr const char* DATA_FEATURE = "DATA_FEATURE";
+constexpr const char* FILTER_IN_CHANNEL = "FILTER_IN_CHANNEL";
+constexpr const char* FILTER_OUT_CHANNEL = "FILTER_OUT_CHANNEL";
+constexpr const char* FILTER_SPATIAL = "FILTER_SPATIAL";
+
+// For type denotation.
+constexpr const char* TENSOR = "TENSOR";
+constexpr const char* IMAGE = "IMAGE";
+constexpr const char* AUDIO = "AUDIO";
+constexpr const char* TEXT = "TEXT";
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/file_utils.h b/third_party/onnx/onnx/common/file_utils.h
new file mode 100644
index 0000000000..1593536ad0
--- /dev/null
+++ b/third_party/onnx/onnx/common/file_utils.h
@@ -0,0 +1,26 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include "onnx/checker.h"
+#include "onnx/common/path.h"
+
+#include <fstream>
+
+namespace ONNX_NAMESPACE {
+
+template <typename T>
+void LoadProtoFromPath(const std::string proto_path, T& proto) {
+  std::fstream proto_stream(proto_path, std::ios::in | std::ios::binary);
+  if (!proto_stream.good()) {
+    fail_check("Unable to open proto file: ", proto_path, ". Please check if it is a valid proto. ");
+  }
+  std::string data{std::istreambuf_iterator<char>{proto_stream}, std::istreambuf_iterator<char>{}};
+  if (!ParseProtoFromBytes(&proto, data.c_str(), data.size())) {
+    fail_check(
+    "Unable to parse proto from file: ", proto_path, ". Please check if it is a valid protobuf file of proto. ");
+  }
+}
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/common/graph_node_list.h b/third_party/onnx/onnx/common/graph_node_list.h
new file mode 100644
index 0000000000..ead527b9a4
--- /dev/null
+++ b/third_party/onnx/onnx/common/graph_node_list.h
@@ -0,0 +1,166 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include "onnx/common/assertions.h"
+
+namespace ONNX_NAMESPACE {
+
+// Intrusive doubly linked lists with sane reverse iterators.
+// The header file is named graph_node_list.h because it is ONLY
+// used for Graph's Node lists, and if you want to use it for other
+// things, you will have to do some refactoring.
+//
+// At the moment, the templated type T must support a few operations:
+//
+//  - It must have a field: T* next_in_graph[2] = { nullptr, nullptr };
+//    which are used for the intrusive linked list pointers.
+//
+//  - It must have a method 'destroy()', which removes T from the
+//    list and frees a T.
+//
+// In practice, we are only using it with Node and const Node.  'destroy()'
+// needs to be renegotiated if you want to use this somewhere else.
+//
+// Besides the benefits of being intrusive, unlike std::list, these lists handle
+// forward and backward iteration uniformly because we require a
+// "before-first-element" sentinel.  This means that reverse iterators
+// physically point to the element they logically point to, rather than
+// the off-by-one behavior for all standard library reverse iterators.
+
+static constexpr size_t kNextDirection = 0;
+static constexpr size_t kPrevDirection = 1;
+
+template <typename T>
+struct generic_graph_node_list;
+
+template <typename T>
+struct generic_graph_node_list_iterator;
+
+struct Node;
+using graph_node_list = generic_graph_node_list<Node>;
+using const_graph_node_list = generic_graph_node_list<const Node>;
+using graph_node_list_iterator = generic_graph_node_list_iterator<Node>;
+using const_graph_node_list_iterator = generic_graph_node_list_iterator<const Node>;
+
+template <typename T>
+struct generic_graph_node_list_iterator final {
+  generic_graph_node_list_iterator()
+    : cur(nullptr), d(kNextDirection) {}
+  generic_graph_node_list_iterator(T * cur, size_t d)
+    : cur(cur), d(d) {}
+  generic_graph_node_list_iterator(const generic_graph_node_list_iterator & rhs)
+    : cur(rhs.cur), d(rhs.d) {}
+  generic_graph_node_list_iterator & operator=(
+    const generic_graph_node_list_iterator&) = default;
+  T * operator*() const { return cur; }
+  T * operator->() const { return cur; }
+  generic_graph_node_list_iterator & operator++() {
+    ONNX_ASSERT(cur);
+    cur = cur->next_in_graph[d];
+    return *this;
+  }
+  generic_graph_node_list_iterator operator++(int) {
+    generic_graph_node_list_iterator old = *this;
+    ++(*this);
+    return old;
+  }
+  generic_graph_node_list_iterator & operator--() {
+    ONNX_ASSERT(cur);
+    cur = cur->next_in_graph[reverseDir()];
+    return *this;
+  }
+  generic_graph_node_list_iterator operator--(int) {
+    generic_graph_node_list_iterator old = *this;
+    --(*this);
+    return old;
+  }
+
+  // erase cur without invalidating this iterator
+  // named differently from destroy so that ->/. bugs do not
+  // silently cause the wrong one to be called.
+  // iterator will point to the previous entry after call
+  void destroyCurrent() {
+    T * n = cur;
+    cur = cur->next_in_graph[reverseDir()];
+    n->destroy();
+  }
+  generic_graph_node_list_iterator reverse() {
+    return generic_graph_node_list_iterator(cur, reverseDir());
+  }
+private:
+  size_t reverseDir() {
+    return d == kNextDirection ? kPrevDirection : kNextDirection;
+  }
+  T * cur;
+  size_t d; //direction 0 is forward 1 is reverse, see next_in_graph
+};
+
+template <typename T>
+struct generic_graph_node_list final {
+  using iterator = generic_graph_node_list_iterator<T>;
+  using const_iterator = generic_graph_node_list_iterator<const T>;
+  generic_graph_node_list_iterator<T> begin() {
+    return generic_graph_node_list_iterator<T>(head->next_in_graph[d], d);
+  }
+  generic_graph_node_list_iterator<const T> begin() const {
+    return generic_graph_node_list_iterator<const T>(head->next_in_graph[d], d);
+  }
+  generic_graph_node_list_iterator<T> end() {
+    return generic_graph_node_list_iterator<T>(head,d);
+  }
+  generic_graph_node_list_iterator<const T> end() const {
+    return generic_graph_node_list_iterator<const T>(head,d);
+  }
+  generic_graph_node_list_iterator<T> rbegin() {
+    return reverse().begin();
+  }
+  generic_graph_node_list_iterator<const T> rbegin() const {
+    return reverse().begin();
+  }
+  generic_graph_node_list_iterator<T> rend() {
+    return reverse().end();
+  }
+  generic_graph_node_list_iterator<const T> rend() const {
+    return reverse().end();
+  }
+  generic_graph_node_list reverse() {
+    return generic_graph_node_list(head, d == kNextDirection ? kPrevDirection : kNextDirection);
+  }
+  const generic_graph_node_list reverse() const {
+    return generic_graph_node_list(head, d == kNextDirection ? kPrevDirection : kNextDirection);
+  }
+  generic_graph_node_list(T * head, size_t d)
+    : head(head), d(d) {}
+private:
+  T * head;
+  size_t d;
+};
+
+template <typename T>
+static inline bool operator==(generic_graph_node_list_iterator<T> a, generic_graph_node_list_iterator<T> b) {
+  return *a == *b;
+}
+
+template <typename T>
+static inline bool operator!=(generic_graph_node_list_iterator<T> a, generic_graph_node_list_iterator<T> b) {
+  return *a != *b;
+}
+
+} // namespace ONNX_NAMESPACE
+
+namespace std {
+
+template<typename T>
+struct iterator_traits<ONNX_NAMESPACE::generic_graph_node_list_iterator<T>> {
+  using difference_type = int64_t;
+  using value_type = T*;
+  using pointer = T**;
+  using reference = T*&;
+  using iterator_category = bidirectional_iterator_tag;
+};
+
+} // namespace std
diff --git a/third_party/onnx/onnx/common/interned_strings.cc b/third_party/onnx/onnx/common/interned_strings.cc
new file mode 100644
index 0000000000..39f3885f2c
--- /dev/null
+++ b/third_party/onnx/onnx/common/interned_strings.cc
@@ -0,0 +1,78 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include <vector>
+#include <stdint.h>
+#include <string>
+#include <unordered_map>
+#include <mutex>
+
+#include "onnx/common/assertions.h"
+#include "onnx/common/interned_strings.h"
+
+namespace ONNX_NAMESPACE {
+
+struct InternedStrings {
+  InternedStrings()
+  : next_sym(kLastSymbol) {
+    #define REGISTER_SYMBOL(s) \
+      string_to_sym_[#s] = k##s; \
+      sym_to_string_[k##s] = #s;
+    FORALL_BUILTIN_SYMBOLS(REGISTER_SYMBOL)
+    #undef REGISTER_SYMBOL
+  }
+  uint32_t symbol(const std::string & s) {
+    std::lock_guard<std::mutex> guard(mutex_);
+    auto it = string_to_sym_.find(s);
+    if(it != string_to_sym_.end())
+      return it->second;
+    uint32_t k = next_sym++;
+    string_to_sym_[s] = k;
+    sym_to_string_[k] = s;
+    return k;
+  }
+  const char * string(Symbol sym) {
+    // Builtin Symbols are also in the maps, but
+    // we can bypass the need to acquire a lock
+    // to read the map for Builtins because we already
+    // know their string value
+    switch(sym) {
+      #define DEFINE_CASE(s) \
+        case k##s: return #s;
+      FORALL_BUILTIN_SYMBOLS(DEFINE_CASE)
+      #undef DEFINE_CASE
+        default:
+          return customString(sym);
+    }
+  }
+private:
+  const char * customString(Symbol sym) {
+    std::lock_guard<std::mutex> guard(mutex_);
+    auto it = sym_to_string_.find(sym);
+    ONNX_ASSERT(it != sym_to_string_.end());
+    return it->second.c_str();
+  }
+  std::unordered_map<std::string, uint32_t> string_to_sym_;
+  std::unordered_map<uint32_t, std::string> sym_to_string_;
+  uint32_t next_sym;
+  std::mutex mutex_;
+};
+
+static InternedStrings & globalStrings() {
+  static InternedStrings s;
+  return s;
+}
+
+const char * Symbol::toString() const {
+  return globalStrings().string(*this);
+}
+
+Symbol::Symbol(const std::string & s)
+  : value(globalStrings().symbol(s)) {
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/interned_strings.h b/third_party/onnx/onnx/common/interned_strings.h
new file mode 100644
index 0000000000..94a053be33
--- /dev/null
+++ b/third_party/onnx/onnx/common/interned_strings.h
@@ -0,0 +1,233 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+#include <stdint.h>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace ONNX_NAMESPACE {
+
+#define FORALL_BUILTIN_SYMBOLS(_) \
+  _(spatial)                      \
+  _(select_last_index)            \
+  _(coordinate_transformation_mode) \
+  _(PythonOp)                     \
+  _(CppOp)                        \
+  _(Param)                        \
+  _(Select)                       \
+  _(Return)                       \
+  _(Eval)                         \
+  _(add)                          \
+  _(Add)                          \
+  _(Div)                          \
+  _(Mul)                          \
+  _(Neg)                          \
+  _(Sub)                          \
+  _(Pow)                          \
+  _(Sigmoid)                      \
+  _(ArgMax)                       \
+  _(Concat)                       \
+  _(Softmax)                      \
+  _(LogSoftmax)                   \
+  _(Dropout)                      \
+  _(Tanh)                         \
+  _(mul)                          \
+  _(neg)                          \
+  _(sigmoid)                      \
+  _(tanh)                         \
+  _(Constant)                     \
+  _(cat)                          \
+  _(Slice)                        \
+  _(Squeeze)                      \
+  _(Undefined)                    \
+  _(FusionGroup)                  \
+  _(MatMul)                       \
+  _(Gemm)                         \
+  _(Tile)                         \
+  _(SubConstant)                  \
+  _(Scale)                        \
+  _(Transpose)                    \
+  _(Pad)                          \
+  _(Reshape)                      \
+  _(split)                        \
+  _(chunk)                        \
+  _(Offset)                       \
+  _(value)                        \
+  _(Subgraph)                     \
+  _(BatchNormalization)           \
+  _(Conv)                         \
+  _(ConvTranspose)                \
+  _(is_test)                      \
+  _(epsilon)                      \
+  _(expand)                       \
+  _(Expand)                       \
+  _(order)                        \
+  _(momentum)                     \
+  _(consumed_inputs)              \
+  _(kernels)                      \
+  _(kernel_shape)                 \
+  _(kernel)                       \
+  _(scale)                        \
+  _(strides)                      \
+  _(stride)                       \
+  _(pads)                         \
+  _(pad)                          \
+  _(beta)                         \
+  _(alpha)                        \
+  _(dilations)                    \
+  _(dilation)                     \
+  _(broadcast)                    \
+  _(axis)                         \
+  _(ratio)                        \
+  _(size)                         \
+  _(dim)                          \
+  _(keepdims)                     \
+  _(perm)                         \
+  _(shape)                        \
+  _(axes)                         \
+  _(group)                        \
+  _(inplace)                      \
+  _(transA)                       \
+  _(transB)                       \
+  _(other)                        \
+  _(__and__)                      \
+  _(__lshift__)                   \
+  _(__or__)                       \
+  _(__rshift__)                   \
+  _(__xor__)                      \
+  _(abs)                          \
+  _(acos)                         \
+  _(asin)                         \
+  _(atan)                         \
+  _(atan2)                        \
+  _(ceil)                         \
+  _(clamp)                        \
+  _(cos)                          \
+  _(cosh)                         \
+  _(div)                          \
+  _(eq)                           \
+  _(equal)                        \
+  _(Exp)                          \
+  _(ends)                         \
+  _(expm1)                        \
+  _(floor)                        \
+  _(fmod)                         \
+  _(frac)                         \
+  _(ge)                           \
+  _(gt)                           \
+  _(le)                           \
+  _(lerp)                         \
+  _(lgamma)                       \
+  _(Log)                          \
+  _(log1p)                        \
+  _(lt)                           \
+  _(max)                          \
+  _(min)                          \
+  _(ne)                           \
+  _(ones)                         \
+  _(pow)                          \
+  _(reciprocal)                   \
+  _(remainder)                    \
+  _(round)                        \
+  _(rsqrt)                        \
+  _(sin)                          \
+  _(sinh)                         \
+  _(Sqrt)                         \
+  _(sub)                          \
+  _(starts)                       \
+  _(tan)                          \
+  _(trunc)                        \
+  _(zeros)                        \
+  _(exponent)                     \
+  _(device)                       \
+  _(mode)                         \
+  _(Identity)                     \
+  _(Loop)                         \
+  _(If)                           \
+  _(body)                         \
+  _(then_branch)                  \
+  _(else_branch)                  \
+  _(Captured)                     \
+  _(__control_inputs)             \
+  _(count_include_pad)            \
+  _(storage_order)                \
+  _(Unsqueeze)                    \
+  _(ReduceL1)                     \
+  _(ReduceL2)                     \
+  _(ReduceLogSum)                 \
+  _(ReduceLogSumExp)              \
+  _(ReduceMax)                    \
+  _(ReduceMean)                   \
+  _(ReduceMin)                    \
+  _(ReduceProd)                   \
+  _(ReduceSum)                    \
+  _(ReduceSumSquare)              \
+  _(Cast)                         \
+  _(to)                           \
+  _(PRelu)                        \
+  _(Greater)                      \
+  _(Less)                         \
+  _(scales)                       \
+  _(Upsample)                     \
+  _(RNN)                          \
+  _(layout)                       \
+  _(k)                            \
+  _(Flatten)                      \
+  _(ScatterElements)              \
+  _(Resize)
+
+enum BuiltinSymbol {
+#define DEFINE_SYMBOL(s) k##s,
+  FORALL_BUILTIN_SYMBOLS(DEFINE_SYMBOL)
+#undef DEFINE_SYMBOL
+      kLastSymbol, // where we start counting for new symbols
+};
+
+struct Symbol {
+  Symbol() {}
+  /*implicit*/ Symbol(BuiltinSymbol value) : value(value) {}
+  explicit Symbol(const std::string& s);
+  explicit Symbol(uint32_t value) : value(value) {}
+
+  operator uint32_t() const {
+    return value;
+  }
+  const char* toString() const;
+
+ private:
+  uint32_t value;
+};
+
+static inline bool operator==(Symbol lhs, Symbol rhs) {
+  return static_cast<uint32_t>(lhs) == static_cast<uint32_t>(rhs);
+}
+// necessary to prevent ambiguous overload resolutions
+static inline bool operator==(BuiltinSymbol lhs, Symbol rhs) {
+  return static_cast<uint32_t>(lhs) == static_cast<uint32_t>(rhs);
+}
+static inline bool operator==(Symbol lhs, BuiltinSymbol rhs) {
+  return static_cast<uint32_t>(lhs) == static_cast<uint32_t>(rhs);
+}
+
+inline Symbol operator"" _sym(const char* s, size_t) {
+  return Symbol(s);
+}
+
+} // namespace ONNX_NAMESPACE
+
+// make symbol behave like an integer in hash tables
+namespace std {
+template <>
+struct hash<ONNX_NAMESPACE::Symbol> {
+  std::size_t operator()(ONNX_NAMESPACE::Symbol s) const {
+    return std::hash<uint32_t>()(static_cast<uint32_t>(s));
+  }
+};
+
+} // namespace std
diff --git a/third_party/onnx/onnx/common/ir.h b/third_party/onnx/onnx/common/ir.h
new file mode 100644
index 0000000000..8d9e5ce61a
--- /dev/null
+++ b/third_party/onnx/onnx/common/ir.h
@@ -0,0 +1,1417 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include <atomic>
+#include <algorithm>
+#include <cstdint>
+#include <functional>
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include <stdint.h>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+#include "onnx/string_utils.h"
+#include "onnx/common/array_ref.h"
+#include "onnx/common/assertions.h"
+#include "onnx/common/interned_strings.h"
+#include "onnx/common/graph_node_list.h"
+#include "onnx/common/tensor.h"
+#include "onnx/common/common.h"
+
+
+#define ONNX_DISALLOW_COPY_AND_ASSIGN(TypeName) \
+  TypeName(const TypeName&) = delete; \
+  TypeName& operator=(const TypeName&) = delete
+
+
+namespace ONNX_NAMESPACE {
+
+// Graph represents one "function" of computation.
+// It uses a simple ownership model where the graph owns all the nodes inside it.
+// All references inside the graph are raw pointers.
+// Destroying the Graph will invalidate any pointers to nodes in the graph.
+struct Graph;
+
+
+// Node is the base class of the IR graph. It represents one computation
+// and dependencies on a list of Values. The "prim-ops", so to speak.
+struct Node;
+
+
+// A Value represents an input or output to node that is either a
+// Tensor or an opaque Handle object, as determined by type().
+struct Value;
+
+
+class ResourceGuard final {
+  std::function<void()> destructor_;
+  bool released_;
+
+public:
+  ResourceGuard(std::function<void()> destructor)
+    : destructor_(std::move(destructor))
+    , released_(false) {}
+
+  ~ResourceGuard() {
+    if (!released_) destructor_();
+  }
+
+  void release() {
+    released_ = true;
+  }
+};
+
+
+struct Dimension final {
+  Dimension() : is_unknown(true) {}
+  Dimension(std::string param)
+    : is_unknown(false), is_int(false), dim(-1), param(std::move(param)) {
+  }
+  Dimension(int64_t dim) : is_unknown(false), is_int(true), dim(dim) {}
+
+  bool is_unknown;
+  bool is_int;
+  int64_t dim;
+  std::string param;
+};
+
+
+enum class AttributeKind : uint8_t {
+  // float, float list, int, int list, string, string list,
+  // tensor, tensor list, subgraph, subgraph list. type proto, type proto list
+  f, fs, i, is, s, ss, t, ts, g, gs, tp, tps
+};
+
+
+static inline const char * toString(AttributeKind kind) {
+  static constexpr const char* names[] = {"f","fs", "i", "is", "s", "ss", "t", "ts", "g", "gs", "tp", "tps"};
+  ONNX_ASSERT(size_t(kind) < sizeof(names) / sizeof(const char*));
+  return names[int(kind)];
+}
+
+
+struct AttributeValue {
+  AttributeValue(Symbol name)
+  : name(name) {}
+  using Ptr = std::unique_ptr<AttributeValue>;
+  Symbol name;
+  virtual AttributeKind kind() const = 0;
+  virtual Ptr clone() const = 0;
+  virtual ~AttributeValue() = default;
+};
+
+
+template<typename T, AttributeKind Kind>
+struct ScalarAttributeValue final : public AttributeValue {
+  using ConstructorType = const T &;
+  using ValueType = T;
+  ScalarAttributeValue(Symbol name, ConstructorType value_)
+  : AttributeValue(name), value_(value_) {}
+  ValueType & value() {
+    return value_;
+  }
+  virtual Ptr clone() const override {
+    return Ptr(new ScalarAttributeValue(name, value_));
+  }
+  virtual AttributeKind kind() const override { return Kind; }
+
+private:
+  ValueType value_;
+};
+
+
+template<typename T, AttributeKind Kind>
+struct VectorAttributeValue final : public AttributeValue {
+  using ConstructorType = const std::vector<T> &&;
+  using ValueType = std::vector<T>;
+  VectorAttributeValue(Symbol name, ConstructorType value_)
+  : AttributeValue(name), value_(std::move(value_)) {}
+  ValueType & value() {
+    return value_;
+  }
+  virtual AttributeKind kind() const override { return Kind; }
+  virtual std::unique_ptr<AttributeValue> clone() const override {
+    auto copy = value_;
+    return Ptr(new VectorAttributeValue(name, std::move(copy)));
+  }
+private:
+  ValueType value_;
+};
+
+
+using FloatAttr = ScalarAttributeValue<double,AttributeKind::f>;
+using FloatsAttr = VectorAttributeValue<double,AttributeKind::fs>;
+using IntAttr = ScalarAttributeValue<int64_t,AttributeKind::i>;
+using IntsAttr = VectorAttributeValue<int64_t,AttributeKind::is>;
+using StringAttr = ScalarAttributeValue<std::string,AttributeKind::s>;
+using StringsAttr = VectorAttributeValue<std::string,AttributeKind::ss>;
+using TensorAttr = ScalarAttributeValue<Tensor,AttributeKind::t>;
+using TensorsAttr = VectorAttributeValue<Tensor,AttributeKind::ts>;
+using GraphAttr = ScalarAttributeValue<std::shared_ptr<Graph>,AttributeKind::g>;
+using GraphsAttr = VectorAttributeValue<std::shared_ptr<Graph>,AttributeKind::gs>;
+using TypeProtoAttr = ScalarAttributeValue<TypeProto,AttributeKind::tp>;
+using TypeProtosAttr = VectorAttributeValue<TypeProto,AttributeKind::tps>;
+
+
+// CRTP so that Node which inherits Attributes can be return for
+// method chaining e.g:
+// Node * n = g->create(kSelect)->set_i(kOffset,3)->set_f(kValue,3.5);
+// we return Derived* pointers because Nodes are normally held as pointers.
+template<typename Derived>
+struct Attributes {
+  Attributes() {}
+  void copyAttributes(const Attributes & rhs) {
+    values_.clear();
+    values_.reserve(rhs.values_.size());
+    for(auto & i : rhs.values_) {
+      values_.push_back(i->clone());
+    }
+  }
+  bool hasAttribute(Symbol name) const {
+    return find(name,false) != values_.end();
+  }
+  AttributeKind kindOf(Symbol name) const {
+    return (*find(name,true))->kind();
+  }
+  Derived* removeAttribute(Symbol name) {
+    values_.erase(find(name,true));
+    return This();
+  }
+  bool hasAttributes() const {
+    return !values_.empty();
+  }
+  // The names are returned in order, since name actually is the index.
+  std::vector<Symbol> attributeNames() const {
+    std::vector<Symbol> names;
+    names.reserve(values_.size());
+    for(auto & a : values_)
+      names.push_back(a->name);
+    return names;
+  }
+
+  #define CREATE_ACCESSOR(Kind, method) \
+  Derived* method##_(Symbol name, Kind##Attr::ConstructorType v) { \
+    return set<Kind##Attr>(name,std::forward<Kind##Attr::ConstructorType>(v)); \
+  } \
+  const Kind##Attr::ValueType& method(Symbol name) const { \
+    return get<Kind##Attr>(name); \
+  }
+  CREATE_ACCESSOR(Float,f)
+  CREATE_ACCESSOR(Floats,fs)
+  CREATE_ACCESSOR(String,s)
+  CREATE_ACCESSOR(Strings,ss)
+  CREATE_ACCESSOR(Int,i)
+  CREATE_ACCESSOR(Ints,is)
+  CREATE_ACCESSOR(Tensor,t)
+  CREATE_ACCESSOR(Tensors,ts)
+  CREATE_ACCESSOR(Graph,g)
+  CREATE_ACCESSOR(Graphs,gs)
+  CREATE_ACCESSOR(TypeProto,tp)
+  CREATE_ACCESSOR(TypeProtos,tps)
+
+  #undef CREATE_ACCESSOR
+
+private:
+  Derived* This() {
+    return static_cast<Derived*>(this);
+  }
+  template<typename T>
+  Derived* set(Symbol name, typename T::ConstructorType v) {
+    auto it = find(name, false);
+    auto nv = AVPtr(new T(name, std::forward<typename T::ConstructorType>(v)));
+    if(it == values_.end()) {
+      values_.push_back(std::move(nv));
+    } else {
+      *it = std::move(nv);
+    }
+    return This();
+  }
+  template<typename T>
+  typename T::ValueType & get(Symbol name) const {
+    auto it = find(name, true);
+    T* child = static_cast<T*>(it->get());
+    return child->value();
+  }
+  using AVPtr = AttributeValue::Ptr;
+  // NB: For determinism, we use a vector rather than a hash map.  This does
+  // mean that lookups are O(n), so you shouldn't use Attributes to store
+  // a big pile of messages.
+  std::vector<AVPtr> values_;
+  using iterator = std::vector<AVPtr>::iterator;
+  iterator find(Symbol name, bool required) {
+    auto it = std::find_if(values_.begin(), values_.end(),[&](const AVPtr & v) {
+      return v->name == name;
+    });
+    ONNX_ASSERT(!required || it != values_.end());
+    return it;
+  }
+  using const_iterator = std::vector<AVPtr>::const_iterator;
+  const_iterator find(Symbol name, bool required) const {
+    auto it = std::find_if(values_.begin(), values_.end(),[&](const AVPtr & v) {
+      return v->name == name;
+    });
+    ONNX_ASSERTM(!required || it != values_.end(),
+        "%s:%u: %s: required undefined attribute '%s'", __FILE__, __LINE__, __func__, name.toString());
+    return it;
+  }
+};
+
+
+
+// Each use is represented by this type, see Node::uses()
+// 'user' is the consumer of the value, offset is the index into
+// 'user's input this where the produces will be found.
+struct Use final {
+  Use(Node * user, size_t offset)
+  : user(user), offset(offset) {}
+  Node * user;
+  size_t offset;
+};
+
+static inline bool operator==(const Use & a, const Use & b) {
+  return a.user == b.user && a.offset == b.offset;
+}
+
+
+// the list types are intentionally simple, but we type-def
+// them here so if we need to change them, refactoring will be easier
+using node_list = std::vector<Node*>;
+using value_list = std::vector<Value*>;
+using use_list = std::vector<Use>;
+using NodeKind = Symbol;
+
+
+struct Value final {
+  ONNX_DISALLOW_COPY_AND_ASSIGN(Value);
+  Value(Node * node_, size_t offset_);
+
+private:
+  friend struct Node;
+  friend struct Graph;
+  Node * node_;
+  size_t offset_;
+  size_t unique_ = 0;          // unique id
+  size_t stage_ = 0;           // 0-forward, 1-backward, 2-double-backward,...
+  use_list uses_in_current_graph_;
+  bool has_unique_name_;
+  std::string unique_name_;
+  int32_t elem_type_;
+  bool has_sizes_;
+  std::vector<Dimension> sizes_;
+
+public:
+  Value* setElemType(int32_t elem_type) {
+    elem_type_ = elem_type;
+    return this;
+  }
+  int32_t elemType() const {
+    return elem_type_;
+  }
+  bool has_sizes() const { return has_sizes_; }
+  Value* setSizes(std::vector<Dimension> sizes) {
+    has_sizes_ = true;
+    sizes_ = std::move(sizes);
+    return this;
+  }
+  Value* wipeSizes() {
+    has_sizes_ = false;
+    sizes_ = std::vector<Dimension>();
+    return this;
+  }
+  const std::vector<Dimension>& sizes() const {
+    return sizes_;
+  }
+  size_t unique() const {
+    return unique_;
+  }
+  bool has_unique_name() const {
+    return has_unique_name_;
+  }
+  std::string uniqueName() const {
+    if(has_unique_name())
+      return unique_name_;
+    return ONNX_NAMESPACE::to_string(unique());
+  }
+  Value* setUniqueName(const std::string &name, bool rename_subgraph_captured_nodes=true);
+  Value* setStage(size_t s) {
+    stage_ = s;
+    return this;
+  }
+  size_t stage() const {
+    return stage_;
+  }
+  Node* node() {
+    return node_;
+  }
+  size_t offset() const {
+    return offset_;
+  }
+  const Node * node() const {
+    return node_;
+  }
+  Graph * owningGraph();
+  const Graph * owningGraph() const;
+  // TODO: make this more const correct
+  const use_list uses() const;
+
+  // Replaces all uses of this node with 'newValue'.
+  //
+  // Given:   %3 = f(%1, %2)
+  //          %4 = g(%3)
+  //          %5 = h(%3, %3)
+  // Execute: %3.replaceAllUsesWith(%6)
+  // Result:  %3 = f(%1, %2)
+  //          %4 = g(%6)
+  //          %5 = h(%6, %6)
+  void replaceAllUsesWith(Value * newValue);
+
+  Value* copyMetadata(Value * from) {
+    setElemType(from->elemType());
+    setSizes(from->sizes());
+    if (from->has_unique_name()) {
+      setUniqueName(from->uniqueName());
+    }
+    return this;
+  }
+
+};
+
+
+struct Node : public Attributes<Node> {
+  ONNX_DISALLOW_COPY_AND_ASSIGN(Node);
+  friend struct Graph;
+  friend struct Value;
+  friend graph_node_list;
+  friend const_graph_node_list;
+  friend graph_node_list_iterator;
+  friend const_graph_node_list_iterator;
+
+private:
+  // each node but Return/Param
+  // is associated with exactly one place in the node list...
+  // of the graph_
+  // this circular is a doubly-linked list, the Return node is used as the sentinel for the beginning and end of the list
+  // such that the list never has null pointers
+  // next_in_graph[0] is next pointer
+  // next_in_graph[1] is prev pointer
+  // using an array to allow the same iterator class for forward and reverse node lists
+  // This list represents a topological sort
+
+  Node* next_in_graph[2] = { nullptr, nullptr };
+  Node* & next() { return next_in_graph[kNextDirection]; }
+  Node* & prev() { return next_in_graph[kPrevDirection]; }
+  Node* const & next() const { return next_in_graph[kNextDirection]; }
+  Node* const & prev() const { return next_in_graph[kPrevDirection]; }
+
+  const NodeKind kind_;
+  std::vector<Value*> inputs_;
+  std::vector<Value*> outputs_;
+  Graph* graph_;
+  size_t stage_;
+  bool has_name_;
+  std::string name_;
+  bool has_domain_;
+  std::string domain_;
+  bool has_doc_string_;
+  std::string doc_string_;
+
+protected:
+  Node(Graph * graph_, NodeKind kind_); // defined after graph
+
+public:
+  bool has_name() const {
+    return has_name_;
+  }
+  const std::string& name() const {
+    return name_;
+  }
+  void setName(std::string name) {
+    has_name_ = true;
+    name_ = std::move(name);
+  }
+  bool has_domain() const {
+    return has_domain_;
+  }
+  const std::string& domain() const {
+    return domain_;
+  }
+  void setDomain(std::string domain) {
+    has_domain_ = true;
+    domain_ = std::move(domain);
+  }
+  bool has_doc_string() const {
+    return has_doc_string_;
+  }
+  const std::string& docString() const {
+    return doc_string_;
+  }
+  void setDocString(std::string doc_string) {
+    has_doc_string_ = true;
+    doc_string_ = std::move(doc_string);
+  }
+  NodeKind kind() const {
+    return kind_;
+  }
+  Graph * owningGraph() {
+    return graph_;
+  }
+  const Graph * owningGraph() const {
+    return graph_;
+  }
+  size_t stage() const {
+    return stage_;
+  }
+  Node* setStage(size_t s) {
+    stage_ = s;
+    return this;
+  }
+  // NB: This returns an ArrayRef; that means that it will
+  // get invalidated if you resize inputs (e.g., using addInput)
+  // We can't return a std::vector<Node*>& because there's no
+  // way to soundly cast to std::vector<const Node*> (an insane
+  // implementation of std::vector could make this representationally
+  // different.)
+  ArrayRef<Value*> inputs() {
+    return inputs_;
+  }
+  ArrayRef<const Value*> inputs() const {
+    // Vectors are not convertible in const-ness of elements, but
+    // raw pointers are.
+    return {inputs_.data(), inputs_.size()};
+  }
+  // NB: This returns an ArrayRef; that means that it will
+  // get invalidated if you resize inputs (e.g., using addInput)
+  // We can't return a std::vector<Node*>& because there's no
+  // way to soundly cast to std::vector<const Node*> (an insane
+  // implementation of std::vector could make this representationally
+  // different.)
+  ArrayRef<Value*> outputs() {
+    return outputs_;
+  }
+  ArrayRef<const Value*> outputs() const {
+    // Vectors are not convertible in const-ness of elements, but
+    // raw pointers are.
+    return {outputs_.data(), outputs_.size()};
+  }
+  bool hasUses() const {
+    for(auto o : outputs()) {
+      if(!o->uses().empty())
+        return true;
+    }
+    return false;
+  }
+  void replaceAllUsesWith(Node * n) {
+    ONNX_ASSERT(outputs().size() == n->outputs().size());
+    size_t nOutputs = outputs().size();
+    for(size_t i = 0; i < nOutputs; i++) {
+      outputs()[i]->replaceAllUsesWith(n->outputs()[i]);
+    }
+  }
+  // lots of things like chunk have a single input or single output, so we have a
+  // helper to make accessing it easier
+  Value * input() {
+    ONNX_ASSERT(inputs_.size() == 1);
+    return inputs_.at(0);
+  }
+  Value * output() {
+    ONNX_ASSERT(outputs_.size() == 1);
+    return outputs_.at(0);
+  }
+  const Value * input() const {
+    ONNX_ASSERT(inputs_.size() == 1);
+    return inputs_.at(0);
+  }
+  Value * output() const {
+    ONNX_ASSERT(outputs_.size() == 1);
+    return outputs_.at(0);
+  }
+  // Access a particular input.  This is a checked index.
+  Value * input(size_t i) {
+    return inputs_.at(i);
+  }
+  const Value * input(size_t i) const {
+    return inputs_.at(i);
+  }
+
+  // Graphs
+
+  // Note [Topological invariant]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // We always maintain an up-to-date topological ordering of all nodes via
+  // the next()/prev() links.  All transformations to graphs must preserve
+  // this topological ordering: for example, it is only valid to 'addInput'
+  // with an input which is topologically before the current node.
+  //
+  // Usually, it is obvious whether or not topological order is maintained;
+  // for example, if you are adding nodes to the end of the topsort, it's
+  // impossible for them to refer to inputs that are not in the topsort.
+  // If it is not obvious, please comment accordingly.
+
+  // Add 'node' as an input to 'this' at the end of existing
+  // arguments.  Returns the added node for ease of chaining.
+  //
+  // Given:   %3 = f(%1, %2)
+  // Execute: %3.addInput(%4)
+  // Result:  %3 = f(%1, %2, %4)
+  Value* addInput(Value * node) {
+    ONNX_ASSERT(graph_ == node->owningGraph());
+    node->uses_in_current_graph_.emplace_back(this, inputs_.size());
+    inputs_.push_back(node);
+    return node;
+  }
+
+  // Replace the input of 'this' at position 'i' with
+  // 'newValue', returning the old node.
+  //
+  // Given:   %3 = f(%1, %2)
+  // Execute: %3.replaceInput(1, %4)
+  // Result:  %3 = f(%1, %4)
+  Value * replaceInput(size_t i, Value * newValue) {
+    ONNX_ASSERT(newValue->owningGraph() == graph_);
+    Value * old = dropInput(i);
+    inputs_[i] = newValue;
+    newValue->uses_in_current_graph_.emplace_back(this, i);
+    return old;
+  }
+
+  // Replace all occurrences of 'from' in the inputs of this
+  // node with 'to'. Corresponds to llvm's replaceUsesOfWith.
+  //
+  // Given:   %3 = f(%1, %2, %1)
+  // Execute: %3.replaceInputWith(%1, %4)
+  // Result:  %3 = f(%4, %2, %4)
+  void replaceInputWith(Value * from, Value * to) {
+    ONNX_ASSERT(from->owningGraph() == graph_);
+    ONNX_ASSERT(to->owningGraph() == graph_);
+    size_t i = 0;
+    for(auto input : inputs()) {
+      if(input == from)
+        replaceInput(i, to);
+      i++;
+    }
+  }
+
+  Value* addOutput() {
+    outputs_.push_back(new Value(this, outputs_.size()));
+    return outputs_.back();
+  }
+
+  void eraseOutput(size_t i);
+
+  // Insert unattached 'this' node after 'n' in the topological order.
+  // Returns this (for chaining).
+  //
+  // Given:   %3 = f(%1, %2)
+  //          %4 = g(%3)
+  // and unattached: %5 = h(%1)
+  // Execute: %5.insertBefore(%4)
+  // Result:  %3 = f(%1, %2)
+  //          %5 = h(%1)
+  //          %4 = g(%3)
+  Node* insertBefore(Node * n) {
+    ONNX_ASSERT(n->inGraphList());
+    insertAfter(n->prev());
+    return this;
+  }
+
+  // Insert unattached 'this' node after 'n' in the topological order.
+  // Returns this (for chaining).
+  //
+  // Given: %3 = f(%1, %2)
+  //        %4 = g(%3)
+  // and unattached: %5 = h(%1)
+  // Execute: %5.insertAfter(%4)
+  // Result:  %3 = f(%1, %2)
+  //          %4 = g(%3)
+  //          %5 = h(%1)
+  Node* insertAfter(Node * n) {
+    ONNX_ASSERT(!inGraphList() && n->inGraphList());
+    Node * next = n->next();
+    n->next() = this;
+    this->prev() = n;
+    this->next() = next;
+    next->prev() = this;
+    return this;
+  }
+
+  // Move 'this' (already in the graph) after 'n' in the topological order.
+  //
+  // Given: %2 = f(%1)
+  //        %3 = g(%1)
+  // Execute: %2.moveAfter(%3)
+  // Result: %3 = g(%1)
+  //         %2 = f(%1)
+  //
+  void moveAfter(Node * n) {
+    removeFromList();
+    insertAfter(n);
+  }
+
+  // Move a node 'n' (already in the graph) before 'this' in the topological order.
+  //
+  // Given: %2 = f(%1)
+  //        %3 = g(%1)
+  // Execute: %3.moveBefore(%2)
+  // Result: %3 = g(%1)
+  //         %2 = f(%1)
+  void moveBefore(Node * n) {
+    removeFromList();
+    insertBefore(n);
+  }
+
+  // Remove the input at 'i' from this node.
+  //
+  // WARNING: This is O(n) in the number of inputs, so avoid repeatedly calling
+  // removeInput.
+  //
+  // Given: %3 = f(%1, %2)
+  // Execute: %3.removeInput(1)
+  // Result: %3 = f(%1)
+  void removeInput(size_t i) {
+    dropInput(i);
+    // everything after this input shifts left,
+    // so we need to update their use offsets to match
+    for(size_t j = i+1; j < inputs_.size(); j++) {
+      auto it = findUseForInput(j);
+      it->offset--;
+    }
+    inputs_.erase(inputs_.begin() + i);
+  }
+
+  // Remove all inputs from a node.
+  //
+  // Given: %3 = f(%1, %2)
+  // Execute: %3.removeAllInputs()
+  // Result: %3 = f()
+  void removeAllInputs() {
+    for(size_t i = 0; i < inputs().size(); ++i)
+      dropInput(i);
+    inputs_.clear();
+  }
+
+  // Check whether this node is before node n in the graph.
+  bool isBefore(Node* n);
+
+  // iterators of the node list starting at this node
+  // useful for resuming a search starting at this node
+  graph_node_list_iterator iterator();
+  graph_node_list_iterator reverseIterator();
+  const_graph_node_list_iterator iterator() const;
+  const_graph_node_list_iterator reverseIterator() const;
+
+  // Remove 'this' from the instruction list and deallocate it.
+  //
+  // Invariant: no outputs of 'this' may have any uses.
+  //
+  // Given: %2 = f(%1)
+  //        %3 = g(%1)
+  // Execute: %2.destroy()
+  // Result: %3 = g(%1)
+  void destroy();
+
+  // Dynamically cast this node to the subclass indicated by the
+  // template variable, returning nullptr if the cast is invalid..
+  //
+  // Example usage: if(auto s = n.cast<Select>()) { ... }
+  //
+  // TODO: Make this const correct
+  template<typename T>
+  T* cast() {
+    if(T::Kind == kind())
+      return static_cast<T*>(this);
+    return nullptr;
+  }
+  template<typename T>
+  T* expect() {
+    ONNX_ASSERTM(T::Kind == kind(), "expected a %s but found a %s", T::Kind.toString(), kind().toString());
+    return static_cast<T*>(this);
+  }
+
+  virtual ~Node() = default;
+
+private:
+  // Lookup iterator in use list of _input i_ that corresponds to its use of _this_
+  use_list::iterator findUseForInput(size_t i) {
+    auto & input_uses = inputs_[i]->uses_in_current_graph_;
+    // O(N) on the use list, but unless we get nodes with +100 uses
+    // vector traversal still is probably faster than linked list
+    auto use_it = std::find(input_uses.begin(), input_uses.end(), Use(this, i));
+    ONNX_ASSERT(use_it != input_uses.end());
+    return use_it;
+  }
+
+  // remove the use of input i, this sets input i to nullptr, but
+  // is only used internally to Node before setting it to a new value
+  // or erasing the entry from the list.
+  Value* dropInput(size_t i) {
+    ONNX_ASSERT(i < inputs_.size());
+    auto input_node = inputs_[i];
+    auto use_it = findUseForInput(i);
+    input_node->uses_in_current_graph_.erase(use_it);
+    inputs_[i] = nullptr;
+    return input_node;
+  }
+
+  bool inGraphList() const {
+    ONNX_ASSERT(next() != nullptr || prev() == nullptr);
+    return next() != nullptr;
+  }
+  void removeFromList() {
+    ONNX_ASSERT(inGraphList());
+    Node * next = this->next();
+    Node * prev = this->prev();
+    prev->next() = next;
+    next->prev() = prev;
+    this->next() = nullptr;
+    this->prev() = nullptr;
+  }
+
+protected:
+  // subclasses must override
+  // this function is used by createClone to initialize a new version
+  // of a node in another graph. It should allocate a new instance of the same
+  // concrete type as 'this', but in graph 'g' which might be different
+  // than graph_
+  virtual Node * allocNewInstance(Graph * g) {
+    return new Node(g, kind());
+  }
+  // create a copy of all properties of Node s into this.
+  // subclasses should extend if they have additional information to copy.
+  // 'this' will be allocated with s->allocNewInstance(g) so it should have
+  // the same concrete type as 's'
+  //
+  // NB: This does NOT clone stages.  You're expected to set the stage correctly
+  // if you are going to preserve it.
+  virtual void cloneFrom(Node * s) {
+    copyAttributes(*s);
+  }
+};
+
+// A class with the same properties as OperatorSetIdProto, but without protobuf
+// overhead, resulting in a simpler and more readable workflow.
+class OpSetID final {
+  private:
+    std::string domain_;
+    int64_t version_;
+
+  public:
+    explicit OpSetID(const OperatorSetIdProto& proto)
+      :domain_(proto.domain()), version_(proto.version()) {}
+
+    // Default Domain Constructor
+    explicit OpSetID(const int64_t version)
+      :domain_(""), version_(version) {}
+
+    explicit OpSetID(const std::string& domain, int64_t version)
+      :domain_(domain), version_(version) {}
+
+    // target must be in the form "<domain>&<version>"
+    std::string toString() const {
+      return domain_ + "$" + ONNX_NAMESPACE::to_string(version_);
+    }
+
+    // target must be in the form "<domain>&<version>"
+    static OpSetID fromString(const std::string& target) {
+      ONNX_TRY {
+        std::string new_domain = target.substr(0, target.find("$"));
+        int new_version = ONNX_NAMESPACE::stoi(target.substr(target.find("$") + 1, target.length()).c_str());
+        return OpSetID(std::move(new_domain), new_version);
+      } ONNX_CATCH (const std::runtime_error& e) {
+        ONNX_HANDLE_EXCEPTION([&]() {
+          ONNX_ASSERTM(false, "Error in fromString: %s", e.what());
+        });
+      }
+
+      // The control will never reach here. 
+      // In the default build where exceptions are turned on in case of any error
+      // the control will enter catch block where an exception will be thrown again.
+      // In case of "no exception build" the code aborts at the site of first exception.
+      // Adding this to appease the warning "control may reach end of non-void function"
+      // as the mac build fails when ONNX_WERROR==ON
+      return OpSetID("", 0);
+    }
+
+    const std::string& domain() const {
+      return domain_;
+    }
+
+    int64_t version() const {
+      return version_;
+    }
+
+    void incrementVersion(int64_t step) {
+      version_ += step;
+    }
+
+    void setVersion(int64_t newVal) {
+      version_ = newVal;
+    }
+};
+
+struct Graph final {
+ONNX_DISALLOW_COPY_AND_ASSIGN(Graph);
+friend struct Node;
+friend struct Value;
+
+private:
+  // only used to keep track of allocated nodes
+  // actual representation of Graph is done with
+  // inputs, outputs, nodes
+
+  std::unordered_set<const Node*> all_nodes;
+  std::unordered_set<const Value*> all_values;
+  size_t next_unique_;
+
+  size_t new_node_stage_;
+
+  // holds outputs in a way that can be reflected
+  // as a Use object
+  // also used as the beginning/end of the circular node list to avoid
+  // having corner cases where the list is empty.
+  Node * const output_;
+  Node * const input_;
+  // Create an independent node list for those initializers do not exist in input
+  Node * const initializer_node_;
+
+  std::vector<Tensor> initializers_;
+  std::vector<std::string> initializer_names_;
+  // Store a name to offset map for erasing initializer node
+  std::map<std::string, int> initializer_to_offset_map_;
+
+  bool has_name_;
+  std::string name_;
+  bool has_doc_string_;
+  std::string doc_string_;
+
+  std::vector <OpSetID> opset_versions_;
+
+  bool isNameUnique(const std::string& name) const {
+    if (std::find(initializer_names_.cbegin(), initializer_names_.cend(), name) !=
+        initializer_names_.cend()) {
+      return false;
+    }
+    const auto f = [&name](const Value* v) { return v->uniqueName() == name; };
+    for (const Node* node : all_nodes) {
+      for (const auto& attr : node->attributeNames()) {
+        if (node->kindOf(attr) == AttributeKind::g) {
+          const auto& subgraph = node->g(attr);
+          if (!subgraph->isNameUnique(name)) {
+            return false;
+          }
+        } else if (node->kindOf(attr) == AttributeKind::gs) {
+          for (const auto& subgraph : node->gs(attr)) {
+            if (!subgraph->isNameUnique(name)) {
+              return false;
+            }
+          }
+        }
+      }
+      const auto found_in =
+          std::find_if(node->inputs().begin(), node->inputs().end(), f);
+      if (found_in != node->inputs().end()) {
+        return false;
+      }
+      const auto found_out =
+          std::find_if(node->outputs().begin(), node->outputs().end(), f);
+      if (found_out != node->outputs().end()) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+
+public:
+  Graph()
+  : next_unique_(0)
+  , new_node_stage_(0)
+  , output_(initOutput(create(kReturn, 0)))
+  , input_(create(kParam, 0))
+  , initializer_node_(create(kParam, 0))
+  , has_name_(false)
+  , has_doc_string_(false) {}
+
+  bool has_doc_string() const {
+    return has_doc_string_;
+  }
+  const std::string& docString() {
+    return doc_string_;
+  }
+  void setDocString(std::string doc_string) {
+    has_doc_string_ = true;
+    doc_string_ = std::move(doc_string);
+  }
+
+  void addInitializer(Tensor& initializer) {
+    if (initializer.name().empty()) {
+      initializer.setName(ONNX_NAMESPACE::to_string(getNextUnique()));
+    }
+    initializers_.push_back(initializer);
+    initializer_names_.push_back(initializer.name());
+  }
+  
+  // For IR >= 4, initializer is not required to exist in input
+  // Add initializer into initializer node list and return its Value
+  Value* addInitializerAndCreateValue(Tensor& initializer) {
+    addInitializer(initializer);
+    auto* init_value = initializer_node_->addOutput();
+    std::vector<Dimension> dim_sizes{initializer.sizes().cbegin(),
+                                    initializer.sizes().cend()};
+    init_value->setUniqueName(initializer.name());
+    init_value->setSizes(dim_sizes);
+    init_value->setElemType(initializer.elem_type());
+    initializer_to_offset_map_[initializer.name()] = init_value->offset();
+    return init_value;
+  }
+
+  void eraseInitializer(const std::string &name) {
+    initializers_.erase(
+        std::remove_if(
+            initializers_.begin(),
+            initializers_.end(),
+            [&name](Tensor& initializer) {
+              return initializer.name() == name;
+            }),
+        initializers_.end());
+    initializer_names_.erase(
+        std::remove(
+            initializer_names_.begin(),
+            initializer_names_.end(),
+            name),
+        initializer_names_.end());
+    if (initializer_to_offset_map_.count(name) > 0) {
+      initializer_node_->eraseOutput(initializer_to_offset_map_[name]);
+      initializer_to_offset_map_.erase(name);
+    }
+  }
+  void clearInitializers() {
+    initializers_.clear();
+    initializer_names_.clear();
+  }
+  const std::vector<Tensor>& initializers() const {
+    return initializers_;
+  }
+  const std::vector<std::string>& initializer_names() const {
+    return initializer_names_;
+  }
+  std::vector<Tensor>::const_iterator getInitializer(const std::string& name) const {
+    for (auto it = initializers_.cbegin(); it != initializers_.cend(); ++it) {
+      if (name == it->name()) {
+        return it;
+      }
+    }
+    return initializers_.end();
+  }
+  ArrayRef<Value*> inputs() {
+    return input_->outputs();
+  }
+  ArrayRef<const Value*> inputs() const {
+    const auto & inputs = input_->outputs();
+    return {inputs.data(), inputs.size()};
+  }
+  ArrayRef<Value*> outputs() {
+    return output_->inputs();
+  }
+  ArrayRef<const Value*> outputs() const {
+    return static_cast<const Node*>(output_)->inputs();
+  }
+  graph_node_list nodes() {
+    return graph_node_list(output_, kNextDirection);
+  }
+  const_graph_node_list nodes() const {
+    return const_graph_node_list(output_, kNextDirection);
+  }
+
+  std::vector<OpSetID>& opset_versions_mutable() {
+    return opset_versions_;
+  }
+
+  size_t getNextUnique() {
+      std::string next_unique_name = ONNX_NAMESPACE::to_string(++next_unique_);
+      while(!isNameUnique(next_unique_name)) {
+          next_unique_name = ONNX_NAMESPACE::to_string(++next_unique_);
+      }
+      return next_unique_;
+  }
+
+  // These invocations of begin() on output of function are OK
+  // because graph_node_list is non-owning, so it doesn't matter
+  // if it immediately dies after the invocation.
+  graph_node_list_iterator begin() {
+    return nodes().begin();
+  }
+  const_graph_node_list_iterator begin() const {
+    return nodes().begin();
+  }
+  graph_node_list_iterator end() {
+    return nodes().end();
+  }
+  const_graph_node_list_iterator end() const {
+    return nodes().end();
+  }
+  graph_node_list_iterator rbegin() {
+    return nodes().rbegin();
+  }
+  const_graph_node_list_iterator rbegin() const {
+    return nodes().rbegin();
+  }
+  graph_node_list_iterator rend() {
+    return nodes().rend();
+  }
+  const_graph_node_list_iterator rend() const {
+    return nodes().rend();
+  }
+  Node * return_node() {
+    return output_;
+  }
+  const Node * return_node() const {
+    return output_;
+  }
+
+  Value * addInput() {
+    return input_->addOutput();
+  }
+  void eraseInput(size_t i) {
+    input_->eraseOutput(i);
+  }
+  void advanceStage() {
+    new_node_stage_++;
+  }
+  void setStage(size_t new_stage) {
+    new_node_stage_ = new_stage;
+  }
+  size_t stage() const {
+    return new_node_stage_;
+  }
+  ResourceGuard setStageTemporary(size_t s) {
+    auto prev_stage = new_node_stage_;
+    new_node_stage_ = s;
+    return ResourceGuard([prev_stage, this]() { this->new_node_stage_ = prev_stage; });
+  }
+
+  size_t registerOutput(Value * n) {
+    output_->addInput(n);
+    return outputs().size() - 1;
+  }
+
+  Node * create(NodeKind kind, size_t num_outputs=1) {
+    // NB: Node constructor adds node to all_nodes
+    auto n = new Node(this, kind);
+    for(size_t i = 0; i < num_outputs; i++)
+      n->addOutput();
+    return n;
+  }
+
+  Node * create(NodeKind kind, ArrayRef<Value*> inputs, size_t num_outputs=1) {
+    auto n = create(kind, num_outputs);
+    for(auto i : inputs)
+      n->addInput(i);
+    return n;
+  }
+
+  Node * appendNode(Node * n) {
+    ONNX_ASSERT(n->graph_ == this && !n->inGraphList());
+    n->insertBefore(output_);
+    return n;
+  }
+
+  Node * prependNode(Node * n) {
+    ONNX_ASSERT(n->graph_ == this && !n->inGraphList());
+    n->insertAfter(output_);
+    return n;
+  }
+
+  // Adds to graph initializer list, initializer names list, and as a graph input
+  // Also syncs the initializer name, tensor name, and value name
+  // Create an initializer whose value is stored in input
+  Value* addInitializerAndInput(const Tensor& initializer, std::string name) {
+    Tensor initializerCopy = initializer;
+    std::vector<Dimension> dim_sizes{initializerCopy.sizes().cbegin(),
+                                     initializerCopy.sizes().cend()};
+    Value* new_init = addInput();
+    initializerCopy.setName(name);
+    new_init->setUniqueName(name);
+    new_init->setSizes(dim_sizes);
+    new_init->setElemType(initializerCopy.elem_type());
+    addInitializer(initializerCopy);
+    return new_init;
+  }
+
+  Value* addInitializerAndInput(const Tensor &initializer) {
+    return addInitializerAndInput(initializer, ONNX_NAMESPACE::to_string(getNextUnique()));
+  }
+
+  // Erases from graph initializer list, initializer names list, and as a graph input
+  // Must have no uses
+  void eraseInitializerAndInput(Value* v) {
+    eraseInitializer(v->uniqueName());
+    eraseInput(v->offset());
+  }
+
+  ~Graph() {
+    for (const Node * n : all_nodes)
+      delete n;
+    for (const Value * v : all_values)
+      delete v;
+  }
+
+  std::string toString() const {
+    std::ostringstream oss;
+    oss << *this;
+    return oss.str();
+  }
+
+  bool has_name() const {
+    return has_name_;
+  }
+
+  const std::string& name() const {
+    return name_;
+  }
+
+  void setName(std::string name) {
+    has_name_ = true;
+    name_ = std::move(name);
+  }
+
+  friend std::ostream& operator<<(std::ostream & out, const Graph & g);
+
+  void forSelfAndEachSubGraph(std::function<void(Graph*)> fn) {
+    fn(this);
+
+    for (const Node* node : all_nodes) {
+      for (const auto& attr : node->attributeNames()) {
+        if (node->kindOf(attr) == AttributeKind::g) {
+          std::shared_ptr<Graph> subgraph = node->g(attr);
+          subgraph->forSelfAndEachSubGraph(fn);
+        } else if (node->kindOf(attr) == AttributeKind::gs) {
+          for (const auto& subgraph : node->gs(attr)) {
+            subgraph->forSelfAndEachSubGraph(fn);
+          }
+        }
+      }
+    }
+  }
+
+  void forSelfAndEachSubGraph(std::function<void(const Graph*)> fn) const {
+    std::function<void(Graph*)> tmp_fn = [fn](Graph* graph) {fn(graph);};
+    const_cast<Graph*>(this)->forSelfAndEachSubGraph(tmp_fn);
+  }
+
+  void forEachNode(std::function<void(Node*)> fn) {
+    forSelfAndEachSubGraph([fn](Graph *graph) {
+      for(Node* node : graph->nodes()) {
+        fn(node);
+      }
+    });
+  }
+
+  void forEachNode(std::function<void(const Node*)> fn) const {
+    std::function<void(Node*)> tmp_fn = [fn](Node* node) {fn(node);};
+    const_cast<Graph*>(this)->forEachNode(tmp_fn);
+  }
+
+private:
+
+  // should only be called in the constructor
+  Node* initOutput(Node* p) {
+    p->next() = p;
+    p->prev() = p;
+    p->setStage(std::numeric_limits<size_t>::max());
+    return p;
+  }
+
+  void freeNode(Node * n) {
+    auto it = all_nodes.find(n);
+    ONNX_ASSERT(it != all_nodes.end());
+    delete *it;
+    all_nodes.erase(it);
+  }
+  void freeValue(Value * v) {
+    auto it = all_values.find(v);
+    ONNX_ASSERT(it != all_values.end());
+    delete *it;
+    all_values.erase(it);
+  }
+};
+
+inline Value::Value(Node *node_, size_t offset_)
+    : node_(node_), offset_(offset_), unique_(node_->graph_->getNextUnique()),
+      stage_(node_->graph_->new_node_stage_), has_unique_name_(false),
+      elem_type_(ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED),
+      has_sizes_(false) {
+  node_->graph_->all_values.emplace(this);
+}
+
+inline Graph * Value::owningGraph() {
+  return node()->owningGraph();
+}
+
+inline const Graph * Value::owningGraph() const {
+  return node()->owningGraph();
+}
+
+// `captured` nodes in subgraph determines which value it captures
+// by storing the value's unique name, so old unique names in `captured` nodes
+// should also be updated.
+inline Value* Value::setUniqueName(const std::string &name, bool rename_subgraph_captured_nodes) {
+  if (has_unique_name() && rename_subgraph_captured_nodes) {
+    auto *graph = owningGraph();
+    graph->forEachNode([this, &name](Node *node) {
+      if (node->owningGraph() == this->owningGraph()) {
+        // skip non-subgraph
+        return;
+      }
+      if (node->kind() == kCaptured) {
+        Value *output = node->output();
+        if (output->uniqueName() == this->uniqueName()) {
+          output->setUniqueName(name, false);
+        }
+      }
+    });
+  }
+  unique_name_ = name;
+  has_unique_name_ = true;
+  return this;
+}
+
+inline void Value::replaceAllUsesWith(Value * newValue) {
+  auto* graph = owningGraph();
+  ONNX_ASSERT(graph == newValue->owningGraph());
+  // propagate sizes and elem type
+  if (this->has_sizes()) {
+    newValue->setSizes(this->sizes());
+  }
+  if (this->elemType() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED) {
+    newValue->setElemType(this->elemType());
+  }
+  const auto unique_name = this->uniqueName();
+  // We do not want the optimization to change the graph output name
+  if (std::find(graph->outputs().rbegin(), graph->outputs().rend(),
+                this) != graph->outputs().rend()) {
+    newValue->setUniqueName(unique_name);
+    // The "unique" semantic of unique_name should be kept or uses()
+    // will return an incorrect result when the value is used in subgraph
+    this->setUniqueName(ONNX_NAMESPACE::to_string(graph->getNextUnique()), false);
+  }
+  newValue->uses_in_current_graph_.reserve(this->uses_in_current_graph_.size());
+  for(auto u : uses_in_current_graph_) {
+    u.user->inputs_[u.offset] = newValue;
+    newValue->uses_in_current_graph_.push_back(u);
+  }
+  graph->forEachNode([this, &newValue, &unique_name](Node *node) {
+    if (node->owningGraph() == this->owningGraph()) {
+      // skip non-subgraph
+      return;
+    }
+    if (node->kind() == kCaptured) {
+      Value *output = node->output();
+      if (output->uniqueName() == unique_name) {
+        output->setUniqueName(newValue->uniqueName());
+      }
+    }
+  });
+  uses_in_current_graph_.clear();
+  assert(this->uses().empty());
+}
+
+inline Node::Node(Graph * graph_, NodeKind kind_) :
+  kind_(kind_),
+  graph_(graph_),
+  stage_(graph_->new_node_stage_),
+  has_name_(false),
+  has_domain_(false),
+  has_doc_string_(false) {
+  graph_->all_nodes.emplace(this);
+}
+
+inline void Node::eraseOutput(size_t i) {
+  ONNX_ASSERT(i < outputs_.size());
+  ONNX_ASSERT(outputs_[i]->uses().empty());
+  Value * n = outputs_[i];
+  outputs_.erase(outputs_.begin() + i);
+  owningGraph()->freeValue(n);
+  for(size_t j = i; j < outputs_.size(); j++) {
+    outputs_[j]->offset_--;
+  }
+}
+
+inline bool Node::isBefore(Node* n) {
+  if (n == nullptr || this == n) {
+    // Bail out early.
+    return false;
+  }
+  // return true if node is Param (in initializers)
+  if (kind_ == kParam) {
+    return true;
+  }
+  // return false if target node is Param (in initializers)
+  if (n->kind() == kParam) {
+    return false;
+  }
+  ONNX_ASSERT(n->inGraphList());
+  for (Node* p = next(); p != *graph_->end(); p = p->next()) {
+    if (p == n) {
+      return true;
+    }
+  }
+  return false;
+}
+
+inline void Node::destroy() {
+  ONNX_ASSERT(inGraphList());
+  while(!outputs().empty())
+    eraseOutput(outputs().size() - 1);
+  removeAllInputs();
+  removeFromList();
+  graph_->freeNode(this);
+}
+
+/************* All nodes not required to be defined before Graph **************/
+
+inline graph_node_list_iterator Node::iterator() {
+  return graph_node_list_iterator(this, 0);
+}
+inline graph_node_list_iterator Node::reverseIterator() {
+  return iterator().reverse();
+}
+inline const_graph_node_list_iterator Node::iterator() const {
+  return const_graph_node_list_iterator(this, 0);
+}
+inline const_graph_node_list_iterator Node::reverseIterator() const {
+  return iterator().reverse();
+}
+
+// Returns a list about which nodes are using this value,
+// nodes in subgraph are also included.
+// This method is usually used to check whether it is
+// safe to delete a Value.
+inline const use_list Value::uses() const {
+  use_list all_uses = uses_in_current_graph_;
+  owningGraph()->forEachNode([this, &all_uses](const Node* node) {
+    if (node->owningGraph() == this->owningGraph()) {
+      // skip non-subgraph
+      return;
+    }
+    if (node->kind() == kCaptured) {
+      const Value* output = node->outputs()[0];
+      if (output->uniqueName() == this->uniqueName()) {
+        const auto output_uses = output->uses();
+        all_uses.insert(all_uses.end(), output_uses.begin(), output_uses.end());
+      }
+    }
+  });
+  return all_uses;
+}
+
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/ir_pb_converter.cc b/third_party/onnx/onnx/common/ir_pb_converter.cc
new file mode 100644
index 0000000000..c16c7b8930
--- /dev/null
+++ b/third_party/onnx/onnx/common/ir_pb_converter.cc
@@ -0,0 +1,699 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include "onnx/common/ir_pb_converter.h"
+#include <sstream>
+
+namespace ONNX_NAMESPACE {
+
+// Part 1: convert ONNX Protobuf to IR
+std::unique_ptr<Graph> graphProtoToGraph(const GraphProto& gp, bool nested,
+  const int ir_version=IR_VERSION);
+
+Tensor tensorProtoToTensor(const ONNX_NAMESPACE::TensorProto& tp) {
+  Tensor ret;
+
+  ret.sizes().reserve(tp.dims_size());
+  for (int i = 0; i < tp.dims_size(); i++) {
+    ret.sizes().push_back(tp.dims(i));
+  }
+
+  ret.elem_type() = tp.data_type();
+  switch (tp.data_type()) {
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
+    case ONNX_NAMESPACE::TensorProto_DataType_COMPLEX64: {
+      ret.floats().reserve(tp.float_data_size());
+      for (int i = 0; i < tp.float_data_size(); i++) {
+        ret.floats().push_back(tp.float_data(i));
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
+    case ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16:
+    case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
+    case ONNX_NAMESPACE::TensorProto_DataType_INT8:
+    case ONNX_NAMESPACE::TensorProto_DataType_INT16:
+    case ONNX_NAMESPACE::TensorProto_DataType_INT32:
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT8:
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT16: {
+      ret.int32s().reserve(tp.int32_data_size());
+      for (int i = 0; i < tp.int32_data_size(); i++) {
+        ret.int32s().push_back(tp.int32_data(i));
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_INT64: {
+      ret.int64s().reserve(tp.int64_data_size());
+      for (int i = 0; i < tp.int64_data_size(); i++) {
+        ret.int64s().push_back(tp.int64_data(i));
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT32:
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT64: {
+      ret.uint64s().reserve(tp.uint64_data_size());
+      for (int i = 0; i < tp.uint64_data_size(); i++) {
+        ret.uint64s().push_back(tp.uint64_data(i));
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE:
+    case ONNX_NAMESPACE::TensorProto_DataType_COMPLEX128: {
+      ret.doubles().reserve(tp.double_data_size());
+      for (int i = 0; i < tp.double_data_size(); i++) {
+        ret.doubles().push_back(tp.double_data(i));
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_STRING: {
+      ret.strings().reserve(tp.string_data_size());
+      for (int i = 0; i < tp.string_data_size(); i++) {
+        ret.strings().push_back(tp.string_data(i));
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED:
+      fail_convert("Unknown tensor data type");
+  }
+
+  // The only way to know if we should be using raw_data or
+  // <type>_data is to look at which of them is size zero.
+  if (tp.has_raw_data()) {
+    ret.set_raw_data(tp.raw_data());
+  }
+
+  if (tp.has_name()) {
+    ret.setName(tp.name());
+  }
+  if (tp.has_segment()) {
+    ret.set_segment_begin_and_end(tp.segment().begin(), tp.segment().end());
+  }
+  return ret;
+}
+
+void convertAttribute(const ONNX_NAMESPACE::AttributeProto& ap, Node* n,
+  const int ir_version=IR_VERSION) {
+  Symbol sym = Symbol(ap.name());
+  switch (ap.type()) {
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_FLOAT:
+      n->f_(sym, ap.f());
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_FLOATS: {
+      std::vector<double> floats;
+      floats.reserve(ap.floats_size());
+      for (int i = 0; i < ap.floats_size(); i++) {
+        floats.push_back(ap.floats(i));
+      }
+      n->fs_(sym, std::move(floats));
+      break;
+    }
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_INT:
+      n->i_(sym, ap.i());
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_INTS: {
+      std::vector<int64_t> ints;
+      ints.reserve(ap.ints_size());
+      for (int i = 0; i < ap.ints_size(); i++) {
+        ints.push_back(ap.ints(i));
+      }
+      n->is_(sym, std::move(ints));
+      break;
+    }
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_STRING:
+      n->s_(sym, ap.s());
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_STRINGS: {
+      std::vector<std::string> strings;
+      strings.reserve(ap.strings_size());
+      for (int i = 0; i < ap.strings_size(); i++) {
+        strings.push_back(ap.strings(i));
+      }
+      n->ss_(sym, std::move(strings));
+      break;
+    }
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_TENSOR:
+      n->t_(sym, tensorProtoToTensor(ap.t()));
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_TENSORS: {
+      std::vector<Tensor> tensors;
+      tensors.reserve(ap.tensors_size());
+      for (int i = 0; i < ap.tensors_size(); i++) {
+        tensors.push_back(tensorProtoToTensor(ap.tensors(i)));
+      }
+      n->ts_(sym, std::move(tensors));
+      break;
+    }
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_TYPE_PROTO:
+      n->tp_(sym, ap.tp());
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_TYPE_PROTOS: {
+      std::vector<TypeProto> types;
+      types.reserve(ap.type_protos_size());
+      for (int i = 0; i < ap.type_protos_size(); i++) {
+        types.push_back(ap.type_protos(i));
+      }
+      n->tps_(sym, std::move(types));
+      break;
+    }
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_GRAPH:
+      n->g_(sym, graphProtoToGraph(ap.g(), true, ir_version));
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_GRAPHS: {
+      std::vector<std::shared_ptr<Graph>> graphs;
+      graphs.reserve(ap.graphs_size());
+      for (int i = 0; i < ap.graphs_size(); i++) {
+        graphs.push_back(graphProtoToGraph(ap.graphs(i), true, ir_version));
+      }
+      n->gs_(sym, std::move(graphs));
+      break;
+    }
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_SPARSE_TENSOR:
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_SPARSE_TENSORS:
+      fail_convert("Sparse tensors not supported.");
+      break;
+    case ONNX_NAMESPACE::AttributeProto_AttributeType_UNDEFINED:
+      fail_convert("Unknown tensor data type");
+      break;
+  }
+}
+
+void convertAttributes(ONNX_NAMESPACE::NodeProto& np, Node* n, const int ir_version=IR_VERSION) {
+  for (int i = 0; i < np.attribute_size(); i++) {
+    convertAttribute(np.attribute(i), n, ir_version);
+  }
+}
+
+std::vector<Dimension> tensorShapeProtoToDimensions(const ONNX_NAMESPACE::TensorShapeProto& tsp) {
+  std::vector<Dimension> dims;
+  dims.reserve(tsp.dim_size());
+  for (int i = 0; i < tsp.dim_size(); i++) {
+    if (tsp.dim(i).has_dim_value()) {
+      dims.push_back(Dimension(static_cast<int>(tsp.dim(i).dim_value())));
+    } else if (tsp.dim(i).has_dim_param()) {
+      dims.push_back(Dimension(tsp.dim(i).dim_param()));
+    } else {
+      // a dimension that has neither dim_value nor dim_param set
+      // represents an unknown dimension unrelated to other unknown dimensions.
+      dims.push_back(Dimension());
+    }
+  }
+  return dims;
+}
+
+void createDummyValue(std::unique_ptr<Graph>& g, const std::string& name, std::unordered_map<std::string, Value*>& value_by_name_of) {
+  auto* undef = g->create(kCaptured, 1);
+  g->appendNode(undef);
+  undef->outputs()[0]->setUniqueName(name);
+  value_by_name_of[name] = undef->outputs()[0];
+}
+
+std::unique_ptr<Graph> graphProtoToGraph(const ONNX_NAMESPACE::GraphProto& gp,
+  bool nested, const int ir_version) {
+  std::unique_ptr<Graph> g(new Graph());
+
+  if (gp.has_name()) {
+    g->setName(gp.name());
+  }
+  if (gp.has_doc_string()) {
+    g->setDocString(gp.doc_string());
+  }
+
+  // Values are created (as in `new Value(..)`) by the Node that
+  // outputs them. Therefore we initialize the Nodes and Values in
+  // several stages.
+  //
+  // 1) add all input (to the graph) Values, owned by the sentinel Param node
+  // 2) add all Nodes and their output Values, but don't intialize inputs
+  // 3) initialize inputs of all Nodes
+  // 4) initialize inputs of the Return sentinel node
+  // 5) fill in type info for graph outputs, and register them as outputs
+  // 5) fill in type info for Values from the value_info list in the graph
+
+  // In ONNX proto land, Values are just strings. We are going to make
+  // objects out of them, and equal strings must be mapped to the same
+  // Value object.
+  std::unordered_map<std::string, Value*> value_by_name_of;
+
+  // We initialize Node inputs in a separate pass from the Nodes
+  // themselves. To do so, we need to have access to the names of the
+  // inputs.
+  std::unordered_map<Node*, std::vector<std::string>> inputs_by_node;
+
+  {
+    // ONNX represents optional arguments in two ways
+    //  - they are simply not provided
+    //  - OR the empty string is passed as the input name
+    // This is to handle that second case, which needs a dummy node to
+    // be representable in the graph IR.
+    auto* n = g->create(kUndefined, 1);
+    g->appendNode(n);
+    n->outputs()[0]->setUniqueName("");
+    value_by_name_of[""] = n->outputs()[0];
+  }
+
+  for (int i = 0; i < gp.input_size(); i++) {
+    auto vip = gp.input(i);
+    auto v = g->addInput();
+    const auto& tensor_type = vip.type().tensor_type();
+    if (tensor_type.has_elem_type()) {
+      v->setElemType(tensor_type.elem_type());
+    }
+    if (tensor_type.has_shape()) {
+      v->setSizes(tensorShapeProtoToDimensions(tensor_type.shape()));
+    }
+    v->setUniqueName(vip.name());
+    value_by_name_of[vip.name()] = v;
+  }
+
+  for (int i = 0; i < gp.node_size(); i++) {
+    auto np = gp.node(i);
+    auto* n = g->create(Symbol(np.op_type()), /* num_outputs = */ np.output_size());
+    g->appendNode(n);
+    for (int j = 0; j < np.output_size(); j++) {
+      auto out = n->outputs()[j];
+      // we don't know the real type here, so that's done in a later pass
+      out->setElemType(ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED);
+      out->setUniqueName(np.output(j));
+      value_by_name_of[np.output(j)] = out;
+    }
+    convertAttributes(np, n, ir_version);
+    std::vector<std::string> inputs;
+    inputs.reserve(np.input_size());
+    for (int j = 0; j < np.input_size(); j++) {
+      inputs.push_back(np.input(j));
+    }
+    inputs_by_node[n] = inputs;
+    if (np.has_doc_string()) {
+      n->setDocString(np.doc_string());
+    }
+    if (np.has_name()) {
+      n->setName(np.name());
+    }
+    if (np.has_domain()) {
+      n->setDomain(np.domain());
+    }
+  }
+
+  for (int i = 0; i < gp.initializer_size(); ++i) {
+    auto init = tensorProtoToTensor(gp.initializer(i));
+    // If ir_version >= 4, initializer does not have to be included in input
+    // Create a Value from initializer by addInitializerNode if name does not exist in input
+    // and save it into value_by_name_of for later use (node input)
+    if (ir_version >= 4 && value_by_name_of.count(init.name()) == 0) {
+      value_by_name_of[init.name()] = g->addInitializerAndCreateValue(init);
+    } else {
+      // If ir_version < 4 or the initializer exists in input
+      // Simply add initializer without creating new value
+      // which means it will prioritize input value over initializer value if both exist
+      g->addInitializer(init);
+    }
+  }
+
+  for (auto n : g->nodes()) {
+    auto search = inputs_by_node.find(n);
+    if (search == inputs_by_node.end()) {
+      continue;
+    }
+    for (auto input : search->second) {
+      if (!value_by_name_of.count(input) && nested) {
+        // Undefined reference to an input in a nested block. This may be a
+        // captured value. Create a dummy node that we ignore later.
+        createDummyValue(g, input, value_by_name_of);
+      }
+
+      if (!value_by_name_of.count(input)) {
+        std::ostringstream msg;
+        msg << "Input " << input << " is undefined!";
+        ONNX_THROW_EX(std::out_of_range(msg.str()));
+      }
+      n->addInput(value_by_name_of.at(input));
+    }
+  }
+
+  for (int i = 0; i < gp.output_size(); i++) {
+    if (!value_by_name_of.count(gp.output(i).name()) && nested) {
+      // Same captured value logic as above. We can consider outputs of a
+      // graph to be "inputs" of a dummy "output" node. The same lexical
+      // scoping rules are valid here, thus we need to add a dummy node
+      // in the case of an undefined reference
+      createDummyValue(g, gp.output(i).name(), value_by_name_of);
+    }
+    const auto& output_tensor_type = gp.output(i).type().tensor_type();
+    if (output_tensor_type.has_elem_type()) {
+      value_by_name_of[gp.output(i).name()]->setElemType(output_tensor_type.elem_type());
+    }
+    if (output_tensor_type.has_shape()) {
+      value_by_name_of[gp.output(i).name()]->setSizes(tensorShapeProtoToDimensions(output_tensor_type.shape()));
+    }
+    g->registerOutput(value_by_name_of[gp.output(i).name()]);
+  }
+
+  for (int i = 0; i < gp.value_info_size(); i++) {
+    const auto& tensor_type = gp.value_info(i).type().tensor_type();
+    if (!value_by_name_of.count(gp.value_info(i).name())) {
+      // Ideally the model should not have a value_info whose name does not exist in the graph (unused); simply skip it
+      continue;
+    }
+    if (tensor_type.has_elem_type()) {
+      value_by_name_of[gp.value_info(i).name()]->setElemType(tensor_type.elem_type());
+    }
+    if (tensor_type.has_shape()) {
+      value_by_name_of[gp.value_info(i).name()]->setSizes(tensorShapeProtoToDimensions(tensor_type.shape()));
+    }
+  }
+
+  return g;
+}
+
+std::unique_ptr<Graph> ImportModelProto(const ModelProto& mp) {
+  if (!mp.has_ir_version()) {
+    return nullptr;
+  } else if (mp.ir_version() == 1) {
+    return nullptr;
+  }
+
+  std::unique_ptr<Graph> g(graphProtoToGraph(mp.graph(), false, mp.ir_version()));
+  for (int i = 0; i < mp.opset_import_size(); i++) {
+    OpSetID new_opset_version(mp.opset_import(i).domain(), mp.opset_import(i).version());
+    g->forSelfAndEachSubGraph(
+        [&new_opset_version](Graph* graph) { graph->opset_versions_mutable().emplace_back(new_opset_version); });
+  }
+  return g;
+}
+
+// Part 2: convert IR to ONNX Protobuf
+std::string value_name(Value* n) {
+  return n->uniqueName();
+}
+
+void encodeGraph(GraphProto* p_g, const std::shared_ptr<Graph>& g);
+
+void encodeTensor(ONNX_NAMESPACE::TensorProto* p, const Tensor& tensor) {
+  if (tensor.hasName()) {
+    p->set_name(tensor.name());
+  }
+  if (tensor.is_segment()) {
+    ONNX_NAMESPACE::TensorProto_Segment segment;
+    segment.set_begin(tensor.segment_begin());
+    segment.set_end(tensor.segment_end());
+    p->mutable_segment()->CopyFrom(segment);
+  }
+  for (auto d : tensor.sizes()) {
+    p->add_dims(d);
+  }
+  p->set_data_type(tensor.elem_type());
+  switch (tensor.elem_type()) {
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
+    case ONNX_NAMESPACE::TensorProto_DataType_COMPLEX64: {
+      for (float x : tensor.floats()) {
+        p->add_float_data(x);
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
+    case ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16:
+    case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
+    case ONNX_NAMESPACE::TensorProto_DataType_INT8:
+    case ONNX_NAMESPACE::TensorProto_DataType_INT16:
+    case ONNX_NAMESPACE::TensorProto_DataType_INT32:
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT8:
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT16: {
+      for (int32_t x : tensor.int32s()) {
+        p->add_int32_data(x);
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_INT64: {
+      for (int64_t x : tensor.int64s()) {
+        p->add_int64_data(x);
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT32:
+    case ONNX_NAMESPACE::TensorProto_DataType_UINT64: {
+      for (uint64_t x : tensor.uint64s()) {
+        p->add_uint64_data(x);
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE:
+    case ONNX_NAMESPACE::TensorProto_DataType_COMPLEX128: {
+      for (double x : tensor.doubles()) {
+        p->add_double_data(x);
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_STRING: {
+      for (const std::string& x : tensor.strings()) {
+        p->add_string_data(x);
+      }
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED:
+      fail_convert("Unknown tensor data type");
+  }
+  if (tensor.is_raw_data()) {
+    p->set_raw_data(tensor.raw());
+  }
+}
+
+void addAttribute(ONNX_NAMESPACE::NodeProto* n_p, Node* n, Symbol name) {
+  auto attr = n_p->add_attribute();
+  attr->set_name(name.toString());
+  switch (n->kindOf(name)) {
+    case AttributeKind::f: {
+      attr->set_f(static_cast<float>(n->f(name)));
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_FLOAT);
+    } break;
+    case AttributeKind::fs: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_FLOATS);
+      for (auto& v : n->fs(name))
+        attr->add_floats(static_cast<float>(v));
+    } break;
+    case AttributeKind::i: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_INT);
+      attr->set_i(n->i(name));
+    } break;
+    case AttributeKind::is: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_INTS);
+      for (auto& v : n->is(name))
+        attr->add_ints(v);
+    } break;
+    case AttributeKind::s: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_STRING);
+      attr->set_s(n->s(name));
+    } break;
+    case AttributeKind::ss: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_STRINGS);
+      for (auto& v : n->ss(name))
+        attr->add_strings(v);
+    } break;
+    case AttributeKind::t: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_TENSOR);
+      auto t = attr->mutable_t();
+      encodeTensor(t, n->t(name));
+    } break;
+    case AttributeKind::ts: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_TENSORS);
+      for (auto& v : n->ts(name)) {
+        auto t = attr->add_tensors();
+        encodeTensor(t, v);
+      }
+    } break;
+    case AttributeKind::g: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_GRAPH);
+      auto g = attr->mutable_g();
+      encodeGraph(g, n->g(name));
+    } break;
+    case AttributeKind::gs: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_GRAPHS);
+      for (auto& v : n->gs(name)) {
+        auto g = attr->add_graphs();
+        encodeGraph(g, v);
+      }
+    } break;
+    case AttributeKind::tp: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_TYPE_PROTO);
+      auto tp = attr->mutable_tp();
+      tp->CopyFrom(n->tp(name));
+    } break;
+    case AttributeKind::tps: {
+      attr->set_type(ONNX_NAMESPACE::AttributeProto_AttributeType_TYPE_PROTOS);
+      for (auto& v : n->tps(name)) {
+        auto tp = attr->add_type_protos();
+        tp->CopyFrom(v);
+      }
+    } break;
+  }
+}
+
+void encodeTypeProtoTensorType(ONNX_NAMESPACE::TypeProto_Tensor* tensor_type, Value* n) {
+  if (n->elemType() != 0) {
+    tensor_type->set_elem_type(n->elemType());
+  }
+  if (n->has_sizes()) {
+    ONNX_NAMESPACE::TensorShapeProto* shape = tensor_type->mutable_shape();
+    for (const Dimension& d : n->sizes()) {
+      auto dim = shape->add_dim();
+      if (!d.is_unknown) {
+        if (d.is_int) {
+          dim->set_dim_value(d.dim);
+        } else {
+          dim->set_dim_param(d.param);
+        }
+      }
+    }
+  }
+}
+
+void encodeValueInfo(ONNX_NAMESPACE::ValueInfoProto* v, Value* n) {
+  v->set_name(value_name(n));
+  if (n->elemType() != 0 || n->has_sizes()) {
+    ONNX_NAMESPACE::TypeProto* t = v->mutable_type();
+    ONNX_NAMESPACE::TypeProto_Tensor* tensor_type = t->mutable_tensor_type();
+    encodeTypeProtoTensorType(tensor_type, n);
+  }
+}
+
+void encodeGraph(GraphProto* p_g, const std::shared_ptr<Graph>& g) {
+  ONNX_ASSERT(p_g != nullptr);
+
+  if (g->has_name()) {
+    p_g->set_name(g->name());
+  }
+
+  if (g->has_doc_string()) {
+    p_g->set_doc_string(g->docString());
+  }
+
+  for (auto input : g->inputs()) {
+    ONNX_NAMESPACE::ValueInfoProto* v = p_g->add_input();
+    encodeValueInfo(v, input);
+  }
+  for (auto output : g->outputs()) {
+    ONNX_NAMESPACE::ValueInfoProto* v = p_g->add_output();
+    encodeValueInfo(v, output);
+  }
+
+  std::unordered_set<Value*> graph_outputs(g->outputs().begin(), g->outputs().end());
+
+  for (auto node : g->nodes()) {
+    if (node->kind() == kUndefined || node->kind() == kCaptured) {
+      // Undefined nodes are used to represent optional inputs that are not
+      // provided.
+      continue;
+    }
+    auto p_n = p_g->add_node();
+    for (auto input : node->inputs()) {
+      if (input->node()->kind() == kUndefined) {
+        p_n->add_input("");
+      } else {
+        p_n->add_input(value_name(input));
+      }
+    }
+    for (auto output : node->outputs()) {
+      p_n->add_output(value_name(output));
+      // only save it if
+      //  - it has actual information worth saving
+      //  - it's not already saved in the graph outputs value info
+      if (graph_outputs.find(output) != graph_outputs.end()) {
+        continue;
+      }
+      if (output->elemType() == TensorProto_DataType_UNDEFINED && output->sizes().empty()) {
+        continue;
+      }
+      ValueInfoProto* v = p_g->add_value_info();
+      encodeValueInfo(v, output);
+    }
+    p_n->set_op_type(node->kind().toString());
+    for (auto attr_name : node->attributeNames()) {
+      addAttribute(p_n, node, attr_name);
+    }
+    if (node->has_doc_string()) {
+      p_n->set_doc_string(node->docString());
+    }
+    if (node->has_name()) {
+      p_n->set_name(node->name());
+    }
+    if (node->has_domain()) {
+      p_n->set_domain(node->domain());
+    }
+  }
+
+  auto num_initializers = g->initializers().size();
+  for (unsigned int i = 0; i < num_initializers; i++) {
+    auto p = p_g->add_initializer();
+    p->set_name(g->initializer_names()[i]);
+    encodeTensor(p, g->initializers()[i]);
+  }
+}
+
+void ExportModelProto(ModelProto* p_m, const std::shared_ptr<Graph>& g) {
+  GraphProto* p_g = p_m->mutable_graph();
+  encodeGraph(p_g, g);
+  // Add new opset_versions
+  p_m->clear_opset_import();
+  for (const OpSetID& opset : g->opset_versions_mutable()) {
+    OperatorSetIdProto* opset_version_output = p_m->add_opset_import();
+    opset_version_output->set_domain(opset.domain());
+    opset_version_output->set_version(opset.version());
+  }
+}
+
+ModelProto PrepareOutput(const ModelProto& mp_in) {
+  ModelProto mp_out{};
+
+  if (mp_in.has_ir_version()) {
+    mp_out.set_ir_version(mp_in.ir_version());
+  }
+  if (mp_in.has_producer_name()) {
+    mp_out.set_producer_name(mp_in.producer_name());
+  }
+  if (mp_in.has_producer_version()) {
+    mp_out.set_producer_version(mp_in.producer_version());
+  }
+  if (mp_in.has_domain()) {
+    mp_out.set_domain(mp_in.domain());
+  }
+  if (mp_in.has_model_version()) {
+    mp_out.set_model_version(mp_in.model_version());
+  }
+  if (mp_in.has_doc_string()) {
+    mp_out.set_doc_string(mp_in.doc_string());
+  }
+  for (int i = 0; i < mp_in.opset_import_size(); i++) {
+    auto& oi_in = mp_in.opset_import(i);
+    auto* oi_out = mp_out.add_opset_import();
+    if (oi_in.has_domain()) {
+      oi_out->set_domain(oi_in.domain());
+    }
+    if (oi_in.has_version()) {
+      oi_out->set_version(oi_in.version());
+    }
+  }
+  for (int i = 0; i < mp_in.metadata_props_size(); i++) {
+    auto& pp_in = mp_in.metadata_props(i);
+    auto* pp_out = mp_out.add_metadata_props();
+    if (pp_in.has_key()) {
+      pp_out->set_key(pp_in.key());
+    }
+    if (pp_in.has_value()) {
+      pp_out->set_value(pp_in.value());
+    }
+  }
+
+  return mp_out;
+}
+
+void assertNonNull(std::shared_ptr<Graph> g) {
+  ONNX_ASSERTM(
+      g.get() != nullptr,
+      "Warning: onnx version converter is unable to parse input model. "
+      "(The IR version of the ONNX model may be too old.)");
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/ir_pb_converter.h b/third_party/onnx/onnx/common/ir_pb_converter.h
new file mode 100644
index 0000000000..4629755a59
--- /dev/null
+++ b/third_party/onnx/onnx/common/ir_pb_converter.h
@@ -0,0 +1,47 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+#include "onnx/common/common.h"
+#include "onnx/common/ir.h"
+#include "onnx/onnx_pb.h"
+
+namespace ONNX_NAMESPACE {
+
+class ConvertError final : public std::runtime_error {
+ public:
+  using std::runtime_error::runtime_error;
+
+  ConvertError(const std::string& message) : std::runtime_error(message) {}
+
+  const char* what() const noexcept override {
+    if (!expanded_message_.empty()) {
+      return expanded_message_.c_str();
+    }
+    return std::runtime_error::what();
+  }
+
+  void AppendContext(const std::string& context) {
+    expanded_message_ = MakeString(
+        std::runtime_error::what(), "\n\n==> Context: ", context);
+  }
+
+ private:
+  std::string expanded_message_;
+};
+
+#define fail_convert(...) \
+  ONNX_THROW_EX(ConvertError(MakeString(__VA_ARGS__)));
+
+void ExportModelProto(ModelProto* p_m, const std::shared_ptr<Graph>& g);
+
+std::unique_ptr<Graph> ImportModelProto(const ModelProto& mp);
+
+ModelProto PrepareOutput(const ModelProto& mp_in);
+
+void assertNonNull(std::shared_ptr<Graph> g);
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/model_helpers.cc b/third_party/onnx/onnx/common/model_helpers.cc
new file mode 100644
index 0000000000..db66a0b45f
--- /dev/null
+++ b/third_party/onnx/onnx/common/model_helpers.cc
@@ -0,0 +1,40 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/common/model_helpers.h"
+#include "onnx/checker.h"
+#include "onnx/defs/schema.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+
+Common::Status BuildNode(
+    const std::string& name,
+    const std::string& domain,
+    const std::string& doc_string,
+    const std::string& op_type,
+    std::vector<std::string> const& inputs,
+    std::vector<std::string> const& outputs,
+    NodeProto* node) {
+  if (node == NULL) {
+    return Common::Status(
+        Common::CHECKER,
+        Common::INVALID_ARGUMENT,
+        "node_proto should not be nullptr.");
+  }
+  node->set_name(name);
+  node->set_domain(domain);
+  node->set_doc_string(doc_string);
+  node->set_op_type(op_type);
+  for (auto& input : inputs) {
+    node->add_input(input);
+  }
+  for (auto& output : outputs) {
+    node->add_output(output);
+  }
+
+  return Common::Status::OK();
+}
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/model_helpers.h b/third_party/onnx/onnx/common/model_helpers.h
new file mode 100644
index 0000000000..5722ea54b8
--- /dev/null
+++ b/third_party/onnx/onnx/common/model_helpers.h
@@ -0,0 +1,26 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "onnx/common/status.h"
+#include "onnx/onnx-operators_pb.h"
+
+namespace ONNX_NAMESPACE {
+
+// Helper function for register nodes in
+// a FunctionProto. Attributes need to be
+// registered separately.
+Common::Status BuildNode(
+    const std::string& name,
+    const std::string& domain,
+    const std::string& doc_string,
+    const std::string& op_type,
+    std::vector<std::string> const& inputs,
+    std::vector<std::string> const& outputs,
+    /*OUT*/ NodeProto* node);
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/path.cc b/third_party/onnx/onnx/common/path.cc
new file mode 100644
index 0000000000..7c0b33aea7
--- /dev/null
+++ b/third_party/onnx/onnx/common/path.cc
@@ -0,0 +1,19 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+#include "onnx/common/path.h"
+
+namespace ONNX_NAMESPACE {
+
+std::string path_join(const std::string& origin, const std::string& append) {
+  if (origin.find_last_of(k_preferred_path_separator) != origin.length() - k_preferred_path_separator.length()) {
+    return origin + k_preferred_path_separator + append;
+  }
+  return origin + append;
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/path.h b/third_party/onnx/onnx/common/path.h
new file mode 100644
index 0000000000..0e62e75186
--- /dev/null
+++ b/third_party/onnx/onnx/common/path.h
@@ -0,0 +1,22 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+#pragma once
+
+#include <string>
+
+namespace ONNX_NAMESPACE {
+
+#ifdef _WIN32
+const std::string k_preferred_path_separator = "\\";
+#else  // POSIX
+const std::string k_preferred_path_separator = "/";
+#endif
+
+std::string path_join(const std::string& origin, const std::string& append);
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/platform_helpers.h b/third_party/onnx/onnx/common/platform_helpers.h
new file mode 100644
index 0000000000..b98a406904
--- /dev/null
+++ b/third_party/onnx/onnx/common/platform_helpers.h
@@ -0,0 +1,18 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+namespace ONNX_NAMESPACE {
+
+// Determine if the processor is little endian or not
+inline bool is_processor_little_endian() {
+  int num = 1;
+  if (*(char*)&num == 1)
+    return true;
+  return false;
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/status.cc b/third_party/onnx/onnx/common/status.cc
new file mode 100644
index 0000000000..d18ff48c45
--- /dev/null
+++ b/third_party/onnx/onnx/common/status.cc
@@ -0,0 +1,86 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "status.h"
+#include <assert.h>
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace Common {
+
+Status::Status(StatusCategory category, int code, const std::string& msg) {
+  assert(static_cast<int>(StatusCode::OK) != code);
+  state_.reset(new State(category, code, msg));
+}
+
+Status::Status(StatusCategory category, int code)
+    : Status(category, code, EmptyString()) {}
+
+bool Status::IsOK() const noexcept {
+  return (state_ == NULL);
+}
+
+StatusCategory Status::Category() const noexcept {
+  return IsOK() ? StatusCategory::NONE : state_->category;
+}
+
+int Status::Code() const noexcept {
+  return IsOK() ? static_cast<int>(StatusCode::OK) : state_->code;
+}
+
+const std::string& Status::ErrorMessage() const {
+  return IsOK() ? EmptyString() : state_->msg;
+}
+
+std::string Status::ToString() const {
+  if (state_ == nullptr) {
+    return std::string("OK");
+  }
+
+  std::string result;
+
+  if (StatusCategory::CHECKER == state_->category) {
+    result += "[CheckerError]";
+  } else if (StatusCategory::OPTIMIZER == state_->category) {
+    result += "[OptimizerError]";
+  }
+
+  result += " : ";
+  result += ONNX_NAMESPACE::to_string(Code());
+  std::string msg;
+
+  switch (static_cast<StatusCode>(Code())) {
+    case INVALID_ARGUMENT:
+      msg = "INVALID_ARGUMENT";
+      break;
+    case INVALID_PROTOBUF:
+      msg = "INVALID_PROTOBUF";
+      break;
+    case FAIL:
+      msg = "FAIL";
+      break;
+    default:
+      msg = "GENERAL ERROR";
+      break;
+  }
+  result += " : ";
+  result += msg;
+  result += " : ";
+  result += state_->msg;
+
+  return result;
+}
+
+const Status& Status::OK() noexcept {
+  static Status s_ok;
+  return s_ok;
+}
+
+const std::string& Status::EmptyString() {
+  static std::string empty_str;
+  return empty_str;
+}
+
+} // namespace Common
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/status.h b/third_party/onnx/onnx/common/status.h
new file mode 100644
index 0000000000..6b4d4e8e7e
--- /dev/null
+++ b/third_party/onnx/onnx/common/status.h
@@ -0,0 +1,95 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include <memory>
+#include <ostream>
+#include <string>
+
+namespace ONNX_NAMESPACE {
+namespace Common {
+
+enum StatusCategory {
+  NONE = 0,
+  CHECKER = 1,
+  OPTIMIZER = 2,
+};
+
+enum StatusCode {
+  OK = 0,
+  FAIL = 1,
+  INVALID_ARGUMENT = 2,
+  INVALID_PROTOBUF = 3,
+};
+
+class Status {
+ public:
+  Status() noexcept {}
+
+  Status(StatusCategory category, int code, const std::string& msg);
+
+  Status(StatusCategory category, int code);
+
+  Status(const Status& other) {
+    *this = other;
+  }
+
+  void operator=(const Status& other) {
+    if (&other != this) {
+      if (nullptr == other.state_) {
+        state_.reset();
+      } else if (state_ != other.state_) {
+        state_.reset(new State(*other.state_));
+      }
+    }
+  }
+
+  Status(Status&&) = default;
+  Status& operator=(Status&&) = default;
+  ~Status() = default;
+
+  bool IsOK() const noexcept;
+
+  int Code() const noexcept;
+
+  StatusCategory Category() const noexcept;
+
+  const std::string& ErrorMessage() const;
+
+  std::string ToString() const;
+
+  bool operator==(const Status& other) const {
+    return (this->state_ == other.state_) || (ToString() == other.ToString());
+  }
+
+  bool operator!=(const Status& other) const {
+    return !(*this == other);
+  }
+
+  static const Status& OK() noexcept;
+
+ private:
+  struct State {
+    State(StatusCategory cat_, int code_, std::string msg_)
+        : category(cat_), code(code_), msg(std::move(msg_)) {}
+
+    StatusCategory category = StatusCategory::NONE;
+    int code = 0;
+    std::string msg;
+  };
+
+  static const std::string& EmptyString();
+
+  // state_ == nullptr when if status code is OK.
+  std::unique_ptr<State> state_;
+};
+
+inline std::ostream& operator<<(std::ostream& out, const Status& status) {
+  return out << status.ToString();
+}
+
+} // namespace Common
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/stl_backports.h b/third_party/onnx/onnx/common/stl_backports.h
new file mode 100644
index 0000000000..fa9ebcf48c
--- /dev/null
+++ b/third_party/onnx/onnx/common/stl_backports.h
@@ -0,0 +1,69 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+// This file contains backports of STL features for newer C++.
+
+#include <memory>
+#include <type_traits>
+
+/*
+ * Use MOVE_CAPTURE_IF_CPP14 in a lambda capture so it gets
+ * copied in C++11 and moved in C++14.
+ * Example:
+ *   std::string mystring;
+ *   auto lambda = [MOVE_CAPTURE_IF_CPP14(mystring)] {
+ *     std::cout << mystring;
+ *   }
+ */
+#ifdef __cpp_init_captures
+  #define MOVE_CAPTURE_IF_CPP14(variable) variable = std::move(variable)
+#else
+  #define MOVE_CAPTURE_IF_CPP14(variable) variable
+#endif
+
+
+/**
+ * For exception safety and consistency with make_shared. Erase me when
+ * we have std::make_unique().
+ *
+ * @author Louis Brandy (ldbrandy@fb.com)
+ * @author Xu Ning (xning@fb.com)
+ * @author Sebastian Messmer (messmer@fb.com)
+ */
+
+#if __cplusplus >= 201402L || __cpp_lib_make_unique >= 201304L || \
+    (__ANDROID__ && __cplusplus >= 201300L) || _MSC_VER >= 1900
+
+namespace ONNX_NAMESPACE {
+  using std::make_unique;
+}
+
+#else
+
+namespace ONNX_NAMESPACE {
+
+template <typename T, typename... Args>
+typename std::enable_if<!std::is_array<T>::value, std::unique_ptr<T>>::type
+make_unique(Args&&... args) {
+  return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
+}
+
+// Allows 'make_unique<T[]>(10)'. (N3690 s20.9.1.4 p3-4)
+template <typename T>
+typename std::enable_if<std::is_array<T>::value, std::unique_ptr<T>>::type
+make_unique(const size_t n) {
+  return std::unique_ptr<T>(new typename std::remove_extent<T>::type[n]());
+}
+
+// Disallows 'make_unique<T[10]>()'. (N3690 s20.9.1.4 p5)
+template <typename T, typename... Args>
+typename std::enable_if<
+  std::extent<T>::value != 0, std::unique_ptr<T>>::type
+make_unique(Args&&...) = delete;
+
+}
+
+#endif
diff --git a/third_party/onnx/onnx/common/tensor.h b/third_party/onnx/onnx/common/tensor.h
new file mode 100644
index 0000000000..f0024f65ad
--- /dev/null
+++ b/third_party/onnx/onnx/common/tensor.h
@@ -0,0 +1,419 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include <cmath>
+#include <functional>
+#include <numeric>
+#include "onnx/common/assertions.h"
+#include "onnx/onnx_pb.h"
+
+namespace ONNX_NAMESPACE {
+
+struct Tensor final {
+private:
+  bool is_segment_;
+  int64_t segment_begin_;
+  int64_t segment_end_;
+  bool has_name_;
+  std::string name_;
+  int32_t elem_type_;
+  std::vector<int64_t> sizes_;
+
+  std::vector<float> float_data_;
+  std::vector<double> double_data_;
+  std::vector<int32_t> int32_data_;
+  std::vector<int64_t> int64_data_;
+  std::vector<uint64_t> uint64_data_;
+  std::vector<std::string> string_data_;
+
+  bool is_raw_data_;
+  std::string raw_data_;
+
+  template <typename F, typename T>
+  void bin_func(const F& f, T* ptr, const T* a_ptr);
+
+  template <typename F, typename T>
+  void un_func(const F& f, T* ptr);
+
+  template <typename T>
+  void scale_dim(T* ptr, const T* s_ptr);
+
+ public:
+  Tensor()
+  : is_segment_(false)
+  , segment_begin_(0)
+  , segment_end_(0)
+  , has_name_(false)
+  , elem_type_(ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED)
+  , is_raw_data_(false)
+  {}
+
+  Tensor(const Tensor &other)
+  : is_segment_(other.is_segment_)
+  , segment_begin_(other.segment_begin_)
+  , segment_end_(other.segment_end_)
+  , has_name_(other.has_name_)
+  , elem_type_(other.elem_type_)
+  , sizes_(other.sizes_)
+  , float_data_(other.float_data_)
+  , double_data_(other.double_data_)
+  , int32_data_(other.int32_data_)
+  , int64_data_(other.int64_data_)
+  , uint64_data_(other.uint64_data_)
+  , is_raw_data_(other.is_raw_data_) {
+    // Deep copy. Avoid copy on write when using gcc<5.0
+    string_data_.resize(other.string_data_.size());
+    for(unsigned int i=0; i<other.string_data_.size(); ++i) {
+      string_data_[i] = std::string( other.string_data_[i].data(), other.string_data_[i].size() );
+    }
+    name_ = std::string(other.name_.data(), other.name_.size());
+    raw_data_ = std::string(other.raw_data_.data(), other.raw_data_.size());
+  }
+
+  friend void swap(Tensor& first, Tensor& second){
+    using std::swap;
+    swap(first.is_segment_, second.is_segment_);
+    swap(first.segment_begin_, second.segment_begin_);
+    swap(first.segment_end_, second.segment_end_);
+    swap(first.has_name_, second.has_name_);
+    swap(first.name_, second.name_);
+    swap(first.elem_type_, second.elem_type_);
+    swap(first.sizes_, second.sizes_);
+    swap(first.float_data_, second.float_data_);
+    swap(first.double_data_, second.double_data_);
+    swap(first.int32_data_, second.int32_data_);
+    swap(first.int64_data_, second.int64_data_);
+    swap(first.uint64_data_, second.uint64_data_);
+    swap(first.is_raw_data_, second.is_raw_data_);
+    swap(first.string_data_, second.string_data_);
+    swap(first.raw_data_, second.raw_data_);
+  }
+
+  Tensor& operator=(Tensor other) noexcept {
+    swap(*this, other);
+    return *this;
+  }
+
+  const std::vector<int64_t>& sizes() const {
+    return sizes_;
+  }
+  std::vector<int64_t>& sizes() {
+    return sizes_;
+  }
+
+  int64_t size_from_dim(int dim) const {
+    if (dim < 0) {
+      dim += (int)sizes_.size();
+    }
+    ONNX_ASSERT(dim >= 0 && (size_t)dim < sizes_.size());
+    return std::accumulate(sizes_.begin() + dim, sizes_.end(), (int64_t)1, std::multiplies<int64_t>{});
+  }
+
+  int32_t elem_type() const {
+    return elem_type_;
+  }
+
+  int32_t& elem_type() {
+    return elem_type_;
+  }
+
+  std::vector<std::string>& strings() {
+    return string_data_;
+  }
+
+  const std::vector<std::string>& strings() const {
+    return string_data_;
+  }
+
+  std::vector<float>& floats() {
+    return float_data_;
+  }
+
+  const std::vector<float>& floats() const {
+    return float_data_;
+  }
+
+  std::vector<double>& doubles() {
+    return double_data_;
+  }
+
+  const std::vector<double>& doubles() const {
+    return double_data_;
+  }
+
+  std::vector<int32_t>& int32s() {
+    return int32_data_;
+  }
+
+  const std::vector<int32_t>& int32s() const {
+    return int32_data_;
+  }
+
+  std::vector<int64_t>& int64s() {
+    return int64_data_;
+  }
+
+  const std::vector<int64_t>& int64s() const {
+    return int64_data_;
+  }
+
+  std::vector<uint64_t>& uint64s() {
+    return uint64_data_;
+  }
+
+  const std::vector<uint64_t>& uint64s() const {
+    return uint64_data_;
+  }
+
+  const std::string& raw() const {
+    return raw_data_;
+  }
+
+  void set_raw_data(std::string raw_data) {
+    is_raw_data_ = true;
+    raw_data_ = std::move(raw_data);
+  }
+
+  template <typename T>
+  T* data();
+
+  template <typename T>
+  const T* data() const;
+
+  bool is_segment() const {
+    return is_segment_;
+  }
+
+  int64_t segment_begin() const {
+    return segment_begin_;
+  }
+
+  int64_t segment_end() const {
+    return segment_end_;
+  }
+
+  void set_segment_begin_and_end(int64_t begin, int64_t end) {
+    is_segment_ = true;
+    segment_begin_ = begin;
+    segment_end_ = end;
+  }
+
+  bool hasName() const {
+    return has_name_;
+  }
+
+  const std::string& name() const {
+    return name_;
+  }
+
+  void setName(std::string name) {
+    has_name_ = true;
+    name_ = std::move(name);
+  }
+
+  bool is_raw_data() const {
+    return is_raw_data_;
+  }
+
+  //this += a
+  //Supported for
+  //FLOAT, BOOL, INT8, INT16, INT32, UINT8, UINT16, INT64,
+  //UINT32, UINT64, DOUBLE,
+  //TODO: Support for FLOAT16, COMPLEX64, COMPLEX128
+  void add(const Tensor& a);
+
+  //this -= a
+  //Supported for
+  //FLOAT, BOOL, INT8, INT16, INT32, UINT8, UINT16, INT64,
+  //UINT32, UINT64, DOUBLE
+  //TODO: Support for FLOAT16, COMPLEX64, COMPLEX128
+  void subtract(const Tensor& a);
+
+  //this *= a
+  //Supported for
+  //FLOAT, BOOL, INT8, INT16, INT32, UINT8, UINT16, INT64,
+  //UINT32, UINT64, DOUBLE
+  //TODO: Support for FLOAT16, COMPLEX64, COMPLEX128
+  void multiply(const Tensor& a);
+
+  //this /= a
+  //Supported for
+  //FLOAT, INT8, INT16, INT32, UINT8, UINT16, INT64,
+  //UINT32, UINT64, DOUBLE
+  //TODO: Support for FLOAT16, COMPLEX64, COMPLEX128
+  void divide(const Tensor& a);
+
+  //Element-wise square root of This
+  //Supported for
+  //FLOAT, DOUBLE,
+  //TODO: Support for FLOAT16
+  void sqrt();
+
+  //Element wise scaling of tensor s
+  //s is one dimensional, has size M, where M is size of first dimension of tensor
+  //s must have has data type corresponding to this
+  //Supported for
+  //FLOAT16, FLOAT, DOUBLE
+  void scale_by_first_dim(const Tensor& s);
+};
+
+#define define_data(type, field)                  \
+  template <>                                     \
+  inline type* Tensor::data<type>() {             \
+    if (is_raw_data_) {                           \
+      return (type*)const_cast<char*>(&raw_data_.data()[0]); \
+    } else {                                      \
+      return field.data();                        \
+    }                                             \
+  }                                               \
+                                                  \
+  template <>                                     \
+  inline const type* Tensor::data<type>() const { \
+    if (is_raw_data_) {                           \
+      return (const type*)(raw_data_.data());     \
+    } else {                                      \
+      return field.data();                        \
+    }                                             \
+  }
+
+define_data(float, float_data_);
+define_data(double, double_data_);
+define_data(int32_t, int32_data_);
+define_data(int64_t, int64_data_);
+define_data(uint64_t, uint64_data_);
+define_data(std::string, string_data_);
+#undef define_data
+
+template <typename F, typename T>
+inline void Tensor::bin_func(const F& f, T* ptr, const T* a_ptr) {
+  const int64_t num_elements = size_from_dim(0);
+  for (int64_t i = 0; i < num_elements; ++i) {
+    ptr[i] = f(ptr[i], a_ptr[i]);
+  }
+}
+
+template <typename F, typename T>
+inline void Tensor::un_func(const F& f, T* ptr) {
+  const int64_t num_elements = size_from_dim(0);
+  for (int64_t i = 0; i < num_elements; ++i) {
+    ptr[i] = f(ptr[i]);
+  }
+}
+
+template <typename T>
+inline void Tensor::scale_dim(T* ptr, const T* s_ptr) {
+  int64_t elems_per_first_dim = size_from_dim(1);
+  int64_t first_dim_size = sizes_[0];
+  int64_t counter = 0;
+  for (int64_t i = 0; i < first_dim_size; ++i) {
+    for (int64_t j = 0; j < elems_per_first_dim; ++j) {
+      ptr[counter++] *= s_ptr[i];
+    }
+  }
+}
+
+#define APPLY_BINARY_FUNCTION(op_name, f)                                  \
+  inline void Tensor::op_name(const Tensor& other) {                       \
+    TENSOR_ASSERTM(                                                        \
+        other.elem_type() == elem_type_,                                   \
+        "Tensor types do not match: %s != %s",                             \
+        to_string(elem_type_).c_str(),                                     \
+        " vs. ",                                                           \
+        to_string(other.elem_type()).c_str());                             \
+    TENSOR_ASSERTM(other.sizes() == sizes_, "Tensor sizes do not match."); \
+    switch (elem_type_) {                                                  \
+      case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {                   \
+        bin_func(f<float>(), data<float>(), other.data<float>());          \
+        break;                                                             \
+      }                                                                    \
+      case ONNX_NAMESPACE::TensorProto_DataType_BOOL:                      \
+      case ONNX_NAMESPACE::TensorProto_DataType_INT8:                      \
+      case ONNX_NAMESPACE::TensorProto_DataType_INT16:                     \
+      case ONNX_NAMESPACE::TensorProto_DataType_INT32:                     \
+      case ONNX_NAMESPACE::TensorProto_DataType_UINT8:                     \
+      case ONNX_NAMESPACE::TensorProto_DataType_UINT16: {                  \
+        bin_func(f<int32_t>(), data<int32_t>(), other.data<int32_t>());    \
+        break;                                                             \
+      }                                                                    \
+      case ONNX_NAMESPACE::TensorProto_DataType_INT64: {                   \
+        bin_func(f<int64_t>(), data<int64_t>(), other.data<int64_t>());    \
+        break;                                                             \
+      }                                                                    \
+      case ONNX_NAMESPACE::TensorProto_DataType_UINT32:                    \
+      case ONNX_NAMESPACE::TensorProto_DataType_UINT64: {                  \
+        bin_func(f<uint64_t>(), data<uint64_t>(), other.data<uint64_t>()); \
+        break;                                                             \
+      }                                                                    \
+      case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {                  \
+        bin_func(f<double>(), data<double>(), other.data<double>());       \
+        break;                                                             \
+      }                                                                    \
+      default:                                                             \
+        TENSOR_ASSERTM(                                                    \
+            false,                                                         \
+            "Operation %s not supported for data type %s",                 \
+            #op_name,                                                      \
+            " not supported for data type ",                               \
+            to_string(elem_type_).c_str());                                \
+    }                                                                      \
+  }
+
+APPLY_BINARY_FUNCTION(add, std::plus)
+APPLY_BINARY_FUNCTION(subtract, std::minus)
+APPLY_BINARY_FUNCTION(multiply, std::multiplies)
+APPLY_BINARY_FUNCTION(divide, std::divides)
+
+#undef APPLY_BINARY_FUNCTION
+
+inline void Tensor::sqrt() {
+  switch(elem_type_) {
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
+      un_func<float (*)(float), float>(std::sqrt, data<float>());
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
+      un_func<double (*)(double), double>(std::sqrt, data<double>());
+      break;
+    }
+    default:
+      TENSOR_ASSERTM(
+          false,
+          "Operation sqrt not supported for data type %s",
+          to_string(elem_type_).c_str());
+  }
+}
+
+inline void Tensor::scale_by_first_dim(const Tensor& other) {
+  ONNX_ASSERT(
+      sizes_.size() > 1 && other.sizes().size() == 1 &&
+      other.sizes()[0] == sizes_[0]);
+  ONNX_ASSERT(other.elem_type() == elem_type_);
+
+  switch(elem_type_) {
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
+      scale_dim(data<float>(), other.data<float>());
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16: {
+      scale_dim(data<int32_t>(), other.data<int32_t>());
+      break;
+    }
+    case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
+      scale_dim(data<double>(), other.data<double>());
+      break;
+    }
+    default:
+      TENSOR_ASSERTM(
+          false,
+          "Operation scale_by_first_dim not supported for data type %s",
+          to_string(elem_type_).c_str());
+  }
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/common/version.h b/third_party/onnx/onnx/common/version.h
new file mode 100644
index 0000000000..28269034ab
--- /dev/null
+++ b/third_party/onnx/onnx/common/version.h
@@ -0,0 +1,13 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+namespace ONNX_NAMESPACE {
+
+// Represents the most recent release version. Updated with every release.
+constexpr const char* LAST_RELEASE_VERSION = "1.11.0";
+
+}
diff --git a/third_party/onnx/onnx/compose.py b/third_party/onnx/onnx/compose.py
new file mode 100644
index 0000000000..76ab93c376
--- /dev/null
+++ b/third_party/onnx/onnx/compose.py
@@ -0,0 +1,638 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import List, Set, Tuple, Text, Optional, MutableMapping
+from onnx import ModelProto, GraphProto, helper, checker
+from onnx import TensorProto as tp
+from onnx import utils
+
+
+def check_overlapping_names(
+    g1: GraphProto,
+    g2: GraphProto,
+    io_map: Optional[List[Tuple[Text, Text]]] = None
+) -> List[Tuple[Text, List[Text]]]:
+    """Checks whether there are name collisions between two graphs
+
+    Returns a list of tuples where the first element represents the member containing overlapping names
+    (One of: "node", "edge", "value_info", "initializer", "sparse_initializer"), and the
+    second element contains a list of names that appear in both graphs on that category.
+
+    Optionally, it takes an io_map, representing the output/inputs to be connected. It provided, overlapping
+    present in the io_map argument will be ignored.
+    """
+    if type(g1) is not GraphProto:
+        raise ValueError("g1 argument is not an ONNX graph")
+    if type(g2) is not GraphProto:
+        raise ValueError("g2 argument is not an ONNX graph")
+
+    def _overlapping(c1: List[Text], c2: List[Text]) -> List[Text]:
+        return list(set(c1) & set(c2))
+
+    def _edge_names(graph: GraphProto, exclude: Set[Text] = set()) -> List[Text]:
+        edges = []
+        for n in graph.node:
+            for i in n.input:
+                if i != '' and i not in exclude:
+                    edges.append(i)
+            for o in n.output:
+                if o != '' and o not in exclude:
+                    edges.append(o)
+        return edges
+
+    result = []
+
+    if not io_map:
+        io_map = []
+    io_map_inputs = set([elem[1] for elem in io_map])
+
+    # Edges already cover input/output
+    overlap = _overlapping(
+        _edge_names(g1), _edge_names(g2, exclude=io_map_inputs)
+    )
+    if len(overlap) > 0:
+        result.append(('edge', overlap))
+
+    overlap = _overlapping([e.name for e in g1.value_info], [e.name for e in g2.value_info])
+    if len(overlap) > 0:
+        result.append(('value_info', overlap))
+
+    overlap = _overlapping([e.name for e in g1.initializer], [e.name for e in g2.initializer])
+    if len(overlap) > 0:
+        result.append(('initializer', overlap))
+
+    overlap = _overlapping([e.values.name for e in g1.sparse_initializer],
+                           [e.values.name for e in g2.sparse_initializer]) + \
+              _overlapping([e.indices.name for e in g1.sparse_initializer],
+                           [e.indices.name for e in g2.sparse_initializer])
+    if len(overlap) > 0:
+        result.append(('sparse_initializer', overlap))
+
+    return result
+
+
+def merge_graphs(
+        g1: GraphProto,
+        g2: GraphProto,
+        io_map: List[Tuple[Text, Text]],
+        inputs: Optional[List[Text]] = None,
+        outputs: Optional[List[Text]] = None,
+        prefix1: Optional[Text] = None,
+        prefix2: Optional[Text] = None,
+        name: Optional[Text] = None,
+        doc_string: Optional[Text] = None,
+) -> GraphProto:
+    """Combines two ONNX graphs into a single one.
+
+    The combined graph is defined by connecting the specified set of outputs/inputs. Those inputs/outputs
+    not specified in the io_map argument will remain as inputs/outputs of the combined graph.
+
+    Arguments:
+        g1 (GraphProto): First graph
+        g2 (GraphProto): Second graph
+        io_map (list of pairs of string): The pairs of names [(out0, in0), (out1, in1), ...]
+                                          representing outputs of the first graph and inputs of the second
+                                          to be connected
+        inputs (list of string): Optional list of inputs to be included in the combined graph
+                                 By default, all inputs not present in the ``io_map`` argument will be
+                                 included in the combined model
+        outputs (list of string): Optional list of outputs to be included in the combined graph
+                                  By default, all outputs not present in the ``io_map`` argument will be
+                                  included in the combined model
+        prefix1 (string): Optional prefix to be added to all names in g1
+        prefix2 (string): Optional prefix to be added to all names in g2
+        name (string): Optional name for the combined graph
+                       By default, the name is g1.name and g2.name concatenated with an undescore delimiter
+        doc_string (string): Optional docstring for the combined graph
+                             If not provided, a default docstring with the concatenation of g1 and g2 docstrings is used
+
+    Returns:
+        GraphProto
+    """
+    if type(g1) is not GraphProto:
+        raise ValueError("g1 argument is not an ONNX graph")
+    if type(g2) is not GraphProto:
+        raise ValueError("g2 argument is not an ONNX graph")
+
+    # Prefixing names in the graph if requested, adjusting io_map accordingly
+    if prefix1 or prefix2:
+        if prefix1:
+            g1_copy = GraphProto()
+            g1_copy.CopyFrom(g1)
+            g1 = g1_copy
+            g1 = add_prefix_graph(g1, prefix=prefix1)
+        if prefix2:
+            g2_copy = GraphProto()
+            g2_copy.CopyFrom(g2)
+            g2 = g2_copy
+            g2 = add_prefix_graph(g2, prefix=prefix2)
+        io_map = [
+            (prefix1 + io[0] if prefix1 else io[0],
+             prefix2 + io[1] if prefix2 else io[1])
+            for io in io_map]
+
+    io_map_g1_outs = set([io[0] for io in io_map])
+    io_map_g2_ins = set([io[1] for io in io_map])
+    reversed_io_map = {in_name: out_name for out_name, in_name in io_map}
+    g1_outs = set([o.name for o in g1.output])
+    g2_ins = set([i.name for i in g2.input])
+
+    # If necessary extract subgraphs
+    if inputs or outputs:
+        if not inputs:
+            g1_inputs = [i.name for i in g1.input]
+            g2_inputs = [i.name for i in g2.input]
+        else:
+            input_set = set(inputs)
+            g1_inputs = [i.name for i in g1.input if i.name in input_set]
+            g2_inputs = [i.name for i in g2.input if i.name in input_set or i.name in io_map_g2_ins]
+
+        if not outputs:
+            g1_outputs = [o.name for o in g1.input]
+            g2_outputs = [o.name for o in g2.input]
+        else:
+            output_set = set(outputs)
+            g1_outputs = [o.name for o in g1.output if o.name in output_set or o.name in io_map_g1_outs]
+            g2_outputs = [o.name for o in g2.output if o.name in output_set]
+
+        if len(g1_inputs) < len(g1.input) or len(g1_outputs) < len(g1.output):
+            e1 = utils.Extractor(helper.make_model(g1))
+            g1 = e1.extract_model(g1_inputs, g1_outputs).graph
+
+        if len(g2_inputs) < len(g2.input) or len(g2_outputs) < len(g2.output):
+            e2 = utils.Extractor(helper.make_model(g2))
+            g2 = e2.extract_model(g2_inputs, g2_outputs).graph
+
+    # Check that input/output names specified in the io_map argument are valid input/output names
+    for g1_out_name, g2_in_name in io_map:
+        if g1_out_name not in g1_outs:
+            raise ValueError(f"Output {g1_out_name} is not present in g1")
+        if g2_in_name not in g2_ins:
+            raise ValueError(f"Input {g2_in_name} is not present in g2")
+
+    # Check for name collision
+    overlapping_names = check_overlapping_names(g1, g2, io_map)
+    if len(overlapping_names) > 0:
+        category, names = overlapping_names[0]
+        raise ValueError(
+            "Cant merge two graphs with overlapping names. "
+            f"Found repeated {category} names: " + ", ".join(names) + "\n"
+            + "Consider using ``onnx.compose.add_prefix`` to add a prefix to names in one of the graphs."
+        )
+
+    g = GraphProto()
+
+    g.node.extend(g1.node)
+    g2_nodes_begin = len(g.node)
+    g.node.extend(g2.node)
+    g2_nodes_end = len(g.node)
+
+    # Connecting outputs of the first graph with the inputs of the second
+    for node_idx in range(g2_nodes_begin, g2_nodes_end):
+        node = g.node[node_idx]
+        for index, name in enumerate(node.input):
+            if name in reversed_io_map:
+                node.input[index] = reversed_io_map[name]
+
+    if inputs:
+        input_set = set(inputs)
+        g.input.extend([i for i in g1.input if i.name in input_set])
+        g.input.extend([i for i in g2.input if i.name in input_set])
+    else:
+        g.input.extend(g1.input)
+        g.input.extend([i for i in g2.input if i.name not in io_map_g2_ins])
+
+    if outputs:
+        output_set = set(outputs)
+        g.output.extend([o for o in g1.output if o.name in output_set])
+        g.output.extend([o for o in g2.output if o.name in output_set])
+    else:
+        g.output.extend([o for o in g1.output if o.name not in io_map_g1_outs])
+        g.output.extend(g2.output)
+
+    g.initializer.extend(g1.initializer)
+    g.initializer.extend(
+        [init for init in g2.initializer if init.name not in io_map_g2_ins])
+
+    g.sparse_initializer.extend(g1.sparse_initializer)
+    g.sparse_initializer.extend(
+        [init for init in g2.sparse_initializer if init.values.name not in io_map_g2_ins])
+
+    g.value_info.extend(g1.value_info)
+    g.value_info.extend([vi for vi in g2.value_info if vi.name not in io_map_g2_ins])
+
+    g.name = name if name is not None else "_".join([g1.name, g2.name])
+
+    if doc_string is None:
+        doc_string = f"Graph combining {g1.name} and {g2.name}\n" + \
+            g1.name + "\n\n" + g1.doc_string + "\n\n" + g2.name + "\n\n" + g2.doc_string
+    g.doc_string = doc_string
+
+    return g
+
+
+def merge_models(
+        m1: ModelProto,
+        m2: ModelProto,
+        io_map: List[Tuple[Text, Text]],
+        inputs: Optional[List[Text]] = None,
+        outputs: Optional[List[Text]] = None,
+        prefix1: Optional[Text] = None,
+        prefix2: Optional[Text] = None,
+        name: Optional[Text] = None,
+        doc_string: Optional[Text] = None,
+        producer_name: Optional[Text] = 'onnx.compose.merge_models',
+        producer_version: Optional[Text] = "1.0",
+        domain: Optional[Text] = "",
+        model_version: Optional[int] = 1
+) -> ModelProto:
+    """Combines two ONNX models into a single one.
+
+    The combined model is defined by connecting the specified set of outputs/inputs.
+    Those inputs/outputs not specified in the io_map argument will remain as
+    inputs/outputs of the combined model.
+
+    Both models should have the same IR version, and same operator sets imported.
+
+    Arguments:
+        m1 (ModelProto): First model
+        m2 (ModelProto): Second model
+        io_map (list of pairs of string): The pairs of names [(out0, in0), (out1, in1), ...]
+                                          representing outputs of the first graph and inputs of the second
+                                          to be connected
+        inputs (list of string): Optional list of inputs to be included in the combined graph
+                                 By default, all inputs not present in the ``io_map`` argument will be
+                                 included in the combined model
+        outputs (list of string): Optional list of outputs to be included in the combined graph
+                                  By default, all outputs not present in the ``io_map`` argument will be
+                                  included in the combined model
+        prefix1 (string): Optional prefix to be added to all names in m1
+        prefix2 (string): Optional prefix to be added to all names in m2
+        name (string): Optional name for the combined graph
+                       By default, the name is g1.name and g2.name concatenated with an undescore delimiter
+        doc_string (string): Optional docstring for the combined graph
+                             If not provided, a default docstring with the concatenation of g1 and g2 docstrings is used
+        producer_name (string): Optional producer name for the combined model. Default: 'onnx.compose'
+        producer_version (string): Optional producer version for the combined model. Default: "1.0"
+        domain (string): Optional domain of the combined model. Default: ""
+        model_version (int): Optional version of the graph encoded. Default: 1
+
+    Returns:
+        ModelProto
+    """
+    if type(m1) is not ModelProto:
+        raise ValueError("m1 argument is not an ONNX model")
+    if type(m2) is not ModelProto:
+        raise ValueError("m2 argument is not an ONNX model")
+
+    if m1.ir_version != m2.ir_version:
+        raise ValueError(
+            f"IR version mismatch {m1.ir_version} != {m2.ir_version}."
+            " Both models should have have the same IR version")
+    ir_version = m1.ir_version
+
+    opset_import_map: MutableMapping[Text, int] = {}
+    opset_imports = \
+        [entry for entry in m1.opset_import] + \
+        [entry for entry in m2.opset_import]
+
+    for entry in opset_imports:
+        if entry.domain in opset_import_map:
+            found_version = opset_import_map[entry.domain]
+            if entry.version != found_version:
+                raise ValueError(
+                    "Can't merge two models with different operator set ids for a given domain. "
+                    f"Got: {m1.opset_import} and {m2.opset_import}")
+        else:
+            opset_import_map[entry.domain] = entry.version
+
+    # Prefixing names in the graph if requested, adjusting io_map accordingly
+    if prefix1 or prefix2:
+        if prefix1:
+            m1_copy = ModelProto()
+            m1_copy.CopyFrom(m1)
+            m1 = m1_copy
+            m1 = add_prefix(m1, prefix=prefix1)
+        if prefix2:
+            m2_copy = ModelProto()
+            m2_copy.CopyFrom(m2)
+            m2 = m2_copy
+            m2 = add_prefix(m2, prefix=prefix2)
+        io_map = [
+            (prefix1 + io[0] if prefix1 else io[0],
+             prefix2 + io[1] if prefix2 else io[1])
+            for io in io_map]
+
+    graph = merge_graphs(m1.graph, m2.graph, io_map,
+                         inputs=inputs, outputs=outputs,
+                         name=name, doc_string=doc_string)
+    model = helper.make_model(graph,
+                              producer_name=producer_name,
+                              producer_version=producer_version,
+                              domain=domain,
+                              model_version=model_version,
+                              opset_imports=opset_imports,
+                              ir_version=ir_version)
+
+    # Merging model metadata props
+    model_props = {}
+    for meta_entry in m1.metadata_props:
+        model_props[meta_entry.key] = meta_entry.value
+    for meta_entry in m2.metadata_props:
+        if meta_entry.key in model_props:
+            value = model_props[meta_entry.key]
+            if value != meta_entry.value:
+                raise ValueError(
+                    "Can't merge models with different values for the same model metadata property."
+                    f" Found: property = {meta_entry.key}, with values {value} and {meta_entry.value}."
+                )
+        else:
+            model_props[meta_entry.key] = meta_entry.value
+    helper.set_model_props(model, model_props)
+
+    # Merging functions
+    function_overlap = list(set([f.name for f in m1.functions]) & set([f.name for f in m2.functions]))
+    if function_overlap:
+        raise ValueError(
+            "Can't merge models with overlapping local function names."
+            " Found in both graphs: " + ', '.join(function_overlap)
+        )
+    model.functions.MergeFrom(m1.functions)
+    model.functions.MergeFrom(m2.functions)
+
+    checker.check_model(model)
+    return model
+
+
+def add_prefix_graph(
+        graph: GraphProto,
+        prefix: Text,
+        rename_nodes: Optional[bool] = True,
+        rename_edges: Optional[bool] = True,
+        rename_inputs: Optional[bool] = True,
+        rename_outputs: Optional[bool] = True,
+        rename_initializers: Optional[bool] = True,
+        rename_value_infos: Optional[bool] = True,
+        inplace: Optional[bool] = False,
+) -> GraphProto:
+    """Adds a prefix to names of elements in a graph: nodes, edges, inputs, outputs,
+    initializers, sparse initializer, value infos.
+
+    It can be used as a utility before merging graphs that have overlapping names.
+    Empty names are not prefixed.
+
+    Arguments:
+        graph (GraphProto): Graph
+        prefix (Text): Prefix to be added to each name in the graph
+        rename_nodes (bool): Whether to prefix node names
+        rename_edges (bool): Whether to prefix node edge names
+        rename_inputs (bool): Whether to prefix input names
+        rename_outputs (bool): Whether to prefix output names
+        rename_initializers (bool): Whether to prefix initializer and sparse initializer names
+        rename_value_infos (bool): Whether to prefix value info names
+        inplace (bool): If True, mutates the graph directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        GraphProto
+    """
+    if type(graph) is not GraphProto:
+        raise ValueError("graph argument is not an ONNX graph")
+
+    if not inplace:
+        g = GraphProto()
+        g.CopyFrom(graph)
+    else:
+        g = graph
+
+    def _prefixed(prefix: Text, name: Text) -> Text:
+        return prefix + name if len(name) > 0 else name
+
+    name_map = {}
+    if rename_edges:
+        for n in g.node:
+            for e in n.input:
+                name_map[e] = _prefixed(prefix, e)
+            for e in n.output:
+                name_map[e] = _prefixed(prefix, e)
+    else:
+        if rename_outputs:
+            for entry in g.output:
+                name_map[entry.name] = _prefixed(prefix, entry.name)
+        if rename_inputs:
+            for entry in g.input:
+                name_map[entry.name] = _prefixed(prefix, entry.name)
+
+    if rename_nodes:
+        for n in g.node:
+            n.name = _prefixed(prefix, n.name)
+
+    if rename_initializers:
+        for init in g.initializer:
+            name_map[init.name] = _prefixed(prefix, init.name)
+        for sparse_init in g.sparse_initializer:
+            name_map[sparse_init.values.name] = _prefixed(prefix, sparse_init.values.name)
+            name_map[sparse_init.indices.name] = _prefixed(prefix, sparse_init.indices.name)
+
+    if rename_value_infos:
+        for entry in g.value_info:
+            name_map[entry.name] = _prefixed(prefix, entry.name)
+
+    for n in g.node:
+        for i in range(len(n.output)):
+            if n.output[i] in name_map:
+                n.output[i] = name_map[n.output[i]]
+        for i in range(len(n.input)):
+            if n.input[i] in name_map:
+                n.input[i] = name_map[n.input[i]]
+
+    for in_desc in g.input:
+        if in_desc.name in name_map:
+            in_desc.name = name_map[in_desc.name]
+    for out_desc in g.output:
+        if out_desc.name in name_map:
+            out_desc.name = name_map[out_desc.name]
+
+    for initializer in g.initializer:
+        if initializer.name in name_map:
+            initializer.name = name_map[initializer.name]
+    for sparse_initializer in g.sparse_initializer:
+        if sparse_initializer.values.name in name_map:
+            sparse_initializer.values.name = name_map[sparse_initializer.values.name]
+        if sparse_initializer.indices.name in name_map:
+            sparse_initializer.indices.name = name_map[sparse_initializer.indices.name]
+
+    for value_info in g.value_info:
+        if value_info.name in name_map:
+            value_info.name = name_map[value_info.name]
+
+    return g
+
+
+def add_prefix(
+        model: ModelProto,
+        prefix: Text,
+        rename_nodes: Optional[bool] = True,
+        rename_edges: Optional[bool] = True,
+        rename_inputs: Optional[bool] = True,
+        rename_outputs: Optional[bool] = True,
+        rename_initializers: Optional[bool] = True,
+        rename_value_infos: Optional[bool] = True,
+        rename_functions: Optional[bool] = True,
+        inplace: Optional[bool] = False,
+) -> ModelProto:
+    """Adds a prefix to names of elements in a graph: nodes, edges, inputs, outputs,
+    initializers, sparse initializer, value infos, and local functions.
+
+    It can be used as a utility before merging graphs that have overlapping names.
+    Empty names are not _prefixed.
+
+    Arguments:
+        model (ModelProto): Model
+        prefix (Text): Prefix to be added to each name in the graph
+        rename_nodes (bool): Whether to prefix node names
+        rename_edges (bool): Whether to prefix node edge names
+        rename_inputs (bool): Whether to prefix input names
+        rename_outputs (bool): Whether to prefix output names
+        rename_initializers (bool): Whether to prefix initializer and sparse initializer names
+        rename_value_infos (bool): Whether to prefix value info nanes
+        rename_functions (bool): Whether to prefix local function names
+        inplace (bool): If True, mutates the model directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        ModelProto
+    """
+    if type(model) is not ModelProto:
+        raise ValueError("model argument is not an ONNX model")
+
+    if not inplace:
+        m = ModelProto()
+        m.CopyFrom(model)
+        model = m
+
+    add_prefix_graph(
+        model.graph, prefix,
+        rename_nodes=rename_nodes,
+        rename_edges=rename_edges,
+        rename_inputs=rename_inputs,
+        rename_outputs=rename_outputs,
+        rename_initializers=rename_initializers,
+        rename_value_infos=rename_value_infos,
+        inplace=True  # No need to create a copy, since it's a new model
+    )
+
+    if rename_functions:
+        f_name_map = {}
+        for f in model.functions:
+            new_f_name = prefix + f.name
+            f_name_map[f.name] = new_f_name
+            f.name = new_f_name
+        # Adjust references to local functions in other local function
+        # definitions
+        for f in model.functions:
+            for n in f.node:
+                if n.op_type in f_name_map:
+                    n.op_type = f_name_map[n.op_type]
+        # Adjust references to local functions in the graph
+        for n in model.graph.node:
+            if n.op_type in f_name_map:
+                n.op_type = f_name_map[n.op_type]
+
+    return model
+
+
+def expand_out_dim_graph(
+        graph: GraphProto,
+        dim_idx: int,
+        inplace: Optional[bool] = False,
+) -> GraphProto:
+    """Inserts an extra dimension with extent 1 to each output in the graph.
+
+    Inserts an Unsqueeze node for each output. It can be used as a utility before merging graphs,
+    for example when the second one expects a batch dimension.
+
+    Arguments:
+        graph (GraphProto): Graph
+        dim_idx (int): Index of the dimension to be inserted.
+                       A negative value means counting dimensions from the back.
+        inplace (bool): If True, mutates the model directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        GraphProto
+    """
+    if type(graph) is not GraphProto:
+        raise ValueError("graph argument is not an ONNX graph")
+
+    if not inplace:
+        g = GraphProto()
+        g.CopyFrom(graph)
+    else:
+        g = graph
+
+    orig_out_names = [output.name for output in g.output]
+
+    for n in g.node:
+        for i in range(len(n.output)):
+            if n.output[i] in orig_out_names:
+                n.output[i] = n.output[i] + f'_collapsed_dim_{dim_idx}'
+        for i in range(len(n.input)):
+            if n.input[i] in orig_out_names:
+                n.input[i] = n.input[i] + f'_collapsed_dim_{dim_idx}'
+
+    expand_dim_k = g.name + "_expand_out_dim_idx"
+    g.node.append(
+        helper.make_node(
+            'Constant', inputs=[], outputs=[expand_dim_k], name=f"{expand_dim_k}-constant",
+            value=helper.make_tensor(name=f"{expand_dim_k}-value", data_type=tp.INT64,
+                                     dims=[1, ], vals=[dim_idx, ]))
+    )
+
+    for _ in range(len(g.output)):
+        o = g.output.pop(0)
+        prev_output = o.name + f'_collapsed_dim_{dim_idx}'
+        g.node.append(
+            helper.make_node('Unsqueeze', inputs=[prev_output, expand_dim_k],
+                             outputs=[o.name], name=f"unsqueeze-{o.name}")
+        )
+        new_shape = [d.dim_value for d in o.type.tensor_type.shape.dim]
+        new_shape.insert(dim_idx, 1)
+        g.output.append(
+            helper.make_tensor_value_info(o.name, o.type.tensor_type.elem_type, new_shape))
+    return g
+
+
+def expand_out_dim(
+        model: ModelProto,
+        dim_idx: int,
+        inplace: Optional[bool] = False,
+) -> ModelProto:
+    """Inserts an extra dimension with extent 1 to each output in the graph.
+
+    Inserts an Unsqueeze node for each output. It can be used as a utility before merging graphs,
+    for example when the second one expects a batch dimension.
+
+    Arguments:
+        model (ModelProto): Model
+        dim_idx (int): Index of the dimension to be inserted.
+                       A negative value means counting dimensions from the back.
+        inplace (bool): If True, mutates the model directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        ModelProto
+    """
+    if type(model) is not ModelProto:
+        raise ValueError("model argument is not an ONNX model")
+
+    if not inplace:
+        m = ModelProto()
+        m.CopyFrom(model)
+        model = m
+
+    expand_out_dim_graph(
+        model.graph,
+        dim_idx,
+        inplace=True  # No need to create a copy, since it's a new model
+    )
+    return model
diff --git a/third_party/onnx/onnx/cpp2py_export.cc b/third_party/onnx/onnx/cpp2py_export.cc
new file mode 100644
index 0000000000..5f9e0333a3
--- /dev/null
+++ b/third_party/onnx/onnx/cpp2py_export.cc
@@ -0,0 +1,352 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <climits>
+#include <limits>
+#include <tuple>
+#include <unordered_map>
+
+#include "onnx/checker.h"
+#include "onnx/defs/function.h"
+#include "onnx/defs/parser.h"
+#include "onnx/defs/schema.h"
+#include "onnx/py_utils.h"
+#include "onnx/shape_inference/implementation.h"
+#include "onnx/version_converter/convert.h"
+
+namespace ONNX_NAMESPACE {
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+template <typename ProtoType>
+static std::tuple<bool, py::bytes, py::bytes> Parse(const char* cstr) {
+  ProtoType proto{};
+  OnnxParser parser(cstr);
+  auto status = parser.Parse(proto);
+  std::string out;
+  proto.SerializeToString(&out);
+  return std::make_tuple(status.IsOK(), py::bytes(status.ErrorMessage()), py::bytes(out));
+}
+
+PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
+  onnx_cpp2py_export.doc() = "Python interface to onnx";
+
+  onnx_cpp2py_export.attr("ONNX_ML") = py::bool_(
+#ifdef ONNX_ML
+      true
+#else // ONNX_ML
+      false
+#endif // ONNX_ML
+  );
+
+  // Submodule `schema`
+  auto defs = onnx_cpp2py_export.def_submodule("defs");
+  defs.doc() = "Schema submodule";
+  py::register_exception<SchemaError>(defs, "SchemaError");
+
+  py::class_<OpSchema> op_schema(defs, "OpSchema");
+  op_schema.def_property_readonly("file", &OpSchema::file)
+      .def_property_readonly("line", &OpSchema::line)
+      .def_property_readonly("support_level", &OpSchema::support_level)
+      .def_property_readonly(
+          "doc", &OpSchema::doc, py::return_value_policy::reference)
+      .def_property_readonly("since_version", &OpSchema::since_version)
+      .def_property_readonly("deprecated", &OpSchema::deprecated)
+      .def_property_readonly("domain", &OpSchema::domain)
+      .def_property_readonly("name", &OpSchema::Name)
+      .def_property_readonly("min_input", &OpSchema::min_input)
+      .def_property_readonly("max_input", &OpSchema::max_input)
+      .def_property_readonly("min_output", &OpSchema::min_output)
+      .def_property_readonly("max_output", &OpSchema::max_output)
+      .def_property_readonly("attributes", &OpSchema::attributes)
+      .def_property_readonly("inputs", &OpSchema::inputs)
+      .def_property_readonly("outputs", &OpSchema::outputs)
+      .def_property_readonly(
+          "has_type_and_shape_inference_function",
+          &OpSchema::has_type_and_shape_inference_function)
+      .def_property_readonly(
+          "has_data_propagation_function",
+          &OpSchema::has_data_propagation_function)
+      .def_property_readonly(
+          "type_constraints", &OpSchema::typeConstraintParams)
+      .def_static(
+          "is_infinite",
+          [](int v) { return v == std::numeric_limits<int>::max(); })
+      .def_property_readonly("has_function", &OpSchema::HasFunction)
+      .def_property_readonly("_function_body", [](OpSchema* op) -> py::bytes {
+        std::string bytes = "";
+        if (op->HasFunction())
+          op->GetFunction()->SerializeToString(&bytes);
+        return py::bytes(bytes);
+      })
+      .def_property_readonly("has_context_dependent_function", &OpSchema::HasContextDependentFunction)
+      .def(
+          "get_context_dependent_function",
+          [](OpSchema* op, const py::bytes& bytes, const std::vector<py::bytes>& input_types_bytes) -> py::bytes {
+            NodeProto proto{};
+            ParseProtoFromPyBytes(&proto, bytes);
+            std::string func_bytes = "";
+            if (op->HasContextDependentFunction()) {
+              std::vector<TypeProto> input_types;
+              input_types.reserve(input_types_bytes.size());
+              for (auto& type_bytes : input_types_bytes) {
+                TypeProto type_proto{};
+                ParseProtoFromPyBytes(&type_proto, type_bytes);
+                input_types.push_back(type_proto);
+              }
+              FunctionBodyBuildContextImpl ctx(proto, input_types);
+              FunctionProto func_proto;
+              op->BuildContextDependentFunction(ctx, func_proto);
+              func_proto.SerializeToString(&func_bytes);
+            }
+            return py::bytes(func_bytes);
+          });
+;
+
+  py::class_<OpSchema::Attribute>(op_schema, "Attribute")
+      .def_readonly("name", &OpSchema::Attribute::name)
+      .def_readonly("description", &OpSchema::Attribute::description)
+      .def_readonly("type", &OpSchema::Attribute::type)
+      .def_property_readonly(
+          "_default_value",
+          [](OpSchema::Attribute* attr) -> py::bytes {
+            std::string out;
+            attr->default_value.SerializeToString(&out);
+            return out;
+          })
+      .def_readonly("required", &OpSchema::Attribute::required);
+
+  py::class_<OpSchema::TypeConstraintParam>(op_schema, "TypeConstraintParam")
+      .def_readonly(
+          "type_param_str", &OpSchema::TypeConstraintParam::type_param_str)
+      .def_readonly("description", &OpSchema::TypeConstraintParam::description)
+      .def_readonly(
+          "allowed_type_strs",
+          &OpSchema::TypeConstraintParam::allowed_type_strs);
+
+  py::enum_<OpSchema::FormalParameterOption>(op_schema, "FormalParameterOption")
+      .value("Single", OpSchema::Single)
+      .value("Optional", OpSchema::Optional)
+      .value("Variadic", OpSchema::Variadic);
+
+  py::enum_<OpSchema::DifferentiationCategory>(op_schema, "DifferentiationCategory")
+      .value("Unknown", OpSchema::Unknown)
+      .value("Differentiable", OpSchema::Differentiable)
+      .value("NonDifferentiable", OpSchema::NonDifferentiable);
+
+  py::class_<OpSchema::FormalParameter>(op_schema, "FormalParameter")
+      .def_property_readonly("name", &OpSchema::FormalParameter::GetName)
+      .def_property_readonly("types", &OpSchema::FormalParameter::GetTypes)
+      .def_property_readonly("typeStr", &OpSchema::FormalParameter::GetTypeStr)
+      .def_property_readonly(
+          "description", &OpSchema::FormalParameter::GetDescription)
+      .def_property_readonly("option", &OpSchema::FormalParameter::GetOption)
+      .def_property_readonly(
+          "isHomogeneous", &OpSchema::FormalParameter::GetIsHomogeneous)
+      .def_property_readonly(
+        "differentiationCategory",
+        &OpSchema::FormalParameter::GetDifferentiationCategory);
+
+  py::enum_<AttributeProto::AttributeType>(op_schema, "AttrType")
+      .value("FLOAT", AttributeProto::FLOAT)
+      .value("INT", AttributeProto::INT)
+      .value("STRING", AttributeProto::STRING)
+      .value("TENSOR", AttributeProto::TENSOR)
+      .value("GRAPH", AttributeProto::GRAPH)
+      .value("FLOATS", AttributeProto::FLOATS)
+      .value("INTS", AttributeProto::INTS)
+      .value("STRINGS", AttributeProto::STRINGS)
+      .value("TENSORS", AttributeProto::TENSORS)
+      .value("GRAPHS", AttributeProto::GRAPHS)
+      .value("SPARSE_TENSOR", AttributeProto::SPARSE_TENSOR)
+      .value("SPARSE_TENSORS", AttributeProto::SPARSE_TENSORS)
+      .value("TYPE_PROTO", AttributeProto::TYPE_PROTO)
+      .value("TYPE_PROTOS", AttributeProto::TYPE_PROTOS);
+
+  py::enum_<OpSchema::SupportType>(op_schema, "SupportType")
+      .value("COMMON", OpSchema::SupportType::COMMON)
+      .value("EXPERIMENTAL", OpSchema::SupportType::EXPERIMENTAL);
+
+  defs.def(
+      "has_schema",
+      [](const std::string& op_type, const std::string& domain) -> bool {
+        return OpSchemaRegistry::Schema(op_type, domain) != nullptr;
+      },
+      "op_type"_a,
+      "domain"_a = ONNX_DOMAIN);
+  defs.def(
+      "schema_version_map",
+      []() -> std::unordered_map<std::string, std::pair<int, int>> {
+        return OpSchemaRegistry::DomainToVersionRange::Instance().Map();
+      });
+  defs.def(
+          "get_schema",
+          [](const std::string& op_type,
+             const int max_inclusive_version,
+             const std::string& domain) -> OpSchema {
+            const auto* schema = OpSchemaRegistry::Schema(
+                op_type, max_inclusive_version, domain);
+            if (!schema) {
+              fail_schema("No schema registered for '" + op_type + "'!");
+            }
+            return *schema;
+          },
+          "op_type"_a,
+          "max_inclusive_version"_a,
+          "domain"_a = ONNX_DOMAIN)
+      .def(
+          "get_schema",
+          [](const std::string& op_type,
+             const std::string& domain) -> OpSchema {
+            const auto* schema = OpSchemaRegistry::Schema(op_type, domain);
+            if (!schema) {
+              fail_schema("No schema registered for '" + op_type + "'!");
+            }
+            return *schema;
+          },
+          "op_type"_a,
+          "domain"_a = ONNX_DOMAIN);
+
+  defs.def("get_all_schemas", []() -> const std::vector<OpSchema> {
+    return OpSchemaRegistry::get_all_schemas();
+  });
+
+  defs.def("get_all_schemas_with_history", []() -> const std::vector<OpSchema> {
+    return OpSchemaRegistry::get_all_schemas_with_history();
+  });
+
+  // Submodule `checker`
+  auto checker = onnx_cpp2py_export.def_submodule("checker");
+  checker.doc() = "Checker submodule";
+
+  py::class_<checker::CheckerContext> checker_context(
+      checker, "CheckerContext");
+  checker_context.def(py::init<>())
+      .def_property(
+          "ir_version",
+          &checker::CheckerContext::get_ir_version,
+          &checker::CheckerContext::set_ir_version)
+      .def_property(
+          "opset_imports",
+          &checker::CheckerContext::get_opset_imports,
+          &checker::CheckerContext::set_opset_imports);
+
+  py::register_exception<checker::ValidationError>(checker, "ValidationError");
+
+  checker.def(
+      "check_value_info",
+      [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+        ValueInfoProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_value_info(proto, ctx);
+      });
+
+  checker.def(
+      "check_tensor",
+      [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+        TensorProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_tensor(proto, ctx);
+      });
+
+  checker.def(
+      "check_sparse_tensor",
+      [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+        SparseTensorProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_sparse_tensor(proto, ctx);
+      });
+
+  checker.def(
+      "check_attribute",
+      [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+        AttributeProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_attribute(proto, ctx, checker::LexicalScopeContext());
+      });
+
+  checker.def(
+      "check_node",
+      [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+        NodeProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::LexicalScopeContext lex_ctx;
+        checker::check_node(proto, ctx, lex_ctx);
+      });
+
+  checker.def(
+      "check_graph",
+      [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+        GraphProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::LexicalScopeContext lex_ctx;
+        checker::check_graph(proto, ctx, lex_ctx);
+      });
+
+  checker.def("check_model", [](const py::bytes& bytes) -> void {
+    ModelProto proto{};
+    ParseProtoFromPyBytes(&proto, bytes);
+    checker::check_model(proto);
+  });
+
+  checker.def(
+      "check_model_path",
+      (void (*)(const std::string&)) & checker::check_model);
+
+  // Submodule `version_converter`
+  auto version_converter =
+      onnx_cpp2py_export.def_submodule("version_converter");
+  version_converter.doc() = "VersionConverter submodule";
+  py::register_exception<ConvertError>(version_converter, "ConvertError");
+
+
+  version_converter.def(
+      "convert_version", [](const py::bytes& bytes, py::int_ target) {
+        ModelProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        shape_inference::InferShapes(proto);
+        auto result = version_conversion::ConvertVersion(proto, target);
+        std::string out;
+        result.SerializeToString(&out);
+        return py::bytes(out);
+      });
+
+  // Submodule `shape_inference`
+  auto shape_inference = onnx_cpp2py_export.def_submodule("shape_inference");
+  shape_inference.doc() = "Shape Inference submodule";
+  py::register_exception<InferenceError>(shape_inference, "InferenceError");
+
+
+  shape_inference.def("infer_shapes", [](const py::bytes& bytes, bool check_type, bool strict_mode, bool data_prop) {
+    ModelProto proto{};
+    ParseProtoFromPyBytes(&proto, bytes);
+    ShapeInferenceOptions options {check_type, strict_mode == true ? 1 : 0, data_prop};
+    shape_inference::InferShapes(proto,
+                                 OpSchemaRegistry::Instance(),
+                                 options);
+    std::string out;
+    proto.SerializeToString(&out);
+    return py::bytes(out);
+  }, "bytes"_a, "check_type"_a = false, "strict_mode"_a = false, "data_prop"_a = false);
+
+  shape_inference.def(
+      "infer_shapes_path",
+      [](const std::string& model_path, const std::string& output_path, bool check_type, bool strict_mode, bool data_prop)  -> void {
+        ShapeInferenceOptions options {check_type, strict_mode == true ? 1 : 0, data_prop};
+        shape_inference::InferShapes(model_path, output_path, 
+                                     OpSchemaRegistry::Instance(),
+                                     options);
+      });
+
+  // Submodule `parser`
+  auto parser = onnx_cpp2py_export.def_submodule("parser");
+  parser.doc() = "Parser submodule";
+
+  parser.def("parse_model", Parse<ModelProto>);
+  parser.def("parse_graph", Parse<GraphProto>);
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/__init__.py b/third_party/onnx/onnx/defs/__init__.py
new file mode 100644
index 0000000000..8afc160feb
--- /dev/null
+++ b/third_party/onnx/onnx/defs/__init__.py
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx import AttributeProto, FunctionProto
+import onnx.onnx_cpp2py_export.defs as C
+
+from collections import defaultdict
+from typing import List, Dict
+
+ONNX_DOMAIN = ""
+ONNX_ML_DOMAIN = 'ai.onnx.ml'
+AI_ONNX_PREVIEW_TRAINING_DOMAIN = 'ai.onnx.preview.training'
+
+
+has = C.has_schema
+get_schema = C.get_schema
+get_all_schemas = C.get_all_schemas
+get_all_schemas_with_history = C.get_all_schemas_with_history
+
+
+def onnx_opset_version() -> int:
+    return C.schema_version_map()[ONNX_DOMAIN][1]
+
+
+@property  # type: ignore
+def _Function_proto(self):  # type: ignore
+    func_proto = FunctionProto()
+    func_proto.ParseFromString(self._function_body)
+    return func_proto
+
+
+OpSchema = C.OpSchema  # type: ignore
+C.OpSchema.function_body = _Function_proto  # type: ignore
+
+
+@property  # type: ignore
+def _Attribute_default_value(self):  # type: ignore
+    attr = AttributeProto()
+    attr.ParseFromString(self._default_value)
+    return attr
+
+
+OpSchema.Attribute.default_value = _Attribute_default_value  # type: ignore
+
+
+def get_function_ops() -> List[OpSchema]:
+    schemas = C.get_all_schemas()
+    return [schema for schema in schemas if schema.has_function or schema.has_context_dependent_function]  # type: ignore
+
+
+SchemaError = C.SchemaError
diff --git a/third_party/onnx/onnx/defs/attr_proto_util.cc b/third_party/onnx/onnx/defs/attr_proto_util.cc
new file mode 100644
index 0000000000..62409655e7
--- /dev/null
+++ b/third_party/onnx/onnx/defs/attr_proto_util.cc
@@ -0,0 +1,72 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "attr_proto_util.h"
+
+namespace ONNX_NAMESPACE {
+
+#define ADD_BASIC_ATTR_IMPL(type, enumType, field)       \
+  AttributeProto MakeAttribute(                          \
+      const std::string& attr_name, const type& value) { \
+    AttributeProto a;                                    \
+    a.set_name(attr_name);                               \
+    a.set_type(enumType);                                \
+    a.set_##field(value);                                \
+    return a;                                            \
+  }
+
+#define ADD_ATTR_IMPL(type, enumType, field)             \
+  AttributeProto MakeAttribute(                          \
+      const std::string& attr_name, const type& value) { \
+    AttributeProto a;                                    \
+    a.set_name(attr_name);                               \
+    a.set_type(enumType);                                \
+    *(a.mutable_##field()) = value;                      \
+    return a;                                            \
+  }
+
+#define ADD_LIST_ATTR_IMPL(type, enumType, field)                      \
+  AttributeProto MakeAttribute(                                        \
+      const std::string& attr_name, const std::vector<type>& values) { \
+    AttributeProto a;                                                  \
+    a.set_name(attr_name);                                             \
+    a.set_type(enumType);                                              \
+    for (const auto& val : values) {                                   \
+      *(a.mutable_##field()->Add()) = val;                             \
+    }                                                                  \
+    return a;                                                          \
+  }
+
+ADD_BASIC_ATTR_IMPL(float, AttributeProto_AttributeType_FLOAT, f)
+ADD_BASIC_ATTR_IMPL(int64_t, AttributeProto_AttributeType_INT, i)
+ADD_BASIC_ATTR_IMPL(std::string, AttributeProto_AttributeType_STRING, s)
+ADD_ATTR_IMPL(TensorProto, AttributeProto_AttributeType_TENSOR, t)
+ADD_ATTR_IMPL(GraphProto, AttributeProto_AttributeType_GRAPH, g)
+ADD_ATTR_IMPL(TypeProto, AttributeProto_AttributeType_TYPE_PROTO, tp)
+ADD_LIST_ATTR_IMPL(float, AttributeProto_AttributeType_FLOATS, floats)
+ADD_LIST_ATTR_IMPL(int64_t, AttributeProto_AttributeType_INTS, ints)
+ADD_LIST_ATTR_IMPL(std::string, AttributeProto_AttributeType_STRINGS, strings)
+ADD_LIST_ATTR_IMPL(TensorProto, AttributeProto_AttributeType_TENSORS, tensors)
+ADD_LIST_ATTR_IMPL(GraphProto, AttributeProto_AttributeType_GRAPHS, graphs)
+ADD_LIST_ATTR_IMPL(TypeProto, AttributeProto_AttributeType_TYPE_PROTOS, type_protos)
+
+AttributeProto MakeRefAttribute(
+    const std::string& attr_name,
+    AttributeProto_AttributeType type) {
+  return MakeRefAttribute(attr_name, attr_name, type);
+}
+
+AttributeProto MakeRefAttribute(
+    const std::string& attr_name,
+    const std::string& referred_attr_name,
+    AttributeProto_AttributeType type) {
+  AttributeProto a;
+  a.set_name(attr_name);
+  a.set_ref_attr_name(referred_attr_name);
+  a.set_type(type);
+  return a;
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/attr_proto_util.h b/third_party/onnx/onnx/defs/attr_proto_util.h
new file mode 100644
index 0000000000..ad93124bd5
--- /dev/null
+++ b/third_party/onnx/onnx/defs/attr_proto_util.h
@@ -0,0 +1,59 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/onnx-operators_pb.h"
+
+namespace ONNX_NAMESPACE {
+
+AttributeProto MakeAttribute(const std::string& attr_name, const float& value);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const int64_t& value);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const std::string& value);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const TensorProto& value);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const GraphProto& value);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const std::vector<float>& values);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const std::vector<int64_t>& values);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const std::vector<std::string>& values);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const std::vector<TensorProto>& values);
+AttributeProto MakeAttribute(
+    const std::string& attr_name,
+    const std::vector<GraphProto>& values);
+
+// Make a "reference" attribute for a node in a function body.
+// <attr_name> specifies the attribute name of both the function node and its
+// function body node. They're using the same attribute name.
+// <type> specifies the attribute type.
+AttributeProto MakeRefAttribute(
+    const std::string& attr_name,
+    AttributeProto_AttributeType type);
+
+// Make a "reference" attribute for a node in a function body.
+// <attr_name> specifies the attribute name of the function body node.
+// <referred_attr_name> specifies the referred attribute name of the function
+// node.
+// <type> specifies the attribute type.
+AttributeProto MakeRefAttribute(
+    const std::string& attr_name,
+    const std::string& referred_attr_name,
+    AttributeProto_AttributeType type);
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/controlflow/defs.cc b/third_party/onnx/onnx/defs/controlflow/defs.cc
new file mode 100644
index 0000000000..24023b28df
--- /dev/null
+++ b/third_party/onnx/onnx/defs/controlflow/defs.cc
@@ -0,0 +1,842 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+#include <assert.h>
+
+namespace ONNX_NAMESPACE {
+using SupportType = OpSchema::SupportType;
+
+static std::vector<std::string> control_flow_types() {
+  auto t = OpSchema::all_tensor_types_with_bfloat();
+  auto s = OpSchema::all_tensor_sequence_types_with_bfloat();
+  auto o = OpSchema::all_optional_types_with_bfloat();
+  t.insert(t.end(), s.begin(), s.end());
+  t.insert(t.end(), o.begin(), o.end());
+  return t;
+}
+
+int handle_negative_axis_validate(
+    const std::string& attrib,
+    int axis,
+    int rank) {
+  if (!(-rank <= axis && axis < rank)) {
+    fail_shape_inference(
+        attrib,
+        " axis value ",
+        axis,
+        " is invalid for a tensor of rank ",
+        rank);
+  }
+  return (axis >= 0 ? axis : axis + rank);
+}
+
+void ScanInferenceFunction(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  auto num_scan_inputs =
+      narrow_cast<size_t>(ctx.getAttribute("num_scan_inputs")->i());
+  auto num_loop_state_vars = num_inputs - num_scan_inputs;
+  auto num_outputs = ctx.getNumOutputs();
+  auto num_scan_outputs = num_outputs - num_loop_state_vars;
+
+  std::vector<int64_t> axes, output_axes;
+  if (getRepeatedAttribute(ctx, "scan_input_axes", axes)) {
+    if (axes.size() != num_scan_inputs) {
+      fail_shape_inference(
+          "Number of scan input axes specified (",
+          axes.size(),
+          ") is not equal to number of scan inputs (",
+          num_scan_inputs,
+          ").");
+    }
+  } else {
+    axes.insert(axes.end(), num_scan_inputs, 0);
+  }
+
+  if (getRepeatedAttribute(ctx, "scan_output_axes", output_axes)) {
+    if (output_axes.size() != num_scan_outputs) {
+      fail_shape_inference(
+          "Number of scan output axes specified (",
+          output_axes.size(),
+          ") is not equal to number of scan outputs (",
+          num_scan_outputs,
+          ").");
+    }
+  } else {
+    output_axes.insert(output_axes.end(), num_scan_outputs, 0);
+  }
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs);
+
+  std::vector<const TypeProto*> subgraph_input_types;
+  subgraph_input_types.reserve(num_inputs);
+
+  TensorShapeProto_Dimension sequence_len_dim;
+
+  for (size_t i = 0; i < num_inputs; ++i) {
+    bool is_loop_state_var = i < num_loop_state_vars;
+    bool has_shape = hasInputShape(ctx, i);
+    const auto* input_type = ctx.getInputType(i);
+
+    // Enforce type constraint for inputs
+    if (!input_type || !input_type->has_tensor_type()) {
+      fail_type_inference("Scan input ", i, " was not a tensor.");
+    }
+
+    if (is_loop_state_var) {
+      // If it's a loop state variable we can propagate type and shape 1:1 to
+      // the matching Scan output.
+      // We can also pass through the type and shape to the subgraph but need to
+      // remove the batch size dimension from the shape.
+      propagateElemTypeFromInputToOutput(ctx, i, i);
+      if (has_shape)
+        propagateShapeFromInputToOutput(ctx, i, i);
+
+      subgraph_input_types.push_back(input_type);
+    } else {
+      // For other inputs there is no fixed relationships to the Scan outputs,
+      // so we don't propagate type/shape information.
+      // We can pass through the type and shape to the subgraph inputs but
+      // need to remove the sequence length dimensions from the shape.
+      if (has_shape) {
+        const auto& shape = input_type->tensor_type().shape();
+
+        // remove sequence length dimensions and add to subgraph_input_types
+        int axis = static_cast<int>(axes[i - num_loop_state_vars]);
+        axis = handle_negative_axis_validate(
+            "scan_input_axes", axis, shape.dim_size());
+
+        // update sequence_len if a value is available
+
+        const auto& dims = shape.dim();
+        mergeInDimensionInfo(dims.Get(axis), sequence_len_dim, 1);
+
+        temporary_type_protos.push_back(
+            RemoveIthDimensionFromShape(*input_type, axis));
+        subgraph_input_types.push_back(&temporary_type_protos.back());
+
+      } else {
+        subgraph_input_types.push_back(input_type);
+      }
+    }
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    input_data.reserve(num_inputs);
+    for (size_t i = 0; i < num_inputs; ++i) {
+      // ctx.getInputData(i), the input to scan, does not represent the input to
+      // scan body. So, we pass in null, to represent an unknown value.
+      input_data.push_back(nullptr);
+    }
+
+    output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!output_types.empty()) {
+    if (output_types.size() != num_outputs) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          output_types.size(),
+          " outputs. Expected ",
+          num_outputs);
+    }
+
+    // propagate type/shape information for loop state variables and outputs
+    for (size_t i = 0; i < num_outputs; ++i) {
+      const bool is_loop_state_var = i < num_loop_state_vars;
+      auto* subgraph_output_type = output_types[i];
+      auto* scan_output_type = ctx.getOutputType(i);
+      auto* mutable_scan_output_tensor_type =
+          scan_output_type->mutable_tensor_type();
+
+      if (!subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Scan 'body' subgraph outputs should all be tensors but output ",
+            i,
+            " was not");
+      }
+      auto& subgraph_output_tensor_type = subgraph_output_type->tensor_type();
+
+      if (is_loop_state_var) {
+        // merge shape; type already propagated
+        mergeInShapeInfo(
+            subgraph_output_tensor_type, *mutable_scan_output_tensor_type);
+      } else {
+        scan_output_type->mutable_tensor_type()->set_elem_type(
+            subgraph_output_tensor_type.elem_type());
+
+        // propagate shape
+        if (subgraph_output_tensor_type.has_shape()) {
+          // infer shape of scan-output from the shape of scan-output-element
+          // by adding sequence-length at the correct axis position
+          const TensorShapeProto& subgraph_output_shape =
+              subgraph_output_tensor_type.shape();
+          TensorShapeProto inferred_shape;
+
+          auto subgraph_output_rank = subgraph_output_shape.dim_size();
+          auto output_rank = subgraph_output_rank + 1;
+          int output_axis =
+              static_cast<int>(output_axes[i - num_loop_state_vars]);
+          output_axis = handle_negative_axis_validate(
+              "scan_output_axes", output_axis, output_rank);
+
+          for (int j = 0; j < output_axis; ++j)
+            *(inferred_shape.add_dim()) = subgraph_output_shape.dim(j);
+          *(inferred_shape.add_dim()) = sequence_len_dim;
+          for (int j = output_axis; j < subgraph_output_rank; ++j)
+            *(inferred_shape.add_dim()) = subgraph_output_shape.dim(j);
+
+          // Merge inferred shape with existing shape information
+          mergeInShapeInfo(inferred_shape, *mutable_scan_output_tensor_type);
+        }
+      }
+    }
+  }
+}
+
+void IfInferenceFunction(InferenceContext& ctx) {
+  // there are no inputs so we just need to run the subgraph inferencing for
+  // then/else subgraphs and apply those to the outputs.
+  std::vector<const TypeProto*> subgraph_input_types; // none
+  std::vector<const TensorProto*> input_data; // none
+
+  std::vector<const TypeProto*> then_output_types;
+  std::vector<const TypeProto*> else_output_types;
+
+  // Run inferencing on the subgraph
+  GraphInferencer* graphInferencer =
+      ctx.getGraphAttributeInferencer("then_branch");
+  if (graphInferencer) {
+    then_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  graphInferencer = ctx.getGraphAttributeInferencer("else_branch");
+  if (graphInferencer) {
+    else_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  auto num_outputs = ctx.getNumOutputs();
+  auto num_then_outputs = then_output_types.size();
+  auto num_else_outputs = else_output_types.size();
+
+  // the output types for then and else should be the same
+  if (num_then_outputs != num_else_outputs) {
+    fail_type_inference(
+        "then_branch and else_branch produce different number of outputs. ",
+        num_then_outputs,
+        " != ",
+        num_else_outputs);
+  }
+
+  if (num_then_outputs != num_outputs) {
+    fail_type_inference(
+        "If node has ",
+        num_outputs,
+        " but subgraphs produce ",
+        num_then_outputs);
+  }
+
+  for (size_t i = 0, end = then_output_types.size(); i < end; ++i) {
+    auto then_output = then_output_types[i];
+    auto else_output = else_output_types[i];
+
+    auto* if_output = ctx.getOutputType(i);
+    *if_output = *then_output;
+
+    UnionTypeInfo(*else_output, *if_output);
+  }
+}
+
+void ClearShape(TypeProto& input_type) {
+  if (input_type.has_tensor_type()) {
+    input_type.mutable_tensor_type()->clear_shape();
+  } else if (input_type.has_sequence_type()) {
+    auto& seq_type = *input_type.mutable_sequence_type();
+    if (seq_type.has_elem_type()) {
+      ClearShape(*(seq_type.mutable_elem_type()));
+    }
+  } else if (input_type.has_optional_type()) {
+    auto& opt_type = *input_type.mutable_optional_type();
+    if (opt_type.has_elem_type()) {
+      ClearShape(*(opt_type.mutable_elem_type()));
+    }
+  }
+}
+
+void LoopInferenceFunction(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  assert(num_inputs >= 2);
+  auto num_loop_state_vars = num_inputs - 2; // skip 'M' and 'cond'
+
+  std::vector<const TypeProto*> subgraph_input_types;
+  subgraph_input_types.reserve(num_inputs);
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs - 2);
+
+  // create TypeProto to validate iteration number type is the same as the
+  // optional 'M' input for max iterations.
+  TypeProto iter_num_type;
+  iter_num_type.mutable_tensor_type()->set_elem_type(
+      TensorProto_DataType_INT64);
+  subgraph_input_types.push_back(&iter_num_type);
+
+  // 'cond'
+  subgraph_input_types.push_back(ctx.getInputType(1));
+
+  // loop state value types get propagated to outputs, but shape may change
+  // across iterations so don't propagate it to the outputs and don't pass it
+  // into the subgraph inferencing
+  for (size_t i = 2; i < num_inputs; ++i) {
+    propagateElemTypeFromInputToOutput(ctx, i, i - 2);
+
+    // copy so we can remove the shape before passing to the subgraph
+    // inferencing
+    temporary_type_protos.push_back(*ctx.getInputType(i));
+    auto& input_type = temporary_type_protos.back();
+
+    ClearShape(input_type);
+    subgraph_input_types.push_back(&input_type);
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> subgraph_output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    input_data.push_back(nullptr); // iteration number
+    for (size_t i = 1; i < num_inputs; ++i) {
+      input_data.push_back(ctx.getInputData(i));
+    }
+
+    subgraph_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!subgraph_output_types.empty()) {
+    auto num_outputs = ctx.getNumOutputs();
+
+    // subgraph outputs the condition value first but that is only used
+    // internally and not returned by Loop.
+    if (subgraph_output_types.size() != num_outputs + 1) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          subgraph_output_types.size(),
+          " outputs. Expected ",
+          num_outputs + 1);
+    }
+
+    // check loop state values match. we should already have type/shape info
+    for (size_t i = 0; i < num_outputs; ++i) {
+      auto* subgraph_output_type = subgraph_output_types[i + 1]; // skip 'cond'
+      auto* loop_output_type = ctx.getOutputType(i);
+
+      const bool is_loop_state_var = i < num_loop_state_vars;
+
+      if (!subgraph_output_type->has_tensor_type() && !subgraph_output_type->has_sequence_type() && !subgraph_output_type->has_optional_type()) {
+        fail_type_inference(
+            "Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output ",
+            i,
+            " was ",
+            subgraph_output_type->value_case());
+      }
+
+      if (!is_loop_state_var && !subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Loop 'body' subgraph scan outputs should all be tensors but output ",
+            i,
+            " was ",
+            subgraph_output_type->value_case());
+      }
+
+      // if there's an existing type check it matches. otherwise propagate
+      propagateElemTypeWithValidation(subgraph_output_type, loop_output_type);
+
+      if (is_loop_state_var) {
+        // shape may change across iterations so ignore.
+      } else {
+        // propogate shape
+        if (subgraph_output_type->tensor_type().has_shape()) {
+          // per iteration output. first dimension will be number of iterations
+          // but we don't know that value yet
+          TypeProto inferred_type(*subgraph_output_type);
+          auto* mutable_inferred_tensor_type =
+              inferred_type.mutable_tensor_type();
+          auto* mutable_inferred_shape =
+              mutable_inferred_tensor_type->mutable_shape();
+
+          mutable_inferred_shape->clear_dim();
+
+          // add empty dimension for number of iterations
+          mutable_inferred_shape->add_dim();
+
+          // add dimensions from subgraph output shape
+          for (const auto& dim :
+               subgraph_output_type->tensor_type().shape().dim()) {
+            (*mutable_inferred_shape->add_dim()) = dim;
+          }
+
+          mergeInShapeInfo(
+              *mutable_inferred_tensor_type,
+              *loop_output_type->mutable_tensor_type());
+        }
+      }
+    }
+  }
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    If,
+    16,
+    OpSchema()
+        .SetDoc("If conditional")
+        .Input(0, "cond", "Condition for the if", "B")
+        .Output(
+            0,
+            "outputs",
+            "Values that are live-out to the enclosing scope. The return values in "
+            "the `then_branch` and `else_branch` must be of the same data type. "
+            "The `then_branch` and `else_branch` may produce tensors with the same "
+            "element type and different shapes. "
+            "If corresponding outputs from the then-branch and the else-branch have "
+            "static shapes S1 and S2, then the shape of the corresponding output "
+            "variable of the if-node (if present) must be compatible with both S1 "
+            "and S2 as it represents the union of both possible shapes."
+            "For example, if in a model file, the the first "
+            "output of `then_branch` is typed float tensor with shape [2] and the "
+            "first output of `else_branch` is another float tensor with shape [3], "
+            "If's first output should have (a) no shape set, or (b) "
+            "a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) "
+            "a shape of rank 1 with a unique `dim_param`. "
+            "In contrast, the first output cannot have the shape [2] since [2] and "
+            "[3] are not compatible.",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "then_branch",
+            "Graph to run if condition is true. Has N outputs: values you wish to "
+            "be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the else_branch.",
+            AttributeProto::GRAPH)
+        .Attr(
+            "else_branch",
+            "Graph to run if condition is false. Has N outputs: values you wish to"
+            " be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the then_branch.",
+            AttributeProto::GRAPH)
+        .TypeConstraint(
+            "V",
+            control_flow_types(),
+            "All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types")
+        .TypeConstraint("B", {"tensor(bool)"}, "Only bool")
+        .TypeAndShapeInferenceFunction(IfInferenceFunction));
+
+static const char* Loop_ver16_doc = R"DOC(
+Generic Looping construct. This loop has multiple termination conditions:
+
+1) Trip count. Iteration count specified at runtime. Set by
+   specifying the input M. Optional. Set to empty string to omit.
+   Note that a static trip count (specified at graph construction time) can be
+   specified by passing in a constant node for input M.
+2) Loop termination condition. This is an input to the op that determines
+   whether to run the first iteration and also a loop-carried dependency for
+   the body graph. The body graph must yield a value for the condition variable,
+   whether this input is provided or not.
+
+This table summarizes the operating modes of this operator with equivalent
+C-style code:
+
+    Operator inputs defined as (max_trip_count, condition_var).
+
+    input ("", ""):
+        for (int i=0; ; ++i) {
+          cond = ... // Note this value is ignored, but is required in the body
+        }
+
+    input ("", cond) // Note this is analogous to a while loop
+        bool cond = ...;
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input ("", 1) // Note this is analogous to a do-while loop
+        bool cond = true
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input (trip_count, "") // Note this is analogous to a for loop
+        int trip_count = ...
+        for (int i=0; i < trip_count; ++i) {
+          cond = ...; // ignored
+        }
+
+    input (trip_count, cond)
+        int trip_count = ...;
+        bool cond = ...;
+        for (int i=0; i < trip_count && cond; ++i) {
+          cond = ...;
+        }
+
+
+*Sample usage - cond as well as trip count*
+
+    graph predict-net {
+      %a = Constant[value = <Scalar Tensor [3]>]()
+      %b = Constant[value = <Scalar Tensor [6]>]()
+      %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+      %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+      %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+      return
+    }
+
+    graph body-net (
+      %i[INT32, scalar]           // iteration number
+      %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+      %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+    ) {
+      %my_local = Add(%a, %b_in)
+      %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+      %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+      %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+      return %keepgoing_out, %b_out, %user_defined_val
+    }
+
+*Sample equivalent C code*
+
+    {
+      /* User-defined code (enclosing scope) */
+      int a = 3, b = 6;
+      bool keepgoing = true; // Analogous to input cond
+      /* End user-defined code */
+
+      /* Implicitly-defined code */
+      const int max_trip_count = 10; // Analogous to input M
+      int user_defined_vals[]; // Imagine this is resizable
+      /* End implicitly-defined code */
+      /* initialize loop-carried variables and scan-output variables */
+      bool keepgoing_out = keepgoing
+      int b_out = b
+
+      for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+        /* Implicitly-defined code: bind actual parameter values
+           to formal parameter variables of loop-body */
+        bool keepgoing_in = keepgoing_out;
+        bool b_in = b_out;
+
+        /* User-defined code (loop body) */
+        int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+        b_out = a - b_in;
+        keepgoing_out = my_local > b_out;
+        user_defined_val = b_in + b_in; // b_in and b_out are different variables
+        /* End user-defined code */
+
+        /* Implicitly defined-code */
+        user_defined_vals[i] = user_defined_val // accumulate scan-output values
+      }
+      // int t = my_local; // Can't do this. my_local is not accessible here.
+
+      // The values below are bound to the output variables of the loop and therefore accessible
+      // b_out; user_defined_vals; keepgoing_out;
+    }
+
+There are several things of note in this code snippet:
+
+1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+   be referenced in the inputs of the loop.
+2) Any values computed in the loop body that needs to be used in a subsequent
+   iteration or after the loop are modelled using a pair of variables in the loop-body,
+   consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+   These are referred to as loop-carried dependences. The loop operation node
+   supplies the input value of the input variable for the first iteration, and
+   returns the output value of the output variable produced by the final
+   iteration.
+3) Scan_output variables are used to implicitly concatenate values computed across
+   all the iterations. In the above example, the value of user_defined_val computed
+   over all iterations are concatenated and returned as the value of user_defined_vals
+   after the loop.
+4) Values created in the body cannot be accessed in the enclosing scope,
+   except using the mechanism described above.
+
+Note that the semantics of this op support "diagonal" or "wavefront" execution.
+(See Step 3 here for an example:
+https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+Frontends should emit multi-layer RNNs as a series of While operators (with
+time being the inner looping dimension), with each successive layer consuming
+the scan_outputs from the previous layer, possibly going through several
+point-wise operators (e.g. dropout, residual connections, linear layer).
+
+The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Loop,
+    16,
+    OpSchema()
+        .SetDoc(Loop_ver16_doc)
+        .Input(
+            0,
+            "M",
+            "A maximum trip-count for the loop specified at runtime. Optional."
+            " Pass empty string to skip.",
+            "I",
+            OpSchema::Optional)
+        .Input(
+            1,
+            "cond",
+            "A boolean termination condition. Optional. Pass empty string to skip.",
+            "B",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "v_initial",
+            "The initial values of any loop-carried dependencies (values that "
+            "change across loop iterations)",
+            "V",
+            OpSchema::Variadic,
+            false,
+            0)
+        .Output(
+            0,
+            "v_final_and_scan_outputs",
+            "Final N loop carried dependency values then K scan_outputs. "
+            "Scan outputs must be Tensors.",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has 2+N inputs: (iteration_num, "
+            "condition, loop carried dependencies...). It has 1+N+K outputs: "
+            "(condition, loop carried dependencies..., scan_outputs...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "output value at the end of each iteration of the loop. It is an error"
+            " if the dimensions or data type of these scan_outputs change across loop"
+            " iterations.",
+            AttributeProto::GRAPH)
+        .TypeConstraint(
+            "V",
+            control_flow_types(),
+            "All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "tensor of int64, which should be a scalar.")
+        .TypeConstraint(
+            "B",
+            {"tensor(bool)"},
+            "tensor of bool, which should be a scalar.")
+        .TypeAndShapeInferenceFunction(LoopInferenceFunction));
+
+static const char* scan_16_doc = R"DOC(
+Scan can be used to iterate over one or more scan_input tensors,
+constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+generalizations of RNN-like constructs for sequence-to-sequence processing.
+Other tensors (referred to as state_variables here) can be used to carry a state
+when iterating from one element to another (similar to hidden-state in RNNs, also referred
+to as loop-carried dependences in the context of loops).
+Many common usages involve a single scan_input tensor (where functionality
+similar to scan, fold and map can be obtained). When more than one scan_input is used,
+a behavior similar to zip is obtained.
+
+The attribute body must be a graph, specifying the computation to be performed in
+every iteration. It takes as input the current values of the state_variables and
+the current iterated element of the scan_inputs. It must return the (updated) values
+of the state_variables and zero or more scan_output_element tensors. The values of the
+scan_output_element tensors are concatenated over all the iterations to produce the
+scan_output values of the scan construct (similar to the concatenated intermediate
+hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+well as scan_output_element tensors) are required to have the same shape in each iteration
+of the loop (a restriction imposed to enable efficient memory allocation).
+
+Note that the iterated element passed to the body subgraph does not have a sequence
+axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+The scan operation returns the final values of the state_variables as well as the
+scan_outputs.
+
+The optional attribute scan_input_directions specifies the direction (forward or backward)
+for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+direction. A bidirectional scan may be performed by specifying the same tensor input twice
+in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+The scan_output of the operation is produced by concatenating the scan_output_element
+values produced by the body in each iteration.  The optional attribute scan_output_directions
+specifies the direction in which scan_output is constructed (by appending or prepending the
+scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+value of 1.
+
+Note that because of the ONNX restriction that only the last parameter of an operator can
+be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+Similarly, the final-states and scan-outputs are listed together as one output parameter.
+The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+The behavior of
+
+    Scan <
+        num_scan_inputs = m,
+        body = loop-body,
+        scan_input_axes = [axis_1, ..., axis_m]
+    > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+is equivalent to the following pseudo-code:
+
+    // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+    // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+    sequence_length = scan_1.shape[axis_1];
+
+    // initialize state-variables
+    st_1 = init_1; ... st_n = init_n;
+    // initialize scan-output variables: [] denotes an empty tensor
+    scan_out_1 = []; ...; scan_out_k = [];
+    // identify number of iterations:
+
+    // execute loop
+    for (int t = 0; t < sequence_length; ++t) {
+        // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+        // of rank one less than T obtained by indexing T at position t along axis k.
+        si_1 = scan_1<axis=axis_1>[t];
+        ... ;
+        si_m = scan_m<axis=axis_m>[t];
+        // execute loop-body
+        st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+        // accumulate the scan-output elements
+        scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+    }
+
+    return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+*Sample usage: Encoding RNN using a Scan*
+
+The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+%Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+    graph rnn-encoding {
+      %H_0 = ...
+      %X = ...
+      %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+      return %Y, %Y_h
+    }
+
+    graph rnn-cell-1 (
+      %H_tminus1[FLOAT, tensor]
+      %X_t[FLOAT, tensor]
+    ) {
+      %Wi = ...
+      %Ri = ...
+      %Wbi = ...
+      %Rbi = ...
+      %t1 = X_t * (Wi^T)
+      %t2 = H_tminus1*(Ri^T)
+      %t3 = Add(%t1, %t2)
+      %t4 = Add(%t3, %Wbi)
+      %t5 = Add(%t4, %Rbi)
+      %Ht = Tanh(%t5)
+      %Accumulate = Identity(%Ht)
+      return %Ht, %Accumulate
+    }
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Scan,
+    16,
+    OpSchema()
+        .SetDoc(scan_16_doc)
+        .Input(
+            0,
+            "initial_state_and_scan_inputs",
+            "Initial values of the loop's N state variables followed by M scan_inputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "final_state_and_scan_outputs",
+            "Final values of the loop's N state variables followed by K scan_outputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has N+M inputs: "
+            "(loop state variables..., scan_input_elts...). It has N+K outputs: "
+            "(loop state variables..., scan_output_elts...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "scan_output_elt value at the end of each iteration of the loop. It is an error"
+            " if the dimensions of these values change across loop iterations.",
+            AttributeProto::GRAPH,
+            true)
+        .Attr(
+            "num_scan_inputs",
+            "An attribute specifying the number of scan_inputs M. ",
+            AttributeProto::INT,
+            true)
+        .Attr(
+            "scan_input_directions",
+            "An optional list of M flags. The i-th element of the list specifies the direction "
+            "to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 "
+            "indicates reverse direction. "
+            "If omitted, all scan_input tensors will be scanned in the forward direction.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_output_directions",
+            "An optional list of K flags, one for each scan_output. The i-th element of the list "
+            "specifies whether the i-th scan_output should be constructed by appending or "
+            "prepending a new value in each iteration: 0 indicates appending and 1 "
+            "indicates prepending. "
+            "If omitted, all scan_output tensors will be produced by appending a value "
+            "in each iteration.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_input_axes",
+            "An optional list of M flags. The i-th element of the list specifies the axis "
+            "to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will "
+            "be used as the scan axis for every scan_input. Negative value for an axis means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_output_axes",
+            "An optional list of K flags. The i-th element of the list specifies the axis "
+            "for the i-th scan_output. The scan outputs are accumulated along the specified "
+            "axis. If omitted, 0 will be used as the scan axis for every scan_output. "
+            "Negative value for an axis means counting dimensions from the back. Accepted "
+            "range is [-r, r-1].",
+            AttributeProto::INTS,
+            false)
+        .TypeConstraint("V", OpSchema::all_tensor_types_with_bfloat(), "All Tensor types")
+        .TypeAndShapeInferenceFunction(ScanInferenceFunction)); // Shares same shape inference as opset 11
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/controlflow/old.cc b/third_party/onnx/onnx/defs/controlflow/old.cc
new file mode 100644
index 0000000000..b7504271a2
--- /dev/null
+++ b/third_party/onnx/onnx/defs/controlflow/old.cc
@@ -0,0 +1,2183 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+namespace ONNX_NAMESPACE {
+using SupportType = OpSchema::SupportType;
+
+void ScanInferenceFunctionOpset8(InferenceContext& ctx) {
+  // NOTE:
+  // The first input to Scan is sequence_lens. We skip that when processing
+  // inputs in many places below, so the - 1 in multiple places is due to that.
+  auto num_inputs = ctx.getNumInputs();
+  auto num_scan_inputs =
+      narrow_cast<size_t>(ctx.getAttribute("num_scan_inputs")->i());
+  auto num_loop_state_vars = num_inputs - 1 - num_scan_inputs;
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs);
+
+  std::vector<const TypeProto*> subgraph_input_types;
+
+  TensorShapeProto_Dimension batch_size_dim;
+  TensorShapeProto_Dimension sequence_len_dim;
+
+  for (size_t i = 1; i < num_inputs; ++i) {
+    bool is_loop_state_var = (i - 1) < num_loop_state_vars;
+    bool has_shape = hasInputShape(ctx, i);
+    const auto* input_type = ctx.getInputType(i);
+
+    // Enforce type constraint for inputs
+    if (!input_type || !input_type->has_tensor_type()) {
+      fail_type_inference("Scan input ", i, " was not a tensor.");
+    }
+
+    if (is_loop_state_var) {
+      // If it's a loop state variable we can propagate type and shape 1:1 to
+      // the matching Scan output.
+      // We can also pass through the type and shape to the subgraph but need to
+      // remove the batch size dimension from the shape.
+      propagateElemTypeFromInputToOutput(ctx, i, i - 1);
+
+      if (has_shape) {
+        propagateShapeFromInputToOutput(ctx, i, i - 1);
+
+        // remove batch size dimension and add to subgraph_input_types
+        temporary_type_protos.push_back(
+            RemoveDimensionsFromShape(*input_type, 1));
+        subgraph_input_types.push_back(&temporary_type_protos.back());
+      } else {
+        subgraph_input_types.push_back(input_type);
+      }
+    } else {
+      // For other inputs there is no fixed relationships to the Scan outputs,
+      // so we don't propagate type/shape information.
+      // We can pass through the type and shape to the subgraph inputs but need
+      // to remove the batch size and sequence length dimensions from the shape.
+      if (has_shape) {
+        // remove batch size and sequence length dimensions and add to
+        // subgraph_input_types
+        temporary_type_protos.push_back(
+            RemoveDimensionsFromShape(*input_type, 2));
+        subgraph_input_types.push_back(&temporary_type_protos.back());
+
+        // update batch_size and sequence_len if a value is available
+        const auto& shape = input_type->tensor_type().shape();
+        if (shape.dim_size() > 2) {
+          const auto& dims = shape.dim();
+          mergeInDimensionInfo(dims.Get(0), batch_size_dim, 0);
+          mergeInDimensionInfo(dims.Get(1), sequence_len_dim, 1);
+        }
+      } else {
+        subgraph_input_types.push_back(input_type);
+      }
+    }
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    for (size_t i = 1; i < num_inputs; ++i) {
+      input_data.push_back(ctx.getInputData(i));
+    }
+
+    output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!output_types.empty()) {
+    auto num_outputs = ctx.getNumOutputs();
+    if (output_types.size() != num_outputs) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          output_types.size(),
+          " outputs. Expected ",
+          num_outputs);
+    }
+
+    // propagate type/shape information for loop state variables and outputs
+    for (size_t i = 0; i < num_outputs; ++i) {
+      const bool is_loop_state_var = i < num_loop_state_vars;
+      auto* subgraph_output_type = output_types[i];
+      auto* scan_output_type = ctx.getOutputType(i);
+
+      if (!subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Scan 'body' subgraph outputs should all be tensors but output ",
+            i,
+            " was not");
+      }
+
+      // propagate output type. loop state vars were done in the above code.
+      if (!is_loop_state_var) {
+        scan_output_type->mutable_tensor_type()->set_elem_type(
+            subgraph_output_type->tensor_type().elem_type());
+      }
+
+      // propagate shape
+      if (subgraph_output_type->tensor_type().has_shape()) {
+        // we need to add in the batch size and sequence length values if
+        // available before merging with any existing info. Create a copy of the
+        // inferred type info from the subgraph to do that.
+        TypeProto inferred_type(*subgraph_output_type);
+        auto* mutable_inferred_tensor_type =
+            inferred_type.mutable_tensor_type();
+        auto* mutable_inferred_shape =
+            mutable_inferred_tensor_type->mutable_shape();
+
+        mutable_inferred_shape->clear_dim();
+        *mutable_inferred_shape->add_dim() = batch_size_dim;
+
+        if (!is_loop_state_var) {
+          *mutable_inferred_shape->add_dim() = sequence_len_dim;
+        }
+
+        for (const auto& dim :
+             subgraph_output_type->tensor_type().shape().dim()) {
+          (*mutable_inferred_shape->add_dim()) = dim;
+        }
+
+        auto* mutable_scan_output_tensor_type =
+            scan_output_type->mutable_tensor_type();
+
+        mergeInShapeInfo(
+            *mutable_inferred_tensor_type, *mutable_scan_output_tensor_type);
+      }
+    }
+  }
+}
+
+int handle_negative_axis_validate_opset9(
+    const std::string& attrib,
+    int axis,
+    int rank) {
+  if (!(-rank <= axis && axis < rank)) {
+    fail_shape_inference(
+        attrib,
+        " axis value ",
+        axis,
+        " is invalid for a tensor of rank ",
+        rank);
+  }
+  return (axis >= 0 ? axis : axis + rank);
+}
+
+void ScanInferenceFunctionOpset9(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  auto num_scan_inputs =
+      narrow_cast<size_t>(ctx.getAttribute("num_scan_inputs")->i());
+  auto num_loop_state_vars = num_inputs - num_scan_inputs;
+  auto num_outputs = ctx.getNumOutputs();
+  auto num_scan_outputs = num_outputs - num_loop_state_vars;
+
+  std::vector<int64_t> axes, output_axes;
+  if (getRepeatedAttribute(ctx, "scan_input_axes", axes)) {
+    if (axes.size() != num_scan_inputs) {
+      fail_shape_inference(
+          "Number of scan input axes specified (",
+          axes.size(),
+          ") is not equal to number of scan inputs (",
+          num_scan_inputs,
+          ").");
+    }
+  } else {
+    axes.insert(axes.end(), num_scan_inputs, 0);
+  }
+
+  if (getRepeatedAttribute(ctx, "scan_output_axes", output_axes)) {
+    if (output_axes.size() != num_scan_outputs) {
+      fail_shape_inference(
+          "Number of scan output axes specified (",
+          output_axes.size(),
+          ") is not equal to number of scan outputs (",
+          num_scan_outputs,
+          ").");
+    }
+  } else {
+    output_axes.insert(output_axes.end(), num_scan_outputs, 0);
+  }
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs);
+
+  std::vector<const TypeProto*> subgraph_input_types;
+
+  TensorShapeProto_Dimension sequence_len_dim;
+
+  for (size_t i = 0; i < num_inputs; ++i) {
+    bool is_loop_state_var = i < num_loop_state_vars;
+    bool has_shape = hasInputShape(ctx, i);
+    const auto* input_type = ctx.getInputType(i);
+
+    // Enforce type constraint for inputs
+    if (!input_type || !input_type->has_tensor_type()) {
+      fail_type_inference("Scan input ", i, " was not a tensor.");
+    }
+
+    if (is_loop_state_var) {
+      // If it's a loop state variable we can propagate type and shape 1:1 to
+      // the matching Scan output.
+      // We can also pass through the type and shape to the subgraph but need to
+      // remove the batch size dimension from the shape.
+      propagateElemTypeFromInputToOutput(ctx, i, i);
+      if (has_shape)
+        propagateShapeFromInputToOutput(ctx, i, i);
+
+      subgraph_input_types.push_back(input_type);
+    } else {
+      // For other inputs there is no fixed relationships to the Scan outputs,
+      // so we don't propagate type/shape information.
+      // We can pass through the type and shape to the subgraph inputs but
+      // need to remove the sequence length dimensions from the shape.
+      if (has_shape) {
+        const auto& shape = input_type->tensor_type().shape();
+
+        // remove sequence length dimensions and add to subgraph_input_types
+        int axis = static_cast<int>(axes[i - num_loop_state_vars]);
+        axis = handle_negative_axis_validate_opset9(
+            "scan_input_axes", axis, shape.dim_size());
+
+        // update sequence_len if a value is available
+
+        const auto& dims = shape.dim();
+        mergeInDimensionInfo(dims.Get(axis), sequence_len_dim, 1);
+
+        temporary_type_protos.push_back(
+            RemoveIthDimensionFromShape(*input_type, axis));
+        subgraph_input_types.push_back(&temporary_type_protos.back());
+
+      } else {
+        subgraph_input_types.push_back(input_type);
+      }
+    }
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    for (size_t i = 0; i < num_inputs; ++i) {
+      // ctx.getInputData(i), the input to scan, does not represent the input to
+      // scan body. So, we pass in null, to represent an unknown value.
+      input_data.push_back(nullptr);
+    }
+
+    output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!output_types.empty()) {
+    if (output_types.size() != num_outputs) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          output_types.size(),
+          " outputs. Expected ",
+          num_outputs);
+    }
+
+    // propagate type/shape information for loop state variables and outputs
+    for (size_t i = 0; i < num_outputs; ++i) {
+      const bool is_loop_state_var = i < num_loop_state_vars;
+      auto* subgraph_output_type = output_types[i];
+      auto* scan_output_type = ctx.getOutputType(i);
+      auto* mutable_scan_output_tensor_type =
+          scan_output_type->mutable_tensor_type();
+
+      if (!subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Scan 'body' subgraph outputs should all be tensors but output ",
+            i,
+            " was not");
+      }
+      auto& subgraph_output_tensor_type = subgraph_output_type->tensor_type();
+
+      if (is_loop_state_var) {
+        // merge shape; type already propagated
+        mergeInShapeInfo(
+            subgraph_output_tensor_type, *mutable_scan_output_tensor_type);
+      } else {
+        scan_output_type->mutable_tensor_type()->set_elem_type(
+            subgraph_output_tensor_type.elem_type());
+
+        // propagate shape
+        if (subgraph_output_tensor_type.has_shape()) {
+          // infer shape of scan-output from the shape of scan-output-element
+          // by adding sequence-length at the correct axis position
+          const TensorShapeProto& subgraph_output_shape =
+              subgraph_output_tensor_type.shape();
+          TensorShapeProto inferred_shape;
+
+          auto subgraph_output_rank = subgraph_output_shape.dim_size();
+          auto output_rank = subgraph_output_rank + 1;
+          int output_axis =
+              static_cast<int>(output_axes[i - num_loop_state_vars]);
+          output_axis = handle_negative_axis_validate_opset9(
+              "scan_output_axes", output_axis, output_rank);
+
+          for (int j = 0; j < output_axis; ++j)
+            *(inferred_shape.add_dim()) = subgraph_output_shape.dim(j);
+          *(inferred_shape.add_dim()) = sequence_len_dim;
+          for (int j = output_axis; j < subgraph_output_rank; ++j)
+            *(inferred_shape.add_dim()) = subgraph_output_shape.dim(j);
+
+          // Merge inferred shape with existing shape information
+          mergeInShapeInfo(inferred_shape, *mutable_scan_output_tensor_type);
+        }
+      }
+    }
+  }
+}
+
+static const char* scan_opset8_doc = R"DOC(
+Scan can be used to iterate over one or more scan_input tensors,
+constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+generalizations of RNN-like constructs for sequence-to-sequence processing.
+Other tensors (referred to as state_variables here) can be used to carry a state
+when iterating from one element to another (similar to hidden-state in RNNs, also referred
+to as loop-carried dependences in the context of loops). All these tensors are required to
+have the same shape in each iteration of the loop (a restriction imposed to enable efficient
+memory allocation). Many common usages involve a single scan_input tensor (where functionality
+similar to scan, fold and map can be obtained). When more than one scan_input is used,
+a behavior similar to zip is obtained.
+
+The attribute body must be a graph, specifying the computation to be performed in
+every iteration. It takes as input the current values of the state_variables and
+the current iterated element of the scan_inputs. It must return the (updated) values
+of the state_variables and zero or more scan_output_element tensors. The values of the
+scan_output_element tensors are concatenated over all the iterations to produce the
+scan_output values of the scan construct (similar to the concatenated intermediate
+hidden-state values of RNN-like constructs).
+
+The scan operation returns the final values of the state_variables as well as the
+scan_outputs.
+
+The operation supports batching, and the batch-axis is required to be 0.
+When multiple scan_input tensors are used, they must all have the same batch-size,
+and they must all have the same maximum-sequence-length (the dimensionality of the
+sequence axis or scan axis). The sequence axis or scan axis is required to be 1.
+
+The operation has an optional sequence_lens input (of shape [BATCH_SIZE]) to
+allow variable length sequences of length <= the maximum-sequence-length. If this
+input is not specified, all sequences are assumed to be of length equal to
+maximum-sequence-length. For variable length input sequences, the scan_outputs
+will consist of a sequence of same length as the input, padded to the
+maximum-sequence-length.
+
+The optional attribute directions can be used to scan a sequence in the reverse direction.
+If this attribute is omitted, all sequences are scanned in the forward direction.
+A bidirectional scan be performed by specifying the same tensor input twice in the
+scan_inputs, once with a forward direction, and once with a backward direction.
+
+Note that because of the ONNX restriction that only the last parameter of an operator can
+be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+Similarly, the final-states and scan-outputs are listed together as one output parameter.
+The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+The behavior of
+
+    Scan <
+        num_scan_inputs = m,
+        body = loop-body
+    > (sequence_lengths, init_1, ..., init_n, scan_1, ..., scan_m)
+
+is equivalent to the following pseudo-code:
+
+    // T.shape[0] denotes the batch-size of T
+    // The batch-size of scan_1, ..., scan_m are all required to be equal
+    batch_size = scan_1.shape[0];
+
+    // scan_i.shape[1] denotes the (max) sequence-length of scan_i
+    // scan_i.shape[1] is required to be equal to scan_j.shape[1] for all i,j.
+    max_sequence_length = scan_1.shape[1];
+
+    for (int batch = 0; batch < batch_size; ++batch) {
+        // initialize state-variables
+        st_1 = init_1; ... st_n = init_n;
+        // initialize scan-output variables: [] denotes an empty tensor
+        scan_out_1 = []; ...; scan_out_k = [];
+        // identify number of iterations:
+        N = (sequence_lengths specified) ? sequence_lengths[batch] : max_sequence_length;
+
+        // execute loop
+        for (int t = 0; t < N; ++t) {
+            // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+            // of rank one less than T obtained by indexing T at position t along axis k.
+            si_1 = (scan_1<axis=0>[batch])<axis=1>[t];
+            ... ;
+            si_m = (scan_m<axis=0>[batch])<axis=1>[t];
+            // execute loop-body
+            st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+            // accumulate the scan-output elements
+            scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+        }
+        // accumulate the outputs for this batch:
+        bst_1[batch] = st_1; ..., bst_n[batch] = st_n;
+        // Note scan-outputs will have size max_sequence_length, but only first N values will be meaningful.
+        // The remaining values have an undefined value.
+        b_scan_out_1[batch] = scan_out_1; ...; b_scan_out_k[batch] = scan_out_k;
+    }
+    return bst_1, ..., bst_n, b_scan_out_1, ..., b_scan_out_k;
+
+
+
+*Sample usage: Encoding RNN using a Scan*
+
+The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+%Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+    graph rnn-encoding {
+      %H_0 = ...
+      %X = ...
+      %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1]("", %H_0, %X)
+      return %Y, %Y_h
+    }
+
+    graph rnn-cell-1 (
+      %H_tminus1[FLOAT, tensor]
+      %X_t[FLOAT, tensor]
+    ) {
+      %Wi = ...
+      %Ri = ...
+      %Wbi = ...
+      %Rbi = ...
+      %t1 = X_t * (Wi^T)
+      %t2 = H_tminus1*(Ri^T)
+      %t3 = Add(%t1, %t2)
+      %t4 = Add(%t3, %Wbi)
+      %t5 = Add(%t4, %Rbi)
+      %Ht = Tanh(%t5)
+      %Accumulate = Identity(%Ht)
+      return %Ht, %Accumulate
+    }
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Scan,
+    8,
+    OpSchema()
+        .SetDoc(scan_opset8_doc)
+        .Input(
+            0,
+            "sequence_lens",
+            "Optional tensor specifying lengths of the sequences in a batch. "
+            "If this input is not specified, all sequences are assumed to be of "
+            "the maximum sequence length (the dimension of the sequence axis of "
+            "the scan_input tensors).",
+            "I",
+            OpSchema::Optional)
+        .Input(
+            1,
+            "initial_state_and_scan_inputs",
+            "Initial values of the loop's N state variables followed by M scan_inputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "final_state_and_scan_outputs",
+            "Final values of the loop's N state variables followed by K scan_outputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has N+M inputs: "
+            "(loop state variables..., scan_input_elts...). It has N+K outputs: "
+            "(loop state variables..., scan_output_elts...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "scan_output_elt value at the end of each iteration of the loop. It is an error"
+            " if the dimensions of these values change across loop iterations.",
+            AttributeProto::GRAPH,
+            true)
+        .Attr(
+            "num_scan_inputs",
+            "An attribute specifying the number of scan_inputs M. ",
+            AttributeProto::INT,
+            true)
+        .Attr(
+            "directions",
+            "An optional list of M flags. The i-th element of the list specifies the direction "
+            "to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 "
+            "indicates reverse direction. "
+            "If omitted, all scan_input tensors will be scanned in the forward direction.",
+            AttributeProto::INTS,
+            false)
+        .TypeConstraint("I", {"tensor(int64)"}, "Int64 tensor")
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeAndShapeInferenceFunction(ScanInferenceFunctionOpset8));
+
+void LoopInferenceFunctionOpset8(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  auto num_loop_state_vars = num_inputs - 2; // skip 'M' and 'cond'
+
+  std::vector<const TypeProto*> subgraph_input_types;
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs - 2);
+
+  // create TypeProto to validate iteration number type is the same as the
+  // optional 'M' input for max iterations.
+  TypeProto iter_num_type;
+  iter_num_type.mutable_tensor_type()->set_elem_type(
+      TensorProto_DataType_INT64);
+  subgraph_input_types.push_back(&iter_num_type);
+
+  // 'cond'
+  subgraph_input_types.push_back(ctx.getInputType(1));
+
+  // loop state value types get propagated to outputs, but shape may change
+  // across iterations so don't propagate it to the outputs and don't pass it
+  // into the subgraph inferencing
+  for (size_t i = 2; i < num_inputs; ++i) {
+    propagateElemTypeFromInputToOutput(ctx, i, i - 2);
+
+    // copy so we can remove the shape before passing to the subgraph
+    // inferencing
+    temporary_type_protos.push_back(*ctx.getInputType(i));
+    auto& input_type = temporary_type_protos.back();
+    input_type.mutable_tensor_type()->clear_shape();
+
+    subgraph_input_types.push_back(&input_type);
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> subgraph_output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    input_data.push_back(nullptr); // iteration number
+    for (size_t i = 1; i < num_inputs; ++i) {
+      input_data.push_back(ctx.getInputData(i));
+    }
+
+    subgraph_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!subgraph_output_types.empty()) {
+    auto num_outputs = ctx.getNumOutputs();
+
+    // subgraph outputs the condition value first but that is only used
+    // internally and not returned by Loop.
+    if (subgraph_output_types.size() != num_outputs + 1) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          subgraph_output_types.size(),
+          " outputs. Expected ",
+          num_outputs + 1);
+    }
+
+    // check loop state values match. we should already have type/shape info
+    for (size_t i = 0; i < num_outputs; ++i) {
+      auto* subgraph_output_type = subgraph_output_types[i + 1]; // skip 'cond'
+      auto* loop_output_type = ctx.getOutputType(i);
+
+      const bool is_loop_state_var = i < num_loop_state_vars;
+
+      if (!subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Loop 'body' subgraph outputs should all be tensors but output ",
+            i,
+            " was ",
+            subgraph_output_type->value_case());
+      }
+
+      // if there's an existing type check it matches. otherwise propagate
+      propagateElemTypeWithValidation(subgraph_output_type, loop_output_type);
+
+      if (is_loop_state_var) {
+        // shape may change across iterations so ignore.
+      } else {
+        // propogate shape
+        if (subgraph_output_type->tensor_type().has_shape()) {
+          // per iteration output. first dimension will be number of iterations
+          // but we don't know that value yet
+          TypeProto inferred_type(*subgraph_output_type);
+          auto* mutable_inferred_tensor_type =
+              inferred_type.mutable_tensor_type();
+          auto* mutable_inferred_shape =
+              mutable_inferred_tensor_type->mutable_shape();
+
+          mutable_inferred_shape->clear_dim();
+
+          // add empty dimension for number of iterations
+          mutable_inferred_shape->add_dim();
+
+          // add dimensions from subgraph output shape
+          for (const auto& dim :
+               subgraph_output_type->tensor_type().shape().dim()) {
+            (*mutable_inferred_shape->add_dim()) = dim;
+          }
+
+          mergeInShapeInfo(
+              *mutable_inferred_tensor_type,
+              *loop_output_type->mutable_tensor_type());
+        }
+      }
+    }
+  }
+}
+
+static const char* Loop_ver1_doc = R"DOC(
+Generic Looping construct. This loop has multiple termination conditions:
+
+1) Trip count. Iteration count specified at runtime. Set by
+   specifying the input M. Optional. Set to empty string to omit.
+   Note that a static trip count (specified at graph construction time) can be
+   specified by passing in a constant node for input M.
+2) Loop termination condition. This is an input to the op that determines
+   whether to run the first iteration and also a loop-carried dependency for
+   the body graph. The body graph must yield a value for the condition variable,
+   whether this input is provided or not.
+
+This table summarizes the operating modes of this operator with equivalent
+C-style code:
+
+    Operator inputs defined as (max_trip_count, condition_var).
+
+    input ("", ""):
+        for (int i=0; ; ++i) {
+          cond = ... // Note this value is ignored, but is required in the body
+        }
+
+    input ("", cond) // Note this is analogous to a while loop
+        bool cond = ...;
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input ("", 1) // Note this is analogous to a do-while loop
+        bool cond = true
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input (trip_count, "") // Note this is analogous to a for loop
+        int trip_count = ...
+        for (int i=0; i < trip_count; ++i) {
+          cond = ...; // ignored
+        }
+
+    input (trip_count, cond)
+        int trip_count = ...;
+        bool cond = ...;
+        for (int i=0; i < trip_count && cond; ++i) {
+          cond = ...;
+        }
+
+
+*Sample usage - cond as well as trip count*
+
+    graph predict-net {
+      %a = Constant[value = <Scalar Tensor [3]>]()
+      %b = Constant[value = <Scalar Tensor [6]>]()
+      %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+      %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+      %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+      return
+    }
+
+    graph body-net (
+      %i[INT32, scalar]
+      %keepgoing[BOOL, scalar]
+      %b[INT32, scalar]
+    ) {
+      %my_local = Add(%a, %b)
+      %b_out = Sub(%a, %b)
+      %keepgoing_out = Greater(%my_local, %b_out)
+      %user_defined_vals = Add(%b, %b)
+      return %keepgoing_out, %b_out, %user_defined_vals
+    }
+
+*Sample equivalent C code*
+
+    {
+      /* User-defined code (enclosing scope) */
+      int a = 3, b = 6;
+      bool keepgoing = true; // Analogous to input cond
+      /* End user-defined code */
+
+      /* Implicitly-defined code */
+      const int max_trip_count = 10; // Analogous to input M
+      int user_defined_vals[]; // Imagine this is resizable
+      /* End implicitly-defined code */
+      for (int i=0; i < max_trip_count && keepgoing; ++i) {
+        /* User-defined code (loop body) */
+        int my_local = a + b; // Reading values in the enclosing scope is fine
+        b = a - b; // writes fine if we specify b as a loop-carried dependency
+        keepgoing = my_local > b; // keepgoing is a loop-carried dependency
+        user_defined_vals[i] = b + b;
+        /* End user-defined code */
+      }
+      // my_local = 123; // Can't do this. my_local was defined in the the body
+
+      // These below values are live-out from the loop and therefore accessible
+      b_out; user_defined_vals; keepgoing_out;
+    }
+
+There are several things of note in this code snippet:
+
+1) Values from the enclosing scope (i.e. variable a here) are in scope and can
+   be referenced in the inputs of the loop.
+2) Any variables which you wish to make available in the enclosing scope (i.e.
+   the variables b and keepgoing) must be declared as either loop-carried
+   dependencies (both at the op inputs and output and at the body net input and
+   output) or scan_outputs.
+3) Values created in the body cannot be accessed in the enclosing scope.
+
+Note that the semantics of this op support "diagonal" or "wavefront" execution.
+(See Step 3 here for an example:
+https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+Frontends should emit multi-layer RNNs as a series of While operators (with
+time being the inner looping dimension), with each successive layer consuming
+the scan_outputs from the previous layer, possibly going through several
+point-wise operators (e.g. dropout, residual connections, linear layer).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Loop,
+    1,
+    OpSchema()
+        .SetDoc(Loop_ver1_doc)
+        .Input(
+            0,
+            "M",
+            "A maximum trip-count for the loop specified at runtime. Optional."
+            " Pass empty string to skip.",
+            "I",
+            OpSchema::Optional)
+        .Input(
+            1,
+            "cond",
+            "A boolean termination condition. Optional. Pass empty string to skip.",
+            "B",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "v_initial",
+            "The initial values of any loop-carried dependencies (values that "
+            "change across loop iterations)",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "v_final_and_scan_outputs",
+            "Final N loop carried dependency values then K scan_outputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has 2+N inputs: (iteration_num, "
+            "condition, loop carried dependencies...). It has 1+N+K outputs: "
+            "(condition, loop carried dependencies..., scan_outputs...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "output value at the end of each iteration of the loop. It is an error"
+            " if the dimensions or data type of these scan_outputs change across loop"
+            " iterations.",
+            AttributeProto::GRAPH)
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "tensor of int64, which should be a scalar.")
+        .TypeConstraint(
+            "B",
+            {"tensor(bool)"},
+            "tensor of bool, which should be a scalar.")
+        .TypeAndShapeInferenceFunction(LoopInferenceFunctionOpset8));
+
+void LoopInferenceFunctionOpset11(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  auto num_loop_state_vars = num_inputs - 2; // skip 'M' and 'cond'
+
+  std::vector<const TypeProto*> subgraph_input_types;
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs - 2);
+
+  // create TypeProto to validate iteration number type is the same as the
+  // optional 'M' input for max iterations.
+  TypeProto iter_num_type;
+  iter_num_type.mutable_tensor_type()->set_elem_type(
+      TensorProto_DataType_INT64);
+  subgraph_input_types.push_back(&iter_num_type);
+
+  // 'cond'
+  subgraph_input_types.push_back(ctx.getInputType(1));
+
+  // loop state value types get propagated to outputs, but shape may change
+  // across iterations so don't propagate it to the outputs and don't pass it
+  // into the subgraph inferencing
+  for (size_t i = 2; i < num_inputs; ++i) {
+    propagateElemTypeFromInputToOutput(ctx, i, i - 2);
+
+    // copy so we can remove the shape before passing to the subgraph
+    // inferencing
+    temporary_type_protos.push_back(*ctx.getInputType(i));
+    auto& input_type = temporary_type_protos.back();
+    input_type.mutable_tensor_type()->clear_shape();
+
+    subgraph_input_types.push_back(&input_type);
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> subgraph_output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    input_data.push_back(nullptr); // iteration number
+    for (size_t i = 1; i < num_inputs; ++i) {
+      input_data.push_back(ctx.getInputData(i));
+    }
+
+    subgraph_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!subgraph_output_types.empty()) {
+    auto num_outputs = ctx.getNumOutputs();
+
+    // subgraph outputs the condition value first but that is only used
+    // internally and not returned by Loop.
+    if (subgraph_output_types.size() != num_outputs + 1) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          subgraph_output_types.size(),
+          " outputs. Expected ",
+          num_outputs + 1);
+    }
+
+    // check loop state values match. we should already have type/shape info
+    for (size_t i = 0; i < num_outputs; ++i) {
+      auto* subgraph_output_type = subgraph_output_types[i + 1]; // skip 'cond'
+      auto* loop_output_type = ctx.getOutputType(i);
+
+      const bool is_loop_state_var = i < num_loop_state_vars;
+
+      if (!subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Loop 'body' subgraph outputs should all be tensors but output ",
+            i,
+            " was ",
+            subgraph_output_type->value_case());
+      }
+
+      // if there's an existing type check it matches. otherwise propagate
+      propagateElemTypeWithValidation(subgraph_output_type, loop_output_type);
+
+      if (is_loop_state_var) {
+        // shape may change across iterations so ignore.
+      } else {
+        // propogate shape
+        if (subgraph_output_type->tensor_type().has_shape()) {
+          // per iteration output. first dimension will be number of iterations
+          // but we don't know that value yet
+          TypeProto inferred_type(*subgraph_output_type);
+          auto* mutable_inferred_tensor_type =
+              inferred_type.mutable_tensor_type();
+          auto* mutable_inferred_shape =
+              mutable_inferred_tensor_type->mutable_shape();
+
+          mutable_inferred_shape->clear_dim();
+
+          // add empty dimension for number of iterations
+          mutable_inferred_shape->add_dim();
+
+          // add dimensions from subgraph output shape
+          for (const auto& dim :
+               subgraph_output_type->tensor_type().shape().dim()) {
+            (*mutable_inferred_shape->add_dim()) = dim;
+          }
+
+          mergeInShapeInfo(
+              *mutable_inferred_tensor_type,
+              *loop_output_type->mutable_tensor_type());
+        }
+      }
+    }
+  }
+}
+
+static const char* Loop_ver11_doc = R"DOC(
+Generic Looping construct. This loop has multiple termination conditions:
+
+1) Trip count. Iteration count specified at runtime. Set by
+   specifying the input M. Optional. Set to empty string to omit.
+   Note that a static trip count (specified at graph construction time) can be
+   specified by passing in a constant node for input M.
+2) Loop termination condition. This is an input to the op that determines
+   whether to run the first iteration and also a loop-carried dependency for
+   the body graph. The body graph must yield a value for the condition variable,
+   whether this input is provided or not.
+
+This table summarizes the operating modes of this operator with equivalent
+C-style code:
+
+    Operator inputs defined as (max_trip_count, condition_var).
+
+    input ("", ""):
+        for (int i=0; ; ++i) {
+          cond = ... // Note this value is ignored, but is required in the body
+        }
+
+    input ("", cond) // Note this is analogous to a while loop
+        bool cond = ...;
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input ("", 1) // Note this is analogous to a do-while loop
+        bool cond = true
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input (trip_count, "") // Note this is analogous to a for loop
+        int trip_count = ...
+        for (int i=0; i < trip_count; ++i) {
+          cond = ...; // ignored
+        }
+
+    input (trip_count, cond)
+        int trip_count = ...;
+        bool cond = ...;
+        for (int i=0; i < trip_count && cond; ++i) {
+          cond = ...;
+        }
+
+
+*Sample usage - cond as well as trip count*
+
+    graph predict-net {
+      %a = Constant[value = <Scalar Tensor [3]>]()
+      %b = Constant[value = <Scalar Tensor [6]>]()
+      %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+      %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+      %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+      return
+    }
+
+    graph body-net (
+      %i[INT32, scalar]           // iteration number
+      %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+      %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+    ) {
+      %my_local = Add(%a, %b_in)
+      %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+      %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+      %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+      return %keepgoing_out, %b_out, %user_defined_val
+    }
+
+*Sample equivalent C code*
+
+    {
+      /* User-defined code (enclosing scope) */
+      int a = 3, b = 6;
+      bool keepgoing = true; // Analogous to input cond
+      /* End user-defined code */
+
+      /* Implicitly-defined code */
+      const int max_trip_count = 10; // Analogous to input M
+      int user_defined_vals[]; // Imagine this is resizable
+      /* End implicitly-defined code */
+      /* initialize loop-carried variables and scan-output variables */
+      bool keepgoing_out = keepgoing
+      int b_out = b
+
+      for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+        /* Implicitly-defined code: bind actual parameter values
+           to formal parameter variables of loop-body */
+        bool keepgoing_in = keepgoing_out;
+        bool b_in = b_out;
+
+        /* User-defined code (loop body) */
+        int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+        b_out = a - b_in;
+        keepgoing_out = my_local > b_out;
+        user_defined_val = b_in + b_in; // b_in and b_out are different variables
+        /* End user-defined code */
+
+        /* Implicitly defined-code */
+        user_defined_vals[i] = user_defined_val // accumulate scan-output values
+      }
+      // int t = my_local; // Can't do this. my_local is not accessible here.
+
+      // The values below are bound to the output variables of the loop and therefore accessible
+      // b_out; user_defined_vals; keepgoing_out;
+    }
+
+There are several things of note in this code snippet:
+
+1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+   be referenced in the inputs of the loop.
+2) Any values computed in the loop body that needs to be used in a subsequent
+   iteration or after the loop are modelled using a pair of variables in the loop-body,
+   consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+   These are referred to as loop-carried dependences. The loop operation node
+   supplies the input value of the input variable for the first iteration, and
+   returns the output value of the output variable produced by the final
+   iteration.
+3) Scan_output variables are used to implicitly concatenate values computed across
+   all the iterations. In the above example, the value of user_defined_val computed
+   over all iterations are concatenated and returned as the value of user_defined_vals
+   after the loop.
+4) Values created in the body cannot be accessed in the enclosing scope,
+   except using the mechanism described above.
+
+Note that the semantics of this op support "diagonal" or "wavefront" execution.
+(See Step 3 here for an example:
+https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+Frontends should emit multi-layer RNNs as a series of While operators (with
+time being the inner looping dimension), with each successive layer consuming
+the scan_outputs from the previous layer, possibly going through several
+point-wise operators (e.g. dropout, residual connections, linear layer).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Loop,
+    11,
+    OpSchema()
+        .SetDoc(Loop_ver11_doc)
+        .Input(
+            0,
+            "M",
+            "A maximum trip-count for the loop specified at runtime. Optional."
+            " Pass empty string to skip.",
+            "I",
+            OpSchema::Optional)
+        .Input(
+            1,
+            "cond",
+            "A boolean termination condition. Optional. Pass empty string to skip.",
+            "B",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "v_initial",
+            "The initial values of any loop-carried dependencies (values that "
+            "change across loop iterations)",
+            "V",
+            OpSchema::Variadic,
+            false,
+            0)
+        .Output(
+            0,
+            "v_final_and_scan_outputs",
+            "Final N loop carried dependency values then K scan_outputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has 2+N inputs: (iteration_num, "
+            "condition, loop carried dependencies...). It has 1+N+K outputs: "
+            "(condition, loop carried dependencies..., scan_outputs...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "output value at the end of each iteration of the loop. It is an error"
+            " if the dimensions or data type of these scan_outputs change across loop"
+            " iterations.",
+            AttributeProto::GRAPH)
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "tensor of int64, which should be a scalar.")
+        .TypeConstraint(
+            "B",
+            {"tensor(bool)"},
+            "tensor of bool, which should be a scalar.")
+        .TypeAndShapeInferenceFunction(LoopInferenceFunctionOpset11));
+
+static const char* scan_9_doc = R"DOC(
+Scan can be used to iterate over one or more scan_input tensors,
+constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+generalizations of RNN-like constructs for sequence-to-sequence processing.
+Other tensors (referred to as state_variables here) can be used to carry a state
+when iterating from one element to another (similar to hidden-state in RNNs, also referred
+to as loop-carried dependences in the context of loops).
+Many common usages involve a single scan_input tensor (where functionality
+similar to scan, fold and map can be obtained). When more than one scan_input is used,
+a behavior similar to zip is obtained.
+
+The attribute body must be a graph, specifying the computation to be performed in
+every iteration. It takes as input the current values of the state_variables and
+the current iterated element of the scan_inputs. It must return the (updated) values
+of the state_variables and zero or more scan_output_element tensors. The values of the
+scan_output_element tensors are concatenated over all the iterations to produce the
+scan_output values of the scan construct (similar to the concatenated intermediate
+hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+well as scan_output_element tensors) are required to have the same shape in each iteration
+of the loop (a restriction imposed to enable efficient memory allocation).
+
+Note that the iterated element passed to the body subgraph does not have a sequence
+axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+The scan operation returns the final values of the state_variables as well as the
+scan_outputs.
+
+The optional attribute scan_input_directions specifies the direction (forward or backward)
+for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+direction. A bidirectional scan may be performed by specifying the same tensor input twice
+in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+The scan_output of the operation is produced by concatenating the scan_output_element
+values produced by the body in each iteration.  The optional attribute scan_output_directions
+specifies the direction in which scan_output is constructed (by appending or prepending the
+scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+value of 1.
+
+Note that because of the ONNX restriction that only the last parameter of an operator can
+be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+Similarly, the final-states and scan-outputs are listed together as one output parameter.
+The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+The behavior of
+
+    Scan <
+        num_scan_inputs = m,
+        body = loop-body,
+        scan_input_axes = [axis_1, ..., axis_m]
+    > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+is equivalent to the following pseudo-code:
+
+    // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+    // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+    sequence_length = scan_1.shape[axis_1];
+
+    // initialize state-variables
+    st_1 = init_1; ... st_n = init_n;
+    // initialize scan-output variables: [] denotes an empty tensor
+    scan_out_1 = []; ...; scan_out_k = [];
+    // identify number of iterations:
+
+    // execute loop
+    for (int t = 0; t < sequence_length; ++t) {
+        // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+        // of rank one less than T obtained by indexing T at position t along axis k.
+        si_1 = scan_1<axis=axis_1>[t];
+        ... ;
+        si_m = scan_m<axis=axis_m>[t];
+        // execute loop-body
+        st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+        // accumulate the scan-output elements
+        scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+    }
+
+    return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+*Sample usage: Encoding RNN using a Scan*
+
+The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+%Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+    graph rnn-encoding {
+      %H_0 = ...
+      %X = ...
+      %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+      return %Y, %Y_h
+    }
+
+    graph rnn-cell-1 (
+      %H_tminus1[FLOAT, tensor]
+      %X_t[FLOAT, tensor]
+    ) {
+      %Wi = ...
+      %Ri = ...
+      %Wbi = ...
+      %Rbi = ...
+      %t1 = X_t * (Wi^T)
+      %t2 = H_tminus1*(Ri^T)
+      %t3 = Add(%t1, %t2)
+      %t4 = Add(%t3, %Wbi)
+      %t5 = Add(%t4, %Rbi)
+      %Ht = Tanh(%t5)
+      %Accumulate = Identity(%Ht)
+      return %Ht, %Accumulate
+    }
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Scan,
+    9,
+    OpSchema()
+        .SetDoc(scan_9_doc)
+        .Input(
+            0,
+            "initial_state_and_scan_inputs",
+            "Initial values of the loop's N state variables followed by M scan_inputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "final_state_and_scan_outputs",
+            "Final values of the loop's N state variables followed by K scan_outputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has N+M inputs: "
+            "(loop state variables..., scan_input_elts...). It has N+K outputs: "
+            "(loop state variables..., scan_output_elts...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "scan_output_elt value at the end of each iteration of the loop. It is an error"
+            " if the dimensions of these values change across loop iterations.",
+            AttributeProto::GRAPH,
+            true)
+        .Attr(
+            "num_scan_inputs",
+            "An attribute specifying the number of scan_inputs M. ",
+            AttributeProto::INT,
+            true)
+        .Attr(
+            "scan_input_directions",
+            "An optional list of M flags. The i-th element of the list specifies the direction "
+            "to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 "
+            "indicates reverse direction. "
+            "If omitted, all scan_input tensors will be scanned in the forward direction.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_output_directions",
+            "An optional list of K flags, one for each scan_output. The i-th element of the list "
+            "specifies whether the i-th scan_output should be constructed by appending or "
+            "prepending a new value in each iteration: 0 indicates appending and 1 "
+            "indicates prepending. "
+            "If omitted, all scan_output tensors will be produced by appending a value "
+            "in each iteration.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_input_axes",
+            "An optional list of M flags. The i-th element of the list specifies the axis "
+            "to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will "
+            "be used as the scan axis for every scan_input.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_output_axes",
+            "An optional list of K flags. The i-th element of the list specifies the axis "
+            "for the i-th scan_output. The scan outputs are accumulated along the specified "
+            "axis. If omitted, 0 will be used as the scan axis for every scan_output.",
+            AttributeProto::INTS,
+            false)
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeAndShapeInferenceFunction(ScanInferenceFunctionOpset9));
+
+void IfInferenceFunction1(InferenceContext& ctx) {
+  // there are no inputs so we just need to run the subgraph inferencing for
+  // then/else subgraphs and apply those to the outputs.
+  std::vector<const TypeProto*> subgraph_input_types; // none
+  std::vector<const TensorProto*> input_data; // none
+
+  std::vector<const TypeProto*> then_output_types;
+  std::vector<const TypeProto*> else_output_types;
+
+  // Run inferencing on the subgraph
+  GraphInferencer* graphInferencer =
+      ctx.getGraphAttributeInferencer("then_branch");
+  if (graphInferencer) {
+    then_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  graphInferencer = ctx.getGraphAttributeInferencer("else_branch");
+  if (graphInferencer) {
+    else_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  auto num_outputs = ctx.getNumOutputs();
+  auto num_then_outputs = then_output_types.size();
+  auto num_else_outputs = else_output_types.size();
+
+  // the output types for then and else should be the same
+  if (num_then_outputs != num_else_outputs) {
+    fail_type_inference(
+        "then_branch and else_branch produce different number of outputs. ",
+        num_then_outputs,
+        " != ",
+        num_else_outputs);
+  }
+
+  if (num_then_outputs != num_outputs) {
+    fail_type_inference(
+        "If node has ",
+        num_outputs,
+        " but subgraphs produce ",
+        num_then_outputs);
+  }
+
+  for (size_t i = 0, end = then_output_types.size(); i < end; ++i) {
+    auto then_output = then_output_types[i];
+    auto else_output = else_output_types[i];
+
+    if (then_output->value_case() != else_output->value_case()) {
+      fail_type_inference(
+          "Mismatched type for output ",
+          i,
+          " then=",
+          then_output->value_case(),
+          " else=",
+          else_output->value_case());
+    }
+
+    auto* if_output = ctx.getOutputType(i);
+    *if_output = *then_output;
+
+    if (then_output->has_tensor_type()) {
+      auto then_elem_type = then_output->tensor_type().elem_type();
+      auto else_elem_type = else_output->tensor_type().elem_type();
+
+      if (then_elem_type != else_elem_type) {
+        fail_type_inference(
+            "Mismatched tensor element type for output ",
+            i,
+            " then=",
+            then_elem_type,
+            " else=",
+            else_elem_type);
+      }
+
+      // merge the 'else' shape information to check it's consistent and
+      // augment the 'if' output if possible
+      mergeInShapeInfo(
+          else_output->tensor_type(), *if_output->mutable_tensor_type());
+    }
+  }
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    If,
+    1,
+    OpSchema()
+        .SetDoc("If conditional")
+        .Input(0, "cond", "Condition for the if", "B")
+        .Output(
+            0,
+            "outputs",
+            "Values that are live-out to the enclosing scope. The return values in "
+            "the `then_branch` and `else_branch` must be of the same shape and same "
+            "data type.",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "then_branch",
+            "Graph to run if condition is true. Has N outputs: values you wish to "
+            "be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the else_branch.",
+            AttributeProto::GRAPH)
+        .Attr(
+            "else_branch",
+            "Graph to run if condition is false. Has N outputs: values you wish to"
+            " be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the then_branch.",
+            AttributeProto::GRAPH)
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeConstraint("B", {"tensor(bool)"}, "Only bool")
+        .TypeAndShapeInferenceFunction(IfInferenceFunction1));
+
+void IfInferenceFunction_11(InferenceContext& ctx) {
+  // there are no inputs so we just need to run the subgraph inferencing for
+  // then/else subgraphs and apply those to the outputs.
+  std::vector<const TypeProto*> subgraph_input_types; // none
+  std::vector<const TensorProto*> input_data; // none
+
+  std::vector<const TypeProto*> then_output_types;
+  std::vector<const TypeProto*> else_output_types;
+
+  // Run inferencing on the subgraph
+  GraphInferencer* graphInferencer =
+      ctx.getGraphAttributeInferencer("then_branch");
+  if (graphInferencer) {
+    then_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  graphInferencer = ctx.getGraphAttributeInferencer("else_branch");
+  if (graphInferencer) {
+    else_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  auto num_outputs = ctx.getNumOutputs();
+  auto num_then_outputs = then_output_types.size();
+  auto num_else_outputs = else_output_types.size();
+
+  // the output types for then and else should be the same
+  if (num_then_outputs != num_else_outputs) {
+    fail_type_inference(
+        "then_branch and else_branch produce different number of outputs. ",
+        num_then_outputs,
+        " != ",
+        num_else_outputs);
+  }
+
+  if (num_then_outputs != num_outputs) {
+    fail_type_inference(
+        "If node has ",
+        num_outputs,
+        " but subgraphs produce ",
+        num_then_outputs);
+  }
+
+  for (size_t i = 0, end = then_output_types.size(); i < end; ++i) {
+    auto then_output = then_output_types[i];
+    auto else_output = else_output_types[i];
+
+    if (then_output->value_case() != else_output->value_case()) {
+      fail_type_inference(
+          "Mismatched type for output ",
+          i,
+          " then=",
+          then_output->value_case(),
+          " else=",
+          else_output->value_case());
+    }
+
+    auto* if_output = ctx.getOutputType(i);
+    *if_output = *then_output;
+
+    if (then_output->has_tensor_type()) {
+      auto then_elem_type = then_output->tensor_type().elem_type();
+      auto else_elem_type = else_output->tensor_type().elem_type();
+
+      if (then_elem_type != else_elem_type) {
+        fail_type_inference(
+            "Mismatched tensor element type for output ",
+            i,
+            " then=",
+            then_elem_type,
+            " else=",
+            else_elem_type);
+      }
+
+      UnionShapeInfo(
+          else_output->tensor_type().shape(), *if_output->mutable_tensor_type());
+    }
+  }
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    If,
+    11,
+    OpSchema()
+        .SetDoc("If conditional")
+        .Input(0, "cond", "Condition for the if", "B")
+        .Output(
+            0,
+            "outputs",
+            "Values that are live-out to the enclosing scope. The return values in "
+            "the `then_branch` and `else_branch` must be of the same data type. "
+            "The `then_branch` and `else_branch` may produce tensors with the same "
+            "element type and different shapes. "
+            "If corresponding outputs from the then-branch and the else-branch have "
+            "static shapes S1 and S2, then the shape of the corresponding output "
+            "variable of the if-node (if present) must be compatible with both S1 "
+            "and S2 as it represents the union of both possible shapes."
+            "For example, if in a model file, the the first "
+            "output of `then_branch` is typed float tensor with shape [2] and the "
+            "first output of `else_branch` is another float tensor with shape [3], "
+            "If's first output should have (a) no shape set, or (b) "
+            "a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) "
+            "a shape of rank 1 with a unique `dim_param`. "
+            "In contrast, the first output cannot have the shape [2] since [2] and "
+            "[3] are not compatible.",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "then_branch",
+            "Graph to run if condition is true. Has N outputs: values you wish to "
+            "be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the else_branch.",
+            AttributeProto::GRAPH)
+        .Attr(
+            "else_branch",
+            "Graph to run if condition is false. Has N outputs: values you wish to"
+            " be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the then_branch.",
+            AttributeProto::GRAPH)
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeConstraint("B", {"tensor(bool)"}, "Only bool")
+        .TypeAndShapeInferenceFunction(IfInferenceFunction_11));
+
+void IfInferenceFunction_13(InferenceContext& ctx) {
+  // there are no inputs so we just need to run the subgraph inferencing for
+  // then/else subgraphs and apply those to the outputs.
+  std::vector<const TypeProto*> subgraph_input_types; // none
+  std::vector<const TensorProto*> input_data; // none
+
+  std::vector<const TypeProto*> then_output_types;
+  std::vector<const TypeProto*> else_output_types;
+
+  // Run inferencing on the subgraph
+  GraphInferencer* graphInferencer =
+      ctx.getGraphAttributeInferencer("then_branch");
+  if (graphInferencer) {
+    then_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  graphInferencer = ctx.getGraphAttributeInferencer("else_branch");
+  if (graphInferencer) {
+    else_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  auto num_outputs = ctx.getNumOutputs();
+  auto num_then_outputs = then_output_types.size();
+  auto num_else_outputs = else_output_types.size();
+
+  // the output types for then and else should be the same
+  if (num_then_outputs != num_else_outputs) {
+    fail_type_inference(
+        "then_branch and else_branch produce different number of outputs. ",
+        num_then_outputs,
+        " != ",
+        num_else_outputs);
+  }
+
+  if (num_then_outputs != num_outputs) {
+    fail_type_inference(
+        "If node has ",
+        num_outputs,
+        " but subgraphs produce ",
+        num_then_outputs);
+  }
+
+  for (size_t i = 0, end = then_output_types.size(); i < end; ++i) {
+    auto then_output = then_output_types[i];
+    auto else_output = else_output_types[i];
+
+    auto* if_output = ctx.getOutputType(i);
+    *if_output = *then_output;
+
+    UnionTypeInfo(*else_output, *if_output);
+  }
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    If,
+    13,
+    OpSchema()
+        .SetDoc("If conditional")
+        .Input(0, "cond", "Condition for the if", "B")
+        .Output(
+            0,
+            "outputs",
+            "Values that are live-out to the enclosing scope. The return values in "
+            "the `then_branch` and `else_branch` must be of the same data type. "
+            "The `then_branch` and `else_branch` may produce tensors with the same "
+            "element type and different shapes. "
+            "If corresponding outputs from the then-branch and the else-branch have "
+            "static shapes S1 and S2, then the shape of the corresponding output "
+            "variable of the if-node (if present) must be compatible with both S1 "
+            "and S2 as it represents the union of both possible shapes."
+            "For example, if in a model file, the the first "
+            "output of `then_branch` is typed float tensor with shape [2] and the "
+            "first output of `else_branch` is another float tensor with shape [3], "
+            "If's first output should have (a) no shape set, or (b) "
+            "a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) "
+            "a shape of rank 1 with a unique `dim_param`. "
+            "In contrast, the first output cannot have the shape [2] since [2] and "
+            "[3] are not compatible.",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "then_branch",
+            "Graph to run if condition is true. Has N outputs: values you wish to "
+            "be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the else_branch.",
+            AttributeProto::GRAPH)
+        .Attr(
+            "else_branch",
+            "Graph to run if condition is false. Has N outputs: values you wish to"
+            " be live-out to the enclosing scope. The number of outputs must match"
+            " the number of outputs in the then_branch.",
+            AttributeProto::GRAPH)
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types();
+              auto s = OpSchema::all_tensor_sequence_types();
+              t.insert(t.end(), s.begin(), s.end());
+              return t;
+            }(),
+            "All Tensor and Sequence types")
+        .TypeConstraint("B", {"tensor(bool)"}, "Only bool")
+        .TypeAndShapeInferenceFunction(IfInferenceFunction_13));
+
+void LoopInferenceFunction_13(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  assert(num_inputs >= 2);
+  auto num_loop_state_vars = num_inputs - 2; // skip 'M' and 'cond'
+
+  std::vector<const TypeProto*> subgraph_input_types;
+  subgraph_input_types.reserve(num_inputs);
+
+  std::vector<TypeProto> temporary_type_protos;
+  temporary_type_protos.reserve(num_inputs - 2);
+
+  // create TypeProto to validate iteration number type is the same as the
+  // optional 'M' input for max iterations.
+  TypeProto iter_num_type;
+  iter_num_type.mutable_tensor_type()->set_elem_type(
+      TensorProto_DataType_INT64);
+  subgraph_input_types.push_back(&iter_num_type);
+
+  // 'cond'
+  subgraph_input_types.push_back(ctx.getInputType(1));
+
+  // loop state value types get propagated to outputs, but shape may change
+  // across iterations so don't propagate it to the outputs and don't pass it
+  // into the subgraph inferencing
+  for (size_t i = 2; i < num_inputs; ++i) {
+    propagateElemTypeFromInputToOutput(ctx, i, i - 2);
+
+    // copy so we can remove the shape before passing to the subgraph
+    // inferencing
+    temporary_type_protos.push_back(*ctx.getInputType(i));
+    auto& input_type = temporary_type_protos.back();
+
+    if (input_type.has_tensor_type()) {
+      input_type.mutable_tensor_type()->clear_shape();
+    } else if (input_type.has_sequence_type()) {
+      auto& seq_type = *input_type.mutable_sequence_type();
+      if (seq_type.has_elem_type() && seq_type.elem_type().has_tensor_type()) {
+        seq_type.mutable_elem_type()->mutable_tensor_type()->clear_shape();
+      }
+    }
+
+    subgraph_input_types.push_back(&input_type);
+  }
+
+  // Run inferencing on the subgraph
+  std::vector<const TypeProto*> subgraph_output_types;
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (graphInferencer) {
+    std::vector<const TensorProto*> input_data;
+    input_data.push_back(nullptr); // iteration number
+    for (size_t i = 1; i < num_inputs; ++i) {
+      input_data.push_back(ctx.getInputData(i));
+    }
+
+    subgraph_output_types =
+        graphInferencer->doInferencing(subgraph_input_types, input_data);
+  }
+
+  // if empty(), assume inferencing was skipped
+  if (!subgraph_output_types.empty()) {
+    auto num_outputs = ctx.getNumOutputs();
+
+    // subgraph outputs the condition value first but that is only used
+    // internally and not returned by Loop.
+    if (subgraph_output_types.size() != num_outputs + 1) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          subgraph_output_types.size(),
+          " outputs. Expected ",
+          num_outputs + 1);
+    }
+
+    // check loop state values match. we should already have type/shape info
+    for (size_t i = 0; i < num_outputs; ++i) {
+      auto* subgraph_output_type = subgraph_output_types[i + 1]; // skip 'cond'
+      auto* loop_output_type = ctx.getOutputType(i);
+
+      const bool is_loop_state_var = i < num_loop_state_vars;
+
+      if (!subgraph_output_type->has_tensor_type() && !subgraph_output_type->has_sequence_type()) {
+        fail_type_inference(
+            "Loop 'body' subgraph outputs should all be tensors or sequences but output ",
+            i,
+            " was ",
+            subgraph_output_type->value_case());
+      }
+
+      if (!is_loop_state_var && !subgraph_output_type->has_tensor_type()) {
+        fail_type_inference(
+            "Loop 'body' subgraph scan outputs should all be tensors but output ",
+            i,
+            " was ",
+            subgraph_output_type->value_case());
+      }
+
+      // if there's an existing type check it matches. otherwise propagate
+      propagateElemTypeWithValidation(subgraph_output_type, loop_output_type);
+
+      if (is_loop_state_var) {
+        // shape may change across iterations so ignore.
+      } else {
+        // propogate shape
+        if (subgraph_output_type->tensor_type().has_shape()) {
+          // per iteration output. first dimension will be number of iterations
+          // but we don't know that value yet
+          TypeProto inferred_type(*subgraph_output_type);
+          auto* mutable_inferred_tensor_type =
+              inferred_type.mutable_tensor_type();
+          auto* mutable_inferred_shape =
+              mutable_inferred_tensor_type->mutable_shape();
+
+          mutable_inferred_shape->clear_dim();
+
+          // add empty dimension for number of iterations
+          mutable_inferred_shape->add_dim();
+
+          // add dimensions from subgraph output shape
+          for (const auto& dim :
+               subgraph_output_type->tensor_type().shape().dim()) {
+            (*mutable_inferred_shape->add_dim()) = dim;
+          }
+
+          mergeInShapeInfo(
+              *mutable_inferred_tensor_type,
+              *loop_output_type->mutable_tensor_type());
+        }
+      }
+    }
+  }
+}
+
+static const char* Loop_ver13_doc = R"DOC(
+Generic Looping construct. This loop has multiple termination conditions:
+
+1) Trip count. Iteration count specified at runtime. Set by
+   specifying the input M. Optional. Set to empty string to omit.
+   Note that a static trip count (specified at graph construction time) can be
+   specified by passing in a constant node for input M.
+2) Loop termination condition. This is an input to the op that determines
+   whether to run the first iteration and also a loop-carried dependency for
+   the body graph. The body graph must yield a value for the condition variable,
+   whether this input is provided or not.
+
+This table summarizes the operating modes of this operator with equivalent
+C-style code:
+
+    Operator inputs defined as (max_trip_count, condition_var).
+
+    input ("", ""):
+        for (int i=0; ; ++i) {
+          cond = ... // Note this value is ignored, but is required in the body
+        }
+
+    input ("", cond) // Note this is analogous to a while loop
+        bool cond = ...;
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input ("", 1) // Note this is analogous to a do-while loop
+        bool cond = true
+        for (int i=0; cond; ++i) {
+          cond = ...;
+        }
+
+    input (trip_count, "") // Note this is analogous to a for loop
+        int trip_count = ...
+        for (int i=0; i < trip_count; ++i) {
+          cond = ...; // ignored
+        }
+
+    input (trip_count, cond)
+        int trip_count = ...;
+        bool cond = ...;
+        for (int i=0; i < trip_count && cond; ++i) {
+          cond = ...;
+        }
+
+
+*Sample usage - cond as well as trip count*
+
+    graph predict-net {
+      %a = Constant[value = <Scalar Tensor [3]>]()
+      %b = Constant[value = <Scalar Tensor [6]>]()
+      %keepgoing = Constant[value = <Scalar Tensor [1]>]()
+      %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
+      %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
+      return
+    }
+
+    graph body-net (
+      %i[INT32, scalar]           // iteration number
+      %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
+      %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
+    ) {
+      %my_local = Add(%a, %b_in)
+      %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
+      %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
+      %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
+      return %keepgoing_out, %b_out, %user_defined_val
+    }
+
+*Sample equivalent C code*
+
+    {
+      /* User-defined code (enclosing scope) */
+      int a = 3, b = 6;
+      bool keepgoing = true; // Analogous to input cond
+      /* End user-defined code */
+
+      /* Implicitly-defined code */
+      const int max_trip_count = 10; // Analogous to input M
+      int user_defined_vals[]; // Imagine this is resizable
+      /* End implicitly-defined code */
+      /* initialize loop-carried variables and scan-output variables */
+      bool keepgoing_out = keepgoing
+      int b_out = b
+
+      for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
+        /* Implicitly-defined code: bind actual parameter values
+           to formal parameter variables of loop-body */
+        bool keepgoing_in = keepgoing_out;
+        bool b_in = b_out;
+
+        /* User-defined code (loop body) */
+        int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
+        b_out = a - b_in;
+        keepgoing_out = my_local > b_out;
+        user_defined_val = b_in + b_in; // b_in and b_out are different variables
+        /* End user-defined code */
+
+        /* Implicitly defined-code */
+        user_defined_vals[i] = user_defined_val // accumulate scan-output values
+      }
+      // int t = my_local; // Can't do this. my_local is not accessible here.
+
+      // The values below are bound to the output variables of the loop and therefore accessible
+      // b_out; user_defined_vals; keepgoing_out;
+    }
+
+There are several things of note in this code snippet:
+
+1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
+   be referenced in the inputs of the loop.
+2) Any values computed in the loop body that needs to be used in a subsequent
+   iteration or after the loop are modelled using a pair of variables in the loop-body,
+   consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
+   These are referred to as loop-carried dependences. The loop operation node
+   supplies the input value of the input variable for the first iteration, and
+   returns the output value of the output variable produced by the final
+   iteration.
+3) Scan_output variables are used to implicitly concatenate values computed across
+   all the iterations. In the above example, the value of user_defined_val computed
+   over all iterations are concatenated and returned as the value of user_defined_vals
+   after the loop.
+4) Values created in the body cannot be accessed in the enclosing scope,
+   except using the mechanism described above.
+
+Note that the semantics of this op support "diagonal" or "wavefront" execution.
+(See Step 3 here for an example:
+https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
+Frontends should emit multi-layer RNNs as a series of While operators (with
+time being the inner looping dimension), with each successive layer consuming
+the scan_outputs from the previous layer, possibly going through several
+point-wise operators (e.g. dropout, residual connections, linear layer).
+
+The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Loop,
+    13,
+    OpSchema()
+        .SetDoc(Loop_ver13_doc)
+        .Input(
+            0,
+            "M",
+            "A maximum trip-count for the loop specified at runtime. Optional."
+            " Pass empty string to skip.",
+            "I",
+            OpSchema::Optional)
+        .Input(
+            1,
+            "cond",
+            "A boolean termination condition. Optional. Pass empty string to skip.",
+            "B",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "v_initial",
+            "The initial values of any loop-carried dependencies (values that "
+            "change across loop iterations)",
+            "V",
+            OpSchema::Variadic,
+            false,
+            0)
+        .Output(
+            0,
+            "v_final_and_scan_outputs",
+            "Final N loop carried dependency values then K scan_outputs. "
+            "Scan outputs must be Tensors.",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has 2+N inputs: (iteration_num, "
+            "condition, loop carried dependencies...). It has 1+N+K outputs: "
+            "(condition, loop carried dependencies..., scan_outputs...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "output value at the end of each iteration of the loop. It is an error"
+            " if the dimensions or data type of these scan_outputs change across loop"
+            " iterations.",
+            AttributeProto::GRAPH)
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types();
+              auto s = OpSchema::all_tensor_sequence_types();
+              t.insert(t.end(), s.begin(), s.end());
+              return t;
+            }(),
+            "All Tensor and Sequence types")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "tensor of int64, which should be a scalar.")
+        .TypeConstraint(
+            "B",
+            {"tensor(bool)"},
+            "tensor of bool, which should be a scalar.")
+        .TypeAndShapeInferenceFunction(LoopInferenceFunction_13));
+
+static const char* scan_11_doc = R"DOC(
+Scan can be used to iterate over one or more scan_input tensors,
+constructing zero or more scan_output tensors. It combines ideas from general recurrences,
+functional programming constructs such as scan, fold, map, and zip, and is intended to enable
+generalizations of RNN-like constructs for sequence-to-sequence processing.
+Other tensors (referred to as state_variables here) can be used to carry a state
+when iterating from one element to another (similar to hidden-state in RNNs, also referred
+to as loop-carried dependences in the context of loops).
+Many common usages involve a single scan_input tensor (where functionality
+similar to scan, fold and map can be obtained). When more than one scan_input is used,
+a behavior similar to zip is obtained.
+
+The attribute body must be a graph, specifying the computation to be performed in
+every iteration. It takes as input the current values of the state_variables and
+the current iterated element of the scan_inputs. It must return the (updated) values
+of the state_variables and zero or more scan_output_element tensors. The values of the
+scan_output_element tensors are concatenated over all the iterations to produce the
+scan_output values of the scan construct (similar to the concatenated intermediate
+hidden-state values of RNN-like constructs). All the output tensors (state_variables as
+well as scan_output_element tensors) are required to have the same shape in each iteration
+of the loop (a restriction imposed to enable efficient memory allocation).
+
+Note that the iterated element passed to the body subgraph does not have a sequence
+axis. It will have a rank one less than the rank of the corresponding scan_input.
+
+The scan operation returns the final values of the state_variables as well as the
+scan_outputs.
+
+The optional attribute scan_input_directions specifies the direction (forward or backward)
+for each scan input. If this attribute is omitted, all sequences are scanned in the forward
+direction. A bidirectional scan may be performed by specifying the same tensor input twice
+in the scan_inputs, once with a forward direction, and once with a backward direction.
+
+The scan_output of the operation is produced by concatenating the scan_output_element
+values produced by the body in each iteration.  The optional attribute scan_output_directions
+specifies the direction in which scan_output is constructed (by appending or prepending the
+scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
+is omitted, the scan_output_element is appended to the scan_output in each iteration.
+
+The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
+If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
+batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
+Note that scanning a non-zero axis may be less efficient than scanning axis zero.
+
+The optional attribute scan_output_axes specifies the axis along which the scan_outputs
+are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
+scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
+value of 1.
+
+Note that because of the ONNX restriction that only the last parameter of an operator can
+be variadic, the initial-states and scan-inputs are listed together as one input parameter.
+Similarly, the final-states and scan-outputs are listed together as one output parameter.
+The attribute num_scan_inputs indicates the number M of scan-inputs.
+
+The behavior of
+
+    Scan <
+        num_scan_inputs = m,
+        body = loop-body,
+        scan_input_axes = [axis_1, ..., axis_m]
+    > (init_1, ..., init_n, scan_1, ..., scan_m)
+
+is equivalent to the following pseudo-code:
+
+    // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
+    // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
+    sequence_length = scan_1.shape[axis_1];
+
+    // initialize state-variables
+    st_1 = init_1; ... st_n = init_n;
+    // initialize scan-output variables: [] denotes an empty tensor
+    scan_out_1 = []; ...; scan_out_k = [];
+    // identify number of iterations:
+
+    // execute loop
+    for (int t = 0; t < sequence_length; ++t) {
+        // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
+        // of rank one less than T obtained by indexing T at position t along axis k.
+        si_1 = scan_1<axis=axis_1>[t];
+        ... ;
+        si_m = scan_m<axis=axis_m>[t];
+        // execute loop-body
+        st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
+        // accumulate the scan-output elements
+        scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
+    }
+
+    return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
+
+*Sample usage: Encoding RNN using a Scan*
+
+The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
+recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
+be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
+%Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
+values are computed in the outer graph, they need to be passed in as extra state_variables.
+
+    graph rnn-encoding {
+      %H_0 = ...
+      %X = ...
+      %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
+      return %Y, %Y_h
+    }
+
+    graph rnn-cell-1 (
+      %H_tminus1[FLOAT, tensor]
+      %X_t[FLOAT, tensor]
+    ) {
+      %Wi = ...
+      %Ri = ...
+      %Wbi = ...
+      %Rbi = ...
+      %t1 = X_t * (Wi^T)
+      %t2 = H_tminus1*(Ri^T)
+      %t3 = Add(%t1, %t2)
+      %t4 = Add(%t3, %Wbi)
+      %t5 = Add(%t4, %Rbi)
+      %Ht = Tanh(%t5)
+      %Accumulate = Identity(%Ht)
+      return %Ht, %Accumulate
+    }
+
+)DOC";
+
+extern void ScanInferenceFunction(InferenceContext& ctx); 
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Scan,
+    11,
+    OpSchema()
+        .SetDoc(scan_11_doc)
+        .Input(
+            0,
+            "initial_state_and_scan_inputs",
+            "Initial values of the loop's N state variables followed by M scan_inputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "final_state_and_scan_outputs",
+            "Final values of the loop's N state variables followed by K scan_outputs",
+            "V",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "body",
+            "The graph run each iteration. It has N+M inputs: "
+            "(loop state variables..., scan_input_elts...). It has N+K outputs: "
+            "(loop state variables..., scan_output_elts...). Each "
+            "scan_output is created by concatenating the value of the specified "
+            "scan_output_elt value at the end of each iteration of the loop. It is an error"
+            " if the dimensions of these values change across loop iterations.",
+            AttributeProto::GRAPH,
+            true)
+        .Attr(
+            "num_scan_inputs",
+            "An attribute specifying the number of scan_inputs M. ",
+            AttributeProto::INT,
+            true)
+        .Attr(
+            "scan_input_directions",
+            "An optional list of M flags. The i-th element of the list specifies the direction "
+            "to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 "
+            "indicates reverse direction. "
+            "If omitted, all scan_input tensors will be scanned in the forward direction.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_output_directions",
+            "An optional list of K flags, one for each scan_output. The i-th element of the list "
+            "specifies whether the i-th scan_output should be constructed by appending or "
+            "prepending a new value in each iteration: 0 indicates appending and 1 "
+            "indicates prepending. "
+            "If omitted, all scan_output tensors will be produced by appending a value "
+            "in each iteration.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_input_axes",
+            "An optional list of M flags. The i-th element of the list specifies the axis "
+            "to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will "
+            "be used as the scan axis for every scan_input. Negative value for an axis means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "scan_output_axes",
+            "An optional list of K flags. The i-th element of the list specifies the axis "
+            "for the i-th scan_output. The scan outputs are accumulated along the specified "
+            "axis. If omitted, 0 will be used as the scan axis for every scan_output. "
+            "Negative value for an axis means counting dimensions from the back. Accepted "
+            "range is [-r, r-1].",
+            AttributeProto::INTS,
+            false)
+        .TypeConstraint("V", OpSchema::all_tensor_types(), "All Tensor types")
+        .TypeAndShapeInferenceFunction(ScanInferenceFunction));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/data_propagators.h b/third_party/onnx/onnx/defs/data_propagators.h
new file mode 100644
index 0000000000..aac4f9b46a
--- /dev/null
+++ b/third_party/onnx/onnx/defs/data_propagators.h
@@ -0,0 +1,52 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/shape_inference.h"
+
+namespace ONNX_NAMESPACE {
+
+inline void appendDimToTensorShapeProto(TensorShapeProto& tsp, const TensorShapeProto_Dimension& dim) {
+  *tsp.add_dim() = dim;
+}
+
+// Returns true if the given axis attribute is 0
+inline bool axisIsZero(DataPropagationContext& ctx, bool defaultZero = false) {
+  auto axisAttr = ctx.getAttribute("axis");
+  // if axis is not defined
+  if (!axisAttr) {
+    if (defaultZero) {
+      return true;
+    } else {
+      fail_shape_inference("Required attribute axis is missing");
+      return false;
+    }
+  }
+  int axis = static_cast<int>(axisAttr->i());
+  auto input_data_0 = ctx.getInputData(0);
+  if (input_data_0 == nullptr) {
+    return false;
+  }
+  int rank = input_data_0->dim_size();
+  if (axis < -rank || axis >= rank) {
+    fail_shape_inference("axis must be in [-rank, rank-1].");
+    return false;
+  }
+  if (axis < 0) {
+    axis += rank;
+  }
+  // Only supports axis = 0 since the data comes from Shape
+  return axis == 0;
+}
+
+inline void PropagateShapeDataFromInputToOutput(DataPropagationContext& ctx, int idx) {
+  // propogate input data
+  const auto input_data = ctx.getInputData(idx);
+  if (input_data != nullptr) {
+    TensorShapeProto tsp;
+    tsp.CopyFrom(*input_data);
+    ctx.addOutputData(0, std::move(tsp));
+  }
+}
+
+}
diff --git a/third_party/onnx/onnx/defs/data_type_utils.cc b/third_party/onnx/onnx/defs/data_type_utils.cc
new file mode 100644
index 0000000000..7679ac9c99
--- /dev/null
+++ b/third_party/onnx/onnx/defs/data_type_utils.cc
@@ -0,0 +1,470 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <cctype>
+#include <iostream>
+#include <iterator>
+#include <sstream>
+
+#include "data_type_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace Utils {
+
+// Singleton wrapper around allowed data types.
+// This implements construct on first use which is needed to ensure
+// static objects are initialized before use. Ops registration does not work
+// properly without this.
+class TypesWrapper final {
+ public:
+  static TypesWrapper& GetTypesWrapper();
+
+  std::unordered_set<std::string>& GetAllowedDataTypes();
+
+  std::unordered_map<std::string, int32_t>& TypeStrToTensorDataType();
+
+  std::unordered_map<int32_t, std::string>& TensorDataTypeToTypeStr();
+
+  ~TypesWrapper() = default;
+  TypesWrapper(const TypesWrapper&) = delete;
+  void operator=(const TypesWrapper&) = delete;
+
+ private:
+  TypesWrapper();
+
+  std::unordered_map<std::string, int> type_str_to_tensor_data_type_;
+  std::unordered_map<int, std::string> tensor_data_type_to_type_str_;
+  std::unordered_set<std::string> allowed_data_types_;
+};
+
+// Simple class which contains pointers to external string buffer and a size.
+// This can be used to track a "valid" range/slice of the string.
+// Caller should ensure StringRange is not used after external storage has
+// been freed.
+class StringRange final {
+ public:
+  StringRange();
+  StringRange(const char* data, size_t size);
+  StringRange(const std::string& str);
+  StringRange(const char* data);
+  const char* Data() const;
+  size_t Size() const;
+  bool Empty() const;
+  char operator[](size_t idx) const;
+  void Reset();
+  void Reset(const char* data, size_t size);
+  void Reset(const std::string& str);
+  bool StartsWith(const StringRange& str) const;
+  bool EndsWith(const StringRange& str) const;
+  bool LStrip();
+  bool LStrip(size_t size);
+  bool LStrip(StringRange str);
+  bool RStrip();
+  bool RStrip(size_t size);
+  bool RStrip(StringRange str);
+  bool LAndRStrip();
+  void ParensWhitespaceStrip();
+  size_t Find(const char ch) const;
+
+  // These methods provide a way to return the range of the string
+  // which was discarded by LStrip(). i.e. We capture the string
+  // range which was discarded.
+  StringRange GetCaptured();
+  void RestartCapture();
+
+ private:
+  // data_ + size tracks the "valid" range of the external string buffer.
+  const char* data_;
+  size_t size_;
+
+  // start_ and end_ track the captured range.
+  // end_ advances when LStrip() is called.
+  const char* start_;
+  const char* end_;
+};
+
+std::unordered_map<std::string, TypeProto>&
+DataTypeUtils::GetTypeStrToProtoMap() {
+  static std::unordered_map<std::string, TypeProto> map;
+  return map;
+}
+
+std::mutex& DataTypeUtils::GetTypeStrLock() {
+  static std::mutex lock;
+  return lock;
+}
+
+DataType DataTypeUtils::ToType(const TypeProto& type_proto) {
+  auto typeStr = ToString(type_proto);
+  std::lock_guard<std::mutex> lock(GetTypeStrLock());
+  if (GetTypeStrToProtoMap().find(typeStr) == GetTypeStrToProtoMap().end()) {
+    TypeProto type;
+    FromString(typeStr, type);
+    GetTypeStrToProtoMap()[typeStr] = type;
+  }
+  return &(GetTypeStrToProtoMap().find(typeStr)->first);
+}
+
+DataType DataTypeUtils::ToType(const std::string& type_str) {
+  TypeProto type;
+  FromString(type_str, type);
+  return ToType(type);
+}
+
+const TypeProto& DataTypeUtils::ToTypeProto(const DataType& data_type) {
+  std::lock_guard<std::mutex> lock(GetTypeStrLock());
+  auto it = GetTypeStrToProtoMap().find(*data_type);
+  if (GetTypeStrToProtoMap().end() == it) {
+    ONNX_THROW_EX(std::invalid_argument("Invalid data type " + *data_type));
+  }
+  return it->second;
+}
+
+std::string DataTypeUtils::ToString(
+    const TypeProto& type_proto,
+    const std::string& left,
+    const std::string& right) {
+  switch (type_proto.value_case()) {
+    case TypeProto::ValueCase::kTensorType: {
+      // Note: We do not distinguish tensors with zero rank (a shape consisting
+      // of an empty sequence of dimensions) here.
+      return left + "tensor(" +
+          ToDataTypeString(type_proto.tensor_type().elem_type()) + ")" + right;
+    }
+    case TypeProto::ValueCase::kSequenceType: {
+      return ToString(
+          type_proto.sequence_type().elem_type(), left + "seq(", ")" + right);
+    }
+    case TypeProto::ValueCase::kOptionalType: {
+      return ToString(
+          type_proto.optional_type().elem_type(), left + "optional(", ")" + right);
+    }
+    case TypeProto::ValueCase::kMapType: {
+      std::string map_str =
+          "map(" + ToDataTypeString(type_proto.map_type().key_type()) + ",";
+      return ToString(
+          type_proto.map_type().value_type(), left + map_str, ")" + right);
+    }
+#ifdef ONNX_ML
+    case TypeProto::ValueCase::kOpaqueType: {
+      static const std::string empty;
+      std::string result;
+      const auto& op_type = type_proto.opaque_type();
+      result.append(left).append("opaque(");
+      if (op_type.has_domain() && !op_type.domain().empty()) {
+        result.append(op_type.domain()).append(",");
+      }
+      if (op_type.has_name() && !op_type.name().empty()) {
+        result.append(op_type.name());
+      }
+      result.append(")").append(right);
+      return result;
+    }
+#endif
+    case TypeProto::ValueCase::kSparseTensorType: {
+      // Note: We do not distinguish tensors with zero rank (a shape consisting
+      // of an empty sequence of dimensions) here.
+      return left + "sparse_tensor(" +
+          ToDataTypeString(type_proto.sparse_tensor_type().elem_type()) + ")" +
+          right;
+    }
+    default:
+      ONNX_THROW_EX(std::invalid_argument("Unsuported type proto value case."));
+  }
+}
+
+std::string DataTypeUtils::ToDataTypeString(int32_t tensor_data_type) {
+  TypesWrapper& t = TypesWrapper::GetTypesWrapper();
+  auto iter = t.TensorDataTypeToTypeStr().find(tensor_data_type);
+  if (t.TensorDataTypeToTypeStr().end() == iter) {
+    ONNX_THROW_EX(std::invalid_argument("Invalid tensor data type "));
+  }
+  return iter->second;
+}
+
+void DataTypeUtils::FromString(
+    const std::string& type_str,
+    TypeProto& type_proto) {
+  StringRange s(type_str);
+  type_proto.Clear();
+  if (s.LStrip("seq")) {
+    s.ParensWhitespaceStrip();
+    return FromString(
+        std::string(s.Data(), s.Size()),
+        *type_proto.mutable_sequence_type()->mutable_elem_type());
+  } else if (s.LStrip("optional")) {
+    s.ParensWhitespaceStrip();
+    return FromString(
+        std::string(s.Data(), s.Size()),
+        *type_proto.mutable_optional_type()->mutable_elem_type());
+  } else if (s.LStrip("map")) {
+    s.ParensWhitespaceStrip();
+    size_t key_size = s.Find(',');
+    StringRange k(s.Data(), key_size);
+    std::string key(k.Data(), k.Size());
+    s.LStrip(key_size);
+    s.LStrip(",");
+    StringRange v(s.Data(), s.Size());
+    int32_t key_type;
+    FromDataTypeString(key, key_type);
+    type_proto.mutable_map_type()->set_key_type(key_type);
+    return FromString(
+        std::string(v.Data(), v.Size()),
+        *type_proto.mutable_map_type()->mutable_value_type());
+  } else
+#ifdef ONNX_ML
+      if (s.LStrip("opaque")) {
+    auto* opaque_type = type_proto.mutable_opaque_type();
+    s.ParensWhitespaceStrip();
+    if (!s.Empty()) {
+      size_t cm = s.Find(',');
+      if (cm != std::string::npos) {
+        if (cm > 0) {
+          opaque_type->mutable_domain()->assign(s.Data(), cm);
+        }
+        s.LStrip(cm + 1); // skip comma
+      }
+      if (!s.Empty()) {
+        opaque_type->mutable_name()->assign(s.Data(), s.Size());
+      }
+    }
+  } else
+#endif
+  if (s.LStrip("sparse_tensor")) {
+    s.ParensWhitespaceStrip();
+    int32_t e;
+    FromDataTypeString(std::string(s.Data(), s.Size()), e);
+    type_proto.mutable_sparse_tensor_type()->set_elem_type(e);
+  } else if (s.LStrip("tensor")) {
+    s.ParensWhitespaceStrip();
+    int32_t e;
+    FromDataTypeString(std::string(s.Data(), s.Size()), e);
+    type_proto.mutable_tensor_type()->set_elem_type(e);
+  } else {
+    // Scalar
+    int32_t e;
+    FromDataTypeString(std::string(s.Data(), s.Size()), e);
+    TypeProto::Tensor* t = type_proto.mutable_tensor_type();
+    t->set_elem_type(e);
+    // Call mutable_shape() to initialize a shape with no dimension.
+    t->mutable_shape();
+  }
+} // namespace Utils
+
+bool DataTypeUtils::IsValidDataTypeString(const std::string& type_str) {
+  TypesWrapper& t = TypesWrapper::GetTypesWrapper();
+  const auto& allowedSet = t.GetAllowedDataTypes();
+  return (allowedSet.find(type_str) != allowedSet.end());
+}
+
+void DataTypeUtils::FromDataTypeString(
+    const std::string& type_str,
+    int32_t& tensor_data_type) {
+  if (!IsValidDataTypeString(type_str)) {
+    ONNX_THROW_EX(std::invalid_argument("DataTypeUtils::FromDataTypeString - Received invalid data type string " + type_str));
+  }
+
+  TypesWrapper& t = TypesWrapper::GetTypesWrapper();
+  tensor_data_type = t.TypeStrToTensorDataType()[type_str];
+}
+
+StringRange::StringRange() : data_(""), size_(0), start_(data_), end_(data_) {}
+
+StringRange::StringRange(const char* p_data, size_t p_size)
+    : data_(p_data), size_(p_size), start_(data_), end_(data_) {
+  assert(p_data != nullptr);
+  LAndRStrip();
+}
+
+StringRange::StringRange(const std::string& p_str)
+    : data_(p_str.data()), size_(p_str.size()), start_(data_), end_(data_) {
+  LAndRStrip();
+}
+
+StringRange::StringRange(const char* p_data)
+    : data_(p_data), size_(strlen(p_data)), start_(data_), end_(data_) {
+  LAndRStrip();
+}
+
+const char* StringRange::Data() const {
+  return data_;
+}
+
+size_t StringRange::Size() const {
+  return size_;
+}
+
+bool StringRange::Empty() const {
+  return size_ == 0;
+}
+
+char StringRange::operator[](size_t idx) const {
+  return data_[idx];
+}
+
+void StringRange::Reset() {
+  data_ = "";
+  size_ = 0;
+  start_ = end_ = data_;
+}
+
+void StringRange::Reset(const char* data, size_t size) {
+  data_ = data;
+  size_ = size;
+  start_ = end_ = data_;
+}
+
+void StringRange::Reset(const std::string& str) {
+  data_ = str.data();
+  size_ = str.size();
+  start_ = end_ = data_;
+}
+
+bool StringRange::StartsWith(const StringRange& str) const {
+  return ((size_ >= str.size_) && (memcmp(data_, str.data_, str.size_) == 0));
+}
+
+bool StringRange::EndsWith(const StringRange& str) const {
+  return (
+      (size_ >= str.size_) &&
+      (memcmp(data_ + (size_ - str.size_), str.data_, str.size_) == 0));
+}
+
+bool StringRange::LStrip() {
+  size_t count = 0;
+  const char* ptr = data_;
+  while (count < size_ && isspace(*ptr)) {
+    count++;
+    ptr++;
+  }
+
+  if (count > 0) {
+    return LStrip(count);
+  }
+  return false;
+}
+
+bool StringRange::LStrip(size_t size) {
+  if (size <= size_) {
+    data_ += size;
+    size_ -= size;
+    end_ += size;
+    return true;
+  }
+  return false;
+}
+
+bool StringRange::LStrip(StringRange str) {
+  if (StartsWith(str)) {
+    return LStrip(str.size_);
+  }
+  return false;
+}
+
+bool StringRange::RStrip() {
+  size_t count = 0;
+  const char* ptr = data_ + size_ - 1;
+  while (count < size_ && isspace(*ptr)) {
+    ++count;
+    --ptr;
+  }
+
+  if (count > 0) {
+    return RStrip(count);
+  }
+  return false;
+}
+
+bool StringRange::RStrip(size_t size) {
+  if (size_ >= size) {
+    size_ -= size;
+    return true;
+  }
+  return false;
+}
+
+bool StringRange::RStrip(StringRange str) {
+  if (EndsWith(str)) {
+    return RStrip(str.size_);
+  }
+  return false;
+}
+
+bool StringRange::LAndRStrip() {
+  bool l = LStrip();
+  bool r = RStrip();
+  return l || r;
+}
+
+void StringRange::ParensWhitespaceStrip() {
+  LStrip();
+  LStrip("(");
+  LAndRStrip();
+  RStrip(")");
+  RStrip();
+}
+
+size_t StringRange::Find(const char ch) const {
+  size_t idx = 0;
+  while (idx < size_) {
+    if (data_[idx] == ch) {
+      return idx;
+    }
+    idx++;
+  }
+  return std::string::npos;
+}
+
+void StringRange::RestartCapture() {
+  start_ = data_;
+  end_ = data_;
+}
+
+StringRange StringRange::GetCaptured() {
+  return StringRange(start_, end_ - start_);
+}
+
+TypesWrapper& TypesWrapper::GetTypesWrapper() {
+  static TypesWrapper types;
+  return types;
+}
+
+std::unordered_set<std::string>& TypesWrapper::GetAllowedDataTypes() {
+  return allowed_data_types_;
+}
+
+std::unordered_map<std::string, int>& TypesWrapper::TypeStrToTensorDataType() {
+  return type_str_to_tensor_data_type_;
+}
+
+std::unordered_map<int, std::string>& TypesWrapper::TensorDataTypeToTypeStr() {
+  return tensor_data_type_to_type_str_;
+}
+
+TypesWrapper::TypesWrapper() {
+  // DataType strings. These should match the DataTypes defined in onnx.proto
+  type_str_to_tensor_data_type_["float"] = TensorProto_DataType_FLOAT;
+  type_str_to_tensor_data_type_["float16"] = TensorProto_DataType_FLOAT16;
+  type_str_to_tensor_data_type_["bfloat16"] = TensorProto_DataType_BFLOAT16;
+  type_str_to_tensor_data_type_["double"] = TensorProto_DataType_DOUBLE;
+  type_str_to_tensor_data_type_["int8"] = TensorProto_DataType_INT8;
+  type_str_to_tensor_data_type_["int16"] = TensorProto_DataType_INT16;
+  type_str_to_tensor_data_type_["int32"] = TensorProto_DataType_INT32;
+  type_str_to_tensor_data_type_["int64"] = TensorProto_DataType_INT64;
+  type_str_to_tensor_data_type_["uint8"] = TensorProto_DataType_UINT8;
+  type_str_to_tensor_data_type_["uint16"] = TensorProto_DataType_UINT16;
+  type_str_to_tensor_data_type_["uint32"] = TensorProto_DataType_UINT32;
+  type_str_to_tensor_data_type_["uint64"] = TensorProto_DataType_UINT64;
+  type_str_to_tensor_data_type_["complex64"] = TensorProto_DataType_COMPLEX64;
+  type_str_to_tensor_data_type_["complex128"] = TensorProto_DataType_COMPLEX128;
+  type_str_to_tensor_data_type_["string"] = TensorProto_DataType_STRING;
+  type_str_to_tensor_data_type_["bool"] = TensorProto_DataType_BOOL;
+
+  for (auto& str_type_pair : type_str_to_tensor_data_type_) {
+    tensor_data_type_to_type_str_[str_type_pair.second] = str_type_pair.first;
+    allowed_data_types_.insert(str_type_pair.first);
+  }
+}
+} // namespace Utils
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/data_type_utils.h b/third_party/onnx/onnx/defs/data_type_utils.h
new file mode 100644
index 0000000000..a7e550d68e
--- /dev/null
+++ b/third_party/onnx/onnx/defs/data_type_utils.h
@@ -0,0 +1,72 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#ifndef ONNX_DATA_TYPE_UTILS_H
+#define ONNX_DATA_TYPE_UTILS_H
+
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include "onnx/common/common.h"
+#include "onnx/onnx_pb.h"
+
+namespace ONNX_NAMESPACE {
+// String pointer as unique TypeProto identifier.
+using DataType = const std::string*;
+
+namespace Utils {
+
+// Data type utility, which maintains a global type string to TypeProto map.
+// DataType (string pointer) is used as unique data type identifier for
+// efficiency.
+//
+// Grammar for data type string:
+// <type> ::= <data_type> |
+//            tensor(<data_type>) |
+//            seq(<type>) |
+//            map(<data_type>, <type>)
+// <data_type> :: = float | int32 | string | bool | uint8
+//                | int8 | uint16 | int16 | int64 | float16 | double
+//
+// NOTE: <type> ::= <data_type> means the data is scalar (zero dimension).
+//
+// Example: float, tensor(float), etc.
+//
+class DataTypeUtils final {
+ public:
+  // If the DataType input is invalid, this function will throw std::invalid_argument exception.
+  // If ONNX_NO_EXCEPTIONS is set it will abort.
+  static DataType ToType(const std::string& type_str);
+
+  // If the DataType input is invalid, this function will throw std::invalid_argument exception.
+  // If ONNX_NO_EXCEPTIONS is set it will abort.
+  static DataType ToType(const TypeProto& type_proto);
+
+  // If the DataType input is invalid, this function will throw std::invalid_argument exception.
+  // If ONNX_NO_EXCEPTIONS is set it will abort.
+  static const TypeProto& ToTypeProto(const DataType& data_type);
+  static std::string ToDataTypeString(int32_t tensor_data_type);
+ 
+ private:
+  static void FromString(const std::string& type_str, TypeProto& type_proto);
+
+  static void FromDataTypeString(const std::string& type_str, int32_t& tensor_data_type);
+
+  static std::string ToString(const TypeProto& type_proto, const std::string& left = "", const std::string& right = "");
+
+  // If int32_t input is invalid, this function will throw an exception.
+  // If ONNX_NO_EXCEPTIONS is set it will abort.
+
+  static bool IsValidDataTypeString(const std::string& type_str);
+
+  static std::unordered_map<std::string, TypeProto>& GetTypeStrToProtoMap();
+
+  // Returns lock used for concurrent updates to TypeStrToProtoMap.
+  static std::mutex& GetTypeStrLock();
+};
+} // namespace Utils
+} // namespace ONNX_NAMESPACE
+
+#endif // ! ONNX_DATA_TYPE_UTILS_H
diff --git a/third_party/onnx/onnx/defs/function.cc b/third_party/onnx/onnx/defs/function.cc
new file mode 100644
index 0000000000..0cb4ac80b2
--- /dev/null
+++ b/third_party/onnx/onnx/defs/function.cc
@@ -0,0 +1,172 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+std::string InteralTensorNameGenerator(const std::string& node_name, const std::string& internal_name) {
+  std::string new_name = "Func_" + node_name + internal_name;
+  return new_name;
+}
+
+void FunctionExpandHelper(
+    const NodeProto& node,
+    const FunctionProto& func,
+    GraphProto& g,
+    const std::string& node_prefix) {
+  // Create a temporary unique node prefix for tensor names
+  std::string uniq_prefix = node_prefix;
+  if (uniq_prefix.empty()) {
+    const void* address = static_cast<const void*>(&node);
+    std::stringstream ss;
+    ss << address;
+    uniq_prefix = ss.str();
+  }
+  std::string node_name = node.has_name() ? node.name() : func.name() + uniq_prefix;
+  std::unordered_map<std::string, std::string> io_names_map;
+  std::unordered_map<std::string, AttributeProto> attr_map;
+
+  for (int idx = 0; idx < node.input_size(); ++idx) {
+    if (idx >= func.input_size()) {
+      ONNX_THROW("Input for function node " + node_name + " is out of bounds");
+    }
+    io_names_map[func.input().Get(idx)] = node.input().Get(idx);
+  }
+  for (int idx = 0; idx < node.output_size(); ++idx) {
+    if (idx >= func.output_size()) {
+      ONNX_THROW("Output for function node " + node_name + " is out of bounds");
+    }
+    // If the node output is missing, the corresponding function output should
+    // be treated as an internal value (not as missing) because it could also be
+    // an intermediate value.
+    if (node.output().Get(idx) == "") {
+      continue;
+    }
+    io_names_map[func.output().Get(idx)] = node.output().Get(idx);
+  }
+
+  for (auto& attr : node.attribute()) {
+    attr_map[attr.name()] = attr;
+  }
+
+  // For undefined attributes of the function node
+  // add default values obtained from the function schema.
+  // get the domain version for function schema
+  int domain_version = -1;
+  for (const auto& opset_import : func.opset_import()) {
+    if (opset_import.domain() == node.domain()) {
+      domain_version = static_cast<int>(opset_import.version());
+    }
+  }
+  if (domain_version == -1) {
+    ONNX_THROW("No opset import registered for domain '" + node.domain() + "' in function proto");
+  }
+
+  const OpSchemaRegistry* schema_registry = OpSchemaRegistry::Instance();
+  const auto schema = schema_registry->GetSchema(node.op_type(), domain_version, node.domain());
+  std::map<std::string, OpSchema::Attribute> default_attrs = schema->attributes();
+
+  for (const auto& pair : default_attrs) {
+    const auto& attr_name = pair.first;
+    const auto& attr = pair.second;
+    if (!attr_map.count(attr_name)) {
+      attr_map[attr_name] = attr.default_value;
+    }
+  }
+
+  for (auto& function_node : func.node()) {
+    NodeProto* new_node = g.add_node();
+    new_node->CopyFrom(function_node);
+    new_node->clear_input();
+    new_node->clear_output();
+    new_node->clear_attribute();
+    for (auto& input : function_node.input()) {
+      if (io_names_map.count(input)) {
+        new_node->add_input(io_names_map[input]);
+      } else {
+        new_node->add_input(InteralTensorNameGenerator(node_name, input));
+      }
+    }
+    for (auto& output : function_node.output()) {
+      if (io_names_map.count(output)) {
+        new_node->add_output(io_names_map[output]);
+      } else {
+        new_node->add_output(InteralTensorNameGenerator(node_name, output));
+      }
+    }
+    for (auto& attr : function_node.attribute()) {
+      if (attr.has_ref_attr_name()) {
+        if (attr_map.count(attr.ref_attr_name())) {
+          AttributeProto* new_attr = new_node->add_attribute();
+          new_attr->CopyFrom(attr_map[attr.ref_attr_name()]);
+          new_attr->set_name(attr.name());
+        }
+      } else {
+        AttributeProto* new_attr = new_node->add_attribute();
+        new_attr->CopyFrom(attr);
+      }
+    }
+  }
+}
+
+std::vector<NodeProto> FunctionBodyHelper::BuildNodes(const std::vector<NodeDef>& node_defs) {
+  std::vector<NodeProto> nodes(node_defs.size());
+
+  for (size_t i = 0; i < node_defs.size(); i++) {
+    const NodeDef& node = node_defs[i];
+    NodeProto& n = nodes[i];
+
+    n.set_op_type(node.op_type);
+    n.set_domain(node.domain);
+    for (const auto& i : node.inputs) {
+      n.add_input(i);
+    }
+    for (const auto& o : node.outputs) {
+      n.add_output(o);
+    }
+    for (const auto& attr : node.attributes) {
+      *(n.add_attribute()) = attr.proto;
+    }
+  }
+
+  return nodes;
+}
+
+void FunctionBodyHelper::BuildNodes(FunctionProto& functionProto, const std::vector<NodeDef>& node_defs) {
+  for (size_t i = 0; i < node_defs.size(); i++) {
+    const NodeDef& node = node_defs[i];
+    auto* np = functionProto.add_node();
+
+    np->set_op_type(node.op_type);
+    np->set_domain(node.domain);
+    for (const auto& inp : node.inputs) {
+      np->add_input(inp);
+    }
+    for (const auto& o : node.outputs) {
+      np->add_output(o);
+    }
+    for (const auto& attr : node.attributes) {
+      *(np->add_attribute()) = attr.proto;
+    }
+  }
+}
+
+bool FunctionBodyHelper::BuildFunctionProto(
+    FunctionProto& functionProto,
+    const OpSchema& schema,
+    const std::vector<NodeDef>& node_defs,
+    const std::vector<OperatorSetIdProto>& relied_opsets) {
+  BuildNodes(functionProto, node_defs);
+
+  for (auto& relied_opset : relied_opsets) {
+    *(functionProto.mutable_opset_import()->Add()) = relied_opset;
+  }
+
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/function.h b/third_party/onnx/onnx/defs/function.h
new file mode 100644
index 0000000000..de529b8e56
--- /dev/null
+++ b/third_party/onnx/onnx/defs/function.h
@@ -0,0 +1,195 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "attr_proto_util.h"
+#include "onnx/common/constants.h"
+#include "onnx/common/status.h"
+#include "onnx/defs/schema.h"
+#include "tensor_proto_util.h"
+#include "onnx/defs/parser.h"
+
+namespace ONNX_NAMESPACE {
+// Helper function to expand a function node given the function proto
+void FunctionExpandHelper(
+    const NodeProto& node,
+    const FunctionProto& func,
+    GraphProto& g,
+    const std::string& node_prefix = "");
+
+class FunctionBodyHelper {
+ public:
+  struct AttributeProtoWrapper {
+    AttributeProto proto;
+
+    AttributeProtoWrapper() {}
+
+    AttributeProtoWrapper(const AttributeProto& attr_prot) {
+      proto = attr_prot;
+    }
+
+    template <typename T>
+    AttributeProtoWrapper(const std::string& attr_name, T value) {
+      proto = MakeAttribute(attr_name, value);
+    }
+  };
+
+  struct NodeDef {
+    NodeDef(
+        std::vector<std::string> outputs,
+        std::string op_type,
+        std::vector<std::string> inputs,
+        std::vector<AttributeProtoWrapper> attributes = {},
+        std::string domain = "")
+        : outputs(std::move(outputs)),
+          op_type(std::move(op_type)),
+          inputs(std::move(inputs)),
+          attributes(std::move(attributes)),
+          domain(std::move(domain)) {}
+
+    std::vector<std::string> outputs;
+    std::string op_type;
+    std::vector<std::string> inputs;
+    std::vector<AttributeProtoWrapper> attributes;
+    std::string domain;
+  };
+
+  /*
+  BuildNodes() is an utility function for easily define a Function Body.
+
+  To build a simple node:
+    {{"Z"}, "Add", {"X", "Y"}} represents Z = Add(X,Y)
+
+  To build a node with attribute:
+    {{"Y"}, "Concat", {"X1", "X2", "X3"}, {{"axis", 1}}}
+      represents Y = Concat(X1,X2,X3) with axis = 1
+    The attribute type are infered from the attribute value's c++ type
+    Supported value types are
+      int64_t -> int, vector<int64_t> -> ints
+      float -> float, vector<float> -> floats
+      string -> string, vector<string> ->strings
+    For refering an attribute from parent, use:
+      {MakeRefAttribute("axes", AttributeProto::INTS)}}
+
+  To build a node which belongs to a domain other than onnx standard domain:
+    {{"Z"}, "Foo", {"X", "Y"}, "customdomain"} represents Z = customdomain.Foo(X,Y)
+    or
+    {{"Y"}, "Bar", {"X1", "X2", "X3"}, {{"axis", 1}}, "customdomain"}
+      represents Y = customdomain.Bar(X1,X2,X3) with axis = 1
+
+  For more examples, please find the references of this function
+  */
+  static std::vector<NodeProto> BuildNodes(const std::vector<NodeDef>& node_defs);
+
+  static void BuildNodes(FunctionProto& functionProto, const std::vector<NodeDef>& node_defs);
+
+  static bool BuildFunctionProto(
+      FunctionProto& functionProto,
+      const OpSchema& schema,
+      const std::vector<NodeDef>& node_defs,
+      const std::vector<OperatorSetIdProto>& relied_opsets);
+
+  template <typename T>
+  static NodeDef Const(const std::string& name, const T& value) {
+    return NodeDef{{name}, "Constant", {}, {{"value", ToTensor<T>(value)}}};
+  }
+
+  template <typename T>
+  static NodeDef Const(const std::string& name, const std::vector<T>& values) {
+    return NodeDef{{name}, "Constant", {}, {{"value", ToTensor<T>(values)}}};
+  }
+};
+
+class FunctionBuilder {
+ public:
+  FunctionBuilder(FunctionProto& funProto_) : funProto(funProto_) {}
+
+  FunctionBuilder& Add(const char* nodes_txt) {
+    OnnxParser parser(nodes_txt);
+    auto& nodes = *funProto.mutable_node();
+
+    while (!parser.EndOfInput()) {
+      auto status = parser.Parse(*nodes.Add());
+      if (!status.IsOK())
+        ONNX_THROW_EX(std::logic_error("Error parsing node:" + status.ErrorMessage()));
+    }
+
+    return *this;
+  }
+
+  FunctionBuilder& Add(const char* node_txt, const AttributeProto& attr) {
+    OnnxParser parser(node_txt);
+    auto& node = *funProto.add_node();
+    auto status = parser.Parse(node);
+    if (!status.IsOK()) {
+      ONNX_THROW_EX(std::logic_error("Error parsing node:" + status.ErrorMessage()));
+    }
+
+    if (!parser.EndOfInput()) {
+      ONNX_THROW_EX(std::logic_error("Error unexpected extra input in node:" + status.ErrorMessage()));
+    }
+
+    *node.add_attribute() = attr;
+
+    return *this;
+  }
+
+  template <typename T>
+  FunctionBuilder& Add(const char* node_txt, const std::string& attr_name, T attr_value) {
+    return Add(node_txt, MakeAttribute(attr_name, attr_value));
+  }
+
+  FunctionBuilder& Const(const std::string& name, const TensorProto& tensor) {
+    std::string constant_op(name);
+    constant_op += " = Constant()";
+    return Add(constant_op.c_str(), MakeAttribute("value", tensor));
+  }
+
+  // Creates a scalar constant (a tensor of rank zero).
+  template <typename T>
+  FunctionBuilder& Const(const std::string& name, T const_value) {
+    std::string constant_op(name);
+    constant_op += " = Constant()";
+    return Add (constant_op.c_str(), MakeAttribute("value", ToTensor(const_value)));
+  }
+
+  // Creates a 1D tensor constant consisting of a single value.
+  template <typename T>
+  FunctionBuilder& Const1D(const std::string& name, T const_value) {
+    std::string constant_op(name);
+    constant_op += " = Constant()";
+    auto tensor = ToTensor(const_value);
+    tensor.add_dims(1);
+    return Add (constant_op.c_str(), MakeAttribute("value", tensor));
+  }
+
+  // Creates a 1D tensor constant consisting of zero or more values.
+  template <typename T>
+  FunctionBuilder& Const(const std::string& name, const std::vector<T>& values) {
+    std::string constant_op(name);
+    constant_op += " = Constant()";
+    auto tensor = ToTensor(values);
+    tensor.add_dims(values.size());  // Treat as 1D tensor.
+
+    return Add (constant_op.c_str(), MakeAttribute("value", tensor));
+  }
+
+  FunctionBuilder& AddOpset(const char* domain, int version) {
+    auto* opset = funProto.add_opset_import();
+    opset->set_domain(domain);
+    opset->set_version(version);
+    return *this;
+  }
+
+ private:
+  FunctionProto& funProto;
+};
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/gen_doc.py b/third_party/onnx/onnx/defs/gen_doc.py
new file mode 100644
index 0000000000..94cee84f6a
--- /dev/null
+++ b/third_party/onnx/onnx/defs/gen_doc.py
@@ -0,0 +1,377 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+from collections import defaultdict
+import io
+import os
+
+import numpy as np  # type: ignore
+
+from onnx import defs, FunctionProto, helper
+from onnx.defs import OpSchema, ONNX_ML_DOMAIN
+from onnx.backend.test.case import collect_snippets
+from onnx.backend.sample.ops import collect_sample_implementations
+from typing import Any, Text, Sequence, Dict, List, NamedTuple, Set, Tuple
+
+
+SNIPPETS = collect_snippets()
+SAMPLE_IMPLEMENTATIONS = collect_sample_implementations()
+ONNX_ML = not bool(os.getenv('ONNX_ML') == '0')
+
+
+ext = '-ml.md' if ONNX_ML else '.md'
+
+
+def display_number(v: int) -> Text:
+    if defs.OpSchema.is_infinite(v):
+        return '&#8734;'
+    return Text(v)
+
+
+def should_render_domain(domain: Text) -> bool:
+    if domain == ONNX_ML_DOMAIN and not ONNX_ML:
+        return False
+    if ONNX_ML and domain != ONNX_ML_DOMAIN:
+        return False
+    return True
+
+
+def format_name_with_domain(domain: Text, schema_name: Text) -> Text:
+    if domain:
+        return '{}.{}'.format(domain, schema_name)
+    return schema_name
+
+
+def format_versions(versions: Sequence[OpSchema]) -> Text:
+    return '{}'.format(', '.join(display_version_link(format_name_with_domain(v.domain, v.name),
+                                               v.since_version) for v in versions[::-1]))
+
+
+def display_attr_type(v: OpSchema.AttrType) -> Text:
+    assert isinstance(v, OpSchema.AttrType)
+    s = Text(v)
+    s = s[s.rfind('.') + 1:].lower()
+    if s[-1] == 's':
+        s = 'list of ' + s
+    return s
+
+
+def display_domain(domain: Text) -> Text:
+    if domain:
+        return "the '{}' operator set".format(domain)
+    return "the default ONNX operator set"
+
+
+def display_domain_short(domain: Text) -> Text:
+    if domain:
+        return domain
+    return 'ai.onnx (default)'
+
+
+def display_version_link(name: Text, version: int) -> Text:
+    changelog_md = 'Changelog' + ext
+    name_with_ver = '{}-{}'.format(name, version)
+    return '<a href="{}#{}">{}</a>'.format(changelog_md, name_with_ver, version)
+
+
+def generate_formal_parameter_tags(formal_parameter: OpSchema.FormalParameter) -> Text:
+    tags: List[Text] = []
+    if OpSchema.FormalParameterOption.Optional == formal_parameter.option:
+        tags = ["optional"]
+    elif OpSchema.FormalParameterOption.Variadic == formal_parameter.option:
+        if formal_parameter.isHomogeneous:
+            tags = ["variadic"]
+        else:
+            tags = ["variadic", "heterogeneous"]
+    differentiable: OpSchema.DifferentiationCategory = OpSchema.DifferentiationCategory.Differentiable
+    non_differentiable: OpSchema.DifferentiationCategory = OpSchema.DifferentiationCategory.NonDifferentiable
+    if differentiable == formal_parameter.differentiationCategory:
+        tags.append('differentiable')
+    elif non_differentiable == formal_parameter.differentiationCategory:
+        tags.append('non-differentiable')
+
+    return '' if len(tags) == 0 else ' (' + ', '.join(tags) + ')'
+
+
+def display_schema(schema: OpSchema, versions: Sequence[OpSchema]) -> Text:
+    s = ''
+
+    # doc
+    if schema.doc:
+        s += '\n'
+        s += '\n'.join(('  ' + line).rstrip()
+                       for line in schema.doc.lstrip().splitlines())
+        s += '\n'
+
+    # since version
+    s += '\n#### Version\n'
+    if schema.support_level == OpSchema.SupportType.EXPERIMENTAL:
+        s += '\nNo versioning maintained for experimental ops.'
+    else:
+        s += '\nThis version of the operator has been ' + ('deprecated' if schema.deprecated else 'available') + ' since version {}'.format(schema.since_version)
+        s += ' of {}.\n'.format(display_domain(schema.domain))
+        if len(versions) > 1:
+            # TODO: link to the Changelog.md
+            s += '\nOther versions of this operator: {}\n'.format(
+                ', '.join(display_version_link(format_name_with_domain(v.domain, v.name),
+                                               v.since_version) for v in versions[:-1]))
+
+    # If this schema is deprecated, don't display any of the following sections
+    if schema.deprecated:
+        return s
+
+    # attributes
+    if schema.attributes:
+        s += '\n#### Attributes\n\n'
+        s += '<dl>\n'
+        for _, attr in sorted(schema.attributes.items()):
+            # option holds either required or default value
+            opt = ''
+            if attr.required:
+                opt = 'required'
+            elif attr.default_value.name:
+                default_value = helper.get_attribute_value(attr.default_value)
+
+                def format_value(value: Any) -> Text:
+                    if isinstance(value, float):
+                        formatted = str(np.round(value, 5))
+                        # use default formatting, unless too long.
+                        if (len(formatted) > 10):
+                            formatted = str("({:e})".format(value))
+                        return formatted
+                    elif isinstance(value, (bytes, bytearray)):
+                        return str(value.decode('utf-8'))
+                    return str(value)
+
+                if isinstance(default_value, list):
+                    default_value = [format_value(val) for val in default_value]
+                else:
+                    default_value = format_value(default_value)
+                opt = 'default is {}'.format(default_value)
+
+            s += '<dt><tt>{}</tt> : {}{}</dt>\n'.format(
+                attr.name,
+                display_attr_type(attr.type),
+                ' ({})'.format(opt) if opt else '')
+            s += '<dd>{}</dd>\n'.format(attr.description)
+        s += '</dl>\n'
+
+    # inputs
+    s += '\n#### Inputs'
+    if schema.min_input != schema.max_input:
+        s += ' ({} - {})'.format(display_number(schema.min_input),
+                                 display_number(schema.max_input))
+    s += '\n\n'
+    if schema.inputs:
+        s += '<dl>\n'
+        for input in schema.inputs:
+            option_str = generate_formal_parameter_tags(input)
+            s += '<dt><tt>{}</tt>{} : {}</dt>\n'.format(input.name, option_str, input.typeStr)
+            s += '<dd>{}</dd>\n'.format(input.description)
+        s += '</dl>\n'
+
+    # outputs
+    s += '\n#### Outputs'
+    if schema.min_output != schema.max_output:
+        s += ' ({} - {})'.format(display_number(schema.min_output),
+                                 display_number(schema.max_output))
+    s += '\n\n'
+
+    if schema.outputs:
+        s += '<dl>\n'
+        for output in schema.outputs:
+            option_str = generate_formal_parameter_tags(output)
+            s += '<dt><tt>{}</tt>{} : {}</dt>\n'.format(output.name, option_str, output.typeStr)
+            s += '<dd>{}</dd>\n'.format(output.description)
+        s += '</dl>\n'
+
+    # type constraints
+    s += '\n#### Type Constraints'
+    s += '\n\n'
+    if schema.type_constraints:
+        s += '<dl>\n'
+        for type_constraint in schema.type_constraints:
+            allowedTypes = type_constraint.allowed_type_strs
+            if (len(allowedTypes) > 0):
+                allowedTypeStr = allowedTypes[0]
+            for allowedType in allowedTypes[1:]:
+                allowedTypeStr += ', ' + allowedType
+            s += '<dt><tt>{}</tt> : {}</dt>\n'.format(
+                type_constraint.type_param_str, allowedTypeStr)
+            s += '<dd>{}</dd>\n'.format(type_constraint.description)
+        s += '</dl>\n'
+
+    # Function Body
+    # TODO: this should be refactored to show the function body graph's picture (DAG).
+    #if schema.has_function or schema.has_context_dependent_function:  # type: ignore
+    #    s += '\n#### Function\n'
+    #    s += '\nThe Function can be represented as a function.\n'
+
+    return s
+
+
+def support_level_str(level: OpSchema.SupportType) -> Text:
+    return \
+        "<sub>experimental</sub> " if level == OpSchema.SupportType.EXPERIMENTAL else ""
+
+
+class Args(NamedTuple):
+    output: str
+    changelog: str
+
+
+def main(args: Args) -> None:
+    with io.open(args.changelog, 'w', newline='') as fout:
+        fout.write('<!--- SPDX-License-Identifier: Apache-2.0 -->\n')
+        fout.write('## Operator Changelog\n')
+        fout.write(
+            "*This file is automatically generated from the\n"
+            "            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).\n"
+            "            Do not modify directly and instead edit operator definitions.*\n"
+            "\n"
+            "For an operator input/output's differentiability, it can be differentiable,\n"
+            "            non-differentiable, or undefined. If a variable's differentiability\n"
+            "            is not specified, that variable has undefined differentiability.\n")
+
+        # domain -> version -> [schema]
+        dv_index: Dict[Text, Dict[int, List[OpSchema]]] = defaultdict(lambda: defaultdict(list))
+        for schema in defs.get_all_schemas_with_history():
+            dv_index[schema.domain][schema.since_version].append(schema)
+
+        fout.write('\n')
+
+        for domain, versionmap in sorted(dv_index.items()):
+            if not should_render_domain(domain):
+                continue
+
+            s = '# {}\n'.format(display_domain_short(domain))
+
+            for version, unsorted_schemas in sorted(versionmap.items()):
+                s += '## Version {} of {}\n'.format(version, display_domain(domain))
+                for schema in sorted(unsorted_schemas, key=lambda s: s.name):
+                    name_with_ver = '{}-{}'.format(format_name_with_domain(domain, schema.name),
+                                                   schema.since_version)
+                    s += ('### <a name="{}"></a>**{}**' + (' (deprecated)' if schema.deprecated else '') + '</a>\n').format(name_with_ver, name_with_ver)
+                    s += display_schema(schema, [schema])
+                    s += '\n'
+
+            fout.write(s)
+
+    with io.open(args.output, 'w', newline='', encoding="utf-8") as fout:
+        fout.write('<!--- SPDX-License-Identifier: Apache-2.0 -->\n')
+        fout.write('## Operator Schemas\n')
+        fout.write(
+            "*This file is automatically generated from the\n"
+            "            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).\n"
+            "            Do not modify directly and instead edit operator definitions.*\n"
+            "\n"
+            "For an operator input/output's differentiability, it can be differentiable,\n"
+            "            non-differentiable, or undefined. If a variable's differentiability\n"
+            "            is not specified, that variable has undefined differentiability.\n")
+
+        # domain -> support level -> name -> [schema]
+        index: Dict[Text, Dict[int, Dict[Text, List[OpSchema]]]] = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
+        for schema in defs.get_all_schemas_with_history():
+            index[schema.domain][int(schema.support_level)][schema.name].append(schema)
+
+        fout.write('\n')
+
+        # Preprocess the Operator Schemas
+        # [(domain, [(support_level, [(schema name, current schema, all versions schemas)])])]
+        operator_schemas: List[Tuple[Text, List[Tuple[int, List[Tuple[Text, OpSchema, List[OpSchema]]]]]]] = list()
+        existing_ops: Set[Text] = set()
+        for domain, _supportmap in sorted(index.items()):
+            if not should_render_domain(domain):
+                continue
+
+            processed_supportmap = list()
+            for _support, _namemap in sorted(_supportmap.items()):
+                processed_namemap = list()
+                for n, unsorted_versions in sorted(_namemap.items()):
+                    versions = sorted(unsorted_versions, key=lambda s: s.since_version)
+                    schema = versions[-1]
+                    if schema.name in existing_ops:
+                        continue
+                    existing_ops.add(schema.name)
+                    processed_namemap.append((n, schema, versions))
+                processed_supportmap.append((_support, processed_namemap))
+            operator_schemas.append((domain, processed_supportmap))
+
+        # Table of contents
+        for domain, supportmap in operator_schemas:
+            s = '### {}\n'.format(display_domain_short(domain))
+            fout.write(s)
+
+            fout.write('|**Operator**|**Since version**|\n')
+            fout.write('|-|-|\n')
+
+            function_ops = list()
+            for _, namemap in supportmap:
+                for n, schema, versions in namemap:
+                    if schema.has_function or schema.has_context_dependent_function:  # type: ignore
+                        function_ops.append((n, schema, versions))
+                        continue
+                    s = '|{}<a href="#{}">{}</a>{}|{}|\n'.format(
+                        support_level_str(schema.support_level),
+                        format_name_with_domain(domain, n),
+                        format_name_with_domain(domain, n),
+                        ' (deprecated)' if schema.deprecated else '',
+                        format_versions(versions))
+                    fout.write(s)
+            if len(function_ops):
+                fout.write('|**Function**|**Since version**|\n')
+                for n, schema, versions in function_ops:
+                    s = '|{}<a href="#{}">{}</a>|{}|\n'.format(
+                        support_level_str(schema.support_level),
+                        format_name_with_domain(domain, n),
+                        format_name_with_domain(domain, n),
+                        format_versions(versions))
+                    fout.write(s)
+
+            fout.write('\n')
+
+        fout.write('\n')
+
+        for domain, supportmap in operator_schemas:
+            s = '## {}\n'.format(display_domain_short(domain))
+            fout.write(s)
+
+            for _, namemap in supportmap:
+                for op_type, schema, versions in namemap:
+                    # op_type
+                    s = ('### {}<a name="{}"></a><a name="{}">**{}**' + (' (deprecated)' if schema.deprecated else '') + '</a>\n').format(
+                        support_level_str(schema.support_level),
+                        format_name_with_domain(domain, op_type),
+                        format_name_with_domain(domain, op_type.lower()),
+                        format_name_with_domain(domain, op_type))
+
+                    s += display_schema(schema, versions)
+
+                    s += '\n\n'
+
+                    if op_type in SNIPPETS:
+                        s += '#### Examples\n\n'
+                        for summary, code in sorted(SNIPPETS[op_type]):
+                            s += '<details>\n'
+                            s += '<summary>{}</summary>\n\n'.format(summary)
+                            s += '```python\n{}\n```\n\n'.format(code)
+                            s += '</details>\n'
+                            s += '\n\n'
+                    if op_type.lower() in SAMPLE_IMPLEMENTATIONS:
+                        s += '#### Sample Implementation\n\n'
+                        s += '<details>\n'
+                        s += '<summary>{}</summary>\n\n'.format(op_type)
+                        s += '```python\n{}\n```\n\n'.format(SAMPLE_IMPLEMENTATIONS[op_type.lower()])
+                        s += '</details>\n'
+                        s += '\n\n'
+
+                    fout.write(s)
+
+
+if __name__ == '__main__':
+    base_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
+    docs_dir = os.path.join(base_dir, 'docs')
+
+    main(Args(os.path.join(docs_dir, 'Operators' + ext),
+              os.path.join(docs_dir, 'Changelog' + ext)))
diff --git a/third_party/onnx/onnx/defs/gen_shape_inference_information.py b/third_party/onnx/onnx/defs/gen_shape_inference_information.py
new file mode 100644
index 0000000000..099df5eb0f
--- /dev/null
+++ b/third_party/onnx/onnx/defs/gen_shape_inference_information.py
@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx import defs
+
+
+def main() -> None:
+    # domain -> support level -> name -> [schema]
+    with_inference = []
+    without_inference = []
+    for schema in defs.get_all_schemas():
+        domain, name, has_inference = schema.domain, schema.name, schema.has_type_and_shape_inference_function
+        elem = (domain, name)
+        if has_inference:
+            with_inference.append(elem)
+        else:
+            without_inference.append(elem)
+    print(len(with_inference), 'operators have a type/shape inference function.')
+    print(len(without_inference), 'do not. These are:')
+    for domain, name in sorted(without_inference):
+        print(domain, name)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/onnx/defs/generator/defs.cc b/third_party/onnx/onnx/defs/generator/defs.cc
new file mode 100755
index 0000000000..9442b125ca
--- /dev/null
+++ b/third_party/onnx/onnx/defs/generator/defs.cc
@@ -0,0 +1,859 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <cmath>
+#include <algorithm>
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+static const char* Constant_ver13_doc = R"DOC(
+This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
+or value_* must be specified.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Constant,
+    13,
+    OpSchema()
+        .SetDoc(Constant_ver13_doc)
+        .Attr(
+            "value",
+            "The value for the elements of the output tensor.",
+            AttributeProto::TENSOR,
+            false)
+        .Attr(
+            "sparse_value",
+            "The value for the elements of the output tensor in sparse format.",
+            AttributeProto::SPARSE_TENSOR,
+            false)
+        .Attr(
+            "value_int",
+            "The value for the sole element for the scalar, int64, output tensor.",
+            AttributeProto::INT,
+            false)
+        .Attr(
+            "value_ints",
+            "The values for the elements for the 1D, int64, output tensor.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "value_float",
+            "The value for the sole element for the scalar, float32, output tensor.",
+            AttributeProto::FLOAT,
+            false)
+        .Attr(
+            "value_floats",
+            "The values for the elements for the 1D, float32, output tensor.",
+            AttributeProto::FLOATS,
+            false)
+        .Attr(
+            "value_string",
+            "The value for the sole element for the scalar, UTF-8 string, output tensor.",
+            AttributeProto::STRING,
+            false)
+        .Attr(
+            "value_strings",
+            "The values for the elements for the 1D, UTF-8 string, output tensor.",
+            AttributeProto::STRINGS,
+            false)
+        .Output(
+            0,
+            "output",
+            "Output tensor containing the same value of the provided tensor.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto* value = ctx.getAttribute("value");
+          auto* sparse_value = ctx.getAttribute("sparse_value");
+          auto* value_int = ctx.getAttribute("value_int");
+          auto* value_ints = ctx.getAttribute("value_ints");
+          auto* value_float = ctx.getAttribute("value_float");
+          auto* value_floats = ctx.getAttribute("value_floats");
+          auto* value_string = ctx.getAttribute("value_string");
+          auto* value_strings = ctx.getAttribute("value_strings");
+
+          std::vector<bool> non_null_attr = {
+            (nullptr != value),
+            (nullptr != sparse_value),
+            (nullptr != value_int),
+            (nullptr != value_ints),
+            (nullptr != value_float),
+            (nullptr != value_floats),
+            (nullptr != value_string),
+            (nullptr != value_strings)
+          };
+          if (std::count(non_null_attr.begin(), non_null_attr.end(), true) != 1) {
+            fail_shape_inference(
+                "One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.");
+          }
+
+          if (nullptr != value) {
+            // OpSchema::Verify check ensures that the attribute value has_t():
+            const TensorProto& tensor_proto = value->t();
+            updateOutputElemType(ctx, 0, tensor_proto.data_type());
+            updateOutputShape(ctx, 0, tensor_proto);
+            return;
+          }
+
+          if (nullptr != value_int) {
+            // OpSchema::Verify check ensures that the attribute value has_i():
+            if (!value_int->has_i()) {
+              fail_shape_inference("Attribute 'value_int' expect an integer.")
+            }
+            updateOutputElemType(ctx, 0, TensorProto::INT64);
+            updateOutputShape(ctx, 0, TensorShapeProto());
+            return;
+          }
+
+          if (nullptr != value_ints) {
+            // OpSchema::Verify check ensures that the attribute value has ints.
+            if (value_ints->ints_size() < 1) {
+              fail_shape_inference("Attribute 'value_ints' expect a list of integers.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::INT64);
+            appendDim(getOutputShape(ctx, 0), value_ints->ints_size());
+            return;
+          }
+
+          if (nullptr != value_float) {
+            // OpSchema::Verify check ensures that the attribute value has_i():
+            if (!value_float->has_f()) {
+              fail_shape_inference("Attribute 'value_float' expect a float.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::FLOAT);
+            updateOutputShape(ctx, 0, TensorShapeProto());
+            return;
+          }
+
+          if (nullptr != value_floats) {
+            // OpSchema::Verify check ensures that the attribute value has ints.
+            if (value_floats->floats_size() < 1) {
+              fail_shape_inference("Attribute 'value_floats' expect a list of floats.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::FLOAT);
+            appendDim(getOutputShape(ctx, 0), value_floats->floats_size());
+            return;
+          }
+
+          if (nullptr != value_string) {
+            // OpSchema::Verify check ensures that the attribute value has_i():
+            if (!value_string->has_s()) {
+              fail_shape_inference("Attribute 'value_string' expect a string.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::STRING);
+            updateOutputShape(ctx, 0, TensorShapeProto());
+            return;
+          }
+
+          if (nullptr != value_strings) {
+            // OpSchema::Verify check ensures that the attribute value has ints.
+            if (value_strings->strings_size() < 1) {
+              fail_shape_inference("Attribute 'value_strings' expect a list of strings.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::STRING);
+            appendDim(getOutputShape(ctx, 0), value_strings->strings_size());
+            return;
+          }
+
+          if (nullptr != sparse_value) {
+            // OpSchema::Verify check ensures that the attribute value
+            // has_sparse_tensor():
+            const SparseTensorProto& sparse = sparse_value->sparse_tensor();
+            // checker.cc::check_sparse_tensor checks that the sparse-value is
+            // well-formed
+            updateOutputElemType(ctx, 0, sparse.values().data_type());
+            auto* output_shape = getOutputShape(ctx, 0);
+            for (int i = 0; i < sparse.dims_size(); ++i)
+              appendDim(output_shape, sparse.dims(i));
+            return;
+          }
+
+          fail_shape_inference(
+              "TypeAndShapeInferenceFunction implementation incomplete: "
+              "this line should never be reached.");
+        }));
+
+static const char* ConstantOfShape_ver9_doc = R"DOC(
+Generate a tensor with given value and shape.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ConstantOfShape,
+    9,
+    OpSchema()
+        .SetDoc(ConstantOfShape_ver9_doc)
+        .Attr(
+            "value",
+            "(Optional) The value of the output elements."
+            "Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "input",
+            "1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar."
+            " All values must be >= 0.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor of shape specified by 'input'."
+            "If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'."
+            "If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype "
+            "defaults to float32.",
+            "T2")
+        .TypeConstraint("T1", {"tensor(int64)"}, "Constrain input types.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain output types to be numerics.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          if (ctx.getAttribute("value") != nullptr) {
+            propagateElemTypeFromDtypeToOutput(
+                ctx, ctx.getAttribute("value"), 0);
+          } else {
+            propagateElemTypeFromDtypeToOutput(ctx, TensorProto::FLOAT, 0);
+          }
+
+          bool found = false;
+          TensorShapeProto output_shape = getShapeInput(ctx, 0, found);
+          if (found) {
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape() = output_shape;
+          }
+        }));
+
+static const char* EyeLike_ver9_doc = R"DOC(
+Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D
+tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the
+same as the input tensor. The data type can be specified by the 'dtype' argument. If
+'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal
+is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals.
+The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+TensorProto message and be valid as an output type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    EyeLike,
+    9,
+    OpSchema()
+        .SetDoc(EyeLike_ver9_doc)
+        .Attr(
+            "k",
+            "(Optional) Index of the diagonal to be populated with ones. Default is 0."
+            " If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, "
+            "k > 0 populates an upper diagonal,  and k < 0 populates a lower diagonal.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "dtype",
+            "(Optional) The data type for the elements of the output tensor. If not specified,"
+            "the data type of the input tensor T1 is used. If input tensor T1 is also not"
+            "specified, then type defaults to 'float'.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "input",
+            "2D input tensor to copy shape, and optionally, type information from.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor, same shape as input tensor T1.",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain input types. Strings and complex are not supported.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain output types. Strings and complex are not supported.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          if (ctx.getAttribute("dtype") != nullptr) {
+            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
+          } else {
+            propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          }
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() != 2) {
+              fail_shape_inference("Input tensor must be 2-dimensional");
+            }
+          }
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+        }));
+
+static const char* RandomUniform_ver1_doc = R"DOC(
+Generate a tensor with random values drawn from a uniform distribution. The shape
+of the tensor is specified by the `shape` argument and the range by `low` and `high`.
+
+The data type is specified by the 'dtype' argument. The 'dtype' argument must
+be one of the data types specified in the 'DataType' enum field in the
+TensorProto message.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RandomUniform,
+    1,
+    OpSchema()
+        .SetDoc(RandomUniform_ver1_doc)
+        .Attr(
+            "low",
+            "Lower boundary of the output values.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "high",
+            "Upper boundary of the output values.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dtype",
+            "The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.",
+            AttributeProto::INT,
+            static_cast<int64_t>(TensorProto::FLOAT))
+        .Attr("shape", "The shape of the output tensor.", AttributeProto::INTS)
+        .Output(
+            0,
+            "output",
+            "Output tensor of random values drawn from uniform distribution",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0, TensorProto::FLOAT);
+          propagateShapeFromAttributeToOutput(ctx, "shape", 0);
+        }));
+
+static const char* RandomNormal_ver1_doc = R"DOC(
+Generate a tensor with random values drawn from a normal distribution. The shape
+of the tensor is specified by the `shape` argument and the parameter of the normal distribution
+specified by `mean` and `scale`.
+
+The data type is specified by the 'dtype' argument. The 'dtype' argument must
+be one of the data types specified in the 'DataType' enum field in the
+TensorProto message.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RandomNormal,
+    1,
+    OpSchema()
+        .SetDoc(RandomNormal_ver1_doc)
+        .Attr(
+            "mean",
+            "The mean of the normal distribution.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "scale",
+            "The standard deviation of the normal distribution.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dtype",
+            "The data type for the elements of the output tensor. Default is TensorProto::FLOAT.",
+            AttributeProto::INT,
+            static_cast<int64_t>(TensorProto::FLOAT))
+        .Attr("shape", "The shape of the output tensor.", AttributeProto::INTS)
+        .Output(
+            0,
+            "output",
+            "Output tensor of random values drawn from normal distribution",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0, TensorProto::FLOAT);
+          propagateShapeFromAttributeToOutput(ctx, "shape", 0);
+        }));
+
+static const char* RandomUniformLike_ver1_doc = R"DOC(
+Generate a tensor with random values drawn from a uniform distribution.
+The shape of the output tensor is copied from the shape of the input tensor,
+and the parameters of the uniform distribution are specified by `low` and `high`.
+
+The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
+The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+TensorProto message and be valid as an output type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RandomUniformLike,
+    1,
+    OpSchema()
+        .SetDoc(RandomUniformLike_ver1_doc)
+        .Attr(
+            "low",
+            "Lower boundary of the output values.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "high",
+            "Upper boundary of the output values.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dtype",
+            "(Optional) The data type for the elements of the output tensor, if not specified, we will use "
+            "the data type of the input tensor.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "input",
+            "Input tensor to copy shape and optionally type information from.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor of random values drawn from uniform distribution",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_tensor_types(),
+            "Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          if (ctx.getAttribute("dtype") != nullptr)
+            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
+          else
+            propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+        }));
+
+static const char* RandomNormalLike_ver1_doc = R"DOC(
+Generate a tensor with random values drawn from a normal distribution.
+The shape of the output tensor is copied from the shape of the input tensor,
+and the parameters of the normal distribution are specified by `mean` and `scale`.
+
+The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
+The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
+TensorProto message, and be valid as an output type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RandomNormalLike,
+    1,
+    OpSchema()
+        .SetDoc(RandomNormalLike_ver1_doc)
+        .Attr(
+            "mean",
+            "The mean of the normal distribution.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "scale",
+            "The standard deviation of the normal distribution.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dtype",
+            "(Optional) The data type for the elements of the output tensor, if not specified, we will use "
+            "the data type of the input tensor.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "input",
+            "Input tensor to copy shape and optionally type information from.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor of random values drawn from normal distribution",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_tensor_types(),
+            "Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          if (ctx.getAttribute("dtype") != nullptr)
+            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
+          else
+            propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+        }));
+
+static const char* Multinomial_ver7_doc = R"DOC(
+Generate a tensor of samples from a multinomial distribution according to the probabilities
+of each of the possible outcomes.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Multinomial,
+    7,
+    OpSchema()
+        .SetDoc(Multinomial_ver7_doc)
+        .Attr(
+            "sample_size",
+            "Number of times to sample.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dtype",
+            "(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.",
+            AttributeProto::INT,
+            static_cast<int64_t>(TensorProto::INT32))
+        .Input(
+            0,
+            "input",
+            "Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain output types to integral tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto dtype = ctx.getAttribute("dtype");
+          auto dataType = TensorProto_DataType::TensorProto_DataType_INT32;
+          if (dtype != nullptr) {
+            dataType = static_cast<TensorProto_DataType>(dtype->i());
+            if (dataType != TensorProto_DataType::TensorProto_DataType_INT32 &&
+                dataType != TensorProto_DataType::TensorProto_DataType_INT64) {
+                  fail_type_inference("Output type must be int32 or int64");
+                }
+          }
+          updateOutputElemType(ctx, 0, dataType);
+
+          TensorShapeProto::Dimension batch_size, sample_size;
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() != 2) {
+              fail_shape_inference("Input tensor must have rank 2");
+            }
+            batch_size = input_shape.dim(0);
+          } // else statically-unknown batch-size
+          sample_size.set_dim_value(getAttribute(ctx, "sample_size", 1));
+          updateOutputShape(ctx, 0, {batch_size, sample_size});
+        }));
+
+static const char* Range_ver11_doc = R"DOC(
+Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta`
+up to `limit` (exclusive).
+
+The number of elements in the output of range is computed as below-
+
+`number_of_elements = max( ceil( (limit - start) / delta ) , 0 )`
+
+The pseudocode determining the contents of the output is shown below-
+
+`for(int i=0; i<number_of_elements; ++i)`
+
+`{`
+
+`    output[i] =  start + (i * delta);  `
+
+`}`
+
+`Example 1`
+Inputs: start = 3, limit = 9, delta = 3
+Output: [3, 6]
+
+`Example 2`
+Inputs: start = 10, limit = 4, delta = -2
+Output: [10, 8, 6]
+
+)DOC";
+
+template <typename T>
+inline int64_t compute_output_dim_for_range(
+    const TensorProto* start,
+    const TensorProto* limit,
+    const TensorProto* delta) {
+  if (start->dims().size() != 0 || limit->dims().size() != 0 ||
+      delta->dims().size() != 0) {
+    fail_shape_inference(
+        "Input to 'Range' op should be scalars (Tensor with only one element and shape empty)");
+  }
+
+  const auto& start_data = ParseData<T>(start);
+  const auto& limit_data = ParseData<T>(limit);
+  const auto& delta_data = ParseData<T>(delta);
+
+  int64_t n = static_cast<int64_t>(
+      ceil((1.0 * (limit_data[0] - start_data[0])) / delta_data[0]));
+
+  if (n < 0)
+    n = 0;
+
+  return n;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Range,
+    11,
+    OpSchema()
+        .SetDoc(Range_ver11_doc)
+        .Input(
+            0,
+            "start",
+            "Scalar. First entry for the range of output values.",
+            "T")
+        .Input(
+            1,
+            "limit",
+            "Scalar. Exclusive upper limit for the range of output values.",
+            "T")
+        .Input(2, "delta", "Scalar. Value to step by.", "T")
+        .Output(
+            0,
+            "output",
+            "A 1-D tensor with same type as the inputs containing generated range of values.",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)"},
+            "Constrain input types to common numeric type tensors.")
+        .FunctionBody(R"ONNX(
+          {
+            sub_result = Sub (limit, start)
+            sub_result_casted = Cast <to = 1> (sub_result)
+            delta_casted = Cast <to = 1> (delta)
+            div_result = Div (sub_result_casted, delta_casted)
+            ceil_result = Ceil (div_result)
+            ceil_result_relu = Relu (ceil_result)
+            ceil_result_relu_int = Cast <to = 7> (ceil_result_relu)
+            ceil_result_relu_bool = Cast <to = 9> (ceil_result_relu)
+            variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start)
+              <body = loop_body_attribute (int64 i, bool cond, prev) => (cond_out, current, range) {
+                cond_out = Identity (cond)
+                current = Add (prev, delta)
+                range = Identity (prev)
+              }>
+          }
+        )ONNX")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          const auto* start_initializer = ctx.getInputData(0);
+          const auto* limit_initializer = ctx.getInputData(1);
+          const auto* delta_initializer = ctx.getInputData(2);
+
+          // Output is always 1-D
+          auto* output_dim = ctx.getOutputType(0)
+                                 ->mutable_tensor_type()
+                                 ->mutable_shape()
+                                 ->add_dim();
+
+          // If any of Range's inputs are not initializers, the output dimension
+          // value would remain unknown.
+          if (start_initializer != nullptr && limit_initializer != nullptr &&
+              delta_initializer != nullptr) {
+            // Make sure the input types are homogeneous
+            if ((start_initializer->data_type() !=
+                 limit_initializer->data_type()) ||
+                (start_initializer->data_type() !=
+                 delta_initializer->data_type())) {
+              fail_shape_inference(
+                  "All inputs to 'Range' op must be of the same type");
+            }
+
+            // Explicitly compute the output dimension if Range's inputs are
+            // stored in initializer list.
+            if (start_initializer->data_type() == TensorProto::FLOAT) {
+              output_dim->set_dim_value(compute_output_dim_for_range<float>(
+                  start_initializer, limit_initializer, delta_initializer));
+            } else if (start_initializer->data_type() == TensorProto::INT32) {
+              output_dim->set_dim_value(compute_output_dim_for_range<int32_t>(
+                  start_initializer, limit_initializer, delta_initializer));
+            } else if (start_initializer->data_type() == TensorProto::INT64) {
+              output_dim->set_dim_value(compute_output_dim_for_range<int64_t>(
+                  start_initializer, limit_initializer, delta_initializer));
+            } else if (start_initializer->data_type() == TensorProto::DOUBLE) {
+              output_dim->set_dim_value(compute_output_dim_for_range<double>(
+                  start_initializer, limit_initializer, delta_initializer));
+            } else {
+              // 'float16' has no native CPU type -
+              // stop with rank inference, no action here
+            }
+
+            return;
+          }
+        }));
+
+static const char* Bernoulli_ver15_doc = R"DOC(
+Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor
+containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number,
+where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p).
+
+This operator is non-deterministic and may not produce the same values in different
+implementations (even if a seed is specified).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Bernoulli,
+    15,
+    OpSchema()
+        .SetDoc(Bernoulli_ver15_doc)
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dtype",
+            "The data type for the elements of the output tensor. if not specified, we will use "
+            "the data type of the input tensor.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "input",
+            "All values in input have to be in the range:[0, 1].",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "The returned output tensor only has values 0 or 1, same shape as input tensor.",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+              "tensor(float)",
+              "tensor(double)"},
+              "Constrain input types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+              "tensor(float)",
+              "tensor(double)",
+              "tensor(bfloat16)",
+              "tensor(uint8)",
+              "tensor(uint16)",
+              "tensor(uint32)",
+              "tensor(uint64)",
+              "tensor(int8)",
+              "tensor(int16)",
+              "tensor(int32)",
+              "tensor(int64)",
+              "tensor(bool)"},
+            "Constrain output types to all numeric tensors and bool tensors.")
+        .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
+          if (ctx.getAttribute("dtype") != nullptr)
+            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
+          else
+            propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+        })
+        .SetContextDependentFunctionBodyBuilder(
+            [](const FunctionBodyBuildContext& ctx,
+               const OpSchema& schema,
+               FunctionProto& functionProto) -> bool {
+              if (ctx.getInputType(0) == nullptr) {
+                // we cannot create a correct function body without knowing the input type
+                return false;
+              }
+              auto input_type = ctx.getInputType(0)->tensor_type().elem_type();
+              auto dtype = ctx.getAttribute("dtype") != nullptr
+                             ? static_cast<TensorProto_DataType>(ctx.getAttribute("dtype")->i())
+                             : input_type;
+              FunctionBuilder builder(functionProto);
+              builder
+                .Add("X_random = RandomUniformLike <low = 0.0, high = 1.0, seed = @seed> (input)", "dtype", int64_t(input_type))
+                .Add("X_greater = Greater (X_random, input)")
+                .Add("output = Cast (X_greater)", "to", int64_t(dtype));
+              schema.BuildFunction(functionProto);
+              return true;
+            }));
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/generator/old.cc b/third_party/onnx/onnx/defs/generator/old.cc
new file mode 100644
index 0000000000..5bf63ee662
--- /dev/null
+++ b/third_party/onnx/onnx/defs/generator/old.cc
@@ -0,0 +1,305 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <functional>
+#include <algorithm>
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+
+static const char* Constant_ver12_doc = R"DOC(
+This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
+or value_* must be specified.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Constant,
+    12,
+    OpSchema()
+        .SetDoc(Constant_ver12_doc)
+        .Attr(
+            "value",
+            "The value for the elements of the output tensor.",
+            AttributeProto::TENSOR,
+            false)
+        .Attr(
+            "sparse_value",
+            "The value for the elements of the output tensor in sparse format.",
+            AttributeProto::SPARSE_TENSOR,
+            false)
+        .Attr(
+            "value_int",
+            "The value for the sole element for the scalar, int64, output tensor.",
+            AttributeProto::INT,
+            false)
+        .Attr(
+            "value_ints",
+            "The values for the elements for the 1D, int64, output tensor.",
+            AttributeProto::INTS,
+            false)
+        .Attr(
+            "value_float",
+            "The value for the sole element for the scalar, float32, output tensor.",
+            AttributeProto::FLOAT,
+            false)
+        .Attr(
+            "value_floats",
+            "The values for the elements for the 1D, float32, output tensor.",
+            AttributeProto::FLOATS,
+            false)
+        .Attr(
+            "value_string",
+            "The value for the sole element for the scalar, UTF-8 string, output tensor.",
+            AttributeProto::STRING,
+            false)
+        .Attr(
+            "value_strings",
+            "The values for the elements for the 1D, UTF-8 string, output tensor.",
+            AttributeProto::STRINGS,
+            false)
+        .Output(
+            0,
+            "output",
+            "Output tensor containing the same value of the provided tensor.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto* value = ctx.getAttribute("value");
+          auto* sparse_value = ctx.getAttribute("sparse_value");
+          auto* value_int = ctx.getAttribute("value_int");
+          auto* value_ints = ctx.getAttribute("value_ints");
+          auto* value_float = ctx.getAttribute("value_float");
+          auto* value_floats = ctx.getAttribute("value_floats");
+          auto* value_string = ctx.getAttribute("value_string");
+          auto* value_strings = ctx.getAttribute("value_strings");
+
+          std::vector<bool> non_null_attr = {(nullptr != value),
+                                             (nullptr != sparse_value),
+                                             (nullptr != value_int),
+                                             (nullptr != value_ints),
+                                             (nullptr != value_float),
+                                             (nullptr != value_floats),
+                                             (nullptr != value_string),
+                                             (nullptr != value_strings)};
+          if (std::count(non_null_attr.begin(), non_null_attr.end(), true) != 1) {
+            fail_shape_inference(
+                "One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.");
+          }
+
+          if (nullptr != value) {
+            // OpSchema::Verify check ensures that the attribute value has_t():
+            const TensorProto& tensor_proto = value->t();
+            updateOutputElemType(ctx, 0, tensor_proto.data_type());
+            updateOutputShape(ctx, 0, tensor_proto);
+            return;
+          }
+
+          if (nullptr != value_int) {
+            // OpSchema::Verify check ensures that the attribute value has_i():
+            if (!value_int->has_i()) {
+              fail_shape_inference("Attribute 'value_int' expect an integer.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::INT64);
+            updateOutputShape(ctx, 0, TensorShapeProto());
+            return;
+          }
+
+          if (nullptr != value_ints) {
+            // OpSchema::Verify check ensures that the attribute value has ints.
+            if (value_ints->ints_size() < 1) {
+              fail_shape_inference(
+                  "Attribute 'value_ints' expect a list of integers.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::INT64);
+            appendDim(getOutputShape(ctx, 0), value_ints->ints_size());
+            return;
+          }
+
+          if (nullptr != value_float) {
+            // OpSchema::Verify check ensures that the attribute value has_i():
+            if (!value_float->has_f()) {
+              fail_shape_inference("Attribute 'value_float' expect a float.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::FLOAT);
+            updateOutputShape(ctx, 0, TensorShapeProto());
+            return;
+          }
+
+          if (nullptr != value_floats) {
+            // OpSchema::Verify check ensures that the attribute value has ints.
+            if (value_floats->floats_size() < 1) {
+              fail_shape_inference(
+                  "Attribute 'value_floats' expect a list of floats.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::FLOAT);
+            appendDim(getOutputShape(ctx, 0), value_floats->floats_size());
+            return;
+          }
+
+          if (nullptr != value_string) {
+            // OpSchema::Verify check ensures that the attribute value has_i():
+            if (!value_string->has_s()) {
+              fail_shape_inference("Attribute 'value_string' expect a string.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::STRING);
+            updateOutputShape(ctx, 0, TensorShapeProto());
+            return;
+          }
+
+          if (nullptr != value_strings) {
+            // OpSchema::Verify check ensures that the attribute value has ints.
+            if (value_strings->strings_size() < 1) {
+              fail_shape_inference("Attribute 'value_strings' expect a list of strings.");
+            }
+            updateOutputElemType(ctx, 0, TensorProto::STRING);
+            appendDim(getOutputShape(ctx, 0), value_strings->strings_size());
+            return;
+          }
+
+          if (nullptr != sparse_value) {
+            // OpSchema::Verify check ensures that the attribute value
+            // has_sparse_tensor():
+            const SparseTensorProto& sparse = sparse_value->sparse_tensor();
+            // checker.cc::check_sparse_tensor checks that the sparse-value is
+            // well-formed
+            updateOutputElemType(ctx, 0, sparse.values().data_type());
+            auto* output_shape = getOutputShape(ctx, 0);
+            for (int i = 0; i < sparse.dims_size(); ++i)
+              appendDim(output_shape, sparse.dims(i));
+            return;
+          }
+
+          fail_shape_inference(
+              "TypeAndShapeInferenceFunction implementation incomplete: "
+              "this line should never be reached.");
+        }));
+
+static const char* Constant_ver1_doc = R"DOC(A constant tensor.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Constant,
+    1,
+    OpSchema()
+        .SetDoc(Constant_ver1_doc)
+        .Attr(
+            "value",
+            "The value for the elements of the output tensor.",
+            AttributeProto::TENSOR)
+        .Output(
+            0,
+            "output",
+            "Output tensor containing the same value of the provided tensor.",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto attr_proto = ctx.getAttribute("value");
+          if (nullptr == attr_proto)
+            return; // attribute not present
+          if (!attr_proto->has_t())
+            return; // attribute has no tensor value
+          const TensorProto& tensor_proto = attr_proto->t();
+          updateOutputElemType(ctx, 0, tensor_proto.data_type());
+          updateOutputShape(ctx, 0, tensor_proto);
+        }));
+
+static const char* Constant_ver9_doc = R"DOC(A constant tensor.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Constant,
+    9,
+    OpSchema()
+        .SetDoc(Constant_ver9_doc)
+        .Attr(
+            "value",
+            "The value for the elements of the output tensor.",
+            AttributeProto::TENSOR)
+        .Output(
+            0,
+            "output",
+            "Output tensor containing the same value of the provided tensor.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto attr_proto = ctx.getAttribute("value");
+          if (nullptr == attr_proto || !attr_proto->has_t())
+            fail_shape_inference(
+                "Attribute 'value' of Constant node must exist with 'Tensor' data.");
+          const TensorProto& tensor_proto = attr_proto->t();
+          updateOutputElemType(ctx, 0, tensor_proto.data_type());
+          updateOutputShape(ctx, 0, tensor_proto);
+        }));
+
+static const char* Constant_ver11_doc = R"DOC(
+A constant tensor. Exactly one of the two attributes, either value or sparse_value,
+must be specified.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Constant,
+    11,
+    OpSchema()
+        .SetDoc(Constant_ver11_doc)
+        .Attr(
+            "value",
+            "The value for the elements of the output tensor.",
+            AttributeProto::TENSOR,
+            false)
+        .Attr(
+            "sparse_value",
+            "The value for the elements of the output tensor in sparse format.",
+            AttributeProto::SPARSE_TENSOR,
+            false)
+        .Output(
+            0,
+            "output",
+            "Output tensor containing the same value of the provided tensor.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto* value = ctx.getAttribute("value");
+          auto* sparse_value = ctx.getAttribute("sparse_value");
+
+          if ((nullptr != value) && (nullptr != sparse_value))
+            fail_shape_inference(
+                "Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.");
+
+          if (nullptr != value) {
+            // OpSchema::Verify check ensures that the attribute value has_t():
+            const TensorProto& tensor_proto = value->t();
+            updateOutputElemType(ctx, 0, tensor_proto.data_type());
+            updateOutputShape(ctx, 0, tensor_proto);
+            return;
+          }
+
+          if (nullptr != sparse_value) {
+            // OpSchema::Verify check ensures that the attribute value
+            // has_sparse_tensor():
+            const SparseTensorProto& sparse = sparse_value->sparse_tensor();
+            // checker.cc::check_sparse_tensor checks that the sparse-value is
+            // well-formed
+            updateOutputElemType(ctx, 0, sparse.values().data_type());
+            auto* output_shape = getOutputShape(ctx, 0);
+            for (int i = 0; i < sparse.dims_size(); ++i)
+              appendDim(output_shape, sparse.dims(i));
+            return;
+          }
+          fail_shape_inference(
+              "One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.");
+        }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/logical/defs.cc b/third_party/onnx/onnx/defs/logical/defs.cc
new file mode 100644
index 0000000000..9986990039
--- /dev/null
+++ b/third_party/onnx/onnx/defs/logical/defs.cc
@@ -0,0 +1,317 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+inline void unaryLogicalOpInference(InferenceContext& ctx) {
+  // Type inference
+  updateOutputElemType(ctx, 0, TensorProto::BOOL);
+  // Shape inference
+  if (hasInputShape(ctx, 0)) {
+    propagateShapeFromInputToOutput(ctx, 0, 0);
+  }
+}
+
+std::function<void(OpSchema&)> BinaryLogicDocGenerator(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Returns the tensor resulted from performing the `{name}` logical operation
+elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "A",
+        "First input operand for the logical operator.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.Input(
+        1,
+        "B",
+        "Second input operand for the logical operator.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.Output(
+        0,
+        "C",
+        "Result tensor.",
+        "T1",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // Type inference
+      updateOutputElemType(ctx, 0, TensorProto::BOOL);
+      // Shape inference
+      if (hasNInputShapes(ctx, 2))
+        bidirectionalBroadcastShapeInference(
+            ctx.getInputType(0)->tensor_type().shape(),
+            ctx.getInputType(1)->tensor_type().shape(),
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    And,
+    7,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("and"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input to boolean tensor.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Or,
+    7,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("or"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input to boolean tensor.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Xor,
+    7,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("xor"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input to boolean tensor.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Greater,
+    13,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("greater"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Less,
+    13,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("less"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Equal,
+    13,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("equal"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+static const char* Not_ver1_doc = R"DOC(
+Returns the negation of the input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Not,
+    1,
+    OpSchema()
+        .SetDoc(Not_ver1_doc)
+        .Input(
+            0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input/output to boolean tensors.")
+        .TypeAndShapeInferenceFunction(unaryLogicalOpInference));
+
+static const char* BitShift_ver11_doc = R"DOC(
+Bitwise shift operator performs element-wise operation. For each input element, if the
+ attribute "direction" is "RIGHT", this operator moves its binary representation toward
+ the right side so that the input value is effectively decreased. If the attribute "direction"
+ is "LEFT", bits of binary representation moves toward the left side, which results the
+ increase of its actual value. The input X is the tensor to be shifted and another input
+ Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4],
+ and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with
+ X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8].
+
+ Because this operator supports Numpy-style broadcasting, X's and Y's shapes are
+ not necessarily identical.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BitShift,
+    11,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(BitShift_ver11_doc) + GenerateBroadcastingDocMul()))
+        .Input(
+            0,
+            "X",
+            "First operand, input to be shifted.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            1,
+            "Y",
+            "Second operand, amounts of shift.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Z",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)"},
+            "Constrain input and output types to integer tensors.")
+        .Attr(
+            "direction",
+            "Direction of moving bits. It can be either \"RIGHT\" (for right shift) "
+            "or \"LEFT\" (for left shift).",
+            AttributeProto::STRING)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape inference
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LessOrEqual,
+    16,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("less_equal"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor.")
+        .TypeAndShapeInferenceFunction(InferenceFunction())
+        .FunctionBody(R"ONNX(
+        {
+            O1 = Less (A, B)
+            O2 = Equal (A, B)
+            C = Or (O1, O2)
+        }
+        )ONNX"));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GreaterOrEqual,
+    16,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("greater_equal"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor.")
+        .TypeAndShapeInferenceFunction(InferenceFunction())
+        .FunctionBody(R"ONNX(
+        {
+            O1 = Greater (A, B)
+            O2 = Equal (A, B)
+            C = Or (O1, O2)
+        }
+        )ONNX"));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/logical/old.cc b/third_party/onnx/onnx/defs/logical/old.cc
new file mode 100644
index 0000000000..1074491ebf
--- /dev/null
+++ b/third_party/onnx/onnx/defs/logical/old.cc
@@ -0,0 +1,336 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+
+using namespace ONNX_NAMESPACE;
+
+namespace ONNX_NAMESPACE {
+
+std::function<void(OpSchema&)> BinaryLogicDocGenerator_opset12(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Returns the tensor resulted from performing the `{name}` logical operation
+elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(0, "A", "First input operand for the logical operator.", "T");
+    schema.Input(1, "B", "Second input operand for the logical operator.", "T");
+    schema.Output(0, "C", "Result tensor.", "T1");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // Type inference
+      updateOutputElemType(ctx, 0, TensorProto::BOOL);
+      // Shape inference
+      if (hasNInputShapes(ctx, 2))
+        bidirectionalBroadcastShapeInference(
+            ctx.getInputType(0)->tensor_type().shape(),
+            ctx.getInputType(1)->tensor_type().shape(),
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Greater,
+    9,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset12("greater"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Less,
+    9,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset12("less"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Equal,
+    11,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset12("equal"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+inline void logicalOpInference_opset1(InferenceContext& ctx) {
+  updateOutputElemType(ctx, 0, TensorProto::BOOL);
+  if (hasInputShape(ctx, 0)) {
+    propagateShapeFromInputToOutput(ctx, 0, 0);
+  }
+}
+
+std::function<void(OpSchema&)> BinaryLogicDocGenerator_opset1(
+    const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Returns the tensor resulted from performing the `{name}` logical operation
+elementwise on the input tensors `A` and `B`.
+
+If broadcasting is enabled, the right-hand-side argument will be broadcasted
+to match the shape of left-hand-side argument. See the doc of `Add` for a
+detailed description of the broadcasting rules.
+)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "broadcast",
+        "Enable broadcasting",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "axis",
+        "If set, defines the broadcast dimensions.",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Input(0, "A", "Left input tensor for the logical operator.", "T");
+    schema.Input(1, "B", "Right input tensor for the logical operator.", "T");
+    schema.Output(0, "C", "Result tensor.", "T1");
+    schema.TypeAndShapeInferenceFunction(logicalOpInference_opset1);
+  };
+}
+
+std::function<void(OpSchema&)> BinaryLogicDocGenerator_opset7(
+    const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Returns the tensor resulted from performing the `{name}` logical operation
+elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
+
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(0, "A", "First input operand for the logical operator.", "T");
+    schema.Input(1, "B", "Second input operand for the logical operator.", "T");
+    schema.Output(0, "C", "Result tensor.", "T1");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      updateOutputElemType(ctx, 0, TensorProto::BOOL);
+      if (hasNInputShapes(ctx, 2))
+        bidirectionalBroadcastShapeInference(
+            ctx.getInputType(0)->tensor_type().shape(),
+            ctx.getInputType(1)->tensor_type().shape(),
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    And,
+    1,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset1("and"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input to boolean tensor.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Or,
+    1,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset1("or"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input to boolean tensor.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Xor,
+    1,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset1("xor"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)"},
+            "Constrain input to boolean tensor.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Greater,
+    1,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset1("greater"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Less,
+    1,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset1("less"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Equal,
+    1,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset1("equal"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)", "tensor(int32)", "tensor(int64)"},
+            "Constrain input to integral tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Equal,
+    7,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset7("equal"))
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)", "tensor(int32)", "tensor(int64)"},
+            "Constrain input to integral tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Greater,
+    7,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset7("greater"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Less,
+    7,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator_opset7("less"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor."));
+
+
+// Shares same doc generator as newer opset 16 version.
+extern std::function<void(OpSchema&)> BinaryLogicDocGenerator(const char* name);
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LessOrEqual,
+    12,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("less_equal"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor.")
+        .TypeAndShapeInferenceFunction(InferenceFunction())
+        .FunctionBody(R"ONNX(
+        {
+            O1 = Less (A, B)
+            O2 = Equal (A, B)
+            C = Or (O1, O2)
+        }
+        )ONNX"));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GreaterOrEqual,
+    12,
+    OpSchema()
+        .FillUsing(BinaryLogicDocGenerator("greater_equal"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input types to all numeric tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output to boolean tensor.")
+        .TypeAndShapeInferenceFunction(InferenceFunction())
+        .FunctionBody(R"ONNX(
+        {
+            O1 = Greater (A, B)
+            O2 = Equal (A, B)
+            C = Or (O1, O2)
+        }
+        )ONNX"));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/math/defs.cc b/third_party/onnx/onnx/defs/math/defs.cc
new file mode 100644
index 0000000000..b49fb63be4
--- /dev/null
+++ b/third_party/onnx/onnx/defs/math/defs.cc
@@ -0,0 +1,3395 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <algorithm>
+#include <functional>
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/tensor_proto_util.h"
+
+namespace ONNX_NAMESPACE {
+
+inline int MathOpTwoIntegers(std::string op_type, int a, int b) {
+  if (op_type == "Add") {
+    return a + b;
+  } else if (op_type == "Sub") {
+    return a - b;
+  } else if (op_type == "Mul") {
+    return a * b;
+  }
+  fail_shape_inference("Wrong op_type name for running propagation: ", op_type);
+}
+
+inline void MathOpDataPropagator(DataPropagationContext& ctx, std::string op_type) {
+  const auto input_0 = ctx.getInputData(0);
+  const auto input_1 = ctx.getInputData(1);
+  if (input_0 == nullptr || input_1 == nullptr) {
+    return;
+  }
+  int size_0 = input_0->dim_size();
+  int size_1 = input_1->dim_size();
+  // Fails to broadcast if the ranks are different and no any rank is 1 
+  if (size_0 != size_1 && size_0 != 1 && size_1 != 1) {
+    fail_shape_inference("Invalid rank for ", op_type, " broadcasting: (",
+        size_0, ") vs (", size_1, ").");
+  }
+  TensorShapeProto tsp;
+  for (int i = 0; i < std::max(size_0, size_1); ++i) {
+    auto& input_dim_0 = input_0->dim(size_0 == 1 ? 0 : i);
+    auto& input_dim_1 = input_1->dim(size_1 == 1 ? 0 : i);
+    if (input_dim_0.has_dim_value() && input_dim_1.has_dim_value()) {
+      tsp.mutable_dim()->Add()->set_dim_value(
+          MathOpTwoIntegers(op_type, input_dim_0.dim_value(), input_dim_1.dim_value()));
+    } else {
+      // Cannot compute the value; simply add an empty dim without value and param
+      tsp.mutable_dim()->Add();
+    }
+  }
+  ctx.addOutputData(0, std::move(tsp));
+}
+
+std::function<void(OpSchema&)> MathDocGenerator(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Performs element-wise binary {name} (with Numpy-style broadcasting support).
+
+{broadcast_doc}
+
+(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(0,
+        "A",
+        "First operand.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(1,
+        "B",
+        "Second operand.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(0,
+        "C",
+        "Result, has same element type as two inputs",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        OpSchema::all_numeric_types_with_bfloat(),
+        "Constrain input and output types to all numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (hasNInputShapes(ctx, 2))
+        bidirectionalBroadcastShapeInference(
+            ctx.getInputType(0)->tensor_type().shape(),
+            ctx.getInputType(1)->tensor_type().shape(),
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+std::function<void(OpSchema&)> SoftmaxFamilyDocGenerator(
+    const char* name,
+    const char* description,
+    const char* equation) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The operator computes the {description} values for the given input:
+
+ {equation}
+
+The "axis" attribute indicates the dimension along which {name}
+will be performed. The output tensor has the same shape
+and contains the {name} values of the corresponding input.
+)DOC";
+                        ReplaceAll(doc, "{name}", name);
+                        ReplaceAll(doc, "{description}", description);
+                        ReplaceAll(doc, "{equation}", equation););
+    std::string axis_attr;
+    POPULATE_OP_DOC_STR(axis_attr = R"DOC(
+Describes the dimension {name} will be performed on.
+Negative value means counting dimensions
+from the back. Accepted range is [-r, r-1] where r = rank(input).
+)DOC";
+                        ReplaceAll(axis_attr, "{name}", name););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "axis", axis_attr, AttributeProto::INT, static_cast<int64_t>(-1));
+    schema.Input(
+        0,
+        "input",
+        "The input tensor of rank >= axis.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "output",
+        "The output values with the same shape as the input tensor.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)",
+         "tensor(float)",
+         "tensor(double)",
+         "tensor(bfloat16)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // Type inference
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+      // Shape inference starts
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      // Validate the value of 'axis'
+      const TensorShapeProto& input_shape =
+          ctx.getInputType(0)->tensor_type().shape();
+      int r = input_shape.dim_size();
+      int axis = static_cast<int>(getAttribute(ctx, "axis", -1));
+      if (axis < -r || axis >= r) {
+        fail_shape_inference(
+            "'axis' must be in [",
+            -r,
+            " , ",
+            (r - 1),
+            "]. Its actual value is: ",
+            axis);
+      }
+
+      // Shape inference
+      propagateShapeFromInputToOutput(ctx, 0, 0);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Add,
+    14,
+    OpSchema().FillUsing(MathDocGenerator("addition"))
+    .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+        MathOpDataPropagator(ctx, "Add");
+    }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sub,
+    14,
+    OpSchema().FillUsing(MathDocGenerator("subtraction"))
+    .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+        MathOpDataPropagator(ctx, "Sub");
+    }));
+
+static const char* Mod_doc = R"DOC(
+  Performs element-wise binary modulus (with Numpy-style broadcasting support).
+    The sign of the remainder is the same as that of the Divisor.
+
+    Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
+    (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
+    This attribute is set to 0 by default causing the behavior to be like integer mod.
+    Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
+
+    If the input type is floating point, then `fmod` attribute must be set to 1.
+
+    In case of dividend being zero, the results will be platform dependent.
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mod,
+    13,
+    OpSchema()
+        .SetDoc(Mod_doc)
+        .Attr(
+            "fmod",
+            "Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0,
+            "A",
+            "Dividend tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(1,
+            "B",
+            "Divisor tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "C",
+            "Remainder tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to high-precision numeric tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mul,
+    14,
+    OpSchema().FillUsing(MathDocGenerator("multiplication"))
+    .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+        MathOpDataPropagator(ctx, "Mul");
+    }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Div,
+    14,
+    OpSchema().FillUsing(MathDocGenerator("division")));
+
+static const char* Neg_ver13_doc = R"DOC(
+Neg takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where each element flipped sign, y = -x, is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Neg,
+    13,
+    OpSchema()
+        .SetDoc(Neg_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(int32)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to signed numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Abs_ver13_doc = R"DOC(
+Absolute takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the absolute is, y = abs(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Abs,
+    13,
+    OpSchema()
+        .SetDoc(Abs_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Reciprocal_ver13_doc = R"DOC(
+Reciprocal takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reciprocal,
+    13,
+    OpSchema()
+        .SetDoc(Reciprocal_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Floor_ver13_doc = R"DOC(
+Floor takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the floor is, y = floor(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Floor,
+    13,
+    OpSchema()
+        .SetDoc(Floor_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Ceil_ver13_doc = R"DOC(
+Ceil takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the ceil is, y = ceil(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Ceil,
+    13,
+    OpSchema()
+        .SetDoc(Ceil_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Sqrt_ver13_doc = R"DOC(
+Square root takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the square root is, y = x^0.5, is applied to
+the tensor elementwise. If x is negative, then it will return NaN.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sqrt,
+    13,
+    OpSchema()
+        .SetDoc(Sqrt_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Relu_ver14_doc = R"DOC(
+Relu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Relu,
+    14,
+    OpSchema()
+        .SetDoc(Relu_ver14_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(int32)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to signed numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* LeakyRelu_ver16_doc = R"DOC(
+LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+`f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+
+**History**
+- Version 16 adds bfloat16 to the types allowed.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LeakyRelu,
+    16,
+    OpSchema()
+        .Attr("alpha", "Coefficient of leakage.", AttributeProto::FLOAT, 0.01f)
+        .SetDoc(LeakyRelu_ver16_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(bfloat16)", "tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* ThresholdedRelu_ver10_doc = R"DOC(
+ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
+is applied to the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ThresholdedRelu,
+    10,
+    OpSchema()
+        .SetDoc(ThresholdedRelu_ver10_doc)
+        .Attr("alpha", "Threshold value", AttributeProto::FLOAT, 1.0f)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Selu_ver6_doc = R"DOC(
+Selu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the scaled exponential linear unit function,
+`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
+is applied to the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Selu,
+    6,
+    OpSchema()
+        .Attr(
+            "alpha",
+            "Coefficient of SELU default to 1.67326319217681884765625 "
+            "(i.e., float32 approximation of 1.6732632423543772848170429916717).",
+            AttributeProto::FLOAT,
+            1.67326319217681884765625f)
+        .Attr(
+            "gamma",
+            "Coefficient of SELU default to 1.05070102214813232421875 "
+            "(i.e., float32 approximation of 1.0507009873554804934193349852946).",
+            AttributeProto::FLOAT,
+            1.05070102214813232421875f)
+        .SetDoc(Selu_ver6_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Elu_ver6_doc = R"DOC(
+Elu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
+0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Elu,
+    6,
+    OpSchema()
+        .Attr("alpha", "Coefficient of ELU.", AttributeProto::FLOAT, 1.0f)
+        .SetDoc(Elu_ver6_doc)
+        .Input(0,
+            "X",
+            "1D input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "1D output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* celu_ver12_doc = R"DOC(
+Continuously Differentiable Exponential Linear Units:
+Perform the linear unit element-wise on the input tensor X
+using formula:
+
+```
+max(0,x) + min(0,alpha*(exp(x/alpha)-1))
+```
+)DOC";
+
+static float celu_default_alpha = 1.0;
+
+TensorProto ToDimensionOneFloatTensor(float value) {
+  auto t = ToTensor(std::vector<float>({value}));
+  t.add_dims(1);
+  return t;
+}
+
+TensorProto ToDimensionOneTensor(int32_t value) {
+  auto t = ToTensor(std::vector<int32_t>({value}));
+  t.add_dims(1);
+  return t;
+}
+
+TensorProto ToDimensionOneInt64Tensor(int64_t value) {
+  auto t = ToTensor(std::vector<int64_t>({value}));
+  t.add_dims(1);
+  return t;
+}
+
+TensorProto ToDimensionOneInt64Tensor(std::vector<int64_t> value) {
+  auto t = ToTensor(value);
+  t.add_dims(value.size());
+  return t;
+}
+
+bool BuildContextDependentFunctionBodyCelu(
+    const FunctionBodyBuildContext& ctx,
+    const OpSchema& schema,
+    FunctionProto& functionProto) {
+
+  float alpha = ctx.getAttribute("alpha") != nullptr ? ctx.getAttribute("alpha")->f() : celu_default_alpha;
+  FunctionBuilder builder(functionProto);
+  builder
+    .Const("alpha", std::vector<float>{alpha})
+    .Add(R"(
+            X_alpha = Div (X, alpha)
+            Elu_Result = Elu <alpha = 1.0>(X_alpha)
+            Y = Mul (alpha, Elu_Result)
+        )");
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Celu,
+    12,
+    OpSchema()
+        .SetDoc(celu_ver12_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Attr(
+            "alpha",
+            "The Alpha value in Celu formula which control the shape of "
+            "the unit. The default value is 1.0.",
+            AttributeProto::FLOAT,
+            celu_default_alpha)
+        .TypeConstraint(
+            "T",
+            {"tensor(float)"},
+            "Constrain input and output types to float32 tensors.")
+        .SetContextDependentFunctionBodyBuilder(
+            BuildContextDependentFunctionBodyCelu)
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Exp_ver13_doc = R"DOC(
+Calculates the exponential of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Exp,
+    13,
+    OpSchema()
+        .SetDoc(Exp_ver13_doc)
+        .Input(0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The exponential of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Log_ver13_doc = R"DOC(
+Calculates the natural log of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Log,
+    13,
+    OpSchema()
+        .SetDoc(Log_ver13_doc)
+        .Input(0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The natural log of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Tanh_ver13_doc = R"DOC(
+Calculates the hyperbolic tangent of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tanh,
+    13,
+    OpSchema()
+        .SetDoc(Tanh_ver13_doc)
+        .Input(0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The hyperbolic tangent values of the input tensor "
+            "computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Pow_ver15_doc = R"DOC(
+Pow takes input data (Tensor<T>) and exponent Tensor, and
+produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pow,
+    15,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Pow_ver15_doc) + GenerateBroadcastingDocMul()))
+        .Input(0,
+            "X",
+            "First operand, base of the exponent.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(1,
+            "Y",
+            "Second operand, power of the exponent.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Z",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input X and output types to float/int tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input Y types to float/int tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+
+static const char* PRelu_ver16_doc = R"DOC(
+PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+`f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+
+**History**
+- Version 16 adds bfloat16 to the types allowed.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    PRelu,
+    16,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(PRelu_ver16_doc) +
+            GenerateBroadcastingDocUni("tensor slope", "input tensor X")))
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "slope",
+            "Slope tensor. The shape of slope can be smaller then first input X; "
+            "if so, its shape must be unidirectional broadcastable to X",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor (same size as X)",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(bfloat16)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)"},
+            "Constrain input and output types to float/int tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Sigmoid_ver13_doc = R"DOC(
+Sigmoid takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sigmoid,
+    13,
+    OpSchema()
+        .SetDoc(Sigmoid_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* HardSigmoid_ver6_doc = R"DOC(
+HardSigmoid takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
+is applied to the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    HardSigmoid,
+    6,
+    OpSchema()
+        .Attr("alpha", "Value of alpha.", AttributeProto::FLOAT, 0.2f)
+        .Attr("beta", "Value of beta.", AttributeProto::FLOAT, 0.5f)
+        .SetDoc(HardSigmoid_ver6_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* HardSwish_ver14_doc = R"DOC(
+HardSwish takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where
+the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid<alpha, beta>(x),
+where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    HardSwish,
+    14,
+    OpSchema()
+        .SetDoc(HardSwish_ver14_doc)
+        .Input(
+            0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
+        .FunctionBody(R"ONNX(
+          {
+            HS_X = HardSigmoid<alpha = 0.16666667163372, beta = 0.5>(X) 
+            Y = Mul (X, HS_X)
+          }
+        )ONNX"));
+
+// Generate opschema for element-wise ops. Leaves type constraint "T"
+// unspecified.
+std::function<void(OpSchema&)> ElementwiseMultiOpDocGenerator(
+    const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support).
+All inputs and outputs must have the same data type.
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "data_0",
+        "List of tensors for " + std::string(name) + ".",
+        "T",
+        OpSchema::Variadic,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(0,
+        name,
+        "Output tensor.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      int num_inputs = static_cast<int>(ctx.getNumInputs());
+      std::vector<const TensorShapeProto*> shapes;
+      shapes.reserve(num_inputs);
+      for (int i = 0; i < num_inputs; ++i) {
+        auto input_type = ctx.getInputType(i);
+        if (nullptr == input_type || !input_type->has_tensor_type() ||
+            !input_type->tensor_type().has_shape()) {
+          return;
+        }
+        shapes.push_back(&input_type->tensor_type().shape());
+      }
+
+      multidirectionalBroadcastShapeInference(
+          shapes,
+          *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Max,
+    13,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator("max"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to numeric tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Min,
+    13,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator("min"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to numeric tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sum,
+    13,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator("sum"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mean,
+    13,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator("mean"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Clip_ver13_doc = R"DOC(
+Clip operator limits the given input within an interval. The interval is
+specified by the inputs 'min' and 'max'. They default to
+numeric_limits::lowest() and numeric_limits::max(), respectively.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Clip,
+    13,
+    OpSchema()
+        .SetDoc(Clip_ver13_doc)
+        .Input(0,
+            "input",
+            "Input tensor whose elements to be clipped",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "min",
+            "Minimum value, under which element is replaced by min. "
+            "It must be a scalar(tensor of empty shape).",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "max",
+            "Maximum value, above which element is replaced by max. "
+            "It must be a scalar(tensor of empty shape).",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "output",
+            "Output tensor with clipped input elements",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Softmax,
+    13,
+    OpSchema()
+        .FillUsing(
+            SoftmaxFamilyDocGenerator("Softmax", "normalized exponential", "Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) "))
+        .SetContextDependentFunctionBodyBuilder(
+            [](const FunctionBodyBuildContext& ctx,
+               const OpSchema& schema,
+               FunctionProto& functionProto) -> bool {
+              int64_t axis = ctx.getAttribute("axis") != nullptr ? ctx.getAttribute("axis")->i() : -1;
+              FunctionBuilder builder(functionProto);
+              builder.Const1D("axes", axis)
+                  .Add("X_ReduceMax = ReduceMax <keepdims = 1> (input)", "axes", std::vector<int64_t>({axis}))
+                  .Add(R"(
+                    X_Sub = Sub (input, X_ReduceMax)
+                    X_Exp = Exp (X_Sub)
+                    X_ReduceSum = ReduceSum <keepdims = 1> (X_Exp, axes)
+                    output = Div (X_Exp, X_ReduceSum)
+                )");
+
+              schema.BuildFunction(functionProto);
+              return true;
+            }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LogSoftmax,
+    13,
+    OpSchema()
+        .FillUsing(SoftmaxFamilyDocGenerator("LogSoftmax", "log of softmax", "LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))"))
+        .SetContextDependentFunctionBodyBuilder(
+            [](const FunctionBodyBuildContext& ctx,
+               const OpSchema& schema,
+               FunctionProto& functionProto) -> bool {
+              const int64_t axis = ctx.getAttribute("axis") != nullptr ? ctx.getAttribute("axis")->i() : -1;
+              FunctionBuilder builder(functionProto);
+              builder.Const1D("axes", axis)
+                  .Add("X_ReduceMax = ReduceMax <keepdims = 1> (input)", "axes", std::vector<int64_t>({axis}))
+                  .Add(R"(
+                    X_Sub = Sub (input, X_ReduceMax)
+                    X_Exp = Exp (X_Sub)
+                    X_ReduceSum = ReduceSum <keepdims = 1> (X_Exp, axes)
+                    X_Log = Log (X_ReduceSum)
+                    output = Sub (X_Sub, X_Log)
+                )");
+
+              schema.BuildFunction(functionProto);
+              return true;
+            }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Hardmax,
+    13,
+    OpSchema().FillUsing(SoftmaxFamilyDocGenerator(
+        "Hardmax",
+        "hardmax",
+        "Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise")));
+
+static const char* Softsign_ver1_doc = R"DOC(
+Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Softsign,
+    1,
+    OpSchema()
+        .SetDoc(Softsign_ver1_doc)
+        .Input(0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The softsign (x/(1+|x|)) values of the input tensor computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Softplus_ver1_doc = R"DOC(
+Softplus takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Softplus,
+    1,
+    OpSchema()
+        .SetDoc(Softplus_ver1_doc)
+        .Input(0,
+            "X",
+            "1D input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "1D input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Gemm_ver13_doc = R"DOC(General Matrix multiplication:
+https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+A' = transpose(A) if transA else A
+
+B' = transpose(B) if transB else B
+
+Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+and output tensor Y has shape (M, N). A will be transposed before doing the
+computation if attribute transA is non-zero, same for B and transB.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gemm,
+    13,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Gemm_ver13_doc) +
+            GenerateBroadcastingDocUni("tensor C", "tensor A * B") + "\n" +
+            GenerateOptionalArgumentsDoc()))
+        .Input(
+            0,
+            "A",
+            "Input tensor A. "
+            "The shape of A should be (M, K) if transA is 0, "
+            "or (K, M) if transA is non-zero.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "B",
+            "Input tensor B. "
+            "The shape of B should be (K, N) if transB is 0, "
+            "or (N, K) if transB is non-zero.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "C",
+            "Optional input tensor C. "
+            "If not specified, the computation is done as if C is a scalar 0. "
+            "The shape of C should be unidirectional broadcastable to (M, N).",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor of shape (M, N).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float/int tensors.")
+        .Attr(
+            "transA",
+            "Whether A should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "transB",
+            "Whether B should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "alpha",
+            "Scalar multiplier for the product of input tensors A * B.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "beta",
+            "Scalar multiplier for input tensor C.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2)) {
+            auto transAAttr = ctx.getAttribute("transA");
+            bool transA =
+                transAAttr ? static_cast<int>(transAAttr->i()) != 0 : false;
+            auto transBAttr = ctx.getAttribute("transB");
+            bool transB =
+                transBAttr ? static_cast<int>(transBAttr->i()) != 0 : false;
+            auto& first_input_shape = getInputShape(ctx, 0);
+            auto& second_input_shape = getInputShape(ctx, 1);
+            if (first_input_shape.dim_size() != 2) {
+              fail_shape_inference("First input does not have rank 2");
+            }
+            if (second_input_shape.dim_size() != 2) {
+              fail_shape_inference("Second input does not have rank 2");
+            }
+            updateOutputShape(
+                ctx,
+                0,
+                {first_input_shape.dim(transA ? 1 : 0),
+                 second_input_shape.dim(transB ? 0 : 1)});
+          }
+        }));
+
+void matmulShapeInference(
+    ONNX_NAMESPACE::InferenceContext& ctx,
+    int input1Idx,
+    int input2Idx) {
+  if (!hasInputShape(ctx, input1Idx) || !hasInputShape(ctx, input2Idx)) {
+    return;
+  }
+
+  const auto shape0 = ctx.getInputType(input1Idx)->tensor_type().shape();
+  const auto shape1 = ctx.getInputType(input2Idx)->tensor_type().shape();
+
+  if (shape0.dim_size() == 0 || shape1.dim_size() == 0) {
+    fail_shape_inference("Input tensors of wrong rank (0).");
+  }
+
+  ONNX_NAMESPACE::TensorShapeProto shapeL, shapeR;
+
+  // First promote each shape to at least rank-2. This logic is
+  // specific to matmul, not generic broadcasting.
+  {
+    if (shape0.dim_size() == 1) {
+      shapeL.add_dim()->set_dim_value(1);
+      *shapeL.add_dim() = shape0.dim(0);
+    } else {
+      *shapeL.mutable_dim() = shape0.dim();
+    }
+    if (shape1.dim_size() == 1) {
+      *shapeR.add_dim() = shape1.dim(0);
+      shapeR.add_dim()->set_dim_value(1);
+    } else {
+      *shapeR.mutable_dim() = shape1.dim();
+    }
+  }
+
+  // Check for compatible matrix multiply dimensions
+  {
+    auto dimL = shapeL.dim(shapeL.dim_size() - 1);
+    auto dimR = shapeR.dim(shapeR.dim_size() - 2);
+    if (dimL.has_dim_value() && dimR.has_dim_value() &&
+        dimL.dim_value() != dimR.dim_value()) {
+      fail_shape_inference("Incompatible dimensions for matrix multiplication");
+    }
+  }
+
+  ONNX_NAMESPACE::TensorShapeProto resultShape;
+
+  // Now call out to generic multidimensional broadcasting for
+  // the broadcastable prefixes.
+  {
+    ONNX_NAMESPACE::TensorShapeProto prefixShapeL, prefixShapeR;
+    for (int i = 0; i < shapeL.dim_size() - 2; ++i) {
+      *prefixShapeL.add_dim() = shapeL.dim(i);
+    }
+    for (int i = 0; i < shapeR.dim_size() - 2; ++i) {
+      *prefixShapeR.add_dim() = shapeR.dim(i);
+    }
+    bidirectionalBroadcastShapeInference(
+        prefixShapeL, prefixShapeR, resultShape);
+  }
+
+  // Back to matmul-specific. Add the trailing dimensions back in.
+  {
+    if (shape0.dim_size() != 1) {
+      *resultShape.add_dim() = shapeL.dim(shapeL.dim_size() - 2);
+    }
+    if (shape1.dim_size() != 1) {
+      *resultShape.add_dim() = shapeR.dim(shapeR.dim_size() - 1);
+    }
+  }
+
+  *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape() = resultShape;
+}
+
+static const char* MatMul_ver13_doc = R"DOC(
+Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MatMul,
+    13,
+    OpSchema()
+        .Input(0,
+            "A", "N-dimensional matrix A",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(1,
+            "B",
+            "N-dimensional matrix B",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Matrix multiply results from A * B",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float/int tensors.")
+        .SetDoc(MatMul_ver13_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          matmulShapeInference(ctx, 0, 1);
+        }));
+
+static const char* TopK_ver11_doc = R"DOC(
+Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of
+shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+  -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+    which contains the values of the top k elements along the specified axis
+  -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+   contains the indices of the top k elements (original indices from the input
+   tensor).
+
+If "largest" is 1 (the default value) then the k largest elements are returned.
+If "sorted" is 1 (the default value) then the resulting k elements will be sorted.
+If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined.
+
+Given two equivalent values, this operator uses the indices along the axis as
+ a tiebreaker. That is, the element with the lower index will appear first.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    TopK,
+    11,
+    OpSchema()
+        .SetDoc(TopK_ver11_doc)
+        .Input(
+            0,
+            "X",
+            "Tensor of shape [a_1, a_2, ..., a_n, r]",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "K",
+            "A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Values",
+            "Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] "
+            "containing top K values from the input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            1,
+            "Indices",
+            "Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] "
+            "containing the corresponding input tensor indices for the top K "
+            "values.",
+            "I",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to numeric tensors.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64")
+        .Attr(
+            "axis",
+            "Dimension on which to do the sort. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .Attr(
+            "largest",
+            "Whether to return the top-K largest or smallest elements.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "sorted",
+            "Whether to return the elements in sorted order.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference:
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          updateOutputElemType(ctx, 1, TensorProto::INT64);
+          // Shape inference:
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int64_t rank = input_shape.dim_size();
+          int64_t axis = getAttribute(ctx, "axis", -1);
+          if (axis < 0)
+            axis += rank;
+          if (axis < 0 || axis >= rank) {
+            fail_shape_inference("Invalid value for attribute axis");
+          }
+
+          const auto& axis_dim = input_shape.dim(static_cast<int>(axis));
+          const auto* k = ctx.getInputData(1);
+
+          // Infer output shape if:
+          // (1) 'K' is available
+          // (2) axis_dim has dim value
+          // Othewise cannot reliably compute output shape as axis dim value is
+          // unknown and hence cannot determine if axis dim value >= k (which
+          // should be enforced)
+          if (nullptr != k && axis_dim.has_dim_value()) {
+            int64_t k_value = 0;
+            if (k->dims_size() != 1 || k->dims(0) != 1) {
+              fail_shape_inference("K input must be a one-dimensional tensor of size 1.");
+            }
+            if (k->data_type() == TensorProto::INT64) {
+              const auto& data = ParseData<int64_t>(k);
+              k_value = data[0];
+            } else {
+                fail_shape_inference("K input must be of type int64.");
+            }
+            if (axis_dim.dim_value() < k_value) {
+              fail_shape_inference("Axis has less than the requested k elements.");
+            }
+
+            TensorShapeProto result_shape = input_shape;
+            result_shape.mutable_dim(static_cast<int>(axis))
+                ->set_dim_value(k_value);
+
+            updateOutputShape(ctx, 0, result_shape);
+            updateOutputShape(ctx, 1, result_shape);
+
+            return;
+          }
+
+          // Infer output shapes' rank in any case
+          auto* output_shape_0 = getOutputShape(ctx, 0);
+          auto* output_shape_1 = getOutputShape(ctx, 1);
+          for (int i = 0; i < input_shape.dim_size(); ++i) {
+            output_shape_0->add_dim();
+            output_shape_1->add_dim();
+          }
+
+          return;
+        }));
+
+static const char* Sin_ver7_doc = R"DOC(
+Calculates the sine of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sin,
+    7,
+    OpSchema()
+        .SetDoc(Sin_ver7_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The sine of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Cos_ver7_doc = R"DOC(
+Calculates the cosine of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Cos,
+    7,
+    OpSchema()
+        .SetDoc(Cos_ver7_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The cosine of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Tan_ver7_doc = R"DOC(
+Calculates the tangent of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tan,
+    7,
+    OpSchema()
+        .SetDoc(Tan_ver7_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The tangent of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Asin_ver7_doc = R"DOC(
+Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Asin,
+    7,
+    OpSchema()
+        .SetDoc(Asin_ver7_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The arcsine of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Acos_ver7_doc = R"DOC(
+Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Acos,
+    7,
+    OpSchema()
+        .SetDoc(Acos_ver7_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The arccosine of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Atan_ver7_doc = R"DOC(
+Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Atan,
+    7,
+    OpSchema()
+        .SetDoc(Atan_ver7_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The arctangent of the input tensor computed "
+            "element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Expand_ver13_doc = R"DOC(
+Broadcast the input tensor following the given shape and the broadcast rule.
+The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):
+Dimensions are right alignment;
+Two corresponding dimensions must have the same value, or one of them is equal to 1.
+Also, this operator is similar to numpy.broadcast_to(input, shape),
+but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size().
+It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1,
+or the shape.ndim < input.shape.ndim.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Expand,
+    13,
+    OpSchema()
+        .SetDoc(Expand_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "shape",
+            "A 1-D tensor indicates the shape you want to expand to, following the broadcast rule",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          // For shape inference, we need both input shape
+          if (hasNInputShapes(ctx, 2)) {
+            const auto& input_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            bool found = false;
+            TensorShapeProto second_shape = getShapeInput(ctx, 1, found);
+            if (found) {
+                bidirectionalBroadcastShapeInference(
+                    input_shape, second_shape, *getOutputShape(ctx, 0));
+            }
+          }
+        }));
+
+static const char* Sinh_ver9_doc = R"DOC(
+Calculates the hyperbolic sine of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sinh,
+    9,
+    OpSchema()
+        .SetDoc(Sinh_ver9_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The hyperbolic sine values of the input tensor "
+            "computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Cosh_ver9_doc = R"DOC(
+Calculates the hyperbolic cosine of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Cosh,
+    9,
+    OpSchema()
+        .SetDoc(Cosh_ver9_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The hyperbolic cosine values of the input tensor "
+            "computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Asinh_ver9_doc = R"DOC(
+Calculates the hyperbolic arcsine of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Asinh,
+    9,
+    OpSchema()
+        .SetDoc(Asinh_ver9_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The hyperbolic arcsine values of the input tensor "
+            "computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Acosh_ver9_doc = R"DOC(
+Calculates the hyperbolic arccosine of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Acosh,
+    9,
+    OpSchema()
+        .SetDoc(Acosh_ver9_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The hyperbolic arccosine values of the input tensor "
+            "computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Atanh_ver9_doc = R"DOC(
+Calculates the hyperbolic arctangent of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Atanh,
+    9,
+    OpSchema()
+        .SetDoc(Atanh_ver9_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The hyperbolic arctangent values of the input tensor "
+            "computed element-wise",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Sign_ver13_doc = R"DOC(
+Calculate the sign of the given input tensor element-wise.
+If input > 0, output 1. if input < 0, output -1. if input == 0, output 0.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sign,
+    13,
+    OpSchema()
+        .SetDoc(Sign_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "The sign of the input tensor "
+            "computed element-wise. It has the same shape and type of the input.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Erf_ver13_doc = R"DOC(
+Computes the error function of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Erf,
+    13,
+    OpSchema()
+        .SetDoc(Erf_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The error function of the input tensor "
+            "computed element-wise. It has the same shape and type of the input.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types_with_bfloat(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* QLinearMatMul_ver10_doc = R"DOC(
+Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, 
+and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). 
+For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 
+Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor 
+(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row 
+or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be 
+an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] 
+for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may 
+have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.
+Production must never overflow, and accumulation may overflow if and only if in 32 bits.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    QLinearMatMul,
+    10,
+    OpSchema()
+        .SetDoc(QLinearMatMul_ver10_doc)
+        .Input(
+            0,
+            "a",
+            "N-dimensional quantized matrix a",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            1,
+            "a_scale",
+            "scale of quantized input a",
+            "tensor(float)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "a_zero_point",
+            "zero point of quantized input a",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            3,
+            "b",
+            "N-dimensional quantized matrix b",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            4,
+            "b_scale",
+            "scale of quantized input b",
+            "tensor(float)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            5,
+            "b_zero_point",
+            "zero point of quantized input b",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            6,
+            "y_scale",
+            "scale of quantized output y",
+            "tensor(float)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            7,
+            "y_zero_point",
+            "zero point of quantized output y",
+            "T3",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "y",
+            "Quantized matrix multiply results from a * b",
+            "T3",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T1",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input a and its zero point data type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input b and its zero point data type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain output y and its zero point data type to 8-bit integer tensor.")
+        .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext&
+                                              ctx) {
+          auto a_type = ctx.getInputType(0);
+          auto b_type = ctx.getInputType(3);
+          if (nullptr == a_type || nullptr == b_type ||
+              a_type->value_case() != ONNX_NAMESPACE::TypeProto::kTensorType ||
+              b_type->value_case() != ONNX_NAMESPACE::TypeProto::kTensorType) {
+            fail_type_inference("inputs are expected to have tensor type.");
+          }
+
+          auto a_zero_point_type = ctx.getInputType(2);
+          if (nullptr == a_zero_point_type ||
+              a_zero_point_type->tensor_type().elem_type() !=
+                  a_type->tensor_type().elem_type()) {
+            fail_type_inference(
+                "input and zero_point pair is expected to have be same type.");
+          }
+
+          auto b_zero_point_type = ctx.getInputType(5);
+          if (nullptr == b_zero_point_type ||
+              b_zero_point_type->tensor_type().elem_type() !=
+                  b_type->tensor_type().elem_type()) {
+            fail_type_inference(
+                "input and zero_point pair is expected to have same type.");
+          }
+
+          propagateElemTypeFromInputToOutput(ctx, 7, 0);
+
+          matmulShapeInference(ctx, 0, 3);
+        }));
+
+static const char* MatMulInteger_ver10_doc = R"DOC(
+Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MatMulInteger,
+    10,
+    OpSchema()
+        .SetDoc(MatMulInteger_ver10_doc)
+        .Input(
+            0,
+            "A",
+            "N-dimensional matrix A",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            1,
+            "B",
+            "N-dimensional matrix B",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "a_zero_point",
+            "Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. "
+            "Scalar refers to per tensor quantization whereas N-D refers to per row quantization. "
+            "If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. "
+            "If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. ",
+            "T1",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            3,
+            "b_zero_point",
+            "Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, "
+            "Scalar refers to per tensor quantization whereas N-D refers to per col quantization. "
+            "If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. "
+            "If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. ",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "Matrix multiply results from A * B",
+            "T3",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T1",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input A data type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input B data type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(int32)"},
+            "Constrain output Y data type as 32-bit integer tensor.")
+        .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext&
+                                              ctx) {
+          auto a_type = ctx.getInputType(0);
+          auto b_type = ctx.getInputType(1);
+          auto y_type = ctx.getOutputType(0);
+          if (nullptr == a_type || nullptr == b_type || nullptr == y_type ||
+              a_type->value_case() != ONNX_NAMESPACE::TypeProto::kTensorType ||
+              b_type->value_case() != ONNX_NAMESPACE::TypeProto::kTensorType) {
+            fail_type_inference(
+                "inputs are expected to have tensor type and output type should not be null.");
+          }
+
+          // Right now we only support int32
+          y_type->mutable_tensor_type()->set_elem_type(
+              ONNX_NAMESPACE::TensorProto::INT32);
+
+          matmulShapeInference(ctx, 0, 1);
+        }));
+static const char* CumSum_ver14_doc = R"DOC(
+Performs cumulative sum of the input elements along the given axis.
+By default, it will do the sum inclusively meaning the first element is copied as is.
+Through an `exclusive` attribute, this behavior can change to exclude the first element.
+It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.
+
+Example:
+```
+input_x = [1, 2, 3]
+axis=0
+output = [1, 3, 6]
+exclusive=1
+output = [0, 1, 3]
+exclusive=0
+reverse=1
+output = [6, 5, 3]
+exclusive=1
+reverse=1
+output = [5, 3, 0]
+```
+ )DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    CumSum,
+    14,
+    OpSchema()
+        .SetDoc(CumSum_ver14_doc)
+        .Attr(
+            "exclusive",
+            "If set to 1 will return exclusive sum in which the top element is not included."
+            " In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements."
+            " Otherwise, it would be the sum of the first j elements.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "reverse",
+            "If set to 1 will perform the sums in reverse direction.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "x",
+            "An input tensor that is to be processed.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "axis",
+            "A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. "
+            "Negative value means counting dimensions from the back.",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "y",
+            "Output tensor of the same type as 'x' with cumulative sums of the x's elements",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::numeric_types_for_math_reduction_with_bfloat(),
+            "Constrain input and output types to high-precision numeric tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int32)", "tensor(int64)"},
+            "axis tensor can be int32 or int64 only")
+        .TypeAndShapeInferenceFunction(
+            ONNX_NAMESPACE::propagateShapeAndTypeFromFirstInput));
+
+static const char* Round_ver11_doc = R"DOC(
+Round takes one input Tensor and rounds the values, element-wise, meaning
+it finds the nearest integer for each value.
+In case of halfs, the rule is to round them to the nearest even integer.
+The output tensor has the same shape and type as the input.
+
+Examples:
+```
+round([0.9]) = [1.0]
+round([2.5]) = [2.0]
+round([2.3]) = [2.0]
+round([1.5]) = [2.0]
+round([-4.5]) = [-4.0]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Round,
+    11,
+    OpSchema()
+        .SetDoc(Round_ver11_doc)
+        .Input(
+            0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Det_ver11_doc = R"DOC(
+Det calculates determinant of a square matrix or batches of square matrices.
+Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions,
+and the inner-most 2 dimensions form square matrices.
+The output is a tensor of shape `[*]`, containing the determinants of all input submatrices.
+e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Det,
+    11,
+    OpSchema()
+        .SetDoc(Det_ver11_doc)
+        .Input(
+            0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to floating-point tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          if (hasInputShape(ctx, 0)) {
+            const TensorShapeProto& input_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            TensorShapeProto* output_shape =
+                ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+            const int rank = static_cast<int>(input_shape.dim_size());
+
+            if (rank < 2) {
+              fail_shape_inference("Input rank must be >= 2.");
+            }
+
+            const auto mat_w = input_shape.dim(rank - 1);
+            const auto mat_h = input_shape.dim(rank - 2);
+            if (mat_w.has_dim_value() && mat_h.has_dim_value() &&
+                (mat_w.dim_value() != mat_h.dim_value())) {
+              fail_shape_inference(
+                  "The inner-most 2 dimensions must have the same size (mat_w:",
+                  mat_w.dim_value(),
+                  " != mat_h:",
+                  mat_h.dim_value(),
+                  ").");
+            }
+
+            for (int i = 0; i < rank - 2; ++i) {
+              auto* dim = output_shape->add_dim();
+              *dim = input_shape.dim(i);
+            }
+          }
+        }));
+
+static const char* NegativeLogLikelihoodLoss_ver13_doc = R"DOC(
+A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss.
+Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0.
+The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C).
+The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes)
+or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples.
+The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as:
+
+    loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].
+
+When an optional "weight" is provided, the sample loss is calculated as:
+
+    loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].
+
+loss is zero for the case when target-value equals ignore_index.
+
+    loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index
+
+If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk).
+If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged:
+
+    mean(loss), if "weight" is not provided,
+
+or if weight is provided,
+
+    sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
+
+If "reduction" attribute is set to "sum", the output is a scalar:
+    sum(loss).
+
+See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
+
+Example 1:
+
+    // negative log likelihood loss, "none" reduction
+    N, C, d1 = 2, 3, 2
+    input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+             [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+    target = [[2, 1], [0, 2]]
+
+    loss = np.zeros((N, d1))
+    for n in range(N):
+        for d_1 in range(d1):
+            c = target[n][d_1]
+            loss[n][d_1] = -input[n][c][d_1]
+
+    // print(loss)
+    // [[-3. -2.]
+    //  [-0. -2.]]
+
+Example 2:
+
+    // weighted negative log likelihood loss, sum reduction
+    N, C, d1 = 2, 3, 2
+    input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+            [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+    target = [[2, 1], [0, 2]]
+    weight = [0.2, 0.3, 0.1]
+    loss = np.zeros((N, d1))
+    for n in range(N):
+        for d_1 in range(d1):
+            c = target[n][d_1]
+            loss[n][d_1] = -input[n][c][d_1] * weight[c]
+
+    loss = np.sum(loss)
+    // print(loss)
+    // -1.1
+
+Example 3:
+
+    // weighted negative log likelihood loss, mean reduction
+    N, C, d1 = 2, 3, 2
+    input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+            [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+    target = [[2, 1], [0, 2]]
+    weight = [0.2, 0.3, 0.1]
+    loss = np.zeros((N, d1))
+    weight_total = 0
+    for n in range(N):
+        for d_1 in range(d1):
+            c = target[n][d_1]
+            loss[n][d_1] = -input[n][c][d_1] * weight[c]
+            weight_total = weight_total + weight[c]
+
+    loss = np.sum(loss) / weight_total
+    // print(loss)
+    // -1.57
+)DOC";
+
+bool BuildContextDependentFunctionBody(
+    const FunctionBodyBuildContext& ctx,
+    const OpSchema& schema,
+    FunctionProto& functionProto) {  
+  if (ctx.getInputType(0) == nullptr) {
+    // we cannot create a correct function body without knowing the input type
+    return false;
+  }
+  auto input_type = ctx.getInputType(0)->tensor_type().elem_type();
+  bool float_input = input_type == TensorProto_DataType_FLOAT;
+  auto reduction_attr_proto = ctx.getAttribute("reduction");
+  std::string reduction_attr =
+      reduction_attr_proto != nullptr && reduction_attr_proto->has_s() ? reduction_attr_proto->s() : "mean";
+
+  FunctionBuilder builder(functionProto);
+  builder.Const1D("const_zero", int64_t(0))
+      .Const1D("const_one", int64_t(1))
+      .Const1D("axes", int64_t(1))
+      .Add("expanded_target = Unsqueeze (target, axes)");
+
+  if (ctx.getAttribute("ignore_index") == nullptr) {
+    builder.Add(R"(
+      input_gather_element = GatherElements <axis = 1> (input, expanded_target)
+      loss_NCdd = Neg (input_gather_element)
+      loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)
+    )");
+
+    if (!ctx.hasInput(2)) {
+      if (reduction_attr == "none") {
+        builder.Add("loss = Squeeze (loss_N1dd, axes)");
+      } else {
+        builder.Add("loss_Ndd = Squeeze (loss_N1dd, axes)");
+        if (reduction_attr == "mean") {
+          builder.Add("loss = ReduceMean <keepdims = 0> (loss_Ndd)");
+        } else {
+          builder.Add("loss = ReduceSum <keepdims = 0> (loss_Ndd)");
+        }
+      }
+    } else {
+      builder.Add("weight_gather = Gather (weight, target)");
+      builder.Add("loss_unweighted = Squeeze (loss_N1dd, axes)");
+      if (reduction_attr == "none") {
+        builder.Add("loss = Mul (loss_unweighted, weight_gather)");
+      } else {
+        builder.Add("loss_Ndd = Mul (loss_unweighted, weight_gather)");
+        if (reduction_attr == "mean") {
+          builder.Add(R"(
+            loss_sum = ReduceSum <keepdims = 0> (loss_Ndd)
+            weight_gather_sum = ReduceSum <keepdims = 0> (weight_gather)
+            loss = Div (loss_sum, weight_gather_sum)
+          )");
+        } else {
+          builder.Add("loss = ReduceSum <keepdims = 0> (loss_Ndd)");
+        }
+      }
+    }
+  } else {
+    builder.Const1D("const_ignore_index", ctx.getAttribute("ignore_index")->i());
+    builder.Add(R"(
+      const_zero_target_typed = Sub (expanded_target, expanded_target)
+      expanded_target_int64 = Cast <to = 7> (expanded_target)
+      mask = Equal (expanded_target_int64, const_ignore_index)
+      transform_targets = Where (mask, const_zero_target_typed, expanded_target)
+    )");
+    builder.Add("input_gather_element = GatherElements <axis = 1> (input, transform_targets)");
+    builder.Const1D("const_zero_float", 0.0f);
+    if (!float_input) {
+      builder.Add("const_zero_casted = Cast (const_zero_float)", "to", static_cast<int64_t>(input_type))
+          .Add("input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)");
+    } else
+      builder.Add("input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)");
+    builder.Add("loss_NCdd = Neg (input_gather_element_transform)");
+    builder.Add("loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)");
+
+    if (!ctx.hasInput(2)) {
+      builder.Add("squeeze_mask = Squeeze (mask, axes)");
+      builder.Const1D("const_one_float", 1.0f);
+      if (!float_input) {
+        builder.Add("const_one_casted = Cast (const_one_float)", "to", static_cast<int64_t>(input_type))
+            .Add("weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)");
+      } else
+        builder.Add("weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)");
+
+    } else {
+      builder.Add("weight_gather_temp = Gather (weight, transform_targets)");
+      builder.Add(
+          float_input ? "weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)"
+                      : "weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)");
+      builder.Add("weight_gather = Squeeze (weight_gather_temp_1, axes)");
+    }
+
+    builder.Add("loss_unweighted = Squeeze (loss_N1dd, axes)");
+    if (reduction_attr == "none") {
+      builder.Add("loss = Mul (loss_unweighted, weight_gather)");
+    } else {
+      builder.Add("loss_Ndd = Mul (loss_unweighted, weight_gather)");
+      if (reduction_attr == "mean") {
+        builder.Add(R"(
+            loss_sum = ReduceSum <keepdims = 0> (loss_Ndd)
+            weight_gather_sum = ReduceSum <keepdims = 0> (weight_gather)
+            loss = Div (loss_sum, weight_gather_sum)
+        )");
+      } else {
+        builder.Add("loss = ReduceSum <keepdims = 0> (loss_Ndd)");
+      }
+    }
+  }
+
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    NegativeLogLikelihoodLoss,
+    13,
+    OpSchema()
+        .SetDoc(NegativeLogLikelihoodLoss_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "target",
+            "Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). "
+            "If ignore_index is specified, it may have a value outside [0, C) and the target values should either be "
+            "in the range [0, C) or have the value ignore_index.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "weight",
+            "Optional rescaling weight tensor. "
+            "If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "loss",
+            "The negative log likelihood loss",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Attr(
+            "reduction",
+            "Type of reduction to apply to loss: none, sum, mean (default). "
+            "'none': the output is the loss for each sample. "
+            "'sum': the output will be summed. "
+            "'mean': the sum of the output will be divided by the sum of applied weights.",
+            AttributeProto::STRING,
+            std::string("mean"))
+        .Attr(
+            "ignore_index",
+            "Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.",
+            AttributeProto::INT,
+            false)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input, weight, and output types to floating-point tensors.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain target to integer types")
+        .SetContextDependentFunctionBodyBuilder(
+            BuildContextDependentFunctionBody)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          if (hasNInputShapes(ctx, 2)) {
+            const TensorShapeProto& input_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            const TensorShapeProto& target_shape =
+                ctx.getInputType(1)->tensor_type().shape();
+
+            const int input_rank = static_cast<int>(input_shape.dim_size());
+            const int target_rank = static_cast<int>(target_shape.dim_size());
+
+            if (input_rank < 2) {
+              fail_shape_inference("Input rank must be >= 2.")
+            }
+            if (target_rank != input_rank - 1) {
+              fail_shape_inference(
+                  "Target rank must be 1 less than the input rank.");
+            }
+
+            // match input dimensions (N, C, d1, ..., dk) with target
+            // dimensions of (C, d1, ..., dk)
+            for (int dim = 0; dim < target_rank; dim++) {
+              const auto input_dim =
+                  dim == 0 ? input_shape.dim(dim) : input_shape.dim(dim + 1);
+              const auto target_dim = target_shape.dim(dim);
+              if (input_dim.has_dim_value() && target_dim.has_dim_value() &&
+                  input_dim.dim_value() != target_dim.dim_value())
+                fail_shape_inference(
+                    "Input and target dimension value mismatch.");
+            }
+
+            if (ctx.getNumInputs() == 3 && hasInputShape(ctx, 2)) {
+              const TensorShapeProto& weight_shape =
+                  ctx.getInputType(2)->tensor_type().shape();
+              if (weight_shape.dim_size() != 1) {
+                fail_shape_inference("Weight rank must be 1.");
+              }
+            }
+
+            TensorShapeProto* output_shape =
+                ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+            if (getAttribute(ctx, "reduction", "mean") == "none") {
+              // output tensor is of shape (N, d1, d2, ..., dk) if
+              // reduction attribute is "none".
+              for (int i = 0; i < input_rank - 1; i++) {
+                auto* dim = output_shape->add_dim();
+                if (i == 0)
+                  *dim = input_shape.dim(i);
+                else
+                  *dim = input_shape.dim(i + 1);
+              }
+            }
+            // otherwise output is a scalar.
+          }
+        }));
+
+void einsumRankInference(
+    ONNX_NAMESPACE::InferenceContext& ctx,
+    std::string equation) {
+  const size_t numInputs = ctx.getNumInputs();
+  if (numInputs < 1 || !hasNInputShapes(ctx, static_cast<int>(numInputs))) {
+    return;
+  }
+
+  auto* output_shape = getOutputShape(ctx, 0);
+  std::string left_equation;
+
+  equation.erase(
+      std::remove(equation.begin(), equation.end(), ' '),
+      equation.end()); // Remove space char
+  auto mid_index = equation.find("->");
+  if (mid_index != std::string::npos) {
+    // Separate right and left hand sides of the equation
+    left_equation = equation.substr(0, mid_index);
+  } else {
+    // No right hand side
+    left_equation = equation;
+  }
+
+  std::string term;
+  size_t num_operands = 0;
+  size_t num_ellipsis = 0;
+  size_t num_ellipsis_indices = 0;
+
+  // Parse the left-hand side
+  std::stringstream str(left_equation);
+  while (!str.eof()) {
+    std::getline(str, term, ',');
+    auto ellipsis_index = term.find("...");
+    if (numInputs <= num_operands) {
+      fail_shape_inference(
+          "Number of input tensors does not match the operands in the equation.");
+    }
+    size_t rank =
+        ctx.getInputType(num_operands)->tensor_type().shape().dim_size();
+    if (ellipsis_index != std::string::npos) {
+      // If there is an ellipsis, the number of dimensions it represents
+      // must be total dim - letter dimensions
+      if (num_ellipsis == 0) {
+        if (rank + 3 < term.size()) {
+          fail_shape_inference("Ellipsis represents incompatible dimensions.");
+        }
+        num_ellipsis_indices = rank - term.size() + 3;
+      } else { // ellipsis has been seen before. Check that if dimensions
+               // are compatible
+        if (num_ellipsis_indices != rank - term.size() + 3) {
+          fail_shape_inference("Ellipsis represents incompatible dimensions.");
+        }
+      }
+      num_ellipsis++;
+    } else {
+      if (rank != term.size()) {
+        fail_shape_inference(
+            "Rank of input ",
+            num_operands,
+            " does not match the equation indices.");
+      }
+    }
+    num_operands++;
+  }
+
+  if (numInputs != num_operands) {
+    fail_shape_inference(
+        "Number of input tensors does not match the operands in the equation.");
+  }
+
+  const size_t number_of_letters = 26;
+  size_t num_letter_occurrences[number_of_letters] = {0};
+  // Parse the provided right-hand side
+  if (mid_index != std::string::npos) {
+    std::string right_equation = equation.substr(mid_index + 2);
+    auto right_ellipsis_index = right_equation.find("...");
+    if (right_ellipsis_index !=
+        std::string::npos) { // Right-hand side contains ellipsis
+      for (size_t i = 0; i < num_ellipsis_indices; ++i) {
+        output_shape->add_dim();
+      }
+    }
+    for (char c : right_equation) { // Add a dimension per each character
+                                    // in right hand equation
+      if (c != '.') {
+        output_shape->add_dim();
+      }
+    }
+  } else { // Infer the dimension for right-hand side
+    // If there's an ellipsis, add it's corresponding dimensions
+    for (size_t i = 0; i < num_ellipsis_indices; i++) {
+      output_shape->add_dim();
+    }
+    for (size_t i = 0; i < left_equation.size();
+         i++) { // Count chars that appear exactly once on left hand side
+      if ((left_equation.at(i) != ',') && (left_equation.at(i) != '.')) {
+        num_letter_occurrences[left_equation.at(i) - 'a']++;
+      }
+    }
+    for (size_t index = 0; index < number_of_letters; index++) {
+      if (num_letter_occurrences[index] == 1) {
+        output_shape->add_dim();
+      }
+    }
+  }
+}
+
+static const char* Einsum_ver12_doc = R"DOC(
+An einsum of the form ```term1, term2 -> output-term``` produces an output tensor using the following equation
+
+```output[output-term] = reduce-sum( input1[term1] * input2[term] )```
+
+where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2)
+that do not occur in the output-term.
+
+The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation
+convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to
+an operand tensor, and the characters within the terms correspond to operands dimensions.
+
+This sequence may be followed by "->" to separate the left and right hand side of the equation.
+If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein
+summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases,
+output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the
+equation.
+
+When a dimension character is repeated in the left-hand side, it represents summation along the dimension.
+
+The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions.
+Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions.
+The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the
+beginning of the output. The equation string may contain space (U+0020) character.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Einsum,
+    12,
+    OpSchema()
+        .SetDoc(Einsum_ver12_doc)
+        .Attr("equation", "Einsum expression string.", AttributeProto::STRING)
+        .Input(
+            0,
+            "Inputs",
+            "Operands",
+            "T",
+            OpSchema::Variadic,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Output",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to all numerical tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          std::string equation = getAttribute(ctx, "equation", "");
+          if (equation.compare("") == 0) {
+            return;
+          }
+          einsumRankInference(ctx, equation);
+        }));
+
+const char* reduction_doc_sce =
+    "Type of reduction to apply to loss: none, sum, mean(default). "
+    "'none': no reduction will be applied, "
+    "'sum': the output will be summed. "
+    "'mean': the sum of the output will be divided by the number of "
+    "elements in the output.";
+
+static const char* SoftmaxCrossEntropyLoss_ver13_doc =
+    R"DOC(Loss function that measures the softmax cross entropy
+between 'scores' and 'labels'.
+This operator first computes a loss tensor whose shape is identical to the labels input.
+If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).
+If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),
+the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.
+After L is available, this operator can optionally do a reduction operator.
+
+shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),
+        with K >= 1 in case of K-dimensional loss.
+shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),
+        with K >= 1 in case of K-dimensional loss.
+
+The loss for one sample, l_i, can caculated as follows:
+    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.
+or
+    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.
+
+loss is zero for the case when label-value equals ignore_index.
+    l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index
+
+where:
+    p = Softmax(scores)
+    y = Log(p)
+    c = labels[i][d1][d2]...[dk]
+
+Finally, L is optionally reduced:
+If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).
+If reduction = 'sum', the output is scalar: Sum(L).
+If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),
+where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].
+)DOC";
+
+bool BuildContextDependentFunctionBodySCE(
+    const FunctionBodyBuildContext& ctx,
+    const OpSchema& schema,
+    FunctionProto& functionProto) {
+  FunctionBuilder builder(functionProto);
+  // Using stable implementation of LogSoftmax
+  builder //
+      .Const("Shape3D", std::vector<int64_t>({0, 0, -1})) //
+      .Add(R"(
+        X_NCD = Reshape (scores, Shape3D)
+        X_NDC = Transpose <perm = [0, 2, 1]> (X_NCD)
+        X_LogSM = LogSoftmax <axis = 2> (X_NDC)
+        X_LogSM_NCD = Transpose <perm = [0, 2, 1]> (X_LogSM)
+        X_shape = Shape (scores)
+        X_Log = Reshape (X_LogSM_NCD, X_shape)
+      )");
+
+  // Review(mzs): Ideally we want to reuse the output from Log for sub-graph
+  // output as well but looking at the graph resolve code it does not include
+  // graph outputs as intermediate outputs, hence if intermediate X_log is
+  // renamed as log_prob then it will be treated as graph output and will not be
+  // available to NegativeLogLikelihoodLoss. May be my understanding is
+  // incorrect or there is a bug in function population code in ORTbut I will
+  // dig further to be 100%. In the meantime we just replicate the log.
+  if (ctx.hasOutput(1)) {
+    builder.Add("log_prob = Identity (X_Log)");
+  }
+
+  builder.Add(
+      ctx.hasInput(2)
+          ? "output = NegativeLogLikelihoodLoss <reduction : string = @reduction, ignore_index : int = @ignore_index> (X_Log, labels, weights)"
+          : "output = NegativeLogLikelihoodLoss <reduction : string = @reduction, ignore_index : int = @ignore_index> (X_Log, labels)");
+
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SoftmaxCrossEntropyLoss,
+    13,
+    OpSchema()
+        .SetDoc(SoftmaxCrossEntropyLoss_ver13_doc)
+        .Attr(
+            "reduction",
+            reduction_doc_sce,
+            AttributeProto::STRING,
+            std::string("mean"))
+        .Attr(
+            "ignore_index",
+            "Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.",
+            AttributeProto::INT,
+            false)
+        .Input(
+            0,
+            "scores",
+            "The predicted outputs with shape [batch_size, class_size], or "
+            "[batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "labels",
+            "The ground truth output tensor, with shape [batch_size], or "
+            "[batch_size, D1, D2, ..., Dk], where K is the number of dimensions. "
+            "Labels element value shall be in range of [0, C). "
+            "If ignore_index is specified, it may have a value outside [0, C) and the label values should either be "
+            "in the range [0, C) or have the value ignore_index.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "weights",
+            "A manual rescaling weight given to each class. If given, it has to "
+            "be a 1D Tensor assigning weight to each of the classes. Otherwise, "
+            "it is treated as if having all ones.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Weighted loss float Tensor. If reduction is 'none', this has the "
+            "shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of "
+            "K-dimensional loss. Otherwise, it is a scalar.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            1,
+            "log_prob",
+            "Log probability tensor. If the output of softmax is prob, its value is log(prob).",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain target to integer types")
+        .SetContextDependentFunctionBodyBuilder(
+            BuildContextDependentFunctionBodySCE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          std::string reduction = getAttribute(ctx, "reduction", "mean");
+          if (reduction.compare("none") == 0) {
+            if (hasInputShape(ctx, 1)) {
+              propagateShapeFromInputToOutput(ctx, 1, 0);
+            }
+          } else {
+            updateOutputShape(ctx, 0, TensorShapeProto());
+          }
+
+          if (ctx.getNumOutputs() == 2) {
+            propagateElemTypeFromInputToOutput(ctx, 0, 1);
+            propagateShapeFromInputToOutput(ctx, 0, 1);
+          }
+        }));
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/math/old.cc b/third_party/onnx/onnx/defs/math/old.cc
new file mode 100644
index 0000000000..dc80df6675
--- /dev/null
+++ b/third_party/onnx/onnx/defs/math/old.cc
@@ -0,0 +1,3609 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <functional>
+#include "onnx/defs/schema.h"
+#include "onnx/defs/tensor_proto_util.h"
+#include "onnx/defs/function.h"
+
+
+namespace ONNX_NAMESPACE {
+
+std::function<void(OpSchema&)> MathDocGenerator_opset13(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Performs element-wise binary {name} (with Numpy-style broadcasting support).
+
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(0,
+        "A",
+        "First operand.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(1,
+        "B",
+        "Second operand.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(0,
+        "C",
+        "Result, has same element type as two inputs",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        OpSchema::numeric_types_for_math_reduction_with_bfloat(),
+        "Constrain input and output types to high-precision numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (hasNInputShapes(ctx, 2))
+        bidirectionalBroadcastShapeInference(
+            ctx.getInputType(0)->tensor_type().shape(),
+            ctx.getInputType(1)->tensor_type().shape(),
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Add,
+    13,
+    OpSchema().FillUsing(MathDocGenerator_opset13("addition")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sub,
+    13,
+    OpSchema().FillUsing(MathDocGenerator_opset13("subtraction")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mul,
+    13,
+    OpSchema().FillUsing(MathDocGenerator_opset13("multiplication")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Div,
+    13,
+    OpSchema().FillUsing(MathDocGenerator_opset13("division")));
+
+std::function<void(OpSchema&)> MathDocGenerator_opset_7(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Performs element-wise binary {name} (with Numpy-style broadcasting support).
+
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(0, "A", "First operand.", "T");
+    schema.Input(1, "B", "Second operand.", "T");
+    schema.Output(0, "C", "Result, has same element type as two inputs", "T");
+    schema.TypeConstraint(
+        "T",
+        OpSchema::numeric_types_for_math_reduction(),
+        "Constrain input and output types to high-precision numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (hasNInputShapes(ctx, 2))
+        bidirectionalBroadcastShapeInference(
+            ctx.getInputType(0)->tensor_type().shape(),
+            ctx.getInputType(1)->tensor_type().shape(),
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Add,
+    7,
+    OpSchema().FillUsing(MathDocGenerator_opset_7("addition")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sub,
+    7,
+    OpSchema().FillUsing(MathDocGenerator_opset_7("subtraction")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mul,
+    7,
+    OpSchema().FillUsing(MathDocGenerator_opset_7("multiplication")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Div,
+    7,
+    OpSchema().FillUsing(MathDocGenerator_opset_7("division")));
+
+std::function<void(OpSchema&)> SoftmaxFamilyDocGenerator_opset_11(
+    const char* name,
+    const char* description) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The operator computes the {name} ({description}) values for each layer in the batch
+ of the given input.
+
+The input does not need to explicitly be a 2D vector; rather, it will be
+coerced into one. For an arbitrary n-dimensional tensor
+input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+the axis provided, then input will be coerced into a 2-dimensional tensor with
+dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+case where axis=1, this means the input tensor will be coerced into a 2D tensor
+of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+Each of these dimensions must be matched correctly, or else the operator
+will throw errors. The output tensor has the same shape
+and contains the {name} values of the corresponding input.
+)DOC";
+                        ReplaceAll(doc, "{name}", name);
+                        ReplaceAll(doc, "{description}", description););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "axis",
+        "Describes the axis of the inputs when coerced "
+        "to 2D; defaults to one because the 0th axis most likely describes "
+        "the batch_size. Negative value means counting dimensions "
+        "from the back. Accepted range is [-r, r-1] where r = rank(input).",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(
+        0,
+        "input",
+        "The input tensor that's coerced into a 2D matrix of size (NxD) "
+        "as described above.",
+        "T");
+    schema.Output(
+        0,
+        "output",
+        "The output values with the same "
+        "shape as input tensor (the original size without coercion).",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // Type inference
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+      // Shape inference starts
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      // Validate the value of 'axis'
+      const TensorShapeProto& input_shape =
+          ctx.getInputType(0)->tensor_type().shape();
+      int r = input_shape.dim_size();
+      int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
+      if (axis < -r || axis >= r) {
+        fail_shape_inference(
+            "'axis' must be in [",
+            -r,
+            " , ",
+            (r - 1),
+            "]. Its actual value is: ",
+            axis);
+      }
+
+      // Shape inference
+      propagateShapeFromInputToOutput(ctx, 0, 0);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Softmax,
+    11,
+    OpSchema().FillUsing(SoftmaxFamilyDocGenerator_opset_11(
+        "softmax",
+        "normalized exponential")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LogSoftmax,
+    11,
+    OpSchema().FillUsing(
+        SoftmaxFamilyDocGenerator_opset_11("logsoftmax", "log of softmax")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Hardmax,
+    11,
+    OpSchema().FillUsing(SoftmaxFamilyDocGenerator_opset_11(
+        "hardmax",
+        "1 for the first maximum value, and 0 for all others")));
+
+static const char* Mod_doc_10 = R"DOC(
+  Performs element-wise binary modulus (with Numpy-style broadcasting support).
+    The sign of the remainder is the same as that of the Divisor.
+
+    Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
+    (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
+    This attribute is set to 0 by default causing the behavior to be like integer mod.
+    Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
+
+    If the input type is floating point, then `fmod` attribute must be set to 1.
+
+    In case of dividend being zero, the results will be platform dependent.
+
+  This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mod,
+    10,
+    OpSchema()
+        .SetDoc(Mod_doc_10)
+        .Attr(
+            "fmod",
+            "Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "A", "Dividend tensor", "T")
+        .Input(1, "B", "Divisor tensor", "T")
+        .Output(0, "C", "Remainder tensor", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to high-precision numeric tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+
+static const char* Neg_ver6_doc = R"DOC(
+Neg takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where each element flipped sign, y = -x, is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Neg,
+    6,
+    OpSchema()
+        .SetDoc(Neg_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(int32)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(double)"},
+            "Constrain input and output types to signed numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Abs_ver6_doc = R"DOC(
+Absolute takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the absolute is, y = abs(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Abs,
+    6,
+    OpSchema()
+        .SetDoc(Abs_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Reciprocal_ver6_doc = R"DOC(
+Reciprocal takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reciprocal,
+    6,
+    OpSchema()
+        .SetDoc(Reciprocal_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Floor_ver6_doc = R"DOC(
+Floor takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the floor is, y = floor(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Floor,
+    6,
+    OpSchema()
+        .SetDoc(Floor_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Ceil_ver6_doc = R"DOC(
+Ceil takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the ceil is, y = ceil(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Ceil,
+    6,
+    OpSchema()
+        .SetDoc(Ceil_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Sqrt_ver6_doc = R"DOC(
+Square root takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the square root is, y = x^0.5, is applied to
+the tensor elementwise. If x is negative, then it will return NaN.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sqrt,
+    6,
+    OpSchema()
+        .SetDoc(Sqrt_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Relu_ver6_doc = R"DOC(
+Relu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Relu,
+    6,
+    OpSchema()
+        .SetDoc(Relu_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Relu_ver13_doc = R"DOC(
+Relu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Relu,
+    13,
+    OpSchema()
+        .SetDoc(Relu_ver13_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Exp_ver6_doc = R"DOC(
+Calculates the exponential of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Exp,
+    6,
+    OpSchema()
+        .SetDoc(Exp_ver6_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The exponential of the input tensor computed "
+            "element-wise",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Log_ver6_doc = R"DOC(
+Calculates the natural log of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Log,
+    6,
+    OpSchema()
+        .SetDoc(Log_ver6_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The natural log of the input tensor computed "
+            "element-wise",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Tanh_ver6_doc = R"DOC(
+Calculates the hyperbolic tangent of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tanh,
+    6,
+    OpSchema()
+        .SetDoc(Tanh_ver6_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The hyperbolic tangent values of the input tensor "
+            "computed element-wise",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Pow_ver13_doc = R"DOC(
+Pow takes input data (Tensor<T>) and exponent Tensor, and
+produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pow,
+    13,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Pow_ver13_doc) + GenerateBroadcastingDocMul()))
+        .Input(0,
+            "X",
+            "First operand, base of the exponent.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(1,
+            "Y",
+            "Second operand, power of the exponent.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Z",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input X and output types to float/int tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Constrain input Y types to float/int tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+        
+static const char* Pow_ver12_doc = R"DOC(
+Pow takes input data (Tensor<T>) and exponent Tensor, and
+produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pow,
+    12,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Pow_ver12_doc) + GenerateBroadcastingDocMul()))
+        .Input(0, "X", "First operand, base of the exponent.", "T")
+        .Input(1, "Y", "Second operand, power of the exponent.", "T1")
+        .Output(0, "Z", "Output tensor.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Constrain input X and output types to float/int tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Constrain input Y types to float/int tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+
+static const char* Sigmoid_ver6_doc = R"DOC(
+Sigmoid takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sigmoid,
+    6,
+    OpSchema()
+        .SetDoc(Sigmoid_ver6_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+// Generate opschema for element-wise ops. Leaves type constraint "T"
+// unspecified.
+std::function<void(OpSchema&)> ElementwiseMultiOpDocGenerator_opset8(
+    const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support).
+All inputs and outputs must have the same data type.
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "data_0",
+        "List of tensors for " + std::string(name) + ".",
+        "T",
+        OpSchema::Variadic);
+    schema.Output(0, name, "Output tensor.", "T");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      int num_inputs = static_cast<int>(ctx.getNumInputs());
+      std::vector<const TensorShapeProto*> shapes;
+      for (int i = 0; i < num_inputs; ++i) {
+        auto input_type = ctx.getInputType(i);
+        if (nullptr == input_type || !input_type->has_tensor_type() ||
+            !input_type->tensor_type().has_shape()) {
+          return;
+        }
+        shapes.push_back(&input_type->tensor_type().shape());
+      }
+
+      multidirectionalBroadcastShapeInference(
+          shapes,
+          *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Max,
+    12,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator_opset8("max"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to numeric tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Min,
+    12,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator_opset8("min"))
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to numeric tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sum,
+    8,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator_opset8("sum"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mean,
+    8,
+    OpSchema()
+        .FillUsing(ElementwiseMultiOpDocGenerator_opset8("mean"))
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Clip_ver12_doc = R"DOC(
+Clip operator limits the given input within an interval. The interval is
+specified by the inputs 'min' and 'max'. They default to
+numeric_limits::lowest() and numeric_limits::max(), respectively.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Clip,
+    12,
+    OpSchema()
+        .SetDoc(Clip_ver12_doc)
+        .Input(0, "input", "Input tensor whose elements to be clipped", "T")
+        .Input(
+            1,
+            "min",
+            "Minimum value, under which element is replaced by min. "
+            "It must be a scalar(tensor of empty shape).",
+            "T",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "max",
+            "Maximum value, above which element is replaced by max. "
+            "It must be a scalar(tensor of empty shape).",
+            "T",
+            OpSchema::Optional)
+        .Output(0, "output", "Output tensor with clipped input elements", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Gemm_ver11_doc = R"DOC(General Matrix multiplication:
+https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+A' = transpose(A) if transA else A
+
+B' = transpose(B) if transB else B
+
+Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+and output tensor Y has shape (M, N). A will be transposed before doing the
+computation if attribute transA is non-zero, same for B and transB.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gemm,
+    11,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Gemm_ver11_doc) +
+            GenerateBroadcastingDocUni("tensor C", "tensor A * B") + "\n" +
+            GenerateOptionalArgumentsDoc()))
+        .Input(
+            0,
+            "A",
+            "Input tensor A. "
+            "The shape of A should be (M, K) if transA is 0, "
+            "or (K, M) if transA is non-zero.",
+            "T")
+        .Input(
+            1,
+            "B",
+            "Input tensor B. "
+            "The shape of B should be (K, N) if transB is 0, "
+            "or (N, K) if transB is non-zero.",
+            "T")
+        .Input(
+            2,
+            "C",
+            "Optional input tensor C. "
+            "If not specified, the computation is done as if C is a scalar 0. "
+            "The shape of C should be unidirectional broadcastable to (M, N).",
+            "T",
+            OpSchema::Optional)
+        .Output(0, "Y", "Output tensor of shape (M, N).", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)"},
+            "Constrain input and output types to float/int tensors.")
+        .Attr(
+            "transA",
+            "Whether A should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "transB",
+            "Whether B should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "alpha",
+            "Scalar multiplier for the product of input tensors A * B.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "beta",
+            "Scalar multiplier for input tensor C.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2)) {
+            auto transAAttr = ctx.getAttribute("transA");
+            bool transA =
+                transAAttr ? static_cast<int>(transAAttr->i()) != 0 : false;
+            auto transBAttr = ctx.getAttribute("transB");
+            bool transB =
+                transBAttr ? static_cast<int>(transBAttr->i()) != 0 : false;
+            auto& first_input_shape = getInputShape(ctx, 0);
+            auto& second_input_shape = getInputShape(ctx, 1);
+            if (first_input_shape.dim_size() != 2) {
+              fail_shape_inference("First input does not have rank 2");
+            }
+            if (second_input_shape.dim_size() != 2) {
+              fail_shape_inference("Second input does not have rank 2");
+            }
+            updateOutputShape(
+                ctx,
+                0,
+                {first_input_shape.dim(transA ? 1 : 0),
+                 second_input_shape.dim(transB ? 0 : 1)});
+          }
+        }));
+
+void matmulShapeInference_opset_9(
+    ONNX_NAMESPACE::InferenceContext& ctx,
+    int input1Idx,
+    int input2Idx) {
+  if (!hasInputShape(ctx, input1Idx) || !hasInputShape(ctx, input2Idx)) {
+    return;
+  }
+
+  const auto shape0 = ctx.getInputType(input1Idx)->tensor_type().shape();
+  const auto shape1 = ctx.getInputType(input2Idx)->tensor_type().shape();
+
+  if (shape0.dim_size() == 0 || shape1.dim_size() == 0) {
+    fail_shape_inference("Input tensors of wrong rank (0).");
+  }
+
+  ONNX_NAMESPACE::TensorShapeProto shapeL, shapeR;
+
+  // First promote each shape to at least rank-2. This logic is
+  // specific to matmul, not generic broadcasting.
+  {
+    if (shape0.dim_size() == 1) {
+      shapeL.add_dim()->set_dim_value(1);
+      *shapeL.add_dim() = shape0.dim(0);
+    } else {
+      *shapeL.mutable_dim() = shape0.dim();
+    }
+    if (shape1.dim_size() == 1) {
+      *shapeR.add_dim() = shape1.dim(0);
+      shapeR.add_dim()->set_dim_value(1);
+    } else {
+      *shapeR.mutable_dim() = shape1.dim();
+    }
+  }
+
+  // Check for compatible matrix multiply dimensions
+  {
+    auto dimL = shapeL.dim(shapeL.dim_size() - 1);
+    auto dimR = shapeR.dim(shapeR.dim_size() - 2);
+    if (dimL.has_dim_value() && dimR.has_dim_value() &&
+        dimL.dim_value() != dimR.dim_value()) {
+      fail_shape_inference("Incompatible dimensions for matrix multiplication");
+    }
+  }
+
+  ONNX_NAMESPACE::TensorShapeProto resultShape;
+
+  // Now call out to generic multidimensional broadcasting for
+  // the broadcastable prefixes.
+  {
+    ONNX_NAMESPACE::TensorShapeProto prefixShapeL, prefixShapeR;
+    for (int i = 0; i < shapeL.dim_size() - 2; ++i) {
+      *prefixShapeL.add_dim() = shapeL.dim(i);
+    }
+    for (int i = 0; i < shapeR.dim_size() - 2; ++i) {
+      *prefixShapeR.add_dim() = shapeR.dim(i);
+    }
+    bidirectionalBroadcastShapeInference(
+        prefixShapeL, prefixShapeR, resultShape);
+  }
+
+  // Back to matmul-specific. Add the trailing dimensions back in.
+  {
+    if (shape0.dim_size() != 1) {
+      *resultShape.add_dim() = shapeL.dim(shapeL.dim_size() - 2);
+    }
+    if (shape1.dim_size() != 1) {
+      *resultShape.add_dim() = shapeR.dim(shapeR.dim_size() - 1);
+    }
+  }
+
+  *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape() = resultShape;
+}
+
+static const char* MatMul_ver9_doc = R"DOC(
+Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MatMul,
+    9,
+    OpSchema()
+        .Input(0, "A", "N-dimensional matrix A", "T")
+        .Input(1, "B", "N-dimensional matrix B", "T")
+        .Output(0, "Y", "Matrix multiply results from A * B", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)"},
+            "Constrain input and output types to float/int tensors.")
+        .SetDoc(MatMul_ver9_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          matmulShapeInference_opset_9(ctx, 0, 1);
+        }));
+
+static const char* Expand_ver8_doc = R"DOC(
+Broadcast the input tensor following the given shape and the broadcast rule.
+The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):
+Dimensions are right alignment;
+Two corresponding dimensions must have the same value, or one of them is equal to 1.
+Also, this operator is similar to numpy.broadcast_to(input, shape),
+but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size().
+It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1,
+or the shape.ndim < input.shape.ndim.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Expand,
+    8,
+    OpSchema()
+        .SetDoc(Expand_ver8_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Input(
+            1,
+            "shape",
+            "A 1-D tensor indicates the shape you want to expand to, following the broadcast rule",
+            "tensor(int64)")
+        .Output(0, "output", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          // For shape inference, we need both input shape
+          const auto* shape_initializer = ctx.getInputData(1);
+          if (hasNInputShapes(ctx, 2)) {
+            const auto& shape_input_shape =
+                ctx.getInputType(1)->tensor_type().shape();
+            if (shape_input_shape.dim_size() != 1) {
+                    fail_shape_inference("'shape' input must be 1D tensor");
+                }
+
+            const auto& input_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            TensorShapeProto second_shape;
+            if (nullptr != shape_initializer) {
+              const auto& shape_data = ParseData<int64_t>(shape_initializer);
+
+              for (const auto& e : shape_data) {
+                auto* dim = second_shape.add_dim();
+                dim->set_dim_value(e);
+              }
+            } else if (shape_input_shape.dim(0).has_dim_value()) {
+              // Attempt rank inference using shape of shape input
+              int64_t dim_value = shape_input_shape.dim(0).dim_value();
+              for (int64_t i = 0; i < dim_value; ++i) {
+                second_shape.add_dim();
+              }
+            } else {
+                return;     
+            }
+            bidirectionalBroadcastShapeInference(
+                input_shape, second_shape, *getOutputShape(ctx, 0));
+          }
+        }));
+
+static const char* Sign_ver9_doc = R"DOC(
+Calculate the sign of the given input tensor element-wise.
+If input > 0, output 1. if input < 0, output -1. if input == 0, output 0.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sign,
+    9,
+    OpSchema()
+        .SetDoc(Sign_ver9_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The sign of the input tensor "
+            "computed element-wise. It has the same shape and type of the input.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Erf_ver9_doc = R"DOC(
+Computes the error function of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Erf,
+    9,
+    OpSchema()
+        .SetDoc(Erf_ver9_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The error function of the input tensor "
+            "computed element-wise. It has the same shape and type of the input.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output types to all numeric tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* CumSum_ver11_doc = R"DOC(
+Performs cumulative sum of the input elements along the given axis.
+By default, it will do the sum inclusively meaning the first element is copied as is.
+Through an `exclusive` attribute, this behavior can change to exclude the first element.
+It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.
+
+Example:
+```
+input_x = [1, 2, 3]
+axis=0
+output = [1, 3, 6]
+exclusive=1
+output = [0, 1, 3]
+exclusive=0
+reverse=1
+output = [6, 5, 3]
+exclusive=1
+reverse=1
+output = [5, 3, 0]
+```
+ )DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    CumSum,
+    11,
+    OpSchema()
+        .SetDoc(CumSum_ver11_doc)
+        .Attr(
+            "exclusive",
+            "If set to 1 will return exclusive sum in which the top element is not included."
+            " In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements."
+            " Otherwise, it would be the sum of the first j elements.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "reverse",
+            "If set to 1 will perform the sums in reverse direction.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "x",
+            "An input tensor that is to be processed.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "axis",
+            "A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. "
+            "Negative value means counting dimensions from the back.",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "y",
+            "Output tensor of the same type as 'x' with cumulative sums of the x's elements",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Input can be of any tensor type.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int32)", "tensor(int64)"},
+            "axis tensor can be int32 or int64 only")
+        .TypeAndShapeInferenceFunction(
+            ONNX_NAMESPACE::propagateShapeAndTypeFromFirstInput));
+
+static const char* NegativeLogLikelihoodLoss_ver12_doc = R"DOC(
+A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss.
+Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0.
+The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C).
+The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes)
+or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples.
+The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as:
+    loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].
+When an optional "weight" is provided, the sample loss is calculated as:
+    loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].
+loss is zero for the case when target-value equals ignore_index.
+
+    loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index
+If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk).
+If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged:
+    mean(loss), if "weight" is not provided,
+or if weight is provided,
+    sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
+If "reduction" attribute is set to "sum", the output is a scalar:
+    sum(loss).
+See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
+Example 1:
+    // negative log likelihood loss, "none" reduction
+    N, C, d1 = 2, 3, 2
+    input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+             [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+    target = [[2, 1], [0, 2]]
+    loss = np.zeros((N, d1))
+    for n in range(N):
+        for d_1 in range(d1):
+            c = target[n][d_1]
+            loss[n][d_1] = -input[n][c][d_1]
+    // print(loss)
+    // [[-3. -2.]
+    //  [-0. -2.]]
+Example 2:
+    // weighted negative log likelihood loss, sum reduction
+    N, C, d1 = 2, 3, 2
+    input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+            [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+    target = [[2, 1], [0, 2]]
+    weight = [0.2, 0.3, 0.1]
+    loss = np.zeros((N, d1))
+    for n in range(N):
+        for d_1 in range(d1):
+            c = target[n][d_1]
+            loss[n][d_1] = -input[n][c][d_1] * weight[c]
+    loss = np.sum(loss)
+    // print(loss)
+    // -1.1
+Example 3:
+    // weighted negative log likelihood loss, mean reduction
+    N, C, d1 = 2, 3, 2
+    input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
+            [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+    target = [[2, 1], [0, 2]]
+    weight = [0.2, 0.3, 0.1]
+    loss = np.zeros((N, d1))
+    weight_total = 0
+    for n in range(N):
+        for d_1 in range(d1):
+            c = target[n][d_1]
+            loss[n][d_1] = -input[n][c][d_1] * weight[c]
+            weight_total = weight_total + weight[c]
+    loss = np.sum(loss) / weight_total
+    // print(loss)
+    // -1.57
+)DOC";
+
+TensorProto ToDimensionOneFloatTensor_old(float value) {
+  auto t = ToTensor(std::vector<float>({value}));
+  t.add_dims(1);
+  return t;
+}
+
+TensorProto ToDimensionOneTensor_old(int32_t value) {
+  auto t = ToTensor(std::vector<int32_t>({value}));
+  t.add_dims(1);
+  return t;
+}
+
+TensorProto ToDimensionOneInt64Tensor_old(int64_t value) {
+  auto t = ToTensor(std::vector<int64_t>({value}));
+  t.add_dims(1);
+  return t;
+}
+
+TensorProto ToDimensionOneInt64Tensor_old(std::vector<int64_t> value) {
+  auto t = ToTensor(value);
+  t.add_dims(value.size());
+  return t;
+}
+
+bool BuildContextDependentFunctionBody_opset12(
+    const FunctionBodyBuildContext& ctx,
+    const OpSchema& schema,
+    FunctionProto& functionProto) {
+  if (ctx.getInputType(0) == nullptr) {
+    // we cannot create a correct function body without knowing the input type
+    return false;
+  }
+  auto input_type = ctx.getInputType(0)->tensor_type().elem_type();
+  bool float_input = input_type == TensorProto_DataType_FLOAT;
+  auto reduction_attr_proto = ctx.getAttribute("reduction");
+  std::string reduction_attr =
+      reduction_attr_proto != nullptr && reduction_attr_proto->has_s() ? reduction_attr_proto->s() : "mean";
+  std::vector<FunctionBodyHelper::NodeDef> body;
+  body.push_back(
+      {{"const_zero"},
+       "Constant",
+       {},
+       {MakeAttribute("value", ToDimensionOneTensor_old(0))}});
+
+  body.push_back(
+      {{"const_one"},
+       "Constant",
+       {},
+       {MakeAttribute("value", ToDimensionOneTensor_old(1))}});
+
+  body.push_back(
+      {{"expanded_target"},
+       "Unsqueeze",
+       {"target"},
+       {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+
+  if (ctx.getAttribute("ignore_index") == nullptr) {
+    body.push_back(
+        {{"input_gather_element"},
+         "GatherElements",
+         {"input", "expanded_target"},
+         {MakeAttribute("axis", (int64_t)1)}});
+
+    body.push_back({{"loss_NCdd"}, "Neg", {"input_gather_element"}});
+
+    body.push_back(
+        {{"loss_N1dd"},
+         "Slice",
+         {"loss_NCdd", "const_zero", "const_one", "const_one"}});
+
+    if (!ctx.hasInput(2)) {
+      if (reduction_attr == "none") {
+        body.push_back(
+            {{"loss"},
+             "Squeeze",
+             {"loss_N1dd"},
+             {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+      } else {
+        body.push_back(
+            {{"loss_Ndd"},
+             "Squeeze",
+             {"loss_N1dd"},
+             {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+        if (reduction_attr == "mean") {
+          body.push_back(
+              {{"loss"},
+               "ReduceMean",
+               {"loss_Ndd"},
+               {MakeAttribute("keepdims", (int64_t)0)}});
+        } else {
+          body.push_back(
+              {{"loss"},
+               "ReduceSum",
+               {"loss_Ndd"},
+               {MakeAttribute("keepdims", (int64_t)0)}});
+        }
+      }
+    } else {
+      body.push_back({{"weight_gather"}, "Gather", {"weight", "target"}});
+      body.push_back(
+          {{"loss_unweighted"},
+           "Squeeze",
+           {"loss_N1dd"},
+           {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+      if (reduction_attr == "none") {
+        body.push_back({{"loss"}, "Mul", {"loss_unweighted", "weight_gather"}});
+      } else {
+        body.push_back(
+            {{"loss_Ndd"}, "Mul", {"loss_unweighted", "weight_gather"}});
+        if (reduction_attr == "mean") {
+          body.push_back(
+              {{"loss_sum"},
+               "ReduceSum",
+               {"loss_Ndd"},
+               {MakeAttribute("keepdims", (int64_t)0)}});
+          body.push_back(
+              {{"weight_gather_sum"},
+               "ReduceSum",
+               {"weight_gather"},
+               {MakeAttribute("keepdims", (int64_t)0)}});
+          body.push_back({{"loss"}, "Div", {"loss_sum", "weight_gather_sum"}});
+        } else {
+          body.push_back(
+              {{"loss"},
+               "ReduceSum",
+               {"loss_Ndd"},
+               {MakeAttribute("keepdims", (int64_t)0)}});
+        }
+      }
+    }
+  } else {
+    body.push_back(
+        {{"const_ignore_index"},
+         "Constant",
+         {},
+         {MakeAttribute(
+             "value",
+             ToDimensionOneInt64Tensor_old(
+                 ctx.getAttribute("ignore_index")->i()))}});
+
+    body.push_back(
+        {{"const_zero_target_typed"},
+         "Sub",
+         {"expanded_target", "expanded_target"}});
+    body.push_back(
+        {{"expanded_target_int64"},
+         "Cast",
+         {"expanded_target"},
+         {MakeAttribute(
+             "to",
+             (int64_t)TensorProto_DataType::TensorProto_DataType_INT64)}});
+
+    body.push_back(
+        {{"mask"}, "Equal", {"expanded_target_int64", "const_ignore_index"}});
+    body.push_back(
+        {{"transform_targets"},
+         "Where",
+         {"mask", "const_zero_target_typed", "expanded_target"}});
+    body.push_back(
+        {{"input_gather_element"},
+         "GatherElements",
+         {"input", "transform_targets"},
+         {MakeAttribute("axis", (int64_t)1)}});
+    body.push_back(
+        {{"const_zero_float"},
+         "Constant",
+         {},
+         {MakeAttribute("value", ToDimensionOneFloatTensor_old(0.0f))}});
+    if (!float_input) {
+      body.push_back(
+          {{"const_zero_casted"}, 
+          "Cast", 
+          {"const_zero_float"}, 
+          {MakeAttribute("to", static_cast<int64_t>(input_type))}});
+    }
+    body.push_back(
+        {{"input_gather_element_transform"},
+         "Where",
+         {"mask", float_input ? "const_zero_float" : "const_zero_casted", "input_gather_element"}});
+    body.push_back({{"loss_NCdd"}, "Neg", {"input_gather_element_transform"}});
+    body.push_back(
+        {{"loss_N1dd"},
+         "Slice",
+         {"loss_NCdd", "const_zero", "const_one", "const_one"}});
+
+    if (!ctx.hasInput(2)) {
+      body.push_back(
+          {{"squeeze_mask"},
+           "Squeeze",
+           {"mask"},
+           {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+
+      body.push_back(
+          {{"const_one_float"},
+           "Constant",
+           {},
+           {MakeAttribute("value", ToDimensionOneFloatTensor_old(1.0f))}});
+      if (!float_input) {
+        body.push_back(
+          {{"const_one_casted"}, 
+           "Cast", 
+           {"const_one_float"}, 
+           {MakeAttribute("to", static_cast<int64_t>(input_type))}});
+      }
+      body.push_back(
+          {{"weight_gather"},
+           "Where",
+           {"squeeze_mask", float_input ? "const_zero_float" : "const_zero_casted", 
+           float_input ? "const_one_float" :"const_one_casted"}});
+
+    } else {
+      body.push_back(
+          {{"weight_gather_temp"}, "Gather", {"weight", "transform_targets"}});
+
+      body.push_back(
+          {{"weight_gather_temp_1"},
+           "Where",
+           {"mask", float_input ? "const_zero_float" : "const_zero_casted", "weight_gather_temp"}});
+
+      body.push_back(
+          {{"weight_gather"},
+           "Squeeze",
+           {"weight_gather_temp_1"},
+           {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+    }
+
+    body.push_back(
+        {{"loss_unweighted"},
+         "Squeeze",
+         {"loss_N1dd"},
+         {MakeAttribute("axes", std::vector<int64_t>({1}))}});
+    if (reduction_attr == "none") {
+      body.push_back({{"loss"}, "Mul", {"loss_unweighted", "weight_gather"}});
+    } else {
+      body.push_back(
+          {{"loss_Ndd"}, "Mul", {"loss_unweighted", "weight_gather"}});
+      if (reduction_attr == "mean") {
+        body.push_back(
+            {{"loss_sum"},
+             "ReduceSum",
+             {"loss_Ndd"},
+             {MakeAttribute("keepdims", (int64_t)0)}});
+        body.push_back(
+            {{"weight_gather_sum"},
+             "ReduceSum",
+             {"weight_gather"},
+             {MakeAttribute("keepdims", (int64_t)0)}});
+        body.push_back({{"loss"}, "Div", {"loss_sum", "weight_gather_sum"}});
+      } else {
+        body.push_back(
+            {{"loss"},
+             "ReduceSum",
+             {"loss_Ndd"},
+             {MakeAttribute("keepdims", (int64_t)0)}});
+      }
+    }
+  }
+
+  auto func_nodes = FunctionBodyHelper::BuildNodes(body);
+  for (const auto& node : func_nodes) {
+    auto new_node = functionProto.add_node();
+    new_node->CopyFrom(node);
+  }
+
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    NegativeLogLikelihoodLoss,
+    12,
+    OpSchema()
+        .SetDoc(NegativeLogLikelihoodLoss_ver12_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).",
+            "T")
+        .Input(
+            1,
+            "target",
+            "Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). "
+            "If ignore_index is specified, it may have a value outside [0, C) and the target values should either be "
+            "in the range [0, C) or have the value ignore_index.",
+            "Tind")
+        .Input(
+            2,
+            "weight",
+            "Optional rescaling weight tensor. "
+            "If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.",
+            "T",
+            OpSchema::Optional)
+        .Output(0, "loss", "The negative log likelihood loss", "T")
+        .Attr(
+            "reduction",
+            "Type of reduction to apply to loss: none, sum, mean (default). "
+            "'none': the output is the loss for each sample. "
+            "'sum': the output will be summed. "
+            "'mean': the sum of the output will be divided by the sum of applied weights.",
+            AttributeProto::STRING,
+            std::string("mean"))
+        .Attr(
+            "ignore_index",
+            "Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.",
+            AttributeProto::INT,
+            false)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input, weight, and output types to floating-point tensors.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain target to integer types")
+        .SetContextDependentFunctionBodyBuilder(
+            BuildContextDependentFunctionBody_opset12)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) { 
+            // Type inference
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+  // Shape inference
+  if (hasNInputShapes(ctx, 2)) {
+    const TensorShapeProto& input_shape =
+     ctx.getInputType(0)->tensor_type().shape();
+    const TensorShapeProto& target_shape =
+     ctx.getInputType(1)->tensor_type().shape();
+
+    const int input_rank = static_cast<int>(input_shape.dim_size());
+    const int target_rank = static_cast<int>(target_shape.dim_size());
+
+    if (input_rank < 2) {
+      fail_shape_inference("Input rank must be >= 2.");
+    }
+    if (target_rank != input_rank - 1) {
+      fail_shape_inference(
+          "Target rank must be 1 less than the input rank.");
+    }
+
+    // match input dimensions (N, C, d1, ..., dk) with target
+    // dimensions of (C, d1, ..., dk)
+    for (int dim = 0; dim < target_rank; dim++) {
+      const auto input_dim =
+       dim == 0 ? input_shape.dim(dim) : input_shape.dim(dim + 1);
+      const auto target_dim = target_shape.dim(dim);
+      if (input_dim.has_dim_value() && target_dim.has_dim_value() &&
+          input_dim.dim_value() != target_dim.dim_value())
+        fail_shape_inference(
+            "Input and target dimension value mismatch.");
+    }
+
+    if (ctx.getNumInputs() == 3 && hasInputShape(ctx, 2)) {
+      const TensorShapeProto& weight_shape =
+          ctx.getInputType(2)->tensor_type().shape();
+      if (weight_shape.dim_size() != 1) {
+        fail_shape_inference("Weight rank must be 1.");
+      }
+    }
+
+    TensorShapeProto* output_shape = ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+    if (getAttribute(ctx, "reduction", "mean") == "none") {
+      // output tensor is of shape (N, d1, d2, ..., dk) if
+      // reduction attribute is "none".
+      for (int i = 0; i < input_rank - 1; i++) {
+        auto* dim = output_shape->add_dim();
+        if (i == 0)
+          *dim = input_shape.dim(i);
+        else
+          *dim = input_shape.dim(i + 1);
+      }
+    }
+    // otherwise output is a scalar.
+  }
+        }));
+
+const char* reduction_doc_sce_opset12 =
+    "Type of reduction to apply to loss: none, sum, mean(default). "
+    "'none': no reduction will be applied, "
+    "'sum': the output will be summed. "
+    "'mean': the sum of the output will be divided by the number of "
+    "elements in the output.";
+
+static const char* SoftmaxCrossEntropyLoss_ver12_doc =
+    R"DOC(Loss function that measures the softmax cross entropy
+between 'scores' and 'labels'.
+This operator first computes a loss tensor whose shape is identical to the labels input.
+If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N).
+If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk),
+the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L.
+After L is available, this operator can optionally do a reduction operator.
+
+shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk),
+        with K >= 1 in case of K-dimensional loss.
+shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),
+        with K >= 1 in case of K-dimensional loss.
+
+The loss for one sample, l_i, can caculated as follows:
+    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.
+or
+    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.
+
+loss is zero for the case when label-value equals ignore_index.
+    l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index
+
+where:
+    p = Softmax(scores)
+    y = Log(p)
+    c = labels[i][d1][d2]...[dk]
+
+Finally, L is optionally reduced:
+If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk).
+If reduction = 'sum', the output is scalar: Sum(L).
+If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),
+where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].
+)DOC";
+
+bool BuildContextDependentFunctionBodySCE_opset12(
+    const FunctionBodyBuildContext& ctx,
+    const OpSchema& schema,
+    FunctionProto& functionProto) {
+  std::vector<FunctionBodyHelper::NodeDef> body;
+
+  // Using stable implementation of LogSoftmax
+  body.push_back(
+      {{"Shape3D"},
+        "Constant",
+        {},
+        {MakeAttribute("value", ToDimensionOneInt64Tensor_old({0,0,-1}))}});
+  body.push_back(
+      {{"X_NCD"},
+       "Reshape",
+       {"scores", "Shape3D"}});
+  body.push_back(
+      {{"X_NDC"},
+       "Transpose",
+       {"X_NCD"},
+       {MakeAttribute("perm", std::vector<int64_t>({0,2,1}))}});
+  body.push_back(
+      {{"X_LogSM"},
+       "LogSoftmax",
+       {"X_NDC"},
+       {MakeAttribute("axis", (int64_t)2)}});
+  body.push_back(
+      {{"X_LogSM_NCD"},
+       "Transpose",
+       {"X_LogSM"},
+       {MakeAttribute("perm", std::vector<int64_t>({0,2,1}))}});
+  body.push_back(
+      {{"X_shape"},
+       "Shape",
+       {"scores"}});
+  body.push_back(
+      {{"X_Log"},
+       "Reshape",
+       {"X_LogSM_NCD", "X_shape"}});
+
+  // Review(mzs): Ideally we want to reuse the output from Log for sub-graph
+  // output as well but looking at the graph resolve code it does not include
+  // graph outputs as intermediate outputs, hence if intermediate X_log is
+  // renamed as log_prob then it will be treated as graph output and will not be
+  // available to NegativeLogLikelihoodLoss. May be my understanding is
+  // incorrect or there is a bug in function population code in ORTbut I will
+  // dig further to be 100%. In the meantime we just replicate the log.
+  if (ctx.hasOutput(1)) {
+    body.push_back({{"log_prob"}, "Identity", {"X_Log"}});
+  }
+
+  std::vector<std::string> input_tensor_names{"X_Log", "labels"};
+  std::vector<FunctionBodyHelper::AttributeProtoWrapper> attributes{
+      MakeRefAttribute("reduction", AttributeProto::STRING)};
+  // Add weights as input if needed.
+  if (ctx.hasInput(2)) {
+    input_tensor_names.push_back("weights");
+  }
+
+  // add ignore_index attributes if needed.
+  if (ctx.getAttribute("ignore_index") != nullptr) {
+    attributes.push_back(MakeRefAttribute("ignore_index", AttributeProto::INT));
+  }
+
+  body.push_back(
+      {{"output"},
+       "NegativeLogLikelihoodLoss",
+       input_tensor_names,
+       attributes});
+
+  auto func_nodes = FunctionBodyHelper::BuildNodes(body);
+  for (const auto& node : func_nodes) {
+    auto new_node = functionProto.add_node();
+    new_node->CopyFrom(node);
+  }
+
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SoftmaxCrossEntropyLoss,
+    12,
+    OpSchema()
+        .SetDoc(SoftmaxCrossEntropyLoss_ver12_doc)
+        .Attr(
+            "reduction",
+            reduction_doc_sce_opset12,
+            AttributeProto::STRING,
+            std::string("mean"))
+        .Attr(
+            "ignore_index",
+            "Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.",
+            AttributeProto::INT,
+            false)
+        .Input(
+            0,
+            "scores",
+            "The predicted outputs with shape [batch_size, class_size], or "
+            "[batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.",
+            "T")
+        .Input(
+            1,
+            "labels",
+            "The ground truth output tensor, with shape [batch_size], or "
+            "[batch_size, D1, D2, ..., Dk], where K is the number of dimensions. "
+            "Labels element value shall be in range of [0, C). "
+            "If ignore_index is specified, it may have a value outside [0, C) and the label values should either be "
+            "in the range [0, C) or have the value ignore_index.",
+            "Tind")
+        .Input(
+            2,
+            "weights",
+            "A manual rescaling weight given to each class. If given, it has to "
+            "be a 1D Tensor assigning weight to each of the classes. Otherwise, "
+            "it is treated as if having all ones.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "output",
+            "Weighted loss float Tensor. If reduction is 'none', this has the "
+            "shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of "
+            "K-dimensional loss. Otherwise, it is a scalar.",
+            "T")
+        .Output(
+            1,
+            "log_prob",
+            "Log probability tensor. If the output of softmax is prob, its value is log(prob).",
+            "T",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain target to integer types")
+        .SetContextDependentFunctionBodyBuilder(
+            BuildContextDependentFunctionBodySCE_opset12)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          std::string reduction = getAttribute(ctx, "reduction", "mean");
+          if (reduction.compare("none") == 0) {
+            if (hasInputShape(ctx, 1)) {
+              propagateShapeFromInputToOutput(ctx, 1, 0);
+            }
+          } else {
+            updateOutputShape(ctx, 0, TensorShapeProto());
+          }
+
+          if (ctx.getNumOutputs() == 2) {
+            propagateElemTypeFromInputToOutput(ctx, 0, 1);
+            propagateShapeFromInputToOutput(ctx, 0, 1);
+          }
+        }));
+
+std::function<void(OpSchema&)> SoftmaxFamilyDocGenerator_opset1(
+    const char* name,
+    const char* description) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The operator computes the {name} ({description}) values for each layer in the batch
+ of the given input. The input is a 2-D tensor (Tensor<float>) of size
+(batch_size x input_feature_dimensions). The output tensor has the same shape
+and contains the {name} values of the corresponding input.
+
+Input does not need to explicitly be a 2D vector; rather, it will be
+coerced into one. For an arbitrary n-dimensional tensor
+input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
+the axis provided, then input will be coerced into a 2-dimensional tensor with
+dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
+case where axis=1, this means the input tensor will be coerced into a 2D tensor
+of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
+In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
+Each of these dimensions must be matched correctly, or else the operator
+will throw errors.
+)DOC";
+                        ReplaceAll(doc, "{name}", name);
+                        ReplaceAll(doc, "{description}", description););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "axis",
+        "Describes the axis of the inputs when coerced "
+        "to 2D; defaults to one because the 0th axis most likely describes "
+        "the batch_size",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(
+        0,
+        "input",
+        "The input tensor that's coerced into a 2D matrix of size (NxD) "
+        "as described above.",
+        "T");
+    schema.Output(
+        0,
+        "output",
+        "The output values with the same "
+        "shape as input tensor (the original size without coercion).",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Softmax,
+    1,
+    OpSchema().FillUsing(
+        SoftmaxFamilyDocGenerator_opset1("softmax", "normalized exponential")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LogSoftmax,
+    1,
+    OpSchema().FillUsing(
+        SoftmaxFamilyDocGenerator_opset1("logsoftmax", "log of softmax")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Hardmax,
+    1,
+    OpSchema().FillUsing(SoftmaxFamilyDocGenerator_opset1(
+        "hardmax",
+        "1 for the first maximum value, and 0 for all others")));
+
+const char* kBroadcastDoc_old = R"DOC(
+If necessary the right-hand-side argument will be broadcasted to match the
+shape of left-hand-side argument. When broadcasting is specified, the second
+tensor can either be of element size 1 (including a scalar tensor and any
+tensor with rank equal to or smaller than the first tensor), or having its
+shape as a contiguous subset of the first tensor's shape. The starting of the
+mutually equal shape is specified by the argument "axis", and if it is not set,
+suffix matching is assumed. 1-dim expansion doesn't work yet.
+
+For example, the following tensor shapes are supported (with broadcast=1):
+
+  shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
+  shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
+  shape(A) = (2, 3, 4, 5), shape(B) = (5,)
+  shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
+  shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
+  shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
+
+Attribute `broadcast=1` needs to be passed to enable broadcasting.
+)DOC";
+
+std::function<void(OpSchema&)> MathDocGenerator_old(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Performs element-wise binary {name} (with limited broadcast support).
+{broadcast_doc})DOC";
+                        ReplaceAll(doc, "{name}", name);
+                        ReplaceAll(doc, "{broadcast_doc}", kBroadcastDoc_old););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "broadcast",
+        "Pass 1 to enable broadcasting",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+
+    // This attribute was added via AllowConsumed API in OpSchema.
+    // After removing the API, we're now using the Attr API to simulate the old
+    // definition.
+    schema.Attr(
+        "consumed_inputs",
+        "legacy optimization attribute.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "axis",
+        "If set, defines the broadcast dimensions. See doc for details.",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "A",
+        "First operand, should share the type with the second operand.",
+        "T");
+    schema.Input(
+        1,
+        "B",
+        "Second operand. With broadcasting can be of smaller size than A. "
+        "If broadcasting is disabled it should be of the same size.",
+        "T");
+    schema.Output(0, "C", "Result, has same dimensions and type as A", "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+  };
+}
+
+std::function<void(OpSchema&)> MathDocGenerator_old_opset6(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Performs element-wise binary {name} (with limited broadcast support).
+{broadcast_doc})DOC";
+                        ReplaceAll(doc, "{name}", name);
+                        ReplaceAll(doc, "{broadcast_doc}", kBroadcastDoc_old););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "broadcast",
+        "Pass 1 to enable broadcasting",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "axis",
+        "If set, defines the broadcast dimensions. See doc for details.",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "A",
+        "First operand, should share the type with the second operand.",
+        "T");
+    schema.Input(
+        1,
+        "B",
+        "Second operand. With broadcasting can be of smaller size than A. "
+        "If broadcasting is disabled it should be of the same size.",
+        "T");
+    schema.Output(0, "C", "Result, has same dimensions and type as A", "T");
+    schema.TypeConstraint(
+        "T",
+        OpSchema::numeric_types_for_math_reduction(),
+        "Constrain input and output types to high-precision numeric tensors.");
+    schema.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Add,
+    1,
+    OpSchema().FillUsing(MathDocGenerator_old("addition")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sub,
+    1,
+    OpSchema().FillUsing(MathDocGenerator_old("subtraction")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mul,
+    1,
+    OpSchema().FillUsing(MathDocGenerator_old("multiplication")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Div,
+    1,
+    OpSchema().FillUsing(MathDocGenerator_old("division")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Add,
+    6,
+    OpSchema().FillUsing(MathDocGenerator_old_opset6("addition")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sub,
+    6,
+    OpSchema().FillUsing(MathDocGenerator_old_opset6("subtraction")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mul,
+    6,
+    OpSchema().FillUsing(MathDocGenerator_old_opset6("multiplication")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Div,
+    6,
+    OpSchema().FillUsing(MathDocGenerator_old_opset6("division")));
+
+static const char* Pow_ver1_doc = R"DOC(
+Pow takes input data (Tensor<T>) and exponent Tensor, and
+produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pow,
+    1,
+    OpSchema()
+        .SetDoc(Pow_ver1_doc + std::string(kBroadcastDoc_old))
+        .Input(0, "X", "Input tensor of any shape, base of the exponent.", "T")
+        .Input(
+            1,
+            "Y",
+            "Input tensor of any shape broadcastable to X shape, "
+            "the exponent component.",
+            "T")
+        .Attr(
+            "broadcast",
+            "Pass 1 to enable broadcasting",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "axis",
+            "If set, defines the broadcast dimensions. See doc for details.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Output(0, "Z", "Output tensor (same size as X)", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Pow_ver7_doc = R"DOC(
+Pow takes input data (Tensor<T>) and exponent Tensor, and
+produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
+is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pow,
+    7,
+    OpSchema()
+        .SetDoc(std::string(Pow_ver7_doc) + GenerateBroadcastingDocMul())
+        .Input(0, "X", "First operand, base of the exponent.", "T")
+        .Input(1, "Y", "Second operand, power of the exponent.", "T")
+        .Output(0, "Z", "Output tensor.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2))
+            bidirectionalBroadcastShapeInference(
+                ctx.getInputType(0)->tensor_type().shape(),
+                ctx.getInputType(1)->tensor_type().shape(),
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+        }));
+
+static const char* Neg_ver1_doc = R"DOC(
+Neg takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where each element flipped sign, y = -x, is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Neg,
+    1,
+    OpSchema()
+        .SetDoc(Neg_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Abs_ver1_doc = R"DOC(
+Absolute takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the absolute is, y = abs(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Abs,
+    1,
+    OpSchema()
+        .SetDoc(Abs_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Reciprocal_ver1_doc = R"DOC(
+Reciprocal takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the reciprocal is, y = 1/x, is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reciprocal,
+    1,
+    OpSchema()
+        .SetDoc(Reciprocal_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Floor_ver1_doc = R"DOC(
+Floor takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the floor is, y = floor(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Floor,
+    1,
+    OpSchema()
+        .SetDoc(Floor_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Ceil_ver1_doc = R"DOC(
+Ceil takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the ceil is, y = ceil(x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Ceil,
+    1,
+    OpSchema()
+        .SetDoc(Ceil_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Sqrt_ver1_doc = R"DOC(
+Square root takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the square root is, y = x^0.5, is applied to
+the tensor elementwise. If x is negative, then it will return NaN.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sqrt,
+    1,
+    OpSchema()
+        .SetDoc(Sqrt_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Relu_ver1_doc = R"DOC(
+Relu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
+the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Relu,
+    1,
+    OpSchema()
+        .SetDoc(Relu_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* LeakyRelu_ver1_doc = R"DOC(
+LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+`f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LeakyRelu,
+    1,
+    OpSchema()
+        .Attr(
+            "alpha",
+            "Coefficient of leakage default to 0.01.",
+            AttributeProto::FLOAT,
+            0.01f)
+        .SetDoc(LeakyRelu_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Selu_ver1_doc = R"DOC(
+Selu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the scaled exponential linear unit function,
+`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
+is applied to the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Selu,
+    1,
+    OpSchema()
+        .Attr(
+            "alpha",
+            "Coefficient of SELU default to 1.6732.",
+            AttributeProto::FLOAT,
+            1.6732f)
+        .Attr(
+            "gamma",
+            "Coefficient of SELU default to 1.0507.",
+            AttributeProto::FLOAT,
+            1.0507f)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Selu_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Elu_ver1_doc = R"DOC(
+Elu takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
+0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Elu,
+    1,
+    OpSchema()
+        .Attr(
+            "alpha",
+            "Coefficient of ELU default to 1.0.",
+            AttributeProto::FLOAT,
+            1.0f)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Elu_ver1_doc)
+        .Input(0, "X", "1D input tensor", "T")
+        .Output(0, "Y", "1D input tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Exp_ver1_doc = R"DOC(
+Calculates the exponential of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Exp,
+    1,
+    OpSchema()
+        .SetDoc(Exp_ver1_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The exponential of the input tensor computed "
+            "element-wise",
+            "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Log_ver1_doc = R"DOC(
+Calculates the natural log of the given input tensor, element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Log,
+    1,
+    OpSchema()
+        .SetDoc(Log_ver1_doc)
+        .Input(0, "input", "Input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The natural log of the input tensor computed "
+            "element-wise",
+            "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Tanh_ver1_doc = R"DOC(
+Calculates the hyperbolic tangent of the given input tensor element-wise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tanh,
+    1,
+    OpSchema()
+        .SetDoc(Tanh_ver1_doc)
+        .Input(0, "input", "1-D input tensor", "T")
+        .Output(
+            0,
+            "output",
+            "The hyperbolic tangent values of the input tensor "
+            "computed element-wise",
+            "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* PRelu_ver1_doc = R"DOC(
+
+PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+`f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    PRelu,
+    1,
+    OpSchema()
+        .SetDoc(PRelu_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Input(
+            1,
+            "slope",
+            "Slope tensor. If `Slope` is of size 1, the value is shared"
+            "across different channels",
+            "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    PRelu,
+    6,
+    OpSchema()
+        .SetDoc(PRelu_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Input(
+            1,
+            "slope",
+            "Slope tensor. If `Slope` is of size 1, the value is shared"
+            "across different channels",
+            "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* PRelu_ver7_doc = R"DOC(
+PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+`f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    PRelu,
+    7,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(PRelu_ver7_doc) +
+            GenerateBroadcastingDocUni("tensor slope", "input tensor X")))
+        .Input(0, "X", "Input tensor", "T")
+        .Input(
+            1,
+            "slope",
+            "Slope tensor. The shape of slope can be smaller then first input X; "
+            "if so, its shape must be unidirectional broadcastable to X",
+            "T")
+        .Output(0, "Y", "Output tensor (same size as X)", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Sigmoid_ver1_doc = R"DOC(
+Sigmoid takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
+tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sigmoid,
+    1,
+    OpSchema()
+        .SetDoc(Sigmoid_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* HardSigmoid_ver1_doc = R"DOC(
+HardSigmoid takes one input data (Tensor<T>) and produces one output data
+(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
+is applied to the tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    HardSigmoid,
+    1,
+    OpSchema()
+        .Attr(
+            "alpha",
+            "Value of alpha default to 0.2",
+            AttributeProto::FLOAT,
+            0.2f)
+        .Attr(
+            "beta",
+            "Value of beta default to 0.5",
+            AttributeProto::FLOAT,
+            0.5f)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(HardSigmoid_ver1_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Max_ver1_doc = R"DOC(
+Element-wise max of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Max,
+    1,
+    OpSchema()
+        .SetDoc(Max_ver1_doc)
+        .Input(0, "data_0", "List of tensors for Max.", "T", OpSchema::Variadic)
+        .Output(0, "max", "Output tensor. Same dimension as inputs.", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Min_ver1_doc = R"DOC(
+Element-wise min of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Min,
+    1,
+    OpSchema()
+        .SetDoc(Min_ver1_doc)
+        .Input(0, "data_0", "List of tensors for Min", "T", OpSchema::Variadic)
+        .Output(0, "min", "Output tensor. Same dimension as inputs.", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Sum_ver1_doc = R"DOC(
+Element-wise sum of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sum,
+    1,
+    OpSchema()
+        .SetDoc(Sum_ver1_doc)
+        .Input(0, "data_0", "List of tensors for Sum.", "T", OpSchema::Variadic)
+        .Output(0, "sum", "Output tensor. Same dimension as inputs.", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Mean_ver1_doc = R"DOC(
+Element-wise mean of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mean,
+    1,
+    OpSchema()
+        .SetDoc(Mean_ver1_doc)
+        .Input(
+            0,
+            "data_0",
+            "List of tensors for Mean.",
+            "T",
+            OpSchema::Variadic)
+        .Output(0, "mean", "Output tensor. Same dimension as inputs.", "T")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Clip_ver1_doc = R"DOC(
+Clip operator limits the given input within an interval. The interval is
+specified with arguments 'min' and 'max'. They default to
+numeric_limits::lowest() and numeric_limits::max() respectively.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Clip,
+    1,
+    OpSchema()
+        .SetDoc(Clip_ver1_doc)
+        .Attr(
+            "min",
+            "Minimum value, under which element is replaced by min",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "max",
+            "Maximum value, above which element is replaced by max",
+            AttributeProto::FLOAT,
+            OPTIONAL_VALUE)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Input(0, "input", "Input tensor whose elements to be clipped", "T")
+        .Output(0, "output", "Output tensor with clipped input elements", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Gemm_ver1_doc = R"DOC(General Matrix multiplication:
+https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+Compute Y = alpha * A * B + beta * C, where input tensor A has
+dimension (M X K), input tensor B has dimension (K X N), input tensor C and
+output tensor Y have dimension (M X N).
+If attribute broadcast is non-zero, input tensor C will be broadcasted to match
+the dimension requirement. A will be transposed before doing the computation
+if attribute transA is non-zero, same for B and transB.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gemm,
+    1,
+    OpSchema()
+        .SetDoc(Gemm_ver1_doc)
+        .Input(0, "A", "Input tensor A", "T")
+        .Input(1, "B", "Input tensor B", "T")
+        .Input(2, "C", "Input tensor C, can be inplace.", "T")
+        .Output(0, "Y", "Output tensor.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "transA",
+            "Whether A should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "transB",
+            "Whether B should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "broadcast",
+            "Whether C should be broadcasted",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "alpha",
+            "Scalar multiplier for the product of input tensors A * B, the default value is 1.0.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "beta",
+            "Scalar multiplier for input tensor C, the default value is 1.0.",
+            AttributeProto::FLOAT,
+            1.0f));
+
+static const char* Gemm_ver6_doc = R"DOC(General Matrix multiplication:
+https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+Compute Y = alpha * A * B + beta * C, where input tensor A has
+dimension (M X K), input tensor B has dimension (K X N), input tensor C and
+output tensor Y have dimension (M X N).
+If attribute broadcast is non-zero, input tensor C will be broadcasted to match
+the dimension requirement. A will be transposed before doing the computation
+if attribute transA is non-zero, same for B and transB.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gemm,
+    6,
+    OpSchema()
+        .SetDoc(Gemm_ver6_doc)
+        .Input(0, "A", "Input tensor A", "T")
+        .Input(1, "B", "Input tensor B", "T")
+        .Input(2, "C", "Input tensor C", "T")
+        .Output(0, "Y", "Output tensor.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .Attr(
+            "transA",
+            "Whether A should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "transB",
+            "Whether B should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "broadcast",
+            "Whether C should be broadcasted",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "alpha",
+            "Scalar multiplier for the product of input tensors A * B, the default value is 1.0.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "beta",
+            "Scalar multiplier for input tensor C, the default value is 1.0.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2)) {
+            auto transAAttr = ctx.getAttribute("transA");
+            bool transA =
+                transAAttr ? static_cast<int>(transAAttr->i()) != 0 : false;
+            auto transBAttr = ctx.getAttribute("transB");
+            bool transB =
+                transBAttr ? static_cast<int>(transBAttr->i()) != 0 : false;
+
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() =
+                ctx.getInputType(0)->tensor_type().shape().dim(transA ? 1 : 0);
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() =
+                ctx.getInputType(1)->tensor_type().shape().dim(transB ? 0 : 1);
+          } else if (
+              hasInputShape(ctx, 2) &&
+              (!ctx.getAttribute("broadcast") ||
+               static_cast<int>(ctx.getAttribute("broadcast")->i()) == 0)) {
+            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape() =
+                ctx.getInputType(2)->tensor_type().shape();
+          }
+        }));
+
+static const char* Gemm_ver7_doc = R"DOC(General Matrix multiplication:
+https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+A' = transpose(A) if transA else A
+
+B' = transpose(B) if transB else B
+
+Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+and output tensor Y has shape (M, N). A will be transposed before doing the
+computation if attribute transA is non-zero, same for B and transB.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gemm,
+    7,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Gemm_ver7_doc) +
+            GenerateBroadcastingDocUni("tensor C", "tensor A * B")))
+        .Input(
+            0,
+            "A",
+            "Input tensor A. "
+            "The shape of A should be (M, K) if transA is 0, "
+            "or (K, M) if transA is non-zero.",
+            "T")
+        .Input(
+            1,
+            "B",
+            "Input tensor B. "
+            "The shape of B should be (K, N) if transB is 0, "
+            "or (N, K) if transB is non-zero.",
+            "T")
+        .Input(
+            2,
+            "C",
+            "Input tensor C. "
+            "The shape of C should be unidirectional broadcastable to (M, N).",
+            "T")
+        .Output(0, "Y", "Output tensor of shape (M, N).", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .Attr(
+            "transA",
+            "Whether A should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "transB",
+            "Whether B should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "alpha",
+            "Scalar multiplier for the product of input tensors A * B.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "beta",
+            "Scalar multiplier for input tensor C.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2)) {
+            auto transAAttr = ctx.getAttribute("transA");
+            bool transA =
+                transAAttr ? static_cast<int>(transAAttr->i()) != 0 : false;
+            auto transBAttr = ctx.getAttribute("transB");
+            bool transB =
+                transBAttr ? static_cast<int>(transBAttr->i()) != 0 : false;
+            auto& first_input_shape = getInputShape(ctx, 0);
+            auto& second_input_shape = getInputShape(ctx, 1);
+            if (first_input_shape.dim_size() != 2) {
+              fail_shape_inference("First input does not have rank 2");
+            }
+            if (second_input_shape.dim_size() != 2) {
+              fail_shape_inference("Second input does not have rank 2");
+            }
+            updateOutputShape(
+                ctx,
+                0,
+                {first_input_shape.dim(transA ? 1 : 0),
+                 second_input_shape.dim(transB ? 0 : 1)});
+          }
+        }));
+
+static const char* Gemm_ver9_doc = R"DOC(General Matrix multiplication:
+https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
+
+A' = transpose(A) if transA else A
+
+B' = transpose(B) if transB else B
+
+Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
+input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
+and output tensor Y has shape (M, N). A will be transposed before doing the
+computation if attribute transA is non-zero, same for B and transB.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gemm,
+    9,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Gemm_ver9_doc) +
+            GenerateBroadcastingDocUni("tensor C", "tensor A * B")))
+        .Input(
+            0,
+            "A",
+            "Input tensor A. "
+            "The shape of A should be (M, K) if transA is 0, "
+            "or (K, M) if transA is non-zero.",
+            "T")
+        .Input(
+            1,
+            "B",
+            "Input tensor B. "
+            "The shape of B should be (K, N) if transB is 0, "
+            "or (N, K) if transB is non-zero.",
+            "T")
+        .Input(
+            2,
+            "C",
+            "Input tensor C. "
+            "The shape of C should be unidirectional broadcastable to (M, N).",
+            "T")
+        .Output(0, "Y", "Output tensor of shape (M, N).", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)"},
+            "Constrain input and output types to float/int tensors.")
+        .Attr(
+            "transA",
+            "Whether A should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "transB",
+            "Whether B should be transposed",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "alpha",
+            "Scalar multiplier for the product of input tensors A * B.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .Attr(
+            "beta",
+            "Scalar multiplier for input tensor C.",
+            AttributeProto::FLOAT,
+            1.0f)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 2)) {
+            auto transAAttr = ctx.getAttribute("transA");
+            bool transA =
+                transAAttr ? static_cast<int>(transAAttr->i()) != 0 : false;
+            auto transBAttr = ctx.getAttribute("transB");
+            bool transB =
+                transBAttr ? static_cast<int>(transBAttr->i()) != 0 : false;
+            auto& first_input_shape = getInputShape(ctx, 0);
+            auto& second_input_shape = getInputShape(ctx, 1);
+            if (first_input_shape.dim_size() != 2) {
+              fail_shape_inference("First input does not have rank 2");
+            }
+            if (second_input_shape.dim_size() != 2) {
+              fail_shape_inference("Second input does not have rank 2");
+            }
+            updateOutputShape(
+                ctx,
+                0,
+                {first_input_shape.dim(transA ? 1 : 0),
+                 second_input_shape.dim(transB ? 0 : 1)});
+          }
+        }));
+
+static const char* Max_ver6_doc = R"DOC(
+Element-wise max of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Max,
+    6,
+    OpSchema()
+        .SetDoc(Max_ver6_doc)
+        .Input(0, "data_0", "List of tensors for Max.", "T", OpSchema::Variadic)
+        .Output(0, "max", "Output tensor. Same dimension as inputs.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Min_ver6_doc = R"DOC(
+Element-wise min of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Min,
+    6,
+    OpSchema()
+        .SetDoc(Min_ver6_doc)
+        .Input(0, "data_0", "List of tensors for Min", "T", OpSchema::Variadic)
+        .Output(0, "min", "Output tensor. Same dimension as inputs.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Sum_ver6_doc = R"DOC(
+Element-wise sum of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Sum,
+    6,
+    OpSchema()
+        .SetDoc(Sum_ver6_doc)
+        .Input(0, "data_0", "List of tensors for Sum.", "T", OpSchema::Variadic)
+        .Output(0, "sum", "Output tensor. Same dimension as inputs.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Mean_ver6_doc = R"DOC(
+Element-wise mean of each of the input tensors. All inputs and outputs must
+have the same shape and data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Mean,
+    6,
+    OpSchema()
+        .SetDoc(Mean_ver6_doc)
+        .Input(
+            0,
+            "data_0",
+            "List of tensors for Mean.",
+            "T",
+            OpSchema::Variadic)
+        .Output(0, "mean", "Output tensor. Same dimension as inputs.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* MatMul_ver1_doc = R"DOC(
+Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MatMul,
+    1,
+    OpSchema()
+        .Input(0, "A", "N-dimensional matrix A", "T")
+        .Input(1, "B", "N-dimensional matrix B", "T")
+        .Output(0, "Y", "Matrix multiply results from A * B", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .SetDoc(MatMul_ver1_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 2)) {
+            return;
+          }
+
+          const auto shape0 = ctx.getInputType(0)->tensor_type().shape();
+          const auto shape1 = ctx.getInputType(1)->tensor_type().shape();
+
+          if (shape0.dim_size() == 0 || shape1.dim_size() == 0) {
+            fail_shape_inference("Input tensors of wrong rank (0).");
+          }
+
+          TensorShapeProto shapeL, shapeR;
+
+          // First promote each shape to at least rank-2. This logic is
+          // specific to matmul, not generic broadcasting.
+          {
+            if (shape0.dim_size() == 1) {
+              shapeL.add_dim()->set_dim_value(1);
+              *shapeL.add_dim() = shape0.dim(0);
+            } else {
+              *shapeL.mutable_dim() = shape0.dim();
+            }
+            if (shape1.dim_size() == 1) {
+              *shapeR.add_dim() = shape1.dim(0);
+              shapeR.add_dim()->set_dim_value(1);
+            } else {
+              *shapeR.mutable_dim() = shape1.dim();
+            }
+          }
+
+          // Check for compatible matrix multiply dimensions
+          {
+            auto dimL = shapeL.dim(shapeL.dim_size() - 1);
+            auto dimR = shapeR.dim(shapeR.dim_size() - 2);
+            if (dimL.has_dim_value() && dimR.has_dim_value() &&
+                dimL.dim_value() != dimR.dim_value()) {
+              fail_shape_inference(
+                  "Incompatible dimensions for matrix multiplication");
+              ;
+            }
+          }
+
+          TensorShapeProto resultShape;
+
+          // Now call out to generic multidimensional broadcasting for
+          // the broadcastable prefixes.
+          {
+            TensorShapeProto prefixShapeL, prefixShapeR;
+            for (int i = 0; i < shapeL.dim_size() - 2; ++i) {
+              *prefixShapeL.add_dim() = shapeL.dim(i);
+            }
+            for (int i = 0; i < shapeR.dim_size() - 2; ++i) {
+              *prefixShapeR.add_dim() = shapeR.dim(i);
+            }
+            bidirectionalBroadcastShapeInference(
+                prefixShapeL, prefixShapeR, resultShape);
+          }
+
+          // Back to matmul-specific. Add the trailing dimensions back in.
+          {
+            if (shape0.dim_size() != 1) {
+              *resultShape.add_dim() = shapeL.dim(shapeL.dim_size() - 2);
+            }
+            if (shape1.dim_size() != 1) {
+              *resultShape.add_dim() = shapeR.dim(shapeR.dim_size() - 1);
+            }
+          }
+
+          *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape() =
+              resultShape;
+        }));
+
+static const char* TopK_ver1_doc = R"DOC(
+Retrieve the top-K elements along a specified axis. Given an input tensor of
+shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+  -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+    which contains the values of the top k elements along the specified axis
+  -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+   contains the indices of the top k elements (original indices from the input
+   tensor).
+Given two equivalent values, this operator uses the indices along the axis  as
+ a tiebreaker. That is, the element with the lower index will appear first.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    TopK,
+    1,
+    OpSchema()
+        .SetDoc(TopK_ver1_doc)
+        .Input(0, "X", "Tensor of shape [a_1, a_2, ..., a_n, r]", "T")
+        .Output(
+            0,
+            "Values",
+            "Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] "
+            "containing top K values from the input tensor",
+            "T")
+        .Output(
+            1,
+            "Indices",
+            "Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] "
+            "containing the corresponding input tensor indices for the top K "
+            "values.",
+            "I")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64")
+        .Attr(
+            "k",
+            "Number of top elements to retrieve",
+            AttributeProto::INT,
+            true)
+        .Attr(
+            "axis",
+            "Dimension on which to do the sort.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference:
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          updateOutputElemType(ctx, 1, TensorProto::INT64);
+
+          // Shape inference:
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int64_t rank = input_shape.dim_size();
+          int64_t axis = getAttribute(ctx, "axis", -1);
+          if (axis < 0)
+            axis += rank;
+          if (axis < 0 || axis >= rank) {
+            fail_shape_inference("Invalid value for attribute axis");
+          }
+          int64_t k = getAttribute(ctx, "k", -1);
+          if (k <= 0) {
+            fail_shape_inference("Invalid value for attribute k");
+          }
+          // TODO: unclear what results should be if axis has less than k
+          // elements.
+          TensorShapeProto result_shape = input_shape;
+          result_shape.mutable_dim(static_cast<int>(axis))->set_dim_value(k);
+          updateOutputShape(ctx, 0, result_shape);
+          updateOutputShape(ctx, 1, result_shape);
+        }));
+
+static const char* TopK_ver10_doc = R"DOC(
+Retrieve the top-K elements along a specified axis. Given an input tensor of
+shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+  -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+    which contains the values of the top k elements along the specified axis
+  -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+   contains the indices of the top k elements (original indices from the input
+   tensor).
+
+Given two equivalent values, this operator uses the indices along the axis  as
+ a tiebreaker. That is, the element with the lower index will appear first.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    TopK,
+    10,
+    OpSchema()
+        .SetDoc(TopK_ver10_doc)
+        .Input(0, "X", "Tensor of shape [a_1, a_2, ..., a_n, r]", "T")
+        .Input(
+            1,
+            "K",
+            "A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve",
+            "tensor(int64)")
+        .Output(
+            0,
+            "Values",
+            "Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] "
+            "containing top K values from the input tensor",
+            "T")
+        .Output(
+            1,
+            "Indices",
+            "Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] "
+            "containing the corresponding input tensor indices for the top K "
+            "values.",
+            "I")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64")
+        .Attr(
+            "axis",
+            "Dimension on which to do the sort.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference:
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          updateOutputElemType(ctx, 1, TensorProto::INT64);
+          // Shape inference:
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int64_t rank = input_shape.dim_size();
+          int64_t axis = getAttribute(ctx, "axis", -1);
+          if (axis < 0)
+            axis += rank;
+          if (axis < 0 || axis >= rank) {
+            fail_shape_inference("Invalid value for attribute axis");
+          }
+
+          const auto& axis_dim = input_shape.dim(static_cast<int>(axis));
+          const auto* k = ctx.getInputData(1);
+
+          // Infer output shape if:
+          // (1) 'K' is available
+          // (2) axis_dim has dim value
+          // Othewise cannot reliably compute output shape as axis dim value is
+          // unknown and hence cannot determine if axis dim value >= k (which
+          // should be enforced)
+          if (nullptr != k && axis_dim.has_dim_value()) {
+            int64_t k_value = 0;
+            if (k->dims_size() != 1 || k->dims(0) != 1) {
+              fail_shape_inference("K input must be a one-dimensional tensor of size 1.");
+            }
+
+            if (k->data_type() == TensorProto::INT64) {
+              const auto& data = ParseData<int64_t>(k);
+              k_value = data[0];
+            } else {
+              fail_shape_inference("K input must be of type int64.");
+            }
+
+            if (axis_dim.dim_value() < k_value) {
+                fail_shape_inference("Axis has less than the requested k elements.");
+            }
+
+            TensorShapeProto result_shape = input_shape;
+            result_shape.mutable_dim(static_cast<int>(axis))
+                ->set_dim_value(k_value);
+
+            updateOutputShape(ctx, 0, result_shape);
+            updateOutputShape(ctx, 1, result_shape);
+
+            return;
+          }
+
+          // Infer output shapes' rank in any case
+          auto* output_shape_0 = getOutputShape(ctx, 0);
+          auto* output_shape_1 = getOutputShape(ctx, 1);
+          for (int i = 0; i < input_shape.dim_size(); ++i) {
+            output_shape_0->add_dim();
+            output_shape_1->add_dim();
+          }
+
+          return;
+        }));
+
+static const char* Clip_ver6_doc = R"DOC(
+Clip operator limits the given input within an interval. The interval is
+specified with arguments 'min' and 'max'. They default to
+numeric_limits::lowest() and numeric_limits::max() respectively.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Clip,
+    6,
+    OpSchema()
+        .SetDoc(Clip_ver6_doc)
+        .Attr(
+            "min",
+            "Minimum value, under which element is replaced by min",
+            AttributeProto::FLOAT,
+            std::numeric_limits<float>::lowest())
+        .Attr(
+            "max",
+            "Maximum value, above which element is replaced by max",
+            AttributeProto::FLOAT,
+            std::numeric_limits<float>::max())
+        .Input(0, "input", "Input tensor whose elements to be clipped", "T")
+        .Output(0, "output", "Output tensor with clipped input elements", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Clip_ver11_doc = R"DOC(
+Clip operator limits the given input within an interval. The interval is
+specified by the inputs 'min' and 'max'. They default to
+numeric_limits::lowest() and numeric_limits::max(), respectively.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Clip,
+    11,
+    OpSchema()
+        .SetDoc(Clip_ver11_doc)
+        .Input(0, "input", "Input tensor whose elements to be clipped", "T")
+        .Input(
+            1,
+            "min",
+            "Minimum value, under which element is replaced by min. "
+            "It must be a scalar(tensor of empty shape).",
+            "T",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "max",
+            "Maximum value, above which element is replaced by max. "
+            "It must be a scalar(tensor of empty shape).",
+            "T",
+            OpSchema::Optional)
+        .Output(0, "output", "Output tensor with clipped input elements", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+std::function<void(OpSchema&)> ElementwiseMultiOpDocGenerator_old(
+    const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support).
+All inputs and outputs must have the same data type.
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "data_0",
+        "List of tensors for " + std::string(name) + ".",
+        "T",
+        OpSchema::Variadic);
+    schema.Output(0, name, "Output tensor.", "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      int num_inputs = static_cast<int>(ctx.getNumInputs());
+      std::vector<const TensorShapeProto*> shapes;
+      for (int i = 0; i < num_inputs; ++i) {
+        auto input_type = ctx.getInputType(i);
+        if (nullptr == input_type || !input_type->has_tensor_type() ||
+            !input_type->tensor_type().has_shape()) {
+          return;
+        }
+        shapes.push_back(&input_type->tensor_type().shape());
+      }
+
+      multidirectionalBroadcastShapeInference(
+          shapes,
+          *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Max,
+    8,
+    OpSchema().FillUsing(ElementwiseMultiOpDocGenerator_old("max")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Min,
+    8,
+    OpSchema().FillUsing(ElementwiseMultiOpDocGenerator_old("min")));
+
+static const char* LeakyRelu_ver6_doc = R"DOC(
+LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
+output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
+`f(x) = x for x >= 0`, is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LeakyRelu,
+    6,
+    OpSchema()
+        .Attr("alpha", "Coefficient of leakage.", AttributeProto::FLOAT, 0.01f)
+        .SetDoc(LeakyRelu_ver6_doc)
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* PRelu_ver9_doc = R"DOC(
+PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
+output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
+`f(x) = x for x >= 0`., is applied to the data tensor elementwise.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    PRelu,
+    9,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(PRelu_ver9_doc) +
+            GenerateBroadcastingDocUni("tensor slope", "input tensor X")))
+        .Input(0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "slope",
+            "Slope tensor. The shape of slope can be smaller then first input X; "
+            "if so, its shape must be unidirectional broadcastable to X",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(0,
+            "Y",
+            "Output tensor (same size as X)",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(int32)",
+             "tensor(int64)"},
+            "Constrain input and output types to float/int tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/nn/defs.cc b/third_party/onnx/onnx/defs/nn/defs.cc
new file mode 100644
index 0000000000..3b3f6c0ebe
--- /dev/null
+++ b/third_party/onnx/onnx/defs/nn/defs.cc
@@ -0,0 +1,2686 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <algorithm>
+#include <cmath>
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+const char* pads_doc =
+    "Padding for the beginning and ending along each spatial axis, it can take any value greater "
+    "than or equal to 0. The value represent the number of pixels added to the beginning "
+    "and end part of the corresponding axis. `pads` format should be as follow "
+    "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
+    "added at the beginning of axis `i` and xi_end, the number of pixels added at "
+    "the end of axis `i`. This attribute cannot be used simultaneously with "
+    "auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.";
+const char* conv_auto_pad_doc =
+    "auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where "
+    "default value is NOTSET, which means explicit padding is used. "
+    "SAME_UPPER or SAME_LOWER mean pad the input so that "
+    "`output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. "
+    "The padding is split between the two sides equally or almost equally (depending "
+    "on whether it is even or odd). In case the padding is an odd number, the extra "
+    "padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.";
+const char* conv_transpose_auto_pad_doc =
+    "auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where "
+    "default value is NOTSET, which means explicit padding is used. "
+    "SAME_UPPER or SAME_LOWER mean pad the input so that "
+    "`output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. "
+    "The padding is split between the two sides equally or almost equally (depending "
+    "on whether it is even or odd). In case the padding is an odd number, the extra "
+    "padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.";
+
+void convPoolShapeInference(
+    InferenceContext& ctx,
+    bool use_dilation,
+    bool require_kernel_shape,
+    int input1Idx,
+    int input2Idx) {
+  // we need the first input shape for this inference.
+  if (!hasInputShape(ctx, input1Idx)) {
+    return;
+  }
+
+  // if kernel shape is an input (and not attribute)
+  // we need the shape of the second input.
+  if (!require_kernel_shape && !hasInputShape(ctx, input2Idx)) {
+    return;
+  }
+
+  auto input_shape = ctx.getInputType(input1Idx)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    fail_shape_inference("Input tensor must have atleast 2 dimensions");
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  // Only MaxPool and Conv support dilation. For
+  // simplicity of the code, we just treat the rest of them as having all-1s
+  // dilation.
+  std::vector<int64_t> dilations;
+  if (use_dilation && getRepeatedAttribute(ctx, "dilations", dilations)) {
+    if (dilations.size() != n_input_dims) {
+      fail_shape_inference("Attribute dilations has incorrect size");
+    }
+  } else {
+    dilations.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> strides;
+  if (getRepeatedAttribute(ctx, "strides", strides)) {
+    if (strides.size() != n_input_dims) {
+      fail_shape_inference("Attribute strides has incorrect size");
+    }
+  } else {
+    strides.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> kernel_shape;
+  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
+    if (kernel_shape.size() != n_input_dims) {
+      fail_shape_inference("Attribute kernel_shape has incorrect size");
+    }
+  } else if (require_kernel_shape) {
+    fail_shape_inference("Attribute kernel_shape must be specified");
+  } else {
+    auto second_input_shape =
+        ctx.getInputType(input2Idx)->tensor_type().shape();
+    for (int i = 2; i < second_input_shape.dim_size(); ++i) {
+      if (!second_input_shape.dim(i).has_dim_value()) {
+        return;
+      }
+      kernel_shape.push_back(second_input_shape.dim(i).dim_value());
+    }
+  }
+
+  std::vector<int64_t> effective_kernel_shape = kernel_shape;
+  for (int i = 0; i < static_cast<int>(kernel_shape.size()); i++) {
+    // accounting for dilation, how big is the kernel in this dimension
+    effective_kernel_shape[i] =
+        (effective_kernel_shape[i] - 1) * dilations[i] + 1;
+  }
+
+  std::vector<int64_t> pads;
+  if (getRepeatedAttribute(ctx, "pads", pads)) {
+    if (pads.size() != n_input_dims * 2) {
+      fail_shape_inference("Attribute pads has incorrect size");
+    }
+  } else {
+    pads.assign(n_input_dims * 2, 0);
+    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
+    if ((nullptr != auto_pad_attr) && (auto_pad_attr->s() != "VALID")) {
+      int input_dims_size = static_cast<int>(n_input_dims);
+      for (int i = 0; i < input_dims_size; ++i) {
+        int64_t residual = 0;
+        int64_t stride = strides[i];
+        if (stride > 1) {
+          if (!input_shape.dim(2 + i).has_dim_value()) {
+            continue;
+          }
+          residual = input_shape.dim(2 + i).dim_value();
+          while (residual >= stride) {
+            residual -= stride;
+          }
+        }
+        int64_t total_pad = residual == 0
+            ? effective_kernel_shape[i] - stride
+            : effective_kernel_shape[i] - residual;
+        if (total_pad < 0)
+          total_pad = 0;
+        int64_t half_pad_small = total_pad >> 1;
+        int64_t half_pad_big = total_pad - half_pad_small;
+        if (auto_pad_attr->s() == "SAME_UPPER") {
+          pads[i] = half_pad_small;
+          pads[i + input_dims_size] = half_pad_big;
+        } else if (auto_pad_attr->s() == "SAME_LOWER") {
+          pads[i] = half_pad_big;
+          pads[i + input_dims_size] = half_pad_small;
+        }
+      }
+    }
+  }
+
+  auto output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  if (require_kernel_shape) {
+    // add the first two dimensions from the input.
+    *output_shape->add_dim() = input_shape.dim(0);
+    *output_shape->add_dim() = input_shape.dim(1);
+  } else {
+    *output_shape->add_dim() = input_shape.dim(0);
+    auto& second_input_shape = getInputShape(ctx, input2Idx);
+    if (second_input_shape.dim_size() < 1) {
+      fail_shape_inference("Second input tensor has wrong dimension");
+    }
+    *output_shape->add_dim() = second_input_shape.dim(0);
+  }
+
+  int kernel_shape_size = static_cast<int>(kernel_shape.size());
+  for (int i = 0; i < kernel_shape_size; ++i) {
+    auto newdim = output_shape->add_dim();
+    if (!input_shape.dim(2 + i).has_dim_value()) {
+      continue;
+    }
+    // how big is the input, including padding
+    int64_t effective_input_size = input_shape.dim(2 + i).dim_value();
+    effective_input_size += pads[i];
+    effective_input_size += pads[i + kernel_shape_size];
+
+    // default is floor mode .i.e. ceil_mode is set to 0
+    auto ceil_mode = getAttribute(ctx, "ceil_mode", 0);
+
+    // how many times we can move the kernel from it's initial position, based
+    // on the stride
+    int64_t strided_kernel_positions;
+
+    if (ceil_mode == 1)
+      strided_kernel_positions = (int64_t)(std::ceil(
+          (effective_input_size - effective_kernel_shape[i]) /
+          float(strides[i])));
+    else
+      strided_kernel_positions =
+          (effective_input_size - effective_kernel_shape[i]) / strides[i];
+
+    // add in the initial position
+    newdim->set_dim_value(1 + strided_kernel_positions);
+  }
+
+  if (ctx.getNumOutputs() > 1) {
+    // MaxPool with two outputs case.
+    auto second_output_shape =
+        ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape();
+    second_output_shape->CopyFrom(*output_shape);
+  }
+}
+
+std::vector<std::string> GetSupportedDataTypesForPoolingOps(bool supports8bit) {
+  if (supports8bit) {
+    return {
+        "tensor(float16)",
+        "tensor(float)",
+        "tensor(double)",
+        "tensor(int8)",
+        "tensor(uint8)"};
+  }
+  return {"tensor(float16)", "tensor(float)", "tensor(double)"};
+}
+
+std::function<void(OpSchema&)> PoolOpSchemaGenerator(
+    const char* name,
+    const char* opName,
+    const char* additionalDescription,
+    bool use_dilation,
+    bool supports8bit = false) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+ {name} consumes an input tensor X and applies {opName} pooling across
+ the tensor according to kernel sizes, stride sizes, and pad lengths.
+ {opName} pooling consisting of computing the {opName} on all values of a
+ subset of the input tensor according to the kernel size and downsampling the
+ data into the output tensor Y for further processing. The output spatial shape will be following:
+ ```
+ output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
+ ```
+ or
+ ```
+ output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
+ ```
+ if ceil_mode is enabled
+
+ ```
+ * pad_shape[i] is sum of pads along axis i
+ ```
+
+ `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+ ```
+ VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
+ SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+ ```
+ And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+ ```
+ pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
+ ```
+ {additionalDescription}
+ )DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(doc, "{opName}", opName);
+        ReplaceAll(doc, "{additionalDescription}", additionalDescription);
+        ReplaceAll(
+            doc,
+            "{kernelSpatialShape}",
+            use_dilation ? "((kernel_spatial_shape[i] - 1) * dilations[i] + 1)"
+                         : "kernel_spatial_shape[i]"););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "kernel_shape",
+        "The size of the kernel along each axis.",
+        AttributeProto::INTS);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        conv_auto_pad_doc,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "ceil_mode",
+        "Whether to use ceil or floor (default) to compute the output shape.",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data. For non image case, the "
+        "dimensions are in the form of "
+        "(N x C x D1 x D2 ... Dn), where N is the batch "
+        "size. Optionally, if dimension denotation is "
+        "in effect, the operation expects the input "
+        "data tensor to arrive with the dimension denotation "
+        "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from average or max pooling across "
+        "the input tensor. Dimensions will vary based "
+        "on various kernel, stride, and pad sizes. Floor value of "
+        "the dimension is used",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        GetSupportedDataTypesForPoolingOps(supports8bit),
+        supports8bit
+            ? "Constrain input and output types to float and 8 bit tensors."
+            : "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([use_dilation](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (ctx.getNumOutputs() > 1) {
+        // MaxPool with two outputs case.
+        auto output_type = ctx.getOutputType(1);
+        if (output_type->value_case() == TypeProto::kTensorType ||
+            output_type->value_case() == TypeProto::VALUE_NOT_SET) {
+          output_type->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
+        }
+      }
+      convPoolShapeInference(ctx, use_dilation, true, 0, 1);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    AveragePool,
+    11,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator(
+            "AveragePool",
+            "average",
+            "The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).",
+            false,
+            false))
+        .Attr(
+            "count_include_pad",
+            "Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxPool,
+    12,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator(
+            "MaxPool",
+            "max",
+            "The output of each pooling window is maximum number of elements exclude pad. ",
+            true,
+            true))
+        .Attr(
+            "storage_order",
+            "The storage order of the tensor. 0 is row major, and 1 is column major.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "dilations",
+            "Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Output(
+            1,
+            "Indices",
+            "Indices tensor from max pooling across the input tensor. "
+            "The dimensions of indices are the same as output tensor. "
+            "The values in indices of are the indices of the selected values during pooling. "
+            "The indices are computed as flatten 1-D tensor, "
+            "and the indices do not consider padding. "
+            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
+            "I",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64"));
+
+void maxUnpoolShapeInference(InferenceContext& ctx) {
+  // we need at least two inputs to have a shape for this inference.
+  if (ctx.getNumInputs() != 2 && ctx.getNumInputs() != 3) {
+    fail_type_inference("MaxUnpool op must have either two or three inputs.");
+  }
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+  if (!hasInputShape(ctx, 0)) {
+    return; // If first input does not have shape, we cannot infer much.
+  }
+  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    fail_shape_inference("Input tensor X must have atleast 2 dimensions.");
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  std::vector<int64_t> pads;
+  if (getRepeatedAttribute(ctx, "pads", pads)) {
+    if (pads.size() != n_input_dims * 2) {
+      fail_shape_inference("Attribute pads has incorrect size.");
+    }
+  } else {
+    pads.assign(n_input_dims * 2, 0);
+  }
+
+  std::vector<int64_t> strides;
+  if (getRepeatedAttribute(ctx, "strides", strides)) {
+    if (strides.size() != n_input_dims) {
+      fail_shape_inference("Attribute strides has incorrect size.");
+    }
+  } else {
+    strides.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> kernel_shape;
+  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
+    if (kernel_shape.size() != n_input_dims) {
+      fail_shape_inference("Attribute kernel_shape has incorrect size.");
+    }
+  } else {
+    fail_shape_inference("Attribute kernel_shape must be specified.");
+  }
+
+  if (ctx.getNumInputs() == 3) {
+    // If the third input, output_size, is specified, then use that instead
+    // of inferring shape from inputs.
+    if (hasInputShape(ctx, 2)) {
+      auto& output_shape = getInputShape(ctx, 2);
+      if (output_shape.dim_size() != 1) {
+        fail_type_inference("'output_shape' must be rank 1 tensor.");
+      }
+      if (output_shape.dim((int)0).has_dim_value() &&
+          static_cast<int>(output_shape.dim((int)0).dim_value()) !=
+              input_shape.dim_size()) {
+        fail_shape_inference(
+            "'output_shape' must have same number of elements as the shape of input tensor X.");
+      }
+    }
+    return; // 'output_shape' is specified as input. Actual shape will be
+            // determined at runtime.
+  }
+
+  auto final_output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  *final_output_shape->add_dim() = input_shape.dim(0);
+  *final_output_shape->add_dim() =
+      ctx.getInputType(1)->tensor_type().shape().dim(
+          1); // channels should be the second dim of second input.
+
+  int kernel_shape_size = static_cast<int>(kernel_shape.size());
+  for (int i = 0; i < kernel_shape_size; ++i) {
+    auto newdim = final_output_shape->add_dim();
+    if (!input_shape.dim(2 + i).has_dim_value()) {
+      continue;
+    }
+
+    int64_t newdim_value =
+        strides[i] * (input_shape.dim(2 + i).dim_value() - 1);
+    newdim_value += kernel_shape[i];
+    newdim_value -= pads[i];
+    newdim_value -= pads[i + kernel_shape_size];
+
+    // add in the initial position
+    newdim->set_dim_value(newdim_value);
+  }
+}
+
+static const char* MaxUnpool_ver9_doc = R"DOC(
+MaxUnpool essentially computes the partial inverse of the MaxPool op.
+ The input information to this op is typically the the output information from a MaxPool op. The first
+ input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
+ from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
+ to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
+ The third (optional) input is a tensor that specifies the output size of the unpooling operation.
+
+MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal
+ values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling
+ the result of an unpooling operation should give back the original input to the unpooling op.
+
+MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.
+ The third input argument, output_size, is meant to disambiguate the op and produce output tensor of
+ known/predictable size.
+
+In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,
+ which define the exact unpooling op. The attributes typically have the same values as the corrsponding
+ pooling op that the unpooling op is trying to invert.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxUnpool,
+    11,
+    OpSchema()
+        .SetDoc(MaxUnpool_ver9_doc)
+        .Attr(
+            "kernel_shape",
+            "The size of the kernel along each axis.",
+            AttributeProto::INTS)
+        .Attr(
+            "strides",
+            "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr("pads", pads_doc, AttributeProto::INTS, OPTIONAL_VALUE)
+        .Input(
+            0,
+            "X",
+            "Input data tensor that has to be unpooled. "
+            "This tensor is typically the first output of the MaxPool op."
+            "Dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non-image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size. Optionally, if dimension denotation is "
+            "in effect, the operation expects the input "
+            "data tensor to arrive with the dimension denotation "
+            "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "I",
+            "Input data tensor containing the indices corresponding to "
+            "elements in the first input tensor X."
+            "This tensor is typically the second output of the MaxPool op."
+            "Dimensions must be the same as input tensor X. "
+            "The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, "
+            "assuming row-major storage. Also, the linear indices should not consider padding. "
+            "So the values in indices are in the range [0, N x C x D1 x ... x Dn).",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "output_shape",
+            "The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, "
+            "'pads' values are ignored.",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Output data tensor that contains the result of the unpooling.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          maxUnpoolShapeInference(ctx);
+        }));
+
+std::function<void(OpSchema&)> LpPoolOpSchemaGenerator(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+ {name} consumes an input tensor X and applies Lp pooling across
+ the tensor according to kernel sizes, stride sizes, and pad lengths.
+ Lp pooling consisting of computing the Lp norm on all values of a subset
+ of the input tensor according to the kernel size and downsampling the
+ data into the output tensor Y for further processing.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "kernel_shape",
+        "The size of the kernel along each axis.",
+        AttributeProto::INTS);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        conv_auto_pad_doc,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "p",
+        "p value of the Lp norm used to pool over the input data.",
+        AttributeProto::INT,
+        static_cast<int64_t>(2));
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data. For non image case, the "
+        "dimensions are in the form of "
+        "(N x C x D1 x D2 ... Dn), where N is the "
+        "batch size.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from Lp pooling across the input "
+        "tensor. Dimensions will vary based on various kernel, stride, and pad "
+        "sizes.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      convPoolShapeInference(ctx, false, true, 0, 1);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LpPool,
+    11,
+    OpSchema().FillUsing(LpPoolOpSchemaGenerator("LpPool")));
+
+// For ROI pool operations.
+void roiPoolTypeShapeInference(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+  // rois is the second input.
+  if (!hasNInputShapes(ctx, 2)) {
+    return;
+  }
+
+  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+  auto rios_shape = ctx.getInputType(1)->tensor_type().shape();
+
+  if (input_shape.dim_size() < 2) {
+    fail_shape_inference("Input tensor must have at least 2 dimensions");
+  }
+  if (rios_shape.dim_size() != 2) {
+    fail_shape_inference("RoIs tensor must have 2 dimensions");
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  std::vector<int64_t> pooled_shape;
+  if (getRepeatedAttribute(ctx, "pooled_shape", pooled_shape)) {
+    if (pooled_shape.size() != n_input_dims) {
+      fail_shape_inference("Attribute pooled_shape has incorrect length");
+    }
+  } else {
+    fail_shape_inference("Attribute pooled_shape must be specified");
+  }
+
+  // (num_rois, channels, pooled_shape[0], pooled_shape[1])
+  auto output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  *output_shape->add_dim() = rios_shape.dim(0);
+  *output_shape->add_dim() = input_shape.dim(1);
+  output_shape->add_dim()->set_dim_value(pooled_shape[0]);
+  output_shape->add_dim()->set_dim_value(pooled_shape[1]);
+}
+
+std::function<void(OpSchema&)> RoiPoolOpSchemaGenerator(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+ ROI {name} pool consumes an input tensor X and region of interests (RoIs) to
+ apply {name} pooling across each RoI, to produce output 4-D tensor of shape
+ (num_rois, channels, pooled_shape[0], pooled_shape[1]).)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "pooled_shape",
+        "ROI pool output shape (height, width).",
+        AttributeProto::INTS);
+    schema.Attr(
+        "spatial_scale",
+        "Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.",
+        AttributeProto::FLOAT,
+        1.f);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(
+        1,
+        "rois",
+        "RoIs (Regions of Interest) to pool over. Should "
+        "be a 2-D tensor of shape (num_rois, 5) given as "
+        "[[batch_id, x1, y1, x2, y2], ...].",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.Output(
+        0,
+        "Y",
+        "RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction(
+        [](InferenceContext& ctx) { roiPoolTypeShapeInference(ctx); });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxRoiPool,
+    1,
+    OpSchema().FillUsing(RoiPoolOpSchemaGenerator("max")));
+
+std::function<void(OpSchema&)> ConvOpSchemaGenerator(const char* filter_desc) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The convolution operator consumes an input tensor and {filter_desc}, and
+computes the output.)DOC";
+                        ReplaceAll(doc, "{filter_desc}", filter_desc););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from previous layer; "
+        "has size (N x C x H x W), where N is the batch size, "
+        "C is the number of channels, and H and W are the "
+        "height and width. Note that this is for the 2D image. "
+        "Otherwise the size is (N x C x D1 x D2 ... x Dn). "
+        "Optionally, if dimension denotation is "
+        "in effect, the operation expects input data tensor "
+        "to arrive with the dimension denotation of [DATA_BATCH, "
+        "DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(
+        1,
+        "W",
+        "The weight tensor that will be used in the "
+        "convolutions; has size (M x C/group x kH x kW), where C "
+        "is the number of channels, and kH and kW are the "
+        "height and width of the kernel, and M is the number "
+        "of feature maps. For more than 2 dimensions, the "
+        "kernel shape will be (M x C/group x k1 x k2 x ... x kn), "
+        "where (k1 x k2 x ... kn) is the dimension of the kernel. "
+        "Optionally, if dimension denotation is in effect, "
+        "the operation expects the weight tensor to arrive "
+        "with the dimension denotation of [FILTER_OUT_CHANNEL, "
+        "FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. "
+        "Assuming zero based indices for the shape array, "
+        "X.shape[1] == (W.shape[1] * group) == C and "
+        "W.shape[0] mod G == 0. Or in other words "
+        "FILTER_IN_CHANNEL multiplied by the number of groups "
+        "should be equal to DATA_CHANNEL and the number of "
+        "feature maps M should be a multiple of the number of "
+        "groups G.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(
+        2,
+        "B",
+        "Optional 1D bias to be added to the convolution, has size of M.",
+        "T",
+        OpSchema::Optional,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor that contains the result of the "
+        "convolution. The output dimensions are functions "
+        "of the kernel size, stride size, and pad lengths.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.Attr(
+        "kernel_shape",
+        "The shape of the convolution kernel. If not present, should be inferred from input W.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "dilations",
+        "dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        conv_auto_pad_doc,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "group",
+        "number of groups input channels and output channels are divided into.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      convPoolShapeInference(ctx, true, false, 0, 1);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Conv,
+    11,
+    OpSchema().FillUsing(ConvOpSchemaGenerator("a filter")));
+
+static const char* QLinearConv_ver10_doc = R"DOC(
+The convolution operator consumes a quantized input tensor, its scale and zero point,
+a quantized filter, its scale and zero point, and output's scale and zero point,
+and computes the quantized output. Each scale and zero-point pair must have same shape.
+It means they must be either scalars (per tensor) or 1-D tensors (per output channel).
+Each input or output and its related zero point must have same type.
+When bias is present it must be quantized using scale = input scale * weight scale and
+zero point as 0.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    QLinearConv,
+    10,
+    OpSchema()
+        .SetDoc(QLinearConv_ver10_doc)
+        .Input(
+            0,
+            "x",
+            "Input data tensor from previous layer; "
+            "has size (N x C x H x W), where N is the batch size, "
+            "C is the number of channels, and H and W are the "
+            "height and width. Note that this is for the 2D image. "
+            "Otherwise the size is (N x C x D1 x D2 ... x Dn). "
+            "Optionally, if dimension denotation is "
+            "in effect, the operation expects input data tensor "
+            "to arrive with the dimension denotation of [DATA_BATCH, "
+            "DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+            "T1")
+        .Input(
+            1,
+            "x_scale",
+            "Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.",
+            "tensor(float)")
+        .Input(
+            2,
+            "x_zero_point",
+            "Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.",
+            "T1")
+        .Input(
+            3,
+            "w",
+            "The weight tensor that will be used in the "
+            "convolutions; has size (M x C/group x kH x kW), where C "
+            "is the number of channels, and kH and kW are the "
+            "height and width of the kernel, and M is the number "
+            "of feature maps. For more than 2 dimensions, the "
+            "kernel shape will be (M x C/group x k1 x k2 x ... x kn), "
+            "where (k1 x k2 x ... kn) is the dimension of the kernel. "
+            "Optionally, if dimension denotation is in effect, "
+            "the operation expects the weight tensor to arrive "
+            "with the dimension denotation of [FILTER_OUT_CHANNEL, "
+            "FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. "
+            "X.shape[1] == (W.shape[1] * group) == C "
+            "(assuming zero based indices for the shape array). "
+            "Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. ",
+            "T2")
+        .Input(
+            4,
+            "w_scale",
+            "Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).",
+            "tensor(float)")
+        .Input(
+            5,
+            "w_zero_point",
+            "Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).",
+            "T2")
+        .Input(
+            6,
+            "y_scale",
+            "Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.",
+            "tensor(float)")
+        .Input(
+            7,
+            "y_zero_point",
+            "Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.",
+            "T3")
+        .Input(
+            8,
+            "B",
+            "Optional 1D bias to be added to the convolution, has size of M. "
+            "Bias must be quantized using scale = x_scale * w_scale and zero_point = 0",
+            "T4",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "y",
+            "Output data tensor that contains the result of the "
+            "convolution. The output dimensions are functions "
+            "of the kernel size, stride size, and pad lengths.",
+            "T3")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain filter type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain output type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T4",
+            {"tensor(int32)"},
+            "Constrain bias type to 32-bit integer tensor.")
+        .Attr(
+            "auto_pad",
+            conv_auto_pad_doc,
+            AttributeProto::STRING,
+            std::string("NOTSET"))
+        .Attr(
+            "kernel_shape",
+            "The shape of the convolution kernel. If not present, should be inferred from input 'w'.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dilations",
+            "dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "strides",
+            "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "pads",
+            "Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0."
+            "The value represent the number of pixels added to the beginning and end part of the corresponding axis."
+            "`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of"
+            "pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`."
+            "This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults"
+            "to 0 along start and end of each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "group",
+            "number of groups input channels and output channels are divided into. default is 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto x_type = ctx.getInputType(0);
+          auto w_type = ctx.getInputType(3);
+          if (nullptr == x_type || nullptr == w_type ||
+              x_type->value_case() != TypeProto::kTensorType ||
+              w_type->value_case() != TypeProto::kTensorType) {
+            fail_type_inference("inputs are expected to have tensor type.");
+          }
+
+          auto x_zero_point_type = ctx.getInputType(2);
+          if (nullptr == x_zero_point_type ||
+              x_zero_point_type->tensor_type().elem_type() !=
+                  x_type->tensor_type().elem_type()) {
+            fail_type_inference(
+                "input and zero_point pair is expected to have be same type.");
+          }
+
+          auto w_zero_point_type = ctx.getInputType(5);
+          if (nullptr == w_zero_point_type ||
+              w_zero_point_type->tensor_type().elem_type() !=
+                  w_type->tensor_type().elem_type()) {
+            fail_type_inference(
+                "weight and zero_point pair is expected to have same type.");
+          }
+
+          propagateElemTypeFromInputToOutput(ctx, 7, 0);
+
+          convPoolShapeInference(ctx, true, false, 0, 3);
+        }));
+
+static const char* ConvInteger_ver10_doc = R"DOC(
+The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point,
+and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ConvInteger,
+    10,
+    OpSchema()
+        .SetDoc(ConvInteger_ver10_doc)
+        .Input(
+            0,
+            "x",
+            "Input data tensor from previous layer; "
+            "has size (N x C x H x W), where N is the batch size, "
+            "C is the number of channels, and H and W are the "
+            "height and width. Note that this is for the 2D image. "
+            "Otherwise the size is (N x C x D1 x D2 ... x Dn). "
+            "Optionally, if dimension denotation is "
+            "in effect, the operation expects input data tensor "
+            "to arrive with the dimension denotation of [DATA_BATCH, "
+            "DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+            "T1")
+        .Input(
+            1,
+            "w",
+            "The weight tensor that will be used in the "
+            "convolutions; has size (M x C/group x kH x kW), where C "
+            "is the number of channels, and kH and kW are the "
+            "height and width of the kernel, and M is the number "
+            "of feature maps. For more than 2 dimensions, the "
+            "kernel shape will be (M x C/group x k1 x k2 x ... x kn), "
+            "where (k1 x k2 x ... kn) is the dimension of the kernel. "
+            "Optionally, if dimension denotation is in effect, "
+            "the operation expects the weight tensor to arrive "
+            "with the dimension denotation of [FILTER_OUT_CHANNEL, "
+            "FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. "
+            "X.shape[1] == (W.shape[1] * group) == C "
+            "(assuming zero based indices for the shape array). "
+            "Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. ",
+            "T2")
+        .Input(
+            2,
+            "x_zero_point",
+            "Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.",
+            "T1",
+            OpSchema::Optional)
+        .Input(
+            3,
+            "w_zero_point",
+            "Zero point tensor for input 'w'. It's optional and default value is 0.  It could be a scalar or a 1-D tensor, "
+            "which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number "
+            "of elements should be equal to the number of output channels (M)",
+            "T2",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "y",
+            "Output data tensor that contains the result of the "
+            "convolution. The output dimensions are functions "
+            "of the kernel size, stride size, and pad lengths.",
+            "T3")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input x and its zero point data type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain input w and its zero point data type to 8-bit integer tensor.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(int32)"},
+            "Constrain output y data type to 32-bit integer tensor.")
+        .Attr(
+            "auto_pad",
+            conv_auto_pad_doc,
+            AttributeProto::STRING,
+            std::string("NOTSET"))
+        .Attr(
+            "kernel_shape",
+            "The shape of the convolution kernel. If not present, should be inferred from input 'w'.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "dilations",
+            "dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "strides",
+            "Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "pads",
+            "Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0."
+            "The value represent the number of pixels added to the beginning and end part of the corresponding axis."
+            "`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of"
+            "pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`."
+            "This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults"
+            "to 0 along start and end of each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "group",
+            "number of groups input channels and output channels are divided into. default is 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto x_type = ctx.getInputType(0);
+          auto w_type = ctx.getInputType(1);
+          auto y_type = ctx.getOutputType(0);
+          if (nullptr == x_type || nullptr == w_type || nullptr == y_type ||
+              x_type->value_case() != TypeProto::kTensorType ||
+              w_type->value_case() != TypeProto::kTensorType) {
+            fail_type_inference(
+                "inputs are expected to have tensor type and output type should not be null.");
+          }
+
+          // Right now we only support int32
+          y_type->mutable_tensor_type()->set_elem_type(TensorProto::INT32);
+
+          convPoolShapeInference(ctx, true, false, 0, 1);
+        }));
+
+void convTransposeShapeInference(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+  // we need at least two inputs to have a shape for this inference.
+  if (!hasNInputShapes(ctx, 2)) {
+    return;
+  }
+
+  int64_t group = getAttribute(ctx, "group", 1);
+
+  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    return; // Input tensor should have at least two dimensions.
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  std::vector<int64_t> dilations;
+  if (getRepeatedAttribute(ctx, "dilations", dilations)) {
+    if (dilations.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    dilations.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> strides;
+  if (getRepeatedAttribute(ctx, "strides", strides)) {
+    if (strides.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    strides.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> kernel_shape;
+  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
+    if (kernel_shape.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    auto second_input_shape = ctx.getInputType(1)->tensor_type().shape();
+    for (int i = 2; i < second_input_shape.dim_size(); ++i) {
+      if (!second_input_shape.dim(i).has_dim_value()) {
+        return;
+      }
+      kernel_shape.push_back(second_input_shape.dim(i).dim_value());
+    }
+  }
+
+  std::vector<int64_t> effective_kernel_shape = kernel_shape;
+  for (int i = 0; i < static_cast<int>(kernel_shape.size()); i++) {
+    // accounting for dilation, how big is the kernel in this dimension
+    effective_kernel_shape[i] =
+        (effective_kernel_shape[i] - 1) * dilations[i] + 1;
+  }
+
+  std::vector<int64_t> pads;
+  if (getRepeatedAttribute(ctx, "pads", pads)) {
+    if (pads.size() != n_input_dims * 2) {
+      fail_shape_inference("Attribute pads has incorrect size");
+    }
+    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
+    if (nullptr != auto_pad_attr && auto_pad_attr->s() != "NOTSET") {
+      fail_shape_inference(
+          "The pads attribute cannot be used simultaneously with auto_pad attribute");
+    }
+  } else {
+    pads.assign(n_input_dims * 2, 0);
+    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
+    if ((nullptr != auto_pad_attr) && (auto_pad_attr->s() != "VALID")) {
+      int input_dims_size = static_cast<int>(n_input_dims);
+      for (int i = 0; i < input_dims_size; ++i) {
+        int64_t total_pad = effective_kernel_shape[i] - strides[i];
+        if (total_pad < 0)
+          total_pad = 0;
+        int64_t half_pad_small = total_pad >> 1;
+        int64_t half_pad_big = total_pad - half_pad_small;
+        if (auto_pad_attr->s() == "SAME_UPPER") {
+          pads[i] = half_pad_small;
+          pads[i + input_dims_size] = half_pad_big;
+        } else if (auto_pad_attr->s() == "SAME_LOWER") {
+          pads[i] = half_pad_big;
+          pads[i + input_dims_size] = half_pad_small;
+        }
+      }
+    }
+  }
+
+  std::vector<int64_t> output_shape;
+  bool output_shape_presented = true;
+  if (getRepeatedAttribute(ctx, "output_shape", output_shape)) {
+    if (output_shape.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    output_shape_presented = false;
+  }
+
+  std::vector<int64_t> output_padding;
+  if (getRepeatedAttribute(ctx, "output_padding", output_padding)) {
+    if (output_padding.size() != n_input_dims) { // Added only to one side.
+      return;
+    }
+  } else {
+    output_padding.assign(n_input_dims, 0);
+  }
+
+  auto final_output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  *final_output_shape->add_dim() = input_shape.dim(0);
+  *final_output_shape->add_dim() =
+      ctx.getInputType(1)->tensor_type().shape().dim(1) *
+      group; // channels should be the second dim of second input multiply
+             // group.
+
+  int size_of_output;
+  if (output_shape_presented) {
+    size_of_output = static_cast<int>(output_shape.size());
+    for (int i = 0; i < size_of_output; ++i) {
+      if (input_shape.dim(i + 2).has_dim_value()) {
+        if (output_shape[i] < input_shape.dim(i + 2).dim_value()) {
+          // TODO: throw exception?
+          return; // output shape value cannot be smaller than the input shape
+                  // value
+        }
+      }
+      final_output_shape->add_dim()->set_dim_value(output_shape[i]);
+    }
+    return;
+  } else {
+    size_of_output = input_shape.dim_size() - 2;
+    for (int i = 0; i < size_of_output; ++i) {
+      if (input_shape.dim(i + 2).has_dim_value()) {
+        int64_t output_shape_dim =
+            strides[i] * (input_shape.dim(i + 2).dim_value() - 1) +
+            output_padding[i] + effective_kernel_shape[i] - pads[i] -
+            pads[i + n_input_dims];
+        final_output_shape->add_dim()->set_dim_value(output_shape_dim);
+      } else {
+        final_output_shape->add_dim();
+      }
+    }
+    return;
+  }
+}
+
+std::function<void(OpSchema&)> ConvTransposeOpSchemaGenerator(
+    const char* filter_desc) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The convolution transpose operator consumes an input tensor and {filter_desc},
+and computes the output.
+
+If the pads parameter is provided the shape of the output is calculated via the following equation:
+
+  output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]
+
+output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
+
+  total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
+  If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
+  Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
+
+    )DOC";
+                        ReplaceAll(doc, "{filter_desc}", filter_desc););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from previous layer; has size (N x C x H x W)"
+        ", where N is the batch size, C is the number of channels, and"
+        " H and W are the height and width. Note that this is for the 2D image. "
+        "Otherwise the size is (N x C x D1 x D2 ... x Dn)",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(
+        1,
+        "W",
+        "The weight tensor that will be used in the "
+        "convolutions; has size (C x M/group x kH x kW), where C "
+        "is the number of channels, and kH and kW are the "
+        "height and width of the kernel, and M is the number "
+        "of feature maps. For more than 2 dimensions, the "
+        "weight shape will be (C x M/group x k1 x k2 x ... x kn), "
+        "where (k1 x k2 x ... x kn) is the dimension of the kernel. "
+        "The number of channels in the output should be equal to W.shape[1] * group "
+        "(assuming zero based indices of the shape array)",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(
+        2,
+        "B",
+        "Optional 1D bias to be added to the convolution, has size of M.",
+        "T",
+        OpSchema::Optional,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor that contains the result of the convolution. The "
+        "output dimensions are functions of the kernel size, stride size, "
+        "pad lengths and group count. "
+        "The number of channels in the output should be equal to W.shape[1] * group "
+        "(assuming zero based indices of the shape array)",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.Attr(
+        "kernel_shape",
+        "The shape of the convolution kernel. If not present, should be inferred from input W.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "output_shape",
+        "The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified "
+        "pads values are ignored. See doc for details for equations to generate pads",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "output_padding",
+        "Additional elements added to the side with higher coordinate indices in the output. "
+        "Each padding value in \"output_padding\" must be less than the corresponding stride/dilation dimension. "
+        "By default, this attribute is a zero vector. "
+        "Note that this attribute doesn't directly affect the computed output values. "
+        "It only controls the selection of the computed values, "
+        "so changing this attribute only adds or removes output elements. "
+        "If \"output_shape\" is explicitly provided, "
+        "\"output_padding\" does not contribute additional size to \"output_shape\" but "
+        "participates in the computation of the needed padding amount. "
+        "This is also called adjs or adjustment in some frameworks.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "dilations",
+        "dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        conv_transpose_auto_pad_doc,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "group",
+        "number of groups input channels and output channels are divided into.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.TypeAndShapeInferenceFunction(
+        [](InferenceContext& ctx) { convTransposeShapeInference(ctx); });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ConvTranspose,
+    11,
+    OpSchema().FillUsing(ConvTransposeOpSchemaGenerator("a filter")));
+
+// For GlobalPool operations.
+void globalPoolTypeShapeInference(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+  // needs at least one input with shape.
+  if (!hasNInputShapes(ctx, 1)) {
+    return;
+  }
+
+  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    return;
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  // (N, C, 1, 1, ..., 1)
+  auto output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+  *output_shape->add_dim() = input_shape.dim(0);
+  *output_shape->add_dim() = input_shape.dim(1);
+
+  for (size_t i = 0; i < n_input_dims; ++i) {
+    output_shape->add_dim()->set_dim_value(1);
+  }
+}
+
+std::function<void(OpSchema&)> GlobalPoolingOpSchemaGenerator(
+    const char* op_type,
+    const char* op) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+ Global{op_type} consumes an input tensor X and applies {op} pooling across
+ the values in the same channel. This is equivalent to {op_type} with kernel size
+ equal to the spatial dimension of input tensor.)DOC";
+                        ReplaceAll(doc, "{op_type}", op_type);
+                        ReplaceAll(doc, "{op}", op););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the width "
+        "of the data. For non image case, the dimensions are "
+        "in the form of (N x C x D1 x D2 ... Dn), "
+        "where N is the batch size.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from pooling across the input "
+        "tensor. The output tensor has the same rank as the input. "
+        "The first two dimensions of output shape are the same as "
+        "the input (N x C), while the other dimensions are all 1.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction(
+        [](InferenceContext& ctx) { globalPoolTypeShapeInference(ctx); });
+  };
+}
+ONNX_OPERATOR_SET_SCHEMA(
+    GlobalAveragePool,
+    1,
+    OpSchema().FillUsing(
+        GlobalPoolingOpSchemaGenerator("AveragePool", "average")));
+ONNX_OPERATOR_SET_SCHEMA(
+    GlobalMaxPool,
+    1,
+    OpSchema().FillUsing(GlobalPoolingOpSchemaGenerator("MaxPool", "max")));
+
+std::function<void(OpSchema&)> GlobalLpPoolingOpSchemaGenerator(
+    const char* op_type,
+    const char* op) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+ Global{op_type} consumes an input tensor X and applies {op} pooling across
+ the values in the same channel. This is equivalent to {op_type} with kernel size
+ equal to the spatial dimension of input tensor.)DOC";
+                        ReplaceAll(doc, "{op_type}", op_type);
+                        ReplaceAll(doc, "{op}", op););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "p",
+        "p value of the Lp norm used to pool over the input data.",
+        AttributeProto::INT,
+        static_cast<int64_t>(2));
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the width "
+        "of the data. For non image case, the dimensions are "
+        "in the form of (N x C x D1 x D2 ... Dn), "
+        "where N is the batch size.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from pooling across the input "
+        "tensor. The output tensor has the same rank as the input. "
+        "The first two dimensions of output shape are the same as "
+        "the input (N x C), while the other dimensions are all 1.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction(
+        [](InferenceContext& ctx) { globalPoolTypeShapeInference(ctx); });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GlobalLpPool,
+    2,
+    OpSchema().FillUsing(
+        GlobalLpPoolingOpSchemaGenerator("LpPool", "lp pool")));
+
+static const char* BatchNormalization_ver15_doc = R"DOC(
+Carries out batch normalization as described in the paper
+https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+There are five required inputs 'X', 'scale', 'B', 'input_mean' and
+'input_var'.
+Note that 'input_mean' and 'input_var' are expected to be the estimated
+statistics in inference mode (training_mode=False, default),
+and the running statistics in training mode (training_mode=True).
+There are multiple cases for the number of outputs, which we list below:
+
+Output case #1: Y, running_mean, running_var (training_mode=True)
+Output case #2: Y (training_mode=False)
+
+When training_mode=False, extra outputs are invalid.
+The outputs are updated as follows when training_mode=True:
+```
+running_mean = input_mean * momentum + current_mean * (1 - momentum)
+running_var = input_var * momentum + current_var * (1 - momentum)
+
+Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
+
+where:
+
+current_mean = ReduceMean(X, axis=all_except_channel_index)
+current_var =  ReduceVar(X, axis=all_except_channel_index)
+
+Notice that ReduceVar refers to the population variance, and it equals to
+sum(sqrd(x_i - x_avg)) / N
+where N is the population size (this formula does not use sample size N - 1).
+
+```
+
+The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs.
+
+When training_mode=False:
+```
+Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
+```
+
+For previous (depreciated) non-spatial cases, implementors are suggested
+to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BatchNormalization,
+    15,
+    OpSchema()
+        .NumOutputs({1, 3})
+        .SetDoc(BatchNormalization_ver15_doc + GenerateOptionalArgumentsDoc())
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Attr(
+            "momentum",
+            "Factor used in computing the running mean and variance."
+            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
+            AttributeProto::FLOAT,
+            0.9f)
+        .Attr(
+            "training_mode",
+            "If set to true, it indicates BatchNormalization is being used for training, and outputs 1, "
+            "2, 3, and 4 would be populated.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions are in the form of (N x C x D1 x D2 ... Dn), "
+            "where N is the batch size, C is the number of channels. "
+            "Statistics are computed for every channel of C over N and D1 to Dn dimensions. "
+            "For image data, input dimensions become (N x C x H x W). "
+            "The op also accepts single dimension input of size N in which case C is assumed to be 1",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "scale",
+            "Scale tensor of shape (C).",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "B",
+            "Bias tensor of shape (C).",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            3,
+            "input_mean",
+            "running (training) or estimated (testing) mean tensor of shape (C).",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            4,
+            "input_var",
+            "running (training) or estimated (testing) variance tensor of shape (C).",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "The output tensor of the same shape as X",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            1,
+            "running_mean",
+            "The running mean after the BatchNormalization operator.",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            2,
+            "running_var",
+            "The running variance after the BatchNormalization operator. This op uses the population size (N) for "
+            "calculating variance, and not the sample size N-1.",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
+            "Constrain scale and bias types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
+            "Constrain mean and variance types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+
+          // Inputs 1 to 4 must be of rank 1.
+          checkInputRank(ctx, 1, 1);
+          checkInputRank(ctx, 2, 1);
+          checkInputRank(ctx, 3, 1);
+          checkInputRank(ctx, 4, 1);
+          
+          Dim num_channels;
+
+          if (hasInputShape(ctx, 0)) {
+            if (getInputShape(ctx, 0).dim_size() > 1)
+              unifyInputDim(ctx, 0, 1, num_channels);
+            else
+              unifyDim(num_channels, 1);
+          }
+
+          unifyInputDim(ctx, 1, 0, num_channels);
+          unifyInputDim(ctx, 2, 0, num_channels);
+          unifyInputDim(ctx, 3, 0, num_channels);
+          unifyInputDim(ctx, 4, 0, num_channels);
+
+          if (ctx.getAttribute("training_mode") &&
+               static_cast<int>(ctx.getAttribute("training_mode")->i()) != 0) {
+            if (ctx.getNumOutputs() != 3)
+              fail_shape_inference(
+                "This number of op outputs should be 3 when Training_mode = True, but it is not.");
+          } else {
+            if (ctx.getNumOutputs() != 1)
+              fail_shape_inference(
+                "This number of op outputs should be 1 when Training_mode = False, but it is not.");
+          }
+
+          if (ctx.getNumOutputs() > 1) {
+            TensorShapeProto outputs_shape;
+            *outputs_shape.add_dim() = num_channels; // channel
+
+            propagateElemTypeFromInputToOutput(ctx, 3, 1);
+            updateOutputShape(ctx, 1, outputs_shape);
+
+            if (ctx.getNumOutputs() > 2) {
+              propagateElemTypeFromInputToOutput(ctx, 4, 2);
+              updateOutputShape(ctx, 2, outputs_shape);
+            }
+          }
+        }));
+
+static const char* InstanceNormalization_ver6_doc = R"DOC(
+Carries out instance normalization as described in the paper
+https://arxiv.org/abs/1607.08022.
+
+y = scale * (x - mean) / sqrt(variance + epsilon) + B,
+where mean and variance are computed per instance per channel.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    InstanceNormalization,
+    6,
+    OpSchema()
+        .SetDoc(InstanceNormalization_ver6_doc)
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Input(
+            0,
+            "input",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "scale",
+            "The input 1-dimensional scale tensor of size C.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "B",
+            "The input 1-dimensional bias tensor of size C.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The output tensor of the same shape as input.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+        }));
+
+static const char* LpNormalization_ver1_doc = R"DOC(
+Given a matrix, apply Lp-normalization along the provided axis.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LpNormalization,
+    1,
+    OpSchema()
+        .Input(
+            0,
+            "input",
+            "Input matrix",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Matrix after normalization",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .SetDoc(LpNormalization_ver1_doc)
+        .Attr(
+            "axis",
+            "The axis on which to apply normalization, -1 mean last axis.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .Attr(
+            "p",
+            "The order of the normalization, only 1 or 2 are supported.",
+            AttributeProto::INT,
+            static_cast<int64_t>(2))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+        }));
+
+static const char* Dropout_ver13_doc = R"DOC(
+Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
+output (floating-point tensor) and mask (optional `Tensor<bool>`). If `training_mode` is true then the output Y will be a random dropout;
+Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
+the user can simply not pass `training_mode` input or set it to false.
+```
+output = scale * data * mask,
+```
+where
+```
+scale = 1. / (1. - ratio).
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Dropout,
+    13,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Dropout_ver13_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "data",
+            "The input data as Tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "ratio",
+            "The ratio of random dropout, with value in [0, 1). If this input was not set, "
+            "or if it was set to 0, the output would be a simple copy of the input. "
+            "If it's non-zero, output will be a random dropout of the scaled input, which is typically "
+            "the case during training. It is an optional value, if not specified it will default to 0.5.",
+            "T1",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "training_mode",
+            "If set to true then it indicates dropout is being used for training. It is an optional value hence unless "
+            "specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where "
+            "nothing will be dropped from the input data and if mask is requested as output it will contain all ones.",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "The output.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            1,
+            "mask",
+            "The output mask.",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input 'ratio' types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(bool)"},
+            "Constrain output 'mask' types to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasInputShape(ctx, 0)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+
+          if (ctx.getNumInputs() > 1 && hasInputShape(ctx, 1)) {
+            auto& ratio_input_shape = getInputShape(ctx, 1);
+            if (static_cast<int>(ratio_input_shape.dim_size()) != 0) {
+              fail_shape_inference("Ratio of Dropout must be a scalar.");
+            }
+          }
+
+          if (ctx.getNumInputs() > 2 && hasInputShape(ctx, 2)) {
+            auto& training_mode_input_shape = getInputShape(ctx, 2);
+            if (static_cast<int>(training_mode_input_shape.dim_size()) != 0) {
+              fail_shape_inference(
+                  "training_mode of Dropout must be a scalar.");
+            }
+          }
+
+          if (ctx.getNumOutputs() == 2) {
+            updateOutputElemType(ctx, 1, TensorProto::BOOL);
+            if (hasNInputShapes(ctx, 1)) {
+              propagateShapeFromInputToOutput(ctx, 0, 1);
+            }
+          }
+        }));
+
+static const char* Shrink_ver9_doc = R"DOC(
+Shrink takes one input data (Tensor<numeric>) and produces one Tensor output,
+having same datatype and shape with input. It has two attributes, lambd and
+bias. The formula of this operator is: If x < -lambd, y = x + bias;
+If x > lambd, y = x - bias; Otherwise, y = 0.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Shrink,
+    9,
+    OpSchema()
+        .SetDoc(Shrink_ver9_doc)
+        .Attr(
+            "lambd",
+            "The lambd value for the Shrink formulation. Default is 0.5.",
+            AttributeProto::FLOAT,
+            0.5f)
+        .Attr(
+            "bias",
+            "The bias value added to output. Default is 0.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Input(
+            0,
+            "input",
+            "The input data as Tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "The output.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input to only numeric types.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Flatten_ver13_doc = R"DOC(
+Flattens the input tensor into a 2D matrix. If input tensor has shape
+(d_0, d_1, ... d_n) then the output will have shape
+(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Flatten,
+    13,
+    OpSchema()
+        .SetDoc(Flatten_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "A tensor of rank >= axis.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "A 2D tensor with the contents of the input tensor, "
+            "with input dimensions up to axis flattened to the outer dimension "
+            "of the output and remaining input dimensions flattened into the inner "
+            "dimension of the output.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output to all tensor types.")
+        .Attr(
+            "axis",
+            "Indicate up to which input dimensions "
+            "(exclusive) should be flattened to the outer dimension of the output. "
+            "The value for axis must be in the range [-r, r], where r is the rank of the input tensor. "
+            "Negative value means counting dimensions from the back. "
+            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
+            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int rank = static_cast<int>(input_shape.dim_size());
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
+          if (axis < 0) {
+            axis += rank;
+          }
+          if (axis > rank || axis < 0) {
+            fail_shape_inference(
+                "Invalid value(", axis, ") for attribute 'axis'");
+          }
+          // TODO: is the operation defined for input-rank < 2?
+          updateOutputShape(
+              ctx,
+              0,
+              {multiplyDims(input_shape, 0, axis),
+               multiplyDims(input_shape, axis, rank)});
+        }));
+
+static const char* LRN_ver13_doc = R"DOC(
+Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+It normalizes over local input regions.
+The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+of shape (N x C x D1 x D2, ..., Dk), its region is
+{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
+
+square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
+
+Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LRN,
+    13,
+    OpSchema()
+        .Attr("size", "The number of channels to sum over", AttributeProto::INT)
+        .Attr("alpha", "Scaling parameter.", AttributeProto::FLOAT, 0.0001f)
+        .Attr("beta", "The exponent.", AttributeProto::FLOAT, 0.75f)
+        .Attr("bias", "", AttributeProto::FLOAT, 1.0f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size. Optionally, if dimension denotation is "
+            "in effect, the operation expects the input "
+            "data tensor to arrive with the dimension denotation "
+            "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "Output tensor, which has the shape and type as input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output "
+            " types to float tensors.")
+        .SetDoc(LRN_ver13_doc)
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* TfIdfVectorizer_ver9_doc = R"DOC(
+This transform extracts n-grams from the input sequence and save them as a vector. Input can
+be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input.
+For 2-D input, the output is also a  2-D tensor whose i-th row is the n-gram representation of the i-th input row.
+More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1].
+If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor.
+
+In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original
+sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips.
+If the number of skips is 2, we should skip two tokens when scanning through the original sequence.
+Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2.
+The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4].
+If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]
+indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively.
+
+The output vector (denoted by Y) stores the count of each n-gram;
+Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping
+between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0],
+ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17],
+respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output.
+Note that we may consider all skips up to S when generating the n-grams.
+
+The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and
+the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF",
+this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute.
+
+Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor.
+If pool_strings is set, the input must be a string tensor.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    TfIdfVectorizer,
+    9,
+    OpSchema()
+        .Input(
+            0,
+            "X",
+            "Input for n-gram extraction",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "Ngram results",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(string)", "tensor(int32)", "tensor(int64)"},
+            "Input is ether string UTF-8 or int32/int64")
+        .TypeConstraint("T1", {"tensor(float)"}, "1-D tensor of floats")
+        .Attr(
+            "max_gram_length",
+            "Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.",
+            AttributeProto::INT)
+        .Attr(
+            "min_gram_length",
+            "Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.",
+            AttributeProto::INT)
+        .Attr(
+            "max_skip_count",
+            "Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. "
+            "If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams "
+            "with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1",
+            AttributeProto::INT)
+        .Attr(
+            "pool_strings",
+            "List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. "
+            "It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. "
+            "The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "pool_int64s",
+            "List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. "
+            "It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. "
+            "The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "ngram_counts",
+            "The starting indexes of 1-grams, 2-grams, and so on in pool. "
+            "It is useful when determining the boundary between two consecutive collections of n-grams. "
+            "For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram "
+            "in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, "
+            "and we choose to use this due to its popularity.",
+            AttributeProto::INTS)
+        .Attr(
+            "ngram_indexes",
+            "list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. "
+            "The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.",
+            AttributeProto::INTS)
+        .Attr(
+            "weights",
+            "list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights "
+            "is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. "
+            "By default, weights is an all-one tensor.This attribute is used when mode is \"IDF\" or \"TFIDF\" "
+            "to scale the associated word counts.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "mode",
+            "The weighting criteria. It can be one of \"TF\" (term frequency), "
+            "\"IDF\" (inverse document frequency), and \"TFIDF\" (the combination of TF and IDF)",
+            AttributeProto::STRING)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          output_elem_type->set_elem_type(TensorProto::FLOAT);
+
+          if (hasInputShape(ctx, 0)) {
+            std::vector<int64_t> ngram_indexes;
+            getRepeatedAttribute(ctx, "ngram_indexes", ngram_indexes);
+            if (ngram_indexes.empty() ||
+                !std::all_of(
+                    ngram_indexes.cbegin(),
+                    ngram_indexes.cend(),
+                    [](int64_t i) { return i >= 0; })) {
+              fail_shape_inference(
+                  "ngram_indexes must be non-empty with no negative values");
+            }
+
+            auto greatest_hit =
+                std::max_element(ngram_indexes.cbegin(), ngram_indexes.cend());
+            auto max_last_axis = *greatest_hit + 1;
+
+            TensorShapeProto output_shape;
+            auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+            auto dim_size = input_shape.dim_size();
+            if (dim_size == 1) {
+              output_shape.add_dim()->set_dim_value(max_last_axis);
+            } else if (dim_size == 2) {
+              *output_shape.add_dim() = input_shape.dim(0);
+              output_shape.add_dim()->set_dim_value(max_last_axis);
+            } else {
+              fail_shape_inference("Input tensor must have rank 1 or 2");
+            }
+            updateOutputShape(ctx, 0, output_shape);
+          }
+        })
+        .SetDoc(TfIdfVectorizer_ver9_doc));
+
+static const char* StringNormalizer_ver10_doc = R"DOC(
+StringNormalization performs string operations for basic cleaning.
+This operator has only one input (denoted by X) and only one output
+(denoted by Y). This operator first examines the elements in the X,
+and removes elements specified in "stopwords" attribute.
+After removing stop words, the intermediate result can be further lowercased,
+uppercased, or just returned depending the "case_change_action" attribute.
+This operator only accepts [C]- and [1, C]-tensor.
+If all elements in X are dropped, the output will be the empty value of string tensor with shape [1]
+if input shape is [C] and shape [1, 1] if input shape is [1, C].
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    StringNormalizer,
+    10,
+    OpSchema()
+        .Input(0, "X", "UTF-8 strings to normalize", "tensor(string)")
+        .Output(0, "Y", "UTF-8 Normalized strings", "tensor(string)")
+        .Attr(
+            std::string("case_change_action"),
+            std::string(
+                "string enum that cases output to be lowercased/uppercases/unchanged."
+                " Valid values are \"LOWER\", \"UPPER\", \"NONE\". Default is \"NONE\""),
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            std::string("is_case_sensitive"),
+            std::string(
+                "Boolean. Whether the identification of stop words in X is case-sensitive. Default is false"),
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "stopwords",
+            "List of stop words. If not set, no word would be removed from X.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "locale",
+            "Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased."
+            "Default en_US or platform specific equivalent as decided by the implementation.",
+            AttributeProto::STRING,
+            OPTIONAL_VALUE)
+        .SetDoc(StringNormalizer_ver10_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          output_elem_type->set_elem_type(TensorProto::STRING);
+          if (!hasInputShape(ctx, 0)) {
+            return;
+          }
+          TensorShapeProto output_shape;
+          auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          auto dim_size = input_shape.dim_size();
+          // Last axis dimension is unknown if we have stop-words since we do
+          // not know how many stop-words are dropped
+          if (dim_size == 1) {
+            // Unknown output dimension
+            output_shape.add_dim();
+          } else if (dim_size == 2) {
+            // Copy B-dim
+            auto& b_dim = input_shape.dim(0);
+            if (!b_dim.has_dim_value() || b_dim.dim_value() != 1) {
+              fail_shape_inference(
+                  "Input shape must have either [C] or [1,C] dimensions where C > 0");
+            }
+            *output_shape.add_dim() = b_dim;
+            output_shape.add_dim();
+          } else {
+            fail_shape_inference(
+                "Input shape must have either [C] or [1,C] dimensions where C > 0");
+          }
+          updateOutputShape(ctx, 0, output_shape);
+        }));
+
+static const char* mvn_ver13_doc = R"DOC(
+      A MeanVarianceNormalization Function: Perform mean variance normalization
+      on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
+)DOC";
+
+static std::vector<int64_t> mvn_default_axes = {0, 2, 3};
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MeanVarianceNormalization,
+    13,
+    OpSchema()
+        .SetDoc(mvn_ver13_doc)
+        .Input(
+            0,
+            "X",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "Output tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Attr(
+            "axes",
+            "A list of integers, along which to reduce. The default is to "
+            "caculate along axes [0,2,3] for calculating mean and variance "
+            "along each channel. Two variables with the same C-coordinate "
+            "are associated with the same mean and variance.",
+            AttributeProto::INTS,
+            mvn_default_axes)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input and output types to all numeric tensors.")
+        .FunctionBody(R"ONNX(
+        {
+          Exponent = Constant <value = float {2.0}>()
+          Epsilon = Constant <value = float {1e-9}>()
+          X_RM = ReduceMean <axes : ints = @axes> (X)
+          EX_squared = Pow (X_RM, Exponent)
+          X_squared = Pow (X, Exponent)
+          E_Xsquared = ReduceMean <axes : ints = @axes> (X_squared)
+          Variance = Sub (E_Xsquared, EX_squared)
+          STD = Sqrt (Variance)
+          X_variance = Sub (X, X_RM)
+          Processed_STD = Add (STD, Epsilon)
+          Y = Div (X_variance, Processed_STD)
+        }
+        )ONNX"
+        ));
+
+static const char* LayerNormalization_ver17_doc = R"DOC(
+      This is layer normalization defined in ONNX as function.
+      The overall computation can be split into two stages.
+      The first stage is standardization, which makes the
+      normalized elements have zero mean and unit variances.
+      The computation required by standardization can be
+      described by the following equations.
+      ```
+      Mean = ReduceMean<axes=normalized_axes>(X)
+      D = Sub(X, Mean)
+      DD = Mul(Diff, Diff)
+      Var = ReduceMean<axes=normalized_axes>(DD)
+      VarEps = Add(Var, epsilon)
+      StdDev = Sqrt(VarEps)
+      InvStdDev = Reciprocal(StdDev)
+      Normalized = Mul(D, InvStdDev)
+      ```
+      where `normalized_axes` is `[axis, ..., rank of X - 1]`.
+      The variables `Var` and `StdDev` stand for variance and
+      standard deviation, respectively. The second output is
+      `Mean` and the last one is `InvStdDev`.
+      Depending on `stash_type` attribute, the actual computation
+      must happen in different floating-point precision.
+      For example, if `stash_type` is 1, this operator casts
+      all input variables to 32-bit float, perform the computation, and
+      finally cast `Normalized` back to the original type of `X`.
+      The second stage then scales and shifts the outcome of the
+      first stage using
+      ```
+      NormalizedScaled = Mul(Normalized, Scale)
+      Y = Add(NormalizedScaled, B)
+      ```
+      The second stage doesn't depends on `stash_type`.
+      All equations are in [this syntax](https://github.com/onnx/onnx/blob/main/docs/Syntax.md).
+      The same variable (i.e., input, output, and attribute) uses
+      the same name in the equations above and this operator's definition.
+      Let `d[i]` indicate the i-th dimension of `X`.
+      If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`,
+      the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`.
+      `Y` and `X` have the same shape.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LayerNormalization,
+    17,
+    OpSchema()
+        .SetDoc(LayerNormalization_ver17_doc)
+        .Attr("axis",
+              "The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. "
+              "Negative value means counting dimensions from the back.",
+              AttributeProto::INT,
+              static_cast<int64_t>(-1))
+        .Attr("epsilon",
+              "The epsilon value to use to avoid division by zero.",
+              AttributeProto::FLOAT,
+              1e-5f)
+        .Attr("stash_type",
+              "Type of Mean and InvStdDev. This also specifies stage one's computation precision.",
+              AttributeProto::INT,
+              static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT))
+        .AllowUncheckedAttributes()
+        .Input(0, "X", "Tensor to be normalized.", "T")
+        .Input(1, "Scale", "Scale tensor.", "T")
+        .Input(2, "B", "Bias tensor.", "T", OpSchema::Optional)
+        .Output(0, "Y", "Normalized tensor.", "T")
+        .Output(1, "Mean", "Saved mean used during training to speed up gradient computation", "U", OpSchema::Optional)
+        .Output(2, "InvStdDev", "Saved inverse standard deviation used during training to speed up gradient computation.", "U", OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
+            "Constrain input types and output Y type to float tensors.")
+        .TypeConstraint(
+            "U",
+            {"tensor(float)", "tensor(bfloat16)"},
+            "Type of Mean and InvStdDev tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          auto stash_type = static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT);
+          auto stash_type_proto = ctx.getAttribute("stash_type");
+          if (stash_type_proto) {
+            stash_type = stash_type_proto->i();
+          }
+          if (ctx.getNumOutputs() > 1) {
+            auto output_type = ctx.getOutputType(1);
+            output_type->mutable_tensor_type()->set_elem_type(static_cast<int32_t>(stash_type));
+          }
+          if (ctx.getNumOutputs() > 2) {
+            auto output_type = ctx.getOutputType(2);
+            output_type->mutable_tensor_type()->set_elem_type(static_cast<int32_t>(stash_type));
+          }
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          int64_t input_ndim = input_shape.dim_size();
+          int64_t axis = -1;
+          auto axis_proto = ctx.getAttribute("axis");
+          if (axis_proto) {
+            axis = axis_proto->i();
+          }
+          if (axis < 0) {
+            // Convert negative axis value to equivalent
+            // positive value.
+            axis += input_ndim;
+          }
+
+          if (ctx.getNumOutputs() > 1) {
+            auto mean_shape = ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape();
+            mean_shape->CopyFrom(input_shape);
+            for (int d = static_cast<int>(axis); d < input_ndim; ++d)
+              mean_shape->mutable_dim(d)->set_dim_value(1);
+          }
+
+          if (ctx.getNumOutputs() > 2) {
+            auto inv_std_dev_shape = ctx.getOutputType(2)->mutable_tensor_type()->mutable_shape();
+            inv_std_dev_shape->CopyFrom(input_shape);
+            for (int d = static_cast<int>(axis); d < input_ndim; ++d)
+              inv_std_dev_shape->mutable_dim(d)->set_dim_value(1);
+          }
+        })
+        .SetContextDependentFunctionBodyBuilder(
+            [](const FunctionBodyBuildContext& ctx, const OpSchema& schema, FunctionProto& functionProto) {
+              // LayerNormalization <axis, epsilon, stash_type> (X, Scale, B) => (Y, Mean?, InvStdDev?)
+              auto* tp = ctx.getInputType(0);
+              if ((tp == nullptr) || (!tp->has_tensor_type()))
+                return false;
+              int64_t T = tp->tensor_type().elem_type();
+
+              auto type_attr = ctx.getAttribute("stash_type");
+              int64_t U = (type_attr != nullptr) ? type_attr->i() : static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT);
+              if ((U != ONNX_NAMESPACE::TensorProto_DataType_FLOAT) && (U != ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16))
+                return false;  // Error
+
+              auto* axis_attr = ctx.getAttribute("axis");
+              int64_t axis = (axis_attr != nullptr) ? axis_attr->i() : -1;
+              auto* epsilon_attr = ctx.getAttribute("epsilon");
+              float epsilon = (epsilon_attr != nullptr) ? epsilon_attr->f() : 1e-5f;
+
+              auto mktensor = [](int64_t val) -> ONNX_NAMESPACE::TensorProto {
+                auto tp = ONNX_NAMESPACE::ToTensor(std::vector<int64_t>{val});
+                tp.add_dims(1);
+                return tp;
+              };
+              // The treatment of "axis" is different in "LayerNormalization" and in Reduction operations.
+              // This complicates the function definition, requiring reshaping inputs/outputs.
+              // Input X shape: [d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]
+              // This is treated as a 2D shape [d[0] * ... * d[axis-1], d[axis] * ... * d[rank-1]]
+              // Normalization is applied to the second dimension.
+              // Output Y has same shape as X
+              // Outputs Mean and InvStdDev have shape: [d[0], ..., d[axis-1], 1, ..., 1]
+              FunctionBuilder builder(functionProto);
+              builder
+                  .AddOpset("", 16)
+                  .Const("FloatEpsilon", ToTensor<float>(epsilon))
+                  .Add("Epsilon = Cast (FloatEpsilon)", "to", U)
+                  .Add("XShape = Shape (X)")                                                    // shape of input tensor: 1D tensor
+                  .Add("Rank = Size (XShape)")                                                  // rank of input tensor: scalar
+                  .Add("Zero1D = Constant()", "value", mktensor(0))                             // [0] : 1D tensor
+                  .Add("Axis1D = Constant()", "value", mktensor(axis))                          // [axis] : 1D tensor
+                  .Add("PrefixShape = Slice (XShape, Zero1D, Axis1D)")                          // [d[0], ..., d[axis-1]]
+                  .Add(axis > 0                                                                 // number of axes that are reduced =
+                          ? "NumReducedAxes = Sub (Rank, Axis1D)"                               // [rank - axis]: 1D tensor
+                          : "NumReducedAxes = Neg (Axis1D)")                                    // [-axis] : 1D tensor
+                  .Add("SuffixShape = ConstantOfShape (NumReducedAxes)", "value", mktensor(1))  // [1, ..., 1] for reduced axes
+                  .Add("ReducedShape = Concat <axis = 0> (PrefixShape, SuffixShape)")           // [d[0], ..., d[axis-1], 1, ..., 1]
+                  .Add("X2D = Flatten (X)", "axis", axis)
+                  .Add("XU = Cast (X2D)", "to", U)
+                  .Add("Mean2D = ReduceMean <axes = [1]> (XU)")
+                  .Add("Square = Mul (XU, XU)")
+                  .Add("MeanOfSquare = ReduceMean <axes = [1]> (Square)")
+                  .Add("SquareOfMean = Mul (Mean2D, Mean2D)")
+                  .Add("Var = Sub (MeanOfSquare, SquareOfMean)")
+                  .Add("VarPlusEpsilon = Add (Var, Epsilon)")
+                  .Add("StdDev = Sqrt (VarPlusEpsilon)")
+                  .Add("Deviation = Sub (XU, Mean2D)")
+                  .Add("Normalized = Div (Deviation, StdDev)")
+                  .Add("NormalizedT = Cast (Normalized)", "to", T)
+                  .Add("Scale2D = Flatten <axis = 0> (Scale)")
+                  .Add("Scaled = Mul (NormalizedT, Scale2D)");
+              if (ctx.hasInput(2)) {
+                builder.Add("B2D = Flatten <axis=0> (B)");
+                builder.Add("Biased = Add (Scaled, B2D)");
+              } else {
+                builder.Add("Biased = Identity (Scaled)");
+              }
+              builder.Add("Y = Reshape (Biased, XShape)");
+              builder.Add("InvStdDev2D = Reciprocal (StdDev)");
+              if (ctx.hasOutput(1))
+                builder.Add("Mean = Reshape (Mean2D, ReducedShape)");
+              if (ctx.hasOutput(2))
+                builder.Add("InvStdDev = Reshape (InvStdDev2D, ReducedShape)");
+
+              schema.BuildFunction(functionProto);
+              return true;
+        }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/nn/old.cc b/third_party/onnx/onnx/defs/nn/old.cc
new file mode 100644
index 0000000000..16a8e9875d
--- /dev/null
+++ b/third_party/onnx/onnx/defs/nn/old.cc
@@ -0,0 +1,2440 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <cmath>
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+static const char* Dropout_ver12_doc = R"DOC(
+Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
+output (floating-point tensor) and mask (optional `Tensor<bool>`). If `training_mode` is true then the output Y will be a random dropout;
+Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
+the user can simply not pass `training_mode` input or set it to false.
+```
+output = scale * data * mask,
+```
+where
+```
+scale = 1. / (1. - ratio).
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Dropout,
+    12,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Dropout_ver12_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "seed",
+            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(0, "data", "The input data as Tensor.", "T")
+        .Input(
+            1,
+            "ratio",
+            "The ratio of random dropout, with value in [0, 1). If this input was not set, "
+            "or if it was set to 0, the output would be a simple copy of the input. "
+            "If it's non-zero, output will be a random dropout of the scaled input, which is typically "
+            "the case during training. It is an optional value, if not specified it will default to 0.5.",
+            "T1",
+            OpSchema::Optional)
+        .Input(
+            2,
+            "training_mode",
+            "If set to true then it indicates dropout is being used for training. It is an optional value hence unless "
+            "specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where "
+            "nothing will be dropped from the input data and if mask is requested as output it will contain all ones.",
+            "T2",
+            OpSchema::Optional)
+        .Output(0, "output", "The output.", "T")
+        .Output(1, "mask", "The output mask.", "T2", OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input 'ratio' types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(bool)"},
+            "Constrain output 'mask' types to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasInputShape(ctx, 0)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+
+          if (ctx.getNumInputs() > 1 && hasInputShape(ctx, 1)) {
+            auto& ratio_input_shape = getInputShape(ctx, 1);
+            if (static_cast<int>(ratio_input_shape.dim_size()) != 0) {
+              fail_shape_inference("Ratio of Dropout must be a scalar.");
+            }
+          }
+
+          if (ctx.getNumInputs() > 2 && hasInputShape(ctx, 2)) {
+            auto& training_mode_input_shape = getInputShape(ctx, 2);
+            if (static_cast<int>(training_mode_input_shape.dim_size()) != 0) {
+              fail_shape_inference(
+                  "training_mode of Dropout must be a scalar.");
+            }
+          }
+
+          if (ctx.getNumOutputs() == 2) {
+            updateOutputElemType(ctx, 1, TensorProto::BOOL);
+            if (hasNInputShapes(ctx, 1)) {
+              propagateShapeFromInputToOutput(ctx, 0, 1);
+            }
+          }
+        }));
+
+static const char* Flatten_ver11_doc = R"DOC(
+Flattens the input tensor into a 2D matrix. If input tensor has shape
+(d_0, d_1, ... d_n) then the output will have shape
+(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Flatten,
+    11,
+    OpSchema()
+        .SetDoc(Flatten_ver11_doc)
+        .Input(0, "input", "A tensor of rank >= axis.", "T")
+        .Output(
+            0,
+            "output",
+            "A 2D tensor with the contents of the input tensor, "
+            "with input dimensions up to axis flattened to the outer dimension "
+            "of the output and remaining input dimensions flattened into the inner "
+            "dimension of the output.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output to all tensor types.")
+        .Attr(
+            "axis",
+            "Indicate up to which input dimensions "
+            "(exclusive) should be flattened to the outer dimension of the output. "
+            "The value for axis must be in the range [-r, r], where r is the rank of the input tensor. "
+            "Negative value means counting dimensions from the back. "
+            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
+            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int rank = static_cast<int>(input_shape.dim_size());
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
+          if (axis < 0) {
+            axis += rank;
+          }
+          if (axis > rank || axis < 0) {
+            fail_shape_inference(
+                "Invalid value(", axis, ") for attribute 'axis'");
+          }
+          // TODO: is the operation defined for input-rank < 2?
+          updateOutputShape(
+              ctx,
+              0,
+              {multiplyDims(input_shape, 0, axis),
+               multiplyDims(input_shape, axis, rank)});
+        }));
+
+static const char* LRN_ver1_doc = R"DOC(
+Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+It normalizes over local input regions.
+The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
+of shape (N x C x D1 x D2, ..., Dk), its region is
+{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
+
+square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
+where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
+
+Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LRN,
+    1,
+    OpSchema()
+        .Attr("size", "The number of channels to sum over", AttributeProto::INT)
+        .Attr("alpha", "Scaling parameter.", AttributeProto::FLOAT, 0.0001f)
+        .Attr("beta", "The exponent.", AttributeProto::FLOAT, 0.75f)
+        .Attr("bias", "", AttributeProto::FLOAT, 1.0f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size. Optionally, if dimension denotation is "
+            "in effect, the operation expects the input "
+            "data tensor to arrive with the dimension denotation "
+            "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+            "T")
+        .Output(
+            0,
+            "Y",
+            "Output tensor, which has the shape and type as input tensor",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output "
+            " types to float tensors.")
+        .SetDoc(LRN_ver1_doc)
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* mvn_ver9_doc = R"DOC(
+      A MeanVarianceNormalization Function: Perform mean variance normalization
+      on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ```
+)DOC";
+
+static std::vector<int64_t> mvn_default_axes = {0, 2, 3};
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MeanVarianceNormalization,
+    9,
+    OpSchema()
+        .SetDoc(mvn_ver9_doc)
+        .Input(0, "X", "Input tensor", "T")
+        .Output(0, "Y", "Output tensor", "T")
+        .Attr(
+            "axes",
+            "A list of integers, along which to reduce. The default is to "
+            "caculate along axes [0,2,3] for calculating mean and variance "
+            "along each channel. Two variables with the same C-coordinate "
+            "are associated with the same mean and variance.",
+            AttributeProto::INTS,
+            mvn_default_axes)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to all numeric tensors.")
+        .FunctionBody(FunctionBodyHelper::BuildNodes(
+            {// nodes: {outputs, op, inputs, attributes}
+             FunctionBodyHelper::Const<float>("Exponent", 2.0f),
+             FunctionBodyHelper::Const<float>("Epsilon", float(1e-9)),
+             {{"X_RM"},
+              "ReduceMean",
+              {"X"},
+              {MakeRefAttribute("axes", AttributeProto::INTS)}},
+             {{"EX_squared"}, "Pow", {"X_RM", "Exponent"}},
+             {{"X_squared"}, "Pow", {"X", "Exponent"}},
+             {{"E_Xsquared"},
+              "ReduceMean",
+              {"X_squared"},
+              {MakeRefAttribute("axes", AttributeProto::INTS)}},
+             {{"Variance"}, "Sub", {"E_Xsquared", "EX_squared"}},
+             {{"STD"}, "Sqrt", {"Variance"}},
+             {{"X_variance"}, "Sub", {"X", "X_RM"}},
+             {{"Processed_STD"}, "Add", {"STD", "Epsilon"}},
+             {{"Y"}, "Div", {"X_variance", "Processed_STD"}}})));
+
+const char* pads_doc2 =
+    "Padding for the beginning and ending along each spatial axis, it can take any value greater "
+    "than or equal to 0. The value represent the number of pixels added to the beginning "
+    "and end part of the corresponding axis. `pads` format should be as follow "
+    "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
+    "added at the beginning of axis `i` and xi_end, the number of pixels added at "
+    "the end of axis `i`. This attribute cannot be used simultaneously with "
+    "auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.";
+const char* auto_pad_doc2 =
+    "auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where "
+    "default value is NOTSET, which means explicit padding is used. "
+    "SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input."
+    "In case of odd number add the extra padding at the end for SAME_UPPER and at the "
+    "beginning for SAME_LOWER. VALID mean no padding.";
+
+void convPoolShapeInference1(
+    InferenceContext& ctx,
+    bool use_dilation,
+    bool require_kernel_shape,
+    int input1Idx,
+    int input2Idx) {
+  // we need the first input shape for this inference.
+  if (!hasInputShape(ctx, input1Idx)) {
+    return;
+  }
+
+  // if kernel shape is an input (and not attribute)
+  // we need the shape of the second input.
+  if (!require_kernel_shape && !hasInputShape(ctx, input2Idx)) {
+    return;
+  }
+
+  auto input_shape = ctx.getInputType(input1Idx)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    fail_shape_inference("Input tensor must have atleast 2 dimensions");
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  // Only MaxPool and Conv support dilation. For
+  // simplicity of the code, we just treat the rest of them as having all-1s
+  // dilation.
+  std::vector<int64_t> dilations;
+  if (use_dilation && getRepeatedAttribute(ctx, "dilations", dilations)) {
+    if (dilations.size() != n_input_dims) {
+      fail_shape_inference("Attribute dilations has incorrect size");
+    }
+  } else {
+    dilations.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> strides;
+  if (getRepeatedAttribute(ctx, "strides", strides)) {
+    if (strides.size() != n_input_dims) {
+      fail_shape_inference("Attribute strides has incorrect size");
+    }
+  } else {
+    strides.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> kernel_shape;
+  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
+    if (kernel_shape.size() != n_input_dims) {
+      fail_shape_inference("Attribute kernel_shape has incorrect size");
+    }
+  } else if (require_kernel_shape) {
+    fail_shape_inference("Attribute kernel_shape must be specified");
+  } else {
+    auto second_input_shape =
+        ctx.getInputType(input2Idx)->tensor_type().shape();
+    for (int i = 2; i < second_input_shape.dim_size(); ++i) {
+      if (!second_input_shape.dim(i).has_dim_value()) {
+        return;
+      }
+      kernel_shape.push_back(second_input_shape.dim(i).dim_value());
+    }
+  }
+
+  std::vector<int64_t> effective_kernel_shape = kernel_shape;
+  for (int i = 0; i < static_cast<int>(kernel_shape.size()); i++) {
+    // accounting for dilation, how big is the kernel in this dimension
+    effective_kernel_shape[i] =
+        (effective_kernel_shape[i] - 1) * dilations[i] + 1;
+  }
+
+  std::vector<int64_t> pads;
+  if (getRepeatedAttribute(ctx, "pads", pads)) {
+    if (pads.size() != n_input_dims * 2) {
+      fail_shape_inference("Attribute pads has incorrect size");
+    }
+  } else {
+    pads.assign(n_input_dims * 2, 0);
+    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
+    if ((nullptr != auto_pad_attr) && (auto_pad_attr->s() != "VALID")) {
+      int input_dims_size = static_cast<int>(n_input_dims);
+      for (int i = 0; i < input_dims_size; ++i) {
+        int64_t residual = 0;
+        int64_t stride = strides[i];
+        if (stride > 1) {
+          if (!input_shape.dim(2 + i).has_dim_value()) {
+            continue;
+          }
+          residual = input_shape.dim(2 + i).dim_value();
+          while (residual >= stride) {
+            residual -= stride;
+          }
+        }
+        int64_t total_pad = residual == 0
+            ? effective_kernel_shape[i] - stride
+            : effective_kernel_shape[i] - residual;
+        if (total_pad < 0)
+          total_pad = 0;
+        int64_t half_pad_small = total_pad >> 1;
+        int64_t half_pad_big = total_pad - half_pad_small;
+        if (auto_pad_attr->s() == "SAME_UPPER") {
+          pads[i] = half_pad_small;
+          pads[i + input_dims_size] = half_pad_big;
+        } else if (auto_pad_attr->s() == "SAME_LOWER") {
+          pads[i] = half_pad_big;
+          pads[i + input_dims_size] = half_pad_small;
+        }
+      }
+    }
+  }
+
+  auto output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  if (require_kernel_shape) {
+    // add the first two dimensions from the input.
+    *output_shape->add_dim() = input_shape.dim(0);
+    *output_shape->add_dim() = input_shape.dim(1);
+  } else {
+    *output_shape->add_dim() = input_shape.dim(0);
+    auto& second_input_shape = getInputShape(ctx, input2Idx);
+    if (second_input_shape.dim_size() < 1) {
+      fail_shape_inference("Second input tensor has wrong dimension");
+    }
+    *output_shape->add_dim() = second_input_shape.dim(0);
+  }
+
+  int kernel_shape_size = static_cast<int>(kernel_shape.size());
+  for (int i = 0; i < kernel_shape_size; ++i) {
+    auto newdim = output_shape->add_dim();
+    if (!input_shape.dim(2 + i).has_dim_value()) {
+      continue;
+    }
+    // how big is the input, including padding
+    int64_t effective_input_size = input_shape.dim(2 + i).dim_value();
+    effective_input_size += pads[i];
+    effective_input_size += pads[i + kernel_shape_size];
+
+    // default is floor mode .i.e. ceil_mode is set to 0
+    auto ceil_mode = getAttribute(ctx, "ceil_mode", 0);
+
+    // how many times we can move the kernel from it's initial position, based
+    // on the stride
+    int64_t strided_kernel_positions;
+
+    if (ceil_mode == 1)
+      strided_kernel_positions = (int64_t)(std::ceil(
+          (effective_input_size - effective_kernel_shape[i]) /
+          float(strides[i])));
+    else
+      strided_kernel_positions =
+          (effective_input_size - effective_kernel_shape[i]) / strides[i];
+
+    // add in the initial position
+    newdim->set_dim_value(1 + strided_kernel_positions);
+  }
+
+  if (ctx.getNumOutputs() > 1) {
+    // MaxPool with two outputs case.
+    auto second_output_shape =
+        ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape();
+    second_output_shape->CopyFrom(*output_shape);
+  }
+}
+
+std::function<void(OpSchema&)> PoolOpSchemaGenerator_9(
+    const char* name,
+    const char* opName,
+    const char* additionalDescription) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+ {name} consumes an input tensor X and applies {opName} pooling across
+ the tensor according to kernel sizes, stride sizes, and pad lengths.
+ {opName} pooling consisting of computing the {opName} on all values of a
+ subset of the input tensor according to the kernel size and downsampling the
+ data into the output tensor Y for further processing. The output spatial shape will be following:
+ ```
+ output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+
+ * pad_shape[i] is sum of pads along axis i
+ ```
+
+ `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+ ```
+ VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+ SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+ ```
+ And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+ ```
+ pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+ ```
+ {additionalDescription}
+ )DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(doc, "{opName}", opName);
+        ReplaceAll(doc, "{additionalDescription}", additionalDescription););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "kernel_shape",
+        "The size of the kernel along each axis.",
+        AttributeProto::INTS);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        auto_pad_doc2,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data. For non image case, the "
+        "dimensions are in the form of "
+        "(N x C x D1 x D2 ... Dn), where N is the batch "
+        "size. Optionally, if dimension denotation is "
+        "in effect, the operation expects the input "
+        "data tensor to arrive with the dimension denotation "
+        "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+        "T");
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from average or max pooling across "
+        "the input tensor. Dimensions will vary based "
+        "on various kernel, stride, and pad sizes. Floor value of "
+        "the dimension is used",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (ctx.getNumOutputs() > 1) {
+        // MaxPool with two outputs case.
+        auto output_type = ctx.getOutputType(1);
+        if (output_type->value_case() == TypeProto::kTensorType ||
+            output_type->value_case() == TypeProto::VALUE_NOT_SET) {
+          output_type->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
+        }
+      }
+      convPoolShapeInference1(ctx, false, true, 0, 1);
+    });
+  };
+}
+
+std::function<void(OpSchema&)> PoolOpSchemaGenerator_10(
+    const char* name,
+    const char* opName,
+    const char* additionalDescription,
+    bool use_dilation,
+    int opsetNum) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+ {name} consumes an input tensor X and applies {opName} pooling across
+ the tensor according to kernel sizes, stride sizes, and pad lengths.
+ {opName} pooling consisting of computing the {opName} on all values of a
+ subset of the input tensor according to the kernel size and downsampling the
+ data into the output tensor Y for further processing. The output spatial shape will be following:
+ ```
+ output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
+ ```
+ or
+ ```
+ output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
+ ```
+ if ceil_mode is enabled
+
+ ```
+ * pad_shape[i] is sum of pads along axis i
+ ```
+
+ `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+ ```
+ VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
+ SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+ ```
+ And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+ ```
+ pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
+ ```
+ {additionalDescription}
+ )DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(doc, "{opName}", opName);
+        ReplaceAll(doc, "{additionalDescription}", additionalDescription);
+        ReplaceAll(
+            doc,
+            "{kernelSpatialShape}",
+            use_dilation ? "((kernel_spatial_shape[i] - 1) * dilations[i] + 1)"
+                         : "kernel_spatial_shape[i]"););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "kernel_shape",
+        "The size of the kernel along each axis.",
+        AttributeProto::INTS);
+    schema.Attr(
+        "strides",
+        opsetNum == 11
+            ? "Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis."
+            : "Stride along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        auto_pad_doc2,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "ceil_mode",
+        "Whether to use ceil or floor (default) to compute the output shape.",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data. For non image case, the "
+        "dimensions are in the form of "
+        "(N x C x D1 x D2 ... Dn), where N is the batch "
+        "size. Optionally, if dimension denotation is "
+        "in effect, the operation expects the input "
+        "data tensor to arrive with the dimension denotation "
+        "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+        "T");
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from average or max pooling across "
+        "the input tensor. Dimensions will vary based "
+        "on various kernel, stride, and pad sizes. Floor value of "
+        "the dimension is used",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([use_dilation](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (ctx.getNumOutputs() > 1) {
+        // MaxPool with two outputs case.
+        auto output_type = ctx.getOutputType(1);
+        if (output_type->value_case() == TypeProto::kTensorType ||
+            output_type->value_case() == TypeProto::VALUE_NOT_SET) {
+          output_type->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
+        }
+      }
+      convPoolShapeInference1(ctx, use_dilation, true, 0, 1);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    AveragePool,
+    1,
+    OpSchema().FillUsing(PoolOpSchemaGenerator_9(
+        "AveragePool",
+        "average",
+        "The output of each pooling window is divided by the number of elements exclude pad.")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    AveragePool,
+    7,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator_9(
+            "AveragePool",
+            "average",
+            "The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero)."))
+        .Attr(
+            "count_include_pad",
+            "Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    AveragePool,
+    10,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator_10(
+            "AveragePool",
+            "average",
+            "The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).",
+            false,
+            10))
+        .Attr(
+            "count_include_pad",
+            "Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxPool,
+    1,
+    OpSchema().FillUsing(PoolOpSchemaGenerator_9(
+        "MaxPool",
+        "max",
+        "The output of each pooling window is maximum number of elements exclude pad.")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxPool,
+    8,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator_9(
+            "MaxPool",
+            "max",
+            "The output of each pooling window is maximum number of elements exclude pad."))
+        .Attr(
+            "storage_order",
+            "The storage order of the tensor. 0 is row major, and 1 is column major.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Output(
+            1,
+            "Indices",
+            "Indices tensor from max pooling across the input tensor. "
+            "The dimensions of indices are the same as output tensor. "
+            "The values in indices of are the indices of the selected values during pooling. "
+            "The indices are computed as flatten 1-D tensor, "
+            "and the indices do not consider padding. "
+            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
+            "I",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64"));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxPool,
+    10,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator_10(
+            "MaxPool",
+            "max",
+            "The output of each pooling window is maximum number of elements exclude pad.",
+            true,
+            10))
+        .Attr(
+            "storage_order",
+            "The storage order of the tensor. 0 is row major, and 1 is column major.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "dilations",
+            "Dilation value along each spatial axis of filter.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Output(
+            1,
+            "Indices",
+            "Indices tensor from max pooling across the input tensor. "
+            "The dimensions of indices are the same as output tensor. "
+            "The values in indices of are the indices of the selected values during pooling. "
+            "The indices are computed as flatten 1-D tensor, "
+            "and the indices do not consider padding. "
+            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
+            "I",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64"));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxPool,
+    11,
+    OpSchema()
+        .FillUsing(PoolOpSchemaGenerator_10(
+            "MaxPool",
+            "max",
+            "The output of each pooling window is maximum number of elements exclude pad.",
+            true,
+            11))
+        .Attr(
+            "storage_order",
+            "The storage order of the tensor. 0 is row major, and 1 is column major.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "dilations",
+            "Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Output(
+            1,
+            "Indices",
+            "Indices tensor from max pooling across the input tensor. "
+            "The dimensions of indices are the same as output tensor. "
+            "The values in indices of are the indices of the selected values during pooling. "
+            "The indices are computed as flatten 1-D tensor, "
+            "and the indices do not consider padding. "
+            "So the values in indices are in [0, N x C x D1 x ... x Dn).",
+            "I",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64"));
+
+void maxUnpoolShapeInference1(InferenceContext& ctx) {
+  // we need at least two inputs to have a shape for this inference.
+  if (ctx.getNumInputs() != 2 && ctx.getNumInputs() != 3) {
+    fail_type_inference("MaxUnpool op must have either two or three inputs.");
+  }
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+  if (!hasInputShape(ctx, 0)) {
+    return; // If first input does not have shape, we cannot infer much.
+  }
+  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    fail_shape_inference("Input tensor X must have atleast 2 dimensions.");
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  std::vector<int64_t> pads;
+  if (getRepeatedAttribute(ctx, "pads", pads)) {
+    if (pads.size() != n_input_dims * 2) {
+      fail_shape_inference("Attribute pads has incorrect size.");
+    }
+  } else {
+    pads.assign(n_input_dims * 2, 0);
+  }
+
+  std::vector<int64_t> strides;
+  if (getRepeatedAttribute(ctx, "strides", strides)) {
+    if (strides.size() != n_input_dims) {
+      fail_shape_inference("Attribute strides has incorrect size.");
+    }
+  } else {
+    strides.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> kernel_shape;
+  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
+    if (kernel_shape.size() != n_input_dims) {
+      fail_shape_inference("Attribute kernel_shape has incorrect size.");
+    }
+  } else {
+    fail_shape_inference("Attribute kernel_shape must be specified.");
+  }
+
+  if (ctx.getNumInputs() == 3) {
+    // If the third input, output_size, is specified, then use that instead
+    // of inferring shape from inputs.
+    if (hasInputShape(ctx, 2)) {
+      auto& output_shape = getInputShape(ctx, 2);
+      if (output_shape.dim_size() != 1) {
+        fail_type_inference("'output_shape' must be rank 1 tensor.");
+      }
+      if (output_shape.dim((int)0).has_dim_value() &&
+          static_cast<int>(output_shape.dim((int)0).dim_value()) !=
+              input_shape.dim_size()) {
+        fail_shape_inference(
+            "'output_shape' must have same number of elements as the shape of input tensor X.");
+      }
+    }
+    return; // 'output_shape' is specified as input. Actual shape will be
+            // determined at runtime.
+  }
+
+  auto final_output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  *final_output_shape->add_dim() = input_shape.dim(0);
+  *final_output_shape->add_dim() =
+      ctx.getInputType(1)->tensor_type().shape().dim(
+          1); // channels should be the second dim of second input.
+
+  int kernel_shape_size = static_cast<int>(kernel_shape.size());
+  for (int i = 0; i < kernel_shape_size; ++i) {
+    auto newdim = final_output_shape->add_dim();
+    if (!input_shape.dim(2 + i).has_dim_value()) {
+      continue;
+    }
+
+    int64_t newdim_value =
+        strides[i] * (input_shape.dim(2 + i).dim_value() - 1);
+    newdim_value += kernel_shape[i];
+    newdim_value -= pads[i];
+    newdim_value -= pads[i + kernel_shape_size];
+
+    // add in the initial position
+    newdim->set_dim_value(newdim_value);
+  }
+}
+
+static const char* MaxUnpool_ver9_doc = R"DOC(
+MaxUnpool essentially computes the partial inverse of the MaxPool op.
+ The input information to this op is typically the the output information from a MaxPool op. The first
+ input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
+ from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
+ to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
+ The third (optional) input is a tensor that specifies the output size of the unpooling operation.
+
+MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal
+ values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling
+ the result of an unpooling operation should give back the original input to the unpooling op.
+
+MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous.
+ The third input argument, output_size, is meant to disambiguate the op and produce output tensor of
+ known/predictable size.
+
+In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads,
+ which define the exact unpooling op. The attributes typically have the same values as the corrsponding
+ pooling op that the unpooling op is trying to invert.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    MaxUnpool,
+    9,
+    OpSchema()
+        .SetDoc(MaxUnpool_ver9_doc)
+        .Attr(
+            "kernel_shape",
+            "The size of the kernel along each axis.",
+            AttributeProto::INTS)
+        .Attr(
+            "strides",
+            "Stride along each spatial axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE)
+        .Input(
+            0,
+            "X",
+            "Input data tensor that has to be unpooled. "
+            "This tensor is typically the first output of the MaxPool op."
+            "Dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non-image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size. Optionally, if dimension denotation is "
+            "in effect, the operation expects the input "
+            "data tensor to arrive with the dimension denotation "
+            "of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+            "T1")
+        .Input(
+            1,
+            "I",
+            "Input data tensor containing the indices corresponding to "
+            "elements in the first input tensor X."
+            "This tensor is typically the second output of the MaxPool op."
+            "Dimensions must be the same as input tensor X. "
+            "The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, "
+            "assuming row-major storage. Also, the linear indices should not consider padding. "
+            "So the values in indices are in the range [0, N x C x D1 x ... x Dn).",
+            "T2")
+        .Input(
+            2,
+            "output_shape",
+            "The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, "
+            "'pads' values are ignored.",
+            "T2",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "output",
+            "Output data tensor that contains the result of the unpooling.",
+            "T1")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain index tensor to int64")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          maxUnpoolShapeInference1(ctx);
+        }));
+
+const char* pads_doc1 =
+    "Padding for the beginning and ending along each axis, it can take any value greater "
+    "than or equal to 0. The value represent the number of pixels added to the beginning "
+    "and end part of the corresponding axis. `pads` format should be as follow "
+    "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
+    "added at the beginning of axis `i` and xi_end, the number of pixels added at "
+    "the end of axis `i`. This attribute cannot be used simultaneously with "
+    "auto_pad attribute.";
+const char* auto_pad_doc1 =
+    "auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where "
+    "default value is NOTSET, which means explicit padding is used. "
+    "SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input."
+    "In case of odd number add the extra padding at the end for SAME_UPPER and at the "
+    "beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is "
+    "only intended to support legacy uses, and for framework authors, one is explicitly "
+    "encouraged to use explicit padding specified in the pads attribute.";
+
+static const char* LpPool_ver1_doc = R"DOC(
+ LpPool consumes an input tensor X and applies Lp pooling across the
+ the tensor according to kernel sizes, stride sizes, and pad lengths.
+ Lp pooling consisting of computing the Lp norm on all values of a subset
+ of the input tensor according to the kernel size and downsampling the
+ data into the output tensor Y for further processing.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LpPool,
+    1,
+    OpSchema()
+        .SetDoc(LpPool_ver1_doc)
+        .Attr(
+            "kernel_shape",
+            "The size of the kernel along each axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "strides",
+            "Stride along each axis.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "auto_pad",
+            auto_pad_doc1,
+            AttributeProto::STRING,
+            std::string("NOTSET"))
+        .Attr("pads", pads_doc1, AttributeProto::INTS, OPTIONAL_VALUE)
+        .Attr(
+            "p",
+            "p value of the Lp norm used to pool over the input data, default is 2.0.",
+            AttributeProto::FLOAT,
+            2.0f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non image case, the "
+            "dimension are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the "
+            "batch size.",
+            "T")
+        .Output(
+            0,
+            "Y",
+            "Output data tensor from Lp pooling across the input "
+            "tensor. Dimensions will vary based on various kernel, stride, and pad "
+            "sizes.",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+std::function<void(OpSchema&)> LpPoolOpSchemaGenerator_10(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+ {name} consumes an input tensor X and applies Lp pooling across
+ the tensor according to kernel sizes, stride sizes, and pad lengths.
+ Lp pooling consisting of computing the Lp norm on all values of a subset
+ of the input tensor according to the kernel size and downsampling the
+ data into the output tensor Y for further processing.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc);
+    schema.Attr(
+        "kernel_shape",
+        "The size of the kernel along each axis.",
+        AttributeProto::INTS);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        auto_pad_doc2,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "p",
+        "p value of the Lp norm used to pool over the input data.",
+        AttributeProto::INT,
+        static_cast<int64_t>(2));
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from the previous operator; "
+        "dimensions for image case are (N x C x H x W), "
+        "where N is the batch size, C is the number of "
+        "channels, and H and W are the height and the "
+        "width of the data. For non image case, the "
+        "dimensions are in the form of "
+        "(N x C x D1 x D2 ... Dn), where N is the "
+        "batch size.",
+        "T");
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor from Lp pooling across the input "
+        "tensor. Dimensions will vary based on various kernel, stride, and pad "
+        "sizes.",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      convPoolShapeInference1(ctx, false, true, 0, 1);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LpPool,
+    2,
+    OpSchema().FillUsing(LpPoolOpSchemaGenerator_10("LpPool")));
+
+static const char* GlobalLpPool_ver1_doc = R"DOC(
+ GlobalLpPool consumes an input tensor X and applies lp pool pooling across the
+ the values in the same channel. This is equivalent to LpPool with kernel size
+ equal to the spatial dimension of input tensor.)DOC";
+
+std::function<void(OpSchema&)> ConvOpSchemaGenerator_10(
+    const char* filter_desc) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The convolution operator consumes an input tensor and {filter_desc}, and
+computes the output.)DOC";
+                        ReplaceAll(doc, "{filter_desc}", filter_desc););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from previous layer; "
+        "has size (N x C x H x W), where N is the batch size, "
+        "C is the number of channels, and H and W are the "
+        "height and width. Note that this is for the 2D image. "
+        "Otherwise the size is (N x C x D1 x D2 ... x Dn). "
+        "Optionally, if dimension denotation is "
+        "in effect, the operation expects input data tensor "
+        "to arrive with the dimension denotation of [DATA_BATCH, "
+        "DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
+        "T");
+    schema.Input(
+        1,
+        "W",
+        "The weight tensor that will be used in the "
+        "convolutions; has size (M x C/group x kH x kW), where C "
+        "is the number of channels, and kH and kW are the "
+        "height and width of the kernel, and M is the number "
+        "of feature maps. For more than 2 dimensions, the "
+        "kernel shape will be (M x C/group x k1 x k2 x ... x kn), "
+        "where (k1 x k2 x ... kn) is the dimension of the kernel. "
+        "Optionally, if dimension denotation is in effect, "
+        "the operation expects the weight tensor to arrive "
+        "with the dimension denotation of [FILTER_OUT_CHANNEL, "
+        "FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. "
+        "X.shape[1] == (W.shape[1] * group) == C "
+        "(assuming zero based indices for the shape array). "
+        "Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. ",
+        "T");
+    schema.Input(
+        2,
+        "B",
+        "Optional 1D bias to be added to the convolution, has size of M.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor that contains the result of the "
+        "convolution. The output dimensions are functions "
+        "of the kernel size, stride size, and pad lengths.",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.Attr(
+        "kernel_shape",
+        "The shape of the convolution kernel. If not present, should be inferred from input W.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "dilations",
+        "dilation value along each spatial axis of the filter.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        auto_pad_doc2,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "group",
+        "number of groups input channels and output channels are divided into.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      convPoolShapeInference1(ctx, true, false, 0, 1);
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Conv,
+    1,
+    OpSchema().FillUsing(ConvOpSchemaGenerator_10("a filter")));
+
+void convTransposeShapeInference1(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+  // we need at least two inputs to have a shape for this inference.
+  if (!hasNInputShapes(ctx, 2)) {
+    return;
+  }
+
+  int64_t group = getAttribute(ctx, "group", 1);
+
+  auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+  if (input_shape.dim_size() < 2) {
+    return; // Input tensor should have at least two dimensions.
+  }
+
+  // first dim is the batch axis and the next is the number of channels.
+  size_t n_input_dims = static_cast<size_t>(input_shape.dim_size() - 2);
+
+  std::vector<int64_t> dilations;
+  if (getRepeatedAttribute(ctx, "dilations", dilations)) {
+    if (dilations.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    dilations.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> strides;
+  if (getRepeatedAttribute(ctx, "strides", strides)) {
+    if (strides.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    strides.assign(n_input_dims, 1);
+  }
+
+  std::vector<int64_t> kernel_shape;
+  if (getRepeatedAttribute(ctx, "kernel_shape", kernel_shape)) {
+    if (kernel_shape.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    auto second_input_shape = ctx.getInputType(1)->tensor_type().shape();
+    for (int i = 2; i < second_input_shape.dim_size(); ++i) {
+      if (!second_input_shape.dim(i).has_dim_value()) {
+        return;
+      }
+      kernel_shape.push_back(second_input_shape.dim(i).dim_value());
+    }
+  }
+
+  std::vector<int64_t> effective_kernel_shape = kernel_shape;
+  for (int i = 0; i < static_cast<int>(kernel_shape.size()); i++) {
+    // accounting for dilation, how big is the kernel in this dimension
+    effective_kernel_shape[i] =
+        (effective_kernel_shape[i] - 1) * dilations[i] + 1;
+  }
+
+  std::vector<int64_t> pads;
+  if (getRepeatedAttribute(ctx, "pads", pads)) {
+    if (pads.size() != n_input_dims * 2) {
+      fail_shape_inference("Attribute pads has incorrect size");
+    }
+  } else {
+    pads.assign(n_input_dims * 2, 0);
+    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
+    if ((nullptr != auto_pad_attr) && (auto_pad_attr->s() != "VALID")) {
+      int input_dims_size = static_cast<int>(n_input_dims);
+      for (int i = 0; i < input_dims_size; ++i) {
+        int64_t total_pad = effective_kernel_shape[i] - strides[i];
+        if (total_pad < 0)
+          total_pad = 0;
+        int64_t half_pad_small = total_pad >> 1;
+        int64_t half_pad_big = total_pad - half_pad_small;
+        if (auto_pad_attr->s() == "SAME_UPPER") {
+          pads[i] = half_pad_small;
+          pads[i + input_dims_size] = half_pad_big;
+        } else if (auto_pad_attr->s() == "SAME_LOWER") {
+          pads[i] = half_pad_big;
+          pads[i + input_dims_size] = half_pad_small;
+        }
+      }
+    }
+  }
+
+  std::vector<int64_t> output_shape;
+  bool output_shape_presented = true;
+  if (getRepeatedAttribute(ctx, "output_shape", output_shape)) {
+    if (output_shape.size() != n_input_dims) {
+      return;
+    }
+  } else {
+    output_shape_presented = false;
+  }
+
+  std::vector<int64_t> output_padding;
+  if (getRepeatedAttribute(ctx, "output_padding", output_padding)) {
+    if (output_padding.size() != n_input_dims) { // Added only to one side.
+      return;
+    }
+  } else {
+    output_padding.assign(n_input_dims, 0);
+  }
+
+  auto final_output_shape =
+      ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+  *final_output_shape->add_dim() = input_shape.dim(0);
+  *final_output_shape->add_dim() =
+      ctx.getInputType(1)->tensor_type().shape().dim(1) *
+      group; // channels should be the second dim of second input multiply
+             // group.
+
+  int size_of_output;
+  if (output_shape_presented) {
+    size_of_output = static_cast<int>(output_shape.size());
+    for (int i = 0; i < size_of_output; ++i) {
+      if (input_shape.dim(i + 2).has_dim_value()) {
+        if (output_shape[i] < input_shape.dim(i + 2).dim_value()) {
+          // TODO: throw exception?
+          return; // output shape value cannot be smaller than the input shape
+                  // value
+        }
+      }
+      final_output_shape->add_dim()->set_dim_value(output_shape[i]);
+    }
+    return;
+  } else {
+    size_of_output = input_shape.dim_size() - 2;
+    for (int i = 0; i < size_of_output; ++i) {
+      if (input_shape.dim(i + 2).has_dim_value()) {
+        int64_t output_shape_dim =
+            strides[i] * (input_shape.dim(i + 2).dim_value() - 1) +
+            output_padding[i] + effective_kernel_shape[i] - pads[i] -
+            pads[i + n_input_dims];
+        final_output_shape->add_dim()->set_dim_value(output_shape_dim);
+      } else {
+        final_output_shape->add_dim();
+      }
+    }
+    return;
+  }
+}
+
+std::function<void(OpSchema&)> ConvTransposeOpSchemaGenerator_10(
+    const char* filter_desc) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+The convolution transpose operator consumes an input tensor and {filter_desc},
+and computes the output.
+
+If the pads parameter is provided the shape of the output is calculated via the following equation:
+
+  output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]
+
+output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
+
+  total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
+  If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
+  Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
+
+    )DOC";
+                        ReplaceAll(doc, "{filter_desc}", filter_desc););
+    schema.SetDoc(doc);
+    schema.Input(
+        0,
+        "X",
+        "Input data tensor from previous layer; has size (N x C x H x W)"
+        ", where N is the batch size, C is the number of channels, and"
+        " H and W are the height and width. Note that this is for the 2D image. "
+        "Otherwise the size is (N x C x D1 x D2 ... x Dn)",
+        "T");
+    schema.Input(
+        1,
+        "W",
+        "The weight tensor that will be used in the "
+        "convolutions; has size (C x M/group x kH x kW), where C "
+        "is the number of channels, and kH and kW are the "
+        "height and width of the kernel, and M is the number "
+        "of feature maps. For more than 2 dimensions, the "
+        "weight shape will be (C x M/group x k1 x k2 x ... x kn), "
+        "where (k1 x k2 x ... x kn) is the dimension of the kernel. "
+        "The number of channels in the output should be equal to W.shape[1] * group "
+        "(assuming zero based indices of the shape array)",
+        "T");
+    schema.Input(
+        2,
+        "B",
+        "Optional 1D bias to be added to the convolution, has size of M.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        0,
+        "Y",
+        "Output data tensor that contains the result of the convolution. The "
+        "output dimensions are functions of the kernel size, stride size, "
+        "pad lengths and group count. "
+        "The number of channels in the output should be equal to W.shape[1] * group "
+        "(assuming zero based indices of the shape array)",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.Attr(
+        "kernel_shape",
+        "The shape of the convolution kernel. If not present, should be inferred from input W.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "output_shape",
+        "The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified "
+        "pads values are ignored. See doc for details for equations to generate pads",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "output_padding",
+        "The zero-padding added to one side of the output."
+        " This is also called adjs/adjustment in some frameworks.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "dilations",
+        "dilation value along each spatial axis of the filter.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "strides",
+        "Stride along each spatial axis.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "auto_pad",
+        auto_pad_doc2,
+        AttributeProto::STRING,
+        std::string("NOTSET"));
+    schema.Attr("pads", pads_doc2, AttributeProto::INTS, OPTIONAL_VALUE);
+    schema.Attr(
+        "group",
+        "number of groups input channels and output channels are divided into.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.TypeAndShapeInferenceFunction(
+        [](InferenceContext& ctx) { convTransposeShapeInference1(ctx); });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ConvTranspose,
+    1,
+    OpSchema().FillUsing(ConvTransposeOpSchemaGenerator_10("a filter")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GlobalLpPool,
+    1,
+    OpSchema()
+        .SetDoc(GlobalLpPool_ver1_doc)
+        .Attr(
+            "p",
+            "p value of the Lp norm used to pool over the input data, default is 2.0.",
+            AttributeProto::FLOAT,
+            2.0f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the width "
+            "of the data. For non image case, the dimension are "
+            "in the form of (N x C x D1 x D2 ... Dn), "
+            "where N is the batch size.",
+            "T")
+        .Output(
+            0,
+            "Y",
+            "Output data tensor from pooling across the input "
+            "tensor. Dimensions will be N x C x 1 x 1",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* BatchNormalization_ver1_doc = R"DOC(
+Carries out batch normalization as described in the paper
+https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+there are multiple cases for the number of outputs, which we list below:
+
+Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+Output case #2: Y (test mode)
+    )DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BatchNormalization,
+    1,
+    OpSchema()
+        .NumOutputs({1, 5})
+        .SetDoc(BatchNormalization_ver1_doc)
+        .Attr(
+            "spatial",
+            "If true, compute the mean and variance across all spatial elements "
+            "If false, compute the mean and variance across per feature."
+            "Default is 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "is_test",
+            "If set to nonzero, run spatial batch normalization in test mode, default is 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero, default is 1e-5f.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Attr(
+            "momentum",
+            "Factor used in computing the running mean and variance."
+            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.",
+            AttributeProto::FLOAT,
+            0.9f)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS)
+        .Input(0, "X", "The input 4-dimensional tensor of shape NCHW.", "T")
+        .Input(
+            1,
+            "scale",
+            "The scale as a 1-dimensional tensor of size C to be applied to the "
+            "output.",
+            "T")
+        .Input(
+            2,
+            "B",
+            "The bias as a 1-dimensional tensor of size C to be applied to the "
+            "output.",
+            "T")
+        .Input(
+            3,
+            "mean",
+            "The running mean (training) or the estimated mean (testing) "
+            "as a 1-dimensional tensor of size C.",
+            "T")
+        .Input(
+            4,
+            "var",
+            "The running variance (training) or the estimated "
+            "variance (testing) as a 1-dimensional tensor of size C.",
+            "T")
+        .Output(
+            0,
+            "Y",
+            "The output 4-dimensional tensor of the same shape as X.",
+            "T")
+        .Output(
+            1,
+            "mean",
+            "The running mean after the BatchNormalization operator. Must be in-place "
+            "with the input mean. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            2,
+            "var",
+            "The running variance after the BatchNormalization operator. Must be "
+            "in-place with the input var. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            3,
+            "saved_mean",
+            "Saved mean used during training to speed up gradient "
+            "computation. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            4,
+            "saved_var",
+            "Saved variance used during training to speed up "
+            "gradient computation. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* BatchNormalization_ver9_doc = R"DOC(
+Carries out batch normalization as described in the paper
+https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+there are multiple cases for the number of outputs, which we list below:
+
+Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+Output case #2: Y (test mode)
+
+For previous (depreciated) non-spatial cases, implementors are suggested
+to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BatchNormalization,
+    9,
+    OpSchema()
+        .NumOutputs({1, 5})
+        .SetDoc(BatchNormalization_ver9_doc + GenerateOptionalArgumentsDoc())
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Attr(
+            "momentum",
+            "Factor used in computing the running mean and variance."
+            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
+            AttributeProto::FLOAT,
+            0.9f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions are in the form of (N x C x D1 x D2 ... Dn), "
+            "where N is the batch size, C is the number of channels. "
+            "Statistics are computed for every channel of C over N and D1 to Dn dimensions. "
+            "For image data, input dimensions become (N x C x H x W). "
+            "The op also accepts single dimension input of size N in which case C is assumed to be 1",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "scale",
+            "Scale tensor of shape (C).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "B",
+            "Bias tensor of shape (C).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            3,
+            "mean",
+            "running (training) or estimated (testing) mean tensor of shape (C).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            4,
+            "var",
+            "running (training) or estimated (testing) variance tensor of shape (C).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "The output tensor of the same shape as X",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            1,
+            "mean",
+            "The running mean after the BatchNormalization operator.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            2,
+            "var",
+            "The running variance after the BatchNormalization operator.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            3,
+            "saved_mean",
+            "Saved mean used during training to speed up gradient "
+            "computation.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            4,
+            "saved_var",
+            "Saved variance used during training to speed up "
+            "gradient computation.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          // TODO in training mode, it may be possible to infer some of
+          // the other outputs as well.
+        }));
+
+static const char* BatchNormalization_ver14_doc = R"DOC(
+Carries out batch normalization as described in the paper
+https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+There are five required inputs 'X', 'scale', 'B', 'input_mean' and
+'input_var'.
+Note that 'input_mean' and 'input_var' are expected to be the estimated
+statistics in inference mode (training_mode=False, default),
+and the running statistics in training mode (training_mode=True).
+There are multiple cases for the number of outputs, which we list below:
+
+Output case #1: Y, running_mean, running_var (training_mode=True)
+Output case #2: Y (training_mode=False)
+
+When training_mode=False, extra outputs are invalid.
+The outputs are updated as follows when training_mode=True:
+```
+running_mean = input_mean * momentum + current_mean * (1 - momentum)
+running_var = input_var * momentum + current_var * (1 - momentum)
+
+Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
+
+where:
+
+current_mean = ReduceMean(X, axis=all_except_channel_index)
+current_var =  ReduceVar(X, axis=all_except_channel_index)
+
+Notice that ReduceVar refers to the population variance, and it equals to
+sum(sqrd(x_i - x_avg)) / N
+where N is the population size (this formula does not use sample size N - 1).
+
+```
+
+When training_mode=False:
+```
+Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
+```
+
+For previous (depreciated) non-spatial cases, implementors are suggested
+to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BatchNormalization,
+    14,
+    OpSchema()
+        .NumOutputs({1, 3})
+        .SetDoc(BatchNormalization_ver14_doc + GenerateOptionalArgumentsDoc())
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Attr(
+            "momentum",
+            "Factor used in computing the running mean and variance."
+            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
+            AttributeProto::FLOAT,
+            0.9f)
+        .Attr(
+            "training_mode",
+            "If set to true, it indicates BatchNormalization is being used for training, and outputs 1, "
+            "2, 3, and 4 would be populated.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions are in the form of (N x C x D1 x D2 ... Dn), "
+            "where N is the batch size, C is the number of channels. "
+            "Statistics are computed for every channel of C over N and D1 to Dn dimensions. "
+            "For image data, input dimensions become (N x C x H x W). "
+            "The op also accepts single dimension input of size N in which case C is assumed to be 1",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "scale",
+            "Scale tensor of shape (C).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "B",
+            "Bias tensor of shape (C).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            3,
+            "input_mean",
+            "running (training) or estimated (testing) mean tensor of shape (C).",
+            "U",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            4,
+            "input_var",
+            "running (training) or estimated (testing) variance tensor of shape (C).",
+            "U",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "Y",
+            "The output tensor of the same shape as X",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            1,
+            "running_mean",
+            "The running mean after the BatchNormalization operator.",
+            "U",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            2,
+            "running_var",
+            "The running variance after the BatchNormalization operator. This op uses the population size (N) for "
+            "calculating variance, and not the sample size N-1.",
+            "U",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "U",
+            {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
+            "Constrain mean and variance types to float tensors. It allows all float type for U.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+
+          // Inputs 1 to 4 must be of rank 1.
+          checkInputRank(ctx, 1, 1);
+          checkInputRank(ctx, 2, 1);
+          checkInputRank(ctx, 3, 1);
+          checkInputRank(ctx, 4, 1);
+          
+          Dim num_channels;
+
+          if (hasInputShape(ctx, 0)) {
+            if (getInputShape(ctx, 0).dim_size() > 1)
+              unifyInputDim(ctx, 0, 1, num_channels);
+            else
+              unifyDim(num_channels, 1);
+          }
+
+          unifyInputDim(ctx, 1, 0, num_channels);
+          unifyInputDim(ctx, 2, 0, num_channels);
+          unifyInputDim(ctx, 3, 0, num_channels);
+          unifyInputDim(ctx, 4, 0, num_channels);
+
+          if (ctx.getAttribute("training_mode") &&
+               static_cast<int>(ctx.getAttribute("training_mode")->i()) != 0) {
+            if (ctx.getNumOutputs() != 3)
+              fail_shape_inference(
+                "This number of op outputs should be 3 when Training_mode = True, but it is not.");
+          } else {
+            if (ctx.getNumOutputs() != 1)
+              fail_shape_inference(
+                "This number of op outputs should be 1 when Training_mode = False, but it is not.");
+          }
+
+          if (ctx.getNumOutputs() > 1) {
+            TensorShapeProto outputs_shape;
+            *outputs_shape.add_dim() = num_channels; // channel
+
+            propagateElemTypeFromInputToOutput(ctx, 3, 1);
+            updateOutputShape(ctx, 1, outputs_shape);
+
+            if (ctx.getNumOutputs() > 2) {
+              propagateElemTypeFromInputToOutput(ctx, 4, 2);
+              updateOutputShape(ctx, 2, outputs_shape);
+            }
+          }
+        }));
+
+static const char* InstanceNormalization_ver1_doc = R"DOC(
+Carries out instance normalization as described in the paper
+https://arxiv.org/abs/1607.08022.
+
+y = scale * (x - mean) / sqrt(variance + epsilon) + B,
+where mean and variance are computed per instance per channel.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    InstanceNormalization,
+    1,
+    OpSchema()
+        .SetDoc(InstanceNormalization_ver1_doc)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero, default is 1e-5f.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Input(0, "input", "The input 4-dimensional tensor of shape NCHW.", "T")
+        .Input(
+            1,
+            "scale",
+            "The input 1-dimensional scale tensor of size C.",
+            "T")
+        .Input(2, "B", "The input 1-dimensional bias tensor of size C.", "T")
+        .Output(
+            0,
+            "output",
+            "The output 4-dimensional tensor of the same shape as input.",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Dropout_old_doc = R"DOC(
+Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
+output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
+test mode or not, the output Y will either be a random dropout, or a simple
+copy of the input. Note that our implementation of Dropout does scaling in
+the training phase, so during testing nothing needs to be done.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Dropout,
+    1,
+    OpSchema()
+        .SetDoc(Dropout_old_doc)
+        .Attr(
+            "ratio",
+            "(float, default 0.5) the ratio of random dropout",
+            AttributeProto::FLOAT,
+            0.5f)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "is_test",
+            "(int, default 0) if nonzero, run dropout in test mode where "
+            "the output is simply Y = X.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "The input data as Tensor.", "T")
+        .Output(0, "output", "The output.", "T")
+        .Output(
+            1,
+            "mask",
+            "The output mask. If is_test is nonzero, this output is not filled.",
+            "T",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Dropout,
+    6,
+    OpSchema()
+        .SetDoc(Dropout_old_doc)
+        .Attr(
+            "ratio",
+            "(float, default 0.5) the ratio of random dropout",
+            AttributeProto::FLOAT,
+            0.5f)
+        .Attr(
+            "is_test",
+            "(int, default 0) if nonzero, run dropout in test mode where "
+            "the output is simply Y = X.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "The input data as Tensor.", "T")
+        .Output(0, "output", "The output.", "T")
+        .Output(
+            1,
+            "mask",
+            "The output mask. If is_test is nonzero, this output is not filled.",
+            "T",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Dropout_ver7_doc = R"DOC(
+Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
+output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
+test mode or not, the output Y will either be a random dropout, or a simple
+copy of the input. Note that our implementation of Dropout does scaling in
+the training phase, so during testing nothing needs to be done.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Dropout,
+    7,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Dropout_ver7_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "ratio",
+            "The ratio of random dropout",
+            AttributeProto::FLOAT,
+            0.5f)
+        .Input(0, "data", "The input data as Tensor.", "T")
+        .Output(0, "output", "The output.", "T")
+        .Output(1, "mask", "The output mask.", "T", OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Dropout_ver10_doc = R"DOC(
+Dropout takes one input floating tensor and produces two tensor outputs,
+output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is
+in test mode or not, the output Y will either be a random dropout, or a simple
+copy of the input. Note that our implementation of Dropout does scaling in
+the training phase, so during testing nothing needs to be done.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Dropout,
+    10,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Dropout_ver10_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "ratio",
+            "The ratio of random dropout",
+            AttributeProto::FLOAT,
+            0.5f)
+        .Input(0, "data", "The input data as Tensor.", "T")
+        .Output(0, "output", "The output.", "T")
+        .Output(1, "mask", "The output mask.", "T1", OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain output mask types to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          if (ctx.getNumOutputs() == 2) {
+            updateOutputElemType(ctx, 1, TensorProto::BOOL);
+            if (hasNInputShapes(ctx, 1)) {
+              propagateShapeFromInputToOutput(ctx, 0, 1);
+            }
+          }
+        }));
+
+static const char* BatchNorm_ver6_doc = R"DOC(
+Carries out batch normalization as described in the paper
+https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+there are multiple cases for the number of outputs, which we list below:
+
+Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+Output case #2: Y (test mode)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BatchNormalization,
+    6,
+    OpSchema()
+        .NumOutputs({1, 5})
+        .SetDoc(BatchNorm_ver6_doc)
+        .Attr(
+            "spatial",
+            "If true, compute the mean and variance across all spatial elements "
+            "If false, compute the mean and variance across per feature."
+            "Default is 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "is_test",
+            "If set to nonzero, run spatial batch normalization in test mode, default is 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero, default is 1e-5f.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Attr(
+            "momentum",
+            "Factor used in computing the running mean and variance."
+            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.",
+            AttributeProto::FLOAT,
+            0.9f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size.",
+            "T")
+        .Input(
+            1,
+            "scale",
+            "The scale as a 1-dimensional tensor of size C to be applied to the "
+            "output.",
+            "T")
+        .Input(
+            2,
+            "B",
+            "The bias as a 1-dimensional tensor of size C to be applied to the "
+            "output.",
+            "T")
+        .Input(
+            3,
+            "mean",
+            "The running mean (training) or the estimated mean (testing) "
+            "as a 1-dimensional tensor of size C.",
+            "T")
+        .Input(
+            4,
+            "var",
+            "The running variance (training) or the estimated "
+            "variance (testing) as a 1-dimensional tensor of size C.",
+            "T")
+        .Output(0, "Y", "The output tensor of the same shape as X.", "T")
+        .Output(
+            1,
+            "mean",
+            "The running mean after the BatchNormalization operator. Must be in-place "
+            "with the input mean. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            2,
+            "var",
+            "The running variance after the BatchNormalization operator. Must be "
+            "in-place with the input var. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            3,
+            "saved_mean",
+            "Saved mean used during training to speed up gradient "
+            "computation. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            4,
+            "saved_var",
+            "Saved variance used during training to speed up "
+            "gradient computation. Should not be used for testing.",
+            "T",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          // TODO in training mode, it may be possible to infer some of
+          // the other outputs as well.
+        }));
+
+static const char* Flatten_ver1_doc = R"DOC(
+Flattens the input tensor into a 2D matrix. If input tensor has shape
+(d_0, d_1, ... d_n) then the output will have shape
+(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Flatten,
+    1,
+    OpSchema()
+        .SetDoc(Flatten_ver1_doc)
+        .Input(0, "input", "A tensor of rank >= axis.", "T")
+        .Output(
+            0,
+            "output",
+            "A 2D tensor with the contents of the input tensor, "
+            "with input dimensions up to axis flattened to the outer dimension "
+            "of the output and remaining input dimensions flattened into the inner "
+            "dimension of the output.",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .Attr(
+            "axis",
+            "Indicate up to which input dimensions "
+            "(exclusive) should be flattened to the outer dimension of the output. "
+            "The value for axis must be in the range [0, R], where R is the rank of the input tensor. "
+            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
+            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int rank = static_cast<int>(input_shape.dim_size());
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
+          if (axis > rank || axis < 0) {
+            fail_shape_inference(
+                "Invalid value(", axis, ") for attribute 'axis'");
+          }
+          // TODO: is the operation defined for input-rank < 2?
+          updateOutputShape(
+              ctx,
+              0,
+              {multiplyDims(input_shape, 0, axis),
+               multiplyDims(input_shape, axis, rank)});
+        }));
+
+static const char* Flatten_ver9_doc = R"DOC(
+Flattens the input tensor into a 2D matrix. If input tensor has shape
+(d_0, d_1, ... d_n) then the output will have shape
+(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Flatten,
+    9,
+    OpSchema()
+        .SetDoc(Flatten_ver9_doc)
+        .Input(0, "input", "A tensor of rank >= axis.", "T")
+        .Output(
+            0,
+            "output",
+            "A 2D tensor with the contents of the input tensor, "
+            "with input dimensions up to axis flattened to the outer dimension "
+            "of the output and remaining input dimensions flattened into the inner "
+            "dimension of the output.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output to all tensor types.")
+        .Attr(
+            "axis",
+            "Indicate up to which input dimensions "
+            "(exclusive) should be flattened to the outer dimension of the output. "
+            "The value for axis must be in the range [0, R], where R is the rank of the input tensor. "
+            "When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), "
+            "where the shape of the input tensor is (d_0, d_1, ... d_n). ",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasInputShape(ctx, 0))
+            return;
+          auto& input_shape = getInputShape(ctx, 0);
+          int rank = static_cast<int>(input_shape.dim_size());
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 1));
+          if (axis > rank || axis < 0) {
+            fail_shape_inference(
+                "Invalid value(", axis, ") for attribute 'axis'");
+          }
+          // TODO: is the operation defined for input-rank < 2?
+          updateOutputShape(
+              ctx,
+              0,
+              {multiplyDims(input_shape, 0, axis),
+               multiplyDims(input_shape, axis, rank)});
+        }));
+
+static const char* BatchNormalization_ver7_doc = R"DOC(
+    Carries out batch normalization as described in the paper
+    https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
+    there are multiple cases for the number of outputs, which we list below:
+
+    Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
+    Output case #2: Y (test mode)
+        )DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    BatchNormalization,
+    7,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(BatchNormalization_ver7_doc) +
+            GenerateOptionalArgumentsDoc()))
+        .NumOutputs({1, 5})
+        .Attr(
+            "spatial",
+            "If true, compute the mean and variance across per activation. "
+            "If false, compute the mean and variance across per feature over "
+            "each mini-batch.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "epsilon",
+            "The epsilon value to use to avoid division by zero.",
+            AttributeProto::FLOAT,
+            1e-5f)
+        .Attr(
+            "momentum",
+            "Factor used in computing the running mean and variance."
+            "e.g., running_mean = running_mean * momentum + mean * (1 - momentum).",
+            AttributeProto::FLOAT,
+            0.9f)
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "dimensions for image case are (N x C x H x W), "
+            "where N is the batch size, C is the number of "
+            "channels, and H and W are the height and the "
+            "width of the data. For non image case, the "
+            "dimensions are in the form of "
+            "(N x C x D1 x D2 ... Dn), where N is the batch "
+            "size.",
+            "T")
+        .Input(
+            1,
+            "scale",
+            "If spatial is true, the dimension of scale is (C). "
+            "If spatial is false, the dimensions of scale are "
+            "(C x D1 x ... x Dn)",
+            "T")
+        .Input(
+            2,
+            "B",
+            "If spatial is true, the dimension of bias is (C). "
+            "If spatial is false, the dimensions of bias are "
+            "(C x D1 x ... x Dn)",
+            "T")
+        .Input(
+            3,
+            "mean",
+            "If spatial is true, the dimension of the running mean "
+            "(training) or the estimated mean (testing) is (C). "
+            "If spatial is false, the dimensions of the running mean "
+            "(training) or the estimated mean (testing) are (C x D1 x ... x Dn).",
+            "T")
+        .Input(
+            4,
+            "var",
+            "If spatial is true, the dimension of the running variance"
+            "(training) or the estimated variance (testing) is (C). "
+            "If spatial is false, the dimensions of the running variance"
+            "(training) or the estimated variance (testing) are (C x D1 x ... x Dn).",
+            "T")
+        .Output(0, "Y", "The output tensor of the same shape as X", "T")
+        .Output(
+            1,
+            "mean",
+            "The running mean after the BatchNormalization operator.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            2,
+            "var",
+            "The running variance after the BatchNormalization operator.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            3,
+            "saved_mean",
+            "Saved mean used during training to speed up gradient "
+            "computation.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            4,
+            "saved_var",
+            "Saved variance used during training to speed up "
+            "gradient computation.",
+            "T",
+            OpSchema::Optional)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateShapeAndTypeFromFirstInput(ctx);
+          // TODO in training mode, it may be possible to infer some of
+          // the other outputs as well.
+        }));
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/object_detection/defs.cc b/third_party/onnx/onnx/defs/object_detection/defs.cc
new file mode 100644
index 0000000000..15bb2ca347
--- /dev/null
+++ b/third_party/onnx/onnx/defs/object_detection/defs.cc
@@ -0,0 +1,220 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+using namespace ONNX_NAMESPACE;
+
+namespace ONNX_NAMESPACE {
+
+static const char* RoiAlign_ver16_doc = R"DOC(
+Region of Interest (RoI) align operation described in the
+[Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
+RoiAlign consumes an input tensor X and region of interests (rois)
+to apply pooling across each RoI; it produces a 4-D tensor of shape
+(num_rois, C, output_height, output_width).
+
+RoiAlign is proposed to avoid the misalignment by removing
+quantizations while converting from original image into feature
+map and from feature map into RoI feature; in each ROI bin,
+the value of the sampled locations are computed directly
+through bilinear interpolation.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RoiAlign,
+    16,
+    OpSchema()
+        .SetDoc(RoiAlign_ver16_doc)
+        .Attr(
+            "spatial_scale",
+            "Multiplicative spatial scale factor to translate ROI coordinates "
+            "from their input spatial scale to the scale used when pooling, "
+            "i.e., spatial scale of the input feature map X relative to the "
+            "input image. E.g.; default is 1.0f. ",
+            AttributeProto::FLOAT,
+            1.f)
+        .Attr(
+            "output_height",
+            "default 1; Pooled output Y's height.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "output_width",
+            "default 1; Pooled output Y's width.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "sampling_ratio",
+            "Number of sampling points in the interpolation grid used to compute "
+            "the output value of each pooled output bin. If > 0, then exactly "
+            "sampling_ratio x sampling_ratio grid points are used. If == 0, then "
+            "an adaptive number of grid points are used (computed as "
+            "ceil(roi_width / output_width), and likewise for height). Default is 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "mode",
+            "The pooling method. Two modes are supported: 'avg' and 'max'. "
+            "Default is 'avg'.",
+            AttributeProto::STRING,
+            std::string("avg"))
+        .Attr(
+            "coordinate_transformation_mode",
+            "Allowed values are 'half_pixel' and 'output_half_pixel'. "
+            "Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). "
+            "Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a "
+            "backward-compatible behavior).",
+            AttributeProto::STRING,
+            std::string("half_pixel"))
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "4-D feature map of shape (N, C, H, W), "
+            "where N is the batch size, C is the number of channels, "
+            "and H and W are the height and the width of the data.",
+            "T1")
+        .Input(
+            1,
+            "rois",
+            "RoIs (Regions of Interest) to pool over; rois is "
+            "2-D input of shape (num_rois, 4) given as "
+            "[[x1, y1, x2, y2], ...]. "
+            "The RoIs' coordinates are in the coordinate system of the input image. "
+            "Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.",
+            "T1")
+        .Input(
+            2,
+            "batch_indices",
+            "1-D tensor of shape (num_rois,) with each element denoting "
+            "the index of the corresponding image in the batch.",
+            "T2")
+        .Output(
+            0,
+            "Y",
+            "RoI pooled output, 4-D tensor of shape "
+            "(num_rois, C, output_height, output_width). The r-th batch element Y[r-1] "
+            "is a pooled feature map corresponding to the r-th RoI X[r-1].",
+            "T1")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain types to int tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          size_t input_param = 0, rois_param = 1, batch_index_param = 2;
+
+          checkInputRank(ctx, input_param, 4);
+          checkInputRank(ctx, rois_param, 2);
+          checkInputRank(ctx, batch_index_param, 1);
+
+          // Output dimensions, initialized to an unknown-dimension-value
+          Dim num_rois, C, ht, width;
+
+          // Get value of C from dim 1 of input_param, if available
+          unifyInputDim(ctx, input_param, 1, C);
+
+          // Get value of num_rois from dim 0 of rois_param, if available
+          unifyInputDim(ctx, rois_param, 0, num_rois);
+          // ... or from dim 0 of batch_index_param, if available
+          unifyInputDim(ctx, batch_index_param, 0, num_rois);
+
+          // Get height from attribute, using default-value of 1
+          unifyDim(ht, getAttribute(ctx, "output_height", 1));
+
+          // Get width from attribute, using default-value of 1
+          unifyDim(width, getAttribute(ctx, "output_width", 1));
+
+          // set output shape:
+          updateOutputShape(ctx, 0, {num_rois, C, ht, width});
+        }));
+
+static const char* NonMaxSuppression_doc = R"DOC(
+Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
+Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box.
+Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to
+orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system
+result in the same boxes being selected by the algorithm.
+The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes.
+The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    NonMaxSuppression,
+    11,
+    OpSchema()
+        .Input(
+            0,
+            "boxes",
+            "An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.",
+            "tensor(float)")
+        .Input(
+            1,
+            "scores",
+            "An input tensor with shape [num_batches, num_classes, spatial_dimension]",
+            "tensor(float)")
+        .Input(
+            2,
+            "max_output_boxes_per_class",
+            "Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.",
+            "tensor(int64)",
+            OpSchema::Optional)
+        .Input(
+            3,
+            "iou_threshold",
+            "Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.",
+            "tensor(float)",
+            OpSchema::Optional)
+        .Input(
+            4,
+            "score_threshold",
+            "Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.",
+            "tensor(float)",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "selected_indices",
+            "selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].",
+            "tensor(int64)")
+        .Attr(
+            "center_point_box",
+            "Integer indicate the format of the box data. The default is 0. "
+            "0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners "
+            "and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. "
+            "1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .SetDoc(NonMaxSuppression_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference - Output is always of type INT64
+          auto* selected_indices_type =
+              ctx.getOutputType(0)->mutable_tensor_type();
+          selected_indices_type->set_elem_type(
+              TensorProto_DataType::TensorProto_DataType_INT64);
+
+          // Shape inference
+          // The exact shape cannot be determined as it depends on the input and
+          // other input configurations for the op But part of the shape can be
+          // established
+
+          auto* selected_indices_shape = getOutputShape(ctx, 0);
+          selected_indices_shape->clear_dim();
+
+          // Output is 2D always
+
+          // The value of the first dim is determined by input data
+          // hence its value cannot be determined statically
+          selected_indices_shape->add_dim();
+
+          // The value of the second dim is 3
+          selected_indices_shape->add_dim()->set_dim_value(3);
+        }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/object_detection/old.cc b/third_party/onnx/onnx/defs/object_detection/old.cc
new file mode 100644
index 0000000000..71efa6ccdd
--- /dev/null
+++ b/third_party/onnx/onnx/defs/object_detection/old.cc
@@ -0,0 +1,195 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+using namespace ONNX_NAMESPACE;
+
+namespace ONNX_NAMESPACE {
+
+static const char* RoiAlign_ver10_doc = R"DOC(
+Region of Interest (RoI) align operation described in the
+[Mask R-CNN paper](https://arxiv.org/abs/1703.06870).
+RoiAlign consumes an input tensor X and region of interests (rois)
+to apply pooling across each RoI; it produces a 4-D tensor of shape
+(num_rois, C, output_height, output_width).
+
+RoiAlign is proposed to avoid the misalignment by removing
+quantizations while converting from original image into feature
+map and from feature map into RoI feature; in each ROI bin,
+the value of the sampled locations are computed directly
+through bilinear interpolation.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RoiAlign,
+    10,
+    OpSchema()
+        .SetDoc(RoiAlign_ver10_doc)
+        .Attr(
+            "spatial_scale",
+            "Multiplicative spatial scale factor to translate ROI coordinates "
+            "from their input spatial scale to the scale used when pooling, "
+            "i.e., spatial scale of the input feature map X relative to the "
+            "input image. E.g.; default is 1.0f. ",
+            AttributeProto::FLOAT,
+            1.f)
+        .Attr(
+            "output_height",
+            "default 1; Pooled output Y's height.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "output_width",
+            "default 1; Pooled output Y's width.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "sampling_ratio",
+            "Number of sampling points in the interpolation grid used to compute "
+            "the output value of each pooled output bin. If > 0, then exactly "
+            "sampling_ratio x sampling_ratio grid points are used. If == 0, then "
+            "an adaptive number of grid points are used (computed as "
+            "ceil(roi_width / output_width), and likewise for height). Default is 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "mode",
+            "The pooling method. Two modes are supported: 'avg' and 'max'. "
+            "Default is 'avg'.",
+            AttributeProto::STRING,
+            std::string("avg"))
+        .Input(
+            0,
+            "X",
+            "Input data tensor from the previous operator; "
+            "4-D feature map of shape (N, C, H, W), "
+            "where N is the batch size, C is the number of channels, "
+            "and H and W are the height and the width of the data.",
+            "T1")
+        .Input(
+            1,
+            "rois",
+            "RoIs (Regions of Interest) to pool over; rois is "
+            "2-D input of shape (num_rois, 4) given as "
+            "[[x1, y1, x2, y2], ...]. "
+            "The RoIs' coordinates are in the coordinate system of the input image. "
+            "Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.",
+            "T1")
+        .Input(
+            2,
+            "batch_indices",
+            "1-D tensor of shape (num_rois,) with each element denoting "
+            "the index of the corresponding image in the batch.",
+            "T2")
+        .Output(
+            0,
+            "Y",
+            "RoI pooled output, 4-D tensor of shape "
+            "(num_rois, C, output_height, output_width). The r-th batch element Y[r-1] "
+            "is a pooled feature map corresponding to the r-th RoI X[r-1].",
+            "T1")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain types to int tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          size_t input_param = 0, rois_param = 1, batch_index_param = 2;
+
+          checkInputRank(ctx, input_param, 4);
+          checkInputRank(ctx, rois_param, 2);
+          checkInputRank(ctx, batch_index_param, 1);
+
+          // Output dimensions, initialized to an unknown-dimension-value
+          Dim num_rois, C, ht, width;
+
+          // Get value of C from dim 1 of input_param, if available
+          unifyInputDim(ctx, input_param, 1, C);
+
+          // Get value of num_rois from dim 0 of rois_param, if available
+          unifyInputDim(ctx, rois_param, 0, num_rois);
+          // ... or from dim 0 of batch_index_param, if available
+          unifyInputDim(ctx, batch_index_param, 0, num_rois);
+
+          // Get height from attribute, using default-value of 1
+          unifyDim(ht, getAttribute(ctx, "output_height", 1));
+
+          // Get width from attribute, using default-value of 1
+          unifyDim(width, getAttribute(ctx, "output_width", 1));
+
+          // set output shape:
+          updateOutputShape(ctx, 0, {num_rois, C, ht, width});
+        }));
+
+static const char* NonMaxSuppression_doc = R"DOC(
+Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
+Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box.
+Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to
+orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system
+result in the same boxes being selected by the algorithm.
+The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes.
+The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    NonMaxSuppression,
+    10,
+    OpSchema()
+        .Input(
+            0,
+            "boxes",
+            "An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.",
+            "tensor(float)")
+        .Input(
+            1,
+            "scores",
+            "An input tensor with shape [num_batches, num_classes, spatial_dimension]",
+            "tensor(float)")
+        .Input(
+            2,
+            "max_output_boxes_per_class",
+            "Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.",
+            "tensor(int64)",
+            OpSchema::Optional)
+        .Input(
+            3,
+            "iou_threshold",
+            "Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.",
+            "tensor(float)",
+            OpSchema::Optional)
+        .Input(
+            4,
+            "score_threshold",
+            "Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.",
+            "tensor(float)",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "selected_indices",
+            "selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].",
+            "tensor(int64)")
+        .Attr(
+            "center_point_box",
+            "Integer indicate the format of the box data. The default is 0. "
+            "0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners "
+            "and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. "
+            "1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .SetDoc(NonMaxSuppression_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto selected_indices_type =
+              ctx.getOutputType(0)->mutable_tensor_type();
+          selected_indices_type->set_elem_type(
+              ::ONNX_NAMESPACE::TensorProto_DataType::
+                  TensorProto_DataType_INT64);
+        }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/operator_sets.h b/third_party/onnx/onnx/defs/operator_sets.h
new file mode 100644
index 0000000000..5931367445
--- /dev/null
+++ b/third_party/onnx/onnx/defs/operator_sets.h
@@ -0,0 +1,1087 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+// Forward declarations for ai.onnx version 1
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Abs);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Add);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, And);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ArgMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ArgMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, AveragePool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, BatchNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Cast);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Ceil);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Clip);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Concat);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Constant);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Conv);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ConvTranspose);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, DepthToSpace);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Div);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Dropout);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Elu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Equal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Exp);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Flatten);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Floor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, GRU);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Gather);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Gemm);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, GlobalAveragePool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, GlobalLpPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, GlobalMaxPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Greater);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, HardSigmoid);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Hardmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Identity);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, If);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, InstanceNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LRN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LSTM);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LeakyRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Less);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Log);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LogSoftmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Loop);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LpNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LpPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, MatMul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Max);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, MaxPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, MaxRoiPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Mean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Min);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Mul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Neg);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Not);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Or);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, PRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Pad);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Pow);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, RNN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, RandomNormal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, RandomNormalLike);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, RandomUniform);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, RandomUniformLike);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Reciprocal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceL1);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceL2);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceLogSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceLogSumExp);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceMean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceProd);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceSumSquare);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Relu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Reshape);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Selu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Shape);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sigmoid);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Size);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Slice);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Softmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Softplus);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Softsign);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, SpaceToDepth);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Split);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sqrt);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Squeeze);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sub);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Tanh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Tile);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, TopK);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Transpose);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Unsqueeze);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Upsample);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Xor);
+
+// Iterate over schema from ai.onnx version 1
+class OpSet_Onnx_ver1 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Abs)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Add)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, And)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ArgMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ArgMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, AveragePool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, BatchNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Cast)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Ceil)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Clip)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Concat)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Constant)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Conv)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, ConvTranspose)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, DepthToSpace)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Div)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Dropout)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Elu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Equal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Exp)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Flatten)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Floor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, GRU)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Gather)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Gemm)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, GlobalAveragePool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, GlobalLpPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, GlobalMaxPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Greater)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, HardSigmoid)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Hardmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Identity)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, If)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, InstanceNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LRN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LSTM)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LeakyRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Less)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Log)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LogSoftmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Loop)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, LpNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, LpPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, MatMul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Max)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, MaxPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, MaxRoiPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Mean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Min)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Mul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Neg)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Not)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Or)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, PRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Pad)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Pow)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, RNN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, RandomNormal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, RandomNormalLike)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, RandomUniform)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, RandomUniformLike)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Reciprocal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceL1)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceL2)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, ReduceLogSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, ReduceLogSumExp)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceMean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceProd)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, ReduceSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, ReduceSumSquare)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Relu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Reshape)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Selu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Shape)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sigmoid)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Size)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Slice)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Softmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Softplus)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Softsign)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 1, SpaceToDepth)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Split)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sqrt)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Squeeze)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sub)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Sum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Tanh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Tile)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, TopK)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Transpose)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Unsqueeze)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Upsample)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 1, Xor)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 2
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, GlobalLpPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, LpPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, Pad);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, Split);
+
+// Iterate over schema from ai.onnx version 2
+class OpSet_Onnx_ver2 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 2, GlobalLpPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, LpPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, Pad)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 2, Split)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 3
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 3, GRU);
+
+// Iterate over schema from ai.onnx version 3
+class OpSet_Onnx_ver3 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 3, GRU)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 4
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 4, Concat);
+
+// Iterate over schema from ai.onnx version 4
+class OpSet_Onnx_ver4 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 4, Concat)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 5
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 5, Reshape);
+
+// Iterate over schema from ai.onnx version 5
+class OpSet_Onnx_ver5 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 5, Reshape)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 6
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Abs);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Add);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, BatchNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Cast);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Ceil);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Clip);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Div);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Dropout);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Elu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Exp);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Floor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Gemm);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, HardSigmoid);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, InstanceNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, LeakyRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Log);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Max);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Mean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Min);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Mul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Neg);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, PRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Reciprocal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Relu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Selu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sigmoid);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sqrt);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sub);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Tanh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Tile);
+
+// Iterate over schema from ai.onnx version 6
+class OpSet_Onnx_ver6 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Abs)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Add)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 6, BatchNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Cast)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Ceil)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Clip)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Div)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Dropout)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Elu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Exp)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Floor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Gemm)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 6, HardSigmoid)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 6, InstanceNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, LeakyRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Log)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Max)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Mean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Min)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Mul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Neg)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, PRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Reciprocal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Relu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Selu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sigmoid)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sqrt)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sub)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Sum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Tanh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 6, Tile)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 7
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Acos);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Add);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, And);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Asin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Atan);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, AveragePool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, BatchNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Cos);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Div);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Dropout);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Equal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Gemm);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Greater);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, GRU);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Less);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, LSTM);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Mul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Or);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Pow);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, RNN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Sin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Sub);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Tan);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Upsample);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Multinomial);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Xor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, PRelu);
+
+// Iterate over schema from ai.onnx version 7
+class OpSet_Onnx_ver7 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Acos)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Add)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, And)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Asin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Atan)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 7, AveragePool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 7, BatchNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Cos)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Div)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Dropout)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Equal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Gemm)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Greater)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, GRU)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Less)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, LSTM)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Mul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Or)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Pow)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, RNN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Sin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Sub)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Tan)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Upsample)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 7, Multinomial)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, Xor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 7, PRelu)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 8
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Expand);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Max);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Min);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Sum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Mean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, MaxPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Scan);
+
+// Iterate over schema from ai.onnx version 8
+class OpSet_Onnx_ver8 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Expand)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Min)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Max)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Sum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Mean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, MaxPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 8, Scan)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 9
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, BatchNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Compress);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, ConstantOfShape);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, EyeLike);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Greater);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Less);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Upsample);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, MaxUnpool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Constant);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, MatMul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, OneHot);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, PRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Gemm);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Flatten);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Sinh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Cosh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Asinh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Acosh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Atanh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Shrink);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, IsNaN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Sign);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Scan);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Erf);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Scatter);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Where);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Cast);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, NonZero);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, TfIdfVectorizer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, MeanVarianceNormalization);
+
+// Iterate over schema from ai.onnx version 9
+class OpSet_Onnx_ver9 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 9, BatchNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Compress)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 9, ConstantOfShape)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, EyeLike)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Greater)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Less)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Upsample)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, MaxUnpool)>());
+    // Add more types' support to Constant/MatMul/PRelu/Gemm/Flatten op.
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Constant)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, MatMul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, OneHot)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, PRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Gemm)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Flatten)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Scatter)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Sinh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Cosh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Asinh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Acosh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Atanh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Shrink)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, IsNaN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Sign)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Scan)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Erf)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Cast)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, Where)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 9, NonZero)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 9, TfIdfVectorizer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 9, MeanVarianceNormalization)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 10
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, StringNormalizer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Upsample);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Resize);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, TopK);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, MaxPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Mod);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, AveragePool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Slice);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, ThresholdedRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Dropout);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, MatMulInteger);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, QLinearMatMul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, ConvInteger);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, QLinearConv);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, QuantizeLinear);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, DequantizeLinear);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, IsInf);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, NonMaxSuppression);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, ReverseSequence);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, RoiAlign);
+
+// Iterate over schema from ai.onnx version 10
+class OpSet_Onnx_ver10 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Upsample)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Resize)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, StringNormalizer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, TopK)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, MaxPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Mod)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, AveragePool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Slice)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, ThresholdedRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, Dropout)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, MatMulInteger)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, QLinearMatMul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, ConvInteger)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, QLinearConv)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, QuantizeLinear)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, DequantizeLinear)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 10, IsInf)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, NonMaxSuppression)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, ReverseSequence)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 10, RoiAlign)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 11
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Loop);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, CumSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Round);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, BitShift);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Unique);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, TopK);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, DepthToSpace);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Equal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Constant);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, DynamicQuantizeLinear);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, GatherElements);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ScatterElements);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Scatter);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Clip);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Resize);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Range);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Det);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ScatterND);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, GatherND);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Gather);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, OneHot);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Slice);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Squeeze);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Unsqueeze);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Flatten);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ArgMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ArgMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceL1);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceL2);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceLogSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceLogSumExp);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceMean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceProd);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceSumSquare);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Compress);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Concat);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Hardmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, LogSoftmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Softmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Scan);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Split);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, AveragePool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, MaxPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, MaxUnpool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, LpPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Conv);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ConvTranspose);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceEmpty);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceConstruct);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceInsert);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceAt);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceErase);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceLength);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SplitToSequence);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ConcatFromSequence);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Pad);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Gemm);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, If);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, NonMaxSuppression);
+
+// Iterate over schema from ai.onnx version 11
+class OpSet_Onnx_ver11 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Loop)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, BitShift)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Unique)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, CumSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Round)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, TopK)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 11, DepthToSpace)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Equal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Constant)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 11, DynamicQuantizeLinear)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 11, GatherElements)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 11, ScatterElements)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Scatter)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Clip)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Resize)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Range)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Det)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ScatterND)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, GatherND)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Gather)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, OneHot)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Slice)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Squeeze)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Unsqueeze)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Flatten)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ArgMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ArgMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceL1)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceL2)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceLogSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceLogSumExp)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceMean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceProd)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ReduceSumSquare)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Compress)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Concat)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Hardmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, LogSoftmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Softmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Scan)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Split)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, AveragePool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, MaxPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, MaxUnpool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, LpPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Conv)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ConvTranspose)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceEmpty)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceConstruct)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceInsert)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceAt)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceErase)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SequenceLength)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, SplitToSequence)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, ConcatFromSequence)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Pad)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, Gemm)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, If)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 11, NonMaxSuppression)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 12
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ArgMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ArgMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Clip);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Einsum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, MaxPool);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ReduceMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ReduceMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, GatherND);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, NegativeLogLikelihoodLoss);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Dropout);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Constant);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Celu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Max);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Min);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, LessOrEqual);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, GreaterOrEqual);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, SoftmaxCrossEntropyLoss);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Pow);
+
+// Iterate over schema from ai.onnx version 12
+class OpSet_Onnx_ver12 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ArgMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ArgMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Clip)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Einsum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, MaxPool)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ReduceMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, ReduceMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, GatherND)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 12, NegativeLogLikelihoodLoss)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Dropout)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Constant)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Celu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Max)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Min)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 12, LessOrEqual)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 12, GreaterOrEqual)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           Onnx, 12, SoftmaxCrossEntropyLoss)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 12, Pow)>());
+  }
+};
+// Forward declarations for ai.onnx version 13
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Constant);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Greater);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Less);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Equal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Add);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sub);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Mul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Div);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Softmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, LogSoftmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Hardmax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Mod);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Neg);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Abs);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Reciprocal);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Floor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Ceil);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sqrt);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Relu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Exp);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Log);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Tanh);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Pow);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sigmoid);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Max);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Min);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Mean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Clip);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Gemm);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, MatMul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Expand);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sign);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Erf);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, SoftmaxCrossEntropyLoss);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, NegativeLogLikelihoodLoss);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Dropout);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Flatten);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, LRN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, MeanVarianceNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceSumSquare);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceMean);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceProd);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceLogSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceLogSumExp);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceL1);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceL2);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ArgMax);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ArgMin);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Cast);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Reshape);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Shape);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Size);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Concat);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Split);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Slice);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Transpose);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ScatterND);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ScatterElements);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Gather);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, GatherElements);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Squeeze);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Unsqueeze);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, SpaceToDepth);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, DepthToSpace);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Tile);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Resize);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Identity);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, IsNaN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, NonZero);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, GatherND);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Pad);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, QuantizeLinear);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, DequantizeLinear);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Loop);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, If);
+
+// Iterate over schema from ai.onnx version 13
+class OpSet_Onnx_ver13 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Constant)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Greater)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Less)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Equal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Add)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sub)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Mul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Div)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Softmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, LogSoftmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Hardmax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Mod)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Neg)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Abs)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Reciprocal)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Floor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Ceil)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sqrt)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Relu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Exp)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Log)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Tanh)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Pow)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sigmoid)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Max)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Min)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Mean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Clip)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Gemm)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, MatMul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Expand)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Sign)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Erf)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, SoftmaxCrossEntropyLoss)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, NegativeLogLikelihoodLoss)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Dropout)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Flatten)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, LRN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, MeanVarianceNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceSumSquare)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceMean)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceProd)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceLogSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceLogSumExp)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceL1)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ReduceL2)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ArgMax)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ArgMin)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Cast)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Reshape)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Shape)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Size)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Concat)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Split)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Slice)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Transpose)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ScatterND)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, ScatterElements)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Gather)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, GatherElements)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Squeeze)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Unsqueeze)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, SpaceToDepth)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, DepthToSpace)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Tile)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Resize)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Identity)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, IsNaN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, NonZero)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, GatherND)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Pad)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, QuantizeLinear)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, DequantizeLinear)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, Loop)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 13, If)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 14
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, CumSum);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Relu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Reshape);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, GRU);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, LSTM);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, RNN);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Trilu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, BatchNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, HardSwish);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Add);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Sub);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Mul);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Div);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Identity);
+
+// Iterate over schema from ai.onnx version 14
+class OpSet_Onnx_ver14 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, CumSum)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Relu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Reshape)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, GRU)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, LSTM)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, RNN)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Trilu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, BatchNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, HardSwish)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Add)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Sub)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Mul)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Div)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 14, Identity)>());
+  }
+};
+
+
+// Forward declarations for ai.onnx version 15
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, BatchNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Bernoulli);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Pow);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Optional);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, OptionalHasElement);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, OptionalGetElement);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, CastLike);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Shape);
+
+// Iterate over schema from ai.onnx version 15
+class OpSet_Onnx_ver15 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, BatchNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Bernoulli)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Pow)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Optional)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, OptionalHasElement)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, OptionalGetElement)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, CastLike)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 15, Shape)>());
+  }
+};
+
+
+// Forward declarations for ai.onnx version 16
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, RoiAlign);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, ScatterND);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, ScatterElements);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, If);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Loop);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Identity);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Where);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, GridSample);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Scan);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, LessOrEqual);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, GreaterOrEqual);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, LeakyRelu);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, PRelu);
+
+// Iterate over schema from ai.onnx version 16
+class OpSet_Onnx_ver16 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, RoiAlign)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, ScatterND)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, ScatterElements)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, If)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Loop)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Identity)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Where)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, GridSample)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, Scan)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, LessOrEqual)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, GreaterOrEqual)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, LeakyRelu)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 16, PRelu)>());
+  }
+};
+
+// Forward declarations for ai.onnx version 17
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 17, LayerNormalization);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 17, SequenceMap);
+
+// Iterate over schema from ai.onnx version 17
+class OpSet_Onnx_ver17 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 17, LayerNormalization)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 17, SequenceMap)>());
+  }
+};
+
+inline void RegisterOnnxOperatorSetSchema() {
+  RegisterOpSetSchema<OpSet_Onnx_ver1>();
+  RegisterOpSetSchema<OpSet_Onnx_ver2>();
+  RegisterOpSetSchema<OpSet_Onnx_ver3>();
+  RegisterOpSetSchema<OpSet_Onnx_ver4>();
+  RegisterOpSetSchema<OpSet_Onnx_ver5>();
+  RegisterOpSetSchema<OpSet_Onnx_ver6>();
+  RegisterOpSetSchema<OpSet_Onnx_ver7>();
+  RegisterOpSetSchema<OpSet_Onnx_ver8>();
+  RegisterOpSetSchema<OpSet_Onnx_ver9>();
+  RegisterOpSetSchema<OpSet_Onnx_ver10>();
+  RegisterOpSetSchema<OpSet_Onnx_ver11>();
+  RegisterOpSetSchema<OpSet_Onnx_ver12>();
+  RegisterOpSetSchema<OpSet_Onnx_ver13>();
+  RegisterOpSetSchema<OpSet_Onnx_ver14>();
+  RegisterOpSetSchema<OpSet_Onnx_ver15>();
+  RegisterOpSetSchema<OpSet_Onnx_ver16>();
+  RegisterOpSetSchema<OpSet_Onnx_ver17>();
+  // 0 means all versions of ONNX schema have been loaded
+  OpSchemaRegistry::Instance()->SetLoadedSchemaVersion(0);
+}
+
+inline void RegisterOnnxOperatorSetSchema(int target_version) {
+  // Update here if opset_version bumps
+  // These calls for schema registration here are required to be in descending order for this to work correctly
+  RegisterOpSetSchema<OpSet_Onnx_ver16>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver15>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver14>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver13>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver12>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver11>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver10>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver9>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver8>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver7>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver6>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver5>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver4>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver3>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver2>(target_version);
+  RegisterOpSetSchema<OpSet_Onnx_ver1>(target_version);
+  // Sets to record the loaded version and prevent the full operator check in Debug mode
+  OpSchemaRegistry::Instance()->SetLoadedSchemaVersion(target_version);
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/operator_sets_ml.h b/third_party/onnx/onnx/defs/operator_sets_ml.h
new file mode 100644
index 0000000000..31bb3c3e7a
--- /dev/null
+++ b/third_party/onnx/onnx/defs/operator_sets_ml.h
@@ -0,0 +1,100 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#ifdef ONNX_ML
+
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+// Forward declarations for ai.onnx.ml version 1
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, ArrayFeatureExtractor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, Binarizer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, CastMap);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, CategoryMapper);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, DictVectorizer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, FeatureVectorizer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, Imputer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, LabelEncoder);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, LinearClassifier);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, LinearRegressor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, Normalizer);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, OneHotEncoder);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, SVMClassifier);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, SVMRegressor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, Scaler);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, TreeEnsembleClassifier);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, TreeEnsembleRegressor);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, ZipMap);
+
+// Iterate over schema from ai.onnx.ml version 1
+class OpSet_OnnxML_ver1 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, ArrayFeatureExtractor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, Binarizer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, CastMap)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, CategoryMapper)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, DictVectorizer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, FeatureVectorizer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, Imputer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, LabelEncoder)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, LinearClassifier)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, LinearRegressor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, Normalizer)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, OneHotEncoder)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, SVMClassifier)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, SVMRegressor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, Scaler)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, TreeEnsembleClassifier)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(
+           OnnxML, 1, TreeEnsembleRegressor)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 1, ZipMap)>());
+  }
+};
+
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 2, LabelEncoder);
+
+class OpSet_OnnxML_ver2 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 2, LabelEncoder)>());
+  }
+};
+
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 3, TreeEnsembleClassifier);
+class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 3, TreeEnsembleRegressor);
+
+class OpSet_OnnxML_ver3 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 3, TreeEnsembleClassifier)>());
+    fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 3, TreeEnsembleRegressor)>());
+  }
+};
+
+inline void RegisterOnnxMLOperatorSetSchema() {
+  RegisterOpSetSchema<OpSet_OnnxML_ver1>();
+  RegisterOpSetSchema<OpSet_OnnxML_ver2>();
+  RegisterOpSetSchema<OpSet_OnnxML_ver3>();
+}
+} // namespace ONNX_NAMESPACE
+
+#endif
diff --git a/third_party/onnx/onnx/defs/operator_sets_preview.h b/third_party/onnx/onnx/defs/operator_sets_preview.h
new file mode 100644
index 0000000000..cf48b269e8
--- /dev/null
+++ b/third_party/onnx/onnx/defs/operator_sets_preview.h
@@ -0,0 +1,38 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+// Declare training operators.
+
+class ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Gradient);
+class ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Momentum);
+class ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Adagrad);
+class ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Adam);
+
+// Iterate over schema from ai.onnx.training version 1
+class OpSet_OnnxPreview_ver1 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> fn) {
+    fn(GetOpSchema<ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Gradient)>());
+    fn(GetOpSchema<ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Momentum)>());
+    fn(GetOpSchema<ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Adagrad)>());
+    fn(GetOpSchema<ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(1, Adam)>());
+  }
+};
+
+// Register preview operators.
+inline void RegisterOnnxPreviewOperatorSetSchema() {
+  // Preview operators should have only one version.
+  // If changes are needed for a specific preview operator,
+  // its spec should be modified without increasing its version.
+  RegisterOpSetSchema<OpSet_OnnxPreview_ver1>();
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/operator_sets_training.h b/third_party/onnx/onnx/defs/operator_sets_training.h
new file mode 100644
index 0000000000..37a667ea3c
--- /dev/null
+++ b/third_party/onnx/onnx/defs/operator_sets_training.h
@@ -0,0 +1,26 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+// Declare training operators.
+
+// Iterate over schema from ai.onnx.training version 1
+class OpSet_OnnxTraining_ver1 {
+ public:
+  static void ForEachSchema(std::function<void(OpSchema&&)> /* fn */) {
+  }
+};
+
+// Register training operators.
+inline void RegisterOnnxTrainingOperatorSetSchema() {
+  RegisterOpSetSchema<OpSet_OnnxTraining_ver1>();
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/optional/defs.cc b/third_party/onnx/onnx/defs/optional/defs.cc
new file mode 100644
index 0000000000..8609b201fe
--- /dev/null
+++ b/third_party/onnx/onnx/defs/optional/defs.cc
@@ -0,0 +1,139 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+#include <algorithm>
+#include <numeric>
+
+namespace ONNX_NAMESPACE {
+
+static const char* Optional_ver15_doc = R"DOC(
+Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute,
+or a non-empty value containing the input element.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Optional,
+    15,
+    OpSchema()
+        .SetDoc(Optional_ver15_doc)
+        .Input(0, "input", "The input element.", "V", OpSchema::Optional)
+        .Attr("type", "Type of the element in the optional output", AttributeProto::TYPE_PROTO, OPTIONAL_VALUE)
+        .Output(0, "output", "The optional output enclosing the input element.", "O")
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types();
+              auto s = OpSchema::all_tensor_sequence_types();
+              t.insert(t.end(), s.begin(), s.end());
+              return t;
+            }(),
+            "Constrain input type to all tensor and sequence types.")
+        .TypeConstraint(
+            "O",
+            OpSchema::all_optional_types(),
+            "Constrain output type to all optional tensor or optional sequence types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const size_t numOutputs = ctx.getNumOutputs();
+          if (numOutputs != 1) {
+            fail_type_inference("Optional is expected to have an output.");
+          }
+
+          const size_t numInputs = ctx.getNumInputs();
+          const auto* attr_proto = ctx.getAttribute("type");
+
+          if ((numInputs == 0) && (attr_proto != nullptr)) {
+            if (!attr_proto->has_tp())
+              fail_type_inference("Attribute 'type' should be a TypeProto and it should specify a type.");
+            auto attr_tp = attr_proto->tp();
+
+            ctx.getOutputType(0)->mutable_optional_type()->mutable_elem_type()->CopyFrom(attr_tp);
+          } else if (numInputs == 1) {
+            auto input_type = ctx.getInputType(0);
+            if (input_type == nullptr) {
+              fail_type_inference("Input type is null. Type information is expected for the input.");
+            }
+            ctx.getOutputType(0)->mutable_optional_type()->mutable_elem_type()->CopyFrom(*input_type);
+          } else {
+            fail_type_inference("Optional is expected to have either an input or the type attribute set.");
+          }
+        }));
+
+static const char* OptionalHasElement_ver1_doc = R"DOC(
+Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns false.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    OptionalHasElement,
+    15,
+    OpSchema()
+        .SetDoc(OptionalHasElement_ver1_doc)
+        .Input(0, "input", "The optional input.", "O")
+        .Output(
+            0,
+            "output",
+            "A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.",
+            "B")
+        .TypeConstraint(
+            "O",
+            OpSchema::all_optional_types(),
+            "Constrain input type to optional tensor and optional sequence types.")
+        .TypeConstraint("B", {"tensor(bool)"}, "Constrain output to a boolean tensor.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const size_t numInputs = ctx.getNumInputs();
+          if (numInputs != 1) {
+            fail_type_inference("OptionalHasElement is expected to have 1 input.");
+          }
+          const size_t numOutputs = ctx.getNumOutputs();
+          if (numOutputs != 1) {
+            fail_type_inference("OptionalHasElement is expected to have 1 output.");
+          }
+          auto* output_tensor_type = ctx.getOutputType(0)->mutable_tensor_type();
+          output_tensor_type->set_elem_type(TensorProto::BOOL);
+          output_tensor_type->mutable_shape()->Clear();
+        }));
+
+static const char* OptionalGetElement_ver1_doc = R"DOC(
+Outputs the element in the optional-type input. It is an error if the input value does not have an element
+and the behavior is undefined in this case.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    OptionalGetElement,
+    15,
+    OpSchema()
+        .SetDoc(OptionalGetElement_ver1_doc)
+        .Input(0, "input", "The optional input.", "O")
+        .Output(0, "output", "Output element in the optional input.", "V")
+        .TypeConstraint(
+            "O",
+            OpSchema::all_optional_types(),
+            "Constrain input type to optional tensor and optional sequence types.")
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types();
+              auto s = OpSchema::all_tensor_sequence_types();
+              t.insert(t.end(), s.begin(), s.end());
+              return t;
+            }(),
+            "Constrain output type to all tensor or sequence types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const size_t numInputs = ctx.getNumInputs();
+          if (numInputs != 1) {
+            fail_type_inference("OptionalGetElement must have an input element.");
+          }
+          auto input_type = ctx.getInputType(0);
+          if (input_type == nullptr) {
+            fail_type_inference("Input type is null. Input must have Type information.");
+          }
+          if (!input_type->has_optional_type() || !input_type->optional_type().has_elem_type()) {
+            fail_type_inference("Input must be an optional-type value containing an element with type information.");
+          }
+          ctx.getOutputType(0)->CopyFrom(input_type->optional_type().elem_type());
+        }));
+
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/defs/parser.cc b/third_party/onnx/onnx/defs/parser.cc
new file mode 100644
index 0000000000..6a5e4bc0ab
--- /dev/null
+++ b/third_party/onnx/onnx/defs/parser.cc
@@ -0,0 +1,582 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Experimental language syntax and parser for ONNX. Please note that the syntax as formalized
+// by this parser is preliminary and may change.
+
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+
+#include "onnx/onnx_pb.h"
+#include "onnx/string_utils.h"
+
+#include "onnx/defs/parser.h"
+
+#define PARSE_TOKEN(x) CHECK_PARSER_STATUS(ParserBase::Parse(x))
+#define PARSE(...) CHECK_PARSER_STATUS(Parse(__VA_ARGS__))
+#define MATCH(...) CHECK_PARSER_STATUS(Match(__VA_ARGS__))
+
+namespace ONNX_NAMESPACE {
+
+Status OnnxParser::Parse(IdList& idlist) {
+  idlist.Clear();
+  std::string id;
+  ParseOptionalIdentifier(id);
+  if (id.empty())
+    return Status::OK(); // Treat as empty list of identifiers
+  *idlist.Add() = id;
+  while (Matches(',')) {
+    ParseOptionalIdentifier(id);
+    *idlist.Add() = id;
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(char open, IdList& idlist, char close) {
+  idlist.Clear();
+  if (Matches(open)) {
+    PARSE(idlist);
+    MATCH(close);
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(TensorShapeProto& shape) {
+  shape.clear_dim();
+  do {
+    if (Matches('?')) {
+      shape.add_dim();
+    } else {
+      // Check for a symbolic identifier ...
+      std::string id;
+      CHECK_PARSER_STATUS(ParseOptionalIdentifier(id));
+      if (!id.empty()) {
+        shape.add_dim()->set_dim_param(id);
+      } else {
+        // ...or a integer value
+        int64_t dimval = 0;
+        PARSE_TOKEN(dimval);
+        shape.add_dim()->set_dim_value(dimval);
+      }
+    }
+  } while (Matches(','));
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(TypeProto& typeProto) {
+  std::string id;
+  CHECK_PARSER_STATUS(ParseIdentifier(id));
+  int dtype = PrimitiveTypeNameMap::Lookup(id);
+  if (dtype != 0) {
+    auto* tensortype = typeProto.mutable_tensor_type();
+    tensortype->set_elem_type(dtype);
+    tensortype->clear_shape();
+    // Grammar:
+    // float indicates scalar (rank 0)
+    // float [] indicates unknown rank tensor (not a zero rank tensor)
+    // float [one-or-more-dimensions] indicates tensor of known rank > 0.
+    if (Matches('[')) {
+      if (!Matches(']')) {
+        PARSE(*tensortype->mutable_shape());
+        MATCH(']');
+      }
+    } else {
+      // Create shape with zero dimensions for scalar
+      (void)(tensortype->mutable_shape());
+    }
+  } else {
+    switch (KeyWordMap::Lookup(id)) {
+      case KeyWordMap::KeyWord::SEQ_TYPE: {
+        // Grammar: seq ( type )
+        MATCH('(');
+        auto* seqtype = typeProto.mutable_sequence_type();
+        PARSE(*seqtype->mutable_elem_type());
+        MATCH(')');
+        break;
+      }
+      case KeyWordMap::KeyWord::MAP_TYPE: {
+        // Grammar: map ( prim-type , type )
+        MATCH('(');
+        auto* maptype = typeProto.mutable_map_type();
+        CHECK_PARSER_STATUS(ParseIdentifier(id));
+        dtype = PrimitiveTypeNameMap::Lookup(id);
+        if (dtype == 0) {
+          return ParseError("Expecting primitive type as map key type.");
+        }
+        maptype->set_key_type(dtype);
+        MATCH(',');
+        PARSE(*maptype->mutable_value_type());
+        MATCH(')');
+        break;
+      }
+      case KeyWordMap::KeyWord::OPTIONAL_TYPE: {
+        // Grammar: optional ( type )
+        MATCH('(');
+        auto* opttype = typeProto.mutable_optional_type();
+        PARSE(*opttype->mutable_elem_type());
+        MATCH(')');
+        break;
+      }
+      case KeyWordMap::KeyWord::SPARSE_TENSOR_TYPE: {
+          // Grammar: sparse_tensor ( tensor-type )
+          MATCH('(');
+          CHECK_PARSER_STATUS(ParseIdentifier(id));
+          dtype = PrimitiveTypeNameMap::Lookup(id);
+          if (dtype != 0) {
+            auto* sparsetype = typeProto.mutable_sparse_tensor_type();
+            sparsetype->set_elem_type(dtype);
+            sparsetype->clear_shape();
+            // Grammar:
+            // float indicates scalar (rank 0)
+            // float [] indicates unknown rank tensor (not a zero rank tensor)
+            // float [one-or-more-dimensions] indicates tensor of known rank > 0.
+            if (Matches('[')) {
+              if (!Matches(']')) {
+                PARSE(*sparsetype->mutable_shape());
+                MATCH(']');
+              }
+            } else {
+              // Create shape with zero dimensions for scalar
+              (void)(sparsetype->mutable_shape());
+            }
+          } else {
+            return ParseError("Unexpected type in sparse-tensor element type.");
+          }
+          MATCH(')');
+          break;
+      }  
+      default:
+        return ParseError("Unexpected type.");
+    }
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(ValueInfoProto& valueinfo) {
+  if (NextIsType())
+    PARSE(*valueinfo.mutable_type());
+  std::string name;
+  CHECK_PARSER_STATUS(ParseIdentifier(name));
+  valueinfo.set_name(name);
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(ValueInfoList& vilist) {
+  vilist.Clear();
+  MATCH('(');
+  if (!Matches(')')) {
+    do {
+      PARSE(*vilist.Add());
+    } while (Matches(','));
+    MATCH(')');
+  }
+  return Status::OK();
+}
+
+// Each input element is a value-info with an optional initializer of the form "= initial-value".
+// The value-info is added to the "inputs", while the initializer is added to initializers.
+Status OnnxParser::ParseInput(ValueInfoList& inputs, TensorList& initializers) {
+  inputs.Clear();
+  if (Matches('(')) {
+    if (!Matches(')')) {
+      do {
+        ValueInfoProto vi;
+        PARSE(vi);
+        *inputs.Add() = vi;
+        if (Matches('=')) {
+          // default value for input
+          TensorProto& tp = *initializers.Add();
+          tp.set_name(vi.name());
+          CHECK_PARSER_STATUS(Parse(tp, vi.type()));
+        }
+      } while (Matches(','));
+      MATCH(')');
+    }
+  }
+  return Status::OK();
+}
+
+// This is handled slightly different from the inputs.
+// Each element is either a value-info or an initializer.
+// A value-info is added to the "value_infos", while an initializer is added to initializers.
+Status OnnxParser::ParseValueInfo(ValueInfoList& value_infos, TensorList& initializers) {
+  value_infos.Clear();
+  if (Matches('<')) {
+    if (!Matches('>')) {
+      do {
+        ValueInfoProto vi;
+        PARSE(vi);
+        if (Matches('=')) {
+          // initializer
+          TensorProto& tp = *initializers.Add();
+          tp.set_name(vi.name());
+          CHECK_PARSER_STATUS(Parse(tp, vi.type()));
+        } else {
+          // valueinfo
+          *value_infos.Add() = vi;
+        }
+      } while (Matches(','));
+      MATCH('>');
+    }
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(TensorProto& tensorProto) {
+  tensorProto = TensorProto();
+  // Parse the concrete tensor-type with numeric dimensions:
+  TypeProto typeProto;
+  PARSE(typeProto);
+  ParseOptionalIdentifier(*tensorProto.mutable_name());
+  (void)Matches('='); // Optional, to unify handling of initializers as well as tensor-protos in other contexts
+  return Parse(tensorProto, typeProto);
+}
+
+// Parse TensorProto data given its type:
+Status OnnxParser::Parse(TensorProto& tensorProto, const TypeProto& tensorTypeProto) {
+  if (!tensorTypeProto.has_tensor_type())
+    return ParseError("Error parsing TensorProto (expected a tensor type).");
+  auto elem_type = tensorTypeProto.tensor_type().elem_type();
+  tensorProto.set_data_type(elem_type);
+  if (!tensorTypeProto.tensor_type().has_shape())
+    return ParseError("Error parsing TensorProto (expected a tensor shape).");
+  uint64_t n = 1;
+  for (auto& dim : tensorTypeProto.tensor_type().shape().dim()) {
+    if (!dim.has_dim_value())
+      return ParseError("Error parsing TensorProto shape (expected numeric dimension).");
+    auto dimval = dim.dim_value();
+    tensorProto.add_dims(dimval);
+    n *= dimval;
+  }
+
+  // tensorProto.mutable_int64_data()->Reserve(n);
+  // Parse the actual values:
+
+  int64_t intval;
+  uint64_t uintval;
+  float floatval;
+  double dblval;
+  std::string strval;
+  MATCH('{');
+  if (!Matches('}')) {
+    do {
+      switch (static_cast<TensorProto::DataType>(elem_type)) {
+        case TensorProto::DataType::TensorProto_DataType_INT8:
+        case TensorProto::DataType::TensorProto_DataType_INT16:
+        case TensorProto::DataType::TensorProto_DataType_INT32:
+        case TensorProto::DataType::TensorProto_DataType_UINT8:
+        case TensorProto::DataType::TensorProto_DataType_UINT16:
+        case TensorProto::DataType::TensorProto_DataType_BOOL:
+          PARSE_TOKEN(intval);
+          // TODO: check values are in the correct range.
+          tensorProto.add_int32_data(intval);
+          break;
+        case TensorProto::DataType::TensorProto_DataType_INT64:
+          PARSE_TOKEN(intval);
+          tensorProto.add_int64_data(intval);
+          break;
+        case TensorProto::DataType::TensorProto_DataType_UINT32:
+        case TensorProto::DataType::TensorProto_DataType_UINT64:
+          PARSE_TOKEN(uintval);
+          tensorProto.add_uint64_data(uintval);
+          break;
+        case TensorProto::DataType::TensorProto_DataType_FLOAT:
+          PARSE_TOKEN(floatval);
+          tensorProto.add_float_data(floatval);
+          break;
+        case TensorProto::DataType::TensorProto_DataType_DOUBLE:
+          PARSE_TOKEN(dblval);
+          tensorProto.add_double_data(dblval);
+          break;
+        case TensorProto::DataType::TensorProto_DataType_STRING:
+          PARSE_TOKEN(strval);
+          tensorProto.add_string_data(strval);
+          break;
+        default:
+          return ParseError("Unhandled type: %d", elem_type);
+      }
+    } while (Matches(','));
+    MATCH('}');
+  }
+  return Status::OK();
+}
+
+bool OnnxParser::NextIsType() {
+  std::string id("");
+  (void)PeekIdentifier(id);
+  return (PrimitiveTypeNameMap::IsTypeName(id));
+}
+
+Status OnnxParser::ParseSingleAttributeValue(AttributeProto& attr) {
+  // Parse a single-value
+  auto next = NextChar();
+  if (isalpha(next) || next == '_') {
+    if (NextIsType()) {
+      attr.set_type(AttributeProto_AttributeType_TENSOR);
+      Parse(*attr.mutable_t());
+    } else {
+      attr.set_type(AttributeProto_AttributeType_GRAPH);
+      Parse(*attr.mutable_g());
+    }
+  } else if (Matches('@')) {
+    std::string name;
+    CHECK_PARSER_STATUS(ParseIdentifier(name));
+    attr.set_ref_attr_name(name);
+  } else {
+    Literal literal;
+    PARSE_TOKEN(literal);
+    switch (literal.type) {
+      case LiteralType::INT_LITERAL:
+        attr.set_type(AttributeProto_AttributeType_INT);
+        attr.set_i(std::stol(literal.value));
+        break;
+      case LiteralType::FLOAT_LITERAL:
+        attr.set_type(AttributeProto_AttributeType_FLOAT);
+        attr.set_f(static_cast<float>(std::stof(literal.value)));
+        break;
+      case LiteralType::STRING_LITERAL:
+        attr.set_type(AttributeProto_AttributeType_STRING);
+        attr.set_s(literal.value);
+        break;
+      default:
+        return ParseError("Unexpected literal type.");
+    }
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(AttributeProto& attr) {
+  std::string name;
+  CHECK_PARSER_STATUS(ParseIdentifier(name));
+  attr.set_name(name);
+  if (Matches(':')) {
+    CHECK_PARSER_STATUS(ParseIdentifier(name));
+    int attrtype = AttributeTypeNameMap::Lookup(name);
+    if (attrtype != 0) {
+      attr.set_type(static_cast<AttributeProto_AttributeType>(attrtype));
+    } else {
+      return ParseError("Unexpected attribute type.");
+    }
+  }
+  MATCH('=');
+  if (NextChar() == '[') {
+    // Parse a list of values. For now, empty list is not allowed, as we need to
+    // figure out a type for the attribute.
+    std::vector<Literal> vals;
+    MATCH('[');
+    do {
+      AttributeProto nextval;
+      CHECK_PARSER_STATUS(ParseSingleAttributeValue(nextval));
+      switch (nextval.type()) {
+        case AttributeProto_AttributeType_INT:
+          attr.set_type(AttributeProto_AttributeType_INTS);
+          attr.add_ints(nextval.i());
+          break;
+        case AttributeProto_AttributeType_FLOAT:
+          attr.set_type(AttributeProto_AttributeType_FLOATS);
+          attr.add_floats(nextval.f());
+          break;
+        case AttributeProto_AttributeType_STRING:
+          attr.add_strings(nextval.s());
+          attr.set_type(AttributeProto_AttributeType_STRINGS);
+          break;
+        default:
+          break;
+      }
+    } while (Matches(','));
+    MATCH(']');
+  } else {
+    CHECK_PARSER_STATUS(ParseSingleAttributeValue(attr));
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(AttrList& attrlist) {
+  attrlist.Clear();
+  if (Matches('<')) {
+    do {
+      PARSE(*attrlist.Add());
+    } while (Matches(','));
+    MATCH('>');
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(NodeProto& node) {
+  PARSE(*node.mutable_output());
+  MATCH('=');
+  std::string domain("");
+  std::string id;
+  ParseIdentifier(id);
+  while (Matches('.')) {
+    if (!domain.empty())
+      domain += ".";
+    domain += id;
+    ParseIdentifier(id);
+  }
+  node.set_domain(domain);
+  node.set_op_type(id);
+  PARSE(*node.mutable_attribute());
+  MATCH('(');
+  PARSE(*node.mutable_input());
+  MATCH(')');
+  if (node.attribute_size() == 0) {
+    // Permit attributes to be specified before or after parameters.
+    PARSE(*node.mutable_attribute());
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(NodeList& nodelist) {
+  nodelist.Clear();
+  MATCH('{');
+  while (!Matches('}')) {
+    PARSE(*nodelist.Add());
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(GraphProto& graph) {
+  std::string id;
+  ParseIdentifier(id);
+  return Parse(id, graph);
+}
+
+Status OnnxParser::Parse(std::string name, GraphProto& graph) {
+  graph.set_name(name);
+  graph.mutable_initializer()->Clear();
+  CHECK_PARSER_STATUS(ParseInput(*graph.mutable_input(), *graph.mutable_initializer()));
+  MATCH('=');
+  MATCH('>', false);
+  PARSE(*graph.mutable_output());
+  CHECK_PARSER_STATUS(ParseValueInfo(*graph.mutable_value_info(), *graph.mutable_initializer()));
+  return Parse(*graph.mutable_node());
+}
+
+Status OnnxParser::Parse(FunctionProto& fn) {
+  fn.Clear();
+  std::string strval;
+  if (Matches('<')) {
+    do {
+      KeyWordMap::KeyWord keyword = KeyWordMap::KeyWord::NONE;
+      PARSE_TOKEN(keyword);
+      MATCH(':');
+      switch (keyword) {
+        case KeyWordMap::KeyWord::OPSET_IMPORT:
+          PARSE(*fn.mutable_opset_import());
+          break;
+        case KeyWordMap::KeyWord::DOC_STRING:
+          PARSE_TOKEN(strval);
+          fn.set_doc_string(strval);
+          break;
+        case KeyWordMap::KeyWord::DOMAIN_KW:
+          PARSE_TOKEN(strval);
+          fn.set_domain(strval);
+          break;
+        default:
+          return ParseError("Unhandled keyword.");
+      }
+    } while (Matches(','));
+    MATCH('>');
+  }
+  std::string id;
+  ParseIdentifier(id);
+  fn.set_name(id);
+
+  PARSE('<', *fn.mutable_attribute(), '>');
+  PARSE('(', *fn.mutable_input(), ')');
+  MATCH('=');
+  MATCH('>', false);
+  PARSE('(', *fn.mutable_output(), ')');
+  return Parse(*fn.mutable_node());
+}
+
+Status OnnxParser::Parse(OpsetIdList& opsets) {
+  std::string strval;
+  int64_t intval = 0;
+  MATCH('[');
+  if (!Matches(']')) {
+    do {
+      auto* import = opsets.Add();
+      PARSE_TOKEN(strval);
+      import->set_domain(strval);
+      MATCH(':');
+      PARSE_TOKEN(intval);
+      import->set_version(intval);
+    } while (Matches(','));
+    MATCH(']');
+  }
+  return Status::OK();
+}
+
+Status OnnxParser::Parse(ModelProto& model) {
+  model.Clear();
+  std::string strval;
+  int64_t intval;
+  if (Matches('<')) {
+    do {
+      KeyWordMap::KeyWord keyword = KeyWordMap::KeyWord::NONE;
+      PARSE_TOKEN(keyword);
+      MATCH(':');
+      switch (keyword) {
+        case KeyWordMap::KeyWord::IR_VERSION:
+          PARSE_TOKEN(intval);
+          model.set_ir_version(intval);
+          break;
+        case KeyWordMap::KeyWord::OPSET_IMPORT:
+          PARSE(*model.mutable_opset_import());
+          break;
+        case KeyWordMap::KeyWord::PRODUCER_NAME:
+          PARSE_TOKEN(strval);
+          model.set_producer_name(strval);
+          break;
+        case KeyWordMap::KeyWord::PRODUCER_VERSION:
+          PARSE_TOKEN(strval);
+          model.set_producer_version(strval);
+          break;
+        case KeyWordMap::KeyWord::DOMAIN_KW:
+          PARSE_TOKEN(strval);
+          model.set_domain(strval);
+          break;
+        case KeyWordMap::KeyWord::MODEL_VERSION:
+          PARSE_TOKEN(intval);
+          model.set_model_version(intval);
+          break;
+        case KeyWordMap::KeyWord::DOC_STRING:
+          PARSE_TOKEN(strval);
+          model.set_doc_string(strval);
+          break;
+        case KeyWordMap::KeyWord::METADATA_PROPS: {
+          auto& metadata_props = *model.mutable_metadata_props();
+          MATCH('[');
+          if (!Matches(']')) {
+            do {
+              auto* metadata = metadata_props.Add();
+              PARSE_TOKEN(strval);
+              metadata->set_key(strval);
+              MATCH(':');
+              PARSE_TOKEN(strval);
+              metadata->set_value(strval);
+            } while (Matches(','));
+            MATCH(']');
+          }
+          break;
+        }
+        default:
+          return ParseError("Unhandled keyword.");
+      }
+    } while (Matches(','));
+    MATCH('>');
+  }
+  PARSE(*model.mutable_graph());
+
+  auto* functions = model.mutable_functions();
+  while (!EndOfInput()) {
+    PARSE(*functions->Add());
+  }
+  return Status::OK();
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/parser.h b/third_party/onnx/onnx/defs/parser.h
new file mode 100644
index 0000000000..84bd09cc38
--- /dev/null
+++ b/third_party/onnx/onnx/defs/parser.h
@@ -0,0 +1,464 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Experimental language syntax and parser for ONNX. Please note that the syntax as formalized
+// by this parser is preliminary and may change.
+
+#pragma once
+
+#include <ctype.h>
+#include <iostream>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+
+#include "onnx/onnx_pb.h"
+
+#include "onnx/common/status.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+
+using namespace ONNX_NAMESPACE::Common;
+
+using IdList = google::protobuf::RepeatedPtrField<std::string>;
+
+using NodeList = google::protobuf::RepeatedPtrField<NodeProto>;
+
+using AttrList = google::protobuf::RepeatedPtrField<AttributeProto>;
+
+using ValueInfoList = google::protobuf::RepeatedPtrField<ValueInfoProto>;
+
+using TensorList = google::protobuf::RepeatedPtrField<TensorProto>;
+
+using OpsetIdList = google::protobuf::RepeatedPtrField<OperatorSetIdProto>;
+
+#define CHECK_PARSER_STATUS(status) \
+  {                                 \
+    auto local_status_ = status;    \
+    if (!local_status_.IsOK())      \
+      return local_status_;         \
+  }
+
+template <typename Map>
+class StringIntMap {
+ public:
+  static const std::unordered_map<std::string, int32_t>& Instance() {
+    static Map instance;
+    return instance.map_;
+  }
+
+  static int32_t Lookup(const std::string& dtype) {
+    auto it = Instance().find(dtype);
+    if (it != Instance().end())
+      return it->second;
+    return 0;
+  }
+
+  static const std::string& ToString(int32_t dtype) {
+    static std::string undefined("undefined");
+    for (const auto& pair : Instance()) {
+      if (pair.second == dtype)
+        return pair.first;
+    }
+    return undefined;
+  }
+
+ protected:
+  std::unordered_map<std::string, int32_t> map_;
+};
+
+class PrimitiveTypeNameMap : public StringIntMap<PrimitiveTypeNameMap> {
+ public:
+  PrimitiveTypeNameMap() : StringIntMap() {
+    map_["float"] = 1;
+    map_["uint8"] = 2;
+    map_["int8"] = 3;
+    map_["uint16"] = 4;
+    map_["int16"] = 5;
+    map_["int32"] = 6;
+    map_["int64"] = 7;
+    map_["string"] = 8;
+    map_["bool"] = 9;
+    map_["float16"] = 10;
+    map_["double"] = 11;
+    map_["uint32"] = 12;
+    map_["uint64"] = 13;
+    map_["complex64"] = 14;
+    map_["complex128"] = 15;
+    map_["bfloat16"] = 16;
+  }
+
+  static bool IsTypeName(const std::string& dtype) {
+    return Lookup(dtype) != 0;
+  }
+};
+
+class AttributeTypeNameMap : public StringIntMap<AttributeTypeNameMap> {
+ public:
+  AttributeTypeNameMap() : StringIntMap() {
+    map_["float"] = 1;
+    map_["int"] = 2;
+    map_["string"] = 3;
+    map_["tensor"] = 4;
+    map_["graph"] = 5;
+    map_["sparse_tensor"] = 11;
+    map_["type_proto"] = 13;
+    map_["floats"] = 6;
+    map_["ints"] = 7;
+    map_["strings"] = 8;
+    map_["tensors"] = 9;
+    map_["graphs"] = 10;
+    map_["sparse_tensors"] = 12;
+    map_["type_protos"] = 14;
+  }
+};
+
+class KeyWordMap {
+ public:
+  enum class KeyWord {
+    NONE,
+    IR_VERSION,
+    OPSET_IMPORT,
+    PRODUCER_NAME,
+    PRODUCER_VERSION,
+    DOMAIN_KW,
+    MODEL_VERSION,
+    DOC_STRING,
+    METADATA_PROPS,
+    SEQ_TYPE,
+    MAP_TYPE,
+    OPTIONAL_TYPE,
+    SPARSE_TENSOR_TYPE
+  };
+
+  KeyWordMap() {
+    map_["ir_version"] = KeyWord::IR_VERSION;
+    map_["opset_import"] = KeyWord::OPSET_IMPORT;
+    map_["producer_name"] = KeyWord::PRODUCER_NAME;
+    map_["producer_version"] = KeyWord::PRODUCER_VERSION;
+    map_["domain"] = KeyWord::DOMAIN_KW;
+    map_["model_version"] = KeyWord::MODEL_VERSION;
+    map_["doc_string"] = KeyWord::DOC_STRING;
+    map_["metadata_props"] = KeyWord::METADATA_PROPS;
+    map_["seq"] = KeyWord::SEQ_TYPE;
+    map_["map"] = KeyWord::MAP_TYPE;
+    map_["optional"] = KeyWord::OPTIONAL_TYPE;
+    map_["sparse_tensor"] = KeyWord::SPARSE_TENSOR_TYPE;
+  }
+
+  static const std::unordered_map<std::string, KeyWord>& Instance() {
+    static KeyWordMap instance;
+    return instance.map_;
+  }
+
+  static KeyWord Lookup(const std::string& id) {
+    auto it = Instance().find(id);
+    if (it != Instance().end())
+      return it->second;
+    return KeyWord::NONE;
+  }
+
+ private:
+  std::unordered_map<std::string, KeyWord> map_;
+};
+
+class ParserBase {
+ public:
+  ParserBase(const std::string& str)
+      : start_(str.data()), next_(str.data()), end_(str.data() + str.length()), saved_pos_(next_) {}
+
+  ParserBase(const char* cstr) : start_(cstr), next_(cstr), end_(cstr + strlen(cstr)), saved_pos_(next_) {}
+
+  void SavePos() {
+    saved_pos_ = next_;
+  }
+
+  void RestorePos() {
+    next_ = saved_pos_;
+  }
+
+  std::string GetCurrentPos() {
+    uint32_t line = 1, col = 1;
+    for (const char* p = start_; p < next_; ++p) {
+      if (*p == '\n') {
+        ++line;
+        col = 1;
+      } else {
+        ++col;
+      }
+    }
+    return ONNX_NAMESPACE::MakeString("(line: ", line, " column: ", col, ")");
+  }
+
+  // Return a suitable suffix of what has been parsed to provide error message context:
+  // return the line containing the last non-space character preceding the error (if it exists).
+  std::string GetErrorContext() {
+    // Special cases: empty input string, and parse-error at first character.
+    const char* p = next_ < end_ ? next_ : next_ - 1;
+    while ((p > start_) && isspace(*p))
+      --p;
+    while ((p > start_) && (*p != '\n'))
+      --p;
+    // Start at character after '\n' unless we are at start of input
+    const char* context_start = (p > start_) ? (p + 1) : start_;
+    for (p = context_start; (p < end_) && (*p != '\n'); ++p)
+      ;
+    return std::string(context_start, p-context_start);
+  }
+
+  template <typename... Args>
+  Status ParseError(const Args&... args) {
+    return Status(
+        NONE,
+        FAIL,
+        ONNX_NAMESPACE::MakeString(
+            "[ParseError at position ", GetCurrentPos(), "]\n", "Error context: ", GetErrorContext(), "\n", args...));
+  }
+
+  void SkipWhiteSpace() {
+    do {
+      while ((next_ < end_) && (isspace(*next_)))
+        ++next_;
+      if ((next_ >= end_) || ((*next_) != '#'))
+        return;
+      // Skip rest of the line:
+      while ((next_ < end_) && ((*next_) != '\n'))
+        ++next_;
+    } while (true);
+  }
+
+  int NextChar(bool skipspace = true) {
+    if (skipspace)
+      SkipWhiteSpace();
+    return (next_ < end_) ? *next_ : 0;
+  }
+
+  bool Matches(char ch, bool skipspace = true) {
+    if (skipspace)
+      SkipWhiteSpace();
+    if ((next_ < end_) && (*next_ == ch)) {
+      ++next_;
+      return true;
+    }
+    return false;
+  }
+
+  Status Match(char ch, bool skipspace = true) {
+    if (!Matches(ch, skipspace))
+      return ParseError("Expected character ", ch, " not found.");
+    return Status::OK();
+  }
+
+  bool EndOfInput() {
+    SkipWhiteSpace();
+    return (next_ >= end_);
+  }
+
+  enum class LiteralType { INT_LITERAL, FLOAT_LITERAL, STRING_LITERAL };
+
+  struct Literal {
+    LiteralType type;
+    std::string value;
+  };
+
+  Status Parse(Literal& result) {
+    bool decimal_point = false;
+    auto nextch = NextChar();
+    auto from = next_;
+    if (nextch == '"') {
+      ++next_;
+      // TODO: Handle escape characters
+      while ((next_ < end_) && (*next_ != '"')) {
+        ++next_;
+      }
+      ++next_;
+      result.type = LiteralType::STRING_LITERAL;
+      result.value = std::string(from + 1, next_ - from - 2); // skip enclosing quotes
+    } else if ((isdigit(nextch) || (nextch == '-'))) {
+      ++next_;
+
+      while ((next_ < end_) && (isdigit(*next_) || (*next_ == '.'))) {
+        if (*next_ == '.') {
+          if (decimal_point)
+            break; // Only one decimal point allowed in numeric literal
+          decimal_point = true;
+        }
+        ++next_;
+      }
+
+      if (next_ == from)
+        return ParseError("Value expected but not found.");
+
+      // Optional exponent syntax: (e|E)(+|-)?[0-9]+
+      if ((next_ < end_) && ((*next_ == 'e') || (*next_ == 'E'))) {
+        decimal_point = true; // treat as float-literal
+        ++next_;
+        if ((next_ < end_) && ((*next_ == '+') || (*next_ == '-')))
+          ++next_;
+        while ((next_ < end_) && (isdigit(*next_)))
+          ++next_;
+      }
+
+      result.value = std::string(from, next_ - from);
+      result.type = decimal_point ? LiteralType::FLOAT_LITERAL : LiteralType::INT_LITERAL;
+    }
+    return Status::OK();
+  }
+
+  Status Parse(int64_t& val) {
+    Literal literal;
+    CHECK_PARSER_STATUS(Parse(literal));
+    if (literal.type != LiteralType::INT_LITERAL)
+      return ParseError("Integer value expected, but not found.");
+    std::string s = literal.value;
+    val = std::stoll(s);
+    return Status::OK();
+  }
+
+  Status Parse(uint64_t& val) {
+    Literal literal;
+    CHECK_PARSER_STATUS(Parse(literal));
+    if (literal.type != LiteralType::INT_LITERAL)
+      return ParseError("Integer value expected, but not found.");
+    std::string s = literal.value;
+    val = std::stoull(s);
+    return Status::OK();
+  }
+
+  Status Parse(float& val) {
+    Literal literal;
+    CHECK_PARSER_STATUS(Parse(literal));
+    switch (literal.type) {
+      case LiteralType::INT_LITERAL:
+      case LiteralType::FLOAT_LITERAL:
+        val = std::stof(literal.value);
+        break;
+      default:
+        return ParseError("Unexpected literal type.");
+    }
+    return Status::OK();
+  }
+
+  Status Parse(double& val) {
+    Literal literal;
+    CHECK_PARSER_STATUS(Parse(literal));
+    switch (literal.type) {
+      case LiteralType::INT_LITERAL:
+      case LiteralType::FLOAT_LITERAL:
+        val = std::stod(literal.value);
+        break;
+      default:
+        return ParseError("Unexpected literal type.");
+    }
+    return Status::OK();
+  }
+
+  // Parse a string-literal enclosed within doube-quotes.
+  Status Parse(std::string& val) {
+    Literal literal;
+    CHECK_PARSER_STATUS(Parse(literal));
+    if (literal.type != LiteralType::STRING_LITERAL)
+      return ParseError("String value expected, but not found.");
+    val = literal.value;
+    return Status::OK();
+  }
+
+  // Parse an identifier, including keywords. If none found, this will
+  // return an empty-string identifier.
+  Status ParseOptionalIdentifier(std::string& id) {
+    SkipWhiteSpace();
+    auto from = next_;
+    if ((next_ < end_) && (isalpha(*next_) || (*next_ == '_'))) {
+      ++next_;
+      while ((next_ < end_) && (isalnum(*next_) || (*next_ == '_')))
+        ++next_;
+    }
+    id = std::string(from, next_ - from);
+    return Status::OK();
+  }
+
+  Status ParseIdentifier(std::string& id) {
+    ParseOptionalIdentifier(id);
+    if (id.empty())
+      return ParseError("Identifier expected but not found.");
+    return Status::OK();
+  }
+
+  Status PeekIdentifier(std::string& id) {
+    SavePos();
+    ParseOptionalIdentifier(id);
+    RestorePos();
+    return Status::OK();
+  }
+
+  Status Parse(KeyWordMap::KeyWord& keyword) {
+    std::string id;
+    CHECK_PARSER_STATUS(ParseIdentifier(id));
+    keyword = KeyWordMap::Lookup(id);
+    return Status::OK();
+  }
+
+ protected:
+  const char* start_;
+  const char* next_;
+  const char* end_;
+  const char* saved_pos_;
+};
+
+class OnnxParser : public ParserBase {
+ public:
+  OnnxParser(const char* cstr) : ParserBase(cstr) {}
+
+  Status Parse(TensorShapeProto& shape);
+
+  Status Parse(TypeProto& typeProto);
+
+  Status Parse(TensorProto& tensorProto);
+
+  Status Parse(AttributeProto& attr);
+
+  Status Parse(AttrList& attrlist);
+
+  Status Parse(NodeProto& node);
+
+  Status Parse(NodeList& nodelist);
+
+  Status Parse(GraphProto& graph);
+
+  Status Parse(FunctionProto& fn);
+
+  Status Parse(ModelProto& model);
+
+  template <typename T>
+  static Status Parse(T& parsedData, const char* input) {
+    OnnxParser parser(input);
+    return parser.Parse(parsedData);
+  }
+
+ private:
+  Status Parse(std::string name, GraphProto& graph);
+
+  Status Parse(IdList& idlist);
+
+  Status Parse(char open, IdList& idlist, char close);
+
+  Status ParseSingleAttributeValue(AttributeProto& attr);
+
+  Status Parse(ValueInfoProto& valueinfo);
+
+  Status Parse(ValueInfoList& vilist);
+
+  Status ParseInput(ValueInfoList& vilist, TensorList& initializers);
+
+  Status ParseValueInfo(ValueInfoList& vilist, TensorList& initializers);
+
+  Status Parse(TensorProto& tensorProto, const TypeProto& tensorTypeProto);
+
+  Status Parse(OpsetIdList& opsets);
+
+  bool NextIsType();
+};
+
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/defs/printer.cc b/third_party/onnx/onnx/defs/printer.cc
new file mode 100644
index 0000000000..dc4f04960c
--- /dev/null
+++ b/third_party/onnx/onnx/defs/printer.cc
@@ -0,0 +1,257 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/printer.h"
+#include "onnx/defs/tensor_proto_util.h"
+
+namespace ONNX_NAMESPACE {
+
+template <typename Collection>
+inline void print(std::ostream& os, const char* open, const char* separator, const char* close, Collection coll) {
+  const char* sep = "";
+  os << open;
+  for (auto& elt : coll) {
+    os << sep << elt;
+    sep = separator;
+  }
+  os << close;
+}
+
+std::ostream& operator<<(std::ostream& os, const TensorShapeProto_Dimension& dim) {
+  if (dim.has_dim_value())
+    os << dim.dim_value();
+  else if (dim.has_dim_param())
+    os << dim.dim_param();
+  else
+    os << "?";
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const TensorShapeProto& shape) {
+  print(os, "[", ",", "]", shape.dim());
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const TypeProto_Tensor& tensortype) {
+  os << PrimitiveTypeNameMap::ToString(tensortype.elem_type());
+  if (tensortype.has_shape()) {
+    if (tensortype.shape().dim_size() > 0)
+      os << tensortype.shape();
+  } else
+    os << "[]";
+
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const TypeProto_Sequence& seqType) {
+  return os << "seq(" << seqType.elem_type() << ")";
+}
+
+std::ostream& operator<<(std::ostream& os, const TypeProto_Map& mapType) {
+  return os << "map(" << PrimitiveTypeNameMap::ToString(mapType.key_type()) << ", " << mapType.value_type() << ")";
+}
+
+std::ostream& operator<<(std::ostream& os, const TypeProto_Optional& optType) {
+  return os << "optional(" << optType.elem_type() << ")";
+}
+
+std::ostream& operator<<(std::ostream& os, const TypeProto_SparseTensor sparseType) {
+  os << "sparse_tensor(" << PrimitiveTypeNameMap::ToString(sparseType.elem_type());
+  if (sparseType.has_shape()) {
+    if (sparseType.shape().dim_size() > 0)
+      os << sparseType.shape();
+  } else
+    os << "[]";
+
+  return os << ")";
+}
+
+std::ostream& operator<<(std::ostream& os, const TypeProto& type) {
+  if (type.has_tensor_type())
+    os << type.tensor_type();
+  else if (type.has_sequence_type())
+    os << type.sequence_type();
+  else if (type.has_map_type())
+    os << type.map_type();
+  else if (type.has_optional_type())
+    os << type.optional_type();
+  else if (type.has_sparse_tensor_type())
+    os << type.sparse_tensor_type();
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const TensorProto& tensor) {
+  os << PrimitiveTypeNameMap::ToString(tensor.data_type());
+  print(os, "[", ",", "]", tensor.dims());
+
+  if (! tensor.name().empty()) {
+    os << " " << tensor.name() << " ";
+  }
+  // TODO: does not yet handle all types or externally stored data.
+  if (tensor.has_raw_data()) {
+    switch (static_cast<TensorProto::DataType>(tensor.data_type())) {
+      case TensorProto::DataType::TensorProto_DataType_INT32:
+        print(os, " {", ",", "}", ParseData<int32_t>(&tensor));
+        break;
+      case TensorProto::DataType::TensorProto_DataType_INT64:
+        print(os, " {", ",", "}", ParseData<int64_t>(&tensor));
+        break;
+      case TensorProto::DataType::TensorProto_DataType_FLOAT:
+        print(os, " {", ",", "}", ParseData<float>(&tensor));
+        break;
+      case TensorProto::DataType::TensorProto_DataType_DOUBLE:
+        print(os, " {", ",", "}", ParseData<double>(&tensor));
+        break;
+      default:
+        os << "..."; // ParseData not instantiated for other types.
+        break;
+    }
+  } else {
+    switch (static_cast<TensorProto::DataType>(tensor.data_type())) {
+      case TensorProto::DataType::TensorProto_DataType_INT8:
+      case TensorProto::DataType::TensorProto_DataType_INT16:
+      case TensorProto::DataType::TensorProto_DataType_INT32:
+      case TensorProto::DataType::TensorProto_DataType_UINT8:
+      case TensorProto::DataType::TensorProto_DataType_UINT16:
+      case TensorProto::DataType::TensorProto_DataType_BOOL:
+        print(os, " {", ",", "}", tensor.int32_data());
+        break;
+      case TensorProto::DataType::TensorProto_DataType_INT64:
+        print(os, " {", ",", "}", tensor.int64_data());
+        break;
+      case TensorProto::DataType::TensorProto_DataType_UINT32:
+      case TensorProto::DataType::TensorProto_DataType_UINT64:
+        print(os, " {", ",", "}", tensor.uint64_data());
+        break;
+      case TensorProto::DataType::TensorProto_DataType_FLOAT:
+        print(os, " {", ",", "}", tensor.float_data());
+        break;
+      case TensorProto::DataType::TensorProto_DataType_DOUBLE:
+        print(os, " {", ",", "}", tensor.double_data());
+        break;
+      case TensorProto::DataType::TensorProto_DataType_STRING: {
+        const char* sep = "{";
+        for (auto& elt : tensor.string_data()) {
+          os << sep << "\"" << elt << "\"";
+          sep = ", ";
+        }
+        os << "}";
+        break;
+      }
+      default:
+        break;
+    }
+  }
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const ValueInfoProto& value_info) {
+  os << value_info.type() << " " << value_info.name();
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const ValueInfoList& vilist) {
+  print(os, "(", ", ", ")", vilist);
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const AttributeProto& attr) {
+  // Special case of attr-ref:
+  if (attr.has_ref_attr_name()) {
+    os << attr.name() << " : " << AttributeTypeNameMap::ToString(attr.type()) << " = @" << attr.ref_attr_name();
+    return os;
+  }
+  // General case:
+  os << attr.name() << " = ";
+  switch (attr.type()) {
+    case AttributeProto_AttributeType_INT:
+      os << attr.i();
+      break;
+    case AttributeProto_AttributeType_INTS:
+      print(os, "[", ", ", "]", attr.ints());
+      break;
+    case AttributeProto_AttributeType_FLOAT:
+      os << attr.f();
+      break;
+    case AttributeProto_AttributeType_FLOATS:
+      print(os, "[", ", ", "]", attr.floats());
+      break;
+    case AttributeProto_AttributeType_STRING:
+      os << "\"" << attr.s() << "\"";
+      break;
+    case AttributeProto_AttributeType_STRINGS: {
+      const char* sep = "[";
+      for (auto& elt : attr.strings()) {
+        os << sep << "\"" << elt << "\"";
+        sep = ", ";
+      }
+      os << "]";
+      break;
+    }
+    case AttributeProto_AttributeType_GRAPH:
+      os << attr.g();
+      break;
+    case AttributeProto_AttributeType_GRAPHS:
+      print(os, "[", ", ", "]", attr.graphs());
+      break;
+    case AttributeProto_AttributeType_TENSOR:
+      os << attr.t();
+      break;
+    case AttributeProto_AttributeType_TENSORS:
+      print(os, "[", ", ", "]", attr.tensors());
+      break;
+    default:
+      break;
+  }
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const AttrList& attrlist) {
+  print(os, "<", ", ", ">", attrlist);
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const NodeProto& node) {
+  print(os, "", ", ", "", node.output());
+  os << " = " << node.op_type();
+  if (node.attribute_size() > 0)
+    os << node.attribute();
+  print(os, "(", ", ", ")", node.input());
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const NodeList& nodelist) {
+  print(os, "{\n", "\n", "\n}\n", nodelist);
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const GraphProto& graph) {
+  os << graph.name() << " " << graph.input() << " => " << graph.output() << " ";
+  os << graph.node();
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const OperatorSetIdProto& opset) {
+  os << "\"" << opset.domain() << "\" : " << opset.version();
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const FunctionProto& fn) {
+  os << "<\n";
+  os << "  " << "domain: \"" << fn.domain() << "\",\n";
+  os << "  " << "opset_import: ";
+  print (os, "[", ",", "]", fn.opset_import());
+  os << "\n>\n";
+  os << fn.name() << " ";
+  if (fn.attribute_size() > 0)
+    print(os, "<", ",", ">", fn.attribute());
+  print(os, "(", ", ", ")", fn.input());
+  os << " => ";
+  print(os, "(", ", ", ")", fn.output());
+  os << "\n";
+  os << fn.node();
+  return os;
+}
+
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/defs/printer.h b/third_party/onnx/onnx/defs/printer.h
new file mode 100644
index 0000000000..89fbc3c858
--- /dev/null
+++ b/third_party/onnx/onnx/defs/printer.h
@@ -0,0 +1,41 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <iostream>
+
+#include "onnx/onnx_pb.h"
+
+#include "onnx/defs/parser.h"
+
+namespace ONNX_NAMESPACE {
+
+std::ostream& operator<<(std::ostream& os, const TensorShapeProto_Dimension& dim);
+
+std::ostream& operator<<(std::ostream& os, const TensorShapeProto& shape);
+
+std::ostream& operator<<(std::ostream& os, const TypeProto_Tensor& tensortype);
+
+std::ostream& operator<<(std::ostream& os, const TypeProto& type);
+
+std::ostream& operator<<(std::ostream& os, const TensorProto& tensor);
+
+std::ostream& operator<<(std::ostream& os, const ValueInfoProto& value_info);
+
+std::ostream& operator<<(std::ostream& os, const ValueInfoList& vilist);
+
+std::ostream& operator<<(std::ostream& os, const AttributeProto& attr);
+
+std::ostream& operator<<(std::ostream& os, const AttrList& attrlist);
+
+std::ostream& operator<<(std::ostream& os, const NodeProto& node);
+
+std::ostream& operator<<(std::ostream& os, const NodeList& nodelist);
+
+std::ostream& operator<<(std::ostream& os, const GraphProto& graph);
+
+std::ostream& operator<<(std::ostream& os, const FunctionProto& fn);
+
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/defs/quantization/defs.cc b/third_party/onnx/onnx/defs/quantization/defs.cc
new file mode 100644
index 0000000000..ca20b93108
--- /dev/null
+++ b/third_party/onnx/onnx/defs/quantization/defs.cc
@@ -0,0 +1,204 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+static const char* QuantizeLinear_ver13_doc = R"DOC(
+The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
+The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
+The quantization formula is y = saturate ((x / y_scale) + y_zero_point).
+For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
+For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    QuantizeLinear,
+    13,
+    OpSchema()
+        .Input(0, "x", "N-D full precision Input tensor to be quantized.", "T1")
+        .Input(
+            1,
+            "y_scale",
+            "Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, "
+            "or a 1-D Tensor for per-axis quantization.",
+            "tensor(float)")
+        .Input(
+            2,
+            "y_zero_point",
+            "Zero point for doing quantization to get 'y'. Shape must match y_scale. "
+            "Default is uint8 with zero point of 0 if it's not specified.",
+            "T2",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "y",
+            "N-D quantized output tensor. It has same shape as input 'x'.",
+            "T2")
+        .Attr(
+            "axis",
+            "(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)", "tensor(int32)"},
+            "Constrain 'x' to float or int32 tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.")
+        .SetDoc(QuantizeLinear_ver13_doc)
+        .TypeAndShapeInferenceFunction(
+            [](ONNX_NAMESPACE::InferenceContext& ctx) {
+              if (ctx.getNumInputs() == 3 && ctx.getInputType(2) != nullptr) {
+                propagateElemTypeFromInputToOutput(ctx, 2, 0);
+              } else {
+                updateOutputElemType(ctx, 0, TensorProto::UINT8);
+              }
+              if (!hasInputShape(ctx, 0)) {
+                return;
+              }
+
+              auto& input_shape = getInputShape(ctx, 0);
+              updateOutputShape(ctx, 0, input_shape);
+        }));
+
+static const char* DequantizeLinear_ver13_doc = R"DOC(
+The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor.
+The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' must have same shape, and can be either a scalar
+for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
+'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
+there's no zero point (zero point is supposed to be 0).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    DequantizeLinear,
+    13,
+    OpSchema()
+        .Input(0, "x", "N-D quantized input tensor to be de-quantized.", "T")
+        .Input(
+            1,
+            "x_scale",
+            "Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, "
+            "or a 1-D tensor for per-axis dequantization.",
+            "tensor(float)")
+        .Input(
+            2,
+            "x_zero_point",
+            "Zero point for input 'x'. Shape must match x_scale. "
+            "It's optional. Zero point is 0 when it's not specified.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "y",
+            "N-D full precision output tensor. It has same shape as input 'x'.",
+            "tensor(float)")
+        .Attr(
+            "axis",
+            "(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeConstraint(
+            "T",
+            {"tensor(int8)", "tensor(uint8)", "tensor(int32)"},
+            "Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.")
+        .SetDoc(DequantizeLinear_ver13_doc)
+        .TypeAndShapeInferenceFunction(
+            [](ONNX_NAMESPACE::InferenceContext& ctx) {
+              auto y_type = ctx.getOutputType(0);
+              // only float is supported
+              y_type->mutable_tensor_type()->set_elem_type(
+                  ONNX_NAMESPACE::TensorProto::FLOAT);
+
+              if (!hasInputShape(ctx, 0))
+                return;
+
+              auto& input_shape = getInputShape(ctx, 0);
+              updateOutputShape(ctx, 0, input_shape);
+            }));
+
+static const char* DynamicQuantizeLinear_ver11_doc = R"DOC(
+A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data.
+Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.
+Scale is calculated as:
+```
+ y_scale = (max(x) - min(x))/(qmax - qmin)
+ * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
+ * data range is adjusted to include 0.
+```
+Zero point is calculated as:
+```
+intermediate_zero_point = qmin - min(x)/y_scale
+y_zero_point = cast(round(saturate(itermediate_zero_point)))
+* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
+* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
+* rounding to nearest ties to even.
+```
+Data quantization formula is:
+```
+y = saturate (round (x / y_scale) + y_zero_point)
+* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
+* rounding to nearest ties to even.
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    DynamicQuantizeLinear,
+    11,
+    OpSchema()
+        .SetDoc(DynamicQuantizeLinear_ver11_doc)
+        .Input(0, "x", "Input tensor", "T1")
+        .Output(0, "y", "Quantized output tensor", "T2")
+        .Output(1, "y_scale", "Output scale. It's a scalar, which means a per-tensor/layer quantization.", "tensor(float)")
+        .Output(2, "y_zero_point", "Output zero point. It's a scalar, which means a per-tensor/layer quantization.", "T2")
+        .TypeConstraint(
+          "T1",
+          {"tensor(float)"},
+          "Constrain 'x' to float tensor.")
+      .TypeConstraint(
+          "T2",
+          {"tensor(uint8)"},
+          "Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.")
+      .FunctionBody(R"ONNX(
+        {
+           Q_Min = Constant<value = float {0.0}>()
+           Q_Max = Constant<value = float {255.0}>()
+           X_Min = ReduceMin <keepdims = 0> (x)
+           X_Min_Adjusted = Min (X_Min, Q_Min)
+           X_Max = ReduceMax <keepdims = 0> (x)
+           X_Max_Adjusted = Max (X_Max, Q_Min)
+           X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted)
+           Scale = Div (X_Range, Q_Max)
+           Min_Scaled = Div (X_Min_Adjusted, Scale)
+           Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled)
+           Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max)
+           Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP)
+           Zeropoint = Cast <to = 2> (Rounded_ZeroPoint_FP)
+           y_scale = Identity (Scale)
+           y_zero_point = Identity (Zeropoint)
+           y = QuantizeLinear (x, Scale, Zeropoint)
+        }
+        )ONNX")
+        .TypeAndShapeInferenceFunction(
+            [](ONNX_NAMESPACE::InferenceContext& ctx) {
+              updateOutputElemType(ctx, 0, TensorProto::UINT8);
+              updateOutputElemType(ctx, 1, TensorProto::FLOAT);
+              updateOutputElemType(ctx, 2, TensorProto::UINT8);
+
+              ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape();
+              ctx.getOutputType(2)->mutable_tensor_type()->mutable_shape();
+
+              if (!hasInputShape(ctx, 0))
+                return;
+
+              auto& input_shape = getInputShape(ctx, 0);
+              updateOutputShape(ctx, 0, input_shape);
+            }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/quantization/old.cc b/third_party/onnx/onnx/defs/quantization/old.cc
new file mode 100644
index 0000000000..211cbfb257
--- /dev/null
+++ b/third_party/onnx/onnx/defs/quantization/old.cc
@@ -0,0 +1,111 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+static const char* QuantizeLinear_ver10_doc = R"DOC(
+The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor.
+The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
+For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    QuantizeLinear,
+    10,
+    OpSchema()
+        .Input(0, "x", "N-D full precision Input tensor to be quantized.", "T1")
+        .Input(
+            1,
+            "y_scale",
+            "Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.",
+            "tensor(float)")
+        .Input(
+            2,
+            "y_zero_point",
+            "Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. "
+            "Default value is uint8 typed 0 if it's not specified.",
+            "T2",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "y",
+            "N-D quantized output tensor. It has same shape as input 'x'.",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)", "tensor(int32)"},
+            "Constrain 'x' to float or int32 tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int8)", "tensor(uint8)"},
+            "Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.")
+        .SetDoc(QuantizeLinear_ver10_doc)
+        .TypeAndShapeInferenceFunction(
+            [](ONNX_NAMESPACE::InferenceContext& ctx) {
+              if (ctx.getNumInputs() == 3 && ctx.getInputType(2) != nullptr) {
+                propagateElemTypeFromInputToOutput(ctx, 2, 0);
+              } else {
+                updateOutputElemType(ctx, 0, TensorProto::UINT8);
+              }
+              if (!hasInputShape(ctx, 0)) {
+                return;
+              }
+
+              auto& input_shape = getInputShape(ctx, 0);
+              updateOutputShape(ctx, 0, input_shape);
+        }));
+
+static const char* DequantizeLinear_ver10_doc = R"DOC(
+The linear dequantization operator. It consumes a quantized tensor, a scale, a zero point to compute the full precision tensor.
+The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' are both scalars.
+'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
+there's no zero point (zero point is supposed to be 0).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    DequantizeLinear,
+    10,
+    OpSchema()
+        .Input(0, "x", "N-D quantized input tensor to be de-quantized.", "T")
+        .Input(
+            1,
+            "x_scale",
+            "Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.",
+            "tensor(float)")
+        .Input(
+            2,
+            "x_zero_point",
+            "Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. "
+            "It's optional. 0 is the default value when it's not specified.",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "y",
+            "N-D full precision output tensor. It has same shape as input 'x'.",
+            "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(int8)", "tensor(uint8)", "tensor(int32)"},
+            "Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.")
+        .SetDoc(DequantizeLinear_ver10_doc)
+        .TypeAndShapeInferenceFunction(
+            [](ONNX_NAMESPACE::InferenceContext& ctx) {
+              auto y_type = ctx.getOutputType(0);
+              // only float is supported
+              y_type->mutable_tensor_type()->set_elem_type(
+                  ONNX_NAMESPACE::TensorProto::FLOAT);
+
+              if (!hasInputShape(ctx, 0))
+                return;
+
+              auto& input_shape = getInputShape(ctx, 0);
+              updateOutputShape(ctx, 0, input_shape);
+            }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/reduction/defs.cc b/third_party/onnx/onnx/defs/reduction/defs.cc
new file mode 100644
index 0000000000..95c73bed2f
--- /dev/null
+++ b/third_party/onnx/onnx/defs/reduction/defs.cc
@@ -0,0 +1,321 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <algorithm>
+#include <functional>
+#include "onnx/defs/schema.h"
+#include "onnx/defs/tensor_proto_util.h"
+
+namespace ONNX_NAMESPACE {
+
+std::vector<std::string> GetSupportedDataTypesForReductionOps(
+    bool supports8bit) {
+  if (supports8bit) {
+    auto data_types = OpSchema::numeric_types_for_math_reduction_with_bfloat();
+    data_types.push_back("tensor(uint8)");
+    data_types.push_back("tensor(int8)");
+
+    return data_types;
+  }
+
+  return OpSchema::numeric_types_for_math_reduction_with_bfloat();
+}
+
+std::function<void(OpSchema&)> ReduceDocGenerator(
+    const char* name,
+    bool supports_8bit_datatypes = false,
+    bool axes_input = false) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Computes the {name} of the input tensor's element along the provided axes. The resulting
+tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
+the resulting tensor has the reduced dimension pruned.
+
+The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+False instead of True.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(
+        0,
+        "data",
+        "An input tensor.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    if (axes_input) {
+      schema.Attr(
+          "noop_with_empty_axes",
+          "Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. "
+          "When axes is empty and this attribute is set to true, input tensor will not be reduced,"
+          "and the output tensor would be equivalent to input tensor.",
+          AttributeProto::INT,
+          static_cast<int64_t>(0));
+      schema.Input(
+          1,
+          "axes",
+          "Optional input list of integers, along which to reduce. "
+          "The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, "
+          "else act as an Identity op when 'noop_with_empty_axes' is true. "
+          "Accepted range is [-r, r-1] where r = rank(data).",
+          "tensor(int64)",
+          OpSchema::Optional,
+          true,
+          1,
+          OpSchema::NonDifferentiable);
+    } else {
+      schema.Attr(
+          "axes",
+          "A list of integers, along which to reduce. The default is to reduce over "
+          "all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).",
+          AttributeProto::INTS,
+          OPTIONAL_VALUE);
+    }
+    schema.Output(
+        0,
+        "reduced",
+        "Reduced output tensor.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        GetSupportedDataTypesForReductionOps(supports_8bit_datatypes),
+        supports_8bit_datatypes
+            ? "Constrain input and output types to high-precision and 8 bit numeric tensors."
+            : "Constrain input and output types to high-precision numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      int64_t keep_dims = 1, noop_with_empty_axes = 0;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      auto noop_attr_proto = ctx.getAttribute("noop_with_empty_axes");
+      if (noop_attr_proto) {
+        noop_with_empty_axes = noop_attr_proto->i();
+      }
+      std::vector<int64_t> axes;
+      size_t num_inputs = ctx.getNumInputs();
+      if ((num_inputs == 2) && ctx.getInputType(1)) { // axes is input
+        if (ctx.getAttribute("axes")) {
+          fail_shape_inference("axes as an input and attribute cannot be specified at the same time.");
+        }
+
+        const TensorProto* axesInitializer = ctx.getInputData(1);
+        if (axesInitializer == nullptr) {
+          // skip if axes is not an initializer
+          return;
+        }
+        std::vector<int64_t> axes_values = ParseData<int64_t>(axesInitializer);
+        axes.assign(axes_values.begin(), axes_values.end());
+      } else { // axes is attribute
+        auto axes_proto = ctx.getAttribute("axes");
+        if (axes_proto)
+          axes.assign(axes_proto->ints().begin(), axes_proto->ints().end());
+      }
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      if (noop_with_empty_axes && axes.empty()) {
+        propagateShapeFromInputToOutput(ctx, 0, 0);
+        return;
+      }
+      int64_t input_ndim = input_shape.dim_size();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+      for (size_t i = 0; i < axes.size(); ++i) {
+        if (axes[i] < -input_ndim || axes[i] >= input_ndim) {
+          fail_shape_inference(
+              "axis must be in [-rank, rank-1]. input rank was ", input_ndim);
+        }
+        if (axes[i] < 0)
+          axes[i] += input_ndim;
+      }
+      for (int i = 0; i < input_ndim; ++i) {
+        // axes empty means reduce all dim
+        if (!axes.empty() &&
+            std::find(axes.begin(), axes.end(), i) == axes.end()) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMax,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("max", true)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMin,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("min", true)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceSum,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("sum", false, true)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceSumSquare,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("sum square")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMean,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("mean")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceProd,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("product")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceLogSum,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("log sum")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceLogSumExp,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("log sum exponent")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceL1,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("L1 norm")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceL2,
+    13,
+    OpSchema().FillUsing(ReduceDocGenerator("L2 norm")));
+
+std::function<void(OpSchema&)> ArgReduceDocGenerator(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Computes the indices of the {name} elements of the input tensor's element along the
+provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
+If select_last_index is True (default False), the index of the last occurrence of the {name}
+is selected if the {name} appears more than once in the input. Otherwise the index of the
+first occurrence is selected.
+The type of the output tensor is integer.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "axis",
+        "The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Attr(
+        "select_last_index",
+        "Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Input(
+        0,
+        "data",
+        "An input tensor.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.Output(
+        0,
+        "reduced",
+        "Reduced output tensor with integer data type.",
+        "tensor(int64)",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.TypeConstraint(
+        "T",
+        OpSchema::all_numeric_types_with_bfloat(),
+        "Constrain input and output types to all numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // set output element type to int64
+      updateOutputElemType(ctx, 0, TensorProto_DataType_INT64);
+
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+      int64_t input_ndim = input_shape.dim_size();
+      int64_t axis = 0; // default to 0
+      auto axis_proto = ctx.getAttribute("axis");
+      if (axis_proto) {
+        axis = axis_proto->i();
+        if (axis < -input_ndim || axis >= input_ndim) {
+          fail_shape_inference(
+              "'axis' must be in [-rank(indices), rank(indices)-1]");
+        }
+        if (axis < 0)
+          axis += input_ndim;
+      }
+
+      int64_t keep_dims = 1;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      // do we need handle negative axis?
+      for (int i = 0; i < input_ndim; ++i) {
+        if (i != axis) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+} // namespace ONNX_NAMESPACE
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMax,
+    13,
+    OpSchema().FillUsing(ArgReduceDocGenerator("max")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMin,
+    13,
+    OpSchema().FillUsing(ArgReduceDocGenerator("min")));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/reduction/old.cc b/third_party/onnx/onnx/defs/reduction/old.cc
new file mode 100644
index 0000000000..8eb0e8fb89
--- /dev/null
+++ b/third_party/onnx/onnx/defs/reduction/old.cc
@@ -0,0 +1,547 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <algorithm>
+#include <functional>
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+std::vector<std::string> GetSupportedDataTypesForReductionOps_opset12(
+    bool supports8bit) {
+  if (supports8bit) {
+    auto data_types = OpSchema::numeric_types_for_math_reduction();
+    data_types.push_back("tensor(uint8)");
+    data_types.push_back("tensor(int8)");
+
+    return data_types;
+  }
+
+  return OpSchema::numeric_types_for_math_reduction();
+}
+
+std::function<void(OpSchema&)> ReduceDocGenerator_opset12(
+    const char* name,
+    bool supports_8bit_datatypes = false) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Computes the {name} of the input tensor's element along the provided axes. The resulting
+tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+the resulted tensor have the reduced dimension pruned.
+
+The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+False instead of True.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "axes",
+        "A list of integers, along which to reduce. The default is to reduce over "
+        "all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(0, "data", "An input tensor.", "T");
+    schema.Output(0, "reduced", "Reduced output tensor.", "T");
+    schema.TypeConstraint(
+        "T",
+        GetSupportedDataTypesForReductionOps_opset12(supports_8bit_datatypes),
+        supports_8bit_datatypes
+            ? "Constrain input and output types to high-precision and 8 bit numeric tensors."
+            : "Constrain input and output types to high-precision numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      int64_t keep_dims = 1;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      int64_t input_ndim = input_shape.dim_size();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+      std::vector<int64_t> axes;
+      auto axes_proto = ctx.getAttribute("axes");
+      if (axes_proto)
+        axes.assign(axes_proto->ints().begin(), axes_proto->ints().end());
+
+      for (size_t i = 0; i < axes.size(); ++i) {
+        if (axes[i] < -input_ndim || axes[i] >= input_ndim) {
+          fail_shape_inference(
+              "axis must be in [-rank, rank-1]. input rank was ", input_ndim);
+        }
+        if (axes[i] < 0)
+          axes[i] += input_ndim;
+      }
+      // do we need handle negative axis?
+      for (int i = 0; i < input_ndim; ++i) {
+        // axes empty means reduce all dim
+        if (!axes.empty() &&
+            std::find(axes.begin(), axes.end(), i) == axes.end()) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMax,
+    12,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("max", true)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMin,
+    12,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("min", true)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceSum,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("sum")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceSumSquare,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("sum square")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMean,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("mean")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceProd,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("product")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceLogSum,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("log sum")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceLogSumExp,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("log sum exponent")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceL1,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("L1 norm")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceL2,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset12("L2 norm")));
+
+std::function<void(OpSchema&)> ArgReduceDocGenerator_opset12(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Computes the indices of the {name} elements of the input tensor's element along the
+provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
+If select_last_index is True (default False), the index of the last occurrence of the {name}
+is selected if the {name} appears more than once in the input. Otherwise the index of the
+first occurrence is selected.
+The type of the output tensor is integer.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "axis",
+        "The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Attr(
+        "select_last_index",
+        "Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Input(0, "data", "An input tensor.", "T");
+    schema.Output(
+        0,
+        "reduced",
+        "Reduced output tensor with integer data type.",
+        "tensor(int64)");
+    schema.TypeConstraint(
+        "T",
+        OpSchema::all_numeric_types(),
+        "Constrain input and output types to all numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // set output element type to int64
+      updateOutputElemType(ctx, 0, TensorProto_DataType_INT64);
+
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+      int64_t input_ndim = input_shape.dim_size();
+      int64_t axis = 0; // default to 0
+      auto axis_proto = ctx.getAttribute("axis");
+      if (axis_proto) {
+        axis = axis_proto->i();
+        if (axis < -input_ndim || axis >= input_ndim) {
+          fail_shape_inference(
+              "'axis' must be in [-rank(indices), rank(indices)-1]");
+        }
+        if (axis < 0)
+          axis += input_ndim;
+      }
+
+      int64_t keep_dims = 1;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      // do we need handle negative axis?
+      for (int i = 0; i < input_ndim; ++i) {
+        if (i != axis) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+} // namespace ONNX_NAMESPACE
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMax,
+    12,
+    OpSchema().FillUsing(ArgReduceDocGenerator_opset12("max")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMin,
+    12,
+    OpSchema().FillUsing(ArgReduceDocGenerator_opset12("min")));
+
+std::function<void(OpSchema&)> ReduceDocGenerator_opset1(
+    const char* name,
+    int opset = 1) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Computes the {name} of the input tensor's element along the provided axes. The resulting
+tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
+the resulted tensor have the reduced dimension pruned.
+
+The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
+False instead of True.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "axes",
+        opset >= 11
+            ? "A list of integers, along which to reduce. The default is to reduce over "
+              "all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data)."
+            : "A list of integers, along which to reduce. The default is to reduce over "
+              "all the dimensions of the input tensor.",
+        AttributeProto::INTS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(0, "data", "An input tensor.", "T");
+    schema.Output(0, "reduced", "Reduced output tensor.", "T");
+    schema.TypeConstraint(
+        "T",
+        OpSchema::numeric_types_for_math_reduction(),
+        "Constrain input and output types to high-precision numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      propagateElemTypeFromInputToOutput(ctx, 0, 0);
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      int64_t keep_dims = 1;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      int64_t input_ndim = input_shape.dim_size();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+      std::vector<int64_t> axes;
+      auto axes_proto = ctx.getAttribute("axes");
+      if (axes_proto)
+        axes.assign(axes_proto->ints().begin(), axes_proto->ints().end());
+
+      for (size_t i = 0; i < axes.size(); ++i) {
+        if (axes[i] < 0)
+          axes[i] += input_ndim;
+      }
+      // do we need handle negative axis?
+      for (int i = 0; i < input_ndim; ++i) {
+        // axes empty means reduce all dim
+        if (!axes.empty() &&
+            std::find(axes.begin(), axes.end(), i) == axes.end()) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMax,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("max")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMin,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("min")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceSum,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("sum")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceSumSquare,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("sum square")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMean,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("mean")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceProd,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("product")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceLogSum,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("log sum")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceLogSumExp,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("log sum exponent")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceL1,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("L1 norm")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceL2,
+    1,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("L2 norm")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMax,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("max", 11)));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReduceMin,
+    11,
+    OpSchema().FillUsing(ReduceDocGenerator_opset1("min", 11)));
+
+std::function<void(OpSchema&)> ArgReduceDocGenerator_opset1(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc;
+    POPULATE_OP_DOC_STR(doc = R"DOC(
+Computes the indices of the {name} elements of the input tensor's element along the
+provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
+The type of the output tensor is integer.)DOC";
+                        ReplaceAll(doc, "{name}", name););
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "axis",
+        "The axis in which to compute the arg indices.",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(0, "data", "An input tensor.", "T");
+    schema.Output(
+        0,
+        "reduced",
+        "Reduced output tensor with integer data type.",
+        "tensor(int64)");
+    schema.TypeConstraint(
+        "T",
+        OpSchema::all_numeric_types(),
+        "Constrain input and output types to all numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // set output element type to int64
+      updateOutputElemType(ctx, 0, TensorProto_DataType_INT64);
+
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+      int64_t input_ndim = input_shape.dim_size();
+      int64_t axis = 0; // default to 0
+      auto axis_proto = ctx.getAttribute("axis");
+      if (axis_proto) {
+        axis = axis_proto->i();
+        if (axis < 0)
+          axis += input_ndim;
+      }
+
+      int64_t keep_dims = 1;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      // do we need handle negative axis?
+      for (int i = 0; i < input_ndim; ++i) {
+        if (i != axis) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+} // namespace ONNX_NAMESPACE
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMax,
+    1,
+    OpSchema().FillUsing(ArgReduceDocGenerator_opset1("max")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMin,
+    1,
+    OpSchema().FillUsing(ArgReduceDocGenerator_opset1("min")));
+
+std::function<void(OpSchema&)> ArgReduceDocGenerator_opset11(const char* name) {
+  return [=](OpSchema& schema) {
+    std::string doc = R"DOC(
+Computes the indices of the {name} elements of the input tensor's element along the
+provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
+If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
+The type of the output tensor is integer.)DOC";
+    ReplaceAll(doc, "{name}", name);
+    schema.SetDoc(doc.c_str());
+    schema.Attr(
+        "axis",
+        "The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "keepdims",
+        "Keep the reduced dimension or not, default 1 means keep reduced dimension.",
+        AttributeProto::INT,
+        static_cast<int64_t>(1));
+    schema.Input(0, "data", "An input tensor.", "T");
+    schema.Output(
+        0,
+        "reduced",
+        "Reduced output tensor with integer data type.",
+        "tensor(int64)");
+    schema.TypeConstraint(
+        "T",
+        OpSchema::all_numeric_types(),
+        "Constrain input and output types to all numeric tensors.");
+    schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+      // set output element type to int64
+      updateOutputElemType(ctx, 0, TensorProto_DataType_INT64);
+
+      if (!hasNInputShapes(ctx, 1)) {
+        return;
+      }
+
+      auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+      auto output_shape =
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+      int64_t input_ndim = input_shape.dim_size();
+      int64_t axis = 0; // default to 0
+      auto axis_proto = ctx.getAttribute("axis");
+      if (axis_proto) {
+        axis = axis_proto->i();
+        if (axis < -input_ndim || axis >= input_ndim) {
+          fail_shape_inference(
+              "'axis' must be in [-rank(indices), rank(indices)-1]");
+        }
+        if (axis < 0)
+          axis += input_ndim;
+      }
+
+      int64_t keep_dims = 1;
+      auto attr_proto = ctx.getAttribute("keepdims");
+      if (attr_proto) {
+        keep_dims = attr_proto->i();
+      }
+      // do we need handle negative axis?
+      for (int i = 0; i < input_ndim; ++i) {
+        if (i != axis) {
+          auto dim = output_shape->add_dim();
+          dim->CopyFrom(input_shape.dim(i));
+        } else {
+          if (keep_dims == 1) {
+            auto dim = output_shape->add_dim();
+            dim->set_dim_value(1);
+          }
+        }
+      }
+    });
+  };
+} // namespace ONNX_NAMESPACE
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMax,
+    11,
+    OpSchema().FillUsing(ArgReduceDocGenerator_opset11("max")));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ArgMin,
+    11,
+    OpSchema().FillUsing(ArgReduceDocGenerator_opset11("min")));
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/rnn/defs.cc b/third_party/onnx/onnx/defs/rnn/defs.cc
new file mode 100644
index 0000000000..497ea16b57
--- /dev/null
+++ b/third_party/onnx/onnx/defs/rnn/defs.cc
@@ -0,0 +1,614 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+void RNNShapeInference(InferenceContext& ctx) {
+  TensorShapeProto::Dimension num_directions, seq_length, batch_size,
+      hidden_size;
+
+  auto direction = getAttribute(ctx, "direction", "forward");
+  if ((direction == "forward") || (direction == "reverse"))
+    num_directions.set_dim_value(1);
+  else if (direction == "bidirectional")
+    num_directions.set_dim_value(2);
+  // else leave num_directions unknown in case of incorrect attribute value
+
+  auto hidden_size_value = getAttribute(ctx, "hidden_size", -1);
+  if (hidden_size_value > 0)
+    hidden_size.set_dim_value(hidden_size_value);
+
+  auto layout_value = getAttribute(ctx, "layout", 0);
+
+  if (hasInputShape(ctx, 0)) {
+    auto& first_input_shape = getInputShape(ctx, 0);
+    if (first_input_shape.dim_size() != 3) {
+      fail_shape_inference("First input tensor must have rank 3");
+    }
+    seq_length = first_input_shape.dim((layout_value == 0) ? 0 : 1);
+    batch_size = first_input_shape.dim((layout_value == 0) ? 1 : 0);
+  }
+
+  auto num_outputs = ctx.getNumOutputs();
+
+  if (num_outputs > 0) {
+    // Y
+    propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+    if (layout_value == 0) {      
+      auto dims = {seq_length, num_directions, batch_size, hidden_size};
+      updateOutputShape(ctx, 0, dims);
+    } else {
+      auto dims = {batch_size, seq_length, num_directions, hidden_size};
+      updateOutputShape(ctx, 0, dims);
+    }
+  }
+
+  if (num_outputs > 1) {
+    // Y_h
+    propagateElemTypeFromInputToOutput(ctx, 0, 1);
+
+    if (layout_value == 0) {      
+      auto dims = {num_directions, batch_size, hidden_size};
+      updateOutputShape(ctx, 1, dims);
+    } else {
+      auto dims = {batch_size, num_directions, hidden_size};
+      updateOutputShape(ctx, 1, dims);
+    }
+  }
+
+  if (num_outputs > 2) {
+    // Y_c : only in the case of LSTM
+    propagateElemTypeFromInputToOutput(ctx, 0, 2);
+
+    if (layout_value == 0) {      
+      auto dims = {num_directions, batch_size, hidden_size};
+      updateOutputShape(ctx, 2, dims);
+    } else {
+      auto dims = {batch_size, num_directions, hidden_size};
+      updateOutputShape(ctx, 2, dims);
+    }
+  }
+}
+
+std::function<void(OpSchema&)> RNNDocGenerator(const char* /*name*/) {
+  return [=](OpSchema& schema) {
+    schema.Attr(
+        "direction",
+        "Specify if the RNN is forward, reverse, or bidirectional. "
+        "Must be one of forward (default), reverse, or bidirectional.",
+        AttributeProto::STRING,
+        std::string("forward"));
+    schema.Attr(
+        "layout",
+        "The shape format of inputs X, initial_h and outputs Y, Y_h. "
+        "If 0, the following shapes are expected: "
+        "X.shape = [seq_length, batch_size, input_size], "
+        "Y.shape = [seq_length, num_directions, batch_size, hidden_size], "
+        "initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. "
+        "If 1, the following shapes are expected: "
+        "X.shape = [batch_size, seq_length, input_size], "
+        "Y.shape = [batch_size, seq_length, num_directions, hidden_size], "
+        "initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "hidden_size",
+        "Number of neurons in the hidden layer",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_alpha",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM. Default values are the same as of corresponding ONNX operators."
+        "For example with LeakyRelu, the default alpha is 0.01.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_beta",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM. Default values are the same as of corresponding ONNX operators.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "clip",
+        "Cell clip threshold. Clipping bounds the elements of a tensor "
+        "in the range of [-threshold, +threshold] and is applied to the input "
+        "of activations. No clip if not specified.",
+        AttributeProto::FLOAT,
+        OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "X",
+        "The input sequences packed (and potentially padded) into one 3-D "
+        "tensor with the shape of `[seq_length, batch_size, input_size]`.",
+        "T",
+        OpSchema::Single,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Input(
+        4,
+        "sequence_lens",
+        "Optional tensor specifying lengths of the sequences in a batch. "
+        "If not specified - assumed all sequences in the batch to have "
+        "length `seq_length`. It has shape `[batch_size]`.",
+        "T1",
+        OpSchema::Optional,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.Input(
+        5,
+        "initial_h",
+        "Optional initial value of the hidden. If not specified - assumed "
+        "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional,
+        true,
+        1,
+        OpSchema::NonDifferentiable);
+    schema.Output(
+        0,
+        "Y",
+        "A tensor that concats all the intermediate output values of the hidden. "
+        "It has shape `[seq_length, num_directions, batch_size, hidden_size]`. ",
+        "T",
+        OpSchema::Optional,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.Output(
+        1,
+        "Y_h",
+        "The last output value of the hidden. It has shape "
+        "`[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional,
+        true,
+        1,
+        OpSchema::Differentiable);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeConstraint(
+        "T1", {"tensor(int32)"}, "Constrain seq_lens to integer tensor.");
+    schema.TypeAndShapeInferenceFunction(RNNShapeInference);
+  };
+}
+
+static const char* RNN_ver14_doc = R"DOC(
+Computes an one-layer simple RNN. This operator is usually supported
+via some custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`t` - time step (t-1 means previous time step)
+
+`Wi` - W parameter weight matrix for input gate
+
+`Ri` - R recurrence weight matrix for input gate
+
+`Wbi` - W parameter bias vector for input gate
+
+`Rbi` - R parameter bias vector for input gate
+
+`WBi` - W parameter weight matrix for backward input gate
+
+`RBi` - R recurrence weight matrix for backward input gate
+
+`WBbi` - WR bias vectors for backward input gate
+
+`RBbi` - RR bias vectors for backward input gate
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Tanh):
+
+  - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RNN,
+    14,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(RNN_ver14_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "activations",
+            "One (or two if bidirectional) activation function for "
+            "input gate. The activation function must be one of the activation "
+            "functions specified above. Optional: Default `Tanh` if not specified.",
+            AttributeProto::STRINGS,
+            std::vector<std::string>{"Tanh", "Tanh"})
+        .Input(
+            1,
+            "W",
+            "The weight tensor for input gate. Concatenation of `Wi` and `WBi` "
+            "(if bidirectional). The tensor has shape "
+            "`[num_directions, hidden_size, input_size]`.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `Ri` and `RBi` "
+            "(if bidirectional). The tensor has shape "
+            "`[num_directions, hidden_size, hidden_size]`.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            3,
+            "B",
+            "The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` "
+            "and `[WBbi, RBbi]` (if bidirectional). The tensor has shape "
+            "`[num_directions, 2*hidden_size]`. Optional: If not specified - assumed "
+            "to be 0.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .FillUsing(RNNDocGenerator("RNN")));
+
+static const char* GRU_ver14_doc = R"DOC(
+Computes an one-layer GRU. This operator is usually supported via some custom
+implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`z` - update gate
+
+`r` - reset gate
+
+`h` - hidden gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh):
+
+  - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
+
+  - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
+
+  - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
+
+  - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
+
+  - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GRU,
+    14,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(GRU_ver14_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "activations",
+            "A list of 2 (or 4 if bidirectional) activation functions "
+            "for update, reset, and hidden gates. The activation functions must be one "
+            "of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "linear_before_reset",
+            "When computing the output of the hidden gate, "
+            "apply the linear transformation before multiplying by the output of the "
+            "reset gate.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, input_size]`.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, hidden_size]`.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            3,
+            "B",
+            "The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and "
+            "`[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor "
+            "has shape `[num_directions, 6*hidden_size]`. Optional: If not specified "
+            "- assumed to be 0",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .FillUsing(RNNDocGenerator("GRU")));
+
+static const char* LSTM_ver14_doc = R"DOC(
+Computes an one-layer LSTM. This operator is usually supported via some
+custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`o` - output gate
+
+`f` - forget gate
+
+`c` - cell gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+`P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+`PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+  - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
+
+  - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
+
+  - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+
+  - Ct = ft (.) Ct-1 + it (.) ct
+
+  - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
+
+  - Ht = ot (.) h(Ct)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LSTM,
+    14,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(LSTM_ver14_doc) + GenerateOptionalArgumentsDoc()))
+        .Attr(
+            "activations",
+            "A list of 3 (or 6 if bidirectional) activation functions "
+            "for input, output, forget, cell, and hidden. The activation functions must "
+            "be one of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+      .Attr(
+          "layout",
+          "The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. "
+          "If 0, the following shapes are expected: "
+          "X.shape = [seq_length, batch_size, input_size], "
+          "Y.shape = [seq_length, num_directions, batch_size, hidden_size], "
+          "initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = "
+          "[num_directions, batch_size, hidden_size]. "
+          "If 1, the following shapes are expected: "
+          "X.shape = [batch_size, seq_length, input_size], "
+          "Y.shape = [batch_size, seq_length, num_directions, hidden_size], "
+          "initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = "
+          "[batch_size, num_directions, hidden_size].",
+          AttributeProto::INT,
+          static_cast<int64_t>(0))
+        .Attr(
+            "input_forget",
+            "Couple the input and forget gates if 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[iofc]` and "
+            "`WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape "
+            "`[num_directions, 4*hidden_size, input_size]`.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[iofc]` and "
+            "`RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 4*hidden_size, hidden_size]`.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            3,
+            "B",
+            "The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, "
+            "and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This "
+            "tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not "
+            "specified - assumed to be 0.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            6,
+            "initial_c",
+            "Optional initial value of the cell. If not specified - assumed "
+            "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            7,
+            "P",
+            "The weight tensor for peepholes. Concatenation of `P[iof]` and "
+            "`PB[iof]` (if bidirectional) along dimension 0. It has shape "
+            "`[num_directions, 3*hidde_size]`. Optional: If not specified - "
+            "assumed to be 0.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .FillUsing(RNNDocGenerator("LSTM"))
+        .Output(
+            2,
+            "Y_c",
+            "The last output value of the cell. It has shape "
+            "`[num_directions, batch_size, hidden_size]`.",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::Differentiable));
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/rnn/old.cc b/third_party/onnx/onnx/defs/rnn/old.cc
new file mode 100644
index 0000000000..d65cabf00c
--- /dev/null
+++ b/third_party/onnx/onnx/defs/rnn/old.cc
@@ -0,0 +1,1215 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+std::function<void(OpSchema&)> RNNDocGeneratorOld(const char* /*name*/) {
+  return [=](OpSchema& schema) {
+    schema.Attr(
+        "direction",
+        "Specify if the RNN is forward, reverse, or bidirectional. "
+        "Must be one of forward (default), reverse, or bidirectional.",
+        AttributeProto::STRING,
+        std::string("foward"));
+    schema.Attr(
+        "hidden_size",
+        "Number of neurons in the hidden layer",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_alpha",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_beta",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "output_sequence",
+        "The sequence output for the hidden is optional if 0. Default 0.",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "clip",
+        "Cell clip threshold. Clipping bounds the elements of a tensor "
+        "in the range of [-threshold, +threshold] and is applied to the input "
+        "of activations. No clip if not specified.",
+        AttributeProto::FLOAT,
+        OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "X",
+        "The input sequences packed (and potentially padded) into one 3-D "
+        "tensor with the shape of `[seq_length, batch_size, input_size]`.",
+        "T");
+    schema.Input(
+        4,
+        "sequence_lens",
+        "Optional tensor specifying lengths of the sequences in a batch. "
+        "If not specified - assumed all sequences in the batch to have "
+        "length `seq_length`. It has shape `[batch_size]`.",
+        "T1",
+        OpSchema::Optional);
+    schema.Input(
+        5,
+        "initial_h",
+        "Optional initial value of the hidden. If not specified - assumed "
+        "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        0,
+        "Y",
+        "A tensor that concats all the intermediate output values of the hidden. "
+        "It has shape `[seq_length, num_directions, batch_size, hidden_size]`. "
+        "It is optional if `output_sequence` is 0.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        1,
+        "Y_h",
+        "The last output value of the hidden. It has shape "
+        "`[num_directions, batch_size, hidden_size]`.",
+        "T");
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeConstraint(
+        "T1", {"tensor(int32)"}, "Constrain seq_lens to integer tensor.");
+  };
+}
+
+static const char* GRU_ver1_doc = R"DOC(
+Computes an one-layer GRU. This operator is usually supported via some custom
+implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`z` - update gate
+
+`r` - reset gate
+
+`h` - hidden gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh):
+
+  - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz)
+
+  - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr)
+
+  - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0
+
+  - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0
+
+  - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GRU,
+    1,
+    OpSchema()
+        .SetDoc(GRU_ver1_doc)
+        .Attr(
+            "activations",
+            "A list of 2 (or 4 if bidirectional) activation functions "
+            "for update, reset, and hidden gates. The activation functions must be one "
+            "of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and "
+            "`[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor "
+            "has shape `[num_directions, 6*hidden_size]`. Optional: If not specified "
+            "- assumed to be 0",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGeneratorOld("GRU")));
+
+// Versions 1 to 6 of RNN/LSTM and versions 3 to 6 of GRU:
+
+void RNNShapeInference1(InferenceContext& ctx) {
+  TensorShapeProto::Dimension num_directions, seq_length, batch_size,
+      hidden_size;
+
+  auto direction = getAttribute(ctx, "direction", "forward");
+  if ((direction == "forward") || (direction == "reverse"))
+    num_directions.set_dim_value(1);
+  else if (direction == "bidirectional")
+    num_directions.set_dim_value(2);
+  // else leave num_directions unknown in case of incorrect attribute value
+
+  auto hidden_size_value = getAttribute(ctx, "hidden_size", -1);
+  if (hidden_size_value > 0)
+    hidden_size.set_dim_value(hidden_size_value);
+
+  if (hasInputShape(ctx, 0)) {
+    auto& first_input_shape = getInputShape(ctx, 0);
+    seq_length = first_input_shape.dim(0);
+    batch_size = first_input_shape.dim(1);
+  }
+
+  // The treatment of outputs is a bit complicated because of the combination of
+  // optional outputs and the output_sequence attribute.
+  bool output_sequence = (getAttribute(ctx, "output_sequence", 0) != 0);
+  auto num_outputs = ctx.getNumOutputs();
+
+  if (num_outputs == 0)
+    return; // Unlikely, but seems legal.
+
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+  if (num_outputs > 1)
+    propagateElemTypeFromInputToOutput(ctx, 0, 1);
+  if (num_outputs > 2)
+    propagateElemTypeFromInputToOutput(ctx, 0, 2);
+
+  if (output_sequence) {
+    // No ambiguity in spec
+    updateOutputShape(
+        ctx, 0, {seq_length, num_directions, batch_size, hidden_size}); // Y
+    if (num_outputs > 1)
+      updateOutputShape(
+          ctx, 1, {num_directions, batch_size, hidden_size}); // Y_h
+    if (num_outputs > 2)
+      updateOutputShape(
+          ctx, 2, {num_directions, batch_size, hidden_size}); // Y_c
+  } else {
+    // Documentation suggests that the output Y is absent in this case
+    // Different tests seem to disagree on whether Y_h and Y_c, if present,
+    // should be in positions 0 & 1 or 1 & 2. updateOutputShape(ctx, 0,
+    // {num_directions, batch_size, hidden_size}); // Y_h if (num_outputs > 1)
+    // updateOutputShape(ctx, 1, {num_directions, batch_size, hidden_size}); //
+    // Y_c
+  }
+}
+
+std::function<void(OpSchema&)> RNNDocGenerator1(const char* /*name*/) {
+  return [=](OpSchema& schema) {
+    schema.Attr(
+        "direction",
+        "Specify if the RNN is forward, reverse, or bidirectional. "
+        "Must be one of forward (default), reverse, or bidirectional.",
+        AttributeProto::STRING,
+        std::string("forward"));
+    schema.Attr(
+        "hidden_size",
+        "Number of neurons in the hidden layer",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_alpha",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM. Default values are the same as of corresponding ONNX operators."
+        "For example with LeakyRelu, the default alpha is 0.01.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_beta",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM. Default values are the same as of corresponding ONNX operators.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "output_sequence",
+        "The sequence output for the hidden is optional if 0. Default 0.",
+        AttributeProto::INT,
+        static_cast<int64_t>(0));
+    schema.Attr(
+        "clip",
+        "Cell clip threshold. Clipping bounds the elements of a tensor "
+        "in the range of [-threshold, +threshold] and is applied to the input "
+        "of activations. No clip if not specified.",
+        AttributeProto::FLOAT,
+        OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "X",
+        "The input sequences packed (and potentially padded) into one 3-D "
+        "tensor with the shape of `[seq_length, batch_size, input_size]`.",
+        "T");
+    schema.Input(
+        4,
+        "sequence_lens",
+        "Optional tensor specifying lengths of the sequences in a batch. "
+        "If not specified - assumed all sequences in the batch to have "
+        "length `seq_length`. It has shape `[batch_size]`.",
+        "T1",
+        OpSchema::Optional);
+    schema.Input(
+        5,
+        "initial_h",
+        "Optional initial value of the hidden. If not specified - assumed "
+        "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        0,
+        "Y",
+        "A tensor that concats all the intermediate output values of the hidden. "
+        "It has shape `[seq_length, num_directions, batch_size, hidden_size]`. "
+        "It is optional if `output_sequence` is 0.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        1,
+        "Y_h",
+        "The last output value of the hidden. It has shape "
+        "`[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeConstraint(
+        "T1", {"tensor(int32)"}, "Constrain seq_lens to integer tensor.");
+    schema.TypeAndShapeInferenceFunction(RNNShapeInference1);
+  };
+}
+
+static const char* RNN_ver1_doc = R"DOC(
+Computes an one-layer simple RNN. This operator is usually supported
+via some custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`t` - time step (t-1 means previous time step)
+
+`Wi` - W parameter weight matrix for input gate
+
+`Ri` - R recurrence weight matrix for input gate
+
+`Wbi` - W parameter bias vector for input gate
+
+`Rbi` - R parameter bias vector for input gate
+
+`WBi` - W parameter weight matrix for backward input gate
+
+`RBi` - R recurrence weight matrix for backward input gate
+
+`WBbi` - WR bias vectors for backward input gate
+
+`RBbi` - RR bias vectors for backward input gate
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Tanh):
+
+  - Ht = f(Xt*(Wi^T) + Ht-1*Ri + Wbi + Rbi)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RNN,
+    1,
+    OpSchema()
+        .SetDoc(RNN_ver1_doc)
+        .Attr(
+            "activations",
+            "One (or two if bidirectional) activation function for "
+            "input gate. The activation function must be one of the activation "
+            "functions specified above. Optional: Default `Tanh` if not specified.",
+            AttributeProto::STRINGS,
+            std::vector<std::string>{"Tanh", "Tanh"})
+        .Input(
+            1,
+            "W",
+            "The weight tensor for input gate. Concatenation of `Wi` and `WBi` "
+            "(if bidirectional). The tensor has shape "
+            "`[num_directions, hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `Ri` and `RBi` "
+            "(if bidirectional). The tensor has shape "
+            "`[num_directions, hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` "
+            "and `[WBbi, RBbi]` (if bidirectional). The tensor has shape "
+            "`[num_directions, 2*hidden_size]`. Optional: If not specified - assumed "
+            "to be 0.",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGenerator1("RNN")));
+
+static const char* GRU_ver3_doc = R"DOC(
+Computes an one-layer GRU. This operator is usually supported via some custom
+implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`z` - update gate
+
+`r` - reset gate
+
+`h` - hidden gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh):
+
+  - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz)
+
+  - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr)
+
+  - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0
+
+  - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0
+
+  - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GRU,
+    3,
+    OpSchema()
+        .SetDoc(GRU_ver3_doc)
+        .Attr(
+            "activations",
+            "A list of 2 (or 4 if bidirectional) activation functions "
+            "for update, reset, and hidden gates. The activation functions must be one "
+            "of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "linear_before_reset",
+            "When computing the output of the hidden gate, "
+            "apply the linear transformation before multiplying by the output of the "
+            "reset gate.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and "
+            "`[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor "
+            "has shape `[num_directions, 6*hidden_size]`. Optional: If not specified "
+            "- assumed to be 0",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGenerator1("GRU")));
+
+static const char* LSTM_ver1_doc = R"DOC(
+Computes an one-layer LSTM. This operator is usually supported via some
+custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`o` - output gate
+
+`f` - forget gate
+
+`c` - cell gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+`P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+`PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+  - it = f(Xt*(Wi^T) + Ht-1*Ri + Pi (.) Ct-1 + Wbi + Rbi)
+
+  - ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf)
+
+  - ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc)
+
+  - Ct = ft (.) Ct-1 + it (.) ct
+
+  - ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo)
+
+  - Ht = ot (.) h(Ct)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LSTM,
+    1,
+    OpSchema()
+        .SetDoc(LSTM_ver1_doc)
+        .Attr(
+            "activations",
+            "A list of 3 (or 6 if bidirectional) activation functions "
+            "for input, output, forget, cell, and hidden. The activation functions must "
+            "be one of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "input_forget",
+            "Couple the input and forget gates if 1, default 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[iofc]` and "
+            "`WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape "
+            "`[num_directions, 4*hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[iofc]` and "
+            "`RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 4*hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, "
+            "and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This "
+            "tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not "
+            "specified - assumed to be 0.",
+            "T",
+            OpSchema::Optional)
+        .Input(
+            6,
+            "initial_c",
+            "Optional initial value of the cell. If not specified - assumed "
+            "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+            "T",
+            OpSchema::Optional)
+        .Input(
+            7,
+            "P",
+            "The weight tensor for peepholes. Concatenation of `P[iof]` and "
+            "`PB[iof]` (if bidirectional) along dimension 0. It has shape "
+            "`[num_directions, 3*hidde_size]`. Optional: If not specified - "
+            "assumed to be 0.",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGenerator1("LSTM"))
+        .Output(
+            2,
+            "Y_c",
+            "The last output value of the cell. It has shape "
+            "`[num_directions, batch_size, hidden_size]`.",
+            "T",
+            OpSchema::Optional));
+
+} // namespace ONNX_NAMESPACE
+
+// Versions 7 to 13 of RNN/LSTM/GRU
+
+namespace ONNX_NAMESPACE {
+void RNNShapeInference2(InferenceContext& ctx) {
+  TensorShapeProto::Dimension num_directions, seq_length, batch_size,
+      hidden_size;
+
+  auto direction = getAttribute(ctx, "direction", "forward");
+  if ((direction == "forward") || (direction == "reverse"))
+    num_directions.set_dim_value(1);
+  else if (direction == "bidirectional")
+    num_directions.set_dim_value(2);
+  // else leave num_directions unknown in case of incorrect attribute value
+
+  auto hidden_size_value = getAttribute(ctx, "hidden_size", -1);
+  if (hidden_size_value > 0)
+    hidden_size.set_dim_value(hidden_size_value);
+
+  if (hasInputShape(ctx, 0)) {
+    auto& first_input_shape = getInputShape(ctx, 0);
+    if (first_input_shape.dim_size() != 3) {
+      fail_shape_inference("First input tensor must have rank 3");
+    }
+    seq_length = first_input_shape.dim(0);
+    batch_size = first_input_shape.dim(1);
+  }
+
+  auto num_outputs = ctx.getNumOutputs();
+
+  if (num_outputs > 0) {
+    // Y
+    propagateElemTypeFromInputToOutput(ctx, 0, 0);
+    updateOutputShape(
+        ctx, 0, {seq_length, num_directions, batch_size, hidden_size});
+  }
+
+  if (num_outputs > 1) {
+    // Y_h
+    propagateElemTypeFromInputToOutput(ctx, 0, 1);
+    updateOutputShape(ctx, 1, {num_directions, batch_size, hidden_size});
+  }
+
+  if (num_outputs > 2) {
+    // Y_c : only in the case of LSTM
+    propagateElemTypeFromInputToOutput(ctx, 0, 2);
+    updateOutputShape(ctx, 2, {num_directions, batch_size, hidden_size});
+  }
+}
+
+std::function<void(OpSchema&)> RNNDocGenerator2(const char* /*name*/) {
+  return [=](OpSchema& schema) {
+    schema.Attr(
+        "direction",
+        "Specify if the RNN is forward, reverse, or bidirectional. "
+        "Must be one of forward (default), reverse, or bidirectional.",
+        AttributeProto::STRING,
+        std::string("forward"));
+    schema.Attr(
+        "hidden_size",
+        "Number of neurons in the hidden layer",
+        AttributeProto::INT,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_alpha",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM. Default values are the same as of corresponding ONNX operators."
+        "For example with LeakyRelu, the default alpha is 0.01.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "activation_beta",
+        "Optional scaling values used by some activation functions. The values "
+        "are consumed in the order of activation functions, for example (f, g, h) "
+        "in LSTM. Default values are the same as of corresponding ONNX operators.",
+        AttributeProto::FLOATS,
+        OPTIONAL_VALUE);
+    schema.Attr(
+        "clip",
+        "Cell clip threshold. Clipping bounds the elements of a tensor "
+        "in the range of [-threshold, +threshold] and is applied to the input "
+        "of activations. No clip if not specified.",
+        AttributeProto::FLOAT,
+        OPTIONAL_VALUE);
+    schema.Input(
+        0,
+        "X",
+        "The input sequences packed (and potentially padded) into one 3-D "
+        "tensor with the shape of `[seq_length, batch_size, input_size]`.",
+        "T");
+    schema.Input(
+        4,
+        "sequence_lens",
+        "Optional tensor specifying lengths of the sequences in a batch. "
+        "If not specified - assumed all sequences in the batch to have "
+        "length `seq_length`. It has shape `[batch_size]`.",
+        "T1",
+        OpSchema::Optional);
+    schema.Input(
+        5,
+        "initial_h",
+        "Optional initial value of the hidden. If not specified - assumed "
+        "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        0,
+        "Y",
+        "A tensor that concats all the intermediate output values of the hidden. "
+        "It has shape `[seq_length, num_directions, batch_size, hidden_size]`. ",
+        "T",
+        OpSchema::Optional);
+    schema.Output(
+        1,
+        "Y_h",
+        "The last output value of the hidden. It has shape "
+        "`[num_directions, batch_size, hidden_size]`.",
+        "T",
+        OpSchema::Optional);
+    schema.TypeConstraint(
+        "T",
+        {"tensor(float16)", "tensor(float)", "tensor(double)"},
+        "Constrain input and output types to float tensors.");
+    schema.TypeConstraint(
+        "T1", {"tensor(int32)"}, "Constrain seq_lens to integer tensor.");
+    schema.TypeAndShapeInferenceFunction(RNNShapeInference2);
+  };
+}
+
+static const char* RNN_ver7_doc = R"DOC(
+Computes an one-layer simple RNN. This operator is usually supported
+via some custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`t` - time step (t-1 means previous time step)
+
+`Wi` - W parameter weight matrix for input gate
+
+`Ri` - R recurrence weight matrix for input gate
+
+`Wbi` - W parameter bias vector for input gate
+
+`Rbi` - R parameter bias vector for input gate
+
+`WBi` - W parameter weight matrix for backward input gate
+
+`RBi` - R recurrence weight matrix for backward input gate
+
+`WBbi` - WR bias vectors for backward input gate
+
+`RBbi` - RR bias vectors for backward input gate
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Tanh):
+
+  - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    RNN,
+    7,
+    OpSchema()
+        .SetDoc(RNN_ver7_doc + GenerateOptionalArgumentsDoc())
+        .Attr(
+            "activations",
+            "One (or two if bidirectional) activation function for "
+            "input gate. The activation function must be one of the activation "
+            "functions specified above. Optional: Default `Tanh` if not specified.",
+            AttributeProto::STRINGS,
+            std::vector<std::string>{"Tanh", "Tanh"})
+        .Input(
+            1,
+            "W",
+            "The weight tensor for input gate. Concatenation of `Wi` and `WBi` "
+            "(if bidirectional). The tensor has shape "
+            "`[num_directions, hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `Ri` and `RBi` "
+            "(if bidirectional). The tensor has shape "
+            "`[num_directions, hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` "
+            "and `[WBbi, RBbi]` (if bidirectional). The tensor has shape "
+            "`[num_directions, 2*hidden_size]`. Optional: If not specified - assumed "
+            "to be 0.",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGenerator2("RNN")));
+
+static const char* GRU_ver7_doc = R"DOC(
+Computes an one-layer GRU. This operator is usually supported via some custom
+implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`z` - update gate
+
+`r` - reset gate
+
+`h` - hidden gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh):
+
+  - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
+
+  - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
+
+  - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
+
+  - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
+
+  - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GRU,
+    7,
+    OpSchema()
+        .SetDoc(GRU_ver7_doc + GenerateOptionalArgumentsDoc())
+        .Attr(
+            "activations",
+            "A list of 2 (or 4 if bidirectional) activation functions "
+            "for update, reset, and hidden gates. The activation functions must be one "
+            "of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "linear_before_reset",
+            "When computing the output of the hidden gate, "
+            "apply the linear transformation before multiplying by the output of the "
+            "reset gate.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` "
+            "(if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 3*hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and "
+            "`[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor "
+            "has shape `[num_directions, 6*hidden_size]`. Optional: If not specified "
+            "- assumed to be 0",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGenerator2("GRU")));
+
+static const char* LSTM_ver7_doc = R"DOC(
+Computes an one-layer LSTM. This operator is usually supported via some
+custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`o` - output gate
+
+`f` - forget gate
+
+`c` - cell gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+`P[iof]`  - P peephole weight vector for input, output, and forget gates
+
+`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+`PB[iof]`  - P peephole weight vector for backward input, output, and forget gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+  Relu(x)                - max(0, x)
+
+  Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})
+
+  Sigmoid(x)             - 1/(1 + e^{-x})
+
+  (NOTE: Below are optional)
+
+  Affine(x)              - alpha*x + beta
+
+  LeakyRelu(x)           - x if x >= 0 else alpha * x
+
+  ThresholdedRelu(x)     - x if x >= alpha else 0
+
+  ScaledTanh(x)          - alpha*Tanh(beta*x)
+
+  HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)
+
+  Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)
+
+  Softsign(x)            - x/(1 + |x|)
+
+  Softplus(x)            - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+  - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
+
+  - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
+
+  - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+
+  - Ct = ft (.) Ct-1 + it (.) ct
+
+  - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
+
+  - Ht = ot (.) h(Ct)
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    LSTM,
+    7,
+    OpSchema()
+        .SetDoc(LSTM_ver7_doc + GenerateOptionalArgumentsDoc())
+        .Attr(
+            "activations",
+            "A list of 3 (or 6 if bidirectional) activation functions "
+            "for input, output, forget, cell, and hidden. The activation functions must "
+            "be one of the activation functions specified above. Optional: See the equations "
+            "for default if not specified.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "input_forget",
+            "Couple the input and forget gates if 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            1,
+            "W",
+            "The weight tensor for the gates. Concatenation of `W[iofc]` and "
+            "`WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape "
+            "`[num_directions, 4*hidden_size, input_size]`.",
+            "T")
+        .Input(
+            2,
+            "R",
+            "The recurrence weight tensor. Concatenation of `R[iofc]` and "
+            "`RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape "
+            "`[num_directions, 4*hidden_size, hidden_size]`.",
+            "T")
+        .Input(
+            3,
+            "B",
+            "The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, "
+            "and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This "
+            "tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not "
+            "specified - assumed to be 0.",
+            "T",
+            OpSchema::Optional)
+        .Input(
+            6,
+            "initial_c",
+            "Optional initial value of the cell. If not specified - assumed "
+            "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.",
+            "T",
+            OpSchema::Optional)
+        .Input(
+            7,
+            "P",
+            "The weight tensor for peepholes. Concatenation of `P[iof]` and "
+            "`PB[iof]` (if bidirectional) along dimension 0. It has shape "
+            "`[num_directions, 3*hidde_size]`. Optional: If not specified - "
+            "assumed to be 0.",
+            "T",
+            OpSchema::Optional)
+        .FillUsing(RNNDocGenerator2("LSTM"))
+        .Output(
+            2,
+            "Y_c",
+            "The last output value of the cell. It has shape "
+            "`[num_directions, batch_size, hidden_size]`.",
+            "T",
+            OpSchema::Optional));
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/schema.cc b/third_party/onnx/onnx/defs/schema.cc
new file mode 100644
index 0000000000..89c1decbc1
--- /dev/null
+++ b/third_party/onnx/onnx/defs/schema.cc
@@ -0,0 +1,956 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/schema.h"
+#include <stdexcept>
+#include <unordered_set>
+#include "onnx/checker.h"
+#include "onnx/defs/operator_sets.h"
+#include "onnx/defs/operator_sets_preview.h"
+#include "onnx/defs/operator_sets_training.h"
+
+#ifdef ONNX_ML
+#include "onnx/defs/operator_sets_ml.h"
+#endif
+
+#include "onnx/common/assertions.h"
+#include "onnx/common/stl_backports.h"
+#include "onnx/defs/parser.h"
+
+namespace ONNX_NAMESPACE {
+// -1 means ONNX schema hasn't been loaded yet
+// 0 means all versions of ONNX schema have been loaded
+// Other positive integer means the ONNX schemas for the specified version have been loaded
+int OpSchemaRegistry::loaded_schema_version = -1;
+
+// By default if opset_version_to_load=0, it registers all opset schema for all opset versions
+// Otherwise, it only registers the latest schema according to opset_version_to_load
+void RegisterSchema(OpSchema schema, int opset_version_to_load) {
+  OpSchemaRegistry::OpSchemaRegisterOnce ONNX_UNUSED registration(schema, opset_version_to_load);
+}
+
+#ifndef NDEBUG
+DbgOperatorSetTracker& DbgOperatorSetTracker::Instance() {
+  static DbgOperatorSetTracker instance;
+  return instance;
+}
+#endif
+
+const std::string& OpSchema::FormalParameter::GetName() const {
+  return name_;
+}
+
+const DataTypeSet& OpSchema::FormalParameter::GetTypes() const {
+  return type_set_;
+}
+
+DataTypeSet& OpSchema::FormalParameter::MutableTypes() {
+  return type_set_;
+}
+
+const std::string& OpSchema::FormalParameter::GetTypeStr() const {
+  return type_str_;
+}
+
+const std::string& OpSchema::FormalParameter::GetDescription() const {
+  return description_;
+}
+
+OpSchema::FormalParameterOption OpSchema::FormalParameter::GetOption() const {
+  return param_option_;
+}
+
+bool OpSchema::FormalParameter::GetIsHomogeneous() const {
+  return is_homogeneous_;
+}
+
+int OpSchema::FormalParameter::GetMinArity() const {
+  return min_arity_;
+}
+
+OpSchema::DifferentiationCategory OpSchema::FormalParameter::GetDifferentiationCategory() const {
+  return differentiation_category_;
+}
+
+OpSchemaRegistry* OpSchemaRegistry::Instance() {
+  static OpSchemaRegistry instance;
+  return &instance;
+}
+
+void OpSchema::CheckInputOutputType(struct InferenceContext& ctx) const {
+  std::unordered_map<std::string, std::string> type_constraints;
+  // check all input types
+  for (size_t in_idx = 0; in_idx < ctx.getNumInputs() && in_idx < inputs_.size(); ++in_idx) {
+    const auto& param = inputs_[in_idx];
+    const auto& type_str = param.GetTypeStr();
+    const auto& param_type = ctx.getInputType(in_idx);
+    const auto& all_types = param.GetTypes();
+    if (nullptr == param_type || param_type->value_case() == TypeProto::VALUE_NOT_SET) {
+      continue;
+    } else if (!all_types.empty() && all_types.find(Utils::DataTypeUtils::ToType(*param_type)) == all_types.end()) {
+      fail_check(
+          param.GetName(),
+          " typestr: ",
+          type_str,
+          ", has unsupported type: ",
+          *Utils::DataTypeUtils::ToType(*param_type));
+    }
+    if (param.GetIsHomogeneous()) {
+      const auto& type_proto = Utils::DataTypeUtils::ToType(*param_type);
+      auto p = type_constraints.emplace(type_str, *type_proto);
+      if (!p.second) {
+        // failed to insert a new element due to a duplication, now check consistency
+        if (p.first->second != *type_proto) {
+          fail_check(param.GetName(), " has inconsistent type ", *Utils::DataTypeUtils::ToType(*param_type));
+        }
+      }
+    }
+  } // for inputs
+  // check all output types
+  for (size_t out_idx = 0; out_idx < ctx.getNumOutputs() && out_idx < outputs_.size(); ++out_idx) {
+    const auto& param = outputs_[out_idx];
+    const auto& type_str = param.GetTypeStr();
+    const auto& param_type = ctx.getOutputType(out_idx);
+    const auto& all_types = param.GetTypes();
+    bool output_type_found = true;
+    // infer type if necessary
+    if (param_type->value_case() == TypeProto::VALUE_NOT_SET) {
+      if (all_types.size() == 1) {
+        *param_type = Utils::DataTypeUtils::ToTypeProto(*all_types.begin());
+      } else if (type_constraints.find(type_str) != type_constraints.end()) {
+        auto data_type = Utils::DataTypeUtils::ToType(type_constraints[type_str]);
+        *param_type = Utils::DataTypeUtils::ToTypeProto(data_type);
+      } else {
+        output_type_found = false;
+      }
+    }
+    if (!output_type_found) {
+      continue;
+    }
+    if (!all_types.empty() && all_types.find(Utils::DataTypeUtils::ToType(*param_type)) == all_types.end()) {
+      fail_check(param.GetName(), " has unsupported type ", *Utils::DataTypeUtils::ToType(*param_type));
+    }
+    if (param.GetIsHomogeneous()) {
+      const auto& type_proto = Utils::DataTypeUtils::ToType(*param_type);
+      if (type_constraints.find(type_str) == type_constraints.end()) {
+        type_constraints[type_str] = *type_proto;
+      } else if (type_constraints[type_str] != *type_proto) {
+        fail_check(param.GetName(), " has inconsistent type ", *Utils::DataTypeUtils::ToType(*param_type));
+      }
+    } // else
+  } // for outputs
+}
+
+void OpSchema::Verify(const NodeProto& node) const {
+  if (deprecated_) {
+    fail_check("Operator '", name_, "' has been deprecated since version ", since_version_);
+  }
+
+  // Check the number of inputs.
+  if (node.input_size() < min_input_ || node.input_size() > max_input_) {
+    fail_check(
+        "Node (",
+        node.name(),
+        ") has input size ",
+        node.input_size(),
+        " not in range [min=",
+        min_input_,
+        ", max=",
+        max_input_,
+        "].");
+  }
+
+  if (!num_inputs_allowed_(node.input_size())) {
+    fail_check("Node (", node.name(), ") has input size ", node.input_size(), " not in allowed input sizes.");
+  }
+
+  // Check the number of outputs.
+  if (node.output_size() < min_output_ || node.output_size() > max_output_) {
+    fail_check(
+        "Node (",
+        node.name(),
+        ") has output size ",
+        node.output_size(),
+        " not in range [min=",
+        min_output_,
+        ", max=",
+        max_output_,
+        "].");
+  }
+
+  if (!num_outputs_allowed_(node.output_size())) {
+    fail_check("Node (", node.name(), "has output size ", node.output_size(), " not in allowed output sizes.");
+  }
+
+  // Check the values of inputs / outputs
+  for (int in_idx = 0; in_idx < node.input_size(); ++in_idx) {
+    if (in_idx >= static_cast<int>(inputs_.size())) {
+      if (inputs_.empty() || Variadic != inputs_.back().GetOption()) {
+        fail_check(
+            "Node (",
+            node.name(),
+            ") has more inputs (",
+            node.input_size(),
+            ") than declared (",
+            inputs_.size(),
+            ") in op definition.");
+      }
+    }
+    if ((in_idx >= static_cast<int>(inputs_.size()) && Variadic == inputs_.back().GetOption()) ||
+      Variadic == inputs_[in_idx].GetOption()) {
+      do {
+        if (node.input(in_idx).empty()) {
+          fail_check(
+              "Node (", node.name(), ")'s input ", in_idx, " is marked Variadic but has an empty string in the graph");
+        }
+      } while (++in_idx < node.input_size());
+    } else if (node.input(in_idx).empty() && (Single == inputs_[in_idx].GetOption())) {
+      fail_check("Node (", node.name(), ")'s input ", in_idx, " is marked single but has an empty string in the graph");
+    }
+  }
+
+  for (int out_idx = 0; out_idx < node.output_size(); ++out_idx) {
+    if (out_idx >= static_cast<int>(outputs_.size())) {
+      if (outputs_.empty() || Variadic != outputs_.back().GetOption()) {
+        fail_check(
+            "Node (",
+            node.name(),
+            ") has more outputs (",
+            node.output_size(),
+            ") than declared (",
+            outputs_.size(),
+            ") in op definition.");
+      }
+    }
+    if ((out_idx >= static_cast<int>(outputs_.size()) && Variadic == outputs_.back().GetOption()) ||
+      Variadic == outputs_[out_idx].GetOption()) {
+      do {
+        if (node.output(out_idx).empty()) {
+          fail_check(
+              "Node (", node.name(), ")'s output ", out_idx, " is marked Variadic but has an empty string in the graph");
+        }
+      } while (++out_idx < node.output_size());
+    } else if (node.output(out_idx).empty() && (Single == outputs_[out_idx].GetOption())) {
+      fail_check(
+          "Node (", node.name(), ")'s output ", out_idx, " is marked single but has an empty string in the graph");
+    }
+  }
+
+  // An internal symbol is defined as starting with two underscores. Attributes
+  // with names meeting this condition are considered implementation details
+  // and should be ignored for the purpose of schema checking.
+  auto isInternalSymbol = [](const std::string& sym) -> bool {
+    return sym.length() >= 2 && sym[0] == '_' && sym[1] == '_';
+  };
+
+  // Check attributes
+  std::unordered_set<std::string> seen_attr_names{};
+  for (const auto& attr_proto : node.attribute()) {
+    const auto& name = attr_proto.name();
+
+    if (!seen_attr_names.insert(name).second) {
+      fail_check("Attribute '", name, "' appeared multiple times.");
+    };
+
+    const auto& search = attributes_.find(name);
+    AttributeProto::AttributeType expected_type;
+    if (search != attributes_.end()) {
+      expected_type = search->second.type;
+    } else if (allows_unchecked_attributes_ || isInternalSymbol(name)) {
+      continue;
+    } else {
+      fail_check("Unrecognized attribute: ", name, " for operator ", node.op_type());
+    }
+
+    // Type would be UNDEFINED if not set
+    if (attr_proto.type() != expected_type) {
+      fail_check("Mismatched attribute type in '", node.name() + " : " + name, "'");
+    }
+
+    // ref_attr_name is only valid when non-empty
+    // we simply read default value if not present
+    if (!attr_proto.ref_attr_name().empty()) {
+      continue;
+    }
+
+    switch (expected_type) {
+      // if attr_proto().type() != UNDEFINED
+      // we consider primitive types to be set even
+      // if proto3 did not output default values into the stream
+      // in which case we will read the default
+      case AttributeProto::FLOAT:
+      case AttributeProto::INT:
+      case AttributeProto::STRING:
+        break;
+      case AttributeProto::TENSOR:
+        if (!attr_proto.has_t()) {
+          fail_check("Attribute '", name, "' is expected to have field 't'");
+        }
+        break;
+      case AttributeProto::SPARSE_TENSOR:
+        if (!attr_proto.has_sparse_tensor()) {
+          fail_check("Attribute '", name, "' is expected to have field 'sparse_tensor'");
+        }
+        break;
+      case AttributeProto::GRAPH:
+        if (!attr_proto.has_g()) {
+          fail_check("Attribute '", name, "' is expected to have field 'g'");
+        }
+        break;
+      case AttributeProto::TYPE_PROTO:
+        if (!attr_proto.has_tp()) {
+          fail_check("Attribute '", name, "' is expected to have field 'type_proto'");
+        }
+        break;
+      case AttributeProto::FLOATS:
+        if (!attr_proto.floats_size()) {
+          fail_check("Attribute '", name, "' is expected to have field 'floats'");
+        }
+        break;
+      case AttributeProto::INTS:
+        if (!attr_proto.ints_size()) {
+          fail_check("Attribute '", name, "' is expected to have field 'ints'");
+        }
+        break;
+      case AttributeProto::STRINGS:
+        if (!attr_proto.strings_size()) {
+          fail_check("Attribute '", name, "' is expected to have field 'strings'");
+        }
+        break;
+      case AttributeProto::TENSORS:
+        if (!attr_proto.tensors_size()) {
+          fail_check("Attribute '", name, "' is expected to have field 'tensors'");
+        }
+        break;
+      case AttributeProto::SPARSE_TENSORS:
+        // Not adding check ... we should likely delete the check in all other
+        // cases, which will not allow us to have an empty list as a valid value
+        // for an attribute and this seems undesirable.
+        break;
+      case AttributeProto::GRAPHS:
+        if (!attr_proto.graphs_size()) {
+          fail_check("Attribute '", name, "' is expected to have field 'graphs'");
+        }
+        break;
+      case AttributeProto::TYPE_PROTOS:
+        if (!attr_proto.type_protos_size()) {
+          fail_check("Attribute '", name, "' is expected to have field 'type_protos'");
+        }
+        break;
+      default:
+        fail_check("Attribute '", name, " has unknown expected type");
+    }
+  }
+  for (const auto& pair : attributes_) {
+    const auto& attr = pair.second;
+    if (!attr.required) {
+      continue;
+    }
+    if (!seen_attr_names.count(attr.name)) {
+      fail_check("Required attribute '", attr.name, "' is missing.");
+    }
+  }
+
+  // Phew. All verifications passed.
+}
+
+OpSchema& OpSchema::SinceVersion(OperatorSetVersion v) {
+  since_version_ = v;
+  return *this;
+}
+
+OpSchema& OpSchema::Deprecate() {
+  deprecated_ = true;
+  return *this;
+}
+
+OpSchema& OpSchema::NumInputs(std::set<int> allowed_input_nums) {
+  num_inputs_allowed_ = [MOVE_CAPTURE_IF_CPP14(allowed_input_nums)](int n) -> bool {
+    return allowed_input_nums.count(n);
+  };
+  return *this;
+}
+
+OpSchema& OpSchema::NumOutputs(std::set<int> allowed_output_nums) {
+  num_outputs_allowed_ = [MOVE_CAPTURE_IF_CPP14(allowed_output_nums)](int n) -> bool {
+    return allowed_output_nums.count(n) > 0;
+  };
+  return *this;
+}
+
+OpSchema& OpSchema::TypeAndShapeInferenceFunction(InferenceFunction inferenceFunction) {
+  tensor_inference_function_ = std::move(inferenceFunction);
+  return *this;
+}
+
+OpSchema& OpSchema::PartialDataPropagationFunction(DataPropagationFunction dataPropagationFunction) {
+  data_propagation_function_ = std::move(dataPropagationFunction);
+  return *this;
+}
+
+OpSchema& OpSchema::SetSupportLevel(SupportType support) {
+  support_ = support;
+  return *this;
+}
+
+// Functions to specify name for the operator schema.
+OpSchema& OpSchema::SetName(std::string name) {
+  name_ = std::move(name);
+  return *this;
+}
+
+OpSchema& OpSchema::SetName(const char* name) {
+  return SetName(std::string(name));
+}
+
+// Functions to specify code location for the operator schema.
+OpSchema& OpSchema::SetLocation(std::string file, int line) {
+  file_ = std::move(file);
+  line_ = line;
+  return *this;
+}
+
+OpSchema& OpSchema::SetLocation(const char* file, int line) {
+  return SetLocation(std::string(file), line);
+}
+
+OpSchema& OpSchema::SetDomain(std::string domain) {
+  domain_ = std::move(domain);
+  return *this;
+}
+
+OpSchema& OpSchema::SetDomain(const char* domain) {
+  return SetDomain(std::string(domain));
+}
+
+OpSchema& OpSchema::Attr(Attribute attr) {
+  auto name = attr.name; // copy name so we can move attr in the next line
+  attributes_.insert(std::make_pair(std::move(name), std::move(attr)));
+  return *this;
+}
+
+OpSchema& OpSchema::Attr(std::string name, std::string description, AttributeProto::AttributeType type, bool required) {
+  Attr(Attribute{std::move(name), std::move(description), type, required});
+  return *this;
+}
+
+OpSchema& OpSchema::Attr(const char* name, const char* description, AttributeProto::AttributeType type, bool required) {
+  return Attr(std::string(name), std::string(description), type, required);
+}
+
+#define ATTR_SETTER_WITH_SINGLE_VALUE(type, field, attrtype)                                                           \
+  OpSchema& OpSchema::Attr(                                                                                            \
+      std::string name, std::string description, AttributeProto::AttributeType attr_type, const type& default_value) { \
+    if (attrtype != attr_type) {                                                                                       \
+      fail_schema("Attribute specification type mismatch.");                                                           \
+    }                                                                                                                  \
+    AttributeProto a;                                                                                                  \
+    a.set_name(name);                                                                                                  \
+    a.set_##field(default_value);                                                                                      \
+    a.set_type(attr_type);                                                                                             \
+    Attr(Attribute(std::move(name), std::move(description), std::move(a)));                                            \
+    return *this;                                                                                                      \
+  }                                                                                                                    \
+  OpSchema& OpSchema::Attr(                                                                                            \
+      const char* name, const char* description, AttributeProto::AttributeType attr_type, const type& default_value) { \
+    return Attr(std::string(name), std::string(description), attr_type, default_value);                                \
+  }
+
+#define ATTR_SETTER_WITH_LIST_VALUE(type, field, attrtype)                  \
+  OpSchema& OpSchema::Attr(                                                 \
+      std::string name,                                                     \
+      std::string description,                                              \
+      AttributeProto::AttributeType attr_type,                              \
+      const std::vector<type>& default_value) {                             \
+    if (attrtype != attr_type) {                                            \
+      fail_schema("Attribute specification type mismatch.");                \
+    }                                                                       \
+    AttributeProto a;                                                       \
+    a.set_name(name);                                                       \
+    a.set_type(attr_type);                                                  \
+    for (const auto& v : default_value) {                                   \
+      a.add_##field(v);                                                     \
+    }                                                                       \
+    Attr(Attribute(std::move(name), std::move(description), std::move(a))); \
+    return *this;                                                           \
+  }
+
+#define ATTR_SETTER_WITH_SINGLE_COMPLEXVALUE(type, field, attrtype)                                                    \
+  OpSchema& OpSchema::Attr(                                                                                            \
+      std::string name, std::string description, AttributeProto::AttributeType attr_type, const type& default_value) { \
+    if (attrtype != attr_type) {                                                                                       \
+      fail_schema("Attribute specification type mismatch.");                                                           \
+    }                                                                                                                  \
+    AttributeProto a;                                                                                                  \
+    a.set_name(name);                                                                                                  \
+    *(a.mutable_##field()) = default_value;                                                                            \
+    a.set_type(attr_type);                                                                                             \
+    Attr(Attribute(std::move(name), std::move(description), a));                                                       \
+    return *this;                                                                                                      \
+  }
+
+#define ATTR_SETTER_WITH_LIST_COMPLEXVALUE(type, field, attrtype)           \
+  OpSchema& OpSchema::Attr(                                                 \
+      std::string name,                                                     \
+      std::string description,                                              \
+      AttributeProto::AttributeType attr_type,                              \
+      const std::vector<type>& default_value) {                             \
+    if (attrtype != attr_type) {                                            \
+      fail_schema("Attribute specification type mismatch.");                \
+    }                                                                       \
+    AttributeProto a;                                                       \
+    a.set_name(name);                                                       \
+    a.set_type(attr_type);                                                  \
+    for (const auto& v : default_value) {                                   \
+      *(a.add_##field()) = v;                                               \
+    }                                                                       \
+    Attr(Attribute(std::move(name), std::move(description), std::move(a))); \
+    return *this;                                                           \
+  }
+
+ATTR_SETTER_WITH_SINGLE_VALUE(int64_t, i, AttributeProto::INT)
+ATTR_SETTER_WITH_SINGLE_VALUE(float, f, AttributeProto::FLOAT)
+ATTR_SETTER_WITH_SINGLE_VALUE(std::string, s, AttributeProto::STRING)
+ATTR_SETTER_WITH_SINGLE_COMPLEXVALUE(TensorProto, t, AttributeProto::TENSOR)
+ATTR_SETTER_WITH_SINGLE_COMPLEXVALUE(GraphProto, g, AttributeProto::GRAPH)
+ATTR_SETTER_WITH_SINGLE_COMPLEXVALUE(TypeProto, tp, AttributeProto::TYPE_PROTO)
+ATTR_SETTER_WITH_LIST_VALUE(int64_t, ints, AttributeProto::INTS)
+ATTR_SETTER_WITH_LIST_VALUE(float, floats, AttributeProto::FLOATS)
+ATTR_SETTER_WITH_LIST_COMPLEXVALUE(std::string, strings, AttributeProto::STRINGS)
+ATTR_SETTER_WITH_LIST_COMPLEXVALUE(TensorProto, tensors, AttributeProto::TENSORS)
+ATTR_SETTER_WITH_LIST_COMPLEXVALUE(GraphProto, graphs, AttributeProto::GRAPHS)
+ATTR_SETTER_WITH_LIST_COMPLEXVALUE(TypeProto, type_protos, AttributeProto::TYPE_PROTOS)
+
+OpSchema& OpSchema::AllowUncheckedAttributes() {
+  allows_unchecked_attributes_ = true;
+  return *this;
+}
+
+OpSchema& OpSchema::Input(
+    int n,
+    std::string name,
+    const std::string& description,
+    std::string type_str,
+    OpSchema::FormalParameterOption param_option,
+    bool is_homogeneous,
+    int min_arity,
+    DifferentiationCategory differentiation_category) {
+  if (int(inputs_.size()) <= n) {
+    inputs_.resize(n + 1);
+  }
+  inputs_[n] = FormalParameter(
+      std::move(name),
+#ifndef __ONNX_NO_DOC_STRINGS
+      description,
+#else
+      std::string(),
+#endif
+      std::move(type_str),
+      param_option,
+      is_homogeneous,
+      min_arity,
+      differentiation_category);
+  return *this;
+}
+
+OpSchema& OpSchema::Input(
+    int n,
+    const char* name,
+    const char* description,
+    const char* type_str,
+    FormalParameterOption param_option,
+    bool is_homogeneous,
+    int min_arity,
+    DifferentiationCategory differentiation_category) {
+  return Input(
+      n,
+      std::string(name),
+#ifndef __ONNX_NO_DOC_STRINGS
+      std::string(description),
+#else
+      std::string(),
+#endif
+      std::string(type_str),
+      param_option,
+      is_homogeneous,
+      min_arity,
+      differentiation_category);
+}
+
+OpSchema& OpSchema::Output(
+    int n,
+    std::string name,
+    const std::string& description,
+    std::string type_str,
+    OpSchema::FormalParameterOption param_option,
+    bool is_homogeneous,
+    int min_arity,
+    DifferentiationCategory differentiation_category) {
+  if (int(outputs_.size()) <= n) {
+    outputs_.resize(n + 1);
+  }
+  outputs_[n] = FormalParameter(
+      std::move(name),
+#ifndef __ONNX_NO_DOC_STRINGS
+      description,
+#else
+      std::string(),
+#endif
+      std::move(type_str),
+      param_option,
+      is_homogeneous,
+      min_arity,
+      differentiation_category);
+  return *this;
+}
+
+OpSchema& OpSchema::Output(
+    int n,
+    const char* name,
+    const char* description,
+    const char* type_str,
+    FormalParameterOption param_option,
+    bool is_homogeneous,
+    int min_arity,
+    DifferentiationCategory differentiation_category) {
+  return Output(
+      n,
+      std::string(name),
+#ifndef __ONNX_NO_DOC_STRINGS
+      std::string(description),
+#else
+      std::string(),
+#endif
+      std::string(type_str),
+      param_option,
+      is_homogeneous,
+      min_arity,
+      differentiation_category);
+}
+
+OpSchema&
+OpSchema::TypeConstraint(std::string type_str, std::vector<std::string> constraints, std::string description) {
+  if (type_constraints_.end() != type_constraints_.find(type_str)) {
+    fail_schema("Duplicate type constraint name");
+  }
+
+  DataTypeSet d;
+  for (const auto& t : constraints) {
+    d.insert(Utils::DataTypeUtils::ToType(t));
+  }
+  type_constraints_.insert(std::make_pair(type_str, std::make_pair(d, description)));
+  type_constraint_params_.push_back(
+      TypeConstraintParam(std::move(type_str), std::move(constraints), std::move(description)));
+  return *this;
+}
+
+OpSchema& OpSchema::TypeConstraint(
+    const char* type_str,
+    std::initializer_list<const char*> constraints,
+    const char* description) {
+  std::vector<std::string> constraints_vector;
+  constraints_vector.reserve(constraints.size());
+  for (auto iter = constraints.begin(); iter != constraints.end(); ++iter) {
+    constraints_vector.push_back(*iter);
+  }
+
+  return TypeConstraint(std::string(type_str), constraints_vector, std::string(description));
+}
+
+void OpSchema::ParseAndSetTypes(
+    /*out*/ std::vector<OpSchema::FormalParameter>* formal_parameters) {
+  for (auto& formal_parameter : *formal_parameters) {
+    auto& type = formal_parameter.GetTypeStr();
+    DataTypeSet allowed_types;
+    auto it = type_constraints_.find(type);
+    if (it != type_constraints_.end()) {
+      allowed_types = it->second.first;
+    } else {
+      allowed_types.emplace(Utils::DataTypeUtils::ToType(type));
+    }
+
+    formal_parameter.MutableTypes() = allowed_types;
+  }
+}
+
+OpSchema& OpSchema::SetContextDependentFunctionBodyBuilder(ContextDependentFunctionBodyBuilder functionBuilder) {
+  functionBuilder_ = std::move(functionBuilder);
+  return *this;
+}
+
+bool OpSchema::BuildContextDependentFunction(const FunctionBodyBuildContext& ctx, FunctionProto& functionProto) const {
+  if (functionBuilder_)
+    return functionBuilder_(ctx, *this, functionProto);
+  else
+    return false;
+}
+
+OpSchema& OpSchema::FunctionBody(const char* func_body) {
+  OnnxParser parser(func_body);
+  auto status = parser.Parse(*function_body_.mutable_node());
+  if (!status.IsOK())
+    ONNX_THROW_EX(std::logic_error("Error parsing function body:" + status.ErrorMessage()));
+  if (!parser.EndOfInput())
+    ONNX_THROW_EX(std::logic_error("Extra unparsed input unexpected."));
+  return *this;
+}
+
+OpSchema& OpSchema::FunctionBody(const std::vector<NodeProto>& func_nodes) {
+  for (const auto& node : func_nodes) {
+    auto new_node = function_body_.add_node();
+    new_node->CopyFrom(node);
+  }
+  return *this;
+}
+
+OpSchema& OpSchema::FunctionBody(
+    const std::vector<NodeProto>& func_nodes,
+    const std::vector<OperatorSetIdProto>& relied_opsets) {
+  for (auto& relied_opset : relied_opsets) {
+    *(function_body_.mutable_opset_import()->Add()) = relied_opset;
+  }
+
+  return FunctionBody(func_nodes);
+}
+
+const FunctionProto* OpSchema::GetFunction() const {
+  return function_body_.node_size() > 0 ? &function_body_ : nullptr;
+}
+
+OpSchema& OpSchema::FillUsing(const std::function<void(OpSchema&)>& populator) {
+  if (populator) {
+    populator(*this);
+  }
+  return *this;
+}
+
+void OpSchema::BuildFunction(FunctionProto& function_body) const {
+  function_body.set_name(this->name_);
+  function_body.set_doc_string(this->doc_);
+  function_body.set_domain(this->domain_);
+  for (auto& i : inputs_) {
+    function_body.add_input(i.GetName());
+  }
+  for (auto& o : outputs_) {
+    function_body.add_output(o.GetName());
+  }
+  for (auto& a : attributes_) {
+    function_body.add_attribute(a.first);
+  }
+
+  // In a typical onnx function where the function and all the
+  // ops in function body belong to the same domain we implicitly add
+  // {domain_, since_version_} to funciton opset imports if it is not already added.
+  // This is simply for convienince. If any of the function body ops do not belong to same
+  // domain as function itself, then the function author needs to explicitly add all the relevant
+  // opset imports.
+  if (function_body.opset_import().size() == 0) {
+    auto* schema_opset = function_body.mutable_opset_import()->Add();
+    schema_opset->set_domain(domain_);
+    schema_opset->set_version(since_version_);
+  }
+}
+
+void OpSchema::Finalize() {
+#define ENFORCE(x)                                                                                      \
+  do {                                                                                                  \
+    if (!(x))                                                                                           \
+      ONNX_THROW_EX(std::logic_error("ONNX Schema " + name_ + ": failed validating the check: " + #x)); \
+  } while (0)
+
+  // Calculate min/max number of inputs.
+  // <Min number of inputs> = <number of "single" inputs> + <number of
+  // "optional" but not trailing inputs>. <Max number of inputs> = <number of
+  // all inputs or std::numeric_limits<int>::max() (if the last input is
+  // variadic).
+
+  // Flag indicates whether an optional input is trailing one (there's no single
+  // or variadic input behind).
+  for (size_t i = 0; i < inputs_.size(); ++i) {
+    switch (inputs_[i].GetOption()) {
+      case OpSchema::Single:
+        ++max_input_;
+        min_input_ = max_input_;
+        break;
+      case OpSchema::Optional:
+        ++max_input_;
+        break;
+      case OpSchema::Variadic:
+        // Only last input formal parameter could be variadic.
+        ENFORCE((inputs_.size() - 1) == i);
+        min_input_ = max_input_ + inputs_[i].GetMinArity();
+        max_input_ = std::numeric_limits<int>::max();
+        break;
+    }
+  }
+
+  // Calculate min/max number of outputs.
+  for (size_t i = 0; i < outputs_.size(); ++i) {
+    switch (outputs_[i].GetOption()) {
+      case OpSchema::Single:
+        ++max_output_;
+        min_output_ = max_output_;
+        break;
+      case OpSchema::Optional:
+        ++max_output_;
+        break;
+      case OpSchema::Variadic:
+        // Only last output formal parameter could be variadic.
+        ENFORCE((outputs_.size() - 1) == i);
+        min_output_ = max_output_ + outputs_[i].GetMinArity();
+        max_output_ = std::numeric_limits<int>::max();
+        break;
+    }
+  }
+
+  // all inputs and outputs have names
+  for (const auto& it : inputs_) {
+    ENFORCE(!(it.GetName().empty()));
+  }
+  for (const auto& it : outputs_) {
+    ENFORCE(!(it.GetName().empty()));
+  }
+
+  ParseAndSetTypes(&inputs_);
+  ParseAndSetTypes(&outputs_);
+
+  if (this->HasFunction()) {
+    BuildFunction(function_body_);
+  }
+}
+
+std::ostream& operator<<(std::ostream& out, const OpSchema& schema) {
+  if (!schema.attributes_.empty()) {
+    out << "Attributes:" << std::endl;
+    for (const auto& pair : schema.attributes_) {
+      out << "  " << pair.second.name << " : " << pair.second.description << std::endl;
+    }
+  }
+  if (schema.max_input_ > 0) {
+    out << "Inputs:" << std::endl;
+    if (!schema.inputs_.empty()) {
+      for (size_t i = 0; i < schema.inputs_.size(); ++i) {
+        const auto& p = schema.inputs_[i];
+        const auto& name = p.GetName();
+        const auto& description = p.GetDescription();
+        const auto& type_str = p.GetTypeStr();
+        out << "  " << i << ", " << (!name.empty() ? name : "(unnamed)") << " : "
+            << (!description.empty() ? description : "(no doc)") << " : "
+            << (!type_str.empty() ? type_str : "(no type)") << std::endl;
+      }
+    } else {
+      out << "  (no explicit description available)" << std::endl;
+    }
+  }
+  if (schema.max_output_ > 0) {
+    out << "Outputs:" << std::endl;
+    if (!schema.outputs_.empty()) {
+      for (size_t i = 0; i < schema.outputs_.size(); ++i) {
+        const auto& p = schema.outputs_[i];
+        const auto& name = p.GetName();
+        const auto& description = p.GetDescription();
+        const auto& type_str = p.GetTypeStr();
+        out << "  " << i << ", " << (!name.empty() ? name : "(unnamed)") << " : "
+            << (!description.empty() ? description : "(no doc)") << " : "
+            << (!type_str.empty() ? type_str : "(no type)") << std::endl;
+      }
+    } else {
+      out << "  (no explicit description available)" << std::endl;
+    }
+  }
+  out << std::endl;
+  if (schema.doc()) {
+    out << schema.doc();
+  } else {
+    out << "(no documentation yet)" << std::endl;
+  }
+  out << std::endl;
+  if (schema.line_) {
+    out << "Defined at " << schema.file_ << ":" << schema.line_ << std::endl;
+  }
+  return out;
+}
+
+OpSchemaRegistry::DomainToVersionRange& OpSchemaRegistry::DomainToVersionRange::Instance() {
+  static DomainToVersionRange domain_to_version_range;
+  return domain_to_version_range;
+};
+
+// Private method used by OpSchemaRegisterOnce and OpSchemaRegistry::map()
+OpName_Domain_Version_Schema_Map& OpSchemaRegistry::GetMapWithoutEnsuringRegistration() {
+  static OpName_Domain_Version_Schema_Map map;
+  return map;
+}
+
+OpName_Domain_Version_Schema_Map& OpSchemaRegistry::map() {
+  auto& map = GetMapWithoutEnsuringRegistration();
+
+  // The following class is used to register operators the
+  // first time this method is called, in a thread-safe fashion.
+  class SchemasRegisterer {
+   public:
+    SchemasRegisterer() {
+      // In debug builds, the number of schema registered in this constructor
+      // is compared against the number of calls to schema registration macros.
+#ifndef NDEBUG
+      size_t dbg_initial_schema_count = GetRegisteredSchemaCount();
+#endif
+
+      RegisterOnnxOperatorSetSchema();
+
+#ifdef ONNX_ML
+      RegisterOnnxMLOperatorSetSchema();
+#endif
+
+      // Invoke register of training operators.
+      RegisterOnnxTrainingOperatorSetSchema();
+
+      // Invoke register of experimental operators.
+      RegisterOnnxPreviewOperatorSetSchema();
+
+#ifndef NDEBUG
+      size_t dbg_registered_schema_count = GetRegisteredSchemaCount() - dbg_initial_schema_count;
+      // Check enabled only if schemas for all opset versions are loaded
+      if (OpSchemaRegistry::Instance()->GetLoadedSchemaVersion() == 0) {
+        ONNX_ASSERTM(
+            dbg_registered_schema_count == ONNX_DBG_GET_COUNT_IN_OPSETS(),
+            "%u schema were exposed from operator sets and automatically placed into the static registry.  "
+            "%u were expected based on calls to registration macros. Operator set functions may need to be updated.",
+            dbg_registered_schema_count,
+            ONNX_DBG_GET_COUNT_IN_OPSETS());
+      }
+#endif
+    }
+
+   private:
+    static size_t GetRegisteredSchemaCount() {
+      size_t count = 0;
+      for (auto& x : GetMapWithoutEnsuringRegistration()) {
+        for (auto& y : x.second) {
+          count += y.second.size();
+        }
+      }
+      return count;
+    }
+  };
+
+#ifndef __ONNX_DISABLE_STATIC_REGISTRATION
+  static SchemasRegisterer schemasRegisterer;
+#endif
+
+  return map;
+}
+
+size_t ReplaceAll(std::string& s, const char* from, const char* to) {
+  size_t numReplaced = 0;
+  std::string::size_type lenFrom = std::strlen(from);
+  std::string::size_type lenTo = std::strlen(to);
+  for (std::string::size_type pos = s.find(from); pos != std::string::npos; pos = s.find(from, pos + lenTo)) {
+    s.replace(pos, lenFrom, to);
+    numReplaced++;
+  }
+  return numReplaced;
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/schema.h b/third_party/onnx/onnx/defs/schema.h
new file mode 100644
index 0000000000..6bf31acceb
--- /dev/null
+++ b/third_party/onnx/onnx/defs/schema.h
@@ -0,0 +1,1401 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include <climits>
+#include <cstring>
+#include <functional>
+#include <initializer_list>
+#include <iostream>
+#include <limits>
+#include <memory>
+#include <ostream>
+#include <set>
+#include <string>
+#include <tuple>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "onnx/common/common.h"
+#include "onnx/common/constants.h"
+#include "onnx/defs/shape_inference.h"
+
+namespace ONNX_NAMESPACE {
+
+struct FunctionBodyBuildContext {
+  virtual const AttributeProto* getAttribute(const std::string& name) const = 0;
+  virtual bool hasInput(int inputIndex) const = 0;
+  virtual bool hasOutput(int inputIndex) const = 0;
+  // getInputType(i) should return null for missing optional inputs, or if
+  // type-inference could not infer the input-type (erroneous model).
+  virtual const TypeProto* getInputType(int inputIndex) const = 0;
+  virtual ~FunctionBodyBuildContext() {}
+};
+
+struct FunctionBodyBuildContextImpl : public FunctionBodyBuildContext {
+  // Input_types: use a default TypeProto for missing types. We use a different convention
+  // here (from FunctionBodyBuildContext) to simplify python interoperability.
+  // The default value for input_types is included only for backward compatibility.
+  // It can be used for functions that do not depend on the type-context, but
+  // will not be sufficient for functions that do use the type-context.
+  FunctionBodyBuildContextImpl(const NodeProto& node_proto, const std::vector<TypeProto>& input_types = {})
+      : node_proto_(node_proto), input_types_(input_types) {
+    for (auto& attr : node_proto.attribute()) {
+      attributesByName_[attr.name()] = &attr;
+    }
+  }
+
+  const AttributeProto* getAttribute(const std::string& name) const override {
+    auto iter = attributesByName_.find(name);
+    if (iter == attributesByName_.end()) {
+      return nullptr;
+    } else {
+      return iter->second;
+    }
+  }
+
+  bool hasInput(int inputIndex) const override {
+    if (inputIndex >= node_proto_.input_size())
+      return false;
+    return node_proto_.input(inputIndex) != "";
+  }
+
+  bool hasOutput(int inputIndex) const override {
+    if (inputIndex >= node_proto_.output_size())
+      return false;
+    return node_proto_.output(inputIndex) != "";
+  }
+
+  const TypeProto* getInputType(int inputIndex) const override {
+    if (inputIndex < 0) return nullptr;
+    size_t j = static_cast<size_t>(inputIndex);
+    if (j >= input_types_.size())
+      return nullptr;
+    // Convert default value (no variant set) into null.
+    if (input_types_[j].value_case() == TypeProto::ValueCase::VALUE_NOT_SET)
+      return nullptr;
+    return &input_types_[j];
+  }
+
+  std::unordered_map<std::string, const AttributeProto*> attributesByName_;
+
+  NodeProto node_proto_;
+  std::vector<TypeProto> input_types_;
+};
+
+using FunctionBodyQueryFunction =
+    std::function<bool(FunctionBodyBuildContext&)>;
+
+class OpSchema;
+using ContextDependentFunctionBodyBuilder = std::function<
+    bool(const FunctionBodyBuildContext&, const OpSchema&, FunctionProto&)>;
+
+class SchemaError final : public std::runtime_error {
+ public:
+  using std::runtime_error::runtime_error;
+
+  SchemaError(const std::string& message) : std::runtime_error(message) {}
+
+  const char* what() const noexcept override {
+    if (!expanded_message_.empty()) {
+      return expanded_message_.c_str();
+    }
+    return std::runtime_error::what();
+  }
+
+  void AppendContext(const std::string& context) {
+    expanded_message_ = ONNX_NAMESPACE::MakeString(
+        std::runtime_error::what(), "\n\n==> Context: ", context);
+  }
+
+ private:
+  std::string expanded_message_;
+};
+
+#define fail_schema(...) \
+  ONNX_THROW_EX(ONNX_NAMESPACE::SchemaError(ONNX_NAMESPACE::MakeString(__VA_ARGS__)));
+
+using OperatorSetVersion = int;
+
+using DataTypeSet = std::unordered_set<DataType>;
+
+// Type constraint map. Key is type string. Value is data type set and
+// description.
+using TypeConstraintMap =
+    std::unordered_map<std::string, std::pair<DataTypeSet, std::string>>;
+
+/**
+ * @brief A class to record the schema of an op.
+ *
+ * OpSchema records the common interface of an op specified by its name.
+ *
+ * To register an OpSchema, one can use the macro ONNX_OPERATOR_SCHEMA(name) and
+ * then append the various functions in the class. For example, for an op
+ * that takes in two inputs, one output, and the first input and output
+ * could be in-place, can be written as
+ *
+ *     ONNX_OPERATOR_SCHEMA(name)
+ *         .NumInputs(2).NumOutputs(1).AllowConsumed({{0, 0}});
+ *
+ * To manufacture methods that may be used to register an OpSchema
+ * non-statically, the following may be used:
+ *
+ *     ONNX_OPERATOR_SET_SCHEMA(name, version, OpSchema()
+ *         .NumInputs(2).NumOutputs(1).AllowConsumed({{0, 0}}));
+ */
+class OpSchema final {
+ public:
+  // Formal parameter options.
+  enum FormalParameterOption : uint8_t {
+    // The formal parameter is single and not optional.
+    // Number of supplied actual parameters must be 1.
+    Single = 0,
+    // The formal parameter is single and optional.
+    // Number of supplied actual parameters may be 0 or 1.
+    Optional = 1,
+    // The formal parameter is variadic.
+    // Number of supplied actual parameters must be N or more, where
+    // the minimum value N is indicated separately (default value 1).
+    Variadic = 2,
+  };
+  enum DifferentiationCategory : uint8_t {
+    // Whether this formal parameter is differentiable or not cannot
+    // be statically determined. It also covers variadic formal
+    // parameters which contain both of differentiable and
+    // non-differentiable variables.
+    Unknown = 0,
+    // This formal parameter is differentiable. That is, this formal
+    // parameter can be differentiable input of Gradient operator.
+    Differentiable = 1,
+    // This formal parameter is not differentiable. That is, this formal
+    // parameter can not be differentiable input of Gradient operator.
+    NonDifferentiable = 2
+  };
+
+  // Formal parameter represenation, including input/output name, typeStr,
+  // description, and type constraints.
+  class FormalParameter final {
+   public:
+    // Constructor.
+    FormalParameter() = default;
+
+    explicit FormalParameter(
+        std::string name,
+        DataTypeSet allowed_type_set,
+        std::string type_str,
+        const std::string& description,
+        FormalParameterOption param_option = Single,
+        bool is_homogeneous = true,
+        int min_arity = 1,
+        DifferentiationCategory differentiation_category = Unknown)
+        : name_(std::move(name)),
+          type_set_(std::move(allowed_type_set)),
+          type_str_(std::move(type_str)),
+#ifndef __ONNX_NO_DOC_STRINGS
+          description_(description),
+#endif
+          param_option_(param_option),
+          is_homogeneous_(is_homogeneous),
+          min_arity_(min_arity),
+          differentiation_category_(differentiation_category) {
+#ifdef __ONNX_NO_DOC_STRINGS
+      ONNX_UNUSED_PARAMETER(description);
+#endif
+    }
+
+    explicit FormalParameter(
+        std::string name,
+        const std::string& description,
+        std::string type_str,
+        FormalParameterOption param_option = Single,
+        bool is_homogeneous = true,
+        int min_arity = 1,
+        DifferentiationCategory differentiation_category = Unknown)
+        : name_(std::move(name)),
+          type_str_(std::move(type_str)),
+#ifndef __ONNX_NO_DOC_STRINGS
+          description_(description),
+#endif
+          param_option_(param_option),
+          is_homogeneous_(is_homogeneous),
+          min_arity_(min_arity),
+          differentiation_category_(differentiation_category) {
+#ifdef __ONNX_NO_DOC_STRINGS
+      ONNX_UNUSED_PARAMETER(description);
+#endif
+    }
+
+    // Get formal parameter name.
+    const std::string& GetName() const;
+
+    // Get allowed data types.
+    const DataTypeSet& GetTypes() const;
+
+    // Get formal parameter type string.
+    const std::string& GetTypeStr() const;
+
+    // Get formal parameter description.
+    const std::string& GetDescription() const;
+
+    // Get the parameter option, it could be Single, Optional or Variadic.
+    FormalParameterOption GetOption() const;
+
+    // Get whether a variadic parameter requires all to be of same type
+    bool GetIsHomogeneous() const;
+
+    // Get minimum arity. Applicable only in the Variadic case.
+    int GetMinArity() const;
+
+    // Get the differentiation property of this formal parameter.
+    DifferentiationCategory GetDifferentiationCategory() const;
+
+   private:
+    friend class OpSchema;
+
+    DataTypeSet& MutableTypes();
+
+    // Formal parameter name.
+    std::string name_;
+
+    // A set of data types supported for <*this> formal parameter.
+    // It should contain at least one element if this formal parameter is good.
+    DataTypeSet type_set_;
+
+    // The <parameter type> string specified when registring an op.
+    // It could be a supported data type or a type constraint key, which
+    // maps to a set of supported data types.
+    std::string type_str_;
+
+    // Formal parameter description.
+    std::string description_;
+
+    // Formal parameter option.
+    FormalParameterOption param_option_;
+
+    // For variadic parameters, a flag indicating if all parameters must be of
+    // same type
+    bool is_homogeneous_;
+
+    // Minimum number of parameters expected. Applicable only for Variadic.
+    int min_arity_;
+
+    // True if this parameter can be an differentiable inputs of Gradient.
+    // Otherwise, using this parameter as an differentiable inputs of Gradient
+    // is prohibited.
+    DifferentiationCategory differentiation_category_;
+  };
+
+  enum class SupportType : uint8_t {
+    COMMON, // Supported by all frameworks that support this IR.
+    EXPERIMENTAL, // This OP is experimental and can be changed or removed in
+                  // the future.
+  };
+
+  OpSchema() : OpSchema("unknown", "unknown", 0) {}
+  OpSchema(std::string name, std::string file, int line)
+      : name_(std::move(name)),
+        file_(std::move(file)),
+        line_(line),
+        support_(SupportType::COMMON) {}
+
+  /**
+   * @brief Returns the file that the op schema is registered from.
+   */
+  const std::string& file() const {
+    return file_;
+  }
+
+  /**
+   * @brief Returns the line in file that the op schema is registered from.
+   */
+  int line() const {
+    return line_;
+  }
+
+  /**
+   * @brief Returns the support level of the op schema.
+   */
+  SupportType support_level() const {
+    return support_;
+  }
+
+  /**
+   * @brief Returns the docstring of the op schema.
+   */
+  const char* doc() const {
+    return doc_.empty() ? nullptr : doc_.c_str();
+  }
+
+  // Check if input and output types fall into valid set and match each other
+  void CheckInputOutputType(struct InferenceContext&) const;
+
+  /**
+   * @brief Verifies if a NodeProto matches the pattern specified in
+   * the schema.
+   */
+  void Verify(const NodeProto& node) const;
+
+  // Functions to set the property of the operator schemas.
+  // Sets the number of inputs, either a fixed number or a min and a max.
+
+  /**
+   * The earliest operator set version which this operator was
+   * present in.  If an operator has had no BC-breaking changes,
+   * this is simply the first operator set the operator was a member
+   * of; if it has had BC-breaking changes, then for the semantics
+   * /as described/ in the OpSchema entry, this version describes
+   * the operator set which introduced the BC-breaking change.
+   *
+   * For example, suppose op Foo was added in v3, and had a BC-breaking
+   * change in v6.  Then there will be an op schema entry for Foo with
+   * SinceVersion(3), and another, updated op schema entry for Foo
+   * with SinceVersion(6).
+   */
+  OpSchema& SinceVersion(OperatorSetVersion n); // aka int
+
+  /**
+   * Marks this op as deprecated as of it's since_version. This will cause the
+   * Schema() lookup functions to return nullptr when the version is in the
+   * deprecated range.
+   */
+  OpSchema& Deprecate();
+
+  bool Deprecated() const {
+    return deprecated_;
+  }
+
+  /**
+   * @brief Input could be one of the values specified in allowed_input_nums.
+   */
+  OpSchema& NumInputs(std::set<int> allowed_input_nums);
+
+  /**
+   * @brief Output could be one of the values specified in allowed_output_nums.
+   */
+  OpSchema& NumOutputs(std::set<int> allowed_output_nums);
+
+  // Shape Inference
+  //
+  // Note that signatures are defined to allow for forward-declaring
+  // any structs used from ir.h
+  OpSchema& TypeAndShapeInferenceFunction(InferenceFunction inferenceFunction);
+  InferenceFunction GetTypeAndShapeInferenceFunction() const {
+    return tensor_inference_function_ ? tensor_inference_function_
+                                      : dummyInferenceFunction;
+  }
+
+  OpSchema& PartialDataPropagationFunction(DataPropagationFunction dataProgationFunction);
+  DataPropagationFunction GetDataPropagationFunction() const {
+    return data_propagation_function_ ? data_propagation_function_ : dummyDataPropagationFunction;
+  }
+
+  // Set the support level for the op schema.
+  OpSchema& SetSupportLevel(SupportType supportType);
+
+  // Functions to do documentation for the operator schema.
+  // This may be disabled to save memory.
+  OpSchema& SetDoc(const char* doc) {
+#ifndef __ONNX_NO_DOC_STRINGS
+    SetDoc(std::string(doc));
+#else
+    ONNX_UNUSED_PARAMETER(doc);
+#endif
+
+    return *this;
+  }
+
+  OpSchema& SetDoc(const std::string& doc) {
+#ifndef __ONNX_NO_DOC_STRINGS
+    doc_ = doc;
+#else
+    ONNX_UNUSED_PARAMETER(doc);
+#endif
+    return *this;
+  }
+
+  // Functions to specify name for the operator schema.
+  OpSchema& SetName(const char* name);
+  OpSchema& SetName(std::string name);
+
+  // Functions to specify code location for the operator schema.
+  OpSchema& SetLocation(const char* file, int line);
+  OpSchema& SetLocation(std::string file, int line);
+
+  // Functions to specify domain for the operator schema.
+  // Default domain value (ONNX_DOMAIN) means it's ONNX domain.
+  OpSchema& SetDomain(const char* domain);
+  OpSchema& SetDomain(std::string domain);
+
+  struct Attribute final {
+    Attribute(
+        std::string name_,
+        std::string description_,
+        AttributeProto::AttributeType type_,
+        bool required_)
+        : name(std::move(name_)),
+          description(std::move(description_)),
+          type(type_),
+          required(required_),
+          default_value() {}
+
+    Attribute(
+        std::string name_,
+        std::string description_,
+        AttributeProto default_value_)
+        : name(std::move(name_)),
+          description(std::move(description_)),
+          type(default_value_.type()),
+          required(false),
+          default_value(std::move(default_value_)) {}
+
+    const std::string name;
+    const std::string description;
+    AttributeProto::AttributeType type;
+    bool required;
+    AttributeProto default_value;
+  };
+
+  OpSchema& Attr(Attribute attr);
+
+// Register "optional" attribute with default value.
+#define ATTR_SETTER_WITH_DEFAULT_VALUE(TypeName) \
+  OpSchema& Attr(                                \
+      std::string name,                          \
+      std::string description,                   \
+      AttributeProto::AttributeType type,        \
+      const TypeName& defaultValue);             \
+  /* non-STL wrapper to reduce binary size */    \
+  OpSchema& Attr(                                \
+      const char* name,                          \
+      const char* description,                   \
+      AttributeProto::AttributeType type,        \
+      const TypeName& defaultValue);             \
+  OpSchema& Attr(                                \
+      std::string name,                          \
+      std::string description,                   \
+      AttributeProto::AttributeType type,        \
+      const std::vector<TypeName>& defaultValue);
+
+  ATTR_SETTER_WITH_DEFAULT_VALUE(int64_t)
+  ATTR_SETTER_WITH_DEFAULT_VALUE(float)
+  ATTR_SETTER_WITH_DEFAULT_VALUE(std::string)
+  ATTR_SETTER_WITH_DEFAULT_VALUE(TensorProto)
+  ATTR_SETTER_WITH_DEFAULT_VALUE(GraphProto)
+  ATTR_SETTER_WITH_DEFAULT_VALUE(TypeProto)
+
+  // Register "required" attribute without default value.
+  OpSchema& Attr(
+      std::string name,
+      std::string description,
+      AttributeProto::AttributeType type,
+      bool required = true);
+
+  // Non-STL wrapper to reduce binary size
+  OpSchema& Attr(
+      const char* name,
+      const char* description,
+      AttributeProto::AttributeType type,
+      bool required = true);
+
+  OpSchema& AllowUncheckedAttributes();
+
+  // Type constraint.
+  struct TypeConstraintParam final {
+    TypeConstraintParam(
+        std::string type_param_str_,
+        std::vector<std::string> allowed_type_strs_,
+        std::string description_)
+        : type_param_str(std::move(type_param_str_)),
+          allowed_type_strs(std::move(allowed_type_strs_)),
+          description(std::move(description_)) {}
+
+    // Type parameter string, for example, "T", "T1", etc.
+    std::string type_param_str;
+    // Allowed type strings for <*this> type parameter, for example,
+    // "tensor(float)".
+    std::vector<std::string> allowed_type_strs;
+    // Type parameter description.
+    std::string description;
+  };
+
+  // Grammar for type strings used in Input(), Output().
+  // <type> ::= <data_type> |
+  //            tensor(<data_type>) |
+  //            seq(<type>) |
+  //            map(<data_type>, <type>) |
+  //            <type_parameter>
+  // <data_type> :: = float | int32 | string | bool | uint8
+  //                | int8 | uint16 | int16 | int64 | float16 | double
+  // <type_parameter> ::= any type parameter string, say "T".
+  //
+  // NOTE: 1) <type_parameter> will always be together with a type constraints
+  // specification.
+  //       2) <type> ::= <data_type> means the data is scalar (zero dimension).
+  //
+  // Example:
+  // ONNX_OPERATOR_SET_SCHEMA(Sum, 1, OpSchema()
+  // .Input(0, "input_a", "the first input", "T")
+  // .Input(1, "input_b", "the second input", "T")
+  // .Output(0, "sum", "the sum of two numbers", "T")
+  // .TypeConstraint("T", {"float", "double", "int32"}, "allowed data types for
+  // sum."))
+  //
+  // Optional = true means that the input might have empty input value
+  // (represented as "") in the graph even though the later inputs have values.
+  // It's useful for complex situation when there are several independent
+  // optional inputs.
+  OpSchema& Input(
+      int n,
+      std::string name,
+      const std::string& description,
+      std::string type_str,
+      FormalParameterOption param_option = Single,
+      bool is_homogeneous = true,
+      int min_arity = 1,
+      DifferentiationCategory differentiation_category = Unknown);
+
+  // Non-STL wrapper to reduce binary size
+  OpSchema& Input(
+      int n,
+      const char* name,
+      const char* description,
+      const char* type_str,
+      FormalParameterOption param_option = Single,
+      bool is_homogeneous = true,
+      int min_arity = 1,
+      DifferentiationCategory differentiation_category = Unknown);
+
+  OpSchema& Output(
+      int n,
+      std::string name,
+      const std::string& description,
+      std::string type_str,
+      FormalParameterOption param_option = Single,
+      bool is_homogeneous = true,
+      int min_arity = 1,
+      DifferentiationCategory differentiation_category = Unknown);
+
+  // Non-STL wrapper to reduce binary size
+  OpSchema& Output(
+      int n,
+      const char* name,
+      const char* description,
+      const char* type_str,
+      FormalParameterOption param_option = Single,
+      bool is_homogeneous = true,
+      int min_arity = 1,
+      DifferentiationCategory differentiation_category = Unknown);
+
+  OpSchema& TypeConstraint(
+      std::string type_str,
+      std::vector<std::string> constraints,
+      std::string description);
+
+  // Non-STL wrapper to reduce binary size
+  OpSchema& TypeConstraint(
+      const char* type_str,
+      std::initializer_list<const char*> constraints,
+      const char* description);
+
+  // Convenience members for types
+
+  // All high-precision numeric types.
+  static const std::vector<std::string>&
+  numeric_types_for_math_reduction_with_bfloat() {
+    static const std::vector<std::string>
+        numeric_types_for_math_reduction_with_bfloat = {"tensor(uint32)",
+                                                        "tensor(uint64)",
+                                                        "tensor(int32)",
+                                                        "tensor(int64)",
+                                                        "tensor(float16)",
+                                                        "tensor(float)",
+                                                        "tensor(double)",
+                                                        "tensor(bfloat16)"};
+    return numeric_types_for_math_reduction_with_bfloat;
+  }
+
+  static const std::vector<std::string>& numeric_types_for_math_reduction() {
+    static const std::vector<std::string> numeric_types_for_math_reduction = {
+        "tensor(uint32)",
+        "tensor(uint64)",
+        "tensor(int32)",
+        "tensor(int64)",
+        "tensor(float16)",
+        "tensor(float)",
+        "tensor(double)"};
+    return numeric_types_for_math_reduction;
+  }
+
+  static const std::vector<std::string>& all_numeric_types_with_bfloat() {
+    static const std::vector<std::string> all_numeric_types_with_bfloat = {
+        "tensor(uint8)",
+        "tensor(uint16)",
+        "tensor(uint32)",
+        "tensor(uint64)",
+        "tensor(int8)",
+        "tensor(int16)",
+        "tensor(int32)",
+        "tensor(int64)",
+        "tensor(float16)",
+        "tensor(float)",
+        "tensor(double)",
+        "tensor(bfloat16)"};
+    return all_numeric_types_with_bfloat;
+  }
+
+  static const std::vector<std::string>& all_numeric_types() {
+    static const std::vector<std::string> all_numeric_types = {
+        "tensor(uint8)",
+        "tensor(uint16)",
+        "tensor(uint32)",
+        "tensor(uint64)",
+        "tensor(int8)",
+        "tensor(int16)",
+        "tensor(int32)",
+        "tensor(int64)",
+        "tensor(float16)",
+        "tensor(float)",
+        "tensor(double)"};
+    return all_numeric_types;
+  }
+
+  static const std::vector<std::string>& all_numeric_sequence_types() {
+    static const std::vector<std::string> all_numeric_sequence_types = {
+        "seq(tensor(uint8))",
+        "seq(tensor(uint16))",
+        "seq(tensor(uint32))",
+        "seq(tensor(uint64))",
+        "seq(tensor(int8))",
+        "seq(tensor(int16))",
+        "seq(tensor(int32))",
+        "seq(tensor(int64))",
+        "seq(tensor(float16))",
+        "seq(tensor(float))",
+        "seq(tensor(double))"};
+    return all_numeric_sequence_types;
+  }
+
+  static const std::vector<std::string>& all_tensor_types() {
+    static const std::vector<std::string> all_tensor_types = {
+        "tensor(uint8)",
+        "tensor(uint16)",
+        "tensor(uint32)",
+        "tensor(uint64)",
+        "tensor(int8)",
+        "tensor(int16)",
+        "tensor(int32)",
+        "tensor(int64)",
+        "tensor(float16)",
+        "tensor(float)",
+        "tensor(double)",
+        "tensor(string)",
+        "tensor(bool)",
+        "tensor(complex64)",
+        "tensor(complex128)"};
+    return all_tensor_types;
+  }
+
+  static const std::vector<std::string>& all_tensor_types_with_bfloat() {
+    static const std::vector<std::string> all_tensor_types_with_bfloat = {
+        "tensor(uint8)",
+        "tensor(uint16)",
+        "tensor(uint32)",
+        "tensor(uint64)",
+        "tensor(int8)",
+        "tensor(int16)",
+        "tensor(int32)",
+        "tensor(int64)",
+        "tensor(bfloat16)",
+        "tensor(float16)",
+        "tensor(float)",
+        "tensor(double)",
+        "tensor(string)",
+        "tensor(bool)",
+        "tensor(complex64)",
+        "tensor(complex128)"};
+    return all_tensor_types_with_bfloat;
+  }
+
+  static const std::vector<std::string>& all_tensor_sequence_types() {
+    static const std::vector<std::string> all_tensor_sequence_types = {
+        "seq(tensor(uint8))",
+        "seq(tensor(uint16))",
+        "seq(tensor(uint32))",
+        "seq(tensor(uint64))",
+        "seq(tensor(int8))",
+        "seq(tensor(int16))",
+        "seq(tensor(int32))",
+        "seq(tensor(int64))",
+        "seq(tensor(float16))",
+        "seq(tensor(float))",
+        "seq(tensor(double))",
+        "seq(tensor(string))",
+        "seq(tensor(bool))",
+        "seq(tensor(complex64))",
+        "seq(tensor(complex128))"};
+    return all_tensor_sequence_types;
+  }
+
+  static const std::vector<std::string>& all_tensor_sequence_types_with_bfloat() {
+    static const std::vector<std::string> all_tensor_sequence_types_with_bfloat = {
+        "seq(tensor(uint8))",
+        "seq(tensor(uint16))",
+        "seq(tensor(uint32))",
+        "seq(tensor(uint64))",
+        "seq(tensor(int8))",
+        "seq(tensor(int16))",
+        "seq(tensor(int32))",
+        "seq(tensor(int64))",
+        "seq(tensor(bfloat16))",
+        "seq(tensor(float16))",
+        "seq(tensor(float))",
+        "seq(tensor(double))",
+        "seq(tensor(string))",
+        "seq(tensor(bool))",
+        "seq(tensor(complex64))",
+        "seq(tensor(complex128))"};
+    return all_tensor_sequence_types_with_bfloat;
+  }
+
+  static const std::vector<std::string>& all_optional_types() {
+    static const std::vector<std::string> all_optional_types = {
+        "optional(seq(tensor(uint8)))",
+        "optional(seq(tensor(uint16)))",
+        "optional(seq(tensor(uint32)))",
+        "optional(seq(tensor(uint64)))",
+        "optional(seq(tensor(int8)))",
+        "optional(seq(tensor(int16)))",
+        "optional(seq(tensor(int32)))",
+        "optional(seq(tensor(int64)))",
+        "optional(seq(tensor(float16)))",
+        "optional(seq(tensor(float)))",
+        "optional(seq(tensor(double)))",
+        "optional(seq(tensor(string)))",
+        "optional(seq(tensor(bool)))",
+        "optional(seq(tensor(complex64)))",
+        "optional(seq(tensor(complex128)))",
+        "optional(tensor(uint8))",
+        "optional(tensor(uint16))",
+        "optional(tensor(uint32))",
+        "optional(tensor(uint64))",
+        "optional(tensor(int8))",
+        "optional(tensor(int16))",
+        "optional(tensor(int32))",
+        "optional(tensor(int64))",
+        "optional(tensor(float16))",
+        "optional(tensor(float))",
+        "optional(tensor(double))",
+        "optional(tensor(string))",
+        "optional(tensor(bool))",
+        "optional(tensor(complex64))",
+        "optional(tensor(complex128))"};
+    return all_optional_types;
+  }
+
+  static const std::vector<std::string>& all_optional_types_with_bfloat() {
+    static const std::vector<std::string> all_optional_types = {
+        "optional(seq(tensor(uint8)))",
+        "optional(seq(tensor(uint16)))",
+        "optional(seq(tensor(uint32)))",
+        "optional(seq(tensor(uint64)))",
+        "optional(seq(tensor(int8)))",
+        "optional(seq(tensor(int16)))",
+        "optional(seq(tensor(int32)))",
+        "optional(seq(tensor(int64)))",
+        "optional(seq(tensor(bfloat16)))",
+        "optional(seq(tensor(float16)))",
+        "optional(seq(tensor(float)))",
+        "optional(seq(tensor(double)))",
+        "optional(seq(tensor(string)))",
+        "optional(seq(tensor(bool)))",
+        "optional(seq(tensor(complex64)))",
+        "optional(seq(tensor(complex128)))",
+        "optional(tensor(uint8))",
+        "optional(tensor(uint16))",
+        "optional(tensor(uint32))",
+        "optional(tensor(uint64))",
+        "optional(tensor(int8))",
+        "optional(tensor(int16))",
+        "optional(tensor(int32))",
+        "optional(tensor(int64))",
+        "optional(tensor(bfloat16))",
+        "optional(tensor(float16))",
+        "optional(tensor(float))",
+        "optional(tensor(double))",
+        "optional(tensor(string))",
+        "optional(tensor(bool))",
+        "optional(tensor(complex64))",
+        "optional(tensor(complex128))"};
+    return all_optional_types;
+  }
+
+  // Calls the passed function with `this` as an argument. Useful for
+  // adding docs for temlated/macro ops.
+  OpSchema& FillUsing(const std::function<void(OpSchema&)>& populator);
+
+  friend std::ostream& operator<<(std::ostream& out, const OpSchema& schema);
+
+  const std::string& domain() const {
+    return domain_;
+  }
+
+  const std::map<std::string, Attribute>& attributes() const {
+    return attributes_;
+  }
+
+  // Get input formal parameters.
+  const std::vector<FormalParameter>& inputs() const {
+    return inputs_;
+  }
+
+  // Get output formal parameters.
+  const std::vector<FormalParameter>& outputs() const {
+    return outputs_;
+  }
+
+  const std::vector<TypeConstraintParam>& typeConstraintParams() const {
+    return type_constraint_params_;
+  }
+
+  const std::string& Name() const {
+    return name_;
+  }
+
+  OperatorSetVersion SinceVersion() const {
+    return since_version_;
+  }
+
+  int since_version() const {
+    return since_version_;
+  }
+
+  bool deprecated() const {
+    return deprecated_;
+  }
+
+  int min_input() const {
+    return min_input_;
+  }
+  int max_input() const {
+    return max_input_;
+  }
+  int min_output() const {
+    return min_output_;
+  }
+  int max_output() const {
+    return max_output_;
+  }
+
+  bool has_type_and_shape_inference_function() const {
+    return tensor_inference_function_ ? true : false;
+  }
+
+  bool has_data_propagation_function() const {
+    return data_propagation_function_ ? true : false;
+  }
+
+  bool HasFunction() const {
+    return function_body_.node_size() > 0;
+  }
+
+  OpSchema& FunctionBody(const std::vector<NodeProto>& func_nodes);
+
+  OpSchema& FunctionBody(const char *func_body);
+
+  OpSchema& FunctionBody(
+      const std::vector<NodeProto>& func_nodes,
+      const std::vector<OperatorSetIdProto>& opsets);
+
+  const FunctionProto* GetFunction() const;
+
+  bool HasContextDependentFunction() const {
+    return functionBuilder_ != nullptr;
+  }
+
+  OpSchema& SetContextDependentFunctionBodyBuilder(
+      ContextDependentFunctionBodyBuilder);
+
+  bool BuildContextDependentFunction(
+      const FunctionBodyBuildContext& ctx,
+      FunctionProto& functionProto) const;
+
+  // Verifies that the schema is valid and all specifications are compatible.
+  // It will also parse all type strings specified for inputs/outputs into valid
+  // TypeProto and create global unique string pointer as the DataType for
+  // efficiency.
+  void Finalize();
+
+  // Build function with information stored in opschema
+  void BuildFunction(
+      FunctionProto& function_body) const;
+
+ private:
+  void ParseAndSetTypes(
+      /*out*/ std::vector<OpSchema::FormalParameter>* formalParameters);
+
+  std::string name_;
+  std::string file_;
+  std::string doc_;
+  // Default domain value ("") means it's ONNX domain.
+  std::string domain_ = ONNX_DOMAIN;
+  std::map<std::string, Attribute> attributes_{};
+  bool allows_unchecked_attributes_ = false;
+  std::vector<FormalParameter> inputs_;
+  std::vector<FormalParameter> outputs_;
+  std::vector<TypeConstraintParam> type_constraint_params_;
+  TypeConstraintMap type_constraints_;
+  int line_ = 0;
+  SupportType support_;
+  int min_input_ = 0;
+  int max_input_ = 0;
+  int min_output_ = 0;
+  int max_output_ = 0;
+  // The default is a little goofy, since it is never what you want
+  OperatorSetVersion since_version_ = 1;
+  bool deprecated_{};
+  std::function<bool(int)> num_inputs_allowed_ = [](int) { return true; };
+  std::function<bool(int)> num_outputs_allowed_ = [](int) { return true; };
+  InferenceFunction tensor_inference_function_;
+  DataPropagationFunction data_propagation_function_;
+  FunctionProto function_body_;
+  ContextDependentFunctionBodyBuilder functionBuilder_;
+};
+
+// Map type to store operator schemas. The format is,
+// <OpName, <Domain, <OperatorSetVersion, OpSchema>>>.
+using OpName_Domain_Version_Schema_Map = std::unordered_map<
+    std::string,
+    std::unordered_map<std::string, std::map<OperatorSetVersion, OpSchema>>>;
+
+class ISchemaRegistry {
+ public:
+  virtual ~ISchemaRegistry() = default;
+
+  virtual const OpSchema* GetSchema(
+      const std::string& key,
+      const int maxInclusiveVersion,
+      const std::string& domain = ONNX_DOMAIN) const = 0;
+};
+
+/**
+ * @brief A registry to hold all the operator schemas.
+ */
+class OpSchemaRegistry final : public ISchemaRegistry {
+ public:
+  // A singleton class to store domain to min/max op_set version map, as well as
+  // domain to last-release op_set version map.
+  class DomainToVersionRange final {
+   public:
+    DomainToVersionRange() {
+      // Increase the highest version when you make BC-breaking changes to the
+      // operator schema on specific domain. Update the lowest version when it's
+      // determined to remove too old version history.
+      map_[ONNX_DOMAIN] = std::make_pair(1, 17);
+      map_[AI_ONNX_ML_DOMAIN] = std::make_pair(1, 3);
+      map_[AI_ONNX_TRAINING_DOMAIN] = std::make_pair(1, 1);
+      // ONNX's preview domain contains operators subject to change, so
+      // versining is not meaningful and that domain should have only one
+      // version.
+      map_[AI_ONNX_PREVIEW_TRAINING_DOMAIN] = std::make_pair(1, 1);
+      // Version corresponding last release of ONNX. Update this to match with
+      // the max version above in a *release* version of ONNX. But in other
+      // versions, the max version may be ahead of the last-release-version.
+      last_release_version_map_[ONNX_DOMAIN] = 16;
+      last_release_version_map_[AI_ONNX_ML_DOMAIN] = 3;
+      last_release_version_map_[AI_ONNX_TRAINING_DOMAIN] = 1;
+      last_release_version_map_[AI_ONNX_PREVIEW_TRAINING_DOMAIN] = 1;
+    }
+
+    const std::unordered_map<std::string, std::pair<int, int>>& Map() const {
+      return map_;
+    }
+
+    const std::unordered_map<std::string, int>& LastReleaseVersionMap() const {
+      return last_release_version_map_;
+    }
+
+    // Add customized domain to min/max version.
+    // Onnx partners are able to use onnx operator schema api to
+    // register customized op in their own domain.
+    // Can optionally specify last_release_version (to make it similar to
+    // standard ONNX domains as above). Custom-domains are free to interpret
+    // this as appropriate (that is, as relative to releases of custom-domain
+    // as opposed to ONNX releases).
+    void AddDomainToVersion(
+        const std::string& domain,
+        int min_version,
+        int max_version,
+        int last_release_version = -1) {
+      std::lock_guard<std::mutex> lock(mutex_);
+      assert(map_.end() == map_.find(domain));
+      map_[domain] = std::make_pair(min_version, max_version);
+      // If a last-release-version is not explicitly specified, use max as
+      // last-release-version.
+      if (last_release_version == -1)
+        last_release_version = max_version;
+      assert(
+          last_release_version_map_.end() ==
+          last_release_version_map_.find(domain));
+      last_release_version_map_[domain] = last_release_version;
+    }
+
+    static DomainToVersionRange& Instance();
+
+   private:
+    // Key: domain. Value: <lowest version, highest version> pair.
+    std::unordered_map<std::string, std::pair<int, int>> map_;
+
+    // Key: domain. Value: most recent release opset version. Note that
+    // the highest opset version may be ahead of the most recent release's opset
+    // version.
+    std::unordered_map<std::string, int> last_release_version_map_;
+
+    std::mutex mutex_;
+  };
+
+  class OpSchemaRegisterOnce final {
+   public:
+    OpSchemaRegisterOnce(OpSchema& op_schema, int opset_version_to_load=0) {
+      ONNX_TRY {
+        op_schema.Finalize();
+
+        auto& m = GetMapWithoutEnsuringRegistration();
+
+        auto& op_name = op_schema.Name();
+        auto& op_domain = op_schema.domain();
+        auto ver = op_schema.SinceVersion();
+        // Stops because the opset_version is higher than opset_version_to_load
+        if (opset_version_to_load != 0 && ver > opset_version_to_load) {
+          return;
+        }
+
+        if (m[op_name][op_domain].count(ver)) {
+          const auto& schema = m[op_name][op_domain][ver];
+          std::stringstream err;
+          err << "Trying to register schema with name " << op_name
+              << " (domain: " << op_domain << " version: " << ver
+              << ") from file " << op_schema.file() << " line "
+              << op_schema.line() << ", but it is already registered from file "
+              << schema.file() << " line " << schema.line() << std::endl;
+          fail_schema(err.str());
+        }
+        // Return early if schema for the targeted opset version has already been loaded
+        if (opset_version_to_load != 0 && !m[op_name][op_domain].empty()) {
+          return;
+        }
+
+        auto ver_range_map = DomainToVersionRange::Instance().Map();
+        auto ver_range_it = ver_range_map.find(op_domain);
+        if (ver_range_it == ver_range_map.end()) {
+          std::stringstream err;
+          err << "Trying to register schema with name " << op_name
+              << " (domain: " << op_domain << " version: " << ver
+              << ") from file " << op_schema.file() << " line "
+              << op_schema.line() << ", but its domain is not"
+              << " known by the checker." << std::endl;
+
+          fail_schema(err.str());
+        }
+        auto lower_bound_incl = ver_range_it->second.first;
+        auto upper_bound_incl = ver_range_it->second.second;
+        if (!(lower_bound_incl <= ver && upper_bound_incl >= ver)) {
+          std::stringstream err;
+          err << "Trying to register schema with name " << op_name
+              << " (domain: " << op_domain << " version: " << ver
+              << ") from file " << op_schema.file() << " line "
+              << op_schema.line() << ", but its version is not "
+              << "in the inclusive range [" << lower_bound_incl << ", "
+              << upper_bound_incl << "] (usually, this means you "
+              << "bumped the operator version but "
+              << "forgot to update the version range in DomainToVersionRange "
+              << "in onnx/defs/schema.h)." << std::endl;
+          fail_schema(err.str());
+        }
+
+        m[op_name][op_domain].insert(
+            std::pair<int, OpSchema&&>(ver, std::move(op_schema)));
+
+      } ONNX_CATCH (const std::exception& e) {
+        ONNX_HANDLE_EXCEPTION([&]() { std::cerr << "Schema error: " << e.what() << std::endl; });
+      }
+    }
+  };
+
+  // Return the latest schema for an operator in specified domain.
+  // Domain with default value ONNX_DOMAIN means ONNX.
+  static const OpSchema* Schema(
+      const std::string& key,
+      const std::string& domain = ONNX_DOMAIN) {
+    auto& m = map();
+    if (m.count(key) && m[key].count(domain)) {
+      return &m[key][domain].rbegin()->second;
+    } else {
+      return nullptr;
+    }
+  }
+
+  // Return the schema with biggest version, which is not greater than specified
+  // <maxInclusiveVersion> in specified domain. Domain with default value
+  // ONNX_DOMAIN means ONNX.
+  static const OpSchema* Schema(
+      const std::string& key,
+      const int maxInclusiveVersion,
+      const std::string& domain = ONNX_DOMAIN) {
+    auto& m = map();
+    if (m.count(key) && m[key].count(domain)) {
+      auto pos = m[key][domain].lower_bound(maxInclusiveVersion);
+      if (m[key][domain].begin() == pos && pos->first > maxInclusiveVersion) {
+        // All versions are greater than specified version.
+        return nullptr;
+      }
+      if (m[key][domain].end() == pos || pos->first > maxInclusiveVersion) {
+        // All versions are less than specified version, or,
+        // The <pos> version is greater than specified version.
+        pos--;
+      }
+
+      // Schema with exact version as specified one exists.
+      return &(pos->second);
+    } else {
+      return nullptr;
+    }
+  }
+
+  static OpSchemaRegistry* Instance();
+
+  const OpSchema* GetSchema(
+      const std::string& key,
+      const int maxInclusiveVersion,
+      const std::string& domain = ONNX_DOMAIN) const override {
+    return Schema(key, maxInclusiveVersion, domain);
+  }
+  static void SetLoadedSchemaVersion(int target_version) {
+    loaded_schema_version = target_version;
+  }
+  static int GetLoadedSchemaVersion() {
+    return loaded_schema_version;
+  }
+ private:
+  // OpSchemaRegistry should not need to be instantiated except statically
+  // within this class
+  OpSchemaRegistry() = default;
+
+  /**
+   * @brief Returns the underlying string to OpSchema map.
+   *
+   * You should not manually manipulate the map object returned. Instead, use
+   * the macros defined such as ONNX_OPERATOR_SET_SCHEMA to register your
+   * operator schema.
+   *
+   * We wrap it inside a function to avoid the static initialization order
+   * fiasco.
+   */
+  static OpName_Domain_Version_Schema_Map& GetMapWithoutEnsuringRegistration();
+  static OpName_Domain_Version_Schema_Map& map();
+  static int loaded_schema_version;
+
+ public:
+  static const std::vector<OpSchema> get_all_schemas_with_history() {
+    std::vector<OpSchema> r;
+    for (auto& x : map()) {
+      for (auto& y : x.second) {
+        for (auto& z : y.second) {
+          r.emplace_back(z.second);
+        }
+      }
+    }
+    return r;
+  }
+
+  static const std::vector<OpSchema> get_all_schemas() {
+    std::vector<OpSchema> r;
+    for (auto& x : map()) {
+      for (auto& y : x.second) {
+        auto& version2schema = y.second;
+        r.emplace_back(version2schema.rbegin()->second);
+      }
+    }
+    return r;
+  }
+};
+
+void RegisterSchema(OpSchema schema, int opset_version_to_load=0);
+
+// Registers the latest opset schema before opset_version_to_load
+// By default opset_version_to_load=0 means it will register all versions
+template <class T>
+void RegisterOpSetSchema(int opset_version_to_load=0) {
+  T::ForEachSchema([opset_version_to_load](OpSchema&& schema) {
+    RegisterSchema(schema, opset_version_to_load);
+  });
+};
+
+// Forward declaration for the non-specialized GetOpSchema method.  This
+// enforces a consistent signature on functions that query individual schema,
+// which are defined as specializations of this function.
+template <typename T>
+OpSchema GetOpSchema();
+
+#define ONNX_OPERATOR_SET_SCHEMA(name, ver, impl) \
+  ONNX_OPERATOR_SET_SCHEMA_EX(name, Onnx, ONNX_DOMAIN, ver, true, impl)
+
+#define ONNX_ML_OPERATOR_SET_SCHEMA(name, ver, impl) \
+  ONNX_OPERATOR_SET_SCHEMA_EX(name, OnnxML, AI_ONNX_ML_DOMAIN, ver, true, impl)
+
+#define ONNX_TRAINING_OPERATOR_SET_SCHEMA(name, ver, impl) \
+  ONNX_OPERATOR_SET_SCHEMA_EX(                             \
+      name, OnnxTraining, AI_ONNX_TRAINING_DOMAIN, ver, true, impl)
+
+#define ONNX_PREVIEW_TRAINING_OPERATOR_SET_SCHEMA(name, ver, impl) \
+  ONNX_OPERATOR_SET_SCHEMA_EX(                                     \
+      name, OnnxPreview, AI_ONNX_PREVIEW_TRAINING_DOMAIN, ver, true, impl)
+
+// Defines specialization of GetOpSchema for a class whose name is determined
+// based on a convention using name, domain, and version.  Operator schema are
+// normally included in operator sets and registered in OpSchemaRegistry::map().
+// In this case, callers should set dbg_included_in_static_opset to true.  This
+// assists with runtime validation in in DEBUG builds ensuring the intended set
+// of operator schema is registered.
+#define ONNX_OPERATOR_SET_SCHEMA_EX(                                        \
+    name, domain, domain_str, ver, dbg_included_in_static_opset, impl)      \
+  class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(domain, ver, name);             \
+  template <>                                                               \
+  OpSchema                                                                  \
+  GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(domain, ver, name)>() {   \
+    return impl.SetName(#name)                                              \
+        .SetDomain(domain_str)                                              \
+        .SinceVersion(ver)                                                  \
+        .SetLocation(__FILE__, __LINE__);                                   \
+  }                                                                         \
+  size_t dbg_count_check_##name##_##domain##_ver##ver =                     \
+      (dbg_included_in_static_opset) ? ONNX_DBG_INCREMENT_COUNT_IN_OPSETS() \
+                                     : 0;
+#ifdef NDEBUG
+#define ONNX_DBG_INCREMENT_COUNT_IN_OPSETS() 0
+#else
+#define ONNX_DBG_INCREMENT_COUNT_IN_OPSETS() \
+  DbgOperatorSetTracker::Instance().IncrementCount()
+#define ONNX_DBG_GET_COUNT_IN_OPSETS() \
+  DbgOperatorSetTracker::Instance().GetCount()
+
+class DbgOperatorSetTracker {
+ public:
+  static DbgOperatorSetTracker& Instance();
+
+  size_t IncrementCount() {
+    return ++count_;
+  }
+
+  size_t GetCount() const {
+    return count_;
+  }
+
+ private:
+  size_t count_ = 0;
+};
+#endif
+
+// Naming convention for operator schema classes
+#define ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(domain, ver, name) \
+  name##_##domain##_ver##ver
+
+// Naming convention for preview operator schema classes
+#define ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(ver, name) \
+  ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxPreview, ver, name)
+
+// Helper function
+size_t ReplaceAll(std::string& s, const char* from, const char* to);
+
+#ifdef __GNUC__
+#define ONNX_UNUSED __attribute__((__unused__))
+#else
+#define ONNX_UNUSED
+#endif
+
+// Legacy macros to register schema at static initialization
+#define ONNX_OPERATOR_SCHEMA(name) \
+  ONNX_OPERATOR_SCHEMA_UNIQ_HELPER(__COUNTER__, name)
+#define ONNX_OPERATOR_SCHEMA_UNIQ_HELPER(Counter, name) \
+  ONNX_OPERATOR_SCHEMA_UNIQ(Counter, name)
+#define ONNX_OPERATOR_SCHEMA_UNIQ(Counter, name)                 \
+  static ONNX_NAMESPACE::OpSchemaRegistry::OpSchemaRegisterOnce( \
+      op_schema_register_once##name##Counter) ONNX_UNUSED =      \
+      OpSchema(#name, __FILE__, __LINE__)
+
+// Helper function
+size_t ReplaceAll(std::string& s, const char* from, const char* to);
+
+inline std::string GenerateOptionalArgumentsDoc() {
+  return "This operator has **optional** inputs/outputs. "
+         "See [the doc](IR.md) for more details about the representation of "
+         "optional arguments. An empty string may be used in the place of "
+         "an actual argument's name to indicate a missing argument. "
+         "Trailing optional arguments (those not followed by an argument "
+         "that is present) may also be simply omitted.\n";
+}
+
+inline std::string GenerateBroadcastingDocMul() {
+  return "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**;"
+         " for more details please check [the doc](Broadcasting.md).";
+}
+
+inline std::string GenerateBroadcastingDocUni(
+    const char* from,
+    const char* to) {
+  std::string ret = "This operator supports **unidirectional broadcasting** (";
+  ret = ret + from + " should be unidirectional broadcastable to " + to +
+      ");"
+      " for more details please check [the doc](Broadcasting.md).";
+  return ret;
+}
+
+/*
+ * Macros for setting operator documentation
+ * Use this macro for simple SetDoc() calls that generate documentation
+ * directly. This is the macro to use in almost all cases.
+ * Sample usage guidelines:
+ * const char* doc_str = "foo";
+ * SetDoc(GET_OP_DOC_STR(doc_str))
+ *
+ * SetDoc(GET_OP_DOC_STR(
+            std::string(BitShift_ver11_doc) + GenerateBroadcastingDocMul()))
+ */
+#ifndef __ONNX_NO_DOC_STRINGS
+#define GET_OP_DOC_STR(doc_str) (doc_str)
+#else
+#define GET_OP_DOC_STR(doc_str) ("")
+#endif
+
+/*
+ * Use this macro when the documentation needs to be populated in some
+ * complicated way like string substitutions, etc before calling SetDoc.
+ * Sample usage guidelines:
+    std::string doc;
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
+Returns the tensor resulted from performing the `{name}` logical operation
+elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting
+support).
+
+{broadcast_doc}
+)DOC";
+        ReplaceAll(doc, "{name}", name);
+        ReplaceAll(
+            doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
+    schema.SetDoc(doc);
+ *
+ */
+#ifndef __ONNX_NO_DOC_STRINGS
+#define POPULATE_OP_DOC_STR(DocPopulatorCode) \
+  do {                                        \
+    DocPopulatorCode                          \
+  } while (0)
+#else
+#define POPULATE_OP_DOC_STR(DocPopulatorCode)
+#endif
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/sequence/defs.cc b/third_party/onnx/onnx/defs/sequence/defs.cc
new file mode 100644
index 0000000000..2210e95fa8
--- /dev/null
+++ b/third_party/onnx/onnx/defs/sequence/defs.cc
@@ -0,0 +1,952 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+#include <numeric>
+#include <algorithm>
+
+namespace ONNX_NAMESPACE {
+
+static const char* SequenceEmpty_ver11_doc = R"DOC(
+Construct an empty tensor sequence, with given data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceEmpty,
+    11,
+    OpSchema()
+        .SetDoc(SequenceEmpty_ver11_doc)
+        .Attr(
+            "dtype",
+            "(Optional) The data type of the tensors in the output sequence. "
+            "The default type is 'float'.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Output(
+            0,
+            "output",
+            "Empty sequence.",
+            "S")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const auto* attr_proto = ctx.getAttribute("dtype");
+          auto elem_type = TensorProto::FLOAT;
+          if (nullptr != attr_proto) {
+            if (!attr_proto->has_i()) {
+              fail_type_inference(
+                  "Attribute dtype should be of integer type and specify a type.");
+            }
+            auto attr_value = attr_proto->i();
+            elem_type = static_cast<TensorProto_DataType>(attr_value);
+          }
+          ctx.getOutputType(0)
+              ->mutable_sequence_type()
+              ->mutable_elem_type()
+              ->mutable_tensor_type()
+              ->set_elem_type(elem_type);
+        }));
+
+static const char* SequenceConstruct_ver11_doc = R"DOC(
+Construct a tensor sequence containing 'inputs' tensors.
+All tensors in 'inputs' must have the same data type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceConstruct,
+    11,
+    OpSchema()
+        .SetDoc(SequenceConstruct_ver11_doc)
+        .Input(
+            0,
+            "inputs",
+            "Tensors.",
+            "T",
+            OpSchema::Variadic)
+        .Output(
+            0,
+            "output_sequence",
+            "Sequence enclosing the input tensors.",
+            "S")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input types to any tensor type.")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const size_t numInputs = ctx.getNumInputs();
+          if (numInputs < 1) {
+            fail_type_inference("SequenceConstruct is expected to have at least 1 input.");
+          }
+
+          std::vector<int> input_elem_types;
+          input_elem_types.reserve(numInputs);
+          for (size_t i = 0; i < numInputs; ++i) {
+            auto input_type = ctx.getInputType(i);
+            if(nullptr == input_type){
+                fail_type_inference("Input type for input at index ", i, " is null. Type info is expected.");
+            }
+            input_elem_types.emplace_back(input_type->tensor_type().elem_type());
+          }
+          if (std::adjacent_find(input_elem_types.begin(), input_elem_types.end(), std::not_equal_to<int>()) != input_elem_types.end()) {
+            // not all input elem types are the same.
+            fail_type_inference("Element type of inputs are expected to be the same.");
+          }
+
+          auto* output_tensor_type =
+              ctx.getOutputType(0)
+                  ->mutable_sequence_type()
+                  ->mutable_elem_type()
+                  ->mutable_tensor_type();
+
+          output_tensor_type->set_elem_type(static_cast<TensorProto_DataType>(input_elem_types[0]));
+
+          if (!hasNInputShapes(ctx, static_cast<int>(numInputs))) {
+            return;
+          }
+
+          *(output_tensor_type->mutable_shape()) = ctx.getInputType(0)->tensor_type().shape();
+
+          for (size_t i = 1; i < numInputs; ++i) {
+            const auto& input_shape = ctx.getInputType(i)->tensor_type().shape();
+            UnionShapeInfo(input_shape, *output_tensor_type);
+          }
+        }));
+
+static const char* SequenceInsert_ver11_doc = R"DOC(
+Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'.
+'tensor' must have the same data type as 'input_sequence'.
+Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'.
+Negative value means counting positions from the back.
+'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceInsert,
+    11,
+    OpSchema()
+        .SetDoc(SequenceInsert_ver11_doc)
+        .Input(
+            0,
+            "input_sequence",
+            "Input sequence.",
+            "S")
+        .Input(
+            1,
+            "tensor",
+            "Input tensor to be inserted into the input sequence.",
+            "T")
+        .Input(
+            2,
+            "position",
+            "Position in the sequence where the new tensor is inserted. "
+            "It is optional and default is to insert to the back of the sequence. "
+            "Negative value means counting positions from the back. "
+            "Accepted range in `[-n, n]`, "
+            "where `n` is the number of tensors in 'input_sequence'. "
+            "It is an error if any of the index values are out of bounds. "
+            "It must be a scalar(tensor of empty shape).",
+            "I",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "output_sequence",
+            "Output sequence that contains the inserted tensor at given position.",
+            "S")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain to any tensor type.")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain to any tensor type.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain position to integral tensor. It must be a scalar(tensor of empty shape).")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const auto input0_type = ctx.getInputType(0);
+          const auto input1_type = ctx.getInputType(1);
+          if(nullptr == input0_type || nullptr == input1_type) {
+            fail_type_inference(
+                "Input Sequence and Tensor are expected to have type info. Current type is null.");
+          }
+          const auto seq_elem_type =
+              input0_type->sequence_type().elem_type().tensor_type().elem_type();
+          const auto tensor_elem_type =
+              input1_type->tensor_type().elem_type();
+          if (seq_elem_type != tensor_elem_type) {
+            fail_type_inference(
+                "Input Sequence and Tensor are expected to have the same elem type. Sequence=",
+                seq_elem_type,
+                " Tensor=",
+                tensor_elem_type);
+          }
+
+          auto *output_tensor_type =
+              ctx.getOutputType(0)
+                  ->mutable_sequence_type()
+                  ->mutable_elem_type()
+                  ->mutable_tensor_type();
+          output_tensor_type->set_elem_type(seq_elem_type);
+
+          if (!hasNInputShapes(ctx, 2)) {
+            return;
+          }
+
+          *(output_tensor_type->mutable_shape()) =
+              input0_type->sequence_type().elem_type().tensor_type().shape();
+
+          UnionShapeInfo(input1_type->tensor_type().shape(), *output_tensor_type);
+        }));
+
+static const char* SequenceAt_ver11_doc = R"DOC(
+Outputs a tensor copy from the tensor at 'position' in 'input_sequence'.
+Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
+Negative value means counting positions from the back.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceAt,
+    11,
+    OpSchema()
+        .SetDoc(SequenceAt_ver11_doc)
+        .Input(
+            0,
+            "input_sequence",
+            "Input sequence.",
+            "S")
+        .Input(
+            1,
+            "position",
+            "Position of the tensor in the sequence. "
+            "Negative value means counting positions from the back. "
+            "Accepted range in `[-n, n - 1]`, "
+            "where `n` is the number of tensors in 'input_sequence'. "
+            "It is an error if any of the index values are out of bounds. "
+            "It must be a scalar(tensor of empty shape).",
+            "I")
+        .Output(
+            0,
+            "tensor",
+            "Output tensor at the specified position in the input sequence.",
+            "T")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain to any tensor type.")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain to any tensor type.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain position to integral tensor. It must be a scalar(tensor of empty shape).")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const auto input0_type = ctx.getInputType(0);
+          if(nullptr == input0_type) {
+              fail_type_inference("Input type for input at index 0 is null. Type info is expected.")
+          }
+          ctx.getOutputType(0)->CopyFrom(input0_type->sequence_type().elem_type());
+        }));
+
+static const char* SequenceErase_ver11_doc = R"DOC(
+Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'.
+Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
+Negative value means counting positions from the back.
+'position' is optional, by default it erases the last tensor from 'input_sequence'.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceErase,
+    11,
+    OpSchema()
+        .SetDoc(SequenceErase_ver11_doc)
+        .Input(
+            0,
+            "input_sequence",
+            "Input sequence.",
+            "S")
+        .Input(
+            1,
+            "position",
+            "Position of the tensor in the sequence. "
+            "Negative value means counting positions from the back. "
+            "Accepted range in `[-n, n - 1]`, "
+            "where `n` is the number of tensors in 'input_sequence'. "
+            "It is an error if any of the index values are out of bounds. "
+            "It must be a scalar(tensor of empty shape).",
+            "I",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "output_sequence",
+            "Output sequence that has the tensor at the specified position removed.",
+            "S")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain to any tensor type.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain position to integral tensor. It must be a scalar(tensor of empty shape).")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const auto input0_type = ctx.getInputType(0);
+          if(nullptr == input0_type) {
+              fail_type_inference("Input type for input at index 0 is null. Type info is expected.")
+          }
+          ctx.getOutputType(0)->CopyFrom(*input0_type);
+        }));
+
+static const char* SequenceLength_ver11_doc = R"DOC(
+Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceLength,
+    11,
+    OpSchema()
+        .SetDoc(SequenceLength_ver11_doc)
+        .Input(
+            0,
+            "input_sequence",
+            "Input sequence.",
+            "S")
+        .Output(
+            0,
+            "length",
+            "Length of input sequence. It must be a scalar(tensor of empty shape).",
+            "I")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain to any tensor type.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int64)"},
+            "Constrain output to integral tensor. It must be a scalar(tensor of empty shape).")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto* output_tensor_type = ctx.getOutputType(0)->mutable_tensor_type();
+          output_tensor_type->set_elem_type(TensorProto::INT64);
+          output_tensor_type->mutable_shape()->Clear();
+        }));
+
+// Updated operators that consume/produce sequence of tensors.
+
+static const char* SplitToSequence_ver11_doc =
+    R"DOC(Split a tensor into a sequence of tensors, along the specified
+'axis'. Lengths of the parts can be specified using argument 'split'.
+'split' must contain only positive numbers.
+'split' is either a scalar (tensor of empty shape), or a 1-D tensor.
+If 'split' is a scalar, then 'input' will be split into equally sized chunks(if possible).
+Last chunk will be smaller if the 'input' size along the given axis 'axis' is not divisible
+by 'split'.
+Otherwise, the tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis'
+specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the
+dimension size of input tensor on 'axis'.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SplitToSequence,
+    11,
+    OpSchema()
+        .Input(0, "input", "The tensor to split", "T")
+        .Input(
+            1,
+            "split",
+            "Length of each output. "
+            "It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. ",
+            "I",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "output_sequence",
+            "One or more outputs forming a sequence of tensors after splitting",
+            "S")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input types to all tensor types.")
+        .TypeConstraint(
+            "I",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain split size to integral tensor.")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain output types to all tensor types.")
+        .Attr(
+            "axis",
+            "Which axis to split on. "
+            "A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "keepdims",
+            "Keep the split dimension or not. Default 1, which means we keep split dimension. "
+            "If input 'split' is specified, this attribute is ignored.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .SetDoc(SplitToSequence_ver11_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const auto input0_type = ctx.getInputType(0);
+          if(nullptr == input0_type) {
+              fail_type_inference("Input type for input at index 0 is null. Type info is expected.")
+          }
+          ctx.getOutputType(0)
+              ->mutable_sequence_type()
+              ->mutable_elem_type()
+              ->mutable_tensor_type()
+              ->set_elem_type(input0_type->tensor_type().elem_type());
+
+          if (!hasInputShape(ctx, 0)) {
+            return;
+          }
+
+          const auto& inputShape = input0_type->tensor_type().shape();
+
+          int r = inputShape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -r || axis > r - 1) {
+            fail_shape_inference(
+                "Invalid value of attribute 'axis'. Rank=",
+                r,
+                " Value=",
+                axis);
+          }
+          if (axis < 0) {
+            axis += r;
+          }
+
+          size_t num_inputs = ctx.getNumInputs();
+          int64_t splitSize = 1;
+          int64_t keepdims = 1;
+          if (num_inputs == 1) {
+            // input split is omitted, default to split by 1.
+            auto attr_proto = ctx.getAttribute("keepdims");
+            if (attr_proto) {
+              keepdims = attr_proto->i();
+            }
+          } else {
+            splitSize = [&]() -> int64_t {
+              // Need input split shape info and initializer data to infer split sizes.
+              if (!hasInputShape(ctx, 1)) {
+                return -1;
+              }
+              const TensorProto* splitInitializer = ctx.getInputData(1);
+              if (nullptr == splitInitializer || !splitInitializer->has_data_type()) {
+                return -1;
+              }
+
+              std::vector<int64_t> splitSizes;
+              if (splitInitializer->data_type() == TensorProto::INT64) {
+                const auto& data = ParseData<int64_t>(splitInitializer);
+                splitSizes.insert(splitSizes.end(), data.begin(), data.end());
+              } else if (splitInitializer->data_type() == TensorProto::INT32) {
+                const auto& data = ParseData<int32_t>(splitInitializer);
+                splitSizes.insert(splitSizes.end(), data.begin(), data.end());
+              } else {
+                // unaccepted data type
+                fail_shape_inference(
+                    "Only supports `int32_t` or `int64_t` inputs for split");
+              }
+
+              if (splitSizes.size() == 0) {
+                fail_shape_inference(
+                    "Input 'split' can not be empty.");
+              }
+
+              const auto& splitDim = inputShape.dim(axis);
+              if (!splitDim.has_dim_value()) {
+                // Unable to verify nor infer exact split dimension size.
+                return -1;
+              }
+
+              int64_t splitDimValue = splitDim.dim_value();
+              const auto& splitShape = getInputShape(ctx, 1);
+              if (splitShape.dim_size() == 0) {
+                // split is scalar
+                if (splitDimValue % splitSizes[0] == 0) {
+                  // all output chunks have the same shape, assign that to output sequence shape.
+                  return splitSizes[0];
+                }
+                return -1;
+              } else {
+                // split is 1-D tensor
+                int64_t splitSizesSum = std::accumulate(splitSizes.begin(), splitSizes.end(), (int64_t)0);
+                if (splitDimValue != splitSizesSum) {
+                  fail_shape_inference(
+                      "Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=",
+                      splitDimValue,
+                      " sum of split values=",
+                      splitSizesSum);
+                }
+                if (std::adjacent_find(splitSizes.begin(), splitSizes.end(), std::not_equal_to<int64_t>()) == splitSizes.end()) {
+                  // all split sizes are the same.
+                  return splitSizes[0];
+                }
+                return -1;
+              }
+            }();
+          }
+
+          if (keepdims) {
+            auto* outputShape =
+                ctx.getOutputType(0)
+                    ->mutable_sequence_type()
+                    ->mutable_elem_type()
+                    ->mutable_tensor_type()
+                    ->mutable_shape();
+            *outputShape = inputShape;
+            auto* dim = outputShape->mutable_dim(axis);
+            // Tensors in sequence could not have different shapes explicitly.
+            // Only assign dim_value when all chunks have the same shape.
+            if (splitSize > 0) {
+              dim->set_dim_value(splitSize);
+            } else {
+              dim->clear_dim_value();
+              dim->clear_dim_param();
+            }
+          } else {
+            TensorShapeProto* outputShape =
+                ctx.getOutputType(0)
+                    ->mutable_sequence_type()
+                    ->mutable_elem_type()
+                    ->mutable_tensor_type()
+                    ->mutable_shape();
+            for (int i = 0; i < inputShape.dim_size(); ++i) {
+              if (i != axis) {
+                auto* dim = outputShape->add_dim();
+                dim->CopyFrom(inputShape.dim(i));
+              }
+            }
+          }
+        }));
+
+static const char* ConcatFromSequence_ver11_doc = R"DOC(
+Concatenate a sequence of tensors into a single tensor.
+All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
+By default 'new_axis' is 0, the behavior is similar to numpy.concatenate.
+When 'new_axis' is 1, the behavior is similar to numpy.stack.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ConcatFromSequence,
+    11,
+    OpSchema()
+        .Attr(
+            "axis",
+            "Which axis to concat on. Accepted range in `[-r, r - 1]`, "
+            "where `r` is the rank of input tensors. "
+            "When `new_axis` is 1, accepted range is `[-r - 1, r]`. ",
+            AttributeProto::INT)
+        .Attr(
+            "new_axis",
+            "Insert and concatenate on a new axis or not, "
+            "default 0 means do not insert new axis.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .SetDoc(ConcatFromSequence_ver11_doc)
+        .Input(
+            0,
+            "input_sequence",
+            "Sequence of tensors for concatenation",
+            "S")
+        .Output(0, "concat_result", "Concatenated tensor", "T")
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain input types to any tensor type.")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          const auto input0_type = ctx.getInputType(0);
+          if(nullptr == input0_type) {
+              fail_type_inference("Input type for input at index 0 is null. Type info is expected.")
+          }
+          auto elem_type = input0_type->sequence_type().elem_type().tensor_type().elem_type();
+          ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(elem_type);
+
+          if (!hasInputShape(ctx, 0)) {
+            return;
+          }
+
+          auto axis_attr = ctx.getAttribute("axis");
+          if (!axis_attr) {
+            fail_shape_inference("Required attribute axis is missing");
+          }
+          int axis = static_cast<int>(axis_attr->i());
+
+          int new_axis = 0;
+          auto new_axis_attr = ctx.getAttribute("new_axis");
+          if (new_axis_attr) {
+            new_axis = static_cast<int>(new_axis_attr->i());
+          }
+
+          const auto& input_shape =
+              ctx.getInputType(0)->sequence_type().elem_type().tensor_type().shape();
+          auto rank = input_shape.dim_size();
+          if (1 != new_axis && 0 != new_axis) {
+            fail_shape_inference("new_axis must be either 0 or 1");
+          }
+
+          auto upper_bound = 1 == new_axis ? rank : rank - 1;
+          auto lower_bound = 1 == new_axis ? -rank - 1 : -rank;
+
+          if (axis < lower_bound || axis > upper_bound) {
+            fail_shape_inference(
+              "Invalid value of attribute 'axis'. Accepted range=[",
+              lower_bound,
+              ", ",
+              upper_bound,
+              "], Value=",
+              axis);
+          }
+
+          if (axis < 0) {
+            axis += (upper_bound + 1);
+          }
+
+          auto* output_shape =
+              ctx.getOutputType(0)
+                  ->mutable_tensor_type()
+                  ->mutable_shape();
+
+          for (int i = 0; i <= upper_bound; ++i) {
+            output_shape->add_dim();
+            if (i != axis) {
+              output_shape->mutable_dim(i)->CopyFrom(input_shape.dim((i > axis && new_axis) ? i - 1 : i));
+            }
+          }
+        }));
+
+static const char* SequenceMap_ver17_doc = R"DOC(
+Applies a sub-graph to each sample in the input sequence(s).
+
+Inputs can be either tensors or sequences, with the exception of the first input which must
+be a sequence. The length of the first input sequence will determine the number of samples in the
+outputs. Any other sequence inputs should have the same number of samples. The number of inputs
+and outputs, should match the one of the subgraph.
+
+For each i-th element in the output, a sample will be extracted from the input sequence(s) at
+the i-th position and the sub-graph will be applied to it.
+The outputs will contain the outputs of the sub-graph for each sample, in the same order as in
+the input.
+
+This operator assumes that processing each sample is independent and could executed in parallel
+or in any order. Users cannot expect any specific ordering in which each subgraph is computed.)DOC";
+
+void SequenceMapInferenceFunction(InferenceContext& ctx) {
+  auto num_inputs = ctx.getNumInputs();
+  assert(num_inputs > 0);
+
+  auto num_outputs = ctx.getNumOutputs();
+  assert(num_outputs > 0);
+
+  std::vector<TypeProto> tmp_type_protos(num_inputs);
+  std::vector<const TypeProto*> subgraph_input_types;
+  subgraph_input_types.reserve(num_inputs);
+  for (size_t inputIndex = 0; inputIndex < num_inputs; inputIndex++) {
+    auto input_type = ctx.getInputType(inputIndex);
+    if (input_type == nullptr) {
+      fail_type_inference("Input ", inputIndex, " expected to have type info");
+    }
+    if (input_type->value_case() == TypeProto::kSequenceType) {
+      tmp_type_protos[inputIndex].CopyFrom(input_type->sequence_type().elem_type());
+      subgraph_input_types.push_back(&tmp_type_protos[inputIndex]);
+    } else {
+      if (inputIndex == 0)
+        fail_type_inference("Input ", inputIndex, " expected to be a sequence type");
+      subgraph_input_types.push_back(input_type);
+    }
+  }
+
+  GraphInferencer* graphInferencer = ctx.getGraphAttributeInferencer("body");
+  if (!graphInferencer)
+    fail_type_inference("Graph attribute inferencer for \"body\" not available");
+
+  std::vector<const TensorProto*> input_data(num_inputs, nullptr);
+  std::vector<const TypeProto*> subgraph_output_types =
+    graphInferencer->doInferencing(subgraph_input_types, input_data);
+
+  // if empty(), assume inferencing was skipped
+  if (!subgraph_output_types.empty()) {
+    if (subgraph_output_types.size() != num_outputs) {
+      fail_type_inference(
+          "Graph attribute inferencing returned type information for ",
+          subgraph_output_types.size(), " outputs. Expected ", num_outputs);
+    }
+
+    for (size_t outputIndex = 0; outputIndex < num_outputs; outputIndex++) {
+      auto* subgraph_output_type = subgraph_output_types[outputIndex];
+      ctx.getOutputType(outputIndex)
+          ->mutable_sequence_type()
+          ->mutable_elem_type()
+          ->CopyFrom(*subgraph_output_type);
+    }
+  }
+}
+
+bool BuildSequenceMapBodyFunc(const FunctionBodyBuildContext& ctx,
+                              const OpSchema& schema,
+                              FunctionProto& functionProto) {
+  schema.BuildFunction(functionProto);
+
+  // variadic input/outputs will be expanded
+  functionProto.clear_input();
+  functionProto.clear_output();
+
+  auto body_attr = ctx.getAttribute("body");
+  if (!body_attr || !body_attr->has_g())
+    ONNX_THROW_EX(
+      std::invalid_argument("Invalid ``body`` argument. Expected a graph"));
+  const GraphProto& body = body_attr->g();
+
+  auto g_inputs = body.input();
+  int ninputs = g_inputs.size();
+  if (ninputs < 1)
+    ONNX_THROW_EX(
+      std::invalid_argument("Expected 1 or more inputs."));
+
+  auto g_outputs = body.output();
+  int noutputs = g_outputs.size();
+  if (noutputs < 1)
+    ONNX_THROW_EX(
+      std::invalid_argument("Expected 1 or more outputs."));
+
+  if (!ctx.hasInput(0))
+    ONNX_THROW_EX(
+      std::invalid_argument(MakeString("Input 0 expected but not provided")));
+
+  const auto* first_input_type = ctx.getInputType(0);
+  assert(first_input_type);
+  if (!first_input_type->has_sequence_type())
+    ONNX_THROW_EX(
+      std::invalid_argument("Expected a sequence type for input 0"));
+
+  auto schema_inputs = schema.inputs();
+  auto input_0_name = schema_inputs[0].GetName();
+  auto input_1_name = schema_inputs[1].GetName();  // variadic input
+
+  *functionProto.add_input() = input_0_name;
+  for (int i = 1; i < ninputs; i++) {
+    if (!ctx.hasInput(i))
+      ONNX_THROW_EX(
+        std::invalid_argument(MakeString("Input ", i, " expected but not provided")));
+    *functionProto.add_input() = MakeString(input_1_name, "_", i);
+  }
+
+  auto schema_outputs = schema.outputs();
+  auto output_0_name = schema_outputs[0].GetName();
+  for (int i = 0; i < noutputs; i++) {
+    if (!ctx.hasOutput(i))
+      ONNX_THROW_EX(
+        std::invalid_argument(MakeString("Output ", i, " expected but not provided")));
+    *functionProto.add_output() = MakeString(output_0_name, "_", i);
+  }
+
+  // Loop body subgraph
+  std::string loopbody_graph_name("SequenceMap_loop_body");
+  GraphProto loopbody_graph;
+  loopbody_graph.set_name(loopbody_graph_name);
+  {
+    TypeProto int64_type;
+    int64_type.mutable_tensor_type()->set_elem_type(TensorProto_DataType_INT64);
+    int64_type.mutable_tensor_type()->mutable_shape()->Clear();
+
+    TypeProto bool_type;
+    bool_type.mutable_tensor_type()->set_elem_type(TensorProto_DataType_BOOL);
+    bool_type.mutable_tensor_type()->mutable_shape()->Clear();
+
+    ValueInfoProto iter_count;
+    std::string iter_count_name = MakeString(loopbody_graph_name, "_itercount");
+    iter_count.set_name(iter_count_name);
+    *iter_count.mutable_type() = int64_type;
+    *loopbody_graph.add_input() = iter_count;
+
+    ValueInfoProto cond_in;
+    std::string cond_in_name = MakeString(loopbody_graph_name, "_cond_in");
+    cond_in.set_name(cond_in_name);
+    *cond_in.mutable_type() = bool_type;
+    *loopbody_graph.add_input() = cond_in;
+
+    ValueInfoProto cond_out;
+    std::string cond_out_name = MakeString(loopbody_graph_name, "_cond_out");
+    cond_out.set_name(cond_out_name);
+    *cond_out.mutable_type() = bool_type;
+    *loopbody_graph.add_output() = cond_out;
+
+    NodeProto cond_identity;
+    cond_identity.set_domain(ONNX_DOMAIN);
+    cond_identity.set_op_type("Identity");
+    cond_identity.add_input(cond_in_name);
+    cond_identity.add_output(cond_out_name);
+    *loopbody_graph.add_node() = cond_identity;
+
+    for (int inputIndex = 0; inputIndex < ninputs; inputIndex++) {
+      const auto* input_type = ctx.getInputType(inputIndex);
+      if (input_type && input_type->has_sequence_type()) {
+        // If it's a sequence input, extract ``iter_count`` element
+        NodeProto seq_at_node;
+        seq_at_node.set_domain(ONNX_DOMAIN);
+        seq_at_node.set_op_type("SequenceAt");
+        seq_at_node.add_input(functionProto.input(inputIndex));
+        seq_at_node.add_input(iter_count_name);
+        seq_at_node.add_output(g_inputs[inputIndex].name());
+        *loopbody_graph.add_node() = seq_at_node;
+      } else {
+        // If not a sequence, simply connect
+        NodeProto identity;
+        identity.set_domain(ONNX_DOMAIN);
+        identity.set_op_type("Identity");
+        identity.add_input(functionProto.input(inputIndex));
+        identity.add_output(g_inputs[inputIndex].name());
+        *loopbody_graph.add_node() = identity;
+      }
+    }
+
+    for (const auto& item : body.node())
+      *loopbody_graph.add_node() = item;
+    for (const auto& item : body.value_info())
+      *loopbody_graph.add_value_info() = item;
+    for (const auto& item : body.initializer())
+      *loopbody_graph.add_initializer() = item;
+    for (const auto& item : body.sparse_initializer())
+      *loopbody_graph.add_sparse_initializer() = item;
+
+    for (int outputIndex = 0; outputIndex < noutputs; outputIndex++) {
+      const auto &body_out_i = body.output(outputIndex);
+      assert(body_out_i.type().has_tensor_type());
+      std::string prefix = MakeString(loopbody_graph_name, "_", body_out_i.name());
+      std::string loopbody_in_name = MakeString(prefix, "_in");
+
+      ValueInfoProto tmp;
+      *tmp.mutable_type()
+          ->mutable_sequence_type()
+          ->mutable_elem_type()
+          ->mutable_tensor_type() = body_out_i.type().tensor_type();
+      tmp.set_name(loopbody_in_name);
+      *loopbody_graph.add_input() = tmp;
+
+      std::string loopbody_out_name = MakeString(prefix, "_out");
+      tmp.set_name(loopbody_out_name);
+      *loopbody_graph.add_output() = tmp;
+
+      NodeProto seq_insert_node;
+      seq_insert_node.set_domain(ONNX_DOMAIN);
+      seq_insert_node.set_op_type("SequenceInsert");
+      seq_insert_node.add_input(loopbody_in_name);
+      seq_insert_node.add_input(body_out_i.name());
+      seq_insert_node.add_output(loopbody_out_name);
+      *loopbody_graph.add_node() = seq_insert_node;
+    }
+  }
+
+  std::vector<FunctionBodyHelper::NodeDef> nodes;
+
+  // TODO: figure out a way to prevent name collisions?
+  auto first_input_name = functionProto.input(0);
+  std::string prefix = MakeString("SequenceMap_", first_input_name);
+  std::string seqlen = MakeString(prefix, "_seqlen");
+  nodes.push_back({{seqlen}, "SequenceLength", {first_input_name}});
+
+  std::string cond_bool = MakeString(prefix, "_cond");
+  nodes.push_back(FunctionBodyHelper::Const<bool>(cond_bool, true));
+
+  std::vector<std::string> loop_node_inputs = {seqlen, cond_bool};
+  std::vector<std::string> loop_node_outputs;
+  for (int outputIndex = 0; outputIndex < noutputs; outputIndex++) {
+    auto output_name = functionProto.output(outputIndex);
+    std::string out_prefix = MakeString("SequenceMap_", output_name);
+
+    std::string seqempty_name = MakeString(out_prefix, "_seqempty");
+    int64_t dtype = g_outputs[outputIndex].type().tensor_type().elem_type();
+    nodes.push_back({
+      {seqempty_name},
+      "SequenceEmpty",
+      {},
+      {MakeAttribute("dtype", dtype)}
+    });
+    loop_node_inputs.push_back(seqempty_name);
+    loop_node_outputs.push_back(output_name);
+  }
+
+  nodes.push_back({
+    loop_node_outputs,
+    "Loop",
+    loop_node_inputs,
+    {MakeAttribute("body", loopbody_graph)}
+  });
+
+  auto func_nodes = FunctionBodyHelper::BuildNodes(nodes);
+  for (const auto& node : func_nodes) {
+    auto new_node = functionProto.add_node();
+    new_node->CopyFrom(node);
+  }
+
+  return true;
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SequenceMap,
+    17,
+    OpSchema()
+        .SetDoc(SequenceMap_ver17_doc)
+        .Attr(
+            "body",
+            "The graph to be run for each sample in the sequence(s). "
+            "It should have as many inputs and outputs as inputs and "
+            "outputs to the SequenceMap function.",
+            AttributeProto::GRAPH)
+        .Input(
+            0,
+            "input_sequence",
+            "Input sequence.",
+            "S")
+        .Input(
+            1,
+            "additional_inputs",
+            "Additional inputs to the graph",
+            "V",
+            OpSchema::Variadic,
+            false,
+            0)
+        .Output(
+            0,
+            "out_sequence",
+            "Output sequence(s)",
+            "S",
+            OpSchema::Variadic,
+            false)
+        .TypeConstraint(
+            "S",
+            OpSchema::all_tensor_sequence_types(),
+            "Constrain input types to any sequence type.")
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types();
+              auto s = OpSchema::all_tensor_sequence_types();
+              t.insert(t.end(), s.begin(), s.end());
+              return t;
+            }(),
+            "Constrain to any tensor or sequence type.")
+        .SetContextDependentFunctionBodyBuilder(BuildSequenceMapBodyFunc)
+        .TypeAndShapeInferenceFunction(SequenceMapInferenceFunction));
+
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/shape_inference.cc b/third_party/onnx/onnx/defs/shape_inference.cc
new file mode 100644
index 0000000000..e196d23428
--- /dev/null
+++ b/third_party/onnx/onnx/defs/shape_inference.cc
@@ -0,0 +1,471 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "shape_inference.h"
+#include "onnx/defs/tensor_proto_util.h"
+
+namespace ONNX_NAMESPACE {
+
+// Note: for all methods below for propagating type or shape, callers are
+// responsible to handle optional inputs/outputs and ensure that the specified
+// index value is less than NumInputs/NumOutputs.
+// Supports mixed tensor and sparse tensor
+void propagateElemTypeFromTensorInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex) {
+  auto input_type = ctx.getInputType(inputIndex);
+  if (nullptr == input_type) {
+    fail_type_inference("Input type was null");
+  }
+
+  const auto input_value_case = input_type->value_case();
+  if (input_value_case != TypeProto::kTensorType && input_value_case != TypeProto::kSparseTensorType) {
+    fail_type_inference(
+        "Input ", inputIndex, " expected to have tensor or sparse tensor type. Got: ", input_value_case);
+  }
+
+  const auto input_elem_type = getTensorElementType(*input_type);
+  if (input_elem_type == TensorProto::UNDEFINED) {
+    fail_type_inference("Element type of input ", inputIndex, " unknown");
+  }
+  auto output_type = ctx.getOutputType(outputIndex);
+  const auto output_value_case = output_type->value_case();
+  if (output_value_case == TypeProto::kTensorType || output_value_case == TypeProto::kSparseTensorType) {
+    setTensorElementType(input_elem_type, output_value_case, *output_type);
+  } else if (output_value_case == TypeProto::VALUE_NOT_SET) {
+    // Assume output will have the same type
+    setTensorElementType(input_elem_type, input_value_case, *output_type);
+  } else {
+    // This is not expected to happen
+    fail_type_inference(
+        "Output ", outputIndex, " expected to have tensor or sparse tensor type. Got: ", output_value_case);
+  }
+}
+
+void propagateElemTypeFromSequenceInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex) {
+  auto input_type = ctx.getInputType(inputIndex);
+  if (nullptr == input_type || input_type->value_case() != TypeProto::kSequenceType) {
+    fail_type_inference("Input ", inputIndex, " expected to have sequence type");
+  }
+  auto input_seq_type = input_type->sequence_type();
+  if (!input_seq_type.has_elem_type()) {
+    fail_type_inference("Element type of sequence input ", inputIndex, " unknown");
+  }
+
+  auto output_type = ctx.getOutputType(outputIndex);
+  output_type->mutable_sequence_type()->mutable_elem_type()->CopyFrom(input_seq_type.elem_type());
+}
+
+void propagateElemTypeFromOptionalInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex) {
+  auto input_type = ctx.getInputType(inputIndex);
+  if (nullptr == input_type || input_type->value_case() != TypeProto::kOptionalType) {
+    fail_type_inference("Input ", inputIndex, " expected to have optional type");
+  }
+  auto input_opt_type = input_type->optional_type();
+  if (!input_opt_type.has_elem_type()) {
+    fail_type_inference("Element type of optional input ", inputIndex, " unknown");
+  }
+
+  auto output_type = ctx.getOutputType(outputIndex);
+  output_type->mutable_optional_type()->mutable_elem_type()->CopyFrom(input_opt_type.elem_type());
+}
+
+void propagateElemTypeFromMapInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex) {
+  auto input_type = ctx.getInputType(inputIndex);
+  if (nullptr == input_type || input_type->value_case() != TypeProto::kMapType) {
+    fail_type_inference("Input ", inputIndex, " expected to have map type");
+  }
+  auto input_map_type = input_type->map_type();
+  if (!input_map_type.has_key_type()) {
+    fail_type_inference("Key type of map input ", inputIndex, " unknown");
+  }
+  if (!input_map_type.has_value_type()) {
+    fail_type_inference("Value type of map input ", inputIndex, " unknown");
+  }
+
+  auto output_type = ctx.getOutputType(outputIndex);
+  output_type->mutable_map_type()->set_key_type(input_map_type.key_type());
+  output_type->mutable_map_type()->mutable_value_type()->CopyFrom(input_map_type.value_type());
+}
+
+void propagateElemTypeFromInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex) {
+  auto input_type = ctx.getInputType(inputIndex);
+  if (nullptr == input_type) {
+    fail_type_inference("Input ", inputIndex, " expected to have type but instead is null");
+  }
+  const auto input_value_case = input_type->value_case();
+  if (input_value_case == TypeProto::kTensorType || input_value_case == TypeProto::kSparseTensorType) {
+    propagateElemTypeFromTensorInputToOutput(ctx, inputIndex, outputIndex);
+  } else if (input_value_case == TypeProto::kSequenceType) {
+    propagateElemTypeFromSequenceInputToOutput(ctx, inputIndex, outputIndex);
+  } else if (input_value_case == TypeProto::kOptionalType) {
+    propagateElemTypeFromOptionalInputToOutput(ctx, inputIndex, outputIndex);
+  } else if (input_value_case == TypeProto::kMapType) {
+    propagateElemTypeFromMapInputToOutput(ctx, inputIndex, outputIndex);
+  }
+}
+
+/*
+Merge shape information from a source shape into a target shape.
+* merges each TensorShapeProto_Dimension separately.
+* prefer values over params.
+* If both have values, values must match.
+* prefer target param over source param if mismatched.
+* Fail if there are mismatches in number of dimensions or dimension values.
+*/
+void mergeInShapeInfo(const TensorShapeProto& source, TensorShapeProto& target) {
+  auto num_source_dims = source.dim_size();
+  auto num_target_dims = target.dim_size();
+  if (num_source_dims != num_target_dims) {
+    fail_shape_inference(
+        "Mismatch between number of source and target dimensions. Source=",
+        num_source_dims,
+        " Target=",
+        num_target_dims);
+  }
+
+  auto& source_dims = source.dim();
+  auto* target_dims = target.mutable_dim();
+
+  for (int i = 0, end = source_dims.size(); i < end; ++i) {
+    auto& source_dim = source_dims.Get(i);
+    auto& target_dim = *target_dims->Mutable(i);
+    mergeInDimensionInfo(source_dim, target_dim, i);
+  }
+}
+
+void mergeInShapeInfo(const TensorShapeProto& source_shape, TypeProto_Tensor& target_type) {
+  if (target_type.has_shape()) {
+    // merge with existing info.
+    mergeInShapeInfo(source_shape, *target_type.mutable_shape());
+  } else {
+    // copy to target
+    (*target_type.mutable_shape()) = source_shape;
+  }
+}
+
+void mergeInShapeInfo(const TensorShapeProto& source_shape, TypeProto_SparseTensor& target_type) {
+  if (target_type.has_shape()) {
+    // merge with existing info.
+    mergeInShapeInfo(source_shape, *target_type.mutable_shape());
+  } else {
+    // copy to target
+    (*target_type.mutable_shape()) = source_shape;
+  }
+}
+
+/*
+Merge the shape information from two TypeProto_Tensor instances.
+Values are merged into target from source.
+If target has no shape information, copy from source.
+If source has no shape information, ignore source.
+If both have shape information:
+- merge each TensorShapeProto_Dimension separately.
+- Prefer values over params. If both have values, values must match.
+- Prefer target param over source param if mismatched.
+Fail if there are mismatches in number of dimensions or dimension values.
+*/
+void mergeInShapeInfo(const TypeProto_Tensor& source, TypeProto_Tensor& target) {
+  if (source.has_shape())
+    mergeInShapeInfo(source.shape(), target);
+}
+
+void mergeInShapeInfo(const TypeProto_SparseTensor& source, TypeProto_SparseTensor& target) {
+  if (source.has_shape())
+    mergeInShapeInfo(source.shape(), target);
+}
+
+/// <summary>
+/// Utility function for UnionShapeInfoForTensor.
+/// Both shapes must be of the same rank
+/// </summary>
+/// <param name="source_shape"></param>
+/// <param name="target_shape">destination shape</param>
+void UnionShapeInfo(const TensorShapeProto& source_shape, TensorShapeProto& target_shape) {
+  auto source_rank = source_shape.dim_size();
+  for (int i = 0; i < source_rank; ++i) {
+    const auto source_dim = source_shape.dim(i);
+    const auto target_dim = target_shape.dim(i);
+    bool is_dims_conflict = [&]() {
+      if (source_dim.has_dim_value()) {
+        if (target_dim.has_dim_value() && target_dim.dim_value() == source_dim.dim_value()) {
+          return false;
+        }
+        return true;
+      }
+
+      if (source_dim.has_dim_param()) {
+        if (target_dim.has_dim_param() && target_dim.dim_param() == source_dim.dim_param()) {
+          return false;
+        }
+        return true;
+      }
+
+      return (target_dim.has_dim_value() || target_dim.has_dim_param());
+    }();
+    if (is_dims_conflict && (target_dim.has_dim_value() || target_dim.has_dim_param())) {
+      auto dim = target_shape.mutable_dim(i);
+      dim->clear_dim_value();
+      dim->clear_dim_param();
+    }
+  }
+}
+
+template<typename TENSOR_TYPE>
+void UnionShapeInfoForTensor(const TensorShapeProto& source_shape, TENSOR_TYPE& target_type) {
+  if (target_type.has_shape()) {
+    TensorShapeProto* target_shape = target_type.mutable_shape();
+
+    auto source_rank = source_shape.dim_size();
+    auto target_rank = target_shape->dim_size();
+    if (source_rank != target_rank) {
+      target_type.clear_shape();
+      return;
+    }
+
+    UnionShapeInfo(source_shape, *target_shape);
+  }
+}
+
+void UnionShapeInfo(const TensorShapeProto& source_shape, TypeProto_Tensor& target_type) {
+  UnionShapeInfoForTensor(source_shape, target_type);
+}
+
+void UnionShapeInfo(const TensorShapeProto& source_shape, TypeProto_SparseTensor& target_type) {
+  UnionShapeInfoForTensor(source_shape, target_type);
+}
+
+
+void UnionTypeInfo(const TypeProto& source_type, TypeProto& target_type) {
+  if (source_type.value_case() != target_type.value_case()) {
+    fail_type_inference("Mismatched type:", " source=", source_type.value_case(), " target=", target_type.value_case());
+  }
+
+  const auto target_case = target_type.value_case();
+  if (target_case == TypeProto::ValueCase::kTensorType) {
+    auto source_elem_type = source_type.tensor_type().elem_type();
+    auto target_elem_type = target_type.tensor_type().elem_type();
+
+    if (source_elem_type != target_elem_type) {
+      fail_type_inference(
+          "Mismatched tensor element type:", " source=", source_elem_type, " target=", target_elem_type);
+    }
+
+    UnionShapeInfoForTensor(source_type.tensor_type().shape(), *target_type.mutable_tensor_type());
+  } else if (target_case == TypeProto::ValueCase::kSparseTensorType) {
+    auto source_elem_type = source_type.sparse_tensor_type().elem_type();
+    auto target_elem_type = target_type.sparse_tensor_type().elem_type();
+    if (source_elem_type != target_elem_type) {
+      fail_type_inference(
+          "Mismatched sparse tensor element type:", " source=", source_elem_type, " target=", target_elem_type);
+    }
+
+    UnionShapeInfoForTensor(source_type.sparse_tensor_type().shape(), *target_type.mutable_sparse_tensor_type());
+  } else if (target_case == TypeProto::ValueCase::kSequenceType) {
+    if (!source_type.sequence_type().has_elem_type()) {
+      fail_type_inference("source sequence type missing element type.");
+    }
+    if (!target_type.sequence_type().has_elem_type()) {
+      fail_type_inference("target sequence type missing element type.");
+    }
+    UnionTypeInfo(source_type.sequence_type().elem_type(), *target_type.mutable_sequence_type()->mutable_elem_type());
+  } else if (target_case == TypeProto::ValueCase::kOptionalType) {
+    if (!source_type.optional_type().has_elem_type()) {
+      fail_type_inference("source optional type missing element type.");
+    }
+    if (!target_type.optional_type().has_elem_type()) {
+      fail_type_inference("target optional type missing element type.");
+    }
+    UnionTypeInfo(source_type.optional_type().elem_type(), *target_type.mutable_optional_type()->mutable_elem_type());
+  } else if (target_case == TypeProto::ValueCase::kMapType) {
+    if (!source_type.map_type().has_key_type()) {
+      fail_type_inference("source map type missing key type.");
+    }
+    if (!target_type.map_type().has_key_type()) {
+      fail_type_inference("target map type missing key type.");
+    }
+    auto source_key_type = source_type.map_type().key_type();
+    auto target_key_type = target_type.map_type().key_type();
+    if (source_key_type != target_key_type) {
+      fail_type_inference(
+          "Mismatched map tensor key type:", " source=", 
+          Utils::DataTypeUtils::ToDataTypeString(source_key_type),
+          " target=", Utils::DataTypeUtils::ToDataTypeString(target_key_type));
+    }
+
+    if (!source_type.map_type().has_value_type()) {
+      fail_type_inference("source map type missing value type.");
+    }
+    if (!target_type.map_type().has_value_type()) {
+      fail_type_inference("target map type missing value type.");
+    }
+    UnionTypeInfo(source_type.map_type().value_type(), *target_type.mutable_map_type()->mutable_value_type());
+  }
+}
+
+// Supports both Tensor and SparseTensor
+// This does not fail if input_type is Tensor and output type is SparseTensor
+// or the other way around. This is to support mixed cases when an op receives
+// sparse input and outputs dense or vice-versa.
+// If the output value_case is not set, then
+// the input value_case is propagated.
+void propagateTensorElemTypeWithValidation(const TypeProto* input_type, TypeProto* output_type) {
+  if (nullptr == input_type) {
+    fail_type_inference("Input type was null");
+  }
+
+  int32_t input_elem_type = TensorProto::UNDEFINED;
+  const auto input_value_case = input_type->value_case();
+  if (input_value_case == TypeProto::kTensorType || input_value_case == TypeProto::kSparseTensorType) {
+    input_elem_type = getTensorElementType(*input_type);
+    if (input_elem_type == TensorProto::UNDEFINED) {
+      fail_type_inference("Element type of tensor or sparse tensor input was unknown");
+    }
+  } else {
+    fail_type_inference("Input was expected to have tensor or sparse tensor type. Got ", input_value_case);
+  }
+
+  const auto output_value_case = output_type->value_case();
+  if (output_value_case == TypeProto::VALUE_NOT_SET) {
+    setTensorElementType(input_elem_type, input_value_case, *output_type);
+  } else if (output_value_case == TypeProto::kTensorType || output_value_case == TypeProto::kSparseTensorType) {
+    const auto output_elem_type = getTensorElementType(*output_type);
+    if (output_elem_type != TensorProto::UNDEFINED) {
+      if (input_elem_type != output_elem_type) {
+        fail_type_inference(
+            "Input element type of ", input_elem_type, " does not match existing output type of ", output_elem_type);
+      }
+    } else {
+      setTensorElementType(input_elem_type, output_value_case, *output_type);
+    }
+  } else {
+    // This is not expected to happen
+    fail_type_inference("Output was expected to have tensor type. Got ", output_value_case);
+  }
+}
+
+void propagateSequenceElemTypeWithValidation(const TypeProto* input_type, TypeProto* output_type) {
+  if (nullptr == input_type) {
+    fail_type_inference("Input type was null");
+  }
+
+  if (input_type->value_case() != TypeProto::kSequenceType) {
+    fail_type_inference("Input was expected to have sequence type. Got ", input_type->value_case());
+  }
+
+  auto input_seq_type = input_type->sequence_type();
+
+  if (input_seq_type.has_elem_type()) {
+    propagateElemTypeWithValidation(
+        &input_seq_type.elem_type(), output_type->mutable_sequence_type()->mutable_elem_type());
+  } else {
+    fail_type_inference("Element type of sequence input was unknown");
+  }
+}
+
+void propagateOptionalElemTypeWithValidation(const TypeProto* input_type, TypeProto* output_type) {
+  if (nullptr == input_type) {
+    fail_type_inference("Input type was null");
+  }
+
+  if (input_type->value_case() != TypeProto::kOptionalType) {
+    fail_type_inference("Input was expected to have optional type. Got ", input_type->value_case());
+  }
+
+  auto input_opt_type = input_type->optional_type();
+
+  if (input_opt_type.has_elem_type()) {
+    propagateElemTypeWithValidation(
+        &input_opt_type.elem_type(), output_type->mutable_optional_type()->mutable_elem_type());
+  } else {
+    fail_type_inference("Element type of optional input was unknown");
+  }
+}
+
+void propagateMapElemTypeWithValidation(const TypeProto* input_type, TypeProto* output_type) {
+  if (nullptr == input_type) {
+    fail_type_inference("Input type was null");
+  }
+
+  if (input_type->value_case() != TypeProto::kMapType) {
+    fail_type_inference("Input was expected to have map type. Got ", input_type->value_case());
+  }
+
+  auto input_map_type = input_type->map_type();
+
+  if (!input_map_type.has_key_type()) {
+    fail_type_inference("Key type of map input was unknown");
+  }
+  if (!input_map_type.has_value_type()) {
+    fail_type_inference("Value type of map input was unknown");
+  }
+  output_type->mutable_map_type()->set_key_type(input_map_type.key_type());
+  propagateElemTypeWithValidation(
+      &input_map_type.value_type(), output_type->mutable_map_type()->mutable_value_type());
+}
+
+// propagate the element type from an input type to an output type.
+// if an existing output element type exists, validate it matches.
+void propagateElemTypeWithValidation(const TypeProto* input_type, TypeProto* output_type) {
+  if (nullptr == input_type) {
+    fail_type_inference("Input type was null");
+  }
+
+  const auto input_value_case = input_type->value_case();
+  if (input_value_case == TypeProto::kTensorType || input_value_case == TypeProto::kSparseTensorType) {
+    propagateTensorElemTypeWithValidation(input_type, output_type);
+  } else if (input_value_case == TypeProto::kSequenceType) {
+    propagateSequenceElemTypeWithValidation(input_type, output_type);
+  } else if (input_value_case == TypeProto::kOptionalType) {
+    propagateOptionalElemTypeWithValidation(input_type, output_type);
+  } else if (input_value_case == TypeProto::kMapType) {
+    propagateMapElemTypeWithValidation(input_type, output_type);
+  } else {
+    fail_type_inference("Input was expected to have either tensor, sequence, optional or map type. Got ", input_value_case);
+  }
+}
+
+TensorShapeProto getShapeInput(InferenceContext& ctx, size_t input_index, bool& found) {
+  TensorShapeProto shape_input;
+
+  // First, check initializer.
+  const TensorProto* shape_initializer = ctx.getInputData(input_index);
+  if (shape_initializer) {
+    const std::vector<int64_t>& shape_data = ParseData<int64_t>(shape_initializer);
+    for (const int64_t& e : shape_data) {
+      shape_input.add_dim()->set_dim_value(e);
+    }
+    found = true;
+    return shape_input;
+  }
+
+  // Then, check symbolic input.
+  const TensorShapeProto* symbolic_input = ctx.getSymbolicInput(input_index);
+  if (symbolic_input) {
+    shape_input.CopyFrom(*symbolic_input);
+    found = true;
+    return shape_input;
+  }
+
+  // Try rank inference.
+  if (hasInputShape(ctx, input_index)) {
+    const TensorShapeProto& shape_input_shape = getInputShape(ctx, input_index);
+    if (shape_input_shape.dim_size() != 1) {
+      fail_shape_inference("shape input must be 1D tensor");
+    }
+    if (shape_input_shape.dim(0).has_dim_value()) {
+      // Attempt rank inference using shape of shape input
+      int64_t dim_value = shape_input_shape.dim(0).dim_value();
+      for (int64_t i = 0; i < dim_value; ++i) {
+          shape_input.add_dim();
+      }
+      found = true;
+      return shape_input;
+    }
+  }
+
+  // Shape input was not found.
+  found = false;
+  return shape_input;
+}
+
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/defs/shape_inference.h b/third_party/onnx/onnx/defs/shape_inference.h
new file mode 100644
index 0000000000..c6acc61e1d
--- /dev/null
+++ b/third_party/onnx/onnx/defs/shape_inference.h
@@ -0,0 +1,800 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include "onnx/defs/data_type_utils.h"
+#include "onnx/proto_utils.h"
+#include "onnx/string_utils.h"
+#include <functional>
+
+namespace ONNX_NAMESPACE {
+
+using Dim = TensorShapeProto_Dimension;
+
+struct ShapeInferenceOptions {
+  // Checks the type-equality for input and output
+  bool check_type;
+  // 1: Will throw any node level shape infer errors
+  // 0: Won't throw node-level shape infer errors, but other errors
+  // like merging existing shape with inferred etc are thrown
+  int error_mode;
+  // Enables data propagation for limited operators
+  // to perform shape computation
+  bool enable_data_propagation;
+  ShapeInferenceOptions(bool check_type_val = false,
+    int strict_mode_val = 0,bool data_prop_val = false):
+    check_type(check_type_val), error_mode(strict_mode_val),
+    enable_data_propagation(data_prop_val) {};
+};
+
+// Maintains a SymbolTable for symbolic shape inference
+class SymbolTable {
+ public:
+    // Adds existing symbols from a main graph or subgraph
+    virtual void addFromGraph(const GraphProto& g) = 0;
+    // Creates a new symbol which is not duplicate as any existing one
+    virtual std::string createNew(const std::string& symbol_prefix) = 0;
+    virtual ~SymbolTable() = default;
+};
+
+class GraphInferencer {
+ public:
+  // Perform inferencing on the graph contained in GraphInferencer.
+  // Returns the graph output types post-inferencing.
+  virtual std::vector<const TypeProto*> doInferencing(
+      const std::vector<const TypeProto*>& inputTypes,
+      const std::vector<const TensorProto*>& inputData) = 0;
+  virtual ~GraphInferencer() = default;
+};
+
+// Exception class used for handling errors in type and shape inference
+
+class InferenceError final : public std::runtime_error {
+ public:
+  using std::runtime_error::runtime_error;
+
+  InferenceError(const std::string& message) : std::runtime_error(message) {}
+
+  const char* what() const noexcept override {
+    if (!expanded_message_.empty()) {
+      return expanded_message_.c_str();
+    }
+    return std::runtime_error::what();
+  }
+
+  void AppendContext(const std::string& context) {
+    expanded_message_ = ONNX_NAMESPACE::MakeString(std::runtime_error::what(), "\n\n==> Context: ", context);
+  }
+
+ private:
+  std::string expanded_message_;
+};
+
+#define fail_type_inference(...) \
+  ONNX_THROW_EX(ONNX_NAMESPACE::InferenceError(ONNX_NAMESPACE::MakeString("[TypeInferenceError] ", __VA_ARGS__)));
+
+#define fail_shape_inference(...) \
+  ONNX_THROW_EX(ONNX_NAMESPACE::InferenceError(ONNX_NAMESPACE::MakeString("[ShapeInferenceError] ", __VA_ARGS__)));
+
+struct InferenceContext {
+  virtual const AttributeProto* getAttribute(const std::string& name) const = 0;
+  virtual size_t getNumInputs() const = 0;
+  virtual const TypeProto* getInputType(size_t index) const = 0;
+  virtual const TensorProto* getInputData(size_t index) const = 0;
+  virtual size_t getNumOutputs() const = 0;
+  virtual TypeProto* getOutputType(size_t index) = 0;
+  virtual GraphInferencer* getGraphAttributeInferencer(const std::string& attribute_name) = 0;
+  virtual ~InferenceContext() {}
+  virtual const SparseTensorProto* getInputSparseData(size_t index) const = 0;
+  // Gets the shape inputs computed by partial data propagation.
+  virtual const TensorShapeProto* getSymbolicInput(size_t index) const = 0;
+};
+
+// We use data propagation to perform partial evaluation of the model, to compute statically
+// known information about tensor values. It is intended to improve the precision of shape
+// inference. We reuse TensorShapeProto to represent the statically known values. One
+// limitation of this is that TensorShapeProto can represent only integer values.
+// As an example, data-propagation is intended to handle code-fragments like below:
+//   shape = Shape(X)
+//   batchsize = Slice(shape, [0], [1])
+//   newshape = Concat (batchsize, [1024, 1024])
+//   Z = Reshape(Y, newshape)
+// If the shape of X is statically known, then data-propagation should be able to determine
+// the value of newshape, as well as the shape of Z.
+struct DataPropagationContext {
+  virtual const AttributeProto* getAttribute(const std::string& name) const = 0;
+  virtual size_t getNumInputs() const = 0;
+  virtual const TypeProto* getInputType(size_t index) const = 0;
+  virtual size_t getNumOutputs() const = 0;
+  virtual const TypeProto* getOutputType(size_t index) const = 0;
+  virtual ~DataPropagationContext() {}
+  virtual const TensorShapeProto* getInputData(size_t index) = 0;
+  virtual void addOutputData(size_t index, TensorShapeProto&& tp) = 0;
+};
+
+using InferenceFunction = std::function<void(InferenceContext&)>;
+using DataPropagationFunction = std::function<void(DataPropagationContext&)>;
+
+// This no-op inference function is used for operators without an
+// inference implementation.
+inline void dummyInferenceFunction(InferenceContext&) {};
+
+// This no-op data propagation function is used for operators without a defined data propagator
+inline void dummyDataPropagationFunction(DataPropagationContext&) {};
+
+template <typename T>
+inline bool getRepeatedAttribute(InferenceContext& ctx, std::string attr_name, std::vector<T>& values) {
+  const auto* attr = ctx.getAttribute(attr_name);
+  if (attr) {
+    values = RetrieveValues<T>(*attr);
+    return true;
+  } else {
+    return false;
+  }
+}
+
+inline int64_t getAttribute(InferenceContext& ctx, const std::string& attributeName, int64_t defaultValue) {
+  auto attr_proto = ctx.getAttribute(attributeName);
+  if ((nullptr != attr_proto) && attr_proto->has_i())
+    return attr_proto->i();
+  return defaultValue;
+}
+
+inline int64_t getAttribute(DataPropagationContext& ctx, const std::string& attributeName, int64_t defaultValue) {
+  auto attr_proto = ctx.getAttribute(attributeName);
+  if ((nullptr != attr_proto) && attr_proto->has_i())
+    return attr_proto->i();
+  return defaultValue;
+}
+
+inline std::string
+getAttribute(InferenceContext& ctx, const std::string& attributeName, const std::string& defaultValue) {
+  auto attr_proto = ctx.getAttribute(attributeName);
+  if ((nullptr != attr_proto) && attr_proto->has_s())
+    return attr_proto->s();
+  return defaultValue;
+}
+
+inline TensorShapeProto::Dimension operator*(TensorShapeProto::Dimension dim1, TensorShapeProto::Dimension dim2) {
+  TensorShapeProto::Dimension result;
+  if (dim1.has_dim_value() && dim2.has_dim_value()) {
+    result.set_dim_value(dim1.dim_value() * dim2.dim_value());
+  } else if (dim1.has_dim_value() && (dim1.dim_value() == 1)) {
+    return dim2;
+  } else if (dim2.has_dim_value() && (dim2.dim_value() == 1)) {
+    return dim1;
+  }
+  return result;
+}
+
+inline TensorShapeProto::Dimension operator*(TensorShapeProto::Dimension dim1, int64_t dim2) {
+  TensorShapeProto::Dimension result;
+  if (dim1.has_dim_value()) {
+    result.set_dim_value(dim1.dim_value() * dim2);
+  } else if (dim2 == 1) {
+    return dim1;
+  }
+  return result;
+}
+
+inline TensorShapeProto::Dimension operator/(TensorShapeProto::Dimension dim1, int64_t dim2) {
+  TensorShapeProto::Dimension result;
+  if (dim1.has_dim_value()) {
+    result.set_dim_value(dim1.dim_value() / dim2);
+  } else if (dim2 == 1) {
+    return dim1;
+  }
+  return result;
+}
+
+// if from >= upto_exclusive, return 1.
+// Caller must make sure upto_exclusive is less than or equal to shape.size()
+// Caller must make sure from>=0
+inline TensorShapeProto::Dimension multiplyDims(const TensorShapeProto& shape, int from, int upto_exclusive) {
+  TensorShapeProto::Dimension dim;
+  dim.set_dim_value(1);
+  for (int i = from; i < upto_exclusive; ++i) {
+    dim = dim * shape.dim(i);
+  }
+  return dim;
+}
+
+inline int32_t getTensorElementType(const TypeProto& type) {
+  int32_t result = TensorProto::UNDEFINED;
+  const auto value_case = type.value_case();
+  if (value_case == TypeProto::kTensorType) {
+    result = type.tensor_type().elem_type();
+  } else if (value_case == TypeProto::kSparseTensorType) {
+    result = type.sparse_tensor_type().elem_type();
+  }
+  return result;
+}
+
+inline void setTensorElementType(int32_t elem_type, TypeProto::ValueCase value_case, TypeProto& type) {
+  if (value_case == TypeProto::kTensorType) {
+    type.mutable_tensor_type()->set_elem_type(elem_type);
+  } else if (value_case == TypeProto::kSparseTensorType) {
+    type.mutable_sparse_tensor_type()->set_elem_type(elem_type);
+  }
+}
+
+void propagateElemTypeWithValidation(const TypeProto* input_type, TypeProto* output_type);
+
+void propagateElemTypeFromInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex);
+
+void propagateElemTypeFromTensorInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex);
+
+inline void propagateElemTypeFromDtypeToOutput(
+    InferenceContext& ctx,
+    const int data_type,
+    size_t outputIndex,
+    TypeProto::ValueCase expected_value_case) {
+  const auto attribute_tensor_datatype = data_type;
+  auto output_type = ctx.getOutputType(outputIndex);
+  const auto output_value_case = output_type->value_case();
+  if (output_value_case == TypeProto::VALUE_NOT_SET || output_value_case == expected_value_case) {
+    setTensorElementType(attribute_tensor_datatype, expected_value_case, *output_type);
+  } else {
+    // This is not expected to happen
+    fail_type_inference(
+        "Output ", outputIndex, " expected to have: ", expected_value_case, " or UNDEFINED. Got: ", output_value_case);
+  }
+}
+
+inline void propagateElemTypeFromDtypeToOutput(InferenceContext& ctx, const int data_type, size_t outputIndex) {
+  propagateElemTypeFromDtypeToOutput(ctx, data_type, outputIndex, TypeProto::kTensorType);
+}
+
+inline void propagateElemTypeFromDtypeToOutput(InferenceContext& ctx, const AttributeProto* attr, size_t outputIndex) {
+  int32_t data_type = TensorProto::UNDEFINED;
+  TypeProto::ValueCase expected_value_case = TypeProto::VALUE_NOT_SET;
+  const auto attr_type = attr->type();
+  if (attr_type == AttributeProto::TENSOR) {
+    if (attr->t().dims().size() != 1) {
+      fail_type_inference("Attribute expected to have a one-dim tensor");
+    }
+    data_type = attr->t().data_type();
+    expected_value_case = TypeProto::kTensorType;
+  } else if (attr_type == AttributeProto::SPARSE_TENSOR) {
+    if (attr->sparse_tensor().dims().size() != 1) {
+      fail_type_inference("Attribute expected to have a one-dim sparse tensor");
+    }
+    data_type = attr->sparse_tensor().values().data_type();
+    expected_value_case = TypeProto::kSparseTensorType;
+  } else {
+    fail_type_inference("Attribute expected to have tensor or sparse tensor type");
+  }
+
+  propagateElemTypeFromDtypeToOutput(ctx, data_type, outputIndex, expected_value_case);
+}
+
+inline bool hasShape(const TypeProto& type) {
+  if (type.has_tensor_type()) {
+    return type.tensor_type().has_shape();
+  } else if (type.has_sparse_tensor_type()) {
+    return type.sparse_tensor_type().has_shape();
+  } else if (type.has_sequence_type() && type.sequence_type().has_elem_type()) {
+    return hasShape(type.sequence_type().elem_type());
+  } else if (type.has_optional_type() && type.optional_type().has_elem_type()) {
+    return hasShape(type.optional_type().elem_type());
+  }
+  return false;
+}
+
+template <typename Context>
+inline bool hasInputShape(Context& ctx, size_t n) {
+  return ctx.getNumInputs() > static_cast<size_t>(n) && ctx.getInputType(n) && hasShape(*ctx.getInputType(n));
+}
+
+template <typename Context>
+inline bool hasNInputShapes(Context& ctx, size_t n) {
+  for (size_t i = 0; i < n; i++) {
+    if (!hasInputShape(ctx, i)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+inline const TensorShapeProto& getInputShape(InferenceContext& ctx, size_t n) {
+  const auto* input_type = ctx.getInputType(n);
+  const auto value_case = input_type->value_case();
+  if (value_case != TypeProto::kTensorType && value_case != TypeProto::kSparseTensorType) {
+    fail_type_inference("Attribute expected to have tensor or sparse tensor type");
+  }
+  if (value_case == TypeProto::kTensorType) {
+    return input_type->tensor_type().shape();
+  } else {
+    return input_type->sparse_tensor_type().shape();
+  }
+}
+
+// Caller must make sure fromDimIndex is strictly less than shape.dim_size()
+inline void appendSingleDimCopiedFromInputTypeToOutputType(
+    InferenceContext& ctx,
+    size_t inputIndex,
+    size_t outputIndex,
+    size_t fromDimIndex) {
+  auto output_type = ctx.getOutputType(outputIndex);
+  const auto output_value_case = output_type->value_case();
+  auto input_type = ctx.getInputType(inputIndex);
+  const auto input_value_case = input_type->value_case();
+  if (output_value_case != input_value_case) {
+    fail_type_inference(
+        "Input: ",
+        inputIndex,
+        " type: ",
+        input_value_case,
+        " does not match type of output: ",
+        outputIndex,
+        "type: ",
+        output_value_case);
+  }
+  if (TypeProto::kTensorType == input_value_case) {
+    auto* dim = output_type->mutable_tensor_type()->mutable_shape()->add_dim();
+    *dim = input_type->tensor_type().shape().dim(static_cast<int>(fromDimIndex));
+  } else if (TypeProto::kSparseTensorType == input_value_case) {
+    auto* dim = output_type->mutable_sparse_tensor_type()->mutable_shape()->add_dim();
+    *dim = input_type->sparse_tensor_type().shape().dim(static_cast<int>(fromDimIndex));
+  } else {
+    fail_type_inference(
+        "Input ", inputIndex, " and Output ", outputIndex, " expected to have tensor or sparse tensor type");
+  }
+}
+
+inline void propagateShape(const TypeProto* from_type, TypeProto* to_type) {
+  const auto from_type_case = from_type->value_case();
+  const auto to_type_case = to_type->value_case();
+  if (from_type_case != to_type_case) {
+    fail_shape_inference("Mismatch between source and target type. Source=", from_type_case, " Target=", to_type_case);
+  }
+
+  if (TypeProto::kTensorType == from_type_case || TypeProto::kSparseTensorType == from_type_case) {
+    // If input shape is "uknown", the corresponding should be "unknown" too.
+    // The way to make output shape unknown is not to assign it any value.
+    if (hasShape(*from_type)) {
+      if (TypeProto::kTensorType == from_type_case) {
+        *to_type->mutable_tensor_type()->mutable_shape() = from_type->tensor_type().shape();
+      } else {
+        *to_type->mutable_sparse_tensor_type()->mutable_shape() = from_type->sparse_tensor_type().shape();
+      }
+    }
+  } else if (TypeProto::kSequenceType == from_type_case) {
+    propagateShape(&from_type->sequence_type().elem_type(), to_type->mutable_sequence_type()->mutable_elem_type());
+  } else if (TypeProto::kOptionalType == from_type_case) {
+    propagateShape(&from_type->optional_type().elem_type(), to_type->mutable_optional_type()->mutable_elem_type());
+  } else if (TypeProto::kMapType == from_type_case) {
+    propagateShape(&from_type->map_type().value_type(), to_type->mutable_map_type()->mutable_value_type());
+  } else {
+    fail_shape_inference("Unsupported Source/Target type=", from_type_case);
+  }
+}
+
+inline void propagateShapeFromInputToOutput(InferenceContext& ctx, size_t inputIndex, size_t outputIndex) {
+  auto output_type = ctx.getOutputType(outputIndex);
+  auto input_type = ctx.getInputType(inputIndex);
+
+  propagateShape(input_type, output_type);
+}
+
+inline void propagateShapeAndTypeFromFirstInput(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+  if (!hasNInputShapes(ctx, 1)) {
+    return;
+  }
+  propagateShapeFromInputToOutput(ctx, 0, 0);
+}
+
+inline void
+updateOutputElemType(InferenceContext& ctx, size_t outputIndex, int32_t elemType, TypeProto::ValueCase expected_type) {
+  auto output_type = ctx.getOutputType(outputIndex);
+  if (output_type == nullptr) {
+    fail_type_inference("Output ", outputIndex, " is null");
+  }
+  if (output_type->value_case() == expected_type || output_type->value_case() == TypeProto::VALUE_NOT_SET) {
+    setTensorElementType(elemType, expected_type, *output_type);
+  } else {
+    // This is not expected to happen
+    fail_type_inference("Output ", outputIndex, " expected to have tensor or sparse tensor type: ", expected_type);
+  }
+}
+
+inline void updateOutputElemType(InferenceContext& ctx, size_t outputIndex, int32_t elemType) {
+  updateOutputElemType(ctx, outputIndex, elemType, TypeProto::kTensorType);
+}
+
+// Infer type of an output from the value of a specified attribute, which is
+// expected to have a valid value representing a TensorProto_DataType.
+inline void propagateElemTypeFromAttributeToOutput(
+    InferenceContext& ctx,
+    const std::string& attributeName,
+    size_t outputIndex,
+    TypeProto::ValueCase expected_type,
+    TensorProto_DataType default_value = TensorProto::UNDEFINED) {
+  auto attr_proto = ctx.getAttribute(attributeName);
+  if (nullptr == attr_proto) { // attribute not present
+    if (default_value != TensorProto::UNDEFINED) {
+      updateOutputElemType(ctx, outputIndex, default_value, expected_type);
+      return;
+    } else {
+      fail_type_inference("Value of attribute ", attributeName, " not specified");
+    }
+  }
+  if (!attr_proto->has_i()) {
+    fail_type_inference("Attribute ", attributeName, " should be of integer type and specify a type.");
+  }
+  auto attr_value = attr_proto->i();
+  auto elem_type = static_cast<TensorProto_DataType>(attr_value);
+  if (!TensorProto_DataType_IsValid(elem_type)) {
+    fail_type_inference("Attribute ", attributeName, " does not specify a valid type.");
+  }
+  updateOutputElemType(ctx, outputIndex, elem_type, expected_type);
+}
+
+inline void propagateElemTypeFromAttributeToOutput(
+    InferenceContext& ctx,
+    const std::string& attributeName,
+    size_t outputIndex,
+    TensorProto_DataType default_value = TensorProto::UNDEFINED) {
+  propagateElemTypeFromAttributeToOutput(ctx, attributeName, outputIndex, TypeProto::kTensorType, default_value);
+}
+
+inline TensorShapeProto* getTensorMutableShape(TypeProto::ValueCase value_case, TypeProto& type) {
+  if (value_case == TypeProto::kTensorType) {
+    return type.mutable_tensor_type()->mutable_shape();
+  } else if (value_case == TypeProto::kSparseTensorType) {
+    return type.mutable_tensor_type()->mutable_shape();
+  }
+  return nullptr;
+}
+
+inline TensorShapeProto*
+getOutputShape(InferenceContext& ctx, size_t n, TypeProto::ValueCase default_type = TypeProto::kTensorType) {
+  auto output_type = ctx.getOutputType(n);
+  if (output_type == nullptr) {
+    fail_type_inference("Output ", n, " expected to have tensor or sparse type");
+  }
+  const auto output_value_case = output_type->value_case();
+  if (output_value_case == TypeProto::kTensorType || output_value_case == TypeProto::kSparseTensorType) {
+    return getTensorMutableShape(output_value_case, *output_type);
+  } else if (output_value_case == TypeProto::VALUE_NOT_SET) {
+    return getTensorMutableShape(default_type, *output_type);
+  } else {
+    fail_type_inference("Output ", n, " expected to have tensor type");
+  }
+}
+
+inline void appendDim(TensorShapeProto* shape, int64_t dim_value) {
+  shape->add_dim()->set_dim_value(dim_value);
+}
+
+inline void updateOutputShape(
+    InferenceContext& ctx,
+    size_t outputIndex,
+    const TensorShapeProto& shape,
+    TypeProto::ValueCase default_type = TypeProto::kTensorType) {
+  auto* output_shape = getOutputShape(ctx, outputIndex, default_type);
+  *output_shape = shape;
+}
+
+inline void updateOutputShape(
+    InferenceContext& ctx,
+    size_t outputIndex,
+    const TensorProto& tensorProto,
+    TypeProto::ValueCase default_type = TypeProto::kTensorType) {
+  auto* output_shape = getOutputShape(ctx, outputIndex, default_type);
+  for (auto d : tensorProto.dims()) {
+    auto* dim = output_shape->add_dim();
+    dim->set_dim_value(d);
+  }
+}
+
+inline void updateOutputShape(
+    InferenceContext& ctx,
+    size_t outputIndex,
+    std::initializer_list<TensorShapeProto::Dimension> dims,
+    TypeProto::ValueCase default_type = TypeProto::kTensorType) {
+  auto* output_shape = getOutputShape(ctx, outputIndex, default_type);
+  for (auto& d : dims) {
+    auto* dim = output_shape->add_dim();
+    *dim = d;
+  }
+}
+
+// Get shape input by first checking initializer and then propagated symbolic data.
+// If neither is available, try rank inference.
+// When one of above succeeds, `true` is stored in `found`.
+// Otherwise, `false` is stored, which means that returned TensorShapeProto does not make sense.
+TensorShapeProto getShapeInput(InferenceContext& ctx, size_t input_index, bool& found);
+
+// Infer shape of an output from the value of a specified attribute, which is
+// expected to be a list of integers specifying a valid shape.
+inline void propagateShapeFromAttributeToOutput(
+    InferenceContext& ctx,
+    const std::string& attributeName,
+    size_t outputIndex,
+    TypeProto::ValueCase default_type = TypeProto::kTensorType) {
+  auto attr_proto = ctx.getAttribute(attributeName);
+  if ((nullptr == attr_proto) || (!attr_proto->has_type()) ||
+      (attr_proto->type() != AttributeProto_AttributeType_INTS)) {
+    fail_shape_inference("Attribute ", attributeName, " should specify a shape");
+  }
+  auto& int_list = attr_proto->ints();
+  TensorShapeProto shape;
+  for (auto dim_size : int_list) {
+    if (dim_size < 0) {
+      fail_shape_inference("Negative values are not allowed in a shape specification");
+    }
+    shape.add_dim()->set_dim_value(dim_size);
+  }
+
+  updateOutputShape(ctx, outputIndex, shape, default_type);
+}
+
+inline void multidirectionalBroadcastShapeInference(
+    const std::vector<const TensorShapeProto*>& shapes,
+    TensorShapeProto& resultShape) {
+  int result_shape_size = 0;
+  // Get the result shape size.
+  for (size_t i = 0; i < shapes.size(); ++i) {
+    if (shapes[i]->dim_size() > result_shape_size) {
+      result_shape_size = shapes[i]->dim_size();
+    }
+  }
+
+  for (int i = 0; i < result_shape_size; ++i) {
+    int64_t dim_value = 1;
+    TensorShapeProto_Dimension symbolic_dim;
+    int num_symbolic_dims = 0;
+    for (size_t j = 0; j < shapes.size(); ++j) {
+      if (i < result_shape_size - shapes[j]->dim_size()) {
+        // Shape j will be filled with 1 at dimension i;
+        continue;
+      }
+
+      auto dim_i_j = shapes[j]->dim(i - result_shape_size + shapes[j]->dim_size());
+      if (dim_i_j.has_dim_value()) {
+        if (dim_i_j.dim_value() != 1) {
+          if (dim_value != dim_i_j.dim_value() && dim_value != 1) {
+            fail_shape_inference("Incompatible dimensions");
+          } else {
+            dim_value = dim_i_j.dim_value();
+          }
+        }
+      } else {
+        if (num_symbolic_dims == 0) {
+          symbolic_dim = dim_i_j;
+          ++num_symbolic_dims;
+        } else if (dim_i_j.dim_param() != symbolic_dim.dim_param()) {
+          ++num_symbolic_dims;
+        }
+      }
+    }
+
+    if (dim_value != 1 || num_symbolic_dims == 0) {
+      resultShape.add_dim()->set_dim_value(dim_value);
+    } else if (num_symbolic_dims == 1) {
+      *resultShape.add_dim() = symbolic_dim;
+    } else {
+      resultShape.add_dim();
+    }
+  }
+}
+
+inline void bidirectionalBroadcastShapeInference(
+    const TensorShapeProto& shapeL,
+    const TensorShapeProto& shapeR,
+    TensorShapeProto& resultShape) {
+  std::vector<const TensorShapeProto*> shapes;
+  shapes.push_back(&shapeL);
+  shapes.push_back(&shapeR);
+  multidirectionalBroadcastShapeInference(shapes, resultShape);
+}
+
+/*
+Merge the dimension information from two TensorShapeProto_Dimension instances.
+Values are merged into target from source.
+If target has no dimension information, copy from source.
+If source has no dimension information, ignore source.
+If both have dimension information:
+ - Prefer values over params. If both have values, values must match.
+ - Prefer target param over source param if mismatched.
+Fail if there are mismatches in dimension values.
+Currently, there is no way to refine/update dimension information for the
+source from information available in the target.
+*/
+inline void mergeInDimensionInfo(
+    const TensorShapeProto_Dimension& source_dim,
+    TensorShapeProto_Dimension& target_dim,
+    int dim_index) {
+  // if source has value, merge into target
+  // else if target has value, preserve it
+  // else merge params
+  if (source_dim.has_dim_value()) {
+    auto source_value = source_dim.dim_value();
+    if (target_dim.has_dim_value()) {
+      auto target_value = target_dim.dim_value();
+      if (target_value != source_value) {
+        fail_shape_inference(
+            "Can't merge shape info. "
+            "Both source and target dimension have values but they differ. Source=",
+            source_value,
+            " Target=",
+            target_value,
+            " Dimension=",
+            dim_index);
+      }
+    } else {
+      target_dim.set_dim_value(source_value);
+    }
+  } else if (target_dim.has_dim_value()) {
+    // if target has a value we preserve it so do nothing
+  } else if (target_dim.has_dim_param()) {
+    // prefer target param over source
+  } else if (source_dim.has_dim_param()) {
+    target_dim.set_dim_param(source_dim.dim_param());
+  }
+}
+
+void mergeInShapeInfo(const TensorShapeProto& source_shape, TypeProto_Tensor& target_type);
+
+void mergeInShapeInfo(const TensorShapeProto& source_shape, TypeProto_SparseTensor& target_type);
+
+/*
+Merge the shape information from two TypeProto_Tensor instances.
+Values are merged into target from source.
+If target has no shape information, copy from source.
+If source has no shape information, ignore source.
+If both have shape information:
+- merge each TensorShapeProto_Dimension separately.
+- Prefer values over params. If both have values, values must match.
+- Prefer target param over source param if mismatched.
+Fail if there are mismatches in number of dimensions or dimension values.
+*/
+void mergeInShapeInfo(const TypeProto_Tensor& source, TypeProto_Tensor& target);
+
+void mergeInShapeInfo(const TypeProto_SparseTensor& source, TypeProto_SparseTensor& target);
+
+// Return a copy of a type, with a specified dimension removed from its shape.
+inline TypeProto RemoveIthDimensionFromShape(const TypeProto& proto, int removed_dim) {
+  TypeProto t(proto);
+  auto mutable_shape = t.mutable_tensor_type()->mutable_shape();
+  mutable_shape->clear_dim();
+
+  const auto& dims = proto.tensor_type().shape().dim();
+
+  for (int j = 0, end = dims.size(); j < end; ++j) {
+    if (j != removed_dim)
+      (*mutable_shape->add_dim()) = dims.Get(j);
+  }
+
+  return t;
+}
+
+// Return a copy of a type, with specified number of dimensions removed from the
+// beginning.
+inline TypeProto RemoveDimensionsFromShape(const TypeProto& proto, int num_dimensions) {
+  TypeProto t(proto);
+  auto mutable_shape = t.mutable_tensor_type()->mutable_shape();
+  mutable_shape->clear_dim();
+
+  const auto& dims = proto.tensor_type().shape().dim();
+
+  // skip first num_dimensions
+  for (int j = num_dimensions, end = dims.size(); j < end; ++j) {
+    (*mutable_shape->add_dim()) = dims.Get(j);
+  }
+
+  return t;
+}
+
+// copied from GSL:
+// https://github.com/Microsoft/GSL/blob/master/include/gsl/gsl_util
+template <class T, class U>
+static constexpr T narrow_cast(U&& u) noexcept {
+  return static_cast<T>(std::forward<U>(u));
+}
+
+inline void checkInputRank(InferenceContext& ctx, size_t input_index, int expected_rank) {
+  // We check the rank only if a rank is known for the input:
+  if (hasInputShape(ctx, input_index)) {
+    auto rank = getInputShape(ctx, input_index).dim_size();
+    if (rank != expected_rank) {
+      fail_shape_inference("Input ", input_index, " expected to have rank ", expected_rank, " but has rank ", rank);
+    }
+  }
+}
+
+// Unification (between dimensions and/or shapes) is at the heart of
+// shape-inference. The current inference algorithm can check input
+// shapes/dimensions of a node and update the output shapes/dimensions. It
+// cannot currently update input shapes and dimensions (even though in some
+// contexts this inference is possible). Hence, we have the variants below to
+// support "const" and "mutable" dimensions/shapes in unification.
+
+inline void checkDimEquality(int64_t value1, int64_t value2) {
+  if (value1 != value2) {
+    fail_shape_inference("Dimension mismatch in unification between ", value1, " and ", value2);
+  }
+}
+
+inline void unifyDim(const Dim& dim1, const Dim& dim2) {
+  if (dim1.has_dim_value() && dim2.has_dim_value())
+    checkDimEquality(dim1.dim_value(), dim2.dim_value());
+}
+
+// TODO: The functionality of unifyDim is similar to that of
+// mergeInDimensionInfo. However, the error messages are different. Leaving this
+// duplication in-place to preserve error message content.
+inline void unifyDim(const Dim& source_dim, Dim& target_dim) {
+  if (source_dim.has_dim_value()) {
+    auto source_value = source_dim.dim_value();
+    if (target_dim.has_dim_value()) {
+      auto target_value = target_dim.dim_value();
+      checkDimEquality(source_value, target_value);
+    } else {
+      target_dim.set_dim_value(source_value);
+    }
+  } else if (target_dim.has_dim_value()) {
+    // if target has a value we preserve it.
+    // we cannot set source dim value.
+  } else if (target_dim.has_dim_param()) {
+    // prefer target param over source
+    // we cannot currently unify the dim_params
+  } else if (source_dim.has_dim_param()) {
+    target_dim.set_dim_param(source_dim.dim_param());
+  }
+}
+
+inline void unifyInputDim(InferenceContext& ctx, size_t input_index, int dim_index, Dim& dim) {
+  // We unify the dimensions only if it is available for specified input:
+  if (hasInputShape(ctx, input_index)) {
+    auto& input_shape = getInputShape(ctx, input_index);
+    // This shape is expected to have rank > dim_index:
+    if (input_shape.dim_size() <= dim_index) {
+      fail_shape_inference(
+          "Input ", input_index, " expected to have rank >", dim_index, " but has rank ", input_shape.dim_size());
+    }
+    const Dim& input_dim = input_shape.dim(dim_index);
+    // Now, unify dim and input_dim:
+    unifyDim(input_dim, dim);
+  }
+}
+
+// unifyDim: unifies a dimension with a constant value. If the dimension
+// already has a value, we check for equality of new value with old value.
+inline void unifyDim(Dim& dim, int64_t value) {
+  if (dim.has_dim_value()) {
+    checkDimEquality(dim.dim_value(), value);
+  } else
+    dim.set_dim_value(value);
+}
+
+// target-shape = Union (target-shape, source_shape)
+// Example 1: same rank, different dimensions
+//    input1 shape: (2, 3, 4, 'x')
+//    input2 shape: (2, 'y', 5, 'x')
+//    output shape: (2, None, None, 'x')
+// Example 2: different rank
+//    input1 shape: (2, 3, 4, 'x')
+//    input2 shape: (2, 3, 4)
+//    output shape: None
+void UnionShapeInfo(const TensorShapeProto& source_shape, TypeProto_Tensor& target_type);
+
+void UnionShapeInfo(const TensorShapeProto& source_shape, TypeProto_SparseTensor& target_type);
+
+// target-type = Union (target-type, source-type)
+// target and source are required to have the same type.
+// Example 1: same tensor type, different shape
+//    source: tensor elem_type: int64, shape: (2, 3, 4, 'x')
+//    target: tensor elem_type: int64, shape: (2, 'y', 5, 'x')
+//    output: tensor elem_type: int64, shape: (2, None, None, 'x')
+// Example 2: same sequence type, different shape
+//    source: sequence of tensor, elem_type: float, shape: (2, 3, 4)
+//    target: sequence of tensor, elem_type: float, shape: None
+//    output: sequence of tensor, elem_type: float, shape: None
+void UnionTypeInfo(const TypeProto& source_type, TypeProto& target_type);
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor/defs.cc b/third_party/onnx/onnx/defs/tensor/defs.cc
new file mode 100755
index 0000000000..b5de361917
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor/defs.cc
@@ -0,0 +1,3915 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/defs/data_propagators.h"
+#include "onnx/defs/tensor/utils.h"
+#include "onnx/defs/function.h"
+#include "onnx/defs/tensor_proto_util.h"
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+
+namespace ONNX_NAMESPACE {
+
+static const char* Cast_ver13_doc = R"DOC(
+The operator casts the elements of a given input tensor to a data type
+specified by the 'to' argument and returns an output tensor of the same size in
+the converted type. The 'to' argument must be one of the data types specified
+in the 'DataType' enum field in the TensorProto message.
+
+Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
+(e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
+result 100. There are some string literals reserved for special floating-point values;
+"+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
+Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
+this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
+to string tensors, plain floating-point representation (such as "314.15926") would be used.
+Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
+of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
+
+Conversion from a numerical type to any numerical type is always allowed.
+User must be aware of precision loss and value change caused by range difference between two types.
+For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
+an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Cast,
+    13,
+    OpSchema()
+        .SetDoc(Cast_ver13_doc)
+        .Attr(
+            "to",
+            "The data type to which the elements of the input tensor are cast. "
+            "Strictly must be one of the types from DataType enum in TensorProto",
+            AttributeProto::INT)
+        .Input(
+            0,
+            "input",
+            "Input tensor to be cast.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Output tensor with the same shape as input with type "
+            "specified by the 'to' argument",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)",
+             "tensor(string)",
+             "tensor(bfloat16)"},
+            "Constrain input types. Casting from complex is not supported.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)",
+             "tensor(string)",
+             "tensor(bfloat16)"},
+            "Constrain output types. Casting to complex is not supported.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromAttributeToOutput(ctx, "to", 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          PropagateShapeDataFromInputToOutput(ctx, 0);
+        }));
+
+static const char* CastLike_ver15_doc = R"DOC(
+The operator casts the elements of a given input tensor (the first input) to
+the same data type as the elements of the second input tensor.
+See documentation of the Cast operator for further details.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+	CastLike,
+	15,
+	OpSchema()
+	.SetDoc(CastLike_ver15_doc)
+	.Input(
+		0,
+		"input",
+		"Input tensor to be cast.",
+		"T1",
+		OpSchema::Single,
+		true,
+		1,
+		OpSchema::Differentiable)
+	.Input(
+		1,
+		"target_type",
+		"The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.",
+		"T2",
+		OpSchema::Single,
+		true,
+		1,
+		OpSchema::NonDifferentiable)
+	.Output(
+		0,
+		"output",
+		"Output tensor produced by casting the first input tensor to have the same type as the second input tensor.",
+		"T2",
+		OpSchema::Single,
+		true,
+		1,
+		OpSchema::Differentiable)
+	.TypeConstraint(
+		"T1",
+		{ "tensor(float16)",
+		 "tensor(float)",
+		 "tensor(double)",
+		 "tensor(int8)",
+		 "tensor(int16)",
+		 "tensor(int32)",
+		 "tensor(int64)",
+		 "tensor(uint8)",
+		 "tensor(uint16)",
+		 "tensor(uint32)",
+		 "tensor(uint64)",
+		 "tensor(bool)",
+		 "tensor(string)",
+		 "tensor(bfloat16)" },
+		"Constrain input types. Casting from complex is not supported.")
+	.TypeConstraint(
+		"T2",
+		{ "tensor(float16)",
+		 "tensor(float)",
+		 "tensor(double)",
+		 "tensor(int8)",
+		 "tensor(int16)",
+		 "tensor(int32)",
+		 "tensor(int64)",
+		 "tensor(uint8)",
+		 "tensor(uint16)",
+		 "tensor(uint32)",
+		 "tensor(uint64)",
+		 "tensor(bool)",
+		 "tensor(string)",
+		 "tensor(bfloat16)" },
+		"Constrain output types. Casting to complex is not supported.")
+	.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+	propagateElemTypeFromInputToOutput(ctx, 1, 0);
+	if (hasNInputShapes(ctx, 1)) {
+		propagateShapeFromInputToOutput(ctx, 0, 0);
+	}
+})
+.SetContextDependentFunctionBodyBuilder(
+	[](const FunctionBodyBuildContext& ctx,
+		const OpSchema& schema,
+		FunctionProto& functionProto) -> bool {
+	auto target_type = ctx.getInputType(1);
+	if ((target_type == nullptr) || (!target_type->has_tensor_type())) {
+		// we cannot create a correct function body without knowing the target element type
+		return false;
+	}
+	auto target_elt_type = target_type->tensor_type().elem_type();
+  FunctionBuilder builder(functionProto);
+  builder.Add("output = Cast (input)", "to", (int64_t)(target_elt_type));
+  schema.BuildFunction(functionProto);
+  return true;
+}));
+
+static const char* Reshape_ver14_doc = R"DOC(
+Reshape the input tensor similar to numpy.reshape.
+First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+At most one dimension of the new shape can be -1. In this case, the value is
+inferred from the size of the tensor and the remaining dimensions. A dimension
+could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
+dimension will be set explicitly to zero (i.e. not taken from input tensor).
+Shape (second input) could be an empty shape, which means converting to a scalar.
+The input tensor's shape and the output tensor's shape are required to have the same number of elements.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reshape,
+    14,
+    OpSchema()
+        .SetDoc(Reshape_ver14_doc)
+        .Attr(
+            "allowzero",
+            "(Optional) By default, when any value in the 'shape' input is equal to zero "
+            "the corresponding dimension value is copied from the input tensor dynamically. "
+            "allowzero=1 indicates that if any value in the 'shape' input is set to zero, "
+            "the zero value is honored, similar to NumPy.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "An input tensor.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable)
+        .Input(
+            1,
+            "shape",
+            "Specified shape for output.",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0, "reshaped", "Reshaped data.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape Inference if 2nd input data (the target shape) is available
+          // or the target shape is generated via partial data propagation
+          const TensorProto* targetShapeInitializer = ctx.getInputData(1);
+          const auto* shapeInput = ctx.getSymbolicInput(1);
+          // The targetShapeProto represents the specified shape for output.
+          TensorShapeProto targetShapeProto;
+          if (targetShapeInitializer) {
+            auto targetShape = ParseData<int64_t>(targetShapeInitializer);
+            for (auto val : targetShape) {
+              targetShapeProto.add_dim()->set_dim_value(val);
+            }
+          } else if (shapeInput) {
+            targetShapeProto.CopyFrom(*shapeInput);
+          } else {
+            return;
+          }
+
+          int allowzero = static_cast<int>(getAttribute(ctx, "allowzero", 0));
+
+          // Iterate through targetShape, adding dimensions in the outputShape
+          // TensorProto. If the targetShape dimension is -1, we do not set the
+          // dimension value in this iteration, but we record the Dimension. If
+          // targetShape dimension is 0, we attempt to propagate the dimension
+          // value/param. If the value cannot be inferred, we set the flag in
+          // the unresolveZeros vector. If targetShape dimension is positive, we
+          // set the dimension value in the outputShape. We track the product of
+          // the dimensions we are setting outputShape in the outputProduct
+          // variable. The outputProduct will potentially be used for inferring
+          // a dimension marked -1.
+          auto* outputShape = ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          TensorShapeProto::Dimension* negativeOneDim = nullptr;
+          const auto& dataInputTensorType = ctx.getInputType(0)->tensor_type();
+          std::vector<bool> unresolvedZeros(targetShapeProto.dim_size(), false);
+          int64_t outputProduct = 1;
+          bool outputProductValid = true;
+          for (int i = 0; i < static_cast<int>(targetShapeProto.dim_size()); ++i) {
+            // Add a new dimension to outputShape
+            auto* new_dim = outputShape->add_dim();
+            if (targetShapeProto.dim(i).has_dim_param()) {
+              // There is a tricky edge case here. It is possible that the value of
+              // symbolic dim can be -1 or 0 at runtime. In that case simply propgating this
+              // symbol can be erroneous. This should be a very rare scenario and in such a
+              // case an option is to turn off data propagation during shape inference.
+              new_dim->set_dim_param(targetShapeProto.dim(i).dim_param());
+              outputProductValid = false;
+            } else {
+              if (!targetShapeProto.dim(i).has_dim_value()) {
+                outputProductValid = false;
+                // treat this dim as unknown dim
+                continue;
+              }
+
+              const auto dim_value = targetShapeProto.dim(i).dim_value();
+
+              if (dim_value == -1) {
+                // Check if multiple -1's. If not, set negativeOneDim, marking
+                // this dimension to potentially be filled in later.
+                if (negativeOneDim) {
+                  fail_shape_inference("Target shape may not have multiple -1 dimensions.");
+                }
+                negativeOneDim = new_dim;
+              } else if (dim_value == 0) {
+                // Check if data input has a shape and if the index i is within
+                // its bounds. If these conditions are satisfied, any dimension
+                // value/param should be propogated. If dimension value cannot be
+                // inferred, set the corresponding  unresolvedZeros flag to true.
+                // If allowzero is set however, do not propagate values, since output
+                // dimension is explicitly zero.
+                if (allowzero == 0) {
+                  unresolvedZeros[i] = true;
+                  if (dataInputTensorType.has_shape()) {
+                    if (i >= dataInputTensorType.shape().dim_size()) {
+                      fail_shape_inference("Invalid position of 0.");
+                    }
+                    if (dataInputTensorType.shape().dim(i).has_dim_value()) {
+                      const auto& input_dim_value = dataInputTensorType.shape().dim(i).dim_value();
+                      new_dim->set_dim_value(input_dim_value);
+                      outputProduct *= input_dim_value;
+                      unresolvedZeros[i] = false;
+                    } else if (dataInputTensorType.shape().dim(i).has_dim_param()) {
+                      new_dim->set_dim_param(dataInputTensorType.shape().dim(i).dim_param());
+                    }
+                  }
+                } else {
+                  new_dim->set_dim_value(dim_value);
+                  outputProduct *= dim_value;
+                }
+              } else if (dim_value > 0) {
+                // Set the dimension value to dim_value
+                new_dim->set_dim_value(dim_value);
+                outputProduct *= dim_value;
+              } else {
+                // Check if value is less than -1; fail if so
+                fail_shape_inference("Invalid dimension value: ", dim_value);
+              }
+            }
+          }
+          // If negativeOneDim has been set, we attempt to infer its value. This
+          // can be done if all dimension values for the data input tensor shape
+          // are known other than the ones corresponding to unresolvedZeros
+          // flags.
+          if (negativeOneDim && outputProductValid) {
+            // First, attempt to compute product of data input shape dimensions
+            // that are not marked by unresolvedZeros. If not possible, set the
+            // inputProductValid flag to false.
+            if (!outputProduct) {
+              fail_shape_inference("Invalid Target shape product of 0. Product cannot be 0 in combination with -1");
+            }
+            int64_t inputProduct = 1;
+            bool inputProductValid = true;
+            if (!dataInputTensorType.has_shape()) {
+              inputProductValid = false;
+            } else {
+              for (int i = 0; i < dataInputTensorType.shape().dim_size(); ++i) {
+                if (dataInputTensorType.shape().dim(i).has_dim_value()) {
+                  inputProduct *= dataInputTensorType.shape().dim(i).dim_value();
+                } else if (i >= static_cast<int>(unresolvedZeros.size()) || !unresolvedZeros[i]) {
+                  inputProductValid = false;
+                  break;
+                }
+              }
+            }
+            if (inputProductValid) {
+              if (inputProduct % outputProduct != 0) {
+                fail_shape_inference("Dimension could not be inferred: incompatible shapes");
+              }
+              negativeOneDim->set_dim_value(inputProduct / outputProduct);
+            }
+          }
+        }));
+
+static const char* Shape_ver15_doc = R"DOC(
+Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+Optional attributes start and end can be used to compute a slice of the input tensor's shape.
+If start axis is omitted, the slice starts from axis 0.
+The end axis, if specified, is exclusive (and the returned value will not include the size of that axis).
+If the end axis is omitted, the axes upto the last one will be included.
+Negative axes indicate counting back from the last axis.
+Note that axes will be clamped to the range [0, r-1], where r is the
+rank of the input tensor if they are out-of-range (after adding r in the case of
+negative axis). Thus, specifying any end value > r is equivalent to specifying an end
+value of r, and specifying any start value < -r is equivalent to specifying a start
+value of 0.
+
+For example:
+Input tensor with shape: [2, 3, 4] 
+No attributes specified.
+Output: [2, 3, 4] 
+
+Input tensor with shape: [2, 3, 4] 
+start: -1
+Output: [4] 
+
+Input tensor with shape: [2, 3, 4] 
+end: -1
+Output: [2, 3]
+
+Input tensor with shape: [2, 3, 4] 
+start: 1
+end: 2
+Output: [3] 
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Shape,
+    15,
+    OpSchema()
+        .SetDoc(Shape_ver15_doc)
+        .Input(0, "data", "An input tensor.", "T", OpSchema::Single, true, 1, OpSchema::NonDifferentiable)
+        .Output(0, "shape", "Shape of the input tensor", "T1", OpSchema::Single, true, 1, OpSchema::NonDifferentiable)
+        .Attr(
+            "start",
+            "(Optional) Starting axis for slicing the shape. Default value is 0."
+            "Negative value means counting dimensions from the back.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "end",
+            "(Optional) Ending axis for slicing the shape. "
+            "Negative value means counting dimensions from the back. "
+            "If omitted, sizes of all axes upto (including) the last one will be included.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .TypeConstraint("T", OpSchema::all_tensor_types_with_bfloat(), "Input tensor can be of arbitrary type.")
+        .TypeConstraint("T1", {"tensor(int64)"}, "Constrain output to int64 tensor.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
+          auto* output_shape = ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          auto* output_length = output_shape->add_dim();
+
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          int64_t rank = static_cast<int64_t>(ctx.getInputType(0)->tensor_type().shape().dim_size());
+          int64_t start = getAttribute(ctx, "start", 0);
+          if (start < 0)
+            start += rank;
+          start = (start < 0) ? 0 : (start > rank) ? rank : start;
+          int64_t end = getAttribute(ctx, "end", rank);
+          if (end < 0)
+            end += rank;
+          end = (end < 0) ? 0 : (end > rank) ? rank : end;
+          output_length->set_dim_value((end - start) < 0 ? 0 : (end - start));
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          if (ctx.getInputType(0)->tensor_type().has_shape()) {
+            auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+            int64_t rank = static_cast<int64_t>(input_shape.dim_size());
+            int64_t start = getAttribute(ctx, "start", 0);
+            if (start < 0)
+              start += rank;
+            start = (start < 0) ? 0 : (start > rank) ? rank : start;
+            int64_t end = getAttribute(ctx, "end", rank);
+            if (end < 0)
+              end += rank;
+            end = (end < 0) ? 0 : (end > rank) ? rank : end;
+            TensorShapeProto output_shape;
+            for (int64_t d = start; d < end; ++d) {
+              *output_shape.add_dim() = input_shape.dim(static_cast<int>(d));
+            }
+            ctx.addOutputData(0, std::move(output_shape));
+          }
+        }));
+
+static const char* Size_ver13_doc = R"DOC(
+Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Size,
+    13,
+    OpSchema()
+        .SetDoc(Size_ver13_doc)
+        .Input(0,
+            "data",
+            "An input tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "size",
+            "Total number of elements of the input tensor",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Input tensor can be of arbitrary type.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain output to int64 tensor, which should be a scalar though.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(
+              TensorProto::INT64);
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          const auto input_data = ctx.getInputData(0);
+          if (input_data != nullptr) {
+            TensorShapeProto tsp;
+            tsp.mutable_dim()->Add()->set_dim_value(input_data->dim_size());
+            ctx.addOutputData(0, std::move(tsp));
+          }
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Concat,
+    13,
+    OpSchema()
+        .Attr(
+            "axis",
+            "Which axis to concat on. A negative value means counting dimensions from the back. "
+            "Accepted range is [-r, r-1] where r = rank(inputs)..",
+            AttributeProto::INT)
+        .SetDoc(
+            "Concatenate a list of tensors into a single tensor. "
+            "All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.")
+        .Input(
+            0,
+            "inputs",
+            "List of tensors for concatenation",
+            "T",
+            OpSchema::Variadic,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "concat_result",
+            "Concatenated tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto numInputs = ctx.getNumInputs();
+          if (numInputs < 1 ||
+              !hasNInputShapes(ctx, static_cast<int>(numInputs))) {
+            return;
+          }
+
+          auto rank = ctx.getInputType(0)->tensor_type().shape().dim_size();
+
+          auto axisAttr = ctx.getAttribute("axis");
+          if (!axisAttr) {
+            fail_shape_inference("Required attribute axis is missing");
+          }
+          int axis = static_cast<int>(axisAttr->i());
+          if (axis < -rank || axis >= rank) {
+            fail_shape_inference("axis must be in [-rank, rank-1].");
+          }
+          if (axis < 0) {
+            axis += rank;
+          }
+
+          if (numInputs == 1) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+            return;
+          }
+
+          bool all_lengths_known = true;
+          int total_length = 0;
+
+          auto* output_shape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          for (int64_t i = 0; i < rank; ++i) {
+            output_shape->add_dim();
+          }
+
+          for (size_t i = 0; i < numInputs; i++) {
+            const auto& shape = ctx.getInputType(i)->tensor_type().shape();
+            if (shape.dim_size() != rank) {
+              fail_shape_inference("All inputs to Concat must have same rank. Input ", i , " has rank ", shape.dim_size(), " != ", rank);
+            }
+            for (int j = 0; j < rank; j++) {
+              if (j == axis) {
+                if (shape.dim(j).has_dim_value()) {
+                  total_length += static_cast<int>(shape.dim(j).dim_value());
+                } else {
+                  all_lengths_known = false;
+                }
+              } else {
+                auto& output_dim = *output_shape->mutable_dim(j);
+                const auto& input_dim = shape.dim(j);
+                mergeInDimensionInfo(input_dim, output_dim, j);
+              }
+            }
+          }
+
+          if (all_lengths_known) {
+            output_shape->mutable_dim(axis)->set_dim_value(total_length);
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          if (!axisIsZero(ctx)) {
+            return;
+          }
+          TensorShapeProto tsp;
+          for (size_t i = 0; i < ctx.getNumInputs(); ++i) {
+            const auto input_data = ctx.getInputData(i);
+            if (input_data == nullptr) {
+              return;
+            }
+            for (int j = 0; j < input_data->dim_size(); ++j) {
+              appendDimToTensorShapeProto(tsp, input_data->dim(j));
+            }
+          }
+          if (tsp.dim_size() > 0) {
+            ctx.addOutputData(0, std::move(tsp));
+          }
+        }));
+
+static const char* Split_ver13_doc =
+    R"DOC(Split a tensor into a list of tensors, along the specified
+'axis'. Lengths of the parts can be specified using input 'split'.
+Otherwise, the tensor is split to equal sized parts.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Split,
+    13,
+    OpSchema()
+        .Input(
+            0,
+            "input",
+            "The tensor to split",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "split",
+            "Optional length of each output. Values should be >= 0."
+            "Sum of the values must be equal to the dim value at 'axis' specified.",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "outputs",
+            "One or more outputs forming list of tensors after splitting",
+            "T",
+            OpSchema::Variadic,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .Attr(
+            "axis",
+            "Which axis to split on. "
+            "A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] "
+            "where r = rank(input).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .SetDoc(Split_ver13_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); ++i) {
+            propagateElemTypeFromInputToOutput(ctx, 0, i);
+          }
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          const auto& shape = ctx.getInputType(0)->tensor_type().shape();
+          int rank = shape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -rank || axis >= rank) {
+            fail_type_inference(
+                "Invalid value of attribute 'axis'. Rank=",
+                rank,
+                " Value=",
+                axis);
+          }
+          if (axis < 0) {
+            axis += rank;
+          }
+          const auto& split_dim = shape.dim(axis);
+          if (!split_dim.has_dim_value()) {
+            for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
+              *ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
+                  shape;
+              ctx.getOutputType(i)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->mutable_dim(axis)
+                  ->Clear();
+            }
+            return;
+          }
+          int split_dim_value = static_cast<int>(split_dim.dim_value());
+
+          std::vector<int64_t> split;
+          size_t num_inputs = ctx.getNumInputs();
+          if ((num_inputs == 2) && ctx.getInputType(1)) { //'split' is input
+            auto split_proto = ctx.getInputData(1);
+            if (split_proto == nullptr) {
+              // skip if split is not an initializer
+              return;
+            }
+            split = ParseData<int64_t>(split_proto);
+            if (split.size() != ctx.getNumOutputs()) {
+              fail_shape_inference(
+                  "Mismatch between number of splits (",
+                  split.size(),
+                  ") and outputs (",
+                  ctx.getNumOutputs(),
+                  ")");
+            }
+            int64_t total_dim = 0;
+            for (int64_t d : split) {
+              total_dim += d;
+            }
+            if (total_dim != split_dim_value) {
+              fail_shape_inference(
+                  "Mismatch between the sum of 'split' (",
+                  total_dim,
+                  ") and the split dimension of the input (",
+                  split_dim_value,
+                  ")");
+            }
+          } else { // no value available for 'split'
+            int num_outputs = static_cast<int>(ctx.getNumOutputs());
+            if (split_dim_value % num_outputs != 0) {
+              fail_shape_inference("The input is not evenly splittable");
+            }
+            int chunk_size = split_dim_value / num_outputs;
+            split.reserve(ctx.getNumOutputs());
+            for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); i++) {
+              split.push_back(chunk_size);
+            }
+          }
+          for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
+            *ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
+                shape;
+            ctx.getOutputType(i)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->mutable_dim(axis)
+                ->set_dim_value(split[i]);
+          }
+        }));
+
+static const char* Slice_ver13_doc = R"DOC(
+Produces a slice of the input tensor along multiple axes. Similar to numpy:
+https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding
+
+Slice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor
+of its input `data` tensor.
+
+An effective `start[i]`, `end[i]`, and `step[i]` must be computed for each `i`
+in `[0, ... r-1]` where `r = rank(input)` as follows:
+
+If `axes` are omitted, they are set to `[0, ..., r-1]`.
+If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+
+The effective values are initialized as `start[i] = 0`, `end[i] = dims[i]` where
+`dims` are the dimensions of `input` and `step[i] = `1.
+
+All negative elements of `axes` are made non-negatve by adding `r` to them, where
+`r =rank(input)`.
+
+All negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them,
+where `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted
+`starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping
+and `[0, dims[axes[i]]-1]` for negative stepping.
+
+The clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must
+accommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping
+`end[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it
+is clamped to `[-1, dims[axes[i]]-1]`.
+
+Finally, `step[axes[i]] = steps[i]`.
+
+For slicing to the end of a dimension with unknown size, it is recommended to pass
+in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward.
+
+Example 1:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  axes = [0, 1]
+  starts = [1, 0]
+  ends = [2, 3]
+  steps = [1, 2]
+  result = [
+      [5, 7],
+  ]
+Example 2:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  starts = [0, 1]
+  ends = [-1, 1000]
+  result = [
+      [2, 3, 4],
+  ]
+)DOC";
+
+inline void processSliceInputs(const int64_t input_rank,
+  int64_t& start, int64_t& end, int64_t& step) {
+  auto clamp = [](int64_t val, int64_t min, int64_t max) -> int64_t {
+    return (val < min) ? min : (val > max) ? max : val;
+  };
+  // process step
+  if (step == 0) {
+    fail_shape_inference("'step' cannot be 0 for Slice");
+  }
+  // process start
+  if (start < 0)
+    start += input_rank;
+  if (step < 0)
+    start = clamp(start, 0, input_rank - 1);
+  else
+    start = clamp(start, 0, input_rank);
+  // process end
+  if (end < 0)
+    end += input_rank;
+  if (step < 0)
+    end = clamp(end, -1, input_rank - 1);
+  else
+    end = clamp(end, 0, input_rank);
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Slice,
+    13,
+    OpSchema()
+        .SetDoc(Slice_ver13_doc)
+        .Input(
+            0,
+            "data",
+            "Tensor of data to extract slices from.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "starts",
+            "1-D tensor of starting indices of corresponding axis in `axes`",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "ends",
+            "1-D tensor of ending indices (exclusive) of corresponding axis in `axes`",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            3,
+            "axes",
+            "1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an "
+            "axis is repeated.",
+            "Tind",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            4,
+            "steps",
+            "1-D tensor of slice step of corresponding axis in `axes`. "
+            "Negative value means slicing backward. 'steps' cannot be 0. "
+            "Defaults to 1s.",
+            "Tind",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Sliced data tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          size_t num_inputs = ctx.getNumInputs();
+          if (num_inputs != 3 && num_inputs != 4 && num_inputs != 5) {
+            fail_type_inference(
+                "Slice op must have either three, four or five inputs.");
+          }
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          // Shape Inference if
+          //     1. 2nd and 3rd input data (starts, ends) are available.
+          // and 2. 4th and 5th optional input (axes, steps) are either not set,
+          // or set and is initializer.
+          const TensorProto* startsInitializer = ctx.getInputData(1);
+          const TensorProto* endsInitializer = ctx.getInputData(2);
+          const TensorProto* axesInitializer =
+              hasInputShape(ctx, 3) ? ctx.getInputData(3) : nullptr;
+          const TensorProto* stepsInitializer =
+              hasInputShape(ctx, 4) ? ctx.getInputData(4) : nullptr;
+
+          if (!startsInitializer || !endsInitializer ||
+              (hasInputShape(ctx, 3) && !ctx.getInputData(3)) ||
+              (hasInputShape(ctx, 4) && !ctx.getInputData(4))) {
+            return;
+          }
+
+          // don't know data_type- can't proceed
+          if (!startsInitializer->has_data_type())
+            return;
+
+          auto get_initializer_data =
+              [](const TensorProto* initializer) -> std::vector<int64_t> {
+            std::vector<int64_t> vec;
+            if (initializer->data_type() == TensorProto::INT64) {
+              const auto& data = ParseData<int64_t>(initializer);
+              vec.insert(vec.end(), data.begin(), data.end());
+            } else if (initializer->data_type() == TensorProto::INT32) {
+              const auto& data = ParseData<int32_t>(initializer);
+              vec.insert(vec.end(), data.begin(), data.end());
+            } else {
+              // unaccepted data type
+              fail_shape_inference(
+                  "Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps");
+            }
+            return vec;
+          };
+
+          std::vector<int64_t> starts = get_initializer_data(startsInitializer);
+          std::vector<int64_t> ends = get_initializer_data(endsInitializer);
+
+          if (starts.size() != ends.size()) {
+            fail_shape_inference(
+                "Incorrect or missing input value for starts and ends");
+          }
+
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+          std::vector<int64_t> axes(starts.size());
+          if (!axesInitializer) {
+            std::iota(axes.begin(), axes.end(), 0);
+          } else {
+            axes = get_initializer_data(axesInitializer);
+            if (axes.size() != starts.size()) {
+              fail_shape_inference("Input axes has incorrect length");
+            }
+          }
+
+          std::vector<int64_t> steps;
+          if (!stepsInitializer) {
+            steps = std::vector<int64_t>(starts.size(), 1);
+          } else {
+            steps = get_initializer_data(stepsInitializer);
+            if (steps.size() != axes.size()) {
+              fail_shape_inference("Input steps has incorrect length");
+            }
+          }
+
+          for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+            // first update rank of output dim
+            auto* output_dim = ctx.getOutputType(0)
+                                   ->mutable_tensor_type()
+                                   ->mutable_shape()
+                                   ->add_dim();
+            const auto& input_dim = input_shape.dim((int)i);
+            if (input_dim.has_dim_value()) {
+              output_dim->set_dim_value(input_dim.dim_value());
+            } else if (input_dim.has_dim_param()) {
+              output_dim->set_dim_param(input_dim.dim_param());
+            }
+          }
+
+          std::unordered_set<int64_t> unique_axes;
+          size_t axes_size = axes.size();
+          for (size_t axis_index = 0; axis_index < axes_size; ++axis_index) {
+            auto axis = axes[axis_index] < 0
+                ? axes[axis_index] + static_cast<int64_t>(input_rank)
+                : axes[axis_index];
+
+            if (axis >= static_cast<int64_t>(input_rank) || axis < 0) {
+              fail_shape_inference("Input axes has invalid data");
+            }
+
+            if (unique_axes.find(axis) != unique_axes.end()) {
+              fail_shape_inference("'axes' has duplicates");
+            }
+
+            unique_axes.insert(axis);
+
+            auto input_dim =
+                ctx.getInputType(0)->tensor_type().shape().dim((int)axis);
+
+            // input dim value is missing - cannot perform shape inference for
+            // this axis
+            if (!input_dim.has_dim_value()) {
+              // Clear any previously propagated dim_param and leave this dimension "empty",
+              // before moving on to the next dimension
+              ctx.getOutputType(0)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->mutable_dim(static_cast<int>(axis))
+                  ->clear_dim_param();
+              continue;
+            }
+            auto start = starts[axis_index];
+            auto end = ends[axis_index];
+            auto step = steps[axis_index];
+            processSliceInputs(input_dim.dim_value(), start, end, step);
+
+            // find output dim value for this axis
+            auto temp = static_cast<int64_t>(ceil(1.0 * (end - start) / step));
+            if (temp < 0)
+              temp = 0;
+
+            // assign output value
+            ctx.getOutputType(0)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->mutable_dim((int)axis)
+                ->set_dim_value(temp);
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          const auto input_data = ctx.getInputData(0);
+          const auto starts = ctx.getInputData(1);
+          const auto ends = ctx.getInputData(2);
+          bool axes_specified = ctx.getNumInputs() >= 4;
+          bool steps_specified = ctx.getNumInputs() >= 5;
+
+          const TensorShapeProto* axes = nullptr;
+          const TensorShapeProto* steps = nullptr;
+          if (axes_specified) {
+            axes = ctx.getInputData(3);
+            if (axes == nullptr) {
+              return;
+            }
+          }
+          if (steps_specified) {
+            steps = ctx.getInputData(4);
+            if (steps == nullptr) {
+              return;
+            }
+          }
+
+          if (input_data == nullptr || starts == nullptr || ends == nullptr) {
+            return;
+          }
+          if (starts->dim_size() != ends->dim_size()) {
+            fail_shape_inference("Input rank for starts and ends should be the same: (",
+            starts->dim_size(), ") vs (", ends->dim_size(), ").");
+          }
+          // Only supports axis = 0 since the data comes from Shape
+          if ((!axes_specified || (axes->dim_size() == 1 && axes->dim(0).dim_value() == 0))
+            && starts->dim_size () == 1 && ends->dim_size() == 1) {
+            auto start = starts->dim(0).dim_value();
+            auto end = ends->dim(0).dim_value();
+            int64_t step = 1; // Default step is 1
+            if (steps_specified) {
+              if (steps->dim_size() != 1) {
+                return;
+              }
+              if (!steps->dim(0).has_dim_value()) {
+                return;
+              }
+              step = steps->dim(0).dim_value();
+            }
+            processSliceInputs(input_data->dim_size(), start, end, step);
+
+            TensorShapeProto tsp;
+            if (step > 0) {
+              for (int i = start; i < end; i += step) {
+                appendDimToTensorShapeProto(tsp, input_data->dim(i));
+              }
+            } else {
+              for (int i = start; i > end; i += step) {
+                appendDimToTensorShapeProto(tsp, input_data->dim(i));
+              }
+            }
+            if (tsp.dim_size() > 0) {
+              ctx.addOutputData(0, std::move(tsp));
+            }
+          }
+        }));
+
+static const char* Transpose_ver13_doc = R"DOC(
+Transpose the input tensor similar to numpy.transpose. For example, when
+perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
+will be (2, 1, 3).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Transpose,
+    13,
+    OpSchema()
+        .SetDoc(Transpose_ver13_doc)
+        .Attr(
+            "perm",
+            "A list of integers. By default, reverse the dimensions, "
+            "otherwise permute the axes according to the values given.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "data",
+            "An input tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "transposed",
+            "Transposed output.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          auto input_type = ctx.getInputType(0);
+          const TensorShapeProto& shape = input_type->tensor_type().shape();
+          std::vector<int64_t> perm;
+          bool has_perm_attr = getRepeatedAttribute(ctx, "perm", perm);
+          if (!has_perm_attr) {
+            perm.reserve(shape.dim_size());
+            for (int i = shape.dim_size() - 1; i >= 0; --i)
+              perm.push_back(i);
+          } else if (!perm.empty()) {
+            // check if every index is valid
+            std::vector<bool> seen(shape.dim_size(), false);
+            for (int64_t fromDimIndex : perm) {
+              if (!(0 <= fromDimIndex && fromDimIndex < shape.dim_size())) {
+                std::ostringstream oss;
+                oss << "Invalid attribute perm {" << perm[0];
+                for (size_t i = 1; i != perm.size(); ++i) {
+                  oss << ", " << perm[i];
+                }
+                oss << "}, input shape = {";
+                if (shape.dim_size() > 0) {
+                  oss << shape.dim(0).dim_value();
+                  for (int i = 1; i != shape.dim_size(); ++i) {
+                    oss << ", " << shape.dim(i).dim_value();
+                  }
+                  oss << "}";
+                }
+                fail_type_inference(oss.str());
+              } else {
+                // check if any perm is repeated
+                if (seen[fromDimIndex]) {
+                  fail_type_inference("Attribute perm for Transpose has repeated value: ", fromDimIndex);
+                }
+                seen[fromDimIndex] = true;
+              }
+            }
+          }
+
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          for (size_t i = 0; i < perm.size(); ++i) {
+            appendSingleDimCopiedFromInputTypeToOutputType(
+                ctx, 0, 0, static_cast<size_t>(perm[i]));
+          }
+        }));
+
+static const char* Scatter_ver11_doc = R"DOC(
+This operator is deprecated. Please use ScatterElements, which provides the same functionality.
+
+Scatter takes three inputs `data`, `updates`, and `indices` of the same
+rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+(by default, the outer-most axis, that is axis 0). The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value
+to values specified by `updates` at specific index positions specified by
+`indices`. Its output shape is the same as the shape of `data`.
+
+For each entry in `updates`, the target index in `data` is obtained by combining
+the corresponding entry in `indices` with the index of the entry itself: the
+index-value for dimension = axis is obtained from the value of the corresponding
+entry in `indices` and the index-value for dimension != axis is obtained from the
+index of the entry itself.
+
+For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+is performed as below:
+```
+  output[indices[i][j]][j] = updates[i][j] if axis = 0,
+  output[i][indices[i][j]] = updates[i][j] if axis = 1,
+```
+
+This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+Example 1:
+```
+  data = [
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+  ]
+  indices = [
+      [1, 0, 2],
+      [0, 2, 1],
+  ]
+  updates = [
+      [1.0, 1.1, 1.2],
+      [2.0, 2.1, 2.2],
+  ]
+  output = [
+      [2.0, 1.1, 0.0]
+      [1.0, 0.0, 2.2]
+      [0.0, 2.1, 1.2]
+  ]
+```
+Example 2:
+```
+  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+  indices = [[1, 3]]
+  updates = [[1.1, 2.1]]
+  axis = 1
+  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Scatter,
+    11,
+    OpSchema()
+        .Deprecate()
+        .SetDoc(Scatter_ver11_doc)
+        .Attr(
+            "axis",
+            "Which axis to scatter on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be "
+            "within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank r >=1 (same rank and shape as indices)",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r >= 1 (same rank as input).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input and output types can be of any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* ScatterND_ver16_doc = R"DOC(
+ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value to values
+specified by `updates` at specific index positions specified by `indices`. Its output shape
+is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+That is, two or more `updates` for the same index-location is not supported.
+
+`indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+ `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+update to a slice of the tensor.
+
+`updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+The remaining dimensions of `updates` correspond to the dimensions of the
+replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+of shapes.
+
+The `output` is calculated via the following equation:
+
+    output = np.copy(data)
+    update_indices = indices.shape[:-1]
+    for idx in np.ndindex(update_indices):
+        output[indices[idx]] = updates[idx]
+
+The order of iteration in the above loop is not specified.
+In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+This ensures that the output value does not depend on the iteration order.
+
+`reduction` allows specification of an optional reduction operation, which is applied to all values in `updates`
+tensor into `output` at the specified `indices`.
+In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, 
+then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order.
+When `reduction` is set to "add", `output` is calculated as follows:
+
+    output = np.copy(data)
+    update_indices = indices.shape[:-1]
+    for idx in np.ndindex(update_indices):
+        output[indices[idx]] += updates[idx]
+
+When `reduction` is set to "mul", `output` is calculated as follows:
+
+    output = np.copy(data)
+    update_indices = indices.shape[:-1]
+    for idx in np.ndindex(update_indices):
+        output[indices[idx]] *= updates[idx]
+
+This operator is the inverse of GatherND.
+
+Example 1:
+```
+  data    = [1, 2, 3, 4, 5, 6, 7, 8]
+  indices = [[4], [3], [1], [7]]
+  updates = [9, 10, 11, 12]
+  output  = [1, 11, 3, 10, 9, 6, 7, 12]
+```
+
+Example 2:
+```
+  data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  indices = [[0], [2]]
+  updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+  output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ScatterND,
+    16,
+    OpSchema()
+        .SetDoc(ScatterND_ver16_doc)
+        .Attr(
+            "reduction",
+            "Type of reduction to apply: none (default), add, mul. "
+            "'none': no reduction applied. "
+            "'add':  reduction using the addition operation. "
+            "'mul': reduction using the multiplication operation.",
+            AttributeProto::STRING,
+            std::string("none"))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of rank q >= 1.",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank q + r - indices_shape[-1] - 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* ScatterElements_ver16_doc = R"DOC(
+ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+(by default, the outer-most axis, that is axis 0). The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value
+to values specified by `updates` at specific index positions specified by
+`indices`. Its output shape is the same as the shape of `data`.
+
+For each entry in `updates`, the target index in `data` is obtained by combining
+the corresponding entry in `indices` with the index of the entry itself: the
+index-value for dimension = axis is obtained from the value of the corresponding
+entry in `indices` and the index-value for dimension != axis is obtained from the
+index of the entry itself.
+
+`reduction` allows specification of an optional reduction operation, which is applied to all values in `updates`
+tensor into `output` at the specified `indices`.
+In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, 
+then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update 
+corresponding to the [i][j] entry is performed as below:
+```
+  output[indices[i][j]][j] = updates[i][j] if axis = 0,
+  output[i][indices[i][j]] = updates[i][j] if axis = 1,
+```
+When `reduction` is set to "add", the update corresponding to the [i][j] entry is performed as below:
+```
+  output[indices[i][j]][j] += updates[i][j] if axis = 0,
+  output[i][indices[i][j]] += updates[i][j] if axis = 1,
+```
+When `reduction` is set to "mul", the update corresponding to the [i][j] entry is performed as below:
+```
+  output[indices[i][j]][j] *= updates[i][j] if axis = 0,
+  output[i][indices[i][j]] *= updates[i][j] if axis = 1,
+```
+
+This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+Example 1:
+```
+  data = [
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+  ]
+  indices = [
+      [1, 0, 2],
+      [0, 2, 1],
+  ]
+  updates = [
+      [1.0, 1.1, 1.2],
+      [2.0, 2.1, 2.2],
+  ]
+  output = [
+      [2.0, 1.1, 0.0]
+      [1.0, 0.0, 2.2]
+      [0.0, 2.1, 1.2]
+  ]
+```
+Example 2:
+```
+  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+  indices = [[1, 3]]
+  updates = [[1.1, 2.1]]
+  axis = 1
+  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ScatterElements,
+    16,
+    OpSchema()
+        .SetDoc(ScatterElements_ver16_doc)
+        .Attr(
+            "axis",
+            "Which axis to scatter on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "reduction",
+            "Type of reduction to apply: none (default), add, mul. "
+            "'none': no reduction applied. "
+            "'add':  reduction using the addition operation. "
+            "'mul': reduction using the multiplication operation.",
+            AttributeProto::STRING,
+            std::string("none"))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be "
+            "within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank r >=1 (same rank and shape as indices)",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r >= 1 (same rank as input).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Input and output types can be of any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* Gather_ver13_doc = R"DOC(
+Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+them in an output tensor of rank q + (r - 1).
+
+axis = 0 :
+
+Let
+k = indices[i_{0}, ..., i_{q-1}]
+Then
+output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]
+
+```
+  data = [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+  indices = [
+      [0, 1],
+      [1, 2],
+  ]
+  output = [
+      [
+          [1.0, 1.2],
+          [2.3, 3.4],
+      ],
+      [
+          [2.3, 3.4],
+          [4.5, 5.7],
+      ],
+  ]
+```
+axis = 1 :
+
+Let
+k = indices[i_{0}, ..., i_{q-1}]
+Then
+output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]
+
+```
+  data = [
+      [1.0, 1.2, 1.9],
+      [2.3, 3.4, 3.9],
+      [4.5, 5.7, 5.9],
+  ]
+  indices = [
+      [0, 2],
+  ]
+  axis = 1,
+  output = [
+          [[1.0, 1.9]],
+          [[2.3, 3.9]],
+          [[4.5, 5.9]],
+  ]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gather,
+    13,
+    OpSchema()
+        .SetDoc(Gather_ver13_doc)
+        .Attr(
+            "axis",
+            "Which axis to gather on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] "
+            "along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank q + (r - 1).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 2)) {
+            return;
+          }
+          const TensorShapeProto& data_shape =
+              ctx.getInputType(0)->tensor_type().shape();
+          const TensorShapeProto& indices_shape =
+              ctx.getInputType(1)->tensor_type().shape();
+          int r = data_shape.dim_size();
+          if (r < 1) {
+            fail_shape_inference("data tensor must have rank >= 1");
+          }
+          int q = indices_shape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -r || axis >= r) {
+            fail_shape_inference("axis must be in [-r, r-1]");
+          }
+          if (axis < 0) {
+            axis += r;
+          }
+          int out_rank = q + r - 1;
+          if (out_rank == 0) {
+            ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          }
+          for (int i = 0; i < out_rank; ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = (i < axis) ? data_shape.dim(i) : // i < axis < r
+                (i >= axis && i < axis + q) ? indices_shape.dim(i - axis)
+                                            : // i - axis < q
+                    data_shape.dim(i - q + 1); // i < out_rank < q + r - 1
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          if (!axisIsZero(ctx, true)) {
+            return;
+          }
+          const auto input_data = ctx.getInputData(0);
+          const auto input_indices = ctx.getInputData(1);
+          if (input_data == nullptr || input_indices == nullptr) {
+            return;
+          }
+          TensorShapeProto tsp;
+          for (int i = 0; i < input_indices->dim_size(); ++i) {
+            if (input_indices->dim(i).has_dim_value()) {
+              int index = input_indices->dim(i).dim_value();
+              appendDimToTensorShapeProto(tsp,
+                input_data->dim((index < 0)? input_data->dim_size() + index : index));
+            } else {
+              return;
+            }
+          }
+          if (tsp.dim_size() > 0) {
+            ctx.addOutputData(0, std::move(tsp));
+          }
+        }));
+
+static const char* GatherElements_ver13_doc = R"DOC(
+
+GatherElements takes two inputs `data` and `indices` of the same rank r >= 1
+and an optional attribute `axis` that identifies an axis of `data`
+(by default, the outer-most axis, that is axis 0). It is an indexing operation
+that produces its output by indexing into the input data tensor at index
+positions determined by elements of the `indices` tensor.
+Its output shape is the same as the shape of `indices` and consists of one value
+(gathered from the `data`) for each element in `indices`.
+
+For instance, in the 3-D case (r = 3), the output produced is determined
+by the following equations:
+```
+  out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
+  out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
+  out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
+```
+
+This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation.
+
+Example 1:
+```
+  data = [
+      [1, 2],
+      [3, 4],
+  ]
+  indices = [
+      [0, 0],
+      [1, 0],
+  ]
+  axis = 1
+  output = [
+      [1, 1],
+      [4, 3],
+  ]
+```
+Example 2:
+```
+  data = [
+      [1, 2, 3],
+      [4, 5, 6],
+      [7, 8, 9],
+  ]
+  indices = [
+      [1, 2, 0],
+      [2, 0, 0],
+  ]
+  axis = 0
+  output = [
+      [4, 8, 3],
+      [7, 2, 3],
+  ]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GatherElements,
+    13,
+    OpSchema()
+        .SetDoc(GatherElements_ver13_doc)
+        .Attr(
+            "axis",
+            "Which axis to gather on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be "
+            "within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of the same shape as indices.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // propagate indices' shape to output if it exists
+          if (hasInputShape(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 1, 0);
+          }
+        }));
+
+static const char* Squeeze_ver13_doc = R"DOC(
+Remove single-dimensional entries from the shape of a tensor.
+Takes an input `axes` with a list of axes to squeeze.
+If `axes` is not provided, all the single dimensions will be removed from
+the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Squeeze,
+    13,
+    OpSchema()
+        .SetDoc(Squeeze_ver13_doc)
+        .Input(
+            0,
+            "data",
+            "Tensors with at least max(dims) dimensions.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "axes",
+            "List of integers indicating the dimensions to squeeze. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "squeezed",
+            "Reshaped tensor with same data as input.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          std::vector<int64_t> axes;
+          size_t num_inputs = ctx.getNumInputs();
+          bool axes_not_specified = false;
+
+          if ((num_inputs == 2) && ctx.getInputType(1)) { //'axes' is input
+            auto axes_proto = ctx.getInputData(1);
+            if (axes_proto == nullptr) {
+              // skip if axes is not an initializer
+              return;
+            }
+            axes = ParseData<int64_t>(axes_proto);
+          } else {
+              // axes not specified
+              axes_not_specified = true;
+          }
+  
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_ndim = input_shape.dim_size();
+          std::transform(
+              axes.begin(),
+              axes.end(),
+              axes.begin(),
+              [&](int64_t axis) -> int64_t {
+                return axis < 0 ? axis + input_ndim : axis;
+              });
+
+          for (int i = 0; i < input_ndim; ++i) {
+            if(!input_shape.dim(i).has_dim_value() && axes_not_specified) {
+                // if dim has a symbolic value and the axes spec want to act on all dims, 
+                // return early because we can't infer the shape
+                return;
+            }
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          for (int i = 0; i < input_ndim; ++i) {
+            if (axes_not_specified && input_shape.dim(i).dim_value() == 1) {
+                // if axes not specified, do not keep shape if the dimension is equal to one
+                continue;
+            } else if (!axes_not_specified && std::find(axes.begin(), axes.end(), i) != axes.end()) {
+              // if axes wants to explictly act on this dim, fail explicitly only if the 
+              // dim is numerical and != 1. If the dim is 1 or symbolic, remove it. If 
+              // the dim is symbolic, runtime engines should check that the dimension is 
+              // actually 1 when the op is evaluated
+              if (input_shape.dim(i).has_dim_value() && input_shape.dim(i).dim_value() != 1) {
+                fail_shape_inference(
+                    "Dimension of input ",
+                    i,
+                    " must be 1 instead of ",
+                    input_shape.dim(i).dim_value());
+              }
+            } else {
+              *ctx.getOutputType(0)
+                   ->mutable_tensor_type()
+                   ->mutable_shape()
+                   ->add_dim() = input_shape.dim(i);
+            }
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          PropagateShapeDataFromInputToOutput(ctx, 0);
+        }));
+
+static const char* Unsqueeze_ver13_doc = R"DOC(
+Insert single-dimensional entries to the shape of an input tensor (`data`).
+Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).
+
+For example:
+  Given an input tensor (`data`) of shape [3, 4, 5], then
+  Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].
+
+The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
+The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
+Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
+The order of values in `axes` does not matter and can come in any order.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Unsqueeze,
+    13,
+    OpSchema()
+        .SetDoc(Unsqueeze_ver13_doc)
+        .Input(
+            0,
+            "data",
+            "Original tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "axes",
+            "List of integers indicating the dimensions to be inserted. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(expanded).",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "expanded",
+            "Reshaped tensor with same data as input.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          std::vector<int64_t> axes;
+          auto axes_proto = ctx.getInputData(1);
+          if (axes_proto == nullptr) {
+            // skip if axes is not an initializer
+            return;
+          }
+          axes = ParseData<int64_t>(axes_proto);
+
+          // validate 'axes' for duplicate entries
+          std::unordered_set<int64_t> unique_values;
+          for (const auto val : axes) {
+            if (unique_values.find(val) != unique_values.end()) {
+              fail_shape_inference(
+                  "'axes' attribute must not contain any duplicates");
+            }
+            unique_values.insert(val);
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_ndim = input_shape.dim_size();
+          const auto output_ndim = input_ndim + static_cast<int>(axes.size());
+          for (auto& axe : axes) {
+            if (axe < -output_ndim || axe >= output_ndim) {
+              fail_shape_inference(
+                  "values in 'axes' are beyond the bounds of the computed output shape");
+            }
+            if (axe < 0) {
+              axe += output_ndim;
+            }
+          }
+
+          // sort after correcting negative axes values (if any) in the previous
+          // step
+          std::sort(axes.begin(), axes.end());
+
+          int j = 0;
+          for (int i = 0; i < input_ndim; ++i) {
+            while (static_cast<size_t>(j) < axes.size() &&
+                   axes[j] ==
+                       ctx.getOutputType(0)->tensor_type().shape().dim_size()) {
+              ctx.getOutputType(0)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->add_dim()
+                  ->set_dim_value(1);
+              ++j;
+            }
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() =
+                ctx.getInputType(0)->tensor_type().shape().dim(i);
+          }
+          while (static_cast<size_t>(j) < axes.size() &&
+                 axes[j] ==
+                     ctx.getOutputType(0)->tensor_type().shape().dim_size()) {
+            ctx.getOutputType(0)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->add_dim()
+                ->set_dim_value(1);
+            ++j;
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          PropagateShapeDataFromInputToOutput(ctx, 0);
+        }));
+
+static const char* SpaceToDepth_ver13_doc =
+    R"DOC(SpaceToDepth rearranges blocks of spatial data into depth. More specifically,
+this op outputs a copy of the input tensor where values from the height and width dimensions
+are moved to the depth dimension.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SpaceToDepth,
+    13,
+    OpSchema()
+        .Attr(
+            "blocksize",
+            "Blocks of [blocksize, blocksize] are moved.",
+            AttributeProto::INT)
+        .SetDoc(SpaceToDepth_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth"
+            ", H is the height and W is the width.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto blocksize = getAttribute(ctx, "blocksize", 0);
+          if (blocksize <= 0) {
+            fail_shape_inference("Blocksize must be positive");
+          }
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() == 4) {
+              // TODO: Clarify what behavior should be if H or W is not a
+              // multiple of blocksize.
+              updateOutputShape(
+                  ctx,
+                  0,
+                  {input_shape.dim(0),
+                   input_shape.dim(1) * (blocksize * blocksize),
+                   input_shape.dim(2) / blocksize,
+                   input_shape.dim(3) / blocksize});
+            } else {
+              fail_shape_inference("Input tensor must be 4-dimensional");
+            }
+          }
+        }));
+
+static const char* DepthToSpace_ver13_doc =
+    R"DOC(DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+the input tensor where values from the depth dimension are moved in spatial blocks to the height
+and width dimensions. By default, `mode` = `DCR`.
+In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
+following order: depth, column, and then row. The output y is computed from the input x as below:
+
+b, c, h, w = x.shape
+
+tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+
+tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+
+y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
+
+
+In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
+following order: column, row, and the depth. The output y is computed from the input x as below:
+
+b, c, h, w = x.shape
+
+tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
+
+tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+
+y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    DepthToSpace,
+    13,
+    OpSchema()
+        .Attr(
+            "blocksize",
+            "Blocks of [blocksize, blocksize] are moved.",
+            AttributeProto::INT)
+        .Attr(
+            "mode",
+            "DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.",
+            AttributeProto::STRING,
+            std::string("DCR"))
+        .SetDoc(DepthToSpace_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth"
+            ", H is the height and W is the width.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto blocksize = getAttribute(ctx, "blocksize", 0);
+          if (blocksize <= 0) {
+            fail_shape_inference("Blocksize must be positive");
+          }
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() == 4) {
+              // TODO: Clarify what behavior should be if C is not a multiple of
+              // blocksize*blocksize.
+              updateOutputShape(
+                  ctx,
+                  0,
+                  {input_shape.dim(0),
+                   input_shape.dim(1) / (blocksize * blocksize),
+                   input_shape.dim(2) * blocksize,
+                   input_shape.dim(3) * blocksize});
+            } else {
+              fail_shape_inference("Input tensor must be 4-dimensional");
+            }
+          }
+        }));
+
+static const char* Tile_ver13_doc =
+    R"DOC(Constructs a tensor by tiling a given tensor.
+This is the same as function `tile` in Numpy, but no broadcast.
+For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tile,
+    13,
+    OpSchema()
+        .SetDoc(Tile_ver13_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of any shape.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "repeats",
+            "1D int64 tensor of the same length as input's dimension number, "
+            "includes numbers of repeated copies along input's dimensions.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Output tensor of the same dimensions and type as tensor input. "
+            "output_dim[i] = input_dim[i] * repeats[i]",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain repeat's type to int64 tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape inference
+
+          // Needs atleast the first input to proceed
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+
+          const auto* repeats_inputs = ctx.getInputData(1);
+
+          auto* output_shape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          if (nullptr != repeats_inputs && hasNInputShapes(ctx, 2)) {
+            // shape inference is possible only when 'repeats' is an initializer
+            const auto& repeats_shape =
+                ctx.getInputType(1)->tensor_type().shape();
+            if (repeats_shape.dim_size() != 1 ||
+                repeats_inputs->data_type() != TensorProto::INT64) {
+                  fail_shape_inference("'Repeats' input must be 1D tensor of type int64");
+                }
+
+            const auto& repeats_data = ParseData<int64_t>(repeats_inputs);
+
+            if (repeats_data.size() != static_cast<size_t>(input_rank)) {
+              fail_shape_inference(
+                  "'Repeats' input has incorrect number of values. "
+                  "The number of values in 'repeats' must be equal "
+                  "to the number of input dimensions.");
+            }
+
+            for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+              const auto& input_dim = input_shape.dim((int)i);
+              auto* output_dim = output_shape->add_dim();
+              if (input_dim.has_dim_value()) {
+                output_dim->set_dim_value(
+                    input_dim.dim_value() * repeats_data[i]);
+              }
+            }
+          } else {
+            // Infer output shape's rank in any case (if repeats data is not
+            // available)
+            auto* output_shape_0 = getOutputShape(ctx, 0);
+            for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+              output_shape_0->add_dim();
+            }
+          }
+          return;
+        }));
+
+static const char* Upsample_ver10_doc = R"DOC(
+Upsample the input tensor.
+Each dimension value of the output tensor is:
+  output_dimension = floor(input_dimension * scale).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Upsample,
+    10,
+    OpSchema()
+        .Deprecate()
+        .Attr(
+            "mode",
+            "Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Input(
+            0,
+            "X",
+            "N-D tensor",
+            "T",
+            OpSchema::Single)
+        .Input(
+            1,
+            "scales",
+            "The scale array along each dimension. It takes value greater than or equal to 1."
+            " The number of elements of 'scales' should be the same as the rank of input 'X'.",
+            "tensor(float)",
+            OpSchema::Single)
+        .Output(
+            0,
+            "Y",
+            "N-D tensor after resizing",
+            "T",
+            OpSchema::Single)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input 'X' and output 'Y' to all tensor types.")
+        .SetDoc(Upsample_ver10_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          resizeShapeInference_opset7_to_10(ctx);
+        }));
+
+static const char* Resize_ver13_doc = R"DOC(
+Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
+Each dimension value of the output tensor is:
+  output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified.
+)DOC";
+
+static const char* Resize_attr_coordinate_transformation_mode_doc = R"DOC(
+This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
+
+The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example.
+Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/>
+
+if coordinate_transformation_mode is "half_pixel", <br/>
+x_original = (x_resized + 0.5) / scale - 0.5, <br/>
+
+if coordinate_transformation_mode is "pytorch_half_pixel", <br/>
+x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/>
+
+if coordinate_transformation_mode is "align_corners", <br/>
+x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
+
+if coordinate_transformation_mode is "asymmetric", <br/>
+x_original = x_resized / scale, <br/>
+
+if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Resize,
+    13,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Three interpolation modes: nearest (default), linear and cubic. "
+            "The \"linear\" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). "
+            "The \"cubic\" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Attr(
+            "cubic_coeff_a",
+            "The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75"
+            " (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. "
+            "This attribute is valid only if \"mode\" is \"cubic\".",
+            AttributeProto::FLOAT,
+            static_cast<float>(-0.75))
+        .Attr(
+            "exclude_outside",
+            "If set to 1, the weight of sampling locations outside the tensor will be set to 0"
+            " and the weight will be renormalized so that their sum is 1.0. The default value is 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "coordinate_transformation_mode",
+            Resize_attr_coordinate_transformation_mode_doc,
+            AttributeProto::STRING,
+            std::string("half_pixel"))
+        .Attr(
+            "nearest_mode",
+            "Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get \"nearest\" pixel in input tensor from x_original, so this attribute is valid only if \"mode\" is \"nearest\".",
+            AttributeProto::STRING,
+            std::string("round_prefer_floor"))
+        .Attr(
+            "extrapolation_value",
+            "When coordinate_transformation_mode is \"tf_crop_and_resize\" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.",
+            AttributeProto::FLOAT,
+            static_cast<float>(0))
+        .Input(
+            0,
+            "X",
+            "N-D tensor",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "roi",
+            "1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is \"tf_crop_and_resize\"",
+            "T2",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "scales",
+            "The scale array along each dimension. It takes value greater than 0. If it's less than 1,"
+            " it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should"
+            " be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.",
+            "tensor(float)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            3,
+            "sizes",
+            "The size of the output tensor. The number of elements of 'sizes' should be the same as the"
+            " rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "N-D tensor after resizing",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input 'X' and output 'Y' to all tensor types.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain roi type to float or double.")
+        .SetDoc(Resize_ver13_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          resizeShapeInference(ctx);
+        }));
+
+static const char* GridSample_ver16_doc = R"DOC(
+Given an `input` and a flow-field `grid`, computes the `output` using `input` values and pixel locations from `grid`.
+Currently, only spatial (4-D) inputs are supported. For `input` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2),
+the `output` will have shape (N, C, H_out, W_out).
+For each output location `output[N, C, H_out, W_out]`, the size-2 vector `grid[N, H_out, W_out]` specifies `input` pixel locations `x` and `y`,
+which are used to interpolate the output value `output[N, C, H_out, W_out]`.
+
+The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025).
+See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GridSample,
+    16,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Three interpolation modes: bilinear (default), nearest and bicubic.",
+            AttributeProto::STRING,
+            std::string("bilinear"))
+        .Attr(
+            "padding_mode",
+            "Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. "
+            "zeros: use 0 for out-of-bound grid locations, "
+            "border: use border values for out-of-bound grid locations, "
+            "reflection: use values at locations reflected by the border for out-of-bound grid locations. "
+            "If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. "
+            "For location far away from the border, it will keep being reflected until becoming in bound. "
+            "If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.",
+            AttributeProto::STRING,
+            std::string("zeros"))
+        .Attr(
+            "align_corners",
+            "If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. "
+            "If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "X",
+            "4-D tensor of shape (N, C, H, W), "
+            "where N is the batch size, C is the numbers of channels, "
+            "H and W are the height and width of the input data.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "grid",
+            "Input offset, 4-D tensor of shape (N, H_out, W_out, 2), "
+            "where H_out and W_out are the height and width of grid and output, "
+            "Grid specifies the sampling pixel locations normalized by the input spatial dimensions. "
+            "Therefore, it should have most values in the range of [-1, 1]. "
+            "If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "4-D tensor of shape (N, C, H_out, W_out).",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_tensor_types(),
+            "Constrain input types to all tensor types.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain output types to float tensors.")
+        .SetDoc(GridSample_ver16_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          size_t input_param = 0, grid_param = 1;
+
+          checkInputRank(ctx, input_param, 4);
+          checkInputRank(ctx, grid_param, 4);
+
+          // Output dimensions, initialized to an unknown-dimension-value
+          Dim N, C, H_out, W_out;
+
+          // Get value of N from dim 0 of input_param, if available
+          unifyInputDim(ctx, input_param, 0, N);
+          // Get value of C from dim 1 of input_param, if available
+          unifyInputDim(ctx, input_param, 1, C);
+
+          // Get value of H_out from dim 1 of grid_param, if available
+          unifyInputDim(ctx, grid_param, 1, H_out);
+          // Get value of W_out from dim 2 of grid_param, if available
+          unifyInputDim(ctx, grid_param, 2, W_out);
+
+          // set output shape:
+          updateOutputShape(ctx, 0, {N, C, H_out, W_out});
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Identity,
+    16,
+    OpSchema()
+        .SetDoc("Identity operator")
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "V",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor to copy input into.",
+            "V",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types_with_bfloat();
+              auto s = OpSchema::all_tensor_sequence_types();
+              auto o = OpSchema::all_optional_types();
+              t.insert(t.end(), s.begin(), s.end());
+              t.insert(t.end(), o.begin(), o.end());
+              return t;
+            }(),
+            "Constrain input and output types to all tensor, sequence, and optional types.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Compress_ver11_doc = R"DOC(
+    Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index.
+    In case axis is not provided, input is flattened before elements are selected.
+    Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html
+    )DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Compress,
+    11,
+    OpSchema()
+        .SetDoc(Compress_ver11_doc)
+        .Attr(
+            "axis",
+            "(Optional) Axis along which to take slices. If not specified, "
+            "input is flattened before elements being selected. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "input",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "condition",
+            "Rank 1 tensor of booleans to indicate which slices or data elements to be selected. "
+            "Its length can be less than the input length along the axis "
+            "or the flattened input size if axis is not specified. "
+            "In such cases data slices or elements exceeding the condition length are discarded.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasInputShape(ctx, 0)) {
+            const TensorShapeProto& indices_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            int r = indices_shape.dim_size();
+            if (r < 1) {
+              fail_shape_inference("Indices tensor must have rank >= 1");
+            }
+            auto axisAttr = ctx.getAttribute("axis");
+            if (axisAttr) {
+              int axis = static_cast<int>(axisAttr->i());
+              if (axis < -r || axis >= r) {
+                fail_shape_inference(
+                    "'axis' must be in [-rank(indices), rank(indices)-1]");
+              }
+              if (axis < 0) {
+                axis += r;
+              }
+            }
+          }
+        }));
+
+static const char* OneHot_ver11_doc = R"DOC(
+    Produces a one-hot tensor based on inputs.
+    The locations represented by the index values in the 'indices' input tensor will have 'on_value'
+    and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'
+    are specified as part of required input argument 'values', which is a two-element tensor of format
+    [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the
+    input tensor. The additional dimension is for one-hot representation. The additional dimension will
+    be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional
+    dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional
+    dimension is specified by required scalar input 'depth'. The type of the output tensor is the same
+    as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside
+    the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the
+    output tensor.
+
+    when axis = 0:
+    output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise.
+
+    when axis = -1:
+    output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    OneHot,
+    11,
+    OpSchema()
+        .SetDoc(OneHot_ver11_doc)
+        .Attr(
+            "axis",
+            "(Optional) Axis along which one-hot representation in added. Default: axis=-1. "
+            "axis=-1 means that the additional dimension will be inserted as the "
+            "innermost/last dimension in the output tensor. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r-1, r] where r = rank(indices).",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .Input(
+            0,
+            "indices",
+            "Input tensor containing indices. Any entries in the 'indices' input tensor with "
+            "values outside the range [-depth, depth-1] will result in one-hot representation with all "
+            "'off_value' values in the output tensor."
+            "In case 'indices' is of non-integer type, the values will be casted to int64 before use.",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            1,
+            "depth",
+            "Scalar specifying the number of classes in one-hot tensor. This is also the size "
+            "of the one-hot dimension (specified by 'axis' attribute) added on in the output "
+            "tensor. The values in the 'indices' input tensor are expected to be "
+            "in the range [-depth, depth-1]. "
+            "In case 'depth' is of non-integer type, it will be casted to int64 before use.",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "values",
+            "Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], "
+            "where 'on_value' is the value used for filling locations specified in 'indices' input "
+            "tensor, and 'off_value' is the value used for filling locations other than those specified "
+            "in 'indices' input tensor. ",
+            "T3",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. "
+            "The data type for the elements of the output tensor is the same as the type of input 'values' "
+            "is used.",
+            "T3",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_numeric_types(),
+            "Constrain input to only numeric types.")
+        .TypeConstraint(
+            "T2",
+            OpSchema::all_numeric_types(),
+            "Constrain input to only numeric types.")
+        .TypeConstraint(
+            "T3",
+            OpSchema::all_tensor_types(),
+            "Constrain to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Check that the node has three inputs.
+          if (ctx.getNumInputs() != 3) {
+            fail_type_inference("OneHot node must have three inputs.");
+          }
+          // Input 'depth' must be a scalar or a single-element vector.
+          // TODO: Ideally to match spec for this input only Scalar should
+          // be allowed. Making this change now can affect backward
+          // compatibility for this op. Since this does not seem like a good
+          // justification to update version for this op, allowing both scalar
+          // and 1 element vector for now. In future when version update for
+          // this op is done we should only allow scalar or chage the spec to
+          // allow both.
+          if (hasInputShape(ctx, 1)) {
+            auto& depth_shape = getInputShape(ctx, 1);
+            if (depth_shape.dim_size() != 0 && depth_shape.dim_size() != 1) {
+              fail_type_inference(
+                  "Input 'depth' must be a scalar or rank 1 tensor.");
+            }
+            if (depth_shape.dim_size() == 1 &&
+                depth_shape.dim((int)0).has_dim_value() &&
+                depth_shape.dim((int)0).dim_value() != 1) {
+              fail_type_inference(
+                  "Input 'depth' must have exactly one element.");
+            }
+          }
+          // Input 'values' must be a two-element vector.
+          if (hasInputShape(ctx, 2)) {
+            auto& values_shape = getInputShape(ctx, 2);
+            if (values_shape.dim_size() != 1) {
+              fail_type_inference("Input 'values' must be rank 1 tensor.");
+            }
+            if (values_shape.dim((int)0).has_dim_value() &&
+                values_shape.dim((int)0).dim_value() != 2) {
+              fail_type_inference(
+                  "Input 'values' must have exactly two elements.");
+            }
+          }
+          // Set output type to be the same as the third input, 'values'.
+          propagateElemTypeFromInputToOutput(ctx, 2, 0);
+          // Set the output shape, if input 0 (indices) shape is available.
+          if (hasInputShape(ctx, 0)) {
+            const TensorShapeProto& indices_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            int r = indices_shape.dim_size();
+            if (r < 1) {
+              fail_shape_inference("Indices tensor must have rank >= 1");
+            }
+            int out_rank = r + 1;
+            int axis = static_cast<int>(getAttribute(ctx, "axis", -1));
+            if (axis < -out_rank || axis >= out_rank) {
+              fail_shape_inference(
+                  "'axis' must be in [-rank(indices), rank(indices)-1]");
+            }
+            if (axis < 0) {
+              axis += out_rank;
+            }
+            auto* output_shape = getOutputShape(ctx, 0);
+            for (int i = 0; i < out_rank; ++i) {
+              auto* dim = output_shape->add_dim();
+              if (i < axis) {
+                if (indices_shape.dim(i).has_dim_value()) {
+                  dim->set_dim_value(indices_shape.dim(i).dim_value());
+                } else if (indices_shape.dim(i).has_dim_param()) {
+                  dim->set_dim_param(indices_shape.dim(i).dim_param());
+                }
+              } else if (i > axis) {
+                if (indices_shape.dim(i - 1).has_dim_value()) {
+                  dim->set_dim_value(indices_shape.dim(i - 1).dim_value());
+                } else if (indices_shape.dim(i - 1).has_dim_param()) {
+                  dim->set_dim_param(indices_shape.dim(i - 1).dim_param());
+                }
+              }
+            }
+          }
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    IsNaN,
+    13,
+    OpSchema()
+        .SetDoc(R"DOC(Returns which elements of the input are NaN.)DOC")
+        .Input(
+            0,
+            "X",
+            "input",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "output",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(bfloat16)"},
+            "Constrain input types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(bool)"},
+            "Constrain output types to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          updateOutputElemType(ctx, 0, TensorProto::BOOL);
+          if (hasInputShape(ctx, 0)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    IsInf,
+    10,
+    OpSchema()
+        .SetDoc(R"DOC(Map infinity to true and other values to false.)DOC")
+        .Input(
+            0,
+            "X",
+            "input",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "output",
+            "T2",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Attr(
+            "detect_positive",
+            "(Optional) Whether map positive infinity to true. Default to 1 "
+            "so that positive infinity induces true. Set this attribute to 0 "
+            "if positive infinity should be mapped to false.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "detect_negative",
+            "(Optional) Whether map negative infinity to true. Default to 1 "
+            "so that negative infinity induces true. Set this attribute to 0 "
+            "if negative infinity should be mapped to false.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(bool)"},
+            "Constrain output types to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          updateOutputElemType(ctx, 0, TensorProto::BOOL);
+          if (hasInputShape(ctx, 0)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* Where_ver16_doc = R"DOC(
+Return elements, either from X or Y, depending on condition.
+Where behaves like
+[numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
+with three parameters.
+
+)DOC";
+
+static const char* Where_ver16_history = R"DOC(
+
+**History**
+- Version 16 adds bfloat16 to the types allowed (for the second and third parameter).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Where,
+    16,
+    OpSchema()
+        .SetDoc(GET_OP_DOC_STR(
+            std::string(Where_ver16_doc) + GenerateBroadcastingDocMul()) +
+            std::string(Where_ver16_history))
+        .Input(
+            0,
+            "condition",
+            "When True (nonzero), yield X, otherwise yield Y",
+            "B",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            1,
+            "X",
+            "values selected at indices where condition is True",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "Y",
+            "values selected at indices where condition is False",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of shape equal to the broadcasted shape of condition, X, and Y.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint("B", {"tensor(bool)"}, "Constrain to boolean tensors.")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types (including bfloat).")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 1, 0);
+          if (hasNInputShapes(ctx, 3)) {
+            std::vector<const TensorShapeProto*> shapes;
+            shapes.push_back(&ctx.getInputType(0)->tensor_type().shape());
+            shapes.push_back(&ctx.getInputType(1)->tensor_type().shape());
+            shapes.push_back(&ctx.getInputType(2)->tensor_type().shape());
+            multidirectionalBroadcastShapeInference(
+                shapes,
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+          }
+        }));
+
+static const char* NonZero_ver13_doc = R"DOC(
+    Returns the indices of the elements that are non-zero
+    (in row-major order - by dimension).
+    NonZero behaves similar to numpy.nonzero:
+    https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    NonZero,
+    13,
+    OpSchema()
+        .SetDoc(NonZero_ver13_doc)
+        .Input(
+            0,
+            "X",
+            "input",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "output",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          updateOutputElemType(ctx, 0, TensorProto::INT64);
+          TensorShapeProto output_shape;
+          auto* dim = output_shape.add_dim();
+          if (hasInputShape(ctx, 0)) {
+            const TensorShapeProto& input_shape = getInputShape(ctx, 0);
+            dim->set_dim_value(input_shape.dim_size());
+          }
+          output_shape.add_dim();
+          updateOutputShape(ctx, 0, output_shape);
+        }));
+
+static const char* ReverseSequence_ver10_doc = R"DOC(
+Reverse batch of sequences having different lengths specified by `sequence_lens`.
+
+For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis,
+and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed
+sequences on the first sequence_lens[i] elements, then have original values copied for the other elements.
+
+Example 1:
+  input = [[0.0, 4.0, 8.0,  12.0],
+           [1.0, 5.0, 9.0,  13.0],
+           [2.0, 6.0, 10.0, 14.0],
+           [3.0, 7.0, 11.0, 15.0]]
+  sequence_lens = [4, 3, 2, 1]
+  time_axis = 0
+  batch_axis = 1
+
+  output = [[3.0, 6.0, 9.0,  12.0],
+            [2.0, 5.0, 8.0,  13.0],
+            [1.0, 4.0, 10.0, 14.0],
+            [0.0, 7.0, 11.0, 15.0]]
+
+Example 2:
+  input = [[0.0,  1.0,  2.0,  3.0 ],
+           [4.0,  5.0,  6.0,  7.0 ],
+           [8.0,  9.0,  10.0, 11.0],
+           [12.0, 13.0, 14.0, 15.0]]
+  sequence_lens = [1, 2, 3, 4]
+  time_axis = 1
+  batch_axis = 0
+
+  output = [[0.0,  1.0,  2.0,  3.0 ],
+            [5.0,  4.0,  6.0,  7.0 ],
+            [10.0, 9.0,  8.0,  11.0],
+            [15.0, 14.0, 13.0, 12.0]]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ReverseSequence,
+    10,
+    OpSchema()
+        .SetDoc(ReverseSequence_ver10_doc)
+        .Attr(
+            "time_axis",
+            "(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "batch_axis",
+            "(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Input(
+            0,
+            "input",
+            "Tensor of rank r >= 2.",
+            "T",
+            OpSchema::Single)
+        .Input(
+            1,
+            "sequence_lens",
+            "Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.",
+            "tensor(int64)",
+            OpSchema::Single)
+        .Output(
+            0,
+            "Y",
+            "Tensor with same shape of input.",
+            "T",
+            OpSchema::Single)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input and output types can be of any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 2)) {
+            return;
+          }
+
+          auto& first_input_shape = getInputShape(ctx, 0);
+          if (first_input_shape.dim_size() < 2) {
+            fail_shape_inference("'input' must have rank >= 2");
+          }
+          auto& seq_len_input_shape = getInputShape(ctx, 1);
+          if (seq_len_input_shape.dim_size() != 1) {
+            fail_shape_inference("'sequence_lens' must have rank of 1");
+          }
+
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+        }));
+
+static const char* Unique_ver11_doc = R"DOC(
+Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned.
+Otherwise the input tensor is flattened and unique values of the flattened tensor are returned.
+
+This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs.
+The first output tensor 'Y' contains all unique values or subtensors of the input.
+The second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'..
+The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. ".
+The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input.
+
+Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.
+
+https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html
+
+Example 1:
+  input_X = [2, 1, 1, 3, 4, 3]
+  attribute_sorted = 0
+  attribute_axis = None
+  output_Y = [2, 1, 3, 4]
+  output_indices = [0, 1, 3, 4]
+  output_inverse_indices = [0, 1, 1, 2, 3, 2]
+  output_counts = [1, 2, 2, 1]
+
+Example 2:
+  input_X = [[1, 3], [2, 3]]
+  attribute_sorted = 1
+  attribute_axis = None
+  output_Y = [1, 2, 3]
+  output_indices = [0, 2, 1]
+  output_inverse_indices = [0, 2, 1, 2]
+  output_counts = [1, 1, 2]
+
+Example 3:
+  input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]
+  attribute_sorted = 1
+  attribute_axis = 0
+  output_Y = [[1, 0, 0], [2, 3, 4]]
+  output_indices = [0, 2]
+  output_inverse_indices = [0, 0, 1]
+  output_counts = [2, 1]
+
+Example 4:
+  input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+             [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]
+  attribute_sorted = 1
+  attribute_axis = 1
+
+  intermediate data are presented below for better understanding:
+
+  there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):
+  A: [[1, 1], [1, 1]],
+     [[0, 1], [0, 1]],
+     [[2, 1], [2, 1]],
+     [[0, 1], [0, 1]].
+
+  there are 3 unique subtensors:
+  [[1, 1], [1, 1]],
+  [[0, 1], [0, 1]],
+  [[2, 1], [2, 1]].
+
+  sorted unique subtensors:
+  B: [[0, 1], [0, 1]],
+     [[1, 1], [1, 1]],
+     [[2, 1], [2, 1]].
+
+  output_Y is constructed from B:
+  [[[0. 1.], [1. 1.], [2. 1.]],
+   [[0. 1.], [1. 1.], [2. 1.]]]
+
+  output_indices is to map from B to A:
+  [1, 0, 2]
+
+  output_inverse_indices is to map from A to B:
+  [1, 0, 2, 0]
+
+  output_counts = [2 1 1]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Unique,
+    11,
+    OpSchema()
+        .SetDoc(Unique_ver11_doc)
+        .Attr(
+            "sorted",
+            "(Optional) Whether to sort the unique elements in ascending order before returning as output. "
+            "Must be one of 0, or 1 (default).",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Attr(
+            "axis",
+            "(Optional) The dimension to apply unique. If not specified, the unique elements of the "
+            "flattened input are returned. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(input).",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(
+            0,
+            "X",
+            "A N-D input tensor that is to be processed.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "Y",
+            "A tensor of the same type as 'X' "
+            "containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted "
+            "or maintained in the same order they occur in input 'X'",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            1,
+            "indices",
+            "A 1-D INT64 tensor "
+            "containing indices of 'Y' elements' first occurance in 'X'. "
+            "When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. "
+            "When 'axis' is not provided, it contains indices to values in the flattened input tensor. ",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            2,
+            "inverse_indices",
+            "A 1-D INT64 tensor "
+            "containing, for elements of 'X', its corresponding indices in 'Y'. "
+            "When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. "
+            "When 'axis' is not provided, it contains indices to values in output 'Y'. ",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            3,
+            "counts",
+            "A 1-D INT64 tensor containing "
+            "the count of each element "
+            "of 'Y' in input 'X'",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input can be of any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          const TypeProto* xTensorProto = ctx.getInputType(0);
+          TypeProto* yTensorProto = ctx.getOutputType(0);
+          TypeProto* indicesTensorProto = nullptr;
+          TypeProto* inverseIndicesTensorProto = nullptr;
+          TypeProto* countsTensorProto = nullptr;
+
+          // 'indices', 'inverse_indices', and 'counts' are 1-D tensors of
+          // unknown dimension.
+          // Shape inference will happen even in case of empty optional outputs,
+          // graph-level shape inference should not propagate the shape downstream for empty optional outputs.
+          auto num_outputs = ctx.getNumOutputs();
+          if (num_outputs >= 2) {
+            indicesTensorProto = ctx.getOutputType(1);
+            updateOutputElemType(ctx, 1, TensorProto::INT64);
+            indicesTensorProto->mutable_tensor_type()
+                ->mutable_shape()
+                ->add_dim();
+          }
+
+          if (num_outputs >= 3) {
+            inverseIndicesTensorProto = ctx.getOutputType(2);
+            updateOutputElemType(ctx, 2, TensorProto::INT64);
+            inverseIndicesTensorProto->mutable_tensor_type()
+                ->mutable_shape()
+                ->add_dim();
+          }
+
+          if (num_outputs >= 4) {
+            countsTensorProto = ctx.getOutputType(3);
+            updateOutputElemType(ctx, 3, TensorProto::INT64);
+            countsTensorProto->mutable_tensor_type()
+                ->mutable_shape()
+                ->add_dim();
+          }
+
+          auto axisAttr = ctx.getAttribute("axis");
+          if (!axisAttr) {
+            // 'axis' is not provided. Input 'X' is flattened.
+            // 'Y' is a 1-D tensor of unknown dimension.
+            yTensorProto->mutable_tensor_type()->mutable_shape()->add_dim();
+          } else {
+            // 'axis' is provided.
+            int axis = static_cast<int>(axisAttr->i());
+            if(!xTensorProto->tensor_type().has_shape()) {
+              return;
+            }
+            const TensorShapeProto& input_shape =
+                xTensorProto->tensor_type().shape();
+            int rank = input_shape.dim_size();
+            if (axis < 0)
+              axis += rank;
+            if (axis < 0 || axis >= rank){
+              fail_shape_inference("Invalid value for attribute axis");
+            }
+            // 'Y' has the same shape as 'X' except in the 'axis' dimension
+            // which is unknown.
+            for (int i = 0; i < input_shape.dim_size(); i++) {
+              auto* dim = yTensorProto->mutable_tensor_type()
+                              ->mutable_shape()
+                              ->add_dim();
+              if (i != axis) {
+                *dim = input_shape.dim(i);
+              }
+            }
+          }
+        }));
+
+static const char* GatherND_ver13_doc = R"DOC(
+Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers
+slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`.
+
+`indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+where each element defines a slice of `data`
+
+`batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of
+`data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension.
+
+Some salient points about the inputs' rank and shape:
+
+1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal.
+
+3) b < min(q, r) is to be honored.
+
+4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive)
+
+5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+   It is an error if any of the index values are out of bounds.
+
+The output is computed as follows:
+
+The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+1) If `indices_shape[-1] > r-b` => error condition
+
+2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors
+   containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions
+   of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]`
+   is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below)
+
+3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor
+   containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding
+   to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor
+   to form the `output` tensor (Examples 2, 3, 4 and 5 below)
+
+This operator is the inverse of `ScatterND`.
+
+`Example 1`
+
+  batch_dims = 0
+
+  data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+  indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+  output  = [0,3]           # output_shape = [2]
+
+`Example 2`
+
+  batch_dims = 0
+
+  data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+  indices = [[1],[0]]      # indices_shape = [2, 1]
+
+  output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+`Example 3`
+
+  batch_dims = 0
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+  output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+`Example 4`
+
+  batch_dims = 0
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+  output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+`Example 5`
+
+  batch_dims = 1
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[1],[0]]             # indices_shape = [2, 1]
+
+  output  = [[2,3],[4,5]]             # output_shape = [2, 2]
+
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GatherND,
+    13,
+    OpSchema()
+        .SetDoc(GatherND_ver13_doc)
+        .Attr(
+            "batch_dims",
+            "The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] "
+            "along axis of size s. It is an error if any of the index values are out of bounds.",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank q + r - indices_shape[-1] - 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          if (!hasNInputShapes(ctx, 2)) {
+            // cannot proceed with shape or rank inference
+            return;
+          }
+
+          const auto& data_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto data_rank = data_shape.dim_size();
+
+          const auto& indices_shape =
+              ctx.getInputType(1)->tensor_type().shape();
+          const auto indices_rank = indices_shape.dim_size();
+
+        int64_t batch_dims_data = getAttribute(ctx, "batch_dims", 0);
+          if (data_rank < 1 || indices_rank < 1) {
+            fail_shape_inference(
+                "Both `data` and `indices` input tensors in GatherND op "
+                "need to have rank larger than 0.");
+          }
+
+          // cannot ascertain if the input shapes are valid if shape of
+          // `indices` is missing last dimension value so return at this point
+          if (!indices_shape.dim(indices_rank - 1).has_dim_value()) {
+            return;
+          }
+
+          const auto last_index_dimension =
+              indices_shape.dim(indices_rank - 1).dim_value() + batch_dims_data;
+
+          if (last_index_dimension > data_rank) {
+            fail_shape_inference(
+                "Last dimension of `indices` input tensor in GatherND op "
+                "must not be larger than the rank of `data` tensor");
+          }
+
+          for (int i = 0; i < indices_rank - 1; ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = indices_shape.dim(i);
+          }
+
+          for (int i = static_cast<int>(last_index_dimension); i < data_rank;
+               ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = data_shape.dim(i);
+          }
+        }));
+
+static const char* Pad_ver13_doc = R"DOC(
+Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`,
+a padded tensor (`output`) is generated.
+
+The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):
+
+1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False)
+
+2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
+
+3) `edge` - pads with the edge values of array
+
+
+Example 1 (`constant` mode):
+  Insert 0 pads to the beginning of the second dimension.
+
+  data =
+  [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+
+  pads = [0, 2, 0, 0]
+
+  mode = 'constant'
+
+  constant_value = 0.0
+
+  output =
+  [
+      [0.0, 0.0, 1.0, 1.2],
+      [0.0, 0.0, 2.3, 3.4],
+      [0.0, 0.0, 4.5, 5.7],
+  ]
+
+
+Example 2 (`reflect` mode):
+  data =
+  [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+
+  pads = [0, 2, 0, 0]
+
+  mode = 'reflect'
+
+  output =
+  [
+      [1.0, 1.2, 1.0, 1.2],
+      [2.3, 3.4, 2.3, 3.4],
+      [4.5, 5.7, 4.5, 5.7],
+  ]
+
+
+Example 3 (`edge` mode):
+  data =
+  [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+
+  pads = [0, 2, 0, 0]
+
+  mode = 'edge'
+
+  output =
+  [
+      [1.0, 1.0, 1.0, 1.2],
+      [2.3, 2.3, 2.3, 3.4],
+      [4.5, 4.5, 4.5, 5.7],
+  ]
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pad,
+    13,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Supported modes: `constant`(default), `reflect`, `edge`",
+            AttributeProto::STRING,
+            std::string("constant"))
+        .SetDoc(Pad_ver13_doc)
+        .Input(
+            0,
+            "data",
+            "Input tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "pads",
+            "Tensor of integers indicating the number of padding elements to add or remove (if negative) "
+            "at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. "
+            "`pads` should be a 1D tensor of shape [2 * input_rank]. "
+            "`pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], "
+            "where xi_begin is the number of pad values added at the beginning of axis `i` and "
+            "xi_end, the number of pad values added at the end of axis `i`.",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "constant_value",
+            "(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, "
+            "empty string or False).",
+            "T",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor after padding.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape inference needs the input data shape
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+
+          // Infer output shape if 'pads' tensor is available
+          const auto* pads_initializer = ctx.getInputData(1);
+          if (nullptr != pads_initializer) {
+            if (pads_initializer->dims_size() != 1 ||
+                pads_initializer->data_type() != TensorProto::INT64) {
+                  fail_shape_inference("'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64");
+            }
+
+            const auto& pads_data = ParseData<int64_t>(pads_initializer);
+            if (pads_data.size() != static_cast<size_t>(2 * input_rank)) {
+              fail_shape_inference("Pads has incorrect number of values");
+            }
+
+            auto* output_shape =
+                ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+            for (size_t i = 0; static_cast<int64_t>(i) < input_rank; ++i) {
+              const auto& input_dim = input_shape.dim((int)i);
+              auto* output_dim = output_shape->add_dim();
+              if (input_dim.has_dim_value()) {
+                output_dim->set_dim_value(
+                    input_dim.dim_value() + pads_data[i] +
+                    pads_data[i + input_rank]);
+              } else if (pads_data[i] + pads_data[i + input_rank] == 0) {
+                *output_dim = input_dim;
+              }
+            }
+          } else {
+            // Infer output shapes' rank in any case
+            auto* output_shape_0 = getOutputShape(ctx, 0);
+            for (size_t i = 0; static_cast<int64_t>(i) < input_rank; ++i) {
+              output_shape_0->add_dim();
+            }
+          }
+          return;
+        }));
+
+static const char* Trilu_ver14_doc = R"DOC(
+Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s).
+The attribute "upper" determines whether the upper or lower part is retained. If set to true,
+the upper triangular matrix is retained. Lower triangular matrix is retained otherwise.
+Default value for the "upper" attribute is true.
+Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists
+of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal.
+All other elements in the matrix are set to zero.
+If k = 0, the triangular part on and above/below the main diagonal is retained.
+If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it.
+A negative k value retains the main diagonal and |k| diagonals below it.
+If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it.
+A negative k value excludes the main diagonal and (|k|-1) diagonals below it.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Trilu,
+    14,
+    OpSchema()
+        .SetDoc(Trilu_ver14_doc)
+        .Attr("upper",
+            "Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .Input(
+            0,
+            "input",
+            "Input tensor of rank 2 or higher.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "k",
+            "A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. "
+            "Default value is 0 if it's not specified.",
+            "tensor(int64)",
+            OpSchema::Optional,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(
+            0,
+            "output",
+	          "Output tensor of the same type and shape as the input tensor.",
+	          "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape inference needs the input data shape
+          if (hasInputShape(ctx, 0)) {
+	          const TensorShapeProto& input_shape =
+	              ctx.getInputType(0)->tensor_type().shape();
+	          const int rank = static_cast<int>(input_shape.dim_size());
+	          if (rank < 2) {
+	            fail_shape_inference("Input rank must be >= 2.")
+	          }
+	          propagateShapeFromInputToOutput(ctx, 0, 0);
+	        }
+        }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor/old.cc b/third_party/onnx/onnx/defs/tensor/old.cc
new file mode 100644
index 0000000000..2bb20fcaea
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor/old.cc
@@ -0,0 +1,4514 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include "onnx/defs/tensor/utils.h"
+
+namespace ONNX_NAMESPACE {
+
+static const char* Cast_ver9_doc = R"DOC(
+The operator casts the elements of a given input tensor to a data type
+specified by the 'to' argument and returns an output tensor of the same size in
+the converted type. The 'to' argument must be one of the data types specified
+in the 'DataType' enum field in the TensorProto message.
+
+Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
+(e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
+result 100. There are some string literals reserved for special floating-point values;
+"+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
+Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
+this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
+to string tensors, plain floating-point representation (such as "314.15926") would be used.
+Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
+of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
+
+Conversion from a numerical type to any numerical type is always allowed.
+User must be aware of precision loss and value change caused by range difference between two types.
+For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
+an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Cast,
+    9,
+    OpSchema()
+        .SetDoc(Cast_ver9_doc)
+        .Attr(
+            "to",
+            "The data type to which the elements of the input tensor are cast. "
+            "Strictly must be one of the types from DataType enum in TensorProto",
+            AttributeProto::INT)
+        .Input(0, "input", "Input tensor to be cast.", "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor with the same shape as input with type "
+            "specified by the 'to' argument",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)",
+             "tensor(string)"},
+            "Constrain input types. Casting from complex is not supported.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)",
+             "tensor(string)"},
+            "Constrain output types. Casting to complex is not supported.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromAttributeToOutput(ctx, "to", 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* Reshape_ver13_doc = R"DOC(
+Reshape the input tensor similar to numpy.reshape.
+First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+At most one dimension of the new shape can be -1. In this case, the value is
+inferred from the size of the tensor and the remaining dimensions. A dimension
+could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
+The input tensor's shape and the output tensor's shape are required to have the same number of elements.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reshape,
+    13,
+    OpSchema()
+        .SetDoc(Reshape_ver13_doc)
+        .Input(0,
+            "data",
+            "An input tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(1,
+            "shape",
+            "Specified shape for output.",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "reshaped",
+            "Reshaped data.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape Inference if 2nd input data (the target shape) is available
+          const TensorProto* targetShapeInitializer = ctx.getInputData(1);
+          if (!targetShapeInitializer) {
+            return;
+          }
+          // Make targetShape (0 -> same as originalShape, -1 -> inferred).
+          // The targetShape vector represents the specified shape for output.
+          std::vector<int64_t> targetShape = ParseData<int64_t>(targetShapeInitializer);
+          // Iterate through targetShape, adding dimensions in the outputShape
+          // TensorProto. If the targertShape dimension is -1, we do not set the
+          // dimension value in this iteration, but we record the Dimension. If
+          // targertShape dimension is 0, we attempt to propagate the dimension
+          // value/param. If the value cannot be inferred, we set the flag in
+          // the unresolveZeros vector. If targetShape dimension is positive, we
+          // set the dimension value in the outputShape. We track the product of
+          // the dimensions we are setting outputShape in the outputProduct
+          // variable. The outputProduct will potentially be used for inferring
+          // a dimension marked -1.
+          auto* outputShape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          TensorShapeProto::Dimension* negativeOneDim = nullptr;
+          const auto& dataInputTensorType = ctx.getInputType(0)->tensor_type();
+          std::vector<bool> unresolvedZeros(targetShape.size(), false);
+          int64_t outputProduct = 1;
+          for (int i = 0; i < static_cast<int>(targetShape.size()); ++i) {
+            // Add a new dimension to outputShape
+            auto* new_dim = outputShape->add_dim();
+            if (targetShape[i] == -1) {
+              // Check if multiple -1's. If not, set negativeOneDim, marking
+              // this dimension to potentially be filled in later.
+              if (negativeOneDim) {
+                fail_shape_inference(
+                    "Target shape may not have multiple -1 dimensions");
+              }
+              negativeOneDim = new_dim;
+            } else if (targetShape[i] == 0) {
+              // Check if data input has a shape and if the index i is within
+              // its bounds. If these conditions are satisfied, any dimension
+              // value/param should be propogated. If dimension value cannot be
+              // inferred, set the corresponding  unresolvedZeros flag to true.
+              unresolvedZeros[i] = true;
+              if (dataInputTensorType.has_shape()) {
+                if (i >= dataInputTensorType.shape().dim_size()) {
+                  fail_shape_inference("Invalid position of 0");
+                }
+                if (dataInputTensorType.shape().dim(i).has_dim_value()) {
+                  const auto& dim_value =
+                      dataInputTensorType.shape().dim(i).dim_value();
+                  new_dim->set_dim_value(dim_value);
+                  outputProduct *= dim_value;
+                  unresolvedZeros[i] = false;
+                } else if (dataInputTensorType.shape().dim(i).has_dim_param()) {
+                  const auto& dim_param =
+                      dataInputTensorType.shape().dim(i).dim_param();
+                  new_dim->set_dim_param(dim_param);
+                }
+              }
+            } else if (targetShape[i] > 0) {
+              // Set the dimension value to targetShape[i]
+              new_dim->set_dim_value(targetShape[i]);
+              outputProduct *= targetShape[i];
+            } else {
+              // Check if value is less than -1; fail if so
+              fail_shape_inference("Invalid dimension value: ", targetShape[i]);
+            }
+          }
+
+          // If negativeOneDim has been set, we attempt to infer its value. This
+          // can be done if all dimension values for the data input tensor shape
+          // are known other than the ones corresponding to unresolvedZeros
+          // flags.
+          if (negativeOneDim) {
+            // First, attempt to compute product of data input shape dimensions
+            // that are not marked by unresolvedZeros. If not possible, set the
+            // inputProductValid flag to false.
+            if (!outputProduct) {
+              fail_shape_inference("Invalid Target shape product of 0");
+            }
+            int64_t inputProduct = 1;
+            bool inputProductValid = true;
+            if (!dataInputTensorType.has_shape()) {
+              inputProductValid = false;
+            } else {
+              for (int i = 0; i < dataInputTensorType.shape().dim_size(); ++i) {
+                if (dataInputTensorType.shape().dim(i).has_dim_value()) {
+                  inputProduct *=
+                      dataInputTensorType.shape().dim(i).dim_value();
+                } else if (
+                    i >= static_cast<int>(unresolvedZeros.size()) ||
+                    !unresolvedZeros[i]) {
+                  inputProductValid = false;
+                  break;
+                }
+              }
+            }
+            if (inputProductValid) {
+              if (inputProduct % outputProduct != 0) {
+                fail_shape_inference(
+                    "Dimension could not be inferred: incompatible shapes");
+              }
+              negativeOneDim->set_dim_value(inputProduct / outputProduct);
+            }
+          }
+        }));
+
+static const char* Reshape_ver5_doc = R"DOC(
+Reshape the input tensor similar to numpy.reshape.
+First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
+At most one dimension of the new shape can be -1. In this case, the value is
+inferred from the size of the tensor and the remaining dimensions. A dimension
+could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
+The input tensor's shape and the output tensor's shape are required to have the same number of elements.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reshape,
+    5,
+    OpSchema()
+        .SetDoc(Reshape_ver5_doc)
+        .Input(0, "data", "An input tensor.", "T")
+        .Input(1, "shape", "Specified shape for output.", "tensor(int64)")
+        .Output(0, "reshaped", "Reshaped data.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape Inference if 2nd input data (the target shape) is available
+          const TensorProto* targetShapeInitializer = ctx.getInputData(1);
+          if (!targetShapeInitializer) {
+            return;
+          }
+          // Make targetShape (0 -> same as originalShape, -1 -> inferred).
+          // The targetShape vector represents the specified shape for output.
+          std::vector<int64_t> targetShape = ParseData<int64_t>(targetShapeInitializer);
+
+          // Iterate through targetShape, adding dimensions in the outputShape
+          // TensorProto. If the targertShape dimension is -1, we do not set the
+          // dimension value in this iteration, but we record the Dimension. If
+          // targertShape dimension is 0, we attempt to propagate the dimension
+          // value/param. If the value cannot be inferred, we set the flag in
+          // the unresolveZeros vector. If targetShape dimension is positive, we
+          // set the dimension value in the outputShape. We track the product of
+          // the dimensions we are setting outputShape in the outputProduct
+          // variable. The outputProduct will potentially be used for inferring
+          // a dimension marked -1.
+          auto* outputShape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          TensorShapeProto::Dimension* negativeOneDim = nullptr;
+          const auto& dataInputTensorType = ctx.getInputType(0)->tensor_type();
+          std::vector<bool> unresolvedZeros(targetShape.size(), false);
+          int64_t outputProduct = 1;
+          for (int i = 0; i < static_cast<int>(targetShape.size()); ++i) {
+            // Add a new dimension to outputShape
+            auto* new_dim = outputShape->add_dim();
+            if (targetShape[i] == -1) {
+              // Check if multiple -1's. If not, set negativeOneDim, marking
+              // this dimension to potentially be filled in later.
+              if (negativeOneDim) {
+                fail_shape_inference(
+                    "Target shape may not have multiple -1 dimensions");
+              }
+              negativeOneDim = new_dim;
+            } else if (targetShape[i] == 0) {
+              // Check if data input has a shape and if the index i is within
+              // its bounds. If these conditions are satisfied, any dimension
+              // value/param should be propogated. If dimension value cannot be
+              // inferred, set the corresponding  unresolvedZeros flag to true.
+              unresolvedZeros[i] = true;
+              if (dataInputTensorType.has_shape()) {
+                if (i >= dataInputTensorType.shape().dim_size()) {
+                  fail_shape_inference("Invalid position of 0");
+                }
+                if (dataInputTensorType.shape().dim(i).has_dim_value()) {
+                  const auto& dim_value =
+                      dataInputTensorType.shape().dim(i).dim_value();
+                  new_dim->set_dim_value(dim_value);
+                  outputProduct *= dim_value;
+                  unresolvedZeros[i] = false;
+                } else if (dataInputTensorType.shape().dim(i).has_dim_param()) {
+                  const auto& dim_param =
+                      dataInputTensorType.shape().dim(i).dim_param();
+                  new_dim->set_dim_param(dim_param);
+                }
+              }
+            } else if (targetShape[i] > 0) {
+              // Set the dimension value to targetShape[i]
+              new_dim->set_dim_value(targetShape[i]);
+              outputProduct *= targetShape[i];
+            } else {
+              // Check if value is less than -1; fail if so
+              fail_shape_inference("Invalid dimension value: ", targetShape[i]);
+            }
+          }
+
+          // If negativeOneDim has been set, we attempt to infer its value. This
+          // can be done if all dimension values for the data input tensor shape
+          // are known other than the ones corresponding to unresolvedZeros
+          // flags.
+          if (negativeOneDim) {
+            // First, attempt to compute product of data input shape dimensions
+            // that are not marked by unresolvedZeros. If not possible, set the
+            // inputProductValid flag to false.
+            if (!outputProduct) {
+              fail_shape_inference("Invalid Target shape product of 0");
+            }
+            int64_t inputProduct = 1;
+            bool inputProductValid = true;
+            if (!dataInputTensorType.has_shape()) {
+              inputProductValid = false;
+            } else {
+              for (int i = 0; i < dataInputTensorType.shape().dim_size(); ++i) {
+                if (dataInputTensorType.shape().dim(i).has_dim_value()) {
+                  inputProduct *=
+                      dataInputTensorType.shape().dim(i).dim_value();
+                } else if (
+                    i >= static_cast<int>(unresolvedZeros.size()) ||
+                    !unresolvedZeros[i]) {
+                  inputProductValid = false;
+                  break;
+                }
+              }
+            }
+            if (inputProductValid) {
+              if (inputProduct % outputProduct != 0) {
+                fail_shape_inference(
+                    "Dimension could not be inferred: incompatible shapes");
+              }
+              negativeOneDim->set_dim_value(inputProduct / outputProduct);
+            }
+          }
+        }));
+
+static const char* Shape_ver13_doc = R"DOC(
+Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+)DOC";
+
+// Data propagation function for Shape op
+// Propagates input shape to output shape
+static void ShapeOp13DataPropagator(DataPropagationContext& ctx) {
+  if (!hasNInputShapes(ctx, 1)) {
+    return;
+  }
+  if (ctx.getInputType(0)->tensor_type().has_shape()) {
+    auto input_shape = ctx.getInputType(0)->tensor_type().shape();
+    TensorShapeProto tsp;
+    tsp.CopyFrom(input_shape);
+    ctx.addOutputData(0, std::move(tsp));
+  }
+}
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Shape,
+    13,
+    OpSchema()
+        .SetDoc(Shape_ver13_doc)
+        .Input(0,
+            "data",
+            "An input tensor.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Output(0,
+            "shape",
+            "Shape of the input tensor",
+            "T1",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Input tensor can be of arbitrary type.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain output to int64 tensor.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(
+              TensorProto::INT64);
+          auto* output_shape = 
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          auto* output_length = output_shape->add_dim();
+		
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          if (ctx.getInputType(0)->tensor_type().has_shape()) {
+            output_length->set_dim_value(
+                ctx.getInputType(0)->tensor_type().shape().dim_size());
+          }
+        })
+        .PartialDataPropagationFunction([](DataPropagationContext& ctx) {
+          ShapeOp13DataPropagator(ctx);
+        }));
+
+static const char* Shape_ver1_doc = R"DOC(
+Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Shape,
+    1,
+    OpSchema()
+        .SetDoc(Shape_ver1_doc)
+        .Input(0, "data", "An input tensor.", "T")
+        .Output(0, "shape", "Shape of the input tensor", "T1")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input tensor can be of arbitrary type.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain output to int64 tensor.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(
+              TensorProto::INT64);
+          auto* output_shape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          auto* output_length = output_shape->add_dim();
+
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          if (ctx.getInputType(0)->tensor_type().has_shape()) {
+            output_length->set_dim_value(
+                ctx.getInputType(0)->tensor_type().shape().dim_size());
+          }
+        }));
+
+static const char* Size_ver1_doc = R"DOC(
+Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Size,
+    1,
+    OpSchema()
+        .SetDoc(Size_ver1_doc)
+        .Input(0, "data", "An input tensor.", "T")
+        .Output(0, "size", "Total number of elements of the input tensor", "T1")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input tensor can be of arbitrary type.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain output to int64 tensor, which should be a scalar though.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(
+              TensorProto::INT64);
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Concat,
+    11,
+    OpSchema()
+        .Attr(
+            "axis",
+            "Which axis to concat on. A negative value means counting dimensions from the back. "
+            "Accepted range is [-r, r-1] where r = rank(inputs)..",
+            AttributeProto::INT)
+        .SetDoc(
+            "Concatenate a list of tensors into a single tensor. "
+            "All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.")
+        .Input(
+            0,
+            "inputs",
+            "List of tensors for concatenation",
+            "T",
+            OpSchema::Variadic)
+        .Output(0, "concat_result", "Concatenated tensor", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto numInputs = ctx.getNumInputs();
+          if (numInputs < 1 ||
+              !hasNInputShapes(ctx, static_cast<int>(numInputs))) {
+            return;
+          }
+
+          auto rank = ctx.getInputType(0)->tensor_type().shape().dim_size();
+
+          auto axisAttr = ctx.getAttribute("axis");
+          if (!axisAttr) {
+            fail_shape_inference("Required attribute axis is missing");
+          }
+          int axis = static_cast<int>(axisAttr->i());
+          if (axis < -rank || axis >= rank) {
+            fail_shape_inference("axis must be in [-rank, rank-1].");
+          }
+          if (axis < 0) {
+            axis += rank;
+          }
+
+          if (numInputs == 1) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+            return;
+          }
+
+          bool all_lengths_known = true;
+          int total_length = 0;
+
+          auto* output_shape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          for (int64_t i = 0; i < rank; ++i) {
+            output_shape->add_dim();
+          }
+
+          for (size_t i = 0; i < numInputs; i++) {
+            const auto& shape = ctx.getInputType(i)->tensor_type().shape();
+            if (shape.dim_size() != rank) {
+              fail_shape_inference("All inputs to Concat must have same rank. Input ", i , " has rank ", shape.dim_size(), " != ", rank);
+            }
+            for (int j = 0; j < rank; j++) {
+              if (j == axis) {
+                if (shape.dim(j).has_dim_value()) {
+                  total_length += static_cast<int>(shape.dim(j).dim_value());
+                } else {
+                  all_lengths_known = false;
+                }
+              } else {
+                auto& output_dim = *output_shape->mutable_dim(j);
+                const auto& input_dim = shape.dim(j);
+                mergeInDimensionInfo(input_dim, output_dim, j);
+              }
+            }
+          }
+
+          if (all_lengths_known) {
+            output_shape->mutable_dim(axis)->set_dim_value(total_length);
+          }
+        }));
+
+static const char* Split_ver11_doc =
+    R"DOC(Split a tensor into a list of tensors, along the specified
+'axis'. Lengths of the parts can be specified using argument 'split'.
+Otherwise, the tensor is split to equal sized parts.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Split,
+    11,
+    OpSchema()
+        .Input(0, "input", "The tensor to split", "T")
+        .Output(
+            0,
+            "outputs",
+            "One or more outputs forming list of tensors after splitting",
+            "T",
+            OpSchema::Variadic)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .Attr(
+            "axis",
+            "Which axis to split on. "
+            "A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] "
+            "where r = rank(input).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "split",
+            "length of each output. Values should be >= 0.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Split_ver11_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); ++i) {
+            propagateElemTypeFromInputToOutput(ctx, 0, i);
+          }
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          const auto& shape = ctx.getInputType(0)->tensor_type().shape();
+          int rank = shape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -rank || axis >= rank) {
+            fail_type_inference(
+                "Invalid value of attribute 'axis'. Rank=",
+                rank,
+                " Value=",
+                axis);
+          }
+          if (axis < 0) {
+            axis += rank;
+          }
+          const auto& split_dim = shape.dim(axis);
+          if (!split_dim.has_dim_value()) {
+            for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
+              *ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
+                  shape;
+              ctx.getOutputType(i)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->mutable_dim(axis)
+                  ->Clear();
+            }
+            return;
+          }
+          int split_dim_value = static_cast<int>(split_dim.dim_value());
+
+          std::vector<int64_t> split;
+          if (getRepeatedAttribute(ctx, "split", split)) {
+            if (split.size() != ctx.getNumOutputs()) {
+              fail_shape_inference(
+                  "Mismatch between number of splits (",
+                  split.size(),
+                  ") and outputs (",
+                  ctx.getNumOutputs(),
+                  ")");
+            }
+            int64_t total_dim = 0;
+            for (int64_t d : split) {
+              total_dim += d;
+            }
+            if (total_dim != split_dim_value) {
+              fail_shape_inference(
+                  "Mismatch between the sum of 'split' (",
+                  total_dim,
+                  ") and the split dimension of the input (",
+                  split_dim_value,
+                  ")");
+            }
+          } else {
+            int num_outputs = static_cast<int>(ctx.getNumOutputs());
+            if (split_dim_value % num_outputs != 0) {
+              fail_shape_inference("The input is not evenly splittable");
+            }
+            int chunk_size = split_dim_value / num_outputs;
+            for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); i++) {
+              split.push_back(chunk_size);
+            }
+          }
+          for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
+            *ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
+                shape;
+            ctx.getOutputType(i)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->mutable_dim(axis)
+                ->set_dim_value(split[i]);
+          }
+        }));
+
+static const char* Slice_ver11_doc = R"DOC(
+Produces a slice of the input tensor along multiple axes. Similar to numpy:
+https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
+dimension and step for each axis in the list of axes, it uses this information to
+slice the input `data` tensor. If a negative value is passed for any of the
+start or end indices, it represents number of elements before the end of that
+dimension. If the value passed to start or end is larger than the `n` (the
+number of elements in this dimension), it represents `n`. For slicing to the
+end of a dimension with unknown size, it is recommended to pass in `INT_MAX`
+when sclicing forward and 'INT_MIN' when slicing backward.
+If a negative value is passed for step, it represents slicing backward.
+However step value cannot be 0.
+If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+Example 1:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  axes = [0, 1]
+  starts = [1, 0]
+  ends = [2, 3]
+  steps = [1, 2]
+  result = [
+      [5, 7],
+  ]
+Example 2:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  starts = [0, 1]
+  ends = [-1, 1000]
+  result = [
+      [2, 3, 4],
+  ]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Slice,
+    11,
+    OpSchema()
+        .SetDoc(Slice_ver11_doc)
+        .Input(0, "data", "Tensor of data to extract slices from.", "T")
+        .Input(
+            1,
+            "starts",
+            "1-D tensor of starting indices of corresponding axis in `axes`",
+            "Tind")
+        .Input(
+            2,
+            "ends",
+            "1-D tensor of ending indices (exclusive) of corresponding axis in `axes`",
+            "Tind")
+        .Input(
+            3,
+            "axes",
+            "1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            "Tind",
+            OpSchema::Optional)
+        .Input(
+            4,
+            "steps",
+            "1-D tensor of slice step of corresponding axis in `axes`. "
+            "Negative value means slicing backward. 'steps' cannot be 0. "
+            "Defaults to 1.",
+            "Tind",
+            OpSchema::Optional)
+        .Output(0, "output", "Sliced data tensor.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          size_t num_inputs = ctx.getNumInputs();
+          if (num_inputs != 3 && num_inputs != 4 && num_inputs != 5) {
+            fail_type_inference(
+                "Slice op must have either three, four or five inputs.");
+          }
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          // Shape Inference if
+          //     1. 2nd and 3rd input data (starts, ends) are available.
+          // and 2. 4th and 5th optional input (axes, steps) are either not set,
+          // or set and is initializer.
+          const TensorProto* startsInitializer = ctx.getInputData(1);
+          const TensorProto* endsInitializer = ctx.getInputData(2);
+          const TensorProto* axesInitializer =
+              hasInputShape(ctx, 3) ? ctx.getInputData(3) : nullptr;
+          const TensorProto* stepsInitializer =
+              hasInputShape(ctx, 4) ? ctx.getInputData(4) : nullptr;
+
+          if (!startsInitializer || !endsInitializer ||
+              (hasInputShape(ctx, 3) && !ctx.getInputData(3)) ||
+              (hasInputShape(ctx, 4) && !ctx.getInputData(4))) {
+            return;
+          }
+
+          // don't know data_type- can't proceed
+          if (!startsInitializer->has_data_type())
+            return;
+
+          auto get_initializer_data =
+              [](const TensorProto* initializer) -> std::vector<int64_t> {
+            std::vector<int64_t> vec;
+            if (initializer->data_type() == TensorProto::INT64) {
+              const auto& data = ParseData<int64_t>(initializer);
+              vec.insert(vec.end(), data.begin(), data.end());
+            } else if (initializer->data_type() == TensorProto::INT32) {
+              const auto& data = ParseData<int32_t>(initializer);
+              vec.insert(vec.end(), data.begin(), data.end());
+            } else {
+              // unaccepted data type
+              fail_shape_inference(
+                  "Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps");
+            }
+            return vec;
+          };
+
+          auto clamp = [](int64_t val, int64_t low, int64_t high) -> int64_t {
+            if (val < low)
+              return low;
+            if (val > high)
+              return high;
+            return val;
+          };
+
+          std::vector<int64_t> starts = get_initializer_data(startsInitializer);
+          std::vector<int64_t> ends = get_initializer_data(endsInitializer);
+
+          if (starts.size() != ends.size()) {
+            fail_shape_inference(
+                "Incorrect or missing input value for starts and ends");
+          }
+
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+          std::vector<int64_t> axes(starts.size());
+          if (!axesInitializer) {
+            std::iota(axes.begin(), axes.end(), 0);
+          } else {
+            axes = get_initializer_data(axesInitializer);
+            if (axes.size() != starts.size()) {
+              fail_shape_inference("Input axes has incorrect length");
+            }
+          }
+
+          std::vector<int64_t> steps;
+          if (!stepsInitializer) {
+            steps = std::vector<int64_t>(starts.size(), 1);
+          } else {
+            steps = get_initializer_data(stepsInitializer);
+            if (steps.size() != axes.size()) {
+              fail_shape_inference("Input steps has incorrect length");
+            }
+          }
+
+          for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+            // first update rank of output dim
+            auto* output_dim = ctx.getOutputType(0)
+                                   ->mutable_tensor_type()
+                                   ->mutable_shape()
+                                   ->add_dim();
+            const auto& input_dim = input_shape.dim((int)i);
+            if (input_dim.has_dim_value()) {
+              output_dim->set_dim_value(input_dim.dim_value());
+            } else if (input_dim.has_dim_param()) {
+              output_dim->set_dim_param(input_dim.dim_param());
+            }
+          }
+
+          std::unordered_set<int64_t> unique_axes;
+          size_t axes_size = axes.size();
+          for (size_t axis_index = 0; axis_index < axes_size; ++axis_index) {
+            auto axis = axes[axis_index] < 0
+                ? axes[axis_index] + static_cast<int64_t>(input_rank)
+                : axes[axis_index];
+
+            if (axis >= static_cast<int64_t>(input_rank) || axis < 0) {
+              fail_shape_inference("Input axes has invalid data");
+            }
+
+            if (unique_axes.find(axis) != unique_axes.end()) {
+              fail_shape_inference("'axes' has duplicates");
+            }
+
+            unique_axes.insert(axis);
+
+            auto input_dim =
+                ctx.getInputType(0)->tensor_type().shape().dim((int)axis);
+
+            // input dim value is missing - cannot perform shape inference for
+            // this axis
+            if (!input_dim.has_dim_value()) {
+              // Clear any previously propagated dim_param and leave this
+              // dimension "empty", before moving on to the next dimension
+              ctx.getOutputType(0)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->mutable_dim(static_cast<int>(axis))
+                  ->clear_dim_param();
+              continue;
+            }
+
+            const auto input_dim_value = input_dim.dim_value();
+
+            // process step
+            auto step = steps[axis_index];
+            if (step == 0) {
+              fail_shape_inference("'step' cannot be 0");
+            }
+
+            // process start
+            auto start = starts[axis_index];
+            if (start < 0)
+              start += input_dim_value;
+            if (step < 0)
+              start = clamp(start, 0, input_dim_value - 1);
+            else
+              start = clamp(start, 0, input_dim_value);
+
+            // process end
+            auto end = ends[axis_index];
+            if (end < 0)
+              end += input_dim_value;
+            if (step < 0)
+              end = clamp(end, -1, input_dim_value);
+            else
+              end = clamp(end, 0, input_dim_value);
+
+            // find output dim value for this axis
+            auto temp = static_cast<int64_t>(ceil(1.0 * (end - start) / step));
+            if (temp < 0)
+              temp = 0;
+
+            // assign output value
+            ctx.getOutputType(0)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->mutable_dim((int)axis)
+                ->set_dim_value(temp);
+          }
+        }));
+
+static const char* Transpose_ver1_doc = R"DOC(
+Transpose the input tensor similar to numpy.transpose. For example, when
+perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
+will be (2, 1, 3).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Transpose,
+    1,
+    OpSchema()
+        .SetDoc(Transpose_ver1_doc)
+        .Attr(
+            "perm",
+            "A list of integers. By default, reverse the dimensions, "
+            "otherwise permute the axes according to the values given.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Input(0, "data", "An input tensor.", "T")
+        .Output(0, "transposed", "Transposed output.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          auto input_type = ctx.getInputType(0);
+          const TensorShapeProto& shape = input_type->tensor_type().shape();
+          std::vector<int64_t> perm;
+          bool has_perm_attr = getRepeatedAttribute(ctx, "perm", perm);
+          if (!has_perm_attr) {
+            for (int i = shape.dim_size() - 1; i >= 0; --i)
+              perm.push_back(i);
+          } else if (!perm.empty()) {
+            // check if every index is valid
+            std::vector<bool> seen(shape.dim_size(), false);
+            for (int64_t fromDimIndex : perm) {
+              if (!(0 <= fromDimIndex && fromDimIndex < shape.dim_size())) {
+                std::ostringstream oss;
+                oss << "Invalid attribute perm {" << perm[0];
+                for (size_t i = 1; i != perm.size(); ++i) {
+                  oss << ", " << perm[i];
+                }
+                oss << "}, input shape = {";
+                if (shape.dim_size() > 0) {
+                  oss << shape.dim(0).dim_value();
+                  for (int i = 1; i != shape.dim_size(); ++i) {
+                    oss << ", " << shape.dim(i).dim_value();
+                  }
+                  oss << "}";
+                }
+                fail_type_inference(oss.str());
+              } else {
+                // check if any perm is repeated
+                if (seen[fromDimIndex]) {
+                  fail_type_inference("Attribute perm for Transpose has repeated value: ", fromDimIndex);
+                }
+                seen[fromDimIndex] = true;
+              }
+            }
+          }
+
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          for (size_t i = 0; i < perm.size(); ++i) {
+            appendSingleDimCopiedFromInputTypeToOutputType(
+                ctx, 0, 0, static_cast<size_t>(perm[i]));
+          }
+        }));
+
+static const char* ScatterND_ver13_doc = R"DOC(
+ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value to values
+specified by `updates` at specific index positions specified by `indices`. Its output shape
+is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+That is, two or more `updates` for the same index-location is not supported.
+
+`indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+ `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+update to a slice of the tensor.
+
+`updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+The remaining dimensions of `updates` correspond to the dimensions of the
+replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+of shapes.
+
+The `output` is calculated via the following equation:
+
+    output = np.copy(data)
+    update_indices = indices.shape[:-1]
+    for idx in np.ndindex(update_indices):
+        output[indices[idx]] = updates[idx]
+
+The order of iteration in the above loop is not specified.
+In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+This ensures that the output value does not depend on the iteration order.
+
+This operator is the inverse of GatherND.
+
+Example 1:
+```
+  data    = [1, 2, 3, 4, 5, 6, 7, 8]
+  indices = [[4], [3], [1], [7]]
+  updates = [9, 10, 11, 12]
+  output  = [1, 11, 3, 10, 9, 6, 7, 12]
+```
+
+Example 2:
+```
+  data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  indices = [[0], [2]]
+  updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+  output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ScatterND,
+    13,
+    OpSchema()
+        .SetDoc(ScatterND_ver13_doc)
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of rank q >= 1.",
+            "tensor(int64)",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank q + r - indices_shape[-1] - 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* ScatterND_ver11_doc = R"DOC(
+ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1,
+and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value to values
+specified by `updates` at specific index positions specified by `indices`. Its output shape
+is the same as the shape of `data`. Note that `indices` should not have duplicate entries.
+That is, two or more `updates` for the same index-location is not supported.
+
+`indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`.
+ `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`.
+Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an
+update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
+update to a slice of the tensor.
+
+`updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
+first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
+The remaining dimensions of `updates` correspond to the dimensions of the
+replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
+corresponding to the trailing (r-k) dimensions of `data`.  Thus, the shape of `updates`
+must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
+of shapes.
+
+The `output` is calculated via the following equation:
+
+    output = np.copy(data)
+    update_indices = indices.shape[:-1]
+    for idx in np.ndindex(update_indices):
+        output[indices[idx]] = updates[idx]
+
+The order of iteration in the above loop is not specified.
+In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
+This ensures that the output value does not depend on the iteration order.
+
+This operator is the inverse of GatherND.
+
+Example 1:
+```
+  data    = [1, 2, 3, 4, 5, 6, 7, 8]
+  indices = [[4], [3], [1], [7]]
+  updates = [9, 10, 11, 12]
+  output  = [1, 11, 3, 10, 9, 6, 7, 12]
+```
+
+Example 2:
+```
+  data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+  indices = [[0], [2]]
+  updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+  output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ScatterND,
+    11,
+    OpSchema()
+        .SetDoc(ScatterND_ver11_doc)
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(1, "indices", "Tensor of rank q >= 1.", "tensor(int64)")
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank q + r - indices_shape[-1] - 1.",
+            "T")
+        .Output(0, "output", "Tensor of rank r >= 1.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* ScatterElements_ver13_doc = R"DOC(
+ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+(by default, the outer-most axis, that is axis 0). The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value
+to values specified by `updates` at specific index positions specified by
+`indices`. Its output shape is the same as the shape of `data`.
+
+For each entry in `updates`, the target index in `data` is obtained by combining
+the corresponding entry in `indices` with the index of the entry itself: the
+index-value for dimension = axis is obtained from the value of the corresponding
+entry in `indices` and the index-value for dimension != axis is obtained from the
+index of the entry itself.
+
+For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+is performed as below:
+```
+  output[indices[i][j]][j] = updates[i][j] if axis = 0,
+  output[i][indices[i][j]] = updates[i][j] if axis = 1,
+```
+
+This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+Example 1:
+```
+  data = [
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+  ]
+  indices = [
+      [1, 0, 2],
+      [0, 2, 1],
+  ]
+  updates = [
+      [1.0, 1.1, 1.2],
+      [2.0, 2.1, 2.2],
+  ]
+  output = [
+      [2.0, 1.1, 0.0]
+      [1.0, 0.0, 2.2]
+      [0.0, 2.1, 1.2]
+  ]
+```
+Example 2:
+```
+  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+  indices = [[1, 3]]
+  updates = [[1.1, 2.1]]
+  axis = 1
+  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ScatterElements,
+    13,
+    OpSchema()
+        .SetDoc(ScatterElements_ver13_doc)
+        .Attr(
+            "axis",
+            "Which axis to scatter on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(
+            0,
+            "data",
+            "Tensor of rank r >= 1.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be "
+            "within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank r >=1 (same rank and shape as indices)",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r >= 1 (same rank as input).",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Input and output types can be of any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* ScatterElements_ver11_doc = R"DOC(
+ScatterElements takes three inputs `data`, `updates`, and `indices` of the same
+rank r >= 1 and an optional attribute axis that identifies an axis of `data`
+(by default, the outer-most axis, that is axis 0). The output of the operation
+is produced by creating a copy of the input `data`, and then updating its value
+to values specified by `updates` at specific index positions specified by
+`indices`. Its output shape is the same as the shape of `data`.
+
+For each entry in `updates`, the target index in `data` is obtained by combining
+the corresponding entry in `indices` with the index of the entry itself: the
+index-value for dimension = axis is obtained from the value of the corresponding
+entry in `indices` and the index-value for dimension != axis is obtained from the
+index of the entry itself.
+
+For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
+is performed as below:
+```
+  output[indices[i][j]][j] = updates[i][j] if axis = 0,
+  output[i][indices[i][j]] = updates[i][j] if axis = 1,
+```
+
+This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
+
+Example 1:
+```
+  data = [
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+  ]
+  indices = [
+      [1, 0, 2],
+      [0, 2, 1],
+  ]
+  updates = [
+      [1.0, 1.1, 1.2],
+      [2.0, 2.1, 2.2],
+  ]
+  output = [
+      [2.0, 1.1, 0.0]
+      [1.0, 0.0, 2.2]
+      [0.0, 2.1, 1.2]
+  ]
+```
+Example 2:
+```
+  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+  indices = [[1, 3]]
+  updates = [[1.1, 2.1]]
+  axis = 1
+  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    ScatterElements,
+    11,
+    OpSchema()
+        .SetDoc(ScatterElements_ver11_doc)
+        .Attr(
+            "axis",
+            "Which axis to scatter on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be "
+            "within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind")
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank r >=1 (same rank and shape as indices)",
+            "T")
+        .Output(0, "output", "Tensor of rank r >= 1 (same rank as input).", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input and output types can be of any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* Gather_ver11_doc = R"DOC(
+Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+them in an output tensor of rank q + (r - 1).
+
+axis = 0 :
+
+Let
+k = indices[i_{0}, ..., i_{q-1}]
+Then
+output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]
+
+```
+  data = [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+  indices = [
+      [0, 1],
+      [1, 2],
+  ]
+  output = [
+      [
+          [1.0, 1.2],
+          [2.3, 3.4],
+      ],
+      [
+          [2.3, 3.4],
+          [4.5, 5.7],
+      ],
+  ]
+```
+axis = 1 :
+
+Let
+k = indices[i_{0}, ..., i_{q-1}]
+Then
+output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]
+
+```
+  data = [
+      [1.0, 1.2, 1.9],
+      [2.3, 3.4, 3.9],
+      [4.5, 5.7, 5.9],
+  ]
+  indices = [
+      [0, 2],
+  ]
+  axis = 1,
+  output = [
+      [
+          [1.0, 1.9],
+          [2.3, 3.9],
+          [4.5, 5.9],
+      ],
+  ]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gather,
+    11,
+    OpSchema()
+        .SetDoc(Gather_ver11_doc)
+        .Attr(
+            "axis",
+            "Which axis to gather on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] "
+            "along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind")
+        .Output(0, "output", "Tensor of rank q + (r - 1).", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 2)) {
+            return;
+          }
+          const TensorShapeProto& data_shape =
+              ctx.getInputType(0)->tensor_type().shape();
+          const TensorShapeProto& indices_shape =
+              ctx.getInputType(1)->tensor_type().shape();
+          int r = data_shape.dim_size();
+          if (r < 1) {
+            fail_shape_inference("data tensor must have rank >= 1");
+          }
+          int q = indices_shape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -r || axis >= r) {
+            fail_shape_inference("axis must be in [-r, r-1]");
+          }
+          if (axis < 0) {
+            axis += r;
+          }
+          int out_rank = q + r - 1;
+          if (out_rank == 0) {
+            ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          }
+          for (int i = 0; i < out_rank; ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = (i < axis) ? data_shape.dim(i) : // i < axis < r
+                (i >= axis && i < axis + q) ? indices_shape.dim(i - axis)
+                                            : // i - axis < q
+                    data_shape.dim(i - q + 1); // i < out_rank < q + r - 1
+          }
+        }));
+
+static const char* GatherElements_ver11_doc = R"DOC(
+
+GatherElements takes two inputs `data` and `indices` of the same rank r >= 1
+and an optional attribute `axis` that identifies an axis of `data`
+(by default, the outer-most axis, that is axis 0). It is an indexing operation
+that produces its output by indexing into the input data tensor at index
+positions determined by elements of the `indices` tensor.
+Its output shape is the same as the shape of `indices` and consists of one value
+(gathered from the `data`) for each element in `indices`.
+
+For instance, in the 3-D case (r = 3), the output produced is determined
+by the following equations:
+```
+  out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
+  out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
+  out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
+```
+
+This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation.
+
+Example 1:
+```
+  data = [
+      [1, 2],
+      [3, 4],
+  ]
+  indices = [
+      [0, 0],
+      [1, 0],
+  ]
+  axis = 1
+  output = [
+      [
+        [1, 1],
+        [4, 3],
+      ],
+  ]
+```
+Example 2:
+```
+  data = [
+      [1, 2, 3],
+      [4, 5, 6],
+      [7, 8, 9],
+  ]
+  indices = [
+      [1, 2, 0],
+      [2, 0, 0],
+  ]
+  axis = 0
+  output = [
+      [
+        [4, 8, 3],
+        [7, 2, 3],
+      ],
+  ]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GatherElements,
+    11,
+    OpSchema()
+        .SetDoc(GatherElements_ver11_doc)
+        .Attr(
+            "axis",
+            "Which axis to gather on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be "
+            "within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.",
+            "Tind")
+        .Output(0, "output", "Tensor of the same shape as indices.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // propagate indices' shape to output if it exists
+          if (hasInputShape(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 1, 0);
+          }
+        }));
+
+static const char* Squeeze_ver11_doc = R"DOC(
+Remove single-dimensional entries from the shape of a tensor.
+Takes a  parameter `axes` with a list of axes to squeeze.
+If `axes` is not provided, all the single dimensions will be removed from
+the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Squeeze,
+    11,
+    OpSchema()
+        .Attr(
+            "axes",
+            "List of integers indicating the dimensions to squeeze. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(data).",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Squeeze_ver11_doc)
+        .Input(0, "data", "Tensors with at least max(dims) dimensions.", "T")
+        .Output(0, "squeezed", "Reshaped tensor with same data as input.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          std::vector<int64_t> axes;
+          if (!getRepeatedAttribute(ctx, "axes", axes)) {
+            return;
+          }
+
+          if (!ctx.getInputType(0)->tensor_type().has_shape()) {
+            return;
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_ndim = input_shape.dim_size();
+          std::transform(
+              axes.begin(),
+              axes.end(),
+              axes.begin(),
+              [&](int64_t axis) -> int64_t {
+                return axis < 0 ? axis + input_ndim : axis;
+              });
+
+          for (int i = 0, j = 0; i < input_ndim; ++i) {
+            if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
+              if (input_shape.dim(i).has_dim_value() &&
+                  input_shape.dim(i).dim_value() != 1) {
+                fail_shape_inference(
+                    "Dimension of input ",
+                    i,
+                    " must be 1 instead of ",
+                    input_shape.dim(i).dim_value());
+              }
+              ++j;
+            } else {
+              *ctx.getOutputType(0)
+                   ->mutable_tensor_type()
+                   ->mutable_shape()
+                   ->add_dim() = input_shape.dim(i);
+            }
+          }
+        }));
+
+static const char* Unsqueeze_ver11_doc = R"DOC(
+Insert single-dimensional entries to the shape of an input tensor (`data`).
+Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).
+
+For example:
+  Given an input tensor (`data`) of shape [3, 4, 5], then
+  Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].
+
+The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
+The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
+Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
+The order of values in `axes` does not matter and can come in any order.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Unsqueeze,
+    11,
+    OpSchema()
+        .Attr(
+            "axes",
+            "List of integers indicating the dimensions to be inserted. Negative value means counting dimensions "
+            "from the back. Accepted range is [-r, r-1] where r = rank(expanded).",
+            AttributeProto::INTS)
+        .SetDoc(Unsqueeze_ver11_doc)
+        .Input(0, "data", "Original tensor", "T")
+        .Output(0, "expanded", "Reshaped tensor with same data as input.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          std::vector<int64_t> axes;
+          if (!getRepeatedAttribute(ctx, "axes", axes)) {
+            return;
+          }
+
+          // validate 'axes' for duplicate entries
+          std::unordered_set<int64_t> unique_values;
+          for (const auto val : axes) {
+            if (unique_values.find(val) != unique_values.end()) {
+              fail_shape_inference(
+                  "'axes' attribute must not contain any duplicates");
+            }
+            unique_values.insert(val);
+          }
+
+          if (!ctx.getInputType(0)->tensor_type().has_shape()) {
+            return;
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_ndim = input_shape.dim_size();
+          const auto output_ndim = input_ndim + static_cast<int>(axes.size());
+          for (auto& axe : axes) {
+            if (axe < -output_ndim || axe >= output_ndim) {
+              fail_shape_inference(
+                  "values in 'axes' are beyond the bounds of the computed output shape");
+            }
+            if (axe < 0) {
+              axe += output_ndim;
+            }
+          }
+
+          // sort after correcting negative axes values (if any) in the previous
+          // step
+          std::sort(axes.begin(), axes.end());
+
+          int j = 0;
+          for (int i = 0; i < input_ndim; ++i) {
+            while (static_cast<size_t>(j) < axes.size() &&
+                   axes[j] ==
+                       ctx.getOutputType(0)->tensor_type().shape().dim_size()) {
+              ctx.getOutputType(0)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->add_dim()
+                  ->set_dim_value(1);
+              ++j;
+            }
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() =
+                ctx.getInputType(0)->tensor_type().shape().dim(i);
+          }
+          while (static_cast<size_t>(j) < axes.size() &&
+                 axes[j] ==
+                     ctx.getOutputType(0)->tensor_type().shape().dim_size()) {
+            ctx.getOutputType(0)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->add_dim()
+                ->set_dim_value(1);
+            ++j;
+          }
+        }));
+
+static const char* SpaceToDepth_ver1_doc =
+    R"DOC(SpaceToDepth rearranges blocks of spatial data into depth. More specifically,
+this op outputs a copy of the input tensor where values from the height and width dimensions
+are moved to the depth dimension.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    SpaceToDepth,
+    1,
+    OpSchema()
+        .Attr(
+            "blocksize",
+            "Blocks of [blocksize, blocksize] are moved.",
+            AttributeProto::INT)
+        .SetDoc(SpaceToDepth_ver1_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth"
+            ", H is the height and W is the width.",
+            "T")
+        .Output(
+            0,
+            "output",
+            "Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto blocksize = getAttribute(ctx, "blocksize", 0);
+          if (blocksize <= 0) {
+            fail_shape_inference("Blocksize must be positive");
+          }
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() == 4) {
+              // TODO: Clarify what behavior should be if H or W is not a
+              // multiple of blocksize.
+              updateOutputShape(
+                  ctx,
+                  0,
+                  {input_shape.dim(0),
+                   input_shape.dim(1) * (blocksize * blocksize),
+                   input_shape.dim(2) / blocksize,
+                   input_shape.dim(3) / blocksize});
+            } else {
+              fail_shape_inference("Input tensor must be 4-dimensional");
+            }
+          }
+        }));
+
+static const char* DepthToSpace_ver11_doc =
+    R"DOC(DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+the input tensor where values from the depth dimension are moved in spatial blocks to the height
+and width dimensions. By default, `mode` = `DCR`.
+In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
+following order: depth, column, and then row. The output y is computed from the input x as below:
+
+b, c, h, w = x.shape
+
+tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+
+tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+
+y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
+
+
+In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
+following order: column, row, and the depth. The output y is computed from the input x as below:
+
+b, c, h, w = x.shape
+
+tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
+
+tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+
+y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    DepthToSpace,
+    11,
+    OpSchema()
+        .Attr(
+            "blocksize",
+            "Blocks of [blocksize, blocksize] are moved.",
+            AttributeProto::INT)
+        .Attr(
+            "mode",
+            "DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.",
+            AttributeProto::STRING,
+            std::string("DCR"))
+        .SetDoc(DepthToSpace_ver11_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth"
+            ", H is the height and W is the width.",
+            "T")
+        .Output(
+            0,
+            "output",
+            "Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto blocksize = getAttribute(ctx, "blocksize", 0);
+          if (blocksize <= 0) {
+            fail_shape_inference("Blocksize must be positive");
+          }
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() == 4) {
+              // TODO: Clarify what behavior should be if C is not a multiple of
+              // blocksize*blocksize.
+              updateOutputShape(
+                  ctx,
+                  0,
+                  {input_shape.dim(0),
+                   input_shape.dim(1) / (blocksize * blocksize),
+                   input_shape.dim(2) * blocksize,
+                   input_shape.dim(3) * blocksize});
+            } else {
+              fail_shape_inference("Input tensor must be 4-dimensional");
+            }
+          }
+        }));
+
+static const char* Tile_ver6_doc =
+    R"DOC(Constructs a tensor by tiling a given tensor.
+This is the same as function `tile` in Numpy, but no broadcast.
+For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tile,
+    6,
+    OpSchema()
+        .SetDoc(Tile_ver6_doc)
+        .Input(0, "input", "Input tensor of any shape.", "T")
+        .Input(
+            1,
+            "repeats",
+            "1D int64 tensor of the same length as input's dimension number, "
+            "includes numbers of repeated copies along input's dimensions.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor of the same dimensions and type as tensor input. "
+            "output_dim[i] = input_dim[i] * repeats[i]",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain repeat's type to int64 tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape inference
+
+          // Needs atleast the first input to proceed
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+
+          const auto* repeats_inputs = ctx.getInputData(1);
+
+          auto* output_shape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          if (nullptr != repeats_inputs && hasNInputShapes(ctx, 2)) {
+            // shape inference is possible only when 'repeats' is an initializer
+            const auto& repeats_shape =
+                ctx.getInputType(1)->tensor_type().shape();
+            if (repeats_shape.dim_size() != 1 ||
+                repeats_inputs->data_type() != TensorProto::INT64) {
+                  fail_shape_inference("'Repeats' input must be 1D tensor of type int64");
+            }
+
+            const auto& repeats_data = ParseData<int64_t>(repeats_inputs);
+
+            if (repeats_data.size() != static_cast<size_t>(input_rank)) {
+              fail_shape_inference(
+                  "'Repeats' input has incorrect number of values. "
+                  "The number of values in 'repeats' must be equal "
+                  "to the number of input dimensions.");
+            }
+
+            for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+              const auto& input_dim = input_shape.dim((int)i);
+              auto* output_dim = output_shape->add_dim();
+              if (input_dim.has_dim_value()) {
+                output_dim->set_dim_value(
+                    input_dim.dim_value() * repeats_data[i]);
+              }
+            }
+          } else {
+            // Infer output shape's rank in any case (if repeats data is not
+            // available)
+            auto* output_shape_0 = getOutputShape(ctx, 0);
+            for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+              output_shape_0->add_dim();
+            }
+          }
+          return;
+        }));
+
+static const char* Resize_ver11_doc = R"DOC(
+Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
+Each dimension value of the output tensor is:
+  output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified.
+)DOC";
+
+static const char* Resize_attr_coordinate_transformation_mode_doc = R"DOC(
+This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
+
+The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example.
+Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/>
+
+if coordinate_transformation_mode is "half_pixel", <br/>
+x_original = (x_resized + 0.5) / scale - 0.5, <br/>
+
+if coordinate_transformation_mode is "pytorch_half_pixel", <br/>
+x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/>
+
+if coordinate_transformation_mode is "align_corners", <br/>
+x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
+
+if coordinate_transformation_mode is "asymmetric", <br/>
+x_original = x_resized / scale, <br/>
+
+if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/>
+x_original = (x_resized + 0.5) / scale, <br/>
+
+if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Resize,
+    11,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Three interpolation modes: nearest (default), linear and cubic. "
+            "The \"linear\" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). "
+            "The \"cubic\" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Attr(
+            "cubic_coeff_a",
+            "The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75"
+            " (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. "
+            "This attribute is valid only if \"mode\" is \"cubic\".",
+            AttributeProto::FLOAT,
+            static_cast<float>(-0.75))
+        .Attr(
+            "exclude_outside",
+            "If set to 1, the weight of sampling locations outside the tensor will be set to 0"
+            " and the weight will be renormalized so that their sum is 1.0. The default value is 0.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "coordinate_transformation_mode",
+            Resize_attr_coordinate_transformation_mode_doc,
+            AttributeProto::STRING,
+            std::string("half_pixel"))
+        .Attr(
+            "nearest_mode",
+            "Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get \"nearest\" pixel in input tensor from x_original, so this attribute is valid only if \"mode\" is \"nearest\".",
+            AttributeProto::STRING,
+            std::string("round_prefer_floor"))
+        .Attr(
+            "extrapolation_value",
+            "When coordinate_transformation_mode is \"tf_crop_and_resize\" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.",
+            AttributeProto::FLOAT,
+            static_cast<float>(0))
+        .Input(0, "X", "N-D tensor", "T1")
+        .Input(
+            1,
+            "roi",
+            "1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is \"tf_crop_and_resize\"",
+            "T2")
+        .Input(
+            2,
+            "scales",
+            "The scale array along each dimension. It takes value greater than 0. If it's less than 1,"
+            " it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should"
+            " be the same as the rank of input 'X'. If 'size' is needed, the user must set 'scales' to an empty tensor.",
+            "tensor(float)")
+        .Input(
+            3,
+            "sizes",
+            "The size of the output tensor. The number of elements of 'sizes' should be the same as the"
+            " rank of input 'X'. May only be set if 'scales' is set to an empty tensor.",
+            "tensor(int64)",
+            OpSchema::Optional)
+        .Output(0, "Y", "N-D tensor after resizing", "T1")
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_tensor_types(),
+            "Constrain input 'X' and output 'Y' to all tensor types.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain roi type to float or double.")
+        .SetDoc(Resize_ver11_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          resizeShapeInference(ctx);
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Identity,
+    13,
+    OpSchema()
+        .SetDoc("Identity operator")
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor to copy input into.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types_with_bfloat(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Identity,
+    1,
+    OpSchema()
+        .SetDoc("Identity operator")
+        .Input(0, "input", "Input tensor", "T")
+        .Output(0, "output", "Tensor to copy input into.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    IsNaN,
+    9,
+    OpSchema()
+        .SetDoc(R"DOC(Returns which elements of the input are NaN.)DOC")
+        .Input(0, "X", "input", "T1")
+        .Output(0, "Y", "output", "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input types to float tensors.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(bool)"},
+            "Constrain output types to boolean tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          updateOutputElemType(ctx, 0, TensorProto::BOOL);
+          if (hasInputShape(ctx, 0)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* NonZero_ver9_doc = R"DOC(
+    Returns the indices of the elements that are non-zero
+    (in row-major order - by dimension).
+    NonZero behaves similar to numpy.nonzero:
+    https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    NonZero,
+    9,
+    OpSchema()
+        .SetDoc(NonZero_ver9_doc)
+        .Input(0, "X", "input", "T")
+        .Output(0, "Y", "output", "tensor(int64)")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          updateOutputElemType(ctx, 0, TensorProto::INT64);
+          TensorShapeProto output_shape;
+          auto* dim = output_shape.add_dim();
+          if (hasInputShape(ctx, 0)) {
+            const TensorShapeProto& input_shape = getInputShape(ctx, 0);
+            dim->set_dim_value(input_shape.dim_size());
+          }
+          output_shape.add_dim();
+          updateOutputShape(ctx, 0, output_shape);
+        }));
+
+static const char* GatherND_ver12_doc = R"DOC(
+Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers
+slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`.
+
+`indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+where each element defines a slice of `data`
+
+`batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of
+`data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension.
+
+Some salient points about the inputs' rank and shape:
+
+1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal.
+
+3) b < min(q, r) is to be honored.
+
+4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive)
+
+5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+   It is an error if any of the index values are out of bounds.
+
+The output is computed as follows:
+
+The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+1) If `indices_shape[-1] > r-b` => error condition
+
+2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors
+   containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions
+   of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]`
+   is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below)
+
+3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor
+   containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding
+   to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor
+   to form the `output` tensor (Examples 2, 3, 4 and 5 below)
+
+This operator is the inverse of `ScatterND`.
+
+`Example 1`
+
+  batch_dims = 0
+
+  data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+  indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+  output  = [0,3]           # output_shape = [2]
+
+`Example 2`
+
+  batch_dims = 0
+
+  data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+  indices = [[1],[0]]      # indices_shape = [2, 1]
+
+  output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+`Example 3`
+
+  batch_dims = 0
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+  output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+`Example 4`
+
+  batch_dims = 0
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+  output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+`Example 5`
+
+  batch_dims = 1
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[1],[0]]             # indices_shape = [2, 1]
+
+  output  = [[2,3],[4,5]]             # output_shape = [2, 2]
+
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GatherND,
+    12,
+    OpSchema()
+        .SetDoc(GatherND_ver12_doc)
+        .Attr(
+            "batch_dims",
+            "The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] "
+            "along axis of size s. It is an error if any of the index values are out of bounds.",
+            "tensor(int64)")
+        .Output(
+            0,
+            "output",
+            "Tensor of rank q + r - indices_shape[-1] - 1.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          if (!hasNInputShapes(ctx, 2)) {
+            // cannot proceed with shape or rank inference
+            return;
+          }
+
+          const auto& data_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto data_rank = data_shape.dim_size();
+
+          const auto& indices_shape =
+              ctx.getInputType(1)->tensor_type().shape();
+          const auto indices_rank = indices_shape.dim_size();
+
+          int64_t batch_dims_data = getAttribute(ctx, "batch_dims", 0);
+          if (data_rank < 1 || indices_rank < 1) {
+            fail_shape_inference(
+                "Both `data` and `indices` input tensors in GatherND op "
+                "need to have rank larger than 0.");
+          }
+
+          // cannot ascertain if the input shapes are valid if shape of
+          // `indices` is missing last dimension value so return at this point
+          if (!indices_shape.dim(indices_rank - 1).has_dim_value()) {
+            return;
+          }
+
+          const auto last_index_dimension =
+              indices_shape.dim(indices_rank - 1).dim_value() + batch_dims_data;
+
+          if (last_index_dimension > data_rank) {
+            fail_shape_inference(
+                "Last dimension of `indices` input tensor in GatherND op "
+                "must not be larger than the rank of `data` tensor");
+          }
+
+          for (int i = 0; i < indices_rank - 1; ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = indices_shape.dim(i);
+          }
+
+          for (int i = static_cast<int>(last_index_dimension); i < data_rank;
+               ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = data_shape.dim(i);
+          }
+        }));
+
+static const char* Pad_ver11_doc = R"DOC(
+Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`,
+a padded tensor (`output`) is generated.
+
+The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):
+
+1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0)
+
+2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
+
+3) `edge` - pads with the edge values of array
+
+
+Example 1 (`constant` mode):
+  Insert 0 pads to the beginning of the second dimension.
+
+  data =
+  [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+
+  pads = [0, 2, 0, 0]
+
+  mode = 'constant'
+
+  constant_value = 0.0
+
+  output =
+  [
+      [0.0, 0.0, 1.0, 1.2],
+      [0.0, 0.0, 2.3, 3.4],
+      [0.0, 0.0, 4.5, 5.7],
+  ]
+
+
+Example 2 (`reflect` mode):
+  data =
+  [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+
+  pads = [0, 2, 0, 0]
+
+  mode = 'reflect'
+
+  output =
+  [
+      [1.0, 1.2, 1.0, 1.2],
+      [2.3, 3.4, 2.3, 3.4],
+      [4.5, 5.7, 4.5, 5.7],
+  ]
+
+
+Example 3 (`edge` mode):
+  data =
+  [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+
+  pads = [0, 2, 0, 0]
+
+  mode = 'edge'
+
+  output =
+  [
+      [1.0, 1.0, 1.0, 1.2],
+      [2.3, 2.3, 2.3, 3.4],
+      [4.5, 4.5, 4.5, 5.7],
+  ]
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pad,
+    11,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Supported modes: `constant`(default), `reflect`, `edge`",
+            AttributeProto::STRING,
+            std::string("constant"))
+        .SetDoc(Pad_ver11_doc)
+        .Input(0, "data", "Input tensor.", "T")
+        .Input(
+            1,
+            "pads",
+            "Tensor of integers indicating the number of padding elements to add or remove (if negative) "
+            "at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. "
+            "`pads` should be a 1D tensor of shape [2 * input_rank]. "
+            "`pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], "
+            "where xi_begin is the number of pad values added at the beginning of axis `i` and "
+            "xi_end, the number of pad values added at the end of axis `i`.",
+            "tensor(int64)")
+        .Input(
+            2,
+            "constant_value",
+            "(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).",
+            "T",
+            OpSchema::Optional)
+        .Output(0, "output", "Tensor after padding.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_numeric_types(),
+            "Constrain input and output to only numeric types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Shape inference needs the input data shape
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+
+          // Infer output shape if 'pads' tensor is available
+          const auto* pads_initializer = ctx.getInputData(1);
+          if (nullptr != pads_initializer) {
+            if (pads_initializer->dims_size() != 1 ||
+                pads_initializer->data_type() != TensorProto::INT64) {
+                  fail_shape_inference("'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64");
+            }
+
+            const auto& pads_data = ParseData<int64_t>(pads_initializer);
+            if (pads_data.size() != static_cast<size_t>(2 * input_rank)) {
+              fail_shape_inference("Pads has incorrect number of values");
+            }
+
+            auto* output_shape =
+                ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+            for (size_t i = 0; static_cast<int64_t>(i) < input_rank; ++i) {
+              const auto& input_dim = input_shape.dim((int)i);
+              auto* output_dim = output_shape->add_dim();
+              if (input_dim.has_dim_value()) {
+                output_dim->set_dim_value(
+                    input_dim.dim_value() + pads_data[i] +
+                    pads_data[i + input_rank]);
+              } else if (pads_data[i] + pads_data[i + input_rank] == 0) {
+                *output_dim = input_dim;
+              }
+            }
+          } else {
+            // Infer output shapes' rank in any case
+            auto* output_shape_0 = getOutputShape(ctx, 0);
+            for (size_t i = 0; static_cast<int64_t>(i) < input_rank; ++i) {
+              output_shape_0->add_dim();
+            }
+          }
+          return;
+        }));
+
+static const char* Cast_ver1_doc = R"DOC(
+The operator casts the elements of a given input tensor to a data type
+specified by the 'to' argument and returns an output tensor of the same size in
+the converted type. The 'to' argument must be one of the data types specified
+in the 'DataType' enum field in the TensorProto message.
+NOTE: Casting to and from strings is not supported yet.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Cast,
+    1,
+    OpSchema()
+        .SetDoc(Cast_ver1_doc)
+        .Attr(
+            "to",
+            "The data type to which the elements of the input tensor are cast. "
+            "Strictly must be one of the types from DataType enum in TensorProto",
+            AttributeProto::STRING)
+        .Input(0, "input", "Input tensor to be cast.", "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor with the same shape as input with type "
+            "specified by the 'to' argument",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain input types. Casting from strings and complex are not supported.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain output types. Casting to strings and complex are not supported."));
+
+static const char* Cast_ver6_doc = R"DOC(
+The operator casts the elements of a given input tensor to a data type
+specified by the 'to' argument and returns an output tensor of the same size in
+the converted type. The 'to' argument must be one of the data types specified
+in the 'DataType' enum field in the TensorProto message.
+NOTE: Casting to and from strings is not supported yet.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Cast,
+    6,
+    OpSchema()
+        .SetDoc(Cast_ver6_doc)
+        .Attr(
+            "to",
+            "The data type to which the elements of the input tensor are cast. "
+            "Strictly must be one of the types from DataType enum in TensorProto",
+            AttributeProto::INT)
+        .Input(0, "input", "Input tensor to be cast.", "T1")
+        .Output(
+            0,
+            "output",
+            "Output tensor with the same shape as input with type "
+            "specified by the 'to' argument",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain input types. Casting from strings and complex are not supported.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(int8)",
+             "tensor(int16)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(uint8)",
+             "tensor(uint16)",
+             "tensor(uint32)",
+             "tensor(uint64)",
+             "tensor(bool)"},
+            "Constrain output types. Casting to strings and complex are not supported.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromAttributeToOutput(ctx, "to", 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* Concat_ver1_doc =
+    R"DOC(Concatenate a list of tensors into a single tensor)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Concat,
+    1,
+    OpSchema()
+        .Attr(
+            "axis",
+            "Which axis to concat on.  Default value is 1.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .SetDoc(Concat_ver1_doc)
+        .Input(
+            0,
+            "inputs",
+            "List of tensors for concatenation",
+            "T",
+            OpSchema::Variadic)
+        .Output(0, "concat_result", "Concatenated tensor", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain output types to float tensors."));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Concat,
+    4,
+    OpSchema()
+        .Attr("axis", "Which axis to concat on", AttributeProto::INT)
+        .SetDoc("Concatenate a list of tensors into a single tensor")
+        .Input(
+            0,
+            "inputs",
+            "List of tensors for concatenation",
+            "T",
+            OpSchema::Variadic)
+        .Output(0, "concat_result", "Concatenated tensor", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto numInputs = ctx.getNumInputs();
+          if (numInputs < 1 ||
+              !hasNInputShapes(ctx, static_cast<int>(numInputs))) {
+            return;
+          }
+
+          auto rank = ctx.getInputType(0)->tensor_type().shape().dim_size();
+
+          auto axisAttr = ctx.getAttribute("axis");
+          if (!axisAttr) {
+            fail_shape_inference("Required attribute axis is missing");
+          }
+          int axis = static_cast<int>(axisAttr->i());
+          if (rank <= axis) {
+            fail_shape_inference("rank must be greater than axis");
+          }
+          if (axis < 0) {
+            return; // TODO: check if negative axis must be supported
+          }
+
+          bool all_lengths_known = true;
+          int total_length = 0;
+
+          auto* output_shape =
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          for (int64_t i = 0; i < rank; ++i) {
+            output_shape->add_dim();
+          }
+
+          for (size_t i = 0; i < numInputs; i++) {
+            const auto& shape = ctx.getInputType(i)->tensor_type().shape();
+            if (shape.dim_size() != rank) {
+              fail_shape_inference("All inputs to Concat must have same rank");
+            }
+            for (int j = 0; j < rank; j++) {
+              if (j == axis) {
+                if (shape.dim(j).has_dim_value()) {
+                  total_length += static_cast<int>(shape.dim(j).dim_value());
+                } else {
+                  all_lengths_known = false;
+                }
+              } else {
+                auto& output_dim = *output_shape->mutable_dim(j);
+                const auto& input_dim = shape.dim(j);
+                mergeInDimensionInfo(input_dim, output_dim, j);
+              }
+            }
+          }
+
+          if (all_lengths_known) {
+            output_shape->mutable_dim(axis)->set_dim_value(total_length);
+          }
+        }));
+
+static const char* Split_ver1_doc =
+    R"DOC(Split a tensor into a list of tensors, along the specified
+'axis'. The lengths of the split can be specified using argument 'axis' or
+optional second input blob to the operator. Otherwise, the tensor is split
+to equal sized parts.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Split,
+    1,
+    OpSchema()
+        .Input(0, "input", "The tensor to split", "T")
+        .Input(
+            1,
+            "split",
+            "Optional list of output lengths (see also arg 'split')",
+            "T",
+            OpSchema::Optional)
+        .Output(
+            0,
+            "outputs...",
+            "One or more outputs forming list of tensors after splitting",
+            "T",
+            OpSchema::Variadic)
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input types to float tensors.")
+        .Attr(
+            "axis",
+            "Which axis to split on",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "split",
+            "length of each output",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Split_ver1_doc));
+
+static const char* Pad_ver1_doc = R"DOC(
+Given `data` tensor, paddings, mode, and value.
+Example:
+  Insert 0 paddings to the beginning of the second dimension.
+  data = [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+  paddings = [0, 0, 2, 0]
+  output = [
+      [
+          [0.0, 0.0, 1.0, 1.2],
+          [0.0, 0.0, 2.3, 3.4],
+          [0.0, 0.0, 4.5, 5.7],
+      ],
+  ]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pad,
+    1,
+    OpSchema()
+        .Attr(
+            "paddings",
+            "List of integers indicate the padding element count at the "
+            "beginning and end of each axis, for 2D it is the number of pixel. "
+            "`paddings` rank should be double of the input's rank. `paddings` format should be as follow "
+            "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
+            "added at the beginning of axis `i` and xi_end, the number of pixels added at "
+            "the end of axis `i`.",
+            AttributeProto::INTS)
+        .Attr(
+            "mode",
+            "Three modes: constant(default), reflect, edge",
+            AttributeProto::STRING,
+            std::string("constant"))
+        .Attr(
+            "value",
+            "One float, indicates the value to be filled, default is 0",
+            AttributeProto::FLOAT,
+            0.0f)
+        .SetDoc(Pad_ver1_doc)
+        .Input(0, "data", "Input tensor.", "T")
+        .Output(0, "output", "Tensor after padding.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Reshape_ver1_doc = R"DOC(
+Reshape the input tensor similar to numpy.reshape.
+It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor.
+At most one dimension of the new shape can be -1. In this case, the value is
+inferred from the size of the tensor and the remaining dimensions. A dimension
+could also be 0, in which case the actual dimension value is unchanged (i.e. taken
+from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
+The input tensor's shape and the output tensor's shape are required to have the same number of elements.)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Reshape,
+    1,
+    OpSchema()
+        .SetDoc(Reshape_ver1_doc)
+        .Attr("shape", "New shape", AttributeProto::INTS, OPTIONAL_VALUE)
+        // This attribute was added via AllowConsumed API in OpSchema.
+        // After removing the API, we're now using the Attr API to simulate the
+        // old definition.
+        .Attr(
+            "consumed_inputs",
+            "legacy optimization attribute.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Input(0, "data", "An input tensor.", "T")
+        .Output(0, "reshaped", "Reshaped data.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors."));
+
+static const char* Upsample_ver1_doc = R"DOC(
+Upsample the input tensor.
+The width and height of the output tensor are:
+  output_width = floor(input_width * width_scale),
+  output_height = floor(input_height * height_scale).
+Example:
+  Given `data` tensor, width_scale, height_scale, mode,
+  Upsample the input 4-D tensor in nearest mode:
+  data = [[[
+      [1, 2],
+      [3, 4]
+  ]]]
+  width_scale = 2
+  height_scale = 2
+  mode = "nearest"
+  output = [[[
+      [1, 1, 2, 2],
+      [1, 1, 2, 2],
+      [3, 3, 4, 4],
+      [3, 3, 4, 4]
+  ]]]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Tile,
+    1,
+    OpSchema()
+        .SetDoc("Repeat the elements of a tensor along an axis.")
+        .Input(0, "input", "Input tensor of any shape.", "T")
+        .Input(
+            1,
+            "tiles",
+            "Number of repeated copies to make of the input tensor.",
+            "T")
+        .Input(2, "axis", "Axis along which to repeat.", "T")
+        .Output(
+            0,
+            "output",
+            "Output tensor of same shape and type as input.",
+            "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input types to float tensors.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int64)"},
+            "Constrain tiles and axis's type to int64 tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          // Only rank of output can be inferred. We can do better if second
+          // input is a constant, but this requires extending InferenceContext
+          // interface to get values of constant inputs.
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Upsample,
+    1,
+    OpSchema()
+        .SetSupportLevel(OpSchema::SupportType::EXPERIMENTAL)
+        .Attr(
+            "width_scale",
+            "The scale along width dimension. It takes value greater than or equal to 1.",
+            AttributeProto::FLOAT)
+        .Attr(
+            "height_scale",
+            "The scale along height dimension. It takes value greater than or equal to 1.",
+            AttributeProto::FLOAT)
+        .Attr(
+            "mode",
+            "Two interpolation modes: nearest(default), bilinear",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Input(0, "X", "4-D tensor, [N,C,H,W]", "T")
+        .Output(0, "Y", "4-D tensor after resizing, [N,C,H,W]", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(bool)",
+             "tensor(int32)",
+             "tensor(int64)",
+             "tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Constrain output types to bool, int32, int64, float16, float, double tensors.")
+        .SetDoc(Upsample_ver1_doc));
+
+static const char* Upsample_ver7_doc = R"DOC(
+Upsample the input tensor.
+Each dimension value of the output tensor is:
+  output_dimension = floor(input_dimension * scale).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Upsample,
+    7,
+    OpSchema()
+        .Attr(
+            "scales",
+            "The scale array along each dimension. It takes value greater than or equal to 1."
+            " The number of elements of 'scales' should be the same as the rank of input 'X'.",
+            AttributeProto::FLOATS)
+        .Attr(
+            "mode",
+            "Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Input(0, "X", "N-D tensor", "T")
+        .Output(0, "Y", "N-D tensor after resizing", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .SetDoc(Upsample_ver7_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          const auto& input_shape = getInputShape(ctx, 0);
+          auto* output_shape = getOutputShape(ctx, 0);
+          const auto* scales = ctx.getAttribute("scales");
+
+          if (output_shape->dim_size() > 0) {
+            if (output_shape->dim_size() != input_shape.dim_size()) {
+              fail_shape_inference(
+                  "Ranks inferred (",
+                  input_shape.dim_size(),
+                  ") is not equal to the existing rank value (",
+                  output_shape->dim_size(),
+                  ").");
+            }
+          } else { // Infer the rank of output anyway
+            for (int i = 0; i < input_shape.dim_size(); ++i) {
+              output_shape->add_dim();
+            }
+          }
+
+          if (nullptr != scales) {
+            // Infer output shape's dimension value if 'scales' is known.
+            if (scales->type() == AttributeProto_AttributeType_FLOATS) {
+              const std::vector<float> scales_data(
+                  scales->floats().begin(), scales->floats().end());
+              if (scales_data.size() !=
+                  static_cast<size_t>(input_shape.dim_size())) {
+                fail_shape_inference(
+                    "Number of elements of attribute 'scales' must be same as rank of input 'X'");
+              }
+              resizeShapeInferenceHelper_opset7_to_10(
+                  input_shape, scales_data, output_shape);
+            } else {
+              fail_shape_inference("Attribute 'scales' must have floats type.");
+            } // scales->type() == float
+          } else {
+            fail_shape_inference("Attribute 'scales' is required.");
+          } // nullptr != scales
+        }));
+
+static const char* Upsample_ver9_doc = R"DOC(
+Upsample the input tensor.
+Each dimension value of the output tensor is:
+  output_dimension = floor(input_dimension * scale).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Upsample,
+    9,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Input(0, "X", "N-D tensor", "T")
+        .Input(
+            1,
+            "scales",
+            "The scale array along each dimension. It takes value greater than or equal to 1."
+            " The number of elements of 'scales' should be the same as the rank of input 'X'.",
+            "tensor(float)")
+        .Output(0, "Y", "N-D tensor after resizing", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input 'X' and output 'Y' to all tensor types.")
+        .SetDoc(Upsample_ver9_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          resizeShapeInference_opset7_to_10(ctx);
+        }));
+
+static const char* Resize_ver10_doc = R"DOC(
+Resize the input tensor.
+Each dimension value of the output tensor is:
+  output_dimension = floor(input_dimension * scale).
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Resize,
+    10,
+    OpSchema()
+        .Attr(
+            "mode",
+            "Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)",
+            AttributeProto::STRING,
+            std::string("nearest"))
+        .Input(0, "X", "N-D tensor", "T")
+        .Input(
+            1,
+            "scales",
+            "The scale array along each dimension. It takes value greater than 0. If it's less than 1,"
+            " it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should"
+            " be the same as the rank of input 'X'.",
+            "tensor(float)")
+        .Output(0, "Y", "N-D tensor after resizing", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input 'X' and output 'Y' to all tensor types.")
+        .SetDoc(Resize_ver10_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          resizeShapeInference_opset7_to_10(ctx);
+        }));
+
+static const char* Slice_ver1_doc = R"DOC(
+Produces a slice of the input tensor along multiple axes. Similar to numpy:
+https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+Slices uses `axes`, `starts` and `ends` attributes to specify the start and end
+dimension for each axis in the list of axes, it uses this information to
+slice the input `data` tensor. If a negative value is passed for any of the
+start or end indices, it represent number of elements before the end of that
+dimension. If the value passed to start or end is larger than the `n` (the
+number of elements in this dimension), it represents `n`. For slicing to the
+end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
+If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+Example 1:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  axes = [0, 1]
+  starts = [1, 0]
+  ends = [2, 3]
+  result = [
+      [5, 6, 7],
+  ]
+Example 2:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  starts = [0, 1]
+  ends = [-1, 1000]
+  result = [
+      [2, 3, 4],
+  ]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Slice,
+    1,
+    OpSchema()
+        .SetDoc(Slice_ver1_doc)
+        .Input(0, "data", "Tensor of data to extract slices from.", "T")
+        .Attr(
+            "axes",
+            "Axes that `starts` and `ends` apply to. "
+            "It's optional. If not present, will be treated as "
+            "[0, 1, ..., len(`starts`) - 1].",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "starts",
+            "Starting indices of corresponding axis in `axes`",
+            AttributeProto::INTS)
+        .Attr(
+            "ends",
+            "Ending indices (exclusive) of corresponding axis in axes`",
+            AttributeProto::INTS)
+        .Output(0, "output", "Sliced data tensor.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          std::vector<int64_t> starts;
+          std::vector<int64_t> ends;
+          if (!getRepeatedAttribute(ctx, "starts", starts) ||
+              !getRepeatedAttribute(ctx, "ends", ends) ||
+              starts.size() != ends.size()) {
+            fail_shape_inference(
+                "Incorrect or missing attribute value for starts and ends");
+          }
+
+          std::vector<int64_t> axes;
+          if (!getRepeatedAttribute(ctx, "axes", axes)) {
+            for (int i = 0; (size_t)i < starts.size(); ++i) {
+              axes.push_back(i);
+            }
+          } else if (axes.size() != starts.size()) {
+            fail_shape_inference("Attribute axes has incorrect length");
+          } else if (!std::is_sorted(axes.begin(), axes.end())) {
+            // TODO support shape inference for unsorted axes
+            return;
+          }
+
+          auto is_negative = [](int64_t index) {
+            return index < 0;
+          };
+          if (std::any_of(starts.begin(), starts.end(), is_negative) ||
+              std::any_of(ends.begin(), ends.end(), is_negative) ||
+              std::any_of(axes.begin(), axes.end(), is_negative)) {
+            // Negative axes were not explicitly discussed in the spec before opset-10.
+            // Hence, they are officially not part of the spec, but some models/runtimes may use them.
+            // So we perform simple rank inference in this case.
+            for (size_t i = 0; (int64_t)i <
+                ctx.getInputType(0)->tensor_type().shape().dim_size();
+                ++i) {
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape()->add_dim();
+            }
+            return;
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          for (size_t i = 0, j = 0; (int64_t)i <
+               ctx.getInputType(0)->tensor_type().shape().dim_size();
+               ++i) {
+            auto* newdim = ctx.getOutputType(0)
+                               ->mutable_tensor_type()
+                               ->mutable_shape()
+                               ->add_dim();
+            if (j < axes.size() && static_cast<size_t>(axes[j]) == i) {
+              // There's a lot of potential behaviors. For now just
+              // handle some simple cases.
+              if (ctx.getInputType(0)
+                      ->tensor_type()
+                      .shape()
+                      .dim((int)i)
+                      .has_dim_value() &&
+                  starts[j] >= 0 && ends[j] >= 0) {
+                auto newval = std::min(
+                                  (int64_t)ctx.getInputType(0)
+                                      ->tensor_type()
+                                      .shape()
+                                      .dim((int)i)
+                                      .dim_value(),
+                                  ends[j]) -
+                    starts[j];
+                if (newval >= 0) {
+                  newdim->set_dim_value(newval);
+                }
+              }
+              ++j;
+            } else {
+              *newdim = ctx.getInputType(0)->tensor_type().shape().dim((int)i);
+            }
+          }
+        }));
+
+static const char* Slice_ver10_doc = R"DOC(
+Produces a slice of the input tensor along multiple axes. Similar to numpy:
+https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
+dimension and step for each axis in the list of axes, it uses this information to
+slice the input `data` tensor. If a negative value is passed for any of the
+start or end indices, it represent number of elements before the end of that
+dimension. If the value passed to start or end is larger than the `n` (the
+number of elements in this dimension), it represents `n`. For slicing to the
+end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
+If a negative value is passed for step, it represents slicing backward.
+If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+Example 1:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  axes = [0, 1]
+  starts = [1, 0]
+  ends = [2, 3]
+  steps = [1, 2]
+  result = [
+      [5, 7],
+  ]
+Example 2:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  starts = [0, 1]
+  ends = [-1, 1000]
+  result = [
+      [2, 3, 4],
+  ]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Slice,
+    10,
+    OpSchema()
+        .SetDoc(Slice_ver10_doc)
+        .Input(0, "data", "Tensor of data to extract slices from.", "T")
+        .Input(
+            1,
+            "starts",
+            "1-D tensor of starting indices of corresponding axis in `axes`",
+            "Tind")
+        .Input(
+            2,
+            "ends",
+            "1-D tensor of ending indices (exclusive) of corresponding axis in `axes`",
+            "Tind")
+        .Input(
+            3,
+            "axes",
+            "1-D tensor of axes that `starts` and `ends` apply to.",
+            "Tind",
+            OpSchema::Optional)
+        .Input(
+            4,
+            "steps",
+            "1-D tensor of slice step of corresponding axis in `axes`. Default to 1. ",
+            "Tind",
+            OpSchema::Optional)
+        .Output(0, "output", "Sliced data tensor.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          size_t num_inputs = ctx.getNumInputs();
+          if (num_inputs != 3 && num_inputs != 4 && num_inputs != 5) {
+            fail_type_inference(
+                "Slice op must have either three, four or five inputs.");
+          }
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+          // Shape Inference if
+          //     1. 2nd and 3rd input data (starts, ends) are available.
+          // and 2. 4th and 5th optional input (axes, steps) are either not set,
+          // or set and is initializer.
+          const TensorProto* startsInitializer = ctx.getInputData(1);
+          const TensorProto* endsInitializer = ctx.getInputData(2);
+          const TensorProto* axesInitializer =
+              hasInputShape(ctx, 3) ? ctx.getInputData(3) : nullptr;
+          const TensorProto* stepsInitializer =
+              hasInputShape(ctx, 4) ? ctx.getInputData(4) : nullptr;
+
+          if (!startsInitializer || !endsInitializer ||
+              (hasInputShape(ctx, 3) && !ctx.getInputData(3)) ||
+              (hasInputShape(ctx, 4) && !ctx.getInputData(4))) {
+            return;
+          }
+
+          // don't know data_type- can't proceed
+          if (!startsInitializer->has_data_type())
+            return;
+
+          auto get_initializer_data =
+              [](const TensorProto* initializer) -> std::vector<int64_t> {
+            std::vector<int64_t> vec;
+            if (initializer->data_type() == TensorProto::INT64) {
+              const auto& data = ParseData<int64_t>(initializer);
+              vec.insert(vec.end(), data.begin(), data.end());
+            } else if (initializer->data_type() == TensorProto::INT32) {
+              const auto& data = ParseData<int32_t>(initializer);
+              vec.insert(vec.end(), data.begin(), data.end());
+            } else {
+              // unaccepted data type
+              fail_shape_inference(
+                  "Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps");
+            }
+            return vec;
+          };
+
+          auto clamp = [](int64_t val, int64_t low, int64_t high) -> int64_t {
+            if (val < low)
+              return low;
+            if (val > high)
+              return high;
+            return val;
+          };
+
+          std::vector<int64_t> starts = get_initializer_data(startsInitializer);
+          std::vector<int64_t> ends = get_initializer_data(endsInitializer);
+
+          if (starts.size() != ends.size()) {
+            fail_shape_inference(
+                "Incorrect or missing input value for starts and ends");
+          }
+
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto input_rank = input_shape.dim_size();
+          std::vector<int64_t> axes(starts.size());
+          if (!axesInitializer) {
+            std::iota(axes.begin(), axes.end(), 0);
+          } else {
+            axes = get_initializer_data(axesInitializer);
+            if (axes.size() != starts.size()) {
+              fail_shape_inference("Input axes has incorrect length");
+            }
+          }
+
+          std::vector<int64_t> steps;
+          if (!stepsInitializer) {
+            steps = std::vector<int64_t>(starts.size(), 1);
+          } else {
+            steps = get_initializer_data(stepsInitializer);
+            if (steps.size() != axes.size()) {
+              fail_shape_inference("Input steps has incorrect length");
+            }
+          }
+
+          for (size_t i = 0; (int64_t)i < input_rank; ++i) {
+            // first update rank of output dim
+            auto* output_dim = ctx.getOutputType(0)
+                                   ->mutable_tensor_type()
+                                   ->mutable_shape()
+                                   ->add_dim();
+            const auto& input_dim = input_shape.dim((int)i);
+            if (input_dim.has_dim_value()) {
+              output_dim->set_dim_value(input_dim.dim_value());
+            } else if (input_dim.has_dim_param()) {
+              output_dim->set_dim_param(input_dim.dim_param());
+            }
+          }
+
+          std::unordered_set<int64_t> unique_axes;
+          size_t axes_size = axes.size();
+          for (size_t axis_index = 0; axis_index < axes_size; ++axis_index) {
+            auto axis = axes[axis_index] < 0
+                ? axes[axis_index] + static_cast<int64_t>(input_rank)
+                : axes[axis_index];
+
+            if (axis >= static_cast<int64_t>(input_rank) || axis < 0) {
+              fail_shape_inference("Input axes has invalid data");
+            }
+
+            if (unique_axes.find(axis) != unique_axes.end()) {
+              fail_shape_inference("'axes' has duplicates");
+            }
+
+            unique_axes.insert(axis);
+            auto input_dim =
+                ctx.getInputType(0)->tensor_type().shape().dim((int)axis);
+
+            // input dim value is missing - cannot perform shape inference for
+            // this axis
+            if (!input_dim.has_dim_value())
+              continue;
+
+            const auto input_dim_value = input_dim.dim_value();
+
+            // process step
+            auto step = steps[axis_index];
+            if (step == 0) {
+              fail_shape_inference("'step' cannot be 0");
+            }
+
+            // process start
+            auto start = starts[axis_index];
+            if (start < 0)
+              start += input_dim_value;
+            if (step < 0)
+              start = clamp(start, 0, input_dim_value - 1);
+            else
+              start = clamp(start, 0, input_dim_value);
+
+            // process end
+            auto end = ends[axis_index];
+            if (end < 0)
+              end += input_dim_value;
+            if (step < 0)
+              end = clamp(end, -1, input_dim_value);
+            else
+              end = clamp(end, 0, input_dim_value);
+
+            // find output dim value for this axis
+            auto temp = static_cast<int64_t>(ceil(1.0 * (end - start) / step));
+            if (temp < 0)
+              temp = 0;
+
+            // assign output value
+            ctx.getOutputType(0)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->mutable_dim((int)axis)
+                ->set_dim_value(temp);
+          }
+        }));
+
+static const char* Scatter_ver9_doc = R"DOC(
+Given `data`, `updates` and `indices` input tensors of rank r >= 1, write the values provided by `updates`
+into the first input, `data`, along `axis` dimension of `data` (by default outer-most one as axis=0) at corresponding `indices`.
+For each entry in `updates`, the target index in `data` is specified by corresponding entry in `indices`
+for dimension = axis, and index in source for dimension != axis. For instance, in a 2-D tensor case,
+data[indices[i][j]][j] = updates[i][j] if axis = 0, or data[i][indices[i][j]] = updates[i][j] if axis = 1,
+where i and j are loop counters from 0 up to the respective size in `updates` - 1.
+Example 1:
+  data = [
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+      [0.0, 0.0, 0.0],
+  ]
+  indices = [
+      [1, 0, 2],
+      [0, 2, 1],
+  ]
+  updates = [
+      [1.0, 1.1, 1.2],
+      [2.0, 2.1, 2.2],
+  ]
+  output = [
+      [2.0, 1.1, 0.0]
+      [1.0, 0.0, 2.2]
+      [0.0, 2.1, 1.2]
+  ]
+Example 2:
+  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+  indices = [[1, 3]]
+  updates = [[1.1, 2.1]]
+  axis = 1
+  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Scatter,
+    9,
+    OpSchema()
+        .SetDoc(Scatter_ver9_doc)
+        .Attr(
+            "axis",
+            "Which axis to scatter on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1]",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of r >= 1 (same rank as input).",
+            "Tind")
+        .Input(
+            2,
+            "updates",
+            "Tensor of rank r >=1 (same rank and shape as indices)",
+            "T")
+        .Output(0, "output", "Tensor of rank r >= 1 (same rank as input).", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Input and output types can be of any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (hasNInputShapes(ctx, 1)) {
+            propagateShapeFromInputToOutput(ctx, 0, 0);
+          }
+        }));
+
+static const char* DepthToSpace_ver1_doc =
+    R"DOC(DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
+This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
+the input tensor where values from the depth dimension are moved in spatial blocks to the height
+and width dimensions.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    DepthToSpace,
+    1,
+    OpSchema()
+        .Attr(
+            "blocksize",
+            "Blocks of [blocksize, blocksize] are moved.",
+            AttributeProto::INT)
+        .SetDoc(DepthToSpace_ver1_doc)
+        .Input(
+            0,
+            "input",
+            "Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth"
+            ", H is the height and W is the width.",
+            "T")
+        .Output(
+            0,
+            "output",
+            "Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          auto blocksize = getAttribute(ctx, "blocksize", 0);
+          if (blocksize <= 0) {
+            fail_shape_inference("Blocksize must be positive");
+          }
+          if (hasInputShape(ctx, 0)) {
+            auto& input_shape = getInputShape(ctx, 0);
+            if (input_shape.dim_size() == 4) {
+              // TODO: Clarify what behavior should be if C is not a multiple of
+              // blocksize*blocksize.
+              updateOutputShape(
+                  ctx,
+                  0,
+                  {input_shape.dim(0),
+                   input_shape.dim(1) / (blocksize * blocksize),
+                   input_shape.dim(2) * blocksize,
+                   input_shape.dim(3) * blocksize});
+            } else {
+              fail_shape_inference("Input tensor must be 4-dimensional");
+            }
+          }
+        }));
+
+static const char* Gather_ver1_doc = R"DOC(
+Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
+entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
+them in an output tensor of rank q + (r - 1).
+Example 1:
+```
+  data = [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+  indices = [
+      [0, 1],
+      [1, 2],
+  ]
+  output = [
+      [
+          [1.0, 1.2],
+          [2.3, 3.4],
+      ],
+      [
+          [2.3, 3.4],
+          [4.5, 5.7],
+      ],
+  ]
+```
+Example 2:
+```
+  data = [
+      [1.0, 1.2, 1.9],
+      [2.3, 3.4, 3.9],
+      [4.5, 5.7, 5.9],
+  ]
+  indices = [
+      [0, 2],
+  ]
+  axis = 1,
+  output = [
+      [
+          [1.0, 1.9],
+          [2.3, 3.9],
+          [4.5, 5.9],
+      ],
+  ]
+```
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Gather,
+    1,
+    OpSchema()
+        .SetDoc(Gather_ver1_doc)
+        .Attr(
+            "axis",
+            "Which axis to gather on. Negative value means "
+            "counting dimensions from the back. Accepted range is [-r, r-1]",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. "
+            "It is an error if any of the index values are out of bounds.",
+            "Tind")
+        .Output(0, "output", "Tensor of rank q + (r - 1).", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to any tensor type.")
+        .TypeConstraint(
+            "Tind",
+            {"tensor(int32)", "tensor(int64)"},
+            "Constrain indices to integer types")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 2)) {
+            return;
+          }
+          const TensorShapeProto& data_shape =
+              ctx.getInputType(0)->tensor_type().shape();
+          const TensorShapeProto& indices_shape =
+              ctx.getInputType(1)->tensor_type().shape();
+          int r = data_shape.dim_size();
+          if (r < 1) {
+            fail_shape_inference("data tensor must have rank >= 1");
+          }
+          int q = indices_shape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -r || axis >= r) {
+            fail_shape_inference("axis must be in [-r, r-1]");
+          }
+          if (axis < 0) {
+            axis += r;
+          }
+          int out_rank = q + r - 1;
+
+          if (out_rank == 0) {
+            ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          }
+          for (int i = 0; i < out_rank; ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = (i < axis) ? data_shape.dim(i) : // i < axis < r
+                (i >= axis && i < axis + q) ? indices_shape.dim(i - axis)
+                                            : // i - axis < q
+                    data_shape.dim(i - q + 1); // i < out_rank < q + r - 1
+          }
+        }));
+
+static const char* Squeeze_ver1_doc = R"DOC(
+Remove single-dimensional entries from the shape of a tensor.
+Takes a  parameter `axes` with a list of axes to squeeze.
+If `axes` is not provided, all the single dimensions will be removed from
+the shape. If an axis is selected with shape entry not equal to one, an error is raised.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Squeeze,
+    1,
+    OpSchema()
+        .Attr(
+            "axes",
+            "List of non-negative integers, indicate the dimensions to squeeze.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Squeeze_ver1_doc)
+        .Input(0, "data", "Tensors with at least max(dims) dimensions.", "T")
+        .Output(0, "squeezed", "Reshaped tensor with same data as input.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          std::vector<int64_t> axes;
+          if (!getRepeatedAttribute(ctx, "axes", axes)) {
+            return;
+          }
+
+          if (!ctx.getInputType(0)->tensor_type().has_shape()) {
+            return;
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+          const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+
+          for (int i = 0, j = 0; i < input_shape.dim_size(); ++i) {
+            if (static_cast<size_t>(j) < axes.size() && axes[j] == i) {
+              if (input_shape.dim(i).has_dim_value() &&
+                  input_shape.dim(i).dim_value() != 1) {
+                fail_shape_inference(
+                    "Dimension of input ",
+                    i,
+                    " must be 1 instead of ",
+                    input_shape.dim(i).dim_value());
+              }
+              ++j;
+            } else {
+              *ctx.getOutputType(0)
+                   ->mutable_tensor_type()
+                   ->mutable_shape()
+                   ->add_dim() = input_shape.dim(i);
+            }
+          }
+        }));
+
+static const char* Unsqueeze_ver1_doc = R"DOC(
+Insert single-dimensional entries to the shape of a tensor.
+Takes one required argument `axes`, a list of dimensions that will be inserted.
+Dimension indices in `axes` are as seen in the output tensor. For example:
+  Given a tensor such that tensor with shape [3, 4, 5], then
+  Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Unsqueeze,
+    1,
+    OpSchema()
+        .Attr(
+            "axes",
+            "List of non-negative integers, indicate the dimensions to be inserted",
+            AttributeProto::INTS)
+        .SetDoc(Unsqueeze_ver1_doc)
+        .Input(0, "data", "Original tensor", "T")
+        .Output(0, "expanded", "Reshaped tensor with same data as input.", "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          std::vector<int64_t> axes;
+          if (!getRepeatedAttribute(ctx, "axes", axes)) {
+            return;
+          }
+          std::sort(axes.begin(), axes.end());
+
+          if (!ctx.getInputType(0)->tensor_type().has_shape()) {
+            return;
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          int j = 0;
+          for (int i = 0;
+               i < ctx.getInputType(0)->tensor_type().shape().dim_size();
+               ++i) {
+            while (static_cast<size_t>(j) < axes.size() &&
+                   axes[j] ==
+                       ctx.getOutputType(0)->tensor_type().shape().dim_size()) {
+              ctx.getOutputType(0)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->add_dim()
+                  ->set_dim_value(1);
+              ++j;
+            }
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() =
+                ctx.getInputType(0)->tensor_type().shape().dim(i);
+          }
+          while (static_cast<size_t>(j) < axes.size() &&
+                 axes[j] ==
+                     ctx.getOutputType(0)->tensor_type().shape().dim_size()) {
+            ctx.getOutputType(0)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->add_dim()
+                ->set_dim_value(1);
+            ++j;
+          }
+        }));
+
+static const char* OneHot_ver9_doc = R"DOC(
+    Produces a one-hot tensor based on inputs.
+    The locations represented by the index values in the 'indices' input tensor will have 'on_value'
+    and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'
+    are specified as part of required input argument 'values', which is a two-element tensor of format
+    [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the
+    input tensor. The additional dimension is for one-hot representation. The additional dimension will
+    be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional
+    dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional
+    dimension is specified by required scalar input 'depth'. The type of the output tensor is the same
+    as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside
+    the range [0, depth) will result in one-hot representation with all 'off_value' values in the
+    output tensor.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    OneHot,
+    9,
+    OpSchema()
+        .SetDoc(OneHot_ver9_doc)
+        .Attr(
+            "axis",
+            "(Optional) Axis along which one-hot representation in added. Default: axis=-1. "
+            "axis=-1 means that the additional dimension will be inserted as the "
+            "innermost/last dimension in the output tensor.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .Input(
+            0,
+            "indices",
+            "Input tensor containing indices. The values must be non-negative integers. "
+            "Any entries in the 'indices' input tensor with values outside the range [0, depth) "
+            "will result in one-hot representation with all 'off_value' values in the output tensor."
+            "In case 'indices' is of non-integer type, the values will be casted to int64 before use.",
+            "T1")
+        .Input(
+            1,
+            "depth",
+            "Scalar specifying the number of classes in one-hot tensor. This is also the size "
+            "of the one-hot dimension (specified by 'axis' attribute) added on in the output "
+            "tensor. The values in the 'indices' input tensor are expected to be "
+            "in the range [0, depth). "
+            "In case 'depth' is of non-integer type, it will be casted to int64 before use.",
+            "T2")
+        .Input(
+            2,
+            "values",
+            "Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], "
+            "where 'on_value' is the value used for filling locations specified in 'indices' input "
+            "tensor, and 'off_value' is the value used for filling locations other than those specified "
+            "in 'indices' input tensor. ",
+            "T3")
+        .Output(
+            0,
+            "output",
+            "Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. "
+            "The data type for the elements of the output tensor is the same as the type of input 'values' "
+            "is used.",
+            "T3")
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_numeric_types(),
+            "Constrain input to only numeric types.")
+        .TypeConstraint(
+            "T2",
+            OpSchema::all_numeric_types(),
+            "Constrain input to only numeric types.")
+        .TypeConstraint(
+            "T3",
+            OpSchema::all_tensor_types(),
+            "Constrain to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Check that the node has three inputs.
+          if (ctx.getNumInputs() != 3) {
+            fail_type_inference("OneHot node must have three inputs.");
+          }
+          // Input 'depth' must be a scalar or a single-element vector.
+          // TODO: Ideally to match spec for this input only allow Scalar should
+          // be allowed. Making this change now can affect backward
+          // compatibility for this op. Since this does not seem like a good
+          // justification to update version for this op, allowing both scalar
+          // and 1 element vector for now. In future when version update for
+          // this op is done we should only allow scalar or chage the spec to
+          // allow both.
+          if (hasInputShape(ctx, 1)) {
+            auto& depth_shape = getInputShape(ctx, 1);
+            if (depth_shape.dim_size() != 0 && depth_shape.dim_size() != 1) {
+              fail_type_inference(
+                  "Input 'depth' must be a scalar or rank 1 tensor.");
+            }
+            if (depth_shape.dim_size() == 1 &&
+                depth_shape.dim((int)0).has_dim_value() &&
+                depth_shape.dim((int)0).dim_value() != 1) {
+              fail_type_inference(
+                  "Input 'depth' must have exactly one element.");
+            }
+          }
+          // Input 'values' must be a two-element vector.
+          if (hasInputShape(ctx, 2)) {
+            auto& values_shape = getInputShape(ctx, 2);
+            if (values_shape.dim_size() != 1) {
+              fail_type_inference("Input 'values' must be rank 1 tensor.");
+            }
+            if (values_shape.dim((int)0).has_dim_value() &&
+                values_shape.dim((int)0).dim_value() != 2) {
+              fail_type_inference(
+                  "Input 'values' must have exactly two elements.");
+            }
+          }
+          // Set output type to be the same as the third input, 'values'.
+          propagateElemTypeFromInputToOutput(ctx, 2, 0);
+          // Set the output shape, if input 0 (indices) shape is available.
+          if (hasInputShape(ctx, 0)) {
+            const TensorShapeProto& indices_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            int r = indices_shape.dim_size();
+            if (r < 1) {
+              fail_shape_inference("Indices tensor must have rank >= 1");
+            }
+            int out_rank = r + 1;
+            int axis = static_cast<int>(getAttribute(ctx, "axis", -1));
+            if (axis < -out_rank || axis >= out_rank) {
+              fail_shape_inference(
+                  "'axis' must be in [-rank(indices)-1, rank(indices)]");
+            }
+            if (axis < 0) {
+              axis += out_rank;
+            }
+            auto* output_shape = getOutputShape(ctx, 0);
+            for (int i = 0; i < out_rank; ++i) {
+              auto* dim = output_shape->add_dim();
+              if (i < axis) {
+                if (indices_shape.dim(i).has_dim_value()) {
+                  dim->set_dim_value(indices_shape.dim(i).dim_value());
+                } else if (indices_shape.dim(i).has_dim_param()) {
+                  dim->set_dim_param(indices_shape.dim(i).dim_param());
+                }
+              } else if (i > axis) {
+                if (indices_shape.dim(i - 1).has_dim_value()) {
+                  dim->set_dim_value(indices_shape.dim(i - 1).dim_value());
+                } else if (indices_shape.dim(i - 1).has_dim_param()) {
+                  dim->set_dim_param(indices_shape.dim(i - 1).dim_param());
+                }
+              }
+            }
+          }
+        }));
+
+static const char* Compress_ver9_doc = R"DOC(
+    Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index.
+    In case axis is not provided, input is flattened before elements are selected.
+    Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html
+    )DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Compress,
+    9,
+    OpSchema()
+        .SetDoc(Compress_ver9_doc)
+        .Attr(
+            "axis",
+            "(Optional) Axis along which to take slices. If not specified, "
+            "input is flattened before elements being selected.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Input(0, "input", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "condition",
+            "Rank 1 tensor of booleans to indicate which slices or data elements to be selected. "
+            "Its length can be less than the input length alone the axis "
+            "or the flattened input size if axis is not specified. "
+            "In such cases data slices or elements exceeding the condition length are discarded.",
+            "T1")
+        .Output(
+            0,
+            "output",
+            "Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeConstraint(
+            "T1",
+            {"tensor(bool)"},
+            "Constrain to boolean tensors."));
+
+static const char* Split_ver2_doc =
+    R"DOC(Split a tensor into a list of tensors, along the specified
+'axis'. Lengths of the parts can be specified using argument 'split'.
+Otherwise, the tensor is split to equal sized parts.
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Split,
+    2,
+    OpSchema()
+        .Input(0, "input", "The tensor to split", "T")
+        .Output(
+            0,
+            "outputs",
+            "One or more outputs forming list of tensors after splitting",
+            "T",
+            OpSchema::Variadic)
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .Attr(
+            "axis",
+            "Which axis to split on. ",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "split",
+            "length of each output",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .SetDoc(Split_ver2_doc)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); ++i) {
+            propagateElemTypeFromInputToOutput(ctx, 0, i);
+          }
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          const auto& shape = ctx.getInputType(0)->tensor_type().shape();
+          int rank = shape.dim_size();
+          int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
+          if (axis < -rank || axis >= rank) {
+            fail_type_inference(
+                "Invalid value of attribute 'axis'. Rank=",
+                rank,
+                " Value=",
+                axis);
+          }
+          // Previously Split-2 does not mention how to deal with negative axis
+          // However, there is an existing test onnx/backend/test/data/pytorch-converted/test_GLU
+          // using Split-2 with negative axis and it is hard to be regenerated.
+          // To compromise, handle negative axis for Split-2 here.
+          if (axis < 0) {
+            axis += rank;
+          }
+          const auto& split_dim = shape.dim(axis);
+          if (!split_dim.has_dim_value()) {
+            for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
+              *ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
+                  shape;
+              ctx.getOutputType(i)
+                  ->mutable_tensor_type()
+                  ->mutable_shape()
+                  ->mutable_dim(axis)
+                  ->Clear();
+            }
+            return;
+          }
+          int split_dim_value = static_cast<int>(split_dim.dim_value());
+
+          std::vector<int64_t> split;
+          if (getRepeatedAttribute(ctx, "split", split)) {
+            if (split.size() != ctx.getNumOutputs()) {
+              fail_shape_inference(
+                  "Mismatch between number of splits (",
+                  split.size(),
+                  ") and outputs (",
+                  ctx.getNumOutputs(),
+                  ")");
+            }
+            int64_t total_dim = 0;
+            for (int64_t d : split) {
+              total_dim += d;
+            }
+            if (total_dim != split_dim_value) {
+              fail_shape_inference(
+                  "Mismatch between the sum of 'split' (",
+                  total_dim,
+                  ") and the split dimension of the input (",
+                  split_dim_value,
+                  ")");
+            }
+          } else {
+            int num_outputs = static_cast<int>(ctx.getNumOutputs());
+            if (split_dim_value % num_outputs != 0) {
+              fail_shape_inference("The input is not evenly splittable");
+            }
+            int chunk_size = split_dim_value / num_outputs;
+            for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); i++) {
+              split.push_back(chunk_size);
+            }
+          }
+          for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
+            *ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
+                shape;
+            ctx.getOutputType(i)
+                ->mutable_tensor_type()
+                ->mutable_shape()
+                ->mutable_dim(axis)
+                ->set_dim_value(split[i]);
+          }
+        }));
+
+static const char* Pad_ver2_doc = R"DOC(
+Given `data` tensor, pads, mode, and value.
+Example:
+  Insert 0 pads to the beginning of the second dimension.
+  data = [
+      [1.0, 1.2],
+      [2.3, 3.4],
+      [4.5, 5.7],
+  ]
+  pads = [0, 2, 0, 0]
+  output = [
+      [
+          [0.0, 0.0, 1.0, 1.2],
+          [0.0, 0.0, 2.3, 3.4],
+          [0.0, 0.0, 4.5, 5.7],
+      ],
+  ]
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Pad,
+    2,
+    OpSchema()
+        .Attr(
+            "pads",
+            "List of integers indicating the number of padding elements to add or remove (if negative) "
+            "at the beginning and end of each axis. For 2D it is the number of pixels. "
+            "`pads` rank should be double of the input's rank. `pads` format should be as follow "
+            "[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels "
+            "added at the beginning of axis `i` and xi_end, the number of pixels added at "
+            "the end of axis `i`.",
+            AttributeProto::INTS)
+        .Attr(
+            "mode",
+            "Three modes: constant(default), reflect, edge",
+            AttributeProto::STRING,
+            std::string("constant"))
+        .Attr(
+            "value",
+            "One float, indicates the value to be filled.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .SetDoc(Pad_ver2_doc)
+        .Input(0, "data", "Input tensor.", "T")
+        .Output(0, "output", "Tensor after padding.", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float16)", "tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+          if (!hasNInputShapes(ctx, 1)) {
+            return;
+          }
+
+          auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
+
+          std::vector<int64_t> pads;
+          if (!getRepeatedAttribute(ctx, "pads", pads)) {
+            fail_shape_inference("Attribute value for pads is required");
+          }
+          if (pads.size() != static_cast<size_t>(input_shape.dim_size() * 2)) {
+            fail_shape_inference("Attribute pads has incorrect length");
+            ;
+          }
+
+          ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
+
+          for (size_t i = 0; (int64_t)i < input_shape.dim_size(); ++i) {
+            auto* newdim = ctx.getOutputType(0)
+                               ->mutable_tensor_type()
+                               ->mutable_shape()
+                               ->add_dim();
+            if (ctx.getInputType(0)
+                    ->tensor_type()
+                    .shape()
+                    .dim((int)i)
+                    .has_dim_value()) {
+              newdim->set_dim_value(
+                  ctx.getInputType(0)
+                      ->tensor_type()
+                      .shape()
+                      .dim((int)i)
+                      .dim_value() +
+                  pads[i] + pads[input_shape.dim_size() + i]);
+            } else if (pads[i] + pads[input_shape.dim_size() + i] == 0) {
+              *newdim = input_shape.dim((int)i);
+            }
+          }
+        }));
+
+static const char* GatherND_ver11_doc = R"DOC(
+Given `data` tensor of rank `r` >= 1, and `indices` tensor of rank `q` >= 1, this operator gathers
+slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1`.
+
+`indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`,
+where each element defines a slice of `data`
+
+Some salient points about the inputs' rank and shape:
+
+1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q`
+
+2) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r` (inclusive)
+
+3) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`.
+   It is an error if any of the index values are out of bounds.
+
+The output is computed as follows:
+
+The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`.
+
+1) If `indices_shape[-1] > r` => error condition
+
+2) If `indices_shape[-1] == r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor
+   containing 1-D tensors of dimension `r`. Let us think of each such `r` ranked tensor as `indices_slice`.
+   Each *scalar value* corresponding to `data[indices_slice]` is filled into the corresponding location of the `(q-1)`-dimensional tensor
+   to form the `output` tensor (Example 1 below)
+
+3) If `indices_shape[-1] < r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor
+   containing 1-D tensors of dimension `< r`. Let us think of each such tensors as `indices_slice`.
+   Each *tensor slice* corresponding to `data[indices_slice , :]` is filled into the corresponding location of the `(q-1)`-dimensional tensor
+   to form the `output` tensor (Examples 2, 3, and 4 below)
+
+This operator is the inverse of `ScatterND`.
+
+`Example 1`
+
+  data    = [[0,1],[2,3]]   # data_shape = [2, 2]
+
+  indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
+
+  output  = [0,3]           # output_shape = [2]
+
+`Example 2`
+
+  data    = [[0,1],[2,3]]  # data_shape = [2, 2]
+
+  indices = [[1],[0]]      # indices_shape = [2, 1]
+
+  output  = [[2,3],[0,1]]  # output_shape = [2, 2]
+
+`Example 3`
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
+
+  output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
+
+`Example 4`
+
+  data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
+
+  indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
+
+  output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    GatherND,
+    11,
+    OpSchema()
+        .SetDoc(GatherND_ver11_doc)
+        .Input(0, "data", "Tensor of rank r >= 1.", "T")
+        .Input(
+            1,
+            "indices",
+            "Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] "
+            "along axis of size s. It is an error if any of the index values are out of bounds.",
+            "tensor(int64)")
+        .Output(
+            0,
+            "output",
+            "Tensor of rank q + r - indices_shape[-1] - 1.",
+            "T")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to any tensor type.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Type inference
+          propagateElemTypeFromInputToOutput(ctx, 0, 0);
+
+          // Shape inference
+          if (!hasNInputShapes(ctx, 2)) {
+            // cannot proceed with shape or rank inference
+            return;
+          }
+
+          const auto& data_shape = ctx.getInputType(0)->tensor_type().shape();
+          const auto data_rank = data_shape.dim_size();
+
+          const auto& indices_shape =
+              ctx.getInputType(1)->tensor_type().shape();
+          const auto indices_rank = indices_shape.dim_size();
+
+          if (data_rank < 1 || indices_rank < 1) {
+            fail_shape_inference(
+                "Both `data` and `indices` input tensors in GatherND op "
+                "need to have rank larger than 0.");
+          }
+
+          // cannot ascertain if the input shapes are valid if shape of
+          // `indices` is missing last dimension value so return at this point
+          if (!indices_shape.dim(indices_rank - 1).has_dim_value()) {
+            return;
+          }
+
+          const auto last_index_dimension =
+              indices_shape.dim(indices_rank - 1).dim_value();
+
+          if (last_index_dimension > data_rank) {
+            fail_shape_inference(
+                "Last dimension of `indices` input tensor in GatherND op "
+                "must not be larger than the rank of `data` tensor");
+          }
+
+          for (int i = 0; i < indices_rank - 1; ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = indices_shape.dim(i);
+          }
+
+          for (int i = static_cast<int>(last_index_dimension); i < data_rank;
+               ++i) {
+            *ctx.getOutputType(0)
+                 ->mutable_tensor_type()
+                 ->mutable_shape()
+                 ->add_dim() = data_shape.dim(i);
+          }
+        }));
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Identity,
+    14,
+    OpSchema()
+        .SetDoc("Identity operator")
+        .Input(
+            0,
+            "input",
+            "Input tensor",
+            "V",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor to copy input into.",
+            "V",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint(
+            "V",
+            [](){
+              auto t = OpSchema::all_tensor_types_with_bfloat();
+              auto s = OpSchema::all_tensor_sequence_types();
+              t.insert(t.end(), s.begin(), s.end());
+              return t;
+            }(),
+            "Constrain input and output types to all tensor and sequence types.")
+        .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
+
+static const char* Where_ver9_doc = R"DOC(
+Return elements, either from X or Y, depending on condition.
+Where behaves like
+[numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
+with three parameters.
+
+)DOC";
+
+ONNX_OPERATOR_SET_SCHEMA(
+    Where,
+    9,
+    OpSchema()
+        .SetDoc(Where_ver9_doc + GenerateBroadcastingDocMul())
+        .Input(
+            0,
+            "condition",
+            "When True (nonzero), yield X, otherwise yield Y",
+            "B",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::NonDifferentiable)
+        .Input(
+            1,
+            "X",
+            "values selected at indices where condition is True",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Input(
+            2,
+            "Y",
+            "values selected at indices where condition is False",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .Output(
+            0,
+            "output",
+            "Tensor of shape equal to the broadcasted shape of condition, X, and Y.",
+            "T",
+            OpSchema::Single,
+            true,
+            1,
+            OpSchema::Differentiable)
+        .TypeConstraint("B", {"tensor(bool)"}, "Constrain to boolean tensors.")
+        .TypeConstraint(
+            "T",
+            OpSchema::all_tensor_types(),
+            "Constrain input and output types to all tensor types.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          propagateElemTypeFromInputToOutput(ctx, 1, 0);
+          if (hasNInputShapes(ctx, 3)) {
+            std::vector<const TensorShapeProto*> shapes;
+            shapes.push_back(&ctx.getInputType(0)->tensor_type().shape());
+            shapes.push_back(&ctx.getInputType(1)->tensor_type().shape());
+            shapes.push_back(&ctx.getInputType(2)->tensor_type().shape());
+            multidirectionalBroadcastShapeInference(
+                shapes,
+                *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
+          }
+        }));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor/utils.cc b/third_party/onnx/onnx/defs/tensor/utils.cc
new file mode 100644
index 0000000000..a5b9c50a49
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor/utils.cc
@@ -0,0 +1,172 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/tensor/utils.h"
+
+namespace ONNX_NAMESPACE {
+void resizeShapeInferenceHelper(
+    const TensorShapeProto& input_shape,
+    const std::vector<int64_t>& sizes_data,
+    TensorShapeProto* output_shape) {
+  if (!sizes_data.empty()) {
+    for (int i = 0; i < input_shape.dim_size(); ++i) {
+      auto* dim = output_shape->mutable_dim(i);
+      dim->set_dim_value(sizes_data[i]);
+    }
+    return;
+  }
+}
+
+void resizeShapeInferenceHelper(
+    const TensorShapeProto& input_shape,
+    const std::vector<float>& scales_data,
+    TensorShapeProto* output_shape) {
+  for (int i = 0; i < input_shape.dim_size(); ++i) {
+    auto* dim = output_shape->mutable_dim(i);
+    // If input_shape has dim_value, we calculate the scaled result
+    // If input_shape doesn's have one, we leave it here
+    if (input_shape.dim(i).has_dim_value()) {
+      int64_t dim_value = static_cast<int64_t>(std::floor(
+          static_cast<float>(input_shape.dim(i).dim_value()) * scales_data[i]));
+      // If output_shape has dim_value, we validate the caculated result
+      // If output_shape doesn's have one, we set it to the scaled result
+      if (dim->has_dim_value()) {
+        if (static_cast<int64_t>(dim->dim_value()) != dim_value) {
+          fail_shape_inference(
+              "Dimension value inferred (",
+              dim_value,
+              ") is not equal to the existing dim value (",
+              dim->dim_value(),
+              ").");
+        }
+      } else {
+        dim->set_dim_value(static_cast<int64_t>(dim_value));
+      } // dim->has_dim_value()
+    } // input_shape.dim(i).has_dim_value()
+  }
+}
+
+void resizeShapeInference(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+  if (!hasNInputShapes(ctx, 1)) {
+    return;
+  }
+  const auto& input_shape = getInputShape(ctx, 0);
+  auto* output_shape = getOutputShape(ctx, 0);
+  const TensorProto* scales = 2 < ctx.getNumInputs() ? ctx.getInputData(2) : nullptr;
+
+  if (output_shape->dim_size() > 0) {
+    if (output_shape->dim_size() != input_shape.dim_size()) {
+      fail_shape_inference(
+          "Ranks inferred (",
+          input_shape.dim_size(),
+          ") is not equal to the existing rank value (",
+          output_shape->dim_size(),
+          ").");
+    }
+  } else { // Infer the rank of output anyway
+    for (int i = 0; i < input_shape.dim_size(); ++i) {
+      output_shape->add_dim();
+    }
+  }
+
+  if (ctx.getNumInputs() == 4) {
+    const auto* sizes = ctx.getInputData(3);
+    if (nullptr != sizes) {
+      if (sizes->data_type() == TensorProto::INT64) {
+        const auto sizes_data = ParseData<int64_t>(sizes);
+        if (sizes_data.size() != static_cast<size_t>(input_shape.dim_size())) {
+          fail_shape_inference(
+              "Number of elements of input 'sizes' must be same as rank of input 'X'");
+        }
+        resizeShapeInferenceHelper(input_shape, sizes_data, output_shape);
+      } else {
+        fail_shape_inference("Input 'sizes' must have int64 element type.");
+      }
+    }
+  } else if (nullptr != scales) {
+    // Infer output shape's dimension value if 'scales' is known.
+    if (scales->data_type() == TensorProto::FLOAT) {
+      const auto& scales_data = ParseData<float>(scales);
+      if (scales_data.size() != static_cast<size_t>(input_shape.dim_size())) {
+        fail_shape_inference(
+            "Number of elements of input 'scales' must be same as rank of input 'X'");
+      }
+      resizeShapeInferenceHelper(input_shape, scales_data, output_shape);
+    } else {
+      fail_shape_inference("Input 'scales' must have float element type.");
+    }
+  } // nullptr != scales
+}
+
+void resizeShapeInferenceHelper_opset7_to_10(
+    const TensorShapeProto& input_shape,
+    const std::vector<float>& scales_data,
+    TensorShapeProto* output_shape) {
+  for (int i = 0; i < input_shape.dim_size(); ++i) {
+    auto* dim = output_shape->mutable_dim(i);
+    // If input_shape has dim_value, we caculate the scaled result
+    // If input_shape doesn's have one, we leave it here
+    if (input_shape.dim(i).has_dim_value()) {
+      int64_t dim_value = static_cast<int64_t>(std::floor(
+          static_cast<float>(input_shape.dim(i).dim_value()) * scales_data[i]));
+      // If output_shape has dim_value, we validate the caculated result
+      // If output_shape doesn's have one, we set it to the scaled result
+      if (dim->has_dim_value()) {
+        if (static_cast<int64_t>(dim->dim_value()) != dim_value) {
+          fail_shape_inference(
+              "Dimension value inferred (",
+              dim_value,
+              ") is not equal to the existing dim value (",
+              dim->dim_value(),
+              ").");
+        }
+      } else {
+        dim->set_dim_value(static_cast<int64_t>(dim_value));
+      } // dim->has_dim_value()
+    } // input_shape.dim(i).has_dim_value()
+  }
+}
+
+void resizeShapeInference_opset7_to_10(InferenceContext& ctx) {
+  propagateElemTypeFromInputToOutput(ctx, 0, 0);
+  if (!hasNInputShapes(ctx, 1)) {
+    return;
+  }
+  const auto& input_shape = getInputShape(ctx, 0);
+  auto* output_shape = getOutputShape(ctx, 0);
+  const auto scales = ctx.getInputData(1);
+
+  if (output_shape->dim_size() > 0) {
+    if (output_shape->dim_size() != input_shape.dim_size()) {
+      fail_shape_inference(
+          "Ranks inferred (",
+          input_shape.dim_size(),
+          ") is not equal to the existing rank value (",
+          output_shape->dim_size(),
+          ").");
+    }
+  } else { // Infer the rank of output anyway
+    for (int i = 0; i < input_shape.dim_size(); ++i) {
+      output_shape->add_dim();
+    }
+  }
+
+  if (nullptr != scales) {
+    // Infer output shape's dimension value if 'scales' is known.
+    if (scales->data_type() == TensorProto::FLOAT) {
+      const auto& scales_data = ParseData<float>(scales);
+      if (scales_data.size() != static_cast<size_t>(input_shape.dim_size())) {
+        fail_shape_inference(
+            "Number of elements of input 'scales' must be same as rank of input 'X'");
+      }
+      resizeShapeInferenceHelper_opset7_to_10(input_shape, scales_data, output_shape);
+    } else {
+      fail_shape_inference("Input 'scales' must have float element type.");
+    } // nullptr != scales
+  }
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor/utils.h b/third_party/onnx/onnx/defs/tensor/utils.h
new file mode 100644
index 0000000000..1dfd7f6429
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor/utils.h
@@ -0,0 +1,34 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/defs/schema.h"
+#include "onnx/defs/tensor_proto_util.h"
+
+#include <cmath>
+
+namespace ONNX_NAMESPACE {
+// The below is called by ops after opset 11, inclusively.
+void resizeShapeInference(InferenceContext& ctx);
+
+void resizeShapeInferenceHelper(
+    const TensorShapeProto& input_shape,
+    const std::vector<float>& scales_data,
+    TensorShapeProto* output_shape);
+
+void resizeShapeInferenceHelper(
+    const TensorShapeProto& input_shape,
+    const std::vector<int64_t>& sizes_data,
+    TensorShapeProto* output_shape);
+
+// The below is called by ops between opset 7 and opset 10, inclusively.
+void resizeShapeInference_opset7_to_10(InferenceContext& ctx);
+
+void resizeShapeInferenceHelper_opset7_to_10(
+    const TensorShapeProto& input_shape,
+    const std::vector<float>& scales_data,
+    TensorShapeProto* output_shape);
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor_proto_util.cc b/third_party/onnx/onnx/defs/tensor_proto_util.cc
new file mode 100644
index 0000000000..b00d0668a9
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor_proto_util.cc
@@ -0,0 +1,126 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "tensor_proto_util.h"
+#include <vector>
+#include "onnx/common/platform_helpers.h"
+#include "onnx/defs/data_type_utils.h"
+#include "onnx/defs/shape_inference.h"
+
+namespace ONNX_NAMESPACE {
+
+#define DEFINE_TO_TENSOR_ONE(type, enumType, field) \
+  template <>                                       \
+  TensorProto ToTensor<type>(const type& value) {   \
+    TensorProto t;                                  \
+    t.set_data_type(enumType);                      \
+    t.add_##field##_data(value);                    \
+    return t;                                       \
+  }
+
+#define DEFINE_TO_TENSOR_LIST(type, enumType, field)            \
+  template <>                                                   \
+  TensorProto ToTensor<type>(const std::vector<type>& values) { \
+    TensorProto t;                                              \
+    t.clear_##field##_data();                                   \
+    t.set_data_type(enumType);                                  \
+    for (const type& val : values) {                            \
+      t.add_##field##_data(val);                                \
+    }                                                           \
+    return t;                                                   \
+  }
+
+#define DEFINE_PARSE_DATA(type, typed_data_fetch, tensorproto_datatype)        \
+  template <>                                                                  \
+  const std::vector<type> ParseData(const TensorProto* tensor_proto) {         \
+    if (!tensor_proto->has_data_type() ||                                      \
+      tensor_proto->data_type() == TensorProto_DataType_UNDEFINED) {           \
+      fail_shape_inference("The type of tensor: ", tensor_proto->name(),       \
+        " is undefined so it cannot be parsed.");                              \
+    } else if (tensor_proto->data_type() != tensorproto_datatype) {            \
+      fail_shape_inference("ParseData type mismatch for tensor: ",             \
+      tensor_proto->name(), ". Expected:",                                     \
+      Utils::DataTypeUtils::ToDataTypeString(tensorproto_datatype), " Actual:",\
+      Utils::DataTypeUtils::ToDataTypeString(tensor_proto->data_type()));      \
+    }                                                                          \
+    std::vector<type> res;                                                     \
+    if (tensor_proto->has_data_location() &&                                   \
+      tensor_proto->data_location() == TensorProto_DataLocation_EXTERNAL) {    \
+      fail_shape_inference("Cannot parse data from external tensors. Please ", \
+        "load external data into raw data for tensor: ", tensor_proto->name());\
+    } else if (!tensor_proto->has_raw_data()) {                                \
+      const auto& data = tensor_proto->typed_data_fetch();                     \
+      int expected_size = 1;                                                   \
+      for (int i = 0; i < tensor_proto->dims_size(); ++i) {                    \
+        expected_size *= tensor_proto->dims(i);                                \
+      }                                                                        \
+      if (tensor_proto->dims_size() != 0 && data.size() != expected_size) {    \
+        fail_shape_inference("Data size mismatch. Tensor: ",                   \
+        tensor_proto->name(), " expected size ", expected_size,                \
+        " does not match the actual size", data.size());                       \
+      }                                                                        \
+      res.insert(res.end(), data.begin(), data.end());                         \
+      return res;                                                              \
+    }                                                                          \
+    if (tensor_proto->data_type() == TensorProto_DataType_STRING) {            \
+      fail_shape_inference(tensor_proto->name()," data type is string. string",\
+      " content is required to be stored in repeated bytes string_data field.",\
+      " raw_data type cannot be string.");                                     \
+    }                                                                          \
+    /* The given tensor does have raw_data itself so parse it by given type */ \
+    /* make copy as we may have to reverse bytes */                            \
+    std::string raw_data = tensor_proto->raw_data();                           \
+    /* okay to remove const qualifier as we have already made a copy */        \
+    char* bytes = const_cast<char*>(raw_data.c_str());                         \
+    /* onnx is little endian serialized always-tweak byte order if needed */   \
+    if (!is_processor_little_endian()) {                                       \
+      const size_t element_size = sizeof(type);                                \
+      const size_t num_elements = raw_data.size() / element_size;              \
+      for (size_t i = 0; i < num_elements; ++i) {                              \
+        char* start_byte = bytes + i * element_size;                           \
+        char* end_byte = start_byte + element_size - 1;                        \
+        /* keep swapping */                                                    \
+        for (size_t count = 0; count < element_size / 2; ++count) {            \
+          char temp = *start_byte;                                             \
+          *start_byte = *end_byte;                                             \
+          *end_byte = temp;                                                    \
+          ++start_byte;                                                        \
+          --end_byte;                                                          \
+        }                                                                      \
+      }                                                                        \
+    }                                                                          \
+    /* raw_data.c_str()/bytes is a byte array and may not be properly  */      \
+    /* aligned for the underlying type */                                      \
+    /* We need to copy the raw_data.c_str()/bytes as byte instead of  */       \
+    /* copying as the underlying type, otherwise we may hit memory   */        \
+    /* misalignment issues on certain platforms, such as arm32-v7a */          \
+    const size_t raw_data_size = raw_data.size();                              \
+    res.resize(raw_data_size / sizeof(type));                                  \
+    memcpy(reinterpret_cast<char*>(res.data()), bytes, raw_data_size);         \
+    return res;                                                                \
+  }
+
+DEFINE_TO_TENSOR_ONE(float, TensorProto_DataType_FLOAT, float)
+DEFINE_TO_TENSOR_ONE(bool, TensorProto_DataType_BOOL, int32)
+DEFINE_TO_TENSOR_ONE(int32_t, TensorProto_DataType_INT32, int32)
+DEFINE_TO_TENSOR_ONE(int64_t, TensorProto_DataType_INT64, int64)
+DEFINE_TO_TENSOR_ONE(uint64_t, TensorProto_DataType_UINT64, uint64)
+DEFINE_TO_TENSOR_ONE(double, TensorProto_DataType_DOUBLE, double)
+DEFINE_TO_TENSOR_ONE(std::string, TensorProto_DataType_STRING, string)
+
+DEFINE_TO_TENSOR_LIST(float, TensorProto_DataType_FLOAT, float)
+DEFINE_TO_TENSOR_LIST(bool, TensorProto_DataType_BOOL, int32)
+DEFINE_TO_TENSOR_LIST(int32_t, TensorProto_DataType_INT32, int32)
+DEFINE_TO_TENSOR_LIST(int64_t, TensorProto_DataType_INT64, int64)
+DEFINE_TO_TENSOR_LIST(uint64_t, TensorProto_DataType_UINT64, uint64)
+DEFINE_TO_TENSOR_LIST(double, TensorProto_DataType_DOUBLE, double)
+DEFINE_TO_TENSOR_LIST(std::string, TensorProto_DataType_STRING, string)
+
+DEFINE_PARSE_DATA(int32_t, int32_data, TensorProto_DataType_INT32)
+DEFINE_PARSE_DATA(int64_t, int64_data, TensorProto_DataType_INT64)
+DEFINE_PARSE_DATA(float, float_data, TensorProto_DataType_FLOAT)
+DEFINE_PARSE_DATA(double, double_data, TensorProto_DataType_DOUBLE)
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor_proto_util.h b/third_party/onnx/onnx/defs/tensor_proto_util.h
new file mode 100644
index 0000000000..a54be0360d
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor_proto_util.h
@@ -0,0 +1,21 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/onnx-operators_pb.h"
+
+namespace ONNX_NAMESPACE {
+
+template <typename T>
+TensorProto ToTensor(const T& value);
+
+template <typename T>
+TensorProto ToTensor(const std::vector<T>& values);
+
+template <typename T>
+const std::vector<T> ParseData(const TensorProto* tensor_proto);
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor_util.cc b/third_party/onnx/onnx/defs/tensor_util.cc
new file mode 100644
index 0000000000..3b6407f834
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor_util.cc
@@ -0,0 +1,58 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "tensor_util.h"
+#include <vector>
+#include "onnx/common/platform_helpers.h"
+
+namespace ONNX_NAMESPACE {
+
+#define DEFINE_PARSE_DATA(type, typed_data_fetch)                          \
+  template <>                                                              \
+  const std::vector<type> ParseData(const Tensor* tensor) {                \
+    std::vector<type> res;                                                 \
+    if (!tensor->is_raw_data()) {                                          \
+      const auto& data = tensor->typed_data_fetch();                       \
+      res.insert(res.end(), data.begin(), data.end());                     \
+      return res;                                                          \
+    }                                                                      \
+    /* make copy as we may have to reverse bytes */                        \
+    std::string raw_data = tensor->raw();                                  \
+    /* okay to remove const qualifier as we have already made a copy */    \
+    char* bytes = const_cast<char*>(raw_data.c_str());                     \
+    /*onnx is little endian serialized always-tweak byte order if needed*/ \
+    if (!is_processor_little_endian()) {                                   \
+      const size_t element_size = sizeof(type);                            \
+      const size_t num_elements = raw_data.size() / element_size;          \
+      for (size_t i = 0; i < num_elements; ++i) {                          \
+        char* start_byte = bytes + i * element_size;                       \
+        char* end_byte = start_byte + element_size - 1;                    \
+        /* keep swapping */                                                \
+        for (size_t count = 0; count < element_size / 2; ++count) {        \
+          char temp = *start_byte;                                         \
+          *start_byte = *end_byte;                                         \
+          *end_byte = temp;                                                \
+          ++start_byte;                                                    \
+          --end_byte;                                                      \
+        }                                                                  \
+      }                                                                    \
+    }                                                                      \
+    /* raw_data.c_str()/bytes is a byte array and may not be properly  */  \
+    /* aligned for the underlying type */                                  \
+    /* We need to copy the raw_data.c_str()/bytes as byte instead of  */   \
+    /* copying as the underlying type, otherwise we may hit memory   */    \
+    /* misalignment issues on certain platforms, such as arm32-v7a */      \
+    const size_t raw_data_size = raw_data.size();                          \
+    res.resize(raw_data_size / sizeof(type));                              \
+    memcpy(reinterpret_cast<char*>(res.data()), bytes, raw_data_size);     \
+    return res;                                                            \
+  }
+
+DEFINE_PARSE_DATA(int32_t, int32s)
+DEFINE_PARSE_DATA(int64_t, int64s)
+DEFINE_PARSE_DATA(float, floats)
+DEFINE_PARSE_DATA(double, doubles)
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/tensor_util.h b/third_party/onnx/onnx/defs/tensor_util.h
new file mode 100644
index 0000000000..13b3d03af4
--- /dev/null
+++ b/third_party/onnx/onnx/defs/tensor_util.h
@@ -0,0 +1,15 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/common/ir.h"
+
+namespace ONNX_NAMESPACE {
+
+template <typename T>
+const std::vector<T> ParseData(const Tensor* tensor);
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/defs/traditionalml/defs.cc b/third_party/onnx/onnx/defs/traditionalml/defs.cc
new file mode 100644
index 0000000000..52c931b064
--- /dev/null
+++ b/third_party/onnx/onnx/defs/traditionalml/defs.cc
@@ -0,0 +1,1268 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+
+#ifdef ONNX_ML
+namespace ONNX_NAMESPACE {
+static const char* ArrayFeatureExtractor_ver1_doc = R"DOC(
+    Select elements of the input tensor based on the indices passed.<br>
+    The indices are applied to the last axes of the tensor.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    ArrayFeatureExtractor,
+    1,
+    OpSchema()
+        .SetDoc(ArrayFeatureExtractor_ver1_doc)
+        .Input(0, "X", "Data to be selected", "T")
+        .Input(
+            1,
+            "Y",
+            "The indices, based on 0 as the first index of any dimension.",
+            "tensor(int64)")
+        .Output(0, "Z", "Selected output data as an array", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)",
+             "tensor(string)"},
+            "The input must be a tensor of a numeric type or string. The output will be of the same tensor type."));
+
+static const char* Binarizer_ver1_doc = R"DOC(
+    Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    Binarizer,
+    1,
+    OpSchema()
+        .SetDoc(Binarizer_ver1_doc)
+        .Input(0, "X", "Data to be binarized", "T")
+        .Output(0, "Y", "Binarized output data", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input must be a tensor of a numeric type. The output will be of the same tensor type.")
+        .Attr(
+            "threshold",
+            "Values greater than this are mapped to 1, others to 0.",
+            AttributeProto::FLOAT,
+            0.f));
+
+static const char* CastMap_ver1_doc = R"DOC(
+    Converts a map to a tensor.<br>The map key must be an int64 and the values will be ordered
+    in ascending order based on this key.<br>The operator supports dense packing or sparse packing.
+    If using sparse packing, the key cannot exceed the max_map-1 value.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    CastMap,
+    1,
+    OpSchema()
+        .SetDoc(CastMap_ver1_doc)
+        .Input(0, "X", "The input map that is to be cast to a tensor", "T1")
+        .Output(
+            0,
+            "Y",
+            "A tensor representing the same data as the input map, ordered by their keys",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"map(int64, string)", "map(int64, float)"},
+            "The input must be an integer map to either string or float.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(float)", "tensor(int64)"},
+            "The output is a 1-D tensor of string, float, or integer.")
+        .Attr(
+            "cast_to",
+            "A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.",
+            AttributeProto::STRING,
+            std::string("TO_FLOAT"))
+        .Attr(
+            "map_form",
+            "Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'.",
+            AttributeProto::STRING,
+            std::string("DENSE"))
+        .Attr(
+            "max_map",
+            "If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto cast_to_attr = ctx.getAttribute("cast_to");
+          auto output_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (nullptr == cast_to_attr) {
+            output_type->set_elem_type(TensorProto::FLOAT);
+            return;
+          }
+          auto& cast_to = cast_to_attr->s();
+          if (0 == cast_to.compare("TO_FLOAT")) {
+            output_type->set_elem_type(TensorProto::FLOAT);
+          } else if (0 == cast_to.compare("TO_INT64")) {
+            output_type->set_elem_type(TensorProto::INT64);
+          } else if (0 == cast_to.compare("TO_STRING")) {
+            output_type->set_elem_type(TensorProto::STRING);
+          }
+        }));
+
+static const char* CategoryMapper_ver1_doc = R"DOC(
+    Converts strings to integers and vice versa.<br>
+    Two sequences of equal length are used to map between integers and strings,
+    with strings and integers at the same index detailing the mapping.<br>
+    Each operator converts either integers to strings or strings to integers, depending
+    on which default value attribute is provided. Only one default value attribute
+    should be defined.<br>
+    If the string default value is set, it will convert integers to strings.
+    If the int default value is set, it will convert strings to integers.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    CategoryMapper,
+    1,
+    OpSchema()
+        .SetDoc(CategoryMapper_ver1_doc)
+        .Input(0, "X", "Input data", "T1")
+        .Output(
+            0,
+            "Y",
+            "Output data. If strings are input, the output values are integers, and vice versa.",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(string)", "tensor(int64)"},
+            "The input must be a tensor of strings or integers, either [N,C] or [C].")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)"},
+            "The output is a tensor of strings or integers. Its shape will be the same as the input shape.")
+        .Attr(
+            "cats_strings",
+            "The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "cats_int64s",
+            "The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "default_string",
+            "A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.",
+            AttributeProto::STRING,
+            std::string("_Unused"))
+        .Attr(
+            "default_int64",
+            "An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto input_elem_type = ctx.getInputType(0)->tensor_type().elem_type();
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (TensorProto::STRING == input_elem_type) {
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          } else if (TensorProto::INT64 == input_elem_type) {
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          }
+        }));
+
+static const char* DictVectorizer_ver1_doc = R"DOC(
+    Uses an index mapping to convert a dictionary to an array.<br>
+    Given a dictionary, each key is looked up in the vocabulary attribute corresponding to
+    the key type. The index into the vocabulary array at which the key is found is then
+    used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.<br>
+    The key type of the input map must correspond to the element type of the defined vocabulary attribute.
+    Therefore, the output array will be equal in length to the index mapping vector parameter.
+    All keys in the input dictionary must be present in the index mapping vector.
+    For each item in the input dictionary, insert its value in the output array.
+    Any keys not present in the input dictionary, will be zero in the output array.<br>
+    For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``,
+    then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``.
+    )DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    DictVectorizer,
+    1,
+    OpSchema()
+        .SetDoc(DictVectorizer_ver1_doc)
+        .Input(0, "X", "A dictionary.", "T1")
+        .Output(
+            0,
+            "Y",
+            "A 1-D tensor holding values from the input dictionary.",
+            "T2")
+        .TypeConstraint(
+            "T1",
+            {"map(string, int64)",
+             "map(int64, string)",
+             "map(int64, float)",
+             "map(int64, double)",
+             "map(string, float)",
+             "map(string, double)"},
+            "The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)",
+             "tensor(float)",
+             "tensor(double)",
+             "tensor(string)"},
+            "The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.")
+        .Attr(
+            "string_vocabulary",
+            "A string vocabulary array.<br>One and only one of the vocabularies must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "int64_vocabulary",
+            "An integer vocabulary array.<br>One and only one of the vocabularies must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto input_elem_type = ctx.getInputType(0)
+                                     ->map_type()
+                                     .value_type()
+                                     .tensor_type()
+                                     .elem_type();
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          output_elem_type->set_elem_type(input_elem_type);
+        }));
+
+static const char* FeatureVectorizer_ver1_doc = R"DOC(
+    Concatenates input tensors into one continuous output.<br>
+    All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C].
+    Inputs are copied to the output maintaining the order of the input arguments.<br>
+    All inputs must be integers or floats, while the output will be all floating point values.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    FeatureVectorizer,
+    1,
+    OpSchema()
+        .SetDoc(FeatureVectorizer_ver1_doc)
+        .Input(
+            0,
+            "X",
+            "An ordered collection of tensors, all with the same element type.",
+            "T1",
+            OpSchema::Variadic)
+        .Output(
+            0,
+            "Y",
+            "The output array, elements ordered as the inputs.",
+            "tensor(float)")
+        .TypeConstraint(
+            "T1",
+            {"tensor(int32)",
+             "tensor(int64)",
+             "tensor(float)",
+             "tensor(double)"},
+            "The input type must be a tensor of a numeric type.")
+        .Attr(
+            "inputdimensions",
+            "The size of each input in the input list",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE));
+
+static const char* Imputer_ver1_doc = R"DOC(
+    Replaces inputs that equal one value with another, leaving all other elements alone.<br>
+    This operator is typically used to replace missing values in situations where they have a canonical
+    representation, such as -1, 0, NaN, or some extreme value.<br>
+    One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor
+    holds floats, integers if the input tensor holds integers. The imputed values must all fit within the
+    width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,
+    which one depends on whether floats or integers are being processed.<br>
+    The imputed_value attribute length can be 1 element, or it can have one element per input feature.<br>In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    Imputer,
+    1,
+    OpSchema()
+        .SetDoc(Imputer_ver1_doc)
+        .Input(0, "X", "Data to be processed.", "T")
+        .Output(0, "Y", "Imputed output data", "T")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.")
+        .Attr(
+            "imputed_value_floats",
+            "Value(s) to change to",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "replaced_value_float",
+            "A value that needs replacing.",
+            AttributeProto::FLOAT,
+            0.f)
+        .Attr(
+            "imputed_value_int64s",
+            "Value(s) to change to.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "replaced_value_int64",
+            "A value that needs replacing.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0)));
+
+static const char* LabelEncoder_ver2_doc = R"DOC(
+    Maps each element in the input tensor to another value.<br>
+    The mapping is determined by the two parallel attributes, 'keys_*' and
+    'values_*' attribute. The i-th value in the specified 'keys_*' attribute
+    would be mapped to the i-th value in the specified 'values_*' attribute. It
+    implies that input's element type and the element type of the specified
+    'keys_*' should be identical while the output type is identical to the
+    specified 'values_*' attribute. If an input element can not be found in the
+    specified 'keys_*' attribute, the 'default_*' that matches the specified
+    'values_*' attribute may be used as its output value.<br>
+    Let's consider an example which maps a string tensor to an integer tensor.
+    Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6],
+    and 'default_int64' is '-1'.  The input ["Dori", "Amy", "Amy", "Sally",
+    "Sally"] would be mapped to [-1, 5, 5, 6, 6].<br>
+    Since this operator is an one-to-one mapping, its input and output shapes
+    are the same. Notice that only one of 'keys_*'/'values_*' can be set.<br>
+    For key look-up, bit-wise comparison is used so even a float NaN can be
+    mapped to a value in 'values_*' attribute.<br>
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    LabelEncoder,
+    2,
+    OpSchema()
+        .SetDoc(LabelEncoder_ver2_doc)
+        .Input(0, "X", "Input data. It can be either tensor or scalar.", "T1")
+        .Output(0, "Y", "Output data.", "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(string)", "tensor(int64)", "tensor(float)"},
+            "The input type is a tensor of any shape.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)", "tensor(float)"},
+            "Output type is determined by the specified 'values_*' attribute.")
+        .Attr(
+            "keys_strings",
+            "A list of strings. One and only one of 'keys_*'s should be set.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr("keys_int64s", "A list of ints.", AttributeProto::INTS, OPTIONAL_VALUE)
+        .Attr(
+            "keys_floats",
+            "A list of floats.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "values_strings",
+            "A list of strings. One and only one of 'value_*'s should be set.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "values_int64s",
+            "A list of ints.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "values_floats",
+            "A list of floats.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "default_string",
+            "A string.",
+            AttributeProto::STRING,
+            std::string("_Unused"))
+        .Attr(
+            "default_int64",
+            "An integer.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .Attr("default_float", "A float.", AttributeProto::FLOAT, -0.f)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          // Label encoder is one-to-one mapping.
+          if (ctx.getNumInputs() != 1) {
+            fail_shape_inference("Label encoder has only one input.");
+          }
+          if (ctx.getNumOutputs() != 1) {
+            fail_shape_inference("Label encoder has only one output.");
+          }
+
+          // Load all key_* attributes.
+          std::vector<std::string> keys_strings;
+          bool keys_strings_result =
+              getRepeatedAttribute(ctx, "keys_strings", keys_strings);
+          std::vector<int64_t> keys_int64s;
+          bool keys_int64s_result =
+              getRepeatedAttribute(ctx, "keys_int64s", keys_int64s);
+          std::vector<float> keys_floats;
+          bool keys_floats_result =
+              getRepeatedAttribute(ctx, "keys_floats", keys_floats);
+
+          // Check if only one keys_* attribute is set.
+          if (static_cast<int>(keys_strings_result) +
+                  static_cast<int>(keys_int64s_result) +
+                  static_cast<int>(keys_floats_result) != 1) {
+                fail_shape_inference("Only one of keys_*'s can be set in label encoder.");
+            }
+
+          // Check if the specified keys_* matches input type.
+          auto input_elem_type = ctx.getInputType(0)->tensor_type().elem_type();
+          if (keys_strings_result && input_elem_type != TensorProto::STRING) {
+            fail_shape_inference("Input type is not string tensor but key_strings is set");
+          }
+          if (keys_int64s_result && input_elem_type != TensorProto::INT64) {
+            fail_shape_inference("Input type is not int64 tensor but keys_int64s is set");
+          }
+          if (keys_floats_result && input_elem_type != TensorProto::FLOAT) {
+            fail_shape_inference("Input type is not float tensor but keys_floats is set");
+          }
+
+          // Load all values_* attributes.
+          std::vector<std::string> values_strings;
+          bool values_strings_result =
+              getRepeatedAttribute(ctx, "values_strings", values_strings);
+          std::vector<int64_t> values_int64s;
+          bool values_int64s_result =
+              getRepeatedAttribute(ctx, "values_int64s", values_int64s);
+          std::vector<float> values_floats;
+          bool values_floats_result =
+              getRepeatedAttribute(ctx, "values_floats", values_floats);
+
+          // Check if only one values_* attribute is set.
+          if (static_cast<int>(values_strings_result) +
+                  static_cast<int>(values_int64s_result) +
+                  static_cast<int>(values_floats_result) != 1) {
+                fail_shape_inference("Only one of values_*'s can be set in label encoder.");
+            }
+
+          // Assign output type based on the specified values_*.
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (values_strings_result)
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          if (values_int64s_result)
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          if (values_floats_result)
+            output_elem_type->set_elem_type(TensorProto::FLOAT);
+
+          // Input and output shapes are the same.
+          propagateShapeFromInputToOutput(ctx, 0, 0);
+
+        }));
+
+static const char* LinearClassifier_ver1_doc = R"DOC(
+    Linear classifier
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    LinearClassifier,
+    1,
+    OpSchema()
+        .SetDoc(LinearClassifier_ver1_doc)
+        .Input(0, "X", "Data to be classified.", "T1")
+        .Output(0, "Y", "Classification outputs (one class per example).", "T2")
+        .Output(
+            1,
+            "Z",
+            "Classification scores ([N,E] - one score for each class and example",
+            "tensor(float)")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)"},
+            "The output will be a tensor of strings or integers.")
+        .Attr(
+            "coefficients",
+            "A collection of weights of the model(s).",
+            AttributeProto::FLOATS)
+        .Attr(
+            "intercepts",
+            "A collection of intercepts.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "multi_class",
+            "Indicates whether to do OvR or multinomial (0=OvR is the default).",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "classlabels_strings",
+            "Class labels when using string labels. One and only one 'classlabels' attribute must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_ints",
+            "Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          std::vector<std::string> label_strs;
+          std::vector<int64_t> label_ints;
+
+          auto labels_strings_present =
+              getRepeatedAttribute(ctx, "classlabels_strings", label_strs);
+          bool using_strings = (labels_strings_present && !label_strs.empty());
+
+          if (!using_strings) {
+            getRepeatedAttribute(ctx, "classlabels_ints", label_ints);
+          }
+
+          // Type inference
+          auto* output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (using_strings) {
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          } else {
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          }
+
+          // second output is always of float type
+          ctx.getOutputType(1)->mutable_tensor_type()->set_elem_type(
+              TensorProto::FLOAT);
+
+          // Shape/Rank inference begins
+
+          // establish the number of classes
+          std::vector<float> intercepts;
+          getRepeatedAttribute(ctx, "intercepts", intercepts);
+          int class_count = static_cast<int>(intercepts.size());
+          if (intercepts.size() == 1 &&
+              ((using_strings && label_strs.size() == 2) ||
+               (!using_strings && label_ints.size() == 2))) {
+            class_count = 2;
+          }
+
+          TensorShapeProto_Dimension batch_size_dim, class_count_dim;
+          class_count_dim.set_dim_value(class_count);
+
+          if (hasNInputShapes(ctx, 1)) {
+            const auto& input_shape =
+                ctx.getInputType(0)->tensor_type().shape();
+            const auto input_rank = input_shape.dim_size();
+            if (input_rank == 1) {
+              // if input_rank is 1, batch_size is interpreted to be 1
+              batch_size_dim.set_dim_value(1);
+            } else if (input_rank == 2) {
+              batch_size_dim = input_shape.dim((int)0);
+            } else {
+              fail_shape_inference("Input's shape should be 1D or 2D");
+            }
+          }
+
+          updateOutputShape(ctx, 0, {batch_size_dim});
+          updateOutputShape(ctx, 1, {batch_size_dim, class_count_dim});
+        }));
+
+static const char* LinearRegressor_ver1_doc = R"DOC(
+    Generalized linear regression evaluation.<br>
+    If targets is set to 1 (default) then univariate regression is performed.<br>
+    If targets is set to M then M sets of coefficients must be passed in as a sequence
+    and M results will be output for each input n in N.<br>
+    The coefficients array is of length n, and the coefficients for each target are contiguous.
+    Intercepts are optional but if provided must match the number of targets.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    LinearRegressor,
+    1,
+    OpSchema()
+        .SetDoc(LinearRegressor_ver1_doc)
+        .Input(0, "X", "Data to be regressed.", "T")
+        .Output(
+            0,
+            "Y",
+            "Regression outputs (one per target, per example).",
+            "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input must be a tensor of a numeric type.")
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            "coefficients",
+            "Weights of the model(s).",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "intercepts",
+            "Weights of the intercepts, if used.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "targets",
+            "The total number of regression targets, 1 if not defined.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1)));
+
+static const char* Normalizer_ver1_doc = R"DOC(
+    Normalize the input.  There are three normalization modes, which have the corresponding formulas,
+    defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':<br>
+<br>
+    Max: Y = X / max(X)<br>
+    L1:  Y = X / sum(X)<br>
+    L2:  Y = sqrt(X^2 / sum(X^2)}<br>
+    In all modes, if the divisor is zero, Y == X.
+<br>
+    For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row
+    of the batch is normalized independently.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    Normalizer,
+    1,
+    OpSchema()
+        .SetDoc(Normalizer_ver1_doc)
+        .Input(
+            0,
+            "X",
+            "Data to be encoded, a tensor of shape [N,C] or [C]",
+            "T")
+        .Output(0, "Y", "Encoded output data", "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input must be a tensor of a numeric type.")
+        .Attr(
+            "norm",
+            "One of 'MAX,' 'L1,' 'L2'",
+            AttributeProto::STRING,
+            std::string("MAX")));
+
+static const char* OneHotEncoder_ver1_doc = R"DOC(
+    Replace each input element with an array of ones and zeros, where a single
+    one is placed at the index of the category that was passed in. The total category count
+    will determine the size of the extra dimension of the output array Y.<br>
+    For example, if we pass a tensor with a single value of 4, and a category count of 8,
+    the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.<br>
+    This operator assumes every input feature is from the same set of categories.<br>
+    If the input is a tensor of float, int32, or double, the data will be cast
+    to integers and the cats_int64s category list will be used for the lookups.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    OneHotEncoder,
+    1,
+    OpSchema()
+        .SetDoc(OneHotEncoder_ver1_doc)
+        .Input(0, "X", "Data to be encoded.", "T")
+        .Output(
+            0,
+            "Y",
+            "Encoded output data, having one more dimension than X.",
+            "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(string)",
+             "tensor(int64)",
+             "tensor(int32)",
+             "tensor(float)",
+             "tensor(double)"},
+            "The input must be a tensor of a numeric type.")
+        .Attr(
+            "cats_int64s",
+            "List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "cats_strings",
+            "List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "zeros",
+            "If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.",
+            AttributeProto::INT,
+            static_cast<int64_t>(1)));
+
+static const char* Scaler_ver1_doc = R"DOC(
+    Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    Scaler,
+    1,
+    OpSchema()
+        .SetDoc(Scaler_ver1_doc)
+        .Input(0, "X", "Data to be scaled.", "T")
+        .Output(0, "Y", "Scaled output data.", "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input must be a tensor of a numeric type.")
+        .Attr(
+            "offset",
+            "First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "scale",
+            "Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset'",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE));
+
+static const char* SVMClassifier_ver1_doc = R"DOC(
+    Support Vector Machine classifier
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    SVMClassifier,
+    1,
+    OpSchema()
+        .SetDoc(SVMClassifier_ver1_doc)
+        .Input(0, "X", "Data to be classified.", "T1")
+        .Output(0, "Y", "Classification outputs (one class per example).", "T2")
+        .Output(
+            1,
+            "Z",
+            "Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.",
+            "tensor(float)")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input must be a tensor of a numeric type, either [C] or [N,C].")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)"},
+            "The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.")
+        .Attr(
+            "kernel_type",
+            "The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.",
+            AttributeProto::STRING,
+            std::string("LINEAR"))
+        .Attr(
+            "kernel_params",
+            "List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr("vectors_per_class", "", AttributeProto::INTS, OPTIONAL_VALUE)
+        .Attr("support_vectors", "", AttributeProto::FLOATS, OPTIONAL_VALUE)
+        .Attr("coefficients", "", AttributeProto::FLOATS, OPTIONAL_VALUE)
+        .Attr(
+            "prob_a",
+            "First set of probability coefficients.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "prob_b",
+            "Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr("rho", "", AttributeProto::FLOATS, OPTIONAL_VALUE)
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            "classlabels_strings",
+            "Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_ints",
+            "Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          std::vector<std::string> label_strs;
+          auto result =
+              getRepeatedAttribute(ctx, "classlabels_strings", label_strs);
+          bool using_strings = (result && !label_strs.empty());
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (using_strings) {
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          } else {
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          }
+        }));
+
+static const char* SVMRegressor_ver1_doc = R"DOC(
+    Support Vector Machine regression prediction and one-class SVM anomaly detection.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    SVMRegressor,
+    1,
+    OpSchema()
+        .SetDoc(SVMRegressor_ver1_doc)
+        .Input(0, "X", "Data to be regressed.", "T")
+        .Output(
+            0,
+            "Y",
+            "Regression outputs (one score per target per example).",
+            "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input type must be a tensor of a numeric type, either [C] or [N,C].")
+        .Attr(
+            "kernel_type",
+            "The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.",
+            AttributeProto::STRING,
+            std::string("LINEAR"))
+        .Attr(
+            "kernel_params",
+            "List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "support_vectors",
+            "Chosen support vectors",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "one_class",
+            "Flag indicating whether the regression is a one-class SVM or not.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "coefficients",
+            "Support vector coefficients.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "n_supports",
+            "The number of support vectors.",
+            AttributeProto::INT,
+            static_cast<int64_t>(0))
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr("rho", "", AttributeProto::FLOATS, OPTIONAL_VALUE));
+
+static const char* TreeEnsembleClassifier_ver3_doc = R"DOC(
+    Tree Ensemble classifier. Returns the top class for each of N inputs.<br>
+    The attributes named 'nodes_X' form a sequence of tuples, associated by
+    index into the sequences, which must all be of equal length. These tuples
+    define the nodes.<br>
+    Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.
+    A leaf may have multiple votes, where each vote is weighted by
+    the associated class_weights index.<br>
+    One and only one of classlabels_strings or classlabels_int64s
+    will be defined. The class_ids are indices into this list.
+    All fields ending with <i>_as_tensor</i> can be used instead of the
+    same parameter without the suffix if the element type is double and not float.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    TreeEnsembleClassifier,
+    3,
+    OpSchema()
+        .SetDoc(TreeEnsembleClassifier_ver3_doc)
+        .Input(0, "X", "Input of shape [N,F]", "T1")
+        .Output(0, "Y", "N, Top class for each point", "T2")
+        .Output(
+            1,
+            "Z",
+            "The class score for each class, for each point, a tensor of shape [N,E].",
+            "tensor(float)")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input type must be a tensor of a numeric type.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)"},
+            "The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.")
+        .Attr(
+            "nodes_treeids",
+            "Tree id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_nodeids",
+            "Node id for each node. Ids may restart at zero for each tree, but it not required to.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_featureids",
+            "Feature id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_values",
+            "Thresholds to do the splitting on for each node.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_values_as_tensor",
+            "Thresholds to do the splitting on for each node.",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_hitrates",
+            "Popularity of each node, used for performance and may be omitted.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_hitrates_as_tensor",
+            "Popularity of each node, used for performance and may be omitted.",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_modes",
+            "The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_truenodeids",
+            "Child node if expression is true.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_falsenodeids",
+            "Child node if expression is false.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_missing_value_tracks_true",
+            "For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_treeids",
+            "The id of the tree that this node is in.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_nodeids",
+            "node id that this weight is for.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_ids",
+            "The index of the class list that each weight is for.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_weights",
+            "The weight for the class in class_id.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_weights_as_tensor",
+            "The weight for the class in class_id.",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_strings",
+            "Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_int64s",
+            "Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            "base_values",
+            "Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "base_values_as_tensor",
+            "Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          std::vector<std::string> label_strs;
+          auto result =
+              getRepeatedAttribute(ctx, "classlabels_strings", label_strs);
+          bool using_strings = (result && !label_strs.empty());
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (using_strings) {
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          } else {
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          }
+
+          auto* nodes_values = ctx.getAttribute("nodes_values");
+          auto* nodes_values_as_tensor = ctx.getAttribute("nodes_values_as_tensor");
+          auto* nodes_hitrates = ctx.getAttribute("nodes_hitrates");
+          auto* nodes_hitrates_as_tensor = ctx.getAttribute("nodes_hitrates_as_tensor");
+          auto* class_weights = ctx.getAttribute("class_weights");
+          auto* class_weights_as_tensor = ctx.getAttribute("class_weights_as_tensor");
+          auto* base_values = ctx.getAttribute("base_values");
+          auto* base_values_as_tensor = ctx.getAttribute("base_values_as_tensor");
+
+          if (nullptr != nodes_values && nullptr != nodes_values_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.");
+          }
+          if (nullptr != nodes_hitrates && nullptr != nodes_hitrates_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.");
+          }
+          if (nullptr != class_weights && nullptr != class_weights_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.");
+          }
+          if (nullptr != base_values && nullptr != base_values_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.");
+          }
+        }));
+
+static const char* TreeEnsembleRegressor_ver3_doc = R"DOC(
+    Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
+    All args with nodes_ are fields of a tuple of tree nodes, and
+    it is assumed they are the same length, and an index i will decode the
+    tuple across these inputs.  Each node id can appear only once
+    for each tree id.<br>
+    All fields prefixed with target_ are tuples of votes at the leaves.<br>
+    A leaf may have multiple votes, where each vote is weighted by
+    the associated target_weights index.<br>
+    All fields ending with <i>_as_tensor</i> can be used instead of the
+    same parameter without the suffix if the element type is double and not float.
+    All trees must have their node ids start at 0 and increment by 1.<br>
+    Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    TreeEnsembleRegressor,
+    3,
+    OpSchema()
+        .SetDoc(TreeEnsembleRegressor_ver3_doc)
+        .Input(0, "X", "Input of shape [N,F]", "T")
+        .Output(0, "Y", "N classes", "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input type must be a tensor of a numeric type.")
+        .Attr(
+            "nodes_treeids",
+            "Tree id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_nodeids",
+            "Node id for each node. Node ids must restart at zero for each tree and increase sequentially.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_featureids",
+            "Feature id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_values",
+            "Thresholds to do the splitting on for each node.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_values_as_tensor",
+            "Thresholds to do the splitting on for each node.",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_hitrates",
+            "Popularity of each node, used for performance and may be omitted.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_hitrates_as_tensor",
+            "Popularity of each node, used for performance and may be omitted.",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_modes",
+            "The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_truenodeids",
+            "Child node if expression is true",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_falsenodeids",
+            "Child node if expression is false",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_missing_value_tracks_true",
+            "For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_treeids",
+            "The id of the tree that each node is in.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_nodeids",
+            "The node id of each weight",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_ids",
+            "The index of the target that each weight is for",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_weights",
+            "The weight for each target",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_weights_as_tensor",
+            "The weight for each target",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .Attr(
+            "n_targets",
+            "The total number of targets.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            "aggregate_function",
+            "Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'",
+            AttributeProto::STRING,
+            std::string("SUM"))
+        .Attr(
+            "base_values",
+            "Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "base_values_as_tensor",
+            "Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)",
+            AttributeProto::TENSOR,
+            OPTIONAL_VALUE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto* nodes_values = ctx.getAttribute("nodes_values");
+          auto* nodes_values_as_tensor = ctx.getAttribute("nodes_values_as_tensor");
+          auto* nodes_hitrates = ctx.getAttribute("nodes_hitrates");
+          auto* nodes_hitrates_as_tensor = ctx.getAttribute("nodes_hitrates_as_tensor");
+          auto* target_weights = ctx.getAttribute("target_weights");
+          auto* target_weights_as_tensor = ctx.getAttribute("target_weights_as_tensor");
+          auto* base_values = ctx.getAttribute("base_values");
+          auto* base_values_as_tensor = ctx.getAttribute("base_values_as_tensor");
+
+          if (nullptr != nodes_values && nullptr != nodes_values_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.");
+          }
+          if (nullptr != nodes_hitrates && nullptr != nodes_hitrates_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.");
+          }
+          if (nullptr != target_weights && nullptr != target_weights_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.");
+          }
+          if (nullptr != base_values && nullptr != base_values_as_tensor) {
+            fail_shape_inference(
+                "Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.");
+          }
+        }));
+
+    static const char* ZipMap_ver1_doc = R"DOC(
+    Creates a map from the input and the attributes.<br>
+    The values are provided by the input tensor, while the keys are specified by the attributes.
+    Must provide keys in either classlabels_strings or classlabels_int64s (but not both).<br>
+    The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.<br>
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    ZipMap,
+    1,
+    OpSchema()
+        .SetDoc(ZipMap_ver1_doc)
+        .Input(0, "X", "The input values", "tensor(float)")
+        .Output(0, "Z", "The output map", "T")
+        .TypeConstraint(
+            "T",
+            {"seq(map(string, float))", "seq(map(int64, float))"},
+            "The output will be a sequence of string or integer maps to float.")
+        .Attr(
+            "classlabels_strings",
+            "The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_int64s",
+            "The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          std::vector<std::string> classlabels_strings;
+          bool result = getRepeatedAttribute(
+              ctx, "classlabels_strings", classlabels_strings);
+          auto output_map_type = ctx.getOutputType(0)
+                                     ->mutable_sequence_type()
+                                     ->mutable_elem_type()
+                                     ->mutable_map_type();
+          output_map_type->mutable_value_type()
+              ->mutable_tensor_type()
+              ->set_elem_type(TensorProto::FLOAT);
+          if (result && !classlabels_strings.empty()) {
+            output_map_type->set_key_type(TensorProto::STRING);
+          }
+          std::vector<int64_t> classlabels_int64s;
+          result = getRepeatedAttribute(
+              ctx, "classlabels_int64s", classlabels_int64s);
+          if (result && !classlabels_int64s.empty()) {
+            output_map_type->set_key_type(TensorProto::INT64);
+          }
+        }));
+
+} // namespace ONNX_NAMESPACE
+#endif
diff --git a/third_party/onnx/onnx/defs/traditionalml/old.cc b/third_party/onnx/onnx/defs/traditionalml/old.cc
new file mode 100644
index 0000000000..6b78929a60
--- /dev/null
+++ b/third_party/onnx/onnx/defs/traditionalml/old.cc
@@ -0,0 +1,310 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include "onnx/defs/schema.h"
+
+#ifdef ONNX_ML
+namespace ONNX_NAMESPACE {
+static const char* LabelEncoder_ver1_doc = R"DOC(
+    Converts strings to integers and vice versa.<br>
+    If the string default value is set, it will convert integers to strings.
+    If the int default value is set, it will convert strings to integers.<br>
+    Each operator converts either integers to strings or strings to integers, depending
+    on which default value attribute is provided. Only one default value attribute
+    should be defined.<br>
+    When converting from integers to strings, the string is fetched from the
+    'classes_strings' list, by simple indexing.<br>
+    When converting from strings to integers, the string is looked up in the list
+    and the index at which it is found is used as the converted value.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    LabelEncoder,
+    1,
+    OpSchema()
+        .SetDoc(LabelEncoder_ver1_doc)
+        .Input(0, "X", "Input data.", "T1")
+        .Output(0, "Y", "Output data. If strings are input, the output values are integers, and vice versa.", "T2")
+        .TypeConstraint(
+            "T1",
+            {"tensor(string)", "tensor(int64)"},
+            "The input type must be a tensor of integers or strings, of any shape.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)"},
+            "The output type will be a tensor of strings or integers, and will have the same shape as the input.")
+        .Attr(
+            "classes_strings",
+            "A list of labels.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "default_int64",
+            "An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.",
+            AttributeProto::INT,
+            static_cast<int64_t>(-1))
+        .Attr(
+            "default_string",
+            "A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.",
+            AttributeProto::STRING,
+            std::string("_Unused"))
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          auto input_elem_type = ctx.getInputType(0)->tensor_type().elem_type();
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (TensorProto::STRING == input_elem_type) {
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          } else if (TensorProto::INT64 == input_elem_type) {
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          }
+        }));
+
+static const char* TreeEnsembleClassifier_ver1_doc = R"DOC(
+    Tree Ensemble classifier.  Returns the top class for each of N inputs.<br>
+    The attributes named 'nodes_X' form a sequence of tuples, associated by
+    index into the sequences, which must all be of equal length. These tuples
+    define the nodes.<br>
+    Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.
+    A leaf may have multiple votes, where each vote is weighted by
+    the associated class_weights index.<br>
+    One and only one of classlabels_strings or classlabels_int64s
+    will be defined. The class_ids are indices into this list.
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    TreeEnsembleClassifier,
+    1,
+    OpSchema()
+        .SetDoc(TreeEnsembleClassifier_ver1_doc)
+        .Input(0, "X", "Input of shape [N,F]", "T1")
+        .Output(0, "Y", "N, Top class for each point", "T2")
+        .Output(
+            1,
+            "Z",
+            "The class score for each class, for each point, a tensor of shape [N,E].",
+            "tensor(float)")
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input type must be a tensor of a numeric type.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(string)", "tensor(int64)"},
+            "The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.")
+        .Attr(
+            "nodes_treeids",
+            "Tree id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_nodeids",
+            "Node id for each node. Ids may restart at zero for each tree, but it not required to.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_featureids",
+            "Feature id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_values",
+            "Thresholds to do the splitting on for each node.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_hitrates",
+            "Popularity of each node, used for performance and may be omitted.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_modes",
+            "The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_truenodeids",
+            "Child node if expression is true.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_falsenodeids",
+            "Child node if expression is false.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_missing_value_tracks_true",
+            "For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_treeids",
+            "The id of the tree that this node is in.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_nodeids",
+            "node id that this weight is for.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_ids",
+            "The index of the class list that each weight is for.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "class_weights",
+            "The weight for the class in class_id.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_strings",
+            "Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "classlabels_int64s",
+            "Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            "base_values",
+            "Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+          std::vector<std::string> label_strs;
+          auto result =
+              getRepeatedAttribute(ctx, "classlabels_strings", label_strs);
+          bool using_strings = (result && !label_strs.empty());
+          auto output_elem_type = ctx.getOutputType(0)->mutable_tensor_type();
+          if (using_strings) {
+            output_elem_type->set_elem_type(TensorProto::STRING);
+          } else {
+            output_elem_type->set_elem_type(TensorProto::INT64);
+          }
+        }));
+
+static const char* TreeEnsembleRegressor_ver1_doc = R"DOC(
+    Tree Ensemble regressor.  Returns the regressed values for each input in N.<br>
+    All args with nodes_ are fields of a tuple of tree nodes, and
+    it is assumed they are the same length, and an index i will decode the
+    tuple across these inputs.  Each node id can appear only once
+    for each tree id.<br>
+    All fields prefixed with target_ are tuples of votes at the leaves.<br>
+    A leaf may have multiple votes, where each vote is weighted by
+    the associated target_weights index.<br>
+    All trees must have their node ids start at 0 and increment by 1.<br>
+    Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
+)DOC";
+
+ONNX_ML_OPERATOR_SET_SCHEMA(
+    TreeEnsembleRegressor,
+    1,
+    OpSchema()
+        .SetDoc(TreeEnsembleRegressor_ver1_doc)
+        .Input(0, "X", "Input of shape [N,F]", "T")
+        .Output(0, "Y", "N classes", "tensor(float)")
+        .TypeConstraint(
+            "T",
+            {"tensor(float)",
+             "tensor(double)",
+             "tensor(int64)",
+             "tensor(int32)"},
+            "The input type must be a tensor of a numeric type.")
+        .Attr(
+            "nodes_treeids",
+            "Tree id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_nodeids",
+            "Node id for each node. Node ids must restart at zero for each tree and increase sequentially.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_featureids",
+            "Feature id for each node.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_values",
+            "Thresholds to do the splitting on for each node.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_hitrates",
+            "Popularity of each node, used for performance and may be omitted.",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_modes",
+            "The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_truenodeids",
+            "Child node if expression is true",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_falsenodeids",
+            "Child node if expression is false",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "nodes_missing_value_tracks_true",
+            "For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_treeids",
+            "The id of the tree that each node is in.",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_nodeids",
+            "The node id of each weight",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_ids",
+            "The index of the target that each weight is for",
+            AttributeProto::INTS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "target_weights",
+            "The weight for each target",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "n_targets",
+            "The total number of targets.",
+            AttributeProto::INT,
+            OPTIONAL_VALUE)
+        .Attr(
+            "post_transform",
+            "Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'",
+            AttributeProto::STRING,
+            std::string("NONE"))
+        .Attr(
+            "aggregate_function",
+            "Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'",
+            AttributeProto::STRING,
+            std::string("SUM"))
+        .Attr(
+            "base_values",
+            "Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)",
+            AttributeProto::FLOATS,
+            OPTIONAL_VALUE));
+
+} // namespace ONNX_NAMESPACE
+#endif
diff --git a/third_party/onnx/onnx/defs/training/defs.cc b/third_party/onnx/onnx/defs/training/defs.cc
new file mode 100644
index 0000000000..32428affb9
--- /dev/null
+++ b/third_party/onnx/onnx/defs/training/defs.cc
@@ -0,0 +1,667 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#include <algorithm>
+#include <cmath>
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+
+static const char* Gradient_ver1_doc = R"DOC(
+Gradient operator computes the partial derivatives of a specific tensor w.r.t.
+some other tensors. This operator is widely used in gradient-based training
+algorithms. To illustrate its use, let's consider a computation graph,
+
+```
+X -----.
+       |
+       v
+W --> Conv --> H --> Gemm --> Y
+                      ^
+                      |
+                      Z
+```
+
+, where W and Z are trainable tensors. Note that operators' attributes are
+omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of
+Y with respect to W (Z). The user can compute gradient by inserting Gradient
+operator to form another graph shown below.
+
+```
+W --> Conv --> H --> Gemm --> Y
+|      ^              ^
+|      |              |
+|      X              Z
+|      |              |
+|      |   .----------'
+|      |   |  (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in
+|      |   |   "xs" followed by "zs")
+|      v   v
+'---> Gradient(xs=["W", "Z"], zs=["X"], y="Y")
+       |   |
+       |   '-----------------------------------> dY/dW (1st output of Gradient)
+       |
+       '---------------------------------------> dY/dZ (2nd output of Gradient)
+```
+
+By definition, the tensor "y" is a function of independent variables in "xs"
+and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable
+variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H"
+cannot appear in "xs" and "zs". The reason is that "H" can be determined by
+tensors "W" and "X" and therefore "H" is not an independent variable.
+
+All outputs are optional. If needed, for example, user can assign an empty
+string to the 1st output name of that Gradient to skip the generation of dY/dW.
+Note that the concept of optional outputs can also be found in ONNX's RNN, GRU,
+and LSTM.
+
+Gradient operator can compute derivative against intermediate tensors. For
+example, the gradient of Y with respect to H can be done via
+
+```
+W --> Conv --> H --> Gemm --> Y
+       ^       |      ^
+       |       |      |
+       X       |      Z
+       .-------'      |
+       |   .----------'
+       |   | (H/Z is the 1st/2nd input of Gradient as shown in "xs")
+       v   v
+      Gradient(xs=["H", "Z"], y="Y")
+       |   |
+       |   '-----------------------------------> dY/dH (1st output of Gradient)
+       |
+       '---------------------------------------> dY/dZ (2nd output of Gradient)
+```
+
+It is possible to represent high-order differentiation using Gradient operators.
+For example, given the following linear model:
+
+```
+W --> Gemm --> Y --> Loss --> O
+       ^              ^
+       |              |
+       X              L
+```
+
+To compute the 2nd order derivative of O with respect to W (denoted by
+d^2O/dW^2), one can do
+
+```
+W --> Gemm --> Y --> Loss --> O
+|      ^              ^
+|      |              |
+|      X .------------L
+|      | |            |
+|      | |            v
++------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient)
+|      | |    |
+|      | |    '---> dO/dW (2nd output of Gradient)
+|      v v
+'---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of
+       |                                                  Gradient)
+       |
+       |
+       '---> d^2O/dW^2 (2nd output of Gradient)
+```
+
+The tensors named in attributes "xs", "zs", and "y" define the differentiated
+computation graph, and the inputs to Gradient node define the values at
+which the gradient is computed. We can feed different tensors to the identified
+graph. For example, one can compute the gradient of Y with respect to H at
+a specific value of H, H_1, by providing that value as an input to the Gradient
+node.
+
+```
+W --> Conv --> H --> Gemm --> Y
+       ^              ^
+       |              |
+       X              Z
+
+          Z_1 (2nd input of Gradient)
+           |
+           v
+H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1.
+           |
+           '------------------------------> dY/dZ (2nd output of Gradient)
+```
+
+When the inputs of Gradient are the tensors named in "xs" and "zs", the
+computation can be optimized. More specifically, intermediate variables in
+forward pass can be reused if the gradient is computed via reverse-mode
+auto-differentiation.
+
+)DOC";
+
+ONNX_PREVIEW_TRAINING_OPERATOR_SET_SCHEMA(
+    Gradient,
+    1,
+    OpSchema()
+        .SetDoc(Gradient_ver1_doc)
+        .Input(
+            0,
+            "Inputs",
+            "The values fed into graph identified by the attributes. "
+            "The i-th input is the value of the i-th tensor specified in the "
+            "concatenated list of the attribute \"xs\" and the attribute "
+            " \"zs\". For example, if xs=[\"A\", \"B\"] and zs=[\"C\"], the "
+            "first input is used as the value of symbol \"A\" and the 3rd "
+            "input is substituted for all the occurrences of \"C\".",
+            "T1",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "Outputs",
+            "The gradient of the tensor specified by the attribute \"y\" "
+            "with respect to each of tensors specified in the "
+            "attribute \"xs\". The i-th output is the gradient of \"y\" with "
+            "respect to the i-th tensor specified in the attribute \"xs\".",
+            "T2",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "xs",
+            "Input tensor names of the differentiated sub-graph. It "
+            "contains only the necessary differentiated "
+            "inputs of a (sub-)graph. Variables (usually called "
+            "intermediate variables) that can be generated from inputs "
+            "cannot be included in this attribute.",
+            AttributeProto::STRINGS)
+        .Attr(
+            "zs",
+            "Input tensor names of the differentiated sub-graph. It "
+            "contains only the necessary non-differentiated "
+            "inputs of a (sub-)graph. Variables (usually called "
+            "intermediate variables) that can be generated from inputs "
+            "cannot be included in this attribute.",
+            AttributeProto::STRINGS,
+            OPTIONAL_VALUE)
+        .Attr(
+            "y",
+            "The targeted tensor. It can be viewed as the output of the "
+            "differentiated function. The attribute \"xs\" and attribute "
+            "\"zs\" are the minimal independent variable set that determines "
+            "the value of \"y\".",
+            AttributeProto::STRING)
+        .TypeConstraint(
+            "T1",
+            OpSchema::all_tensor_types(),
+            "Allow outputs to be any kind of tensor.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(float16)",
+             "tensor(float)",
+             "tensor(double)"},
+            "Allow inputs to be any kind of floating-point tensor."));
+
+static const char* Adagrad_ver1_doc = R"DOC(
+    Compute one iteration of ADAGRAD, a stochastic gradient based optimization
+    algorithm. This operator can conduct the optimization of multiple tensor variables.
+
+    Let's define the behavior of this operator. As you can imagine, ADAGRAD requires
+    some parameters:
+
+     - The initial learning-rate "R".
+     - The update count "T". That is, the number of training iterations conducted.
+     - A L2-norm regularization coefficient "norm_coefficient".
+     - A learning-rate decay factor "decay_factor".
+     - A small constant "epsilon" to avoid dividing-by-zero.
+
+    At each ADAGRAD iteration, the optimized tensors are moved along a direction
+    computed based on their estimated gradient and accumulated squared gradient. Assume
+    that only a single tensor "X" is updated by this operator. We need the value of "X",
+    its gradient "G", and its accumulated squared gradient "H". Therefore, variables in
+    this operator's input list are sequentially "R", "T", "X", "G", and "H". Other
+    parameters are given as attributes because they are usually constants. Also, the
+    corresponding output tensors are the new value of "X" (called "X_new"), and then
+    the new accumulated squared gradient (called "H_new"). Those outputs are computed
+    from the given inputs following the pseudo code below.
+
+    Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
+    numpy-style broadcasting support. The pseudo code to compute those outputs is:
+
+      // Compute a scalar learning-rate factor. At the first update of X, T is generally
+      // 0 (0-based update index) or 1 (1-based update index).
+      r = R / (1 + T * decay_factor);
+
+      // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
+      G_regularized = norm_coefficient * X + G;
+
+      // Compute new accumulated squared gradient.
+      H_new = H + G_regularized * G_regularized;
+
+      // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...)
+      // computes element-wise square-root.
+      H_adaptive = Sqrt(H_new) + epsilon
+
+      // Compute the new value of "X".
+      X_new = X - r * G_regularized / H_adaptive;
+
+    If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same
+    pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a
+    concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
+    be concatenated too) and then just reuse the entire pseudo code.
+
+    Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
+    In that reference paper, this operator is a special case of the Figure 1's composite mirror
+    descent update.
+)DOC";
+
+ONNX_PREVIEW_TRAINING_OPERATOR_SET_SCHEMA(
+    Adagrad,
+    1,
+    OpSchema()
+        .SetDoc(Adagrad_ver1_doc)
+        .Input(0, "R", "The initial learning rate.", "T1")
+        .Input(1, "T", "The update count of \"X\". It should be a scalar.", "T2")
+        .Input(
+            2,
+            "inputs",
+            "The current values of optimized tensors, followed by their "
+            "respective gradients, followed by their respective accumulated squared gradients."
+            "For example, if two tensor \"X_1\" and \"X_2\" " "are optimized, "
+            "The input list would be "
+            "[\"X_1\", \"X_2\", "
+            "gradient of \"X_1\", "
+            "gradient of \"X_2\", "
+            "accumulated squared gradient of \"X_1\", "
+            "accumulated squared gradient of \"X_2\"].",
+            "T3",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "outputs",
+            "Updated values of optimized tensors, followed by their updated "
+            "values of accumulated squared gradients. For example, "
+            "if two tensor \"X_1\" and \"X_2\" are "
+            "optimized, the output list would be [new value of \"X_1,\" new value of \"X_2\" "
+            "new accumulated squared gradient of \"X_1\", new accumulated squared gradient of \"X_2\"].",
+            "T3",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "epsilon",
+            "Small scalar to avoid dividing by zero.",
+            AttributeProto::FLOAT,
+            1e-6f)
+        .Attr(
+            "decay_factor",
+            "The decay factor of learning rate after one update."
+            "The effective learning rate is computed by r = R / (1 + T * decay_factor). "
+            "Default to 0 so that increasing update counts doesn't reduce the learning rate.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "norm_coefficient",
+            "Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, "
+            "which means no regularization.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input types to float scalars.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain input types to 64-bit integer scalars.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+            // In comments below, we assume that the input list is
+            // [R, T, X1, X2, G1, G2, H1, H2] and the output list is
+            // [X1_new, X2_new, H1_new, H2_new].
+
+            // Compute the number of tuples (X, G, H).
+            auto num_optimized_tensors = (ctx.getNumInputs() - 2) / 3;
+            for (size_t i = 0; i < num_optimized_tensors; ++i) {
+              // Pass X1's and X2's shapes to X1_new and X2_new, respectively.
+              size_t i_in = 2 + i;
+              size_t i_out = i;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+
+              // Pass H1's and H2's shapes to H1_new and H2_new, respectively.
+              i_in = 2 + 2 * num_optimized_tensors + i;
+              i_out = i + num_optimized_tensors;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+            }}));
+
+static const char* Momentum_ver1_doc = R"DOC(
+    Compute one iteration of stochastic gradient update with momentum.
+    This operator can conduct the optimization of multiple tensor variables.
+
+    Let's define the behavior of this operator. As you can imagine, SG with momentum requires
+    several parameters:
+
+     - The learning-rate "R".
+     - The update count "T". That is, the number of conducted training iterations. It should
+       be zero in the first training iteration.
+     - A L2-norm regularization coefficient "norm_coefficient".
+     - A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha".
+     - The scaling coefficient of current gradient "beta".
+     - An attribute to choose either standard momentum or Nesterov's momentum "mode" should
+       be used.
+
+    For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized.
+    Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This
+    Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new
+    momentum (called "V_new").
+
+    This operator supports two different momentum algorithms. Set the attribute "mode" to
+    "nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to
+    "standard" to use standard momentum. Computation details are described subsequently.
+
+    Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting.
+
+    Pseudo code for SG with standard momentum:
+
+      // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
+      // values of all elements in X.
+      G_regularized = norm_coefficient * X + G
+
+      // In the first training iteration, beta should always be 1.
+      beta_adjusted = T > 0 ? beta : 1
+
+      // Compute the current momentum based on previous momentum and the current gradient.
+      V_new = alpha * V + beta_adjusted * G_regularized
+
+      // Update X.
+      X_new = X - R * V_new
+
+    Pseudo code for SG with Nesterov's momentum:
+
+      // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
+      // values of all elements in X.
+      G_regularized = norm_coefficient * X + G;
+
+      // In the first training iteration, beta should always be 1.
+      beta_adjusted = T > 0 ? beta : 1
+
+      // Compute the current momentum based on previous momentum and the current gradient.
+      V_new = alpha * V + beta_adjusted * G_regularized;
+
+      // Compute final update direction and then update X.
+      X_new = X - R * (G_regularized + alpha * V_new)
+
+    If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same
+    pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a
+    concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
+    be concatenated too) and then our pseudo code becomes applicable.
+)DOC";
+
+ONNX_PREVIEW_TRAINING_OPERATOR_SET_SCHEMA(
+    Momentum,
+    1,
+    OpSchema()
+        .SetDoc(Momentum_ver1_doc)
+        .Input(0, "R", "The learning rate.", "T1")
+        .Input(1, "T", "Update count of \"X\". It should be a scalar.", "T2")
+        .Input(
+            2,
+            "inputs",
+            "It sequentially contains the current values of optimized tensors, then their "
+            "gradient tensors, and finally their momentum tensors. For example, if two tensors "
+            "\"X_1\" and \"X_2\" are optimized, The expected input list would be "
+            "[\"X_1\", \"X_2\", gradient of \"X_1\", gradient of \"X_2\", momentum of \"X_1\", momentum of \"X_2\"].",
+            "T3",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "outputs",
+            "It sequentially contains the new values of optimized tensors and then the new "
+            "values of their momentum tensors. For example, if two tensors \"X_1\" and \"X_2\" are "
+            "optimized, the output list would be [new value of \"X_1,\" new value of \"X_2\" "
+            "new momentum of \"X_1\", new momentum of \"X_2\"].",
+            "T3",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "alpha",
+            "The decay factor of momentum. It should be a scalar.",
+            AttributeProto::FLOAT)
+        .Attr(
+            "beta",
+            "The coefficient of gradient in computing new momentum. It should be a scalar.",
+            AttributeProto::FLOAT)
+        .Attr(
+            "norm_coefficient",
+            "Coefficient of 0.5 * norm_coefficient * ||X||^2.",
+            AttributeProto::FLOAT)
+        .Attr(
+            "mode",
+            "Its value should be either \"nesterov\" or \"standard\". The value \"nesterov\" leads "
+            "to the use of Nesterov's momentum while \"standard\" invokes stochastic gradient method "
+            "using standard momentum",
+            AttributeProto::STRING)
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input types to float scalars.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain input types to 64-bit integer scalars.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+            // Assume that the input list is [R, T, X1, X2, G1, G2, V1, V2] and
+            // output list is [X1_new, X2_new, V1_new, V2_new] for explaining
+            // the code below in a simpler way.
+
+            // The count of input tensors excluding "R" and "T".
+            auto num_adjustable_tensors = ctx.getNumInputs() - 2;
+
+            // Check number of (optimized tensor, gradient, momentum) tuples.
+            if (num_adjustable_tensors % 3 != 0) {
+                fail_shape_inference(
+                  "The sum of optimized tensor count and momentum tensor count ",
+                  "should be a multiple of 2 in the input list of Momentum operator");
+            }
+
+            // The count of "X1" and "X2".
+            auto num_optimized_tensors = num_adjustable_tensors / 3;
+            for (size_t i = 0; i < num_optimized_tensors; ++i){
+              // Pass X1's/X2's shapes to X1_new/X2_new.
+              size_t i_in = 2 + i;
+              size_t i_out = i;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+              // Pass V1's/V2's shapes to V1_new/V2_new.
+              i_in = 2 + 2 * num_optimized_tensors + i;
+              i_out = i + num_optimized_tensors;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+            }
+        }));
+
+static const char* Adam_ver1_doc = R"DOC(
+    Compute one iteration of Adam, a stochastic gradient based optimization
+    algorithm. This operator can conduct the optimization of multiple tensor variables.
+
+    Let's define the behavior of this operator. First of all, Adam requires
+    some parameters:
+
+     - The learning-rate "R".
+     - The update count "T". That is, the number of training iterations conducted.
+     - A L2-norm regularization coefficient "norm_coefficient".
+     - A small constant "epsilon" to avoid dividing-by-zero.
+     - Two coefficients, "alpha" and "beta".
+
+    At each Adam iteration, the optimized tensors are moved along a direction
+    computed based on their exponentially-averaged historical gradient and
+    exponentially-averaged historical squared gradient. Assume that only a tensor
+    "X" is being optimized. The rest of required information is
+
+     - the value of "X",
+     - "X"'s gradient (denoted by "G"),
+     - "X"'s exponentially-averaged historical gradient (denoted by "V"), and
+     - "X"'s exponentially-averaged historical squared gradient (denoted by "H").
+
+    Some of those parameters are passed into this operator as input tensors and others
+    are stored as this operator's attributes. Specifically, this operator's input tensor
+    list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is
+    the second input, and so on. Other parameters are given as attributes because they
+    are constants. Moreover, the corresponding output tensors are
+
+     - the new value of "X" (called "X_new"),
+     - the new exponentially-averaged historical gradient (denoted by "V_new"), and
+     - the new exponentially-averaged historical squared gradient (denoted by "H_new").
+
+    Those outputs are computed following the pseudo code below.
+
+    Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
+    numpy-style broadcasting support. The pseudo code to compute those outputs is:
+
+      // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
+      G_regularized = norm_coefficient * X + G
+
+      // Update exponentially-averaged historical gradient.
+      V_new = alpha * V + (1 - alpha) * G_regularized
+
+      // Update exponentially-averaged historical squared gradient.
+      H_new = beta * H + (1 - beta) * G_regularized * G_regularized
+
+      // Compute the element-wise square-root of H_new. V_new will be element-wisely
+      // divided by H_sqrt for a better update direction.
+      H_sqrt = Sqrt(H_new) + epsilon
+
+      // Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power.
+      R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R
+
+      // Compute new value of "X".
+      X_new = X - R_adjusted * V_new / H_sqrt
+
+      // Post-update regularization.
+      X_final = (1 - norm_coefficient_post) * X_new
+
+    If there are multiple inputs to be optimized, the pseudo code will be applied
+    independently to each of them.
+)DOC";
+
+ONNX_PREVIEW_TRAINING_OPERATOR_SET_SCHEMA(
+    Adam,
+    1,
+    OpSchema()
+        .SetDoc(Adam_ver1_doc)
+        .Input(0, "R", "The initial learning rate.", "T1")
+        .Input(1, "T", "The update count of \"X\". It should be a scalar.", "T2")
+        .Input(
+            2,
+            "inputs",
+            "The tensors to be optimized, followed by their respective gradients, "
+            "followed by their respective accumulated gradients (aka momentum), "
+            "followed by their respective accumulated squared gradients. For example, "
+            "to optimize tensors \"X_1\" and \"X_2,\", the input list would be "
+            "[\"X_1\", \"X_2\", "
+            "gradient of \"X_1\", gradient of \"X_2\", "
+            "accumulated gradient of \"X_1\", accumulated gradient of \"X_2\", "
+            "accumulated squared gradient of \"X_1\", accumulated squared gradient of \"X_2\"].",
+            "T3",
+            OpSchema::Variadic,
+            false)
+        .Output(
+            0,
+            "outputs",
+            "New values of optimized tensors, "
+            "followed by their respective new accumulated gradients, "
+            "followed by their respective new accumulated squared gradients. "
+            "For example, if two tensors \"X_1\" and \"X_2\" are optimized, "
+            "the outputs list would be "
+            "[new value of \"X_1\", new value of \"X_2\", "
+            "new accumulated gradient of \"X_1\", "
+            "new accumulated gradient of \"X_2\", "
+            "new accumulated squared gradient of \"X_1\", "
+            "new accumulated squared gradient of \"X_2\"].",
+            "T3",
+            OpSchema::Variadic,
+            false)
+        .Attr(
+            "alpha",
+            "Coefficient of previously accumulated gradient in running average. Default to 0.9.",
+            AttributeProto::FLOAT,
+            0.9f)
+        .Attr(
+            "beta",
+            "Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.",
+            AttributeProto::FLOAT,
+            0.999f)
+        .Attr(
+            "norm_coefficient",
+            "Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, "
+            "which means no regularization.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "norm_coefficient_post",
+            "Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, "
+            "which means no regularization.",
+            AttributeProto::FLOAT,
+            0.0f)
+        .Attr(
+            "epsilon",
+            "Small scalar to avoid dividing by zero.",
+            AttributeProto::FLOAT,
+            1e-6f)
+        .TypeConstraint(
+            "T1",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input types to float scalars.")
+        .TypeConstraint(
+            "T2",
+            {"tensor(int64)"},
+            "Constrain input types to 64-bit integer scalars.")
+        .TypeConstraint(
+            "T3",
+            {"tensor(float)", "tensor(double)"},
+            "Constrain input and output types to float tensors.")
+        .TypeAndShapeInferenceFunction([](InferenceContext &ctx) {
+            // Assume that the input list is [R, T, X1, X2, G1, G2, V1, V2, H1, H2] and
+            // output list is [X1_new, X2_new, V1_new, V2_new, H1_new, H2_new] for explaining
+            // the code below in a simpler way.
+
+            // The count of input tensors excluding "R" and "T".
+            auto num_adjustable_tensors = ctx.getNumInputs() - 2;
+
+            // Check number of (optimized tensor, gradient, momentum) tuples.
+            if (num_adjustable_tensors % 4 != 0) {
+                fail_shape_inference(
+                  "The sum of optimized tensor count, gradient tensor count, momentum tensor count, ",
+                  "accumulated squared-gradient tensor count should be a multiple of 4 in the ",
+                  "\"inputs\" of Adam operator.");
+            }
+
+            // The count of "X1" and "X2".
+            auto num_optimized_tensors = num_adjustable_tensors / 4;
+            for (size_t i = 0; i < num_optimized_tensors; ++i){
+              // Pass X1's/X2's shapes to X1_new/X2_new.
+              size_t i_in = 2 + i;
+              size_t i_out = i;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+
+              // Pass V1's/V2's shapes to V1_new/V2_new.
+              i_in = 2 + 2 * num_optimized_tensors + i;
+              i_out = num_optimized_tensors + i;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+
+              // Pass H1's/H2's shapes to H1_new/H2_new.
+              i_in = 2 + 3 * num_optimized_tensors + i;
+              i_out = 2 * num_optimized_tensors + i;
+              propagateElemTypeFromInputToOutput(ctx, i_in, i_out);
+              propagateShapeFromInputToOutput(ctx, i_in, i_out);
+        }}));
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/examples/Protobufs.ipynb b/third_party/onnx/onnx/examples/Protobufs.ipynb
new file mode 100644
index 0000000000..568c81f252
--- /dev/null
+++ b/third_party/onnx/onnx/examples/Protobufs.ipynb
@@ -0,0 +1,543 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from onnx import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Int attribute:\n",
+      "\n",
+      "name: \"this_is_an_int\"\n",
+      "i: 1701\n",
+      "type: INT\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Int Attibute\n",
+    "arg = helper.make_attribute(\"this_is_an_int\", 1701)\n",
+    "print(\"\\nInt attribute:\\n\")\n",
+    "print(arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Float attribute:\n",
+      "\n",
+      "name: \"this_is_a_float\"\n",
+      "f: 3.1400001049\n",
+      "type: 1\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "# Float Attribute\n",
+    "arg = helper.make_attribute(\"this_is_a_float\", 3.14)\n",
+    "print(\"\\nFloat attribute:\\n\")\n",
+    "print(arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "String attribute:\n",
+      "\n",
+      "name: \"this_is_a_string\"\n",
+      "s: \"string_content\"\n",
+      "type: STRING\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# String Attribute\n",
+    "arg = helper.make_attribute(\"this_is_a_string\", \"string_content\")\n",
+    "print(\"\\nString attribute:\\n\")\n",
+    "print(arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Repeated int attribute:\n",
+      "\n",
+      "name: \"this_is_a_repeated_int\"\n",
+      "ints: 1\n",
+      "ints: 2\n",
+      "ints: 3\n",
+      "ints: 4\n",
+      "type: INTS\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Repeated Attribute\n",
+    "arg = helper.make_attribute(\"this_is_a_repeated_int\", [1, 2, 3, 4])\n",
+    "print(\"\\nRepeated int attribute:\\n\")\n",
+    "print(arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "NodeProto:\n",
+      "\n",
+      "input: \"X\"\n",
+      "output: \"Y\"\n",
+      "op_type: \"Relu\"\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# node\n",
+    "node_proto = helper.make_node(\"Relu\", [\"X\"], [\"Y\"])\n",
+    "\n",
+    "print(\"\\nNodeProto:\\n\")\n",
+    "print(node_proto)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "NodeProto:\n",
+      "\n",
+      "input: \"X\"\n",
+      "input: \"W\"\n",
+      "input: \"B\"\n",
+      "output: \"Y\"\n",
+      "op_type: \"Conv\"\n",
+      "attribute {\n",
+      "  name: \"kernel\"\n",
+      "  i: 3\n",
+      "  type: INT\n",
+      "}\n",
+      "attribute {\n",
+      "  name: \"pad\"\n",
+      "  i: 1\n",
+      "  type: INT\n",
+      "}\n",
+      "attribute {\n",
+      "  name: \"stride\"\n",
+      "  i: 1\n",
+      "  type: INT\n",
+      "}\n",
+      "\n",
+      "\n",
+      "More Readable NodeProto (no args yet):\n",
+      "\n",
+      "%Y = Conv[kernel = 3, pad = 1, stride = 1](%X, %W, %B)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# node with args\n",
+    "node_proto = helper.make_node(\n",
+    "    \"Conv\", [\"X\", \"W\", \"B\"], [\"Y\"],\n",
+    "    kernel=3, stride=1, pad=1)\n",
+    "\n",
+    "# This is just for making the attributes to be printed in order\n",
+    "node_proto.attribute.sort(key=lambda attr: attr.name)\n",
+    "print(\"\\nNodeProto:\\n\")\n",
+    "print(node_proto)\n",
+    "\n",
+    "print(\"\\nMore Readable NodeProto (no args yet):\\n\")\n",
+    "print(helper.printable_node(node_proto))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "graph proto:\n",
+      "\n",
+      "node {\n",
+      "  input: \"X\"\n",
+      "  input: \"W1\"\n",
+      "  input: \"B1\"\n",
+      "  output: \"H1\"\n",
+      "  op_type: \"FC\"\n",
+      "}\n",
+      "node {\n",
+      "  input: \"H1\"\n",
+      "  output: \"R1\"\n",
+      "  op_type: \"Relu\"\n",
+      "}\n",
+      "node {\n",
+      "  input: \"R1\"\n",
+      "  input: \"W2\"\n",
+      "  input: \"B2\"\n",
+      "  output: \"Y\"\n",
+      "  op_type: \"FC\"\n",
+      "}\n",
+      "name: \"MLP\"\n",
+      "input {\n",
+      "  name: \"X\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "input {\n",
+      "  name: \"W1\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "input {\n",
+      "  name: \"B1\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "input {\n",
+      "  name: \"W2\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "input {\n",
+      "  name: \"B2\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "output {\n",
+      "  name: \"Y\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "\n",
+      "\n",
+      "More Readable GraphProto:\n",
+      "\n",
+      "graph MLP (\n",
+      "  %X[FLOAT, 1]\n",
+      "  %W1[FLOAT, 1]\n",
+      "  %B1[FLOAT, 1]\n",
+      "  %W2[FLOAT, 1]\n",
+      "  %B2[FLOAT, 1]\n",
+      ") {\n",
+      "  %H1 = FC(%X, %W1, %B1)\n",
+      "  %R1 = Relu(%H1)\n",
+      "  %Y = FC(%R1, %W2, %B2)\n",
+      "  return %Y\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# graph\n",
+    "graph_proto = helper.make_graph(\n",
+    "    [\n",
+    "        helper.make_node(\"FC\", [\"X\", \"W1\", \"B1\"], [\"H1\"]),\n",
+    "        helper.make_node(\"Relu\", [\"H1\"], [\"R1\"]),\n",
+    "        helper.make_node(\"FC\", [\"R1\", \"W2\", \"B2\"], [\"Y\"]),\n",
+    "    ],\n",
+    "    \"MLP\",\n",
+    "    [\n",
+    "        helper.make_tensor_value_info('X' , TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('W1', TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('B1', TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('W2', TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('B2', TensorProto.FLOAT, [1]),\n",
+    "    ],\n",
+    "    [\n",
+    "        helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1]),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "print(\"\\ngraph proto:\\n\")\n",
+    "print(graph_proto)\n",
+    "\n",
+    "print(\"\\nMore Readable GraphProto:\\n\")\n",
+    "print(helper.printable_graph(graph_proto))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "NodeProto that contains a graph:\n",
+      "\n",
+      "input: \"Input\"\n",
+      "input: \"W1\"\n",
+      "input: \"B1\"\n",
+      "input: \"W2\"\n",
+      "input: \"B2\"\n",
+      "output: \"Output\"\n",
+      "op_type: \"graph\"\n",
+      "attribute {\n",
+      "  name: \"graph\"\n",
+      "  graphs {\n",
+      "    node {\n",
+      "      input: \"X\"\n",
+      "      input: \"W1\"\n",
+      "      input: \"B1\"\n",
+      "      output: \"H1\"\n",
+      "      op_type: \"FC\"\n",
+      "    }\n",
+      "    node {\n",
+      "      input: \"H1\"\n",
+      "      output: \"R1\"\n",
+      "      op_type: \"Relu\"\n",
+      "    }\n",
+      "    node {\n",
+      "      input: \"R1\"\n",
+      "      input: \"W2\"\n",
+      "      input: \"B2\"\n",
+      "      output: \"Y\"\n",
+      "      op_type: \"FC\"\n",
+      "    }\n",
+      "    name: \"MLP\"\n",
+      "    input {\n",
+      "      name: \"X\"\n",
+      "      type {\n",
+      "        tensor_type {\n",
+      "          elem_type: 1\n",
+      "          shape {\n",
+      "            dim {\n",
+      "              dim_value: 1\n",
+      "            }\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "    input {\n",
+      "      name: \"W1\"\n",
+      "      type {\n",
+      "        tensor_type {\n",
+      "          elem_type: 1\n",
+      "          shape {\n",
+      "            dim {\n",
+      "              dim_value: 1\n",
+      "            }\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "    input {\n",
+      "      name: \"B1\"\n",
+      "      type {\n",
+      "        tensor_type {\n",
+      "          elem_type: 1\n",
+      "          shape {\n",
+      "            dim {\n",
+      "              dim_value: 1\n",
+      "            }\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "    input {\n",
+      "      name: \"W2\"\n",
+      "      type {\n",
+      "        tensor_type {\n",
+      "          elem_type: 1\n",
+      "          shape {\n",
+      "            dim {\n",
+      "              dim_value: 1\n",
+      "            }\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "    input {\n",
+      "      name: \"B2\"\n",
+      "      type {\n",
+      "        tensor_type {\n",
+      "          elem_type: 1\n",
+      "          shape {\n",
+      "            dim {\n",
+      "              dim_value: 1\n",
+      "            }\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "    output {\n",
+      "      name: \"Y\"\n",
+      "      type {\n",
+      "        tensor_type {\n",
+      "          elem_type: 1\n",
+      "          shape {\n",
+      "            dim {\n",
+      "              dim_value: 1\n",
+      "            }\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "  type: GRAPHS\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An node that is also a graph\n",
+    "graph_proto = helper.make_graph(\n",
+    "    [\n",
+    "        helper.make_node(\"FC\", [\"X\", \"W1\", \"B1\"], [\"H1\"]),\n",
+    "        helper.make_node(\"Relu\", [\"H1\"], [\"R1\"]),\n",
+    "        helper.make_node(\"FC\", [\"R1\", \"W2\", \"B2\"], [\"Y\"]),\n",
+    "    ],\n",
+    "    \"MLP\",\n",
+    "    [\n",
+    "        helper.make_tensor_value_info('X' , TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('W1', TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('B1', TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('W2', TensorProto.FLOAT, [1]),\n",
+    "        helper.make_tensor_value_info('B2', TensorProto.FLOAT, [1]),\n",
+    "    ],\n",
+    "    [\n",
+    "        helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1]),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# output = ThisSpecificgraph([input, w1, b1, w2, b2])\n",
+    "node_proto = helper.make_node(\n",
+    "    \"graph\",\n",
+    "    [\"Input\", \"W1\", \"B1\", \"W2\", \"B2\"],\n",
+    "    [\"Output\"],\n",
+    "    graph=[graph_proto],\n",
+    ")\n",
+    "\n",
+    "print(\"\\nNodeProto that contains a graph:\\n\")\n",
+    "print(node_proto)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/third_party/onnx/onnx/examples/check_model.ipynb b/third_party/onnx/onnx/examples/check_model.ipynb
new file mode 100644
index 0000000000..a5315125e0
--- /dev/null
+++ b/third_party/onnx/onnx/examples/check_model.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model is:\n",
+      "ir_version: 3\n",
+      "producer_name: \"backend-test\"\n",
+      "graph {\n",
+      "  node {\n",
+      "    input: \"x\"\n",
+      "    output: \"y\"\n",
+      "    name: \"test\"\n",
+      "    op_type: \"Relu\"\n",
+      "  }\n",
+      "  name: \"SingleRelu\"\n",
+      "  input {\n",
+      "    name: \"x\"\n",
+      "    type {\n",
+      "      tensor_type {\n",
+      "        elem_type: 1\n",
+      "        shape {\n",
+      "          dim {\n",
+      "            dim_value: 1\n",
+      "          }\n",
+      "          dim {\n",
+      "            dim_value: 2\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "  output {\n",
+      "    name: \"y\"\n",
+      "    type {\n",
+      "      tensor_type {\n",
+      "        elem_type: 1\n",
+      "        shape {\n",
+      "          dim {\n",
+      "            dim_value: 1\n",
+      "          }\n",
+      "          dim {\n",
+      "            dim_value: 2\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "opset_import {\n",
+      "  version: 6\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnx\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "# Preprocessing: load the ONNX model\n",
+    "model_path = os.path.join('resources', 'single_relu.onnx')\n",
+    "onnx_model = onnx.load(model_path)\n",
+    "\n",
+    "print('The model is:\\n{}'.format(onnx_model))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model is valid!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the model\n",
+    "try:\n",
+    "    onnx.checker.check_model(onnx_model)\n",
+    "except onnx.checker.ValidationError as e:\n",
+    "    print('The model is invalid: %s' % e)\n",
+    "else:\n",
+    "    print('The model is valid!')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/onnx/onnx/examples/load_model.ipynb b/third_party/onnx/onnx/examples/load_model.ipynb
new file mode 100644
index 0000000000..a31e59bafc
--- /dev/null
+++ b/third_party/onnx/onnx/examples/load_model.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ir_version: 3\n",
+      "producer_name: \"backend-test\"\n",
+      "graph {\n",
+      "  node {\n",
+      "    input: \"x\"\n",
+      "    output: \"y\"\n",
+      "    name: \"test\"\n",
+      "    op_type: \"Relu\"\n",
+      "  }\n",
+      "  name: \"SingleRelu\"\n",
+      "  input {\n",
+      "    name: \"x\"\n",
+      "    type {\n",
+      "      tensor_type {\n",
+      "        elem_type: 1\n",
+      "        shape {\n",
+      "          dim {\n",
+      "            dim_value: 1\n",
+      "          }\n",
+      "          dim {\n",
+      "            dim_value: 2\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "  output {\n",
+      "    name: \"y\"\n",
+      "    type {\n",
+      "      tensor_type {\n",
+      "        elem_type: 1\n",
+      "        shape {\n",
+      "          dim {\n",
+      "            dim_value: 1\n",
+      "          }\n",
+      "          dim {\n",
+      "            dim_value: 2\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "opset_import {\n",
+      "  version: 6\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnx\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "# Load the ONNX model\n",
+    "onnx_model = onnx.load(os.path.join('resources', 'single_relu.onnx'))\n",
+    "print(onnx_model)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/onnx/onnx/examples/make_model.ipynb b/third_party/onnx/onnx/examples/make_model.ipynb
new file mode 100644
index 0000000000..ecb42ac93e
--- /dev/null
+++ b/third_party/onnx/onnx/examples/make_model.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The producer_name in model: onnx-example\n",
+      "\n",
+      "The graph in model:\n",
+      "node {\n",
+      "  input: \"X\"\n",
+      "  input: \"Pads\"\n",
+      "  output: \"Y\"\n",
+      "  op_type: \"Pad\"\n",
+      "  attribute {\n",
+      "    name: \"mode\"\n",
+      "    s: \"constant\"\n",
+      "    type: STRING\n",
+      "  }\n",
+      "}\n",
+      "name: \"test-model\"\n",
+      "initializer {\n",
+      "  dims: 4\n",
+      "  data_type: 7\n",
+      "  int64_data: 0\n",
+      "  int64_data: 0\n",
+      "  int64_data: 1\n",
+      "  int64_data: 1\n",
+      "  name: \"Pads\"\n",
+      "}\n",
+      "input {\n",
+      "  name: \"X\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "        dim {\n",
+      "          dim_value: 2\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "input {\n",
+      "  name: \"Pads\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 7\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 4\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "output {\n",
+      "  name: \"Y\"\n",
+      "  type {\n",
+      "    tensor_type {\n",
+      "      elem_type: 1\n",
+      "      shape {\n",
+      "        dim {\n",
+      "          dim_value: 1\n",
+      "        }\n",
+      "        dim {\n",
+      "          dim_value: 4\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "\n",
+      "The model is checked!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnx\n",
+    "from onnx import helper\n",
+    "from onnx import AttributeProto, TensorProto, GraphProto\n",
+    "\n",
+    "\n",
+    "# The protobuf definition can be found here:\n",
+    "# https://github.com/onnx/onnx/blob/main/onnx/onnx.proto\n",
+    "\n",
+    "\n",
+    "# Create one input (ValueInfoProto)\n",
+    "X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 2])\n",
+    "\n",
+    "# Create second input (ValueInfoProto)\n",
+    "Pads = helper.make_tensor_value_info('Pads', TensorProto.INT64, [4])\n",
+    "\n",
+    "# Create one output (ValueInfoProto)\n",
+    "Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 4])\n",
+    "\n",
+    "# Create a node (NodeProto)\n",
+    "node_def = helper.make_node(\n",
+    "    'Pad', # node name\n",
+    "    ['X', 'Pads'], # inputs\n",
+    "    ['Y'], # outputs\n",
+    "    mode='constant', # Attributes\n",
+    ")\n",
+    "\n",
+    "# Create the graph (GraphProto)\n",
+    "graph_def = helper.make_graph(\n",
+    "    [node_def],\n",
+    "    \"test-model\",\n",
+    "    [X, Pads],\n",
+    "    [Y],\n",
+    "    [helper.make_tensor('Pads', TensorProto.INT64, [4,], [0, 0, 1, 1,])],\n",
+    ")\n",
+    "\n",
+    "# Create the model (ModelProto)\n",
+    "model_def = helper.make_model(graph_def,\n",
+    "                              producer_name='onnx-example')\n",
+    "\n",
+    "print('The producer_name in model: {}\\n'.format(model_def.producer_name))\n",
+    "print('The graph in model:\\n{}'.format(model_def.graph))\n",
+    "onnx.checker.check_model(model_def)\n",
+    "print('The model is checked!')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/onnx/onnx/examples/np_array_tensorproto.ipynb b/third_party/onnx/onnx/examples/np_array_tensorproto.ipynb
new file mode 100644
index 0000000000..1c5d289b01
--- /dev/null
+++ b/third_party/onnx/onnx/examples/np_array_tensorproto.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Numpy array:\n",
+      "[[1. 2. 3.]\n",
+      " [4. 5. 6.]]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "import onnx\n",
+    "import os\n",
+    "from onnx import numpy_helper\n",
+    "\n",
+    "\n",
+    "# Preprocessing: create a Numpy array\n",
+    "numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float)\n",
+    "print('Original Numpy array:\\n{}\\n'.format(numpy.array2string(numpy_array)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TensorProto:\n",
+      "dims: 2\n",
+      "dims: 3\n",
+      "data_type: 11\n",
+      "raw_data: \"\\000\\000\\000\\000\\000\\000\\360?\\000\\000\\000\\000\\000\\000\\000@\\000\\000\\000\\000\\000\\000\\010@\\000\\000\\000\\000\\000\\000\\020@\\000\\000\\000\\000\\000\\000\\024@\\000\\000\\000\\000\\000\\000\\030@\"\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the Numpy array to a TensorProto\n",
+    "tensor = numpy_helper.from_array(numpy_array)\n",
+    "print('TensorProto:\\n{}'.format(tensor))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After round trip, Numpy array:\n",
+      "[[1. 2. 3.]\n",
+      " [4. 5. 6.]]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the TensorProto to a Numpy array\n",
+    "new_array = numpy_helper.to_array(tensor)\n",
+    "print('After round trip, Numpy array:\\n{}\\n'.format(numpy.array2string(numpy_array)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the TensorProto\n",
+    "with open(os.path.join('resources', 'tensor.pb'), 'wb') as f:\n",
+    "    f.write(tensor.SerializeToString())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After saving and loading, new TensorProto:\n",
+      "dims: 2\n",
+      "dims: 3\n",
+      "data_type: 11\n",
+      "raw_data: \"\\000\\000\\000\\000\\000\\000\\360?\\000\\000\\000\\000\\000\\000\\000@\\000\\000\\000\\000\\000\\000\\010@\\000\\000\\000\\000\\000\\000\\020@\\000\\000\\000\\000\\000\\000\\024@\\000\\000\\000\\000\\000\\000\\030@\"\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the TensorProto\n",
+    "new_tensor = onnx.TensorProto()\n",
+    "with open(os.path.join('resources', 'tensor.pb'), 'rb') as f:\n",
+    "    new_tensor.ParseFromString(f.read())\n",
+    "print('After saving and loading, new TensorProto:\\n{}'.format(new_tensor))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/onnx/onnx/examples/resources/single_relu.onnx b/third_party/onnx/onnx/examples/resources/single_relu.onnx
new file mode 100644
index 0000000000..b69364d1a5
--- /dev/null
+++ b/third_party/onnx/onnx/examples/resources/single_relu.onnx
@@ -0,0 +1,12 @@
+backend-test:J
+
+
x
ytest"Relu
+SingleReluZ
+
x
+

+

+b
+
y
+

+

+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/examples/resources/single_relu_new.onnx b/third_party/onnx/onnx/examples/resources/single_relu_new.onnx
new file mode 100644
index 0000000000..b69364d1a5
--- /dev/null
+++ b/third_party/onnx/onnx/examples/resources/single_relu_new.onnx
@@ -0,0 +1,12 @@
+backend-test:J
+
+
x
ytest"Relu
+SingleReluZ
+
x
+

+

+b
+
y
+

+

+B
\ No newline at end of file
diff --git a/third_party/onnx/onnx/examples/resources/tensor.pb b/third_party/onnx/onnx/examples/resources/tensor.pb
new file mode 100644
index 0000000000..713e7fa930
Binary files /dev/null and b/third_party/onnx/onnx/examples/resources/tensor.pb differ
diff --git a/third_party/onnx/onnx/examples/resources/two_transposes.onnx b/third_party/onnx/onnx/examples/resources/two_transposes.onnx
new file mode 100644
index 0000000000..8a8ab2fe67
Binary files /dev/null and b/third_party/onnx/onnx/examples/resources/two_transposes.onnx differ
diff --git a/third_party/onnx/onnx/examples/save_model.ipynb b/third_party/onnx/onnx/examples/save_model.ipynb
new file mode 100644
index 0000000000..bc0dfa1299
--- /dev/null
+++ b/third_party/onnx/onnx/examples/save_model.ipynb
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model is saved.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnx\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "# Preprocessing: load the old model\n",
+    "old_model_path = os.path.join('resources', 'single_relu.onnx')\n",
+    "onnx_model = onnx.load(old_model_path)\n",
+    "\n",
+    "# Preprocessing: get the path to the saved model\n",
+    "new_model_path = os.path.join('resources', 'single_relu_new.onnx')\n",
+    "\n",
+    "# Save the ONNX model\n",
+    "onnx.save(onnx_model, new_model_path)\n",
+    "\n",
+    "print('The model is saved.')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/onnx/onnx/examples/shape_inference.ipynb b/third_party/onnx/onnx/examples/shape_inference.ipynb
new file mode 100644
index 0000000000..855355d0a3
--- /dev/null
+++ b/third_party/onnx/onnx/examples/shape_inference.ipynb
@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before shape inference, the shape info of Y is:\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnx\n",
+    "from onnx import helper, shape_inference\n",
+    "from onnx import TensorProto\n",
+    "\n",
+    "\n",
+    "# Preprocessing: create a model with two nodes, Y's shape is unknown\n",
+    "node1 = helper.make_node('Transpose', ['X'], ['Y'], perm=[1, 0, 2])\n",
+    "node2 = helper.make_node('Transpose', ['Y'], ['Z'], perm=[1, 0, 2])\n",
+    "\n",
+    "graph = helper.make_graph(\n",
+    "    [node1, node2],\n",
+    "    'two-transposes',\n",
+    "    [helper.make_tensor_value_info('X', TensorProto.FLOAT, (2, 3, 4))],\n",
+    "    [helper.make_tensor_value_info('Z', TensorProto.FLOAT, (2, 3, 4))],\n",
+    ")\n",
+    "\n",
+    "original_model = helper.make_model(graph, producer_name='onnx-examples')\n",
+    "\n",
+    "# Check the model and print Y's shape information\n",
+    "onnx.checker.check_model(original_model)\n",
+    "print('Before shape inference, the shape info of Y is:\\n{}'.format(original_model.graph.value_info))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After shape inference, the shape info of Y is:\n",
+      "[name: \"Y\"\n",
+      "type {\n",
+      "  tensor_type {\n",
+      "    elem_type: 1\n",
+      "    shape {\n",
+      "      dim {\n",
+      "        dim_value: 3\n",
+      "      }\n",
+      "      dim {\n",
+      "        dim_value: 2\n",
+      "      }\n",
+      "      dim {\n",
+      "        dim_value: 4\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply shape inference on the model\n",
+    "inferred_model = shape_inference.infer_shapes(original_model)\n",
+    "\n",
+    "# Check the model and print Y's shape information\n",
+    "onnx.checker.check_model(inferred_model)\n",
+    "print('After shape inference, the shape info of Y is:\\n{}'.format(inferred_model.graph.value_info))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/third_party/onnx/onnx/external_data_helper.py b/third_party/onnx/onnx/external_data_helper.py
new file mode 100644
index 0000000000..f26869d2b0
--- /dev/null
+++ b/third_party/onnx/onnx/external_data_helper.py
@@ -0,0 +1,287 @@
+# SPDX-License-Identifier: Apache-2.0
+import uuid
+import os
+import re
+import sys
+from itertools import chain
+from typing import Callable, Iterable, Text, Optional
+
+from .onnx_pb import TensorProto, ModelProto, AttributeProto, GraphProto
+
+
+class ExternalDataInfo(object):
+
+    def __init__(self, tensor: TensorProto) -> None:
+        self.location = ''
+        self.offset = None
+        self.length = None
+        self.checksum = None
+        self.basepath = ''
+
+        for entry in tensor.external_data:
+            setattr(self, entry.key, entry.value)
+
+        if self.offset:
+            self.offset = int(self.offset)
+
+        if self.length:
+            self.length = int(self.length)
+
+
+def load_external_data_for_tensor(tensor: TensorProto, base_dir: Text) -> None:
+    """
+    Loads data from an external file for tensor.
+    Ideally TensorProto should not hold any raw data but if it does it will be ignored.
+
+    Arguments:
+        tensor: a TensorProto object.
+        base_dir: directory that contains the external data.
+    """
+    info = ExternalDataInfo(tensor)
+    file_location = _sanitize_path(info.location)
+    external_data_file_path = os.path.join(base_dir, file_location)
+
+    with open(external_data_file_path, 'rb') as data_file:
+
+        if info.offset:
+            data_file.seek(info.offset)
+
+        if info.length:
+            tensor.raw_data = data_file.read(info.length)
+        else:
+            tensor.raw_data = data_file.read()
+
+
+def load_external_data_for_model(model: ModelProto, base_dir: Text) -> None:
+    """
+    Loads external tensors into model
+
+    Arguments:
+        model: ModelProto to load external data to
+        base_dir: directory that contains external data
+    """
+    for tensor in _get_all_tensors(model):
+        if uses_external_data(tensor):
+            load_external_data_for_tensor(tensor, base_dir)
+            # After loading raw_data from external_data, change the state of tensors
+            tensor.data_location = TensorProto.DEFAULT
+            # and remove external data
+            del tensor.external_data[:]
+
+
+def set_external_data(tensor: TensorProto,
+                      location: Text,
+                      offset: Optional[int] = None,
+                      length: Optional[int] = None,
+                      checksum: Optional[Text] = None,
+                      basepath: Optional[Text] = None
+                      ) -> None:
+    if not tensor.HasField("raw_data"):
+        raise ValueError("Tensor " + tensor.name + "does not have raw_data field. Cannot set external data for this tensor.")
+
+    del tensor.external_data[:]
+    tensor.data_location = TensorProto.EXTERNAL
+    for (k, v) in {
+        'location': location,
+        'offset': int(offset) if offset is not None else None,
+        'length': int(length) if length is not None else None,
+        'checksum': checksum,
+        'basepath': basepath
+    }.items():
+        if v is not None:
+            entry = tensor.external_data.add()
+            entry.key = k
+            entry.value = str(v)
+
+
+def convert_model_to_external_data(model: ModelProto, all_tensors_to_one_file: bool = True, location: Optional[Text] = None, size_threshold: int = 1024, convert_attribute: bool = False) -> None:
+    """
+    Call to set all tensors with raw data as external data. This call should preceed 'save_model'.
+    'save_model' saves all the tensors data as external data after calling this function.
+
+    Arguments:
+        model (ModelProto): Model to be converted.
+        all_tensors_to_one_file (bool): If true, save all tensors to one external file specified by location.
+            If false, save each tensor to a file named with the tensor name.
+        location: specify the external file that all tensors to save to.
+            If not specified, will use the model name.
+        size_threshold: Threshold for size of data. Only when tensor's data is >= the size_threshold
+            it will be converted to external data. To convert every tensor with raw data to external data set size_threshold=0.
+        convert_attribute (bool): If true, convert all tensors to external data
+                       If false, convert only non-attribute tensors to external data
+    """
+    tensors = _get_initializer_tensors(model)
+    if convert_attribute:
+        tensors = _get_all_tensors(model)
+
+    if all_tensors_to_one_file:
+        file_name = Text(uuid.uuid1())
+        if location:
+            file_name = location
+        for tensor in tensors:
+            if tensor.HasField("raw_data") and sys.getsizeof(tensor.raw_data) >= size_threshold:
+                set_external_data(tensor, file_name)
+    else:
+        for tensor in tensors:
+            if tensor.HasField("raw_data") and sys.getsizeof(tensor.raw_data) >= size_threshold:
+                tensor_location = tensor.name
+                if not _is_valid_filename(tensor_location):
+                    tensor_location = Text(uuid.uuid1())
+                set_external_data(tensor, tensor_location)
+
+
+def convert_model_from_external_data(model: ModelProto) -> None:
+    """
+    Call to set all tensors which use external data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
+
+    Arguments:
+        model (ModelProto): Model to be converted.
+    """
+    for tensor in _get_all_tensors(model):
+        if uses_external_data(tensor):
+            if not tensor.HasField("raw_data"):
+                raise ValueError("raw_data field doesn't exist.")
+            del tensor.external_data[:]
+            tensor.data_location = TensorProto.DEFAULT
+
+
+def save_external_data(tensor: TensorProto, base_path: Text) -> None:
+    """
+    Writes tensor data to an external file according to information in the `external_data` field.
+
+    Arguments:
+        tensor (TensorProto): Tensor object to be serialized
+        base_path: System path of a folder where tensor data is to be stored
+    """
+    info = ExternalDataInfo(tensor)
+    external_data_file_path = os.path.join(base_path, info.location)
+
+    # Retrieve the tensor's data from raw_data or load external file
+    if not tensor.HasField("raw_data"):
+        raise ValueError("raw_data field doesn't exist.")
+
+    # Create file if it doesn't exist
+    if not os.path.isfile(external_data_file_path):
+        open(external_data_file_path, 'ab').close()
+
+    # Open file for reading and writing at random locations ('r+b')
+    with open(external_data_file_path, 'r+b') as data_file:
+        data_file.seek(0, 2)
+        if info.offset is not None:
+            # Pad file to required offset if needed
+            file_size = data_file.tell()
+            if info.offset > file_size:
+                data_file.write(b"\0" * (info.offset - file_size))
+
+            data_file.seek(info.offset)
+        offset = data_file.tell()
+        data_file.write(tensor.raw_data)
+        set_external_data(tensor, info.location, offset, data_file.tell() - offset)
+
+
+def _get_all_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
+    """Scan an ONNX model for all tensors and return as an iterator."""
+    return chain(_get_initializer_tensors(onnx_model_proto),
+                 _get_attribute_tensors(onnx_model_proto))
+
+
+def _recursive_attribute_processor(attribute: AttributeProto, func: Callable[[GraphProto], Iterable[TensorProto]]) -> Iterable[TensorProto]:
+    """Create an iterator through processing ONNX model attributes with functor."""
+    if attribute.type == AttributeProto.GRAPH:
+        yield from func(attribute.g)
+    if attribute.type == AttributeProto.GRAPHS:
+        for graph in attribute.graphs:
+            yield from func(graph)
+
+
+def _get_initializer_tensors_from_graph(onnx_model_proto_graph: GraphProto) -> Iterable[TensorProto]:
+    """Create an iterator of initializer tensors from ONNX model graph."""
+    for initializer in onnx_model_proto_graph.initializer:
+        yield initializer
+    for node in onnx_model_proto_graph.node:
+        for attribute in node.attribute:
+            yield from _recursive_attribute_processor(attribute, _get_initializer_tensors_from_graph)
+
+
+def _get_initializer_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
+    """Create an iterator of initializer tensors from ONNX model."""
+    yield from _get_initializer_tensors_from_graph(onnx_model_proto.graph)
+
+
+def _get_attribute_tensors_from_graph(onnx_model_proto_graph: GraphProto) -> Iterable[TensorProto]:
+    """Create an iterator of tensors from node attributes of an ONNX model graph."""
+    for node in onnx_model_proto_graph.node:
+        for attribute in node.attribute:
+            if attribute.HasField("t"):
+                yield attribute.t
+            for tensor in attribute.tensors:
+                yield tensor
+            yield from _recursive_attribute_processor(attribute, _get_attribute_tensors_from_graph)
+
+
+def _get_attribute_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
+    """Create an iterator of tensors from node attributes of an ONNX model."""
+    yield from _get_attribute_tensors_from_graph(onnx_model_proto.graph)
+
+
+def _sanitize_path(path: Text) -> Text:
+    """Remove path components which would allow traversing up a directory tree from a base path.
+
+    Note: This method is currently very basic and should be expanded.
+    """
+    return path.lstrip('/.')
+
+
+def _is_valid_filename(filename: Text) -> bool:
+    """Utility to check whether the provided filename is valid."""
+    exp = re.compile("^[^<>:;,?\"*|/]+$")
+    match = exp.match(filename)
+    if match:
+        return True
+    else:
+        return False
+
+
+def uses_external_data(tensor: TensorProto) -> bool:
+    """Returns true if the tensor stores data in an external location."""
+    return tensor.HasField("data_location") and tensor.data_location == TensorProto.EXTERNAL
+
+
+def remove_external_data_field(tensor: TensorProto, field_key: Text) -> None:
+    """
+    Removes a field from a Tensor's external_data key-value store.
+
+    Modifies tensor object in place.
+
+    Arguments:
+        tensor (TensorProto): Tensor object from which value will be removed
+        field_key (string): The key of the field to be removed
+    """
+    for (i, field) in enumerate(tensor.external_data):
+        if field.key == field_key:
+            del tensor.external_data[i]
+
+
+def write_external_data_tensors(model: ModelProto, filepath: Text) -> ModelProto:
+    """
+    Serializes data for all the tensors which have data location set to TensorProto.External.
+
+    Note: This function also strips basepath information from all tensors' external_data fields.
+
+    Arguments:
+        model (ModelProto): Model object which is the source of tensors to serialize.
+        filepath: System path to the directory which should be treated as base path for external data.
+
+    Returns:
+        ModelProto: The modified model object.
+    """
+    for tensor in _get_all_tensors(model):
+        # Writing to external data happens in 2 passes:
+        # 1. Tensors with raw data which pass the necessary conditions (size threshold etc) are marked for serialization
+        # 2. The raw data in these tensors is serialized to a file
+        # Thus serialize only if tensor has raw data and it was marked for serialization
+        if uses_external_data(tensor) and tensor.HasField("raw_data"):
+            save_external_data(tensor, filepath)
+            tensor.ClearField(str('raw_data'))
+
+    return model
diff --git a/third_party/onnx/onnx/frontend/__init__.py b/third_party/onnx/onnx/frontend/__init__.py
new file mode 100644
index 0000000000..9881313609
--- /dev/null
+++ b/third_party/onnx/onnx/frontend/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/third_party/onnx/onnx/gen_proto.py b/third_party/onnx/onnx/gen_proto.py
new file mode 100755
index 0000000000..da69e9806d
--- /dev/null
+++ b/third_party/onnx/onnx/gen_proto.py
@@ -0,0 +1,232 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import io
+import os
+import re
+import glob
+import subprocess
+from textwrap import dedent
+from typing import Iterable, Text
+
+autogen_header = """\
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+"""
+
+LITE_OPTION = '''
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
+'''
+
+DEFAULT_PACKAGE_NAME = "onnx"
+
+IF_ONNX_ML_REGEX = re.compile(r'\s*//\s*#if\s+ONNX-ML\s*$')
+ENDIF_ONNX_ML_REGEX = re.compile(r'\s*//\s*#endif\s*$')
+ELSE_ONNX_ML_REGEX = re.compile(r'\s*//\s*#else\s*$')
+
+
+def process_ifs(lines: Iterable[Text], onnx_ml: bool) -> Iterable[Text]:
+    in_if = 0
+    for line in lines:
+        if IF_ONNX_ML_REGEX.match(line):
+            assert 0 == in_if
+            in_if = 1
+        elif ELSE_ONNX_ML_REGEX.match(line):
+            assert 1 == in_if
+            in_if = 2
+        elif ENDIF_ONNX_ML_REGEX.match(line):
+            assert (1 == in_if or 2 == in_if)
+            in_if = 0
+        else:
+            if 0 == in_if:
+                yield line
+            elif (1 == in_if and onnx_ml):
+                yield line
+            elif (2 == in_if and not onnx_ml):
+                yield line
+
+
+IMPORT_REGEX = re.compile(r'(\s*)import\s*"([^"]*)\.proto";\s*$')
+PACKAGE_NAME_REGEX = re.compile(r'\{PACKAGE_NAME\}')
+ML_REGEX = re.compile(r'(.*)\-ml')
+
+
+def process_package_name(lines: Iterable[Text], package_name: Text) -> Iterable[Text]:
+    need_rename = (package_name != DEFAULT_PACKAGE_NAME)
+    for line in lines:
+        m = IMPORT_REGEX.match(line) if need_rename else None
+        if m:
+            include_name = m.group(2)
+            ml = ML_REGEX.match(include_name)
+            if ml:
+                include_name = "{}_{}-ml".format(ml.group(1), package_name)
+            else:
+                include_name = "{}_{}".format(include_name, package_name)
+            yield m.group(1) + 'import "{}.proto";'.format(include_name)
+        else:
+            yield PACKAGE_NAME_REGEX.sub(package_name, line)
+
+
+PROTO_SYNTAX_REGEX = re.compile(r'(\s*)syntax\s*=\s*"proto2"\s*;\s*$')
+OPTIONAL_REGEX = re.compile(r'(\s*)optional\s(.*)$')
+
+
+def convert_to_proto3(lines: Iterable[Text]) -> Iterable[Text]:
+    for line in lines:
+        # Set the syntax specifier
+        m = PROTO_SYNTAX_REGEX.match(line)
+        if m:
+            yield m.group(1) + 'syntax = "proto3";'
+            continue
+
+        # Remove optional keywords
+        m = OPTIONAL_REGEX.match(line)
+        if m:
+            yield m.group(1) + m.group(2)
+            continue
+
+        # Rewrite import
+        m = IMPORT_REGEX.match(line)
+        if m:
+            yield m.group(1) + 'import "{}.proto3";'.format(m.group(2))
+            continue
+
+        yield line
+
+
+def gen_proto3_code(protoc_path: Text, proto3_path: Text, include_path: Text, cpp_out: Text, python_out: Text) -> None:
+    print("Generate pb3 code using {}".format(protoc_path))
+    build_args = [protoc_path, proto3_path, '-I', include_path]
+    build_args.extend(['--cpp_out', cpp_out, '--python_out', python_out])
+    subprocess.check_call(build_args)
+
+
+def translate(source: Text, proto: int, onnx_ml: bool, package_name: Text) -> Text:
+    lines: Iterable[Text] = source.splitlines()
+    lines = process_ifs(lines, onnx_ml=onnx_ml)
+    lines = process_package_name(lines, package_name=package_name)
+    if proto == 3:
+        lines = convert_to_proto3(lines)
+    else:
+        assert proto == 2
+    return "\n".join(lines)  # TODO: not Windows friendly
+
+
+def qualify(f: Text, pardir: Text = os.path.realpath(os.path.dirname(__file__))) -> Text:
+    return os.path.join(pardir, f)
+
+
+def convert(stem: Text, package_name: Text, output: Text, do_onnx_ml: bool = False, lite: bool = False, protoc_path: Text = '') -> None:
+    proto_in = qualify("{}.in.proto".format(stem))
+    need_rename = (package_name != DEFAULT_PACKAGE_NAME)
+    # Having a separate variable for import_ml ensures that the import statements for the generated
+    # proto files can be set separately from the ONNX_ML environment variable setting.
+    import_ml = do_onnx_ml
+    # We do not want to generate the onnx-data-ml.proto files for onnx-data.in.proto,
+    # as there is no change between onnx-data.proto and the ML version.
+    if 'onnx-data' in proto_in:
+        do_onnx_ml = False
+    if do_onnx_ml:
+        proto_base = "{}_{}-ml".format(stem, package_name) if need_rename else "{}-ml".format(stem)
+    else:
+        proto_base = "{}_{}".format(stem, package_name) if need_rename else "{}".format(stem)
+    proto = qualify("{}.proto".format(proto_base), pardir=output)
+    proto3 = qualify("{}.proto3".format(proto_base), pardir=output)
+
+    print("Processing {}".format(proto_in))
+    with io.open(proto_in, 'r') as fin:
+        source = fin.read()
+        print("Writing {}".format(proto))
+        with io.open(proto, 'w', newline='') as fout:
+            fout.write(autogen_header)
+            fout.write(translate(source, proto=2, onnx_ml=import_ml, package_name=package_name))
+            if lite:
+                fout.write(LITE_OPTION)
+        print("Writing {}".format(proto3))
+        with io.open(proto3, 'w', newline='') as fout:
+            fout.write(autogen_header)
+            fout.write(translate(source, proto=3, onnx_ml=import_ml, package_name=package_name))
+            if lite:
+                fout.write(LITE_OPTION)
+        if protoc_path:
+            porto3_dir = os.path.dirname(proto3)
+            base_dir = os.path.dirname(porto3_dir)
+            gen_proto3_code(protoc_path, proto3, base_dir, base_dir, base_dir)
+            pb3_files = glob.glob(os.path.join(porto3_dir, '*.proto3.*'))
+            for pb3_file in pb3_files:
+                print("Removing {}".format(pb3_file))
+                os.remove(pb3_file)
+
+        if need_rename:
+            if do_onnx_ml:
+                proto_header = qualify("{}-ml.pb.h".format(stem), pardir=output)
+            else:
+                proto_header = qualify("{}.pb.h".format(stem), pardir=output)
+            print("Writing {}".format(proto_header))
+            with io.open(proto_header, 'w', newline='') as fout:
+                fout.write("#pragma once\n")
+                fout.write("#include \"{}.pb.h\"\n".format(proto_base))
+
+    # Generate py mapping
+    # "-" is invalid in python module name, replaces '-' with '_'
+    pb_py = qualify('{}_pb.py'.format(stem.replace('-', '_')), pardir=output)
+    if need_rename:
+        pb2_py = qualify('{}_pb2.py'.format(proto_base.replace('-', '_')), pardir=output)
+    else:
+        if do_onnx_ml:
+            pb2_py = qualify('{}_ml_pb2.py'.format(stem.replace('-', '_')), pardir=output)
+        else:
+            pb2_py = qualify('{}_pb2.py'.format(stem.replace('-', '_')), pardir=output)
+
+    print('generating {}'.format(pb_py))
+    with open(pb_py, 'w') as f:
+        f.write(str(dedent('''\
+        # This file is generated by setup.py. DO NOT EDIT!
+
+
+        from .{} import *  # noqa
+        '''.format(os.path.splitext(os.path.basename(pb2_py))[0]))))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description='Generates .proto file variations from .in.proto')
+    parser.add_argument('-p', '--package', default='onnx',
+                        help='package name in the generated proto files'
+                        ' (default: %(default)s)')
+    parser.add_argument('-m', '--ml', action='store_true', help='ML mode')
+    parser.add_argument('-l', '--lite', action='store_true',
+                        help='generate lite proto to use with protobuf-lite')
+    parser.add_argument('-o', '--output',
+                        default=os.path.realpath(os.path.dirname(__file__)),
+                        help='output directory (default: %(default)s)')
+    parser.add_argument('--protoc_path',
+                        default='',
+                        help='path to protoc for proto3 file validation')
+    parser.add_argument('stems', nargs='*', default=['onnx', 'onnx-operators', 'onnx-data'],
+                        help='list of .in.proto file stems '
+                        '(default: %(default)s)')
+    args = parser.parse_args()
+
+    if not os.path.exists(args.output):
+        os.makedirs(args.output)
+
+    for stem in args.stems:
+        convert(stem,
+                package_name=args.package,
+                output=args.output,
+                do_onnx_ml=args.ml,
+                lite=args.lite,
+                protoc_path=args.protoc_path)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/onnx/helper.py b/third_party/onnx/onnx/helper.py
new file mode 100644
index 0000000000..5ed1a721c4
--- /dev/null
+++ b/third_party/onnx/onnx/helper.py
@@ -0,0 +1,1015 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import collections.abc  # type: ignore
+import numbers
+
+import google.protobuf.message
+from onnx import TensorProto, SparseTensorProto, AttributeProto, ValueInfoProto, \
+    TensorShapeProto, NodeProto, ModelProto, GraphProto, OperatorSetIdProto, \
+    TypeProto, SequenceProto, MapProto, IR_VERSION, TrainingInfoProto, OptionalProto, \
+    FunctionProto
+from onnx import defs
+from onnx import mapping
+from onnx.mapping import STORAGE_TENSOR_TYPE_TO_FIELD
+from typing import Text, Sequence, Any, Optional, Dict, Union, TypeVar, Callable, Tuple, List, cast
+import numpy as np  # type: ignore
+import warnings
+
+VersionRowType = Union[Tuple[Text, int, int, int], Tuple[Text, int, int, int, int]]
+VersionTableType = List[VersionRowType]
+AssignmentBindingType = List[Tuple[Text, Text]]
+
+# This is a copy of the documented version in https://github.com/onnx/onnx/blob/main/docs/Versioning.md#released-versions
+# Both must be updated whenever a new version of ONNX is released.
+VERSION_TABLE: VersionTableType = [
+    # Release-version, IR version, ai.onnx version, ai.onnx.ml version, (optional) ai.onnx.training version
+    ('1.0', 3, 1, 1),
+    ('1.1', 3, 5, 1),
+    ('1.1.2', 3, 6, 1),
+    ('1.2', 3, 7, 1),
+    ('1.3', 3, 8, 1),
+    ('1.4.1', 4, 9, 1),
+    ('1.5.0', 5, 10, 1),
+    ('1.6.0', 6, 11, 2),
+    ('1.7.0', 7, 12, 2, 1),
+    ('1.8.0', 7, 13, 2, 1),
+    ('1.8.1', 7, 13, 2, 1),
+    ('1.9.0', 7, 14, 2, 1),
+    ('1.10.0', 8, 15, 2, 1),
+    ('1.10.1', 8, 15, 2, 1),
+    ('1.10.2', 8, 15, 2, 1),
+    ('1.11.0', 8, 16, 3, 1)
+]
+
+VersionMapType = Dict[Tuple[Text, int], int]
+
+
+def create_op_set_id_version_map(table: VersionTableType) -> VersionMapType:
+    """create a map from (opset-domain, opset-version) to ir-version from above table"""
+    result: VersionMapType = dict()
+
+    def process(release_version: Text, ir_version: int, *args: Any) -> None:
+        for pair in zip(['ai.onnx', 'ai.onnx.ml', 'ai.onnx.training'], args):
+            if (pair not in result):
+                result[pair] = ir_version
+    for row in table:
+        process(*row)
+    return result
+
+
+OP_SET_ID_VERSION_MAP = create_op_set_id_version_map(VERSION_TABLE)
+
+
+def find_min_ir_version_for(opsetidlist: List[OperatorSetIdProto]) -> int:
+    """Given list of opset ids, determine minimum IR version required"""
+    default_min_version = 3
+
+    def find_min(domain: Union[Text, None], version: int) -> int:
+        key = (domain if domain else 'ai.onnx', version)
+        if (key in OP_SET_ID_VERSION_MAP):
+            return OP_SET_ID_VERSION_MAP[key]
+        else:
+            raise ValueError("Unsupported opset-version.")
+    if (opsetidlist):
+        return max([find_min(x.domain, x.version) for x in opsetidlist])
+    return default_min_version  # if no opsets specified
+
+
+def make_node(
+        op_type: Text,
+        inputs: Sequence[Text],
+        outputs: Sequence[Text],
+        name: Optional[Text] = None,
+        doc_string: Optional[Text] = None,
+        domain: Optional[Text] = None,
+        **kwargs: Any
+) -> NodeProto:
+    """Construct a NodeProto.
+
+    Arguments:
+        op_type (string): The name of the operator to construct
+        inputs (list of string): list of input names
+        outputs (list of string): list of output names
+        name (string, default None): optional unique identifier for NodeProto
+        doc_string (string, default None): optional documentation string for NodeProto
+        domain (string, default None): optional domain for NodeProto.
+            If it's None, we will just use default domain (which is empty)
+        **kwargs (dict): the attributes of the node.  The acceptable values
+            are documented in :func:`make_attribute`.
+    Returns:
+        NodeProto
+    """
+
+    node = NodeProto()
+    node.op_type = op_type
+    node.input.extend(inputs)
+    node.output.extend(outputs)
+    if name:
+        node.name = name
+    if doc_string:
+        node.doc_string = doc_string
+    if domain is not None:
+        node.domain = domain
+    if kwargs:
+        node.attribute.extend(
+            make_attribute(key, value)
+            for key, value in sorted(kwargs.items())
+            if value is not None)
+    return node
+
+
+def make_operatorsetid(
+        domain: Text,
+        version: int,
+) -> OperatorSetIdProto:
+    """Construct an OperatorSetIdProto.
+
+    Arguments:
+        domain (string): The domain of the operator set id
+        version (integer): Version of operator set id
+    Returns:
+        OperatorSetIdProto
+    """
+    operatorsetid = OperatorSetIdProto()
+    operatorsetid.domain = domain
+    operatorsetid.version = version
+    return operatorsetid
+
+
+def make_graph(
+    nodes: Sequence[NodeProto],
+    name: Text,
+    inputs: Sequence[ValueInfoProto],
+    outputs: Sequence[ValueInfoProto],
+    initializer: Optional[Sequence[TensorProto]] = None,
+    doc_string: Optional[Text] = None,
+    value_info: Sequence[ValueInfoProto] = [],
+    sparse_initializer: Optional[Sequence[SparseTensorProto]] = None,
+) -> GraphProto:
+    """Construct a GraphProto
+
+    Arguments:
+        nodes: list of NodeProto
+        name (string): graph name
+        inputs: list of ValueInfoProto
+        outputs: list of ValueInfoProto
+        initializer: list of TensorProto
+        doc_string (string): graph documentation
+        value_info: list of ValueInfoProto
+        sparse_initializer: list of SparseTensorProto
+    Returns:
+        GraphProto
+    """
+    if initializer is None:
+        initializer = []
+    if sparse_initializer is None:
+        sparse_initializer = []
+    if value_info is None:
+        value_info = []
+    graph = GraphProto()
+    graph.node.extend(nodes)
+    graph.name = name
+    graph.input.extend(inputs)
+    graph.output.extend(outputs)
+    graph.initializer.extend(initializer)
+    graph.sparse_initializer.extend(sparse_initializer)
+    graph.value_info.extend(value_info)
+    if doc_string:
+        graph.doc_string = doc_string
+    return graph
+
+
+def make_opsetid(domain: Text, version: int) -> OperatorSetIdProto:
+    """Construct an OperatorSetIdProto.
+
+    Arguments:
+        domain (string): The domain of the operator set id
+        version (integer): Version of operator set id
+    Returns:
+        OperatorSetIdProto
+    """
+    opsetid = OperatorSetIdProto()
+    opsetid.domain = domain
+    opsetid.version = version
+    return opsetid
+
+
+def make_function(
+    domain: Text,
+    fname: Text,
+    inputs: Sequence[Text],
+    outputs: Sequence[Text],
+    nodes: Sequence[NodeProto],
+    opset_imports: Sequence[OperatorSetIdProto],
+    attributes: Optional[Sequence[Text]] = [],
+    doc_string: Optional[Text] = None
+) -> FunctionProto:
+    f = FunctionProto()
+    f.domain = domain
+    f.name = fname
+    f.input.extend(inputs)
+    f.output.extend(outputs)
+    f.node.extend(nodes)
+    f.opset_import.extend(opset_imports)
+    f.attribute.extend(attributes)
+    if doc_string:
+        f.doc_string = doc_string
+    return f
+
+
+def make_model(graph: GraphProto, **kwargs: Any) -> ModelProto:
+    """Construct a ModelProto
+
+    Arguments:
+        graph (GraphProto): *make_graph* returns
+        **kwargs: any attribute to add to the returned instance
+    Returns:
+        ModelProto
+    """
+    model = ModelProto()
+    # Touch model.ir_version so it is stored as the version from which it is
+    # generated.
+    model.ir_version = IR_VERSION
+    model.graph.CopyFrom(graph)
+
+    opset_imports: Optional[Sequence[OperatorSetIdProto]] = None
+    opset_imports = kwargs.pop('opset_imports', None)  # type: ignore
+    if opset_imports is not None:
+        model.opset_import.extend(opset_imports)
+    else:
+        # Default import
+        imp = model.opset_import.add()
+        imp.version = defs.onnx_opset_version()
+
+    functions: Optional[Sequence[FunctionProto]] = None
+    functions = kwargs.pop('functions', None)  # type: ignore
+    if functions is not None:
+        model.functions.extend(functions)
+
+    for k, v in kwargs.items():
+        # TODO: Does this work with repeated fields?
+        setattr(model, k, v)
+    return model
+
+
+# An extension of make_model that infers an IR_VERSION for the model,
+# if not specified, using a best-effort-basis.
+def make_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto:
+    ir_version_field = str('ir_version')
+    if (ir_version_field not in kwargs):
+        opset_imports_field = str('opset_imports')
+        imports = (kwargs[opset_imports_field] if opset_imports_field in kwargs else [])
+        kwargs[ir_version_field] = find_min_ir_version_for(imports)
+    return make_model(graph, **kwargs)
+
+
+def set_model_props(model: ModelProto, dict_value: Dict[Text, Text]) -> None:
+    del model.metadata_props[:]
+    for (k, v) in dict_value.items():
+        entry = model.metadata_props.add()
+        entry.key = k
+        entry.value = v
+        # model.metadata_properties.append(entry)
+
+
+def split_complex_to_pairs(ca: Sequence[np.complex64]) -> Sequence[int]:
+    return [(ca[i // 2].real if (i % 2 == 0) else ca[i // 2].imag)
+            for i in range(len(ca) * 2)]
+
+
+def make_tensor(
+        name: Text,
+        data_type: int,
+        dims: Sequence[int],
+        vals: Any,
+        raw: bool = False
+) -> TensorProto:
+    '''
+    Make a TensorProto with specified arguments.  If raw is False, this
+    function will choose the corresponding proto field to store the
+    values based on data_type. If raw is True, use "raw_data" proto
+    field to store the values, and values should be of type bytes in
+    this case.
+
+    Arguments:
+        name (string): tensor name
+        data_type (int): a value such as onnx.TensorProto.FLOAT
+        dims (List[int]): shape
+        vals: values
+        raw (bool): if True, vals contains the seralized content of the tensor,
+            otherwise, vals should be a list of values of the type defined by *data_type*
+
+    Returns:
+        TensorProto
+    '''
+    tensor = TensorProto()
+    tensor.data_type = data_type
+    tensor.name = name
+
+    if data_type == TensorProto.STRING:
+        assert not raw, "Can not use raw_data to store string type"
+
+    # Check number of vals specified equals tensor size
+    expected_size = 1 if (not raw) else (mapping.TENSOR_TYPE_TO_NP_TYPE[data_type].itemsize)
+    # Flatten a numpy array if its rank > 1
+    if type(vals) is np.ndarray and len(vals.shape) > 1:
+        vals = vals.flatten()
+    for d in dims:
+        expected_size = expected_size * d
+
+    if len(vals) != expected_size:
+        raise ValueError("Number of values does not match tensor's size. Expected {}, but it is {}. "
+            .format(expected_size, len(vals)))
+
+    if raw:
+        tensor.raw_data = vals
+    else:
+        if (data_type == TensorProto.COMPLEX64
+                or data_type == TensorProto.COMPLEX128):
+            vals = split_complex_to_pairs(vals)
+        # floa16/bfloat16 are stored as uint16
+        elif (data_type == TensorProto.FLOAT16
+                or data_type == TensorProto.BFLOAT16):
+            vals = np.array(vals).astype(np.float16).view(dtype=np.uint16).flatten().tolist()
+        field = mapping.STORAGE_TENSOR_TYPE_TO_FIELD[
+            mapping.TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE[data_type]]
+        getattr(tensor, field).extend(vals)
+    tensor.dims.extend(dims)
+    return tensor
+
+
+def make_sparse_tensor(
+    values: TensorProto,
+    indices: TensorProto,
+    dims: Sequence[int]
+) -> SparseTensorProto:
+    """Construct a SparseTensorProto
+
+    Arguments:
+        values (TensorProto): the values
+        indices (TensorProto): the indices
+        dims: the shape
+
+    Returns:
+        SparseTensorProto
+    """
+    sparse = SparseTensorProto()
+    sparse.values.CopyFrom(values)
+    sparse.indices.CopyFrom(indices)
+    sparse.dims.extend(dims)
+    return sparse
+
+
+def make_sequence(
+        name: Text,
+        elem_type: SequenceProto.DataType,
+        values: Sequence[Any],
+) -> SequenceProto:
+    '''
+    Make a Sequence with specified value arguments.
+    '''
+    sequence = SequenceProto()
+    sequence.name = name
+    sequence.elem_type = elem_type
+    values_field = mapping.STORAGE_ELEMENT_TYPE_TO_FIELD[elem_type]
+    getattr(sequence, values_field).extend(values)
+    return sequence
+
+
+def make_map(
+        name: Text,
+        key_type: int,
+        keys: List[Any],
+        values: SequenceProto
+) -> MapProto:
+    '''
+    Make a Map with specified key-value pair arguments.
+
+    Criteria for conversion:
+    - Keys and Values must have the same number of elements
+    - Every key in keys must be of the same type
+    - Every value in values must be of the same type
+    '''
+    map = MapProto()
+    valid_key_int_types = [TensorProto.INT8, TensorProto.INT16, TensorProto.INT32,
+                           TensorProto.INT64, TensorProto.UINT8, TensorProto.UINT16,
+                           TensorProto.UINT32, TensorProto.UINT64]
+    map.name = name
+    map.key_type = key_type
+    if key_type == TensorProto.STRING:
+        map.string_keys.extend(keys)
+    elif key_type in valid_key_int_types:
+        map.keys.extend(keys)
+    map.values.CopyFrom(values)
+    return map
+
+
+def make_optional(
+        name: Text,
+        elem_type: OptionalProto.DataType,
+        value: Optional[Any],
+) -> OptionalProto:
+    '''
+    Make an Optional with specified value arguments.
+    '''
+    optional = OptionalProto()
+    optional.name = name
+    optional.elem_type = elem_type
+    if elem_type != 0:
+        values_field = mapping.OPTIONAL_ELEMENT_TYPE_TO_FIELD[elem_type]
+        getattr(optional, values_field).CopyFrom(value)
+    return optional
+
+
+def _to_bytes_or_false(val: Union[Text, bytes]) -> Union[bytes, bool]:
+    """An internal graph to convert the input to a bytes or to False.
+
+    The criteria for conversion is as follows and should be python 2 and 3
+    compatible:
+    - If val is py2 str or py3 bytes: return bytes
+    - If val is py2 unicode or py3 str: return val.decode('utf-8')
+    - Otherwise, return False
+    """
+    if isinstance(val, bytes):
+        return val
+    try:
+        return val.encode('utf-8')
+    except AttributeError:
+        return False
+
+
+def make_attribute(
+        key: Text,
+        value: Any,
+        doc_string: Optional[Text] = None
+) -> AttributeProto:
+    """Makes an AttributeProto based on the value type."""
+    attr = AttributeProto()
+    attr.name = key
+    if doc_string:
+        attr.doc_string = doc_string
+
+    is_iterable = isinstance(value, collections.abc.Iterable)
+    bytes_or_false = _to_bytes_or_false(value)
+    # First, singular cases
+    # float
+    if isinstance(value, float):
+        attr.f = value
+        attr.type = AttributeProto.FLOAT
+    # integer
+    elif isinstance(value, numbers.Integral):
+        attr.i = cast(int, value)
+        attr.type = AttributeProto.INT
+    # string
+    elif bytes_or_false is not False:
+        assert isinstance(bytes_or_false, bytes)
+        attr.s = bytes_or_false
+        attr.type = AttributeProto.STRING
+    elif isinstance(value, TensorProto):
+        attr.t.CopyFrom(value)
+        attr.type = AttributeProto.TENSOR
+    elif isinstance(value, SparseTensorProto):
+        attr.sparse_tensor.CopyFrom(value)
+        attr.type = AttributeProto.SPARSE_TENSOR
+    elif isinstance(value, GraphProto):
+        attr.g.CopyFrom(value)
+        attr.type = AttributeProto.GRAPH
+    elif isinstance(value, TypeProto):
+        attr.tp.CopyFrom(value)
+        attr.type = AttributeProto.TYPE_PROTO
+    # third, iterable cases
+    elif is_iterable:
+        byte_array = [_to_bytes_or_false(v) for v in value]
+        if all(isinstance(v, numbers.Integral) for v in value):
+            # Turn np.int32/64 into Python built-in int.
+            attr.ints.extend(int(v) for v in value)
+            attr.type = AttributeProto.INTS
+        elif all(isinstance(v, numbers.Real) for v in value):
+            # Since ints and floats are members of Real, this allows a mix of ints and floats
+            # (and converts the ints to floats).
+            attr.floats.extend(float(v) for v in value)
+            attr.type = AttributeProto.FLOATS
+        elif all(map(lambda bytes_or_false: bytes_or_false is not False, byte_array)):
+            attr.strings.extend(cast(List[bytes], byte_array))
+            attr.type = AttributeProto.STRINGS
+        elif all(isinstance(v, TensorProto) for v in value):
+            attr.tensors.extend(value)
+            attr.type = AttributeProto.TENSORS
+        elif all(isinstance(v, SparseTensorProto) for v in value):
+            attr.sparse_tensors.extend(value)
+            attr.type = AttributeProto.SPARSE_TENSORS
+        elif all(isinstance(v, GraphProto) for v in value):
+            attr.graphs.extend(value)
+            attr.type = AttributeProto.GRAPHS
+        elif all(isinstance(tp, TypeProto) for tp in value):
+            attr.type_protos.extend(value)
+            attr.type = AttributeProto.TYPE_PROTOS
+        else:
+            raise ValueError(
+                "You passed in an iterable attribute but I cannot figure out "
+                "its applicable type.")
+    else:
+        raise TypeError(
+            'value "{}" is not valid attribute data type.'.format(value))
+    return attr
+
+
+def get_attribute_value(attr: AttributeProto) -> Any:
+    if attr.type == AttributeProto.FLOAT:
+        return attr.f
+    if attr.type == AttributeProto.INT:
+        return attr.i
+    if attr.type == AttributeProto.STRING:
+        return attr.s
+    if attr.type == AttributeProto.TENSOR:
+        return attr.t
+    if attr.type == AttributeProto.SPARSE_TENSOR:
+        return attr.sparse_tensor
+    if attr.type == AttributeProto.GRAPH:
+        return attr.g
+    if attr.type == AttributeProto.TYPE_PROTO:
+        return attr.tp
+    if attr.type == AttributeProto.FLOATS:
+        return list(attr.floats)
+    if attr.type == AttributeProto.INTS:
+        return list(attr.ints)
+    if attr.type == AttributeProto.STRINGS:
+        return list(attr.strings)
+    if attr.type == AttributeProto.TENSORS:
+        return list(attr.tensors)
+    if attr.type == AttributeProto.SPARSE_TENSORS:
+        return list(attr.sparse_tensors)
+    if attr.type == AttributeProto.GRAPHS:
+        return list(attr.graphs)
+    if attr.type == AttributeProto.TYPE_PROTOS:
+        return list(attr.type_protos)
+    raise ValueError("Unsupported ONNX attribute: {}".format(attr))
+
+
+def make_empty_tensor_value_info(name: Text) -> ValueInfoProto:
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    return value_info_proto
+
+
+def make_tensor_type_proto(
+        elem_type: int,
+        shape: Optional[Sequence[Union[Text, int, None]]],
+        shape_denotation: Optional[List[Text]] = None,
+) -> TypeProto:
+    """Makes a Tensor TypeProto based on the data type and shape."""
+
+    type_proto = TypeProto()
+    tensor_type_proto = type_proto.tensor_type
+    tensor_type_proto.elem_type = elem_type
+    tensor_shape_proto = tensor_type_proto.shape
+
+    if shape is not None:
+        # You might think this is a no-op (extending a normal Python
+        # list by [] certainly is), but protobuf lists work a little
+        # differently; if a field is never set, it is omitted from the
+        # resulting protobuf; a list that is explicitly set to be
+        # empty will get an (empty) entry in the protobuf. This
+        # difference is visible to our consumers, so make sure we emit
+        # an empty shape!
+        tensor_shape_proto.dim.extend([])
+
+        if shape_denotation:
+            if len(shape_denotation) != len(shape):
+                raise ValueError(
+                    'Invalid shape_denotation. '
+                    'Must be of the same length as shape.')
+
+        for i, d in enumerate(shape):
+            dim = tensor_shape_proto.dim.add()
+            if d is None:
+                pass
+            elif isinstance(d, int):
+                dim.dim_value = d
+            elif isinstance(d, str):
+                dim.dim_param = d
+            else:
+                raise ValueError(
+                    'Invalid item in shape: {}. '
+                    'Needs to be of int or str.'.format(d))
+
+            if shape_denotation:
+                dim.denotation = shape_denotation[i]
+
+    return type_proto
+
+
+def make_tensor_value_info(
+        name: Text,
+        elem_type: int,
+        shape: Optional[Sequence[Union[Text, int, None]]],
+        doc_string: Text = "",
+        shape_denotation: Optional[List[Text]] = None,
+) -> ValueInfoProto:
+    """Makes a ValueInfoProto based on the data type and shape."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    tensor_type_proto = make_tensor_type_proto(elem_type, shape, shape_denotation)
+    value_info_proto.type.CopyFrom(tensor_type_proto)
+    return value_info_proto
+
+
+def make_sparse_tensor_type_proto(
+        elem_type: int,
+        shape: Optional[Sequence[Union[Text, int, None]]],
+        shape_denotation: Optional[List[Text]] = None,
+) -> TypeProto:
+    """Makes a SparseTensor TypeProto based on the data type and shape."""
+
+    type_proto = TypeProto()
+    sparse_tensor_type_proto = type_proto.sparse_tensor_type
+    sparse_tensor_type_proto.elem_type = elem_type
+    sparse_tensor_shape_proto = sparse_tensor_type_proto.shape
+
+    if shape is not None:
+        # You might think this is a no-op (extending a normal Python
+        # list by [] certainly is), but protobuf lists work a little
+        # differently; if a field is never set, it is omitted from the
+        # resulting protobuf; a list that is explicitly set to be
+        # empty will get an (empty) entry in the protobuf. This
+        # difference is visible to our consumers, so make sure we emit
+        # an empty shape!
+        sparse_tensor_shape_proto.dim.extend([])
+
+        if shape_denotation:
+            if len(shape_denotation) != len(shape):
+                raise ValueError(
+                    'Invalid shape_denotation. '
+                    'Must be of the same length as shape.')
+
+        for i, d in enumerate(shape):
+            dim = sparse_tensor_shape_proto.dim.add()
+            if d is None:
+                pass
+            elif isinstance(d, int):
+                dim.dim_value = d
+            elif isinstance(d, str):
+                dim.dim_param = d
+            else:
+                raise ValueError(
+                    'Invalid item in shape: {}. '
+                    'Needs to be of int or text.'.format(d))
+
+            if shape_denotation:
+                dim.denotation = shape_denotation[i]
+
+    return type_proto
+
+
+def make_sparse_tensor_value_info(
+        name: Text,
+        elem_type: int,
+        shape: Optional[Sequence[Union[Text, int, None]]],
+        doc_string: Text = "",
+        shape_denotation: Optional[List[Text]] = None,
+) -> ValueInfoProto:
+    """Makes a SparseTensor ValueInfoProto based on the data type and shape."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    sparse_tensor_type_proto = make_sparse_tensor_type_proto(elem_type, shape, shape_denotation)
+    value_info_proto.type.sparse_tensor_type.CopyFrom(sparse_tensor_type_proto.sparse_tensor_type)
+    return value_info_proto
+
+
+def make_sequence_type_proto(
+        inner_type_proto: TypeProto,
+) -> TypeProto:
+    """Makes a sequence TypeProto."""
+    type_proto = TypeProto()
+    type_proto.sequence_type.elem_type.CopyFrom(inner_type_proto)
+    return type_proto
+
+
+def make_optional_type_proto(
+        inner_type_proto: TypeProto,
+) -> TypeProto:
+    """Makes an optional TypeProto."""
+    type_proto = TypeProto()
+    type_proto.optional_type.elem_type.CopyFrom(inner_type_proto)
+    return type_proto
+
+
+def make_value_info(
+        name: Text,
+        type_proto: TypeProto,
+        doc_string: Text = "",
+) -> ValueInfoProto:
+    """Makes a ValueInfoProto with the given type_proto."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    value_info_proto.type.CopyFrom(type_proto)
+    return value_info_proto
+
+
+def _sanitize_str(s: Union[Text, bytes]) -> Text:
+    if isinstance(s, str):
+        sanitized = s
+    elif isinstance(s, bytes):
+        sanitized = s.decode('utf-8', errors='ignore')
+    else:
+        sanitized = str(s)
+    if len(sanitized) < 64:
+        return sanitized
+    return sanitized[:64] + '...<+len=%d>' % (len(sanitized) - 64)
+
+
+def make_tensor_sequence_value_info(
+        name: Text,
+        elem_type: int,
+        shape: Optional[Sequence[Union[Text, int, None]]],
+        doc_string: Text = "",
+        elem_shape_denotation: Optional[List[Text]] = None,
+) -> ValueInfoProto:
+    """Makes a Sequence[Tensors] ValueInfoProto based on the data type and shape."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    tensor_type_proto = make_tensor_type_proto(elem_type, shape, elem_shape_denotation)
+    sequence_type_proto = make_sequence_type_proto(tensor_type_proto)
+    value_info_proto.type.sequence_type.CopyFrom(sequence_type_proto.sequence_type)
+
+    return value_info_proto
+
+
+def printable_attribute(attr: AttributeProto, subgraphs: bool = False) -> Union[Text, Tuple[Text, List[GraphProto]]]:
+    content = []
+    content.append(attr.name)
+    content.append("=")
+
+    def str_float(f: float) -> Text:
+        # NB: Different Python versions print different numbers of trailing
+        # decimals, specifying this explicitly keeps it consistent for all
+        # versions
+        return '{:.15g}'.format(f)
+
+    def str_int(i: int) -> Text:
+        return str(i)
+
+    _T = TypeVar('_T')  # noqa
+
+    def str_list(str_elem: Callable[[_T], Text], xs: Sequence[_T]) -> Text:
+        return '[' + ', '.join(map(str_elem, xs)) + ']'
+
+    # for now, this logic should continue to work as long as we are running on a proto3
+    # implementation. If/when we switch to proto3, we will need to use attr.type
+
+    # To support printing subgraphs, if we find a graph attribute, print out
+    # its name here and pass the graph itself up to the caller for later
+    # printing.
+    graphs = []
+    if attr.HasField("f"):
+        content.append(str_float(attr.f))
+    elif attr.HasField("i"):
+        content.append(str_int(attr.i))
+    elif attr.HasField("s"):
+        # TODO: Bit nervous about Python 2 / Python 3 determinism implications
+        content.append(repr(_sanitize_str(attr.s)))
+    elif attr.HasField("t"):
+        if len(attr.t.dims) > 0:
+            content.append("<Tensor>")
+        else:
+            # special case to print scalars
+            field = STORAGE_TENSOR_TYPE_TO_FIELD[attr.t.data_type]
+            content.append('<Scalar Tensor {}>'.format(str(getattr(attr.t, field))))
+    elif attr.HasField("g"):
+        content.append("<graph {}>".format(attr.g.name))
+        graphs.append(attr.g)
+    elif attr.HasField("tp"):
+        content.append("<Type Proto {}>".format(attr.tp))
+    elif attr.floats:
+        content.append(str_list(str_float, attr.floats))
+    elif attr.ints:
+        content.append(str_list(str_int, attr.ints))
+    elif attr.strings:
+        # TODO: Bit nervous about Python 2 / Python 3 determinism implications
+        content.append(str(list(map(_sanitize_str, attr.strings))))
+    elif attr.tensors:
+        content.append("[<Tensor>, ...]")
+    elif attr.type_protos:
+        content.append('[')
+        for i, tp in enumerate(attr.type_protos):
+            comma = ',' if i != len(attr.type_protos) - 1 else ''
+            content.append('<Type Proto {}>{}'.format(tp, comma))
+        content.append(']')
+    elif attr.graphs:
+        content.append('[')
+        for i, g in enumerate(attr.graphs):
+            comma = ',' if i != len(attr.graphs) - 1 else ''
+            content.append('<graph {}>{}'.format(g.name, comma))
+        content.append(']')
+        graphs.extend(attr.graphs)
+    else:
+        content.append("<Unknown>")
+    if subgraphs:
+        return ' '.join(content), graphs
+    else:
+        return ' '.join(content)
+
+
+def printable_dim(dim: TensorShapeProto.Dimension) -> Text:
+    which = dim.WhichOneof('value')
+    assert which is not None
+    return str(getattr(dim, which))
+
+
+def printable_type(t: TypeProto) -> Text:
+    if t.WhichOneof('value') == "tensor_type":
+        s = TensorProto.DataType.Name(t.tensor_type.elem_type)
+        if t.tensor_type.HasField('shape'):
+            if len(t.tensor_type.shape.dim):
+                s += str(', ' + 'x'.join(map(printable_dim, t.tensor_type.shape.dim)))
+            else:
+                s += str(', scalar')
+        return s
+    if t.WhichOneof('value') is None:
+        return ""
+    return 'Unknown type {}'.format(t.WhichOneof('value'))
+
+
+def printable_value_info(v: ValueInfoProto) -> Text:
+    s = '%{}'.format(v.name)
+    if v.type:
+        s = '{}[{}]'.format(s, printable_type(v.type))
+    return s
+
+
+def printable_tensor_proto(t: TensorProto) -> Text:
+    s = '%{}['.format(t.name)
+    s += TensorProto.DataType.Name(t.data_type)
+    if t.dims is not None:
+        if len(t.dims):
+            s += str(', ' + 'x'.join(map(str, t.dims)))
+        else:
+            s += str(', scalar')
+    s += ']'
+    return s
+
+
+def printable_node(node: NodeProto, prefix: Text = '', subgraphs: bool = False) -> Union[Text, Tuple[Text, List[GraphProto]]]:
+    content = []
+    if len(node.output):
+        content.append(
+            ', '.join(['%{}'.format(name) for name in node.output]))
+        content.append('=')
+    # To deal with nested graphs
+    graphs: List[GraphProto] = []
+    printed_attrs = []
+    for attr in node.attribute:
+        if subgraphs:
+            printed_attr_subgraphs = printable_attribute(attr, subgraphs)
+            assert isinstance(printed_attr_subgraphs[1], list)
+            graphs.extend(printed_attr_subgraphs[1])
+            printed_attrs.append(printed_attr_subgraphs[0])
+        else:
+            printed = printable_attribute(attr)
+            assert isinstance(printed, Text)
+            printed_attrs.append(printed)
+    printed_attributes = ', '.join(sorted(printed_attrs))
+    printed_inputs = ', '.join(['%{}'.format(name) for name in node.input])
+    if node.attribute:
+        content.append("{}[{}]({})".format(node.op_type, printed_attributes, printed_inputs))
+    else:
+        content.append("{}({})".format(node.op_type, printed_inputs))
+    if subgraphs:
+        return prefix + ' '.join(content), graphs
+    else:
+        return prefix + ' '.join(content)
+
+
+def printable_graph(graph: GraphProto, prefix: Text = '') -> Text:
+    """
+    Display a GraphProto as a string.
+
+    Arguments:
+        graph (GraphProto): the graph to display
+        prefix (string): prefix of every line
+
+    Returns:
+        string
+    """
+    content = []
+    indent = prefix + '  '
+    # header
+    header = ['graph', graph.name]
+    initializers = {t.name for t in graph.initializer}
+    if len(graph.input):
+        header.append("(")
+        in_strs = []  # required inputs
+        in_with_init_strs = []  # optional inputs with initializer providing default value
+        for inp in graph.input:
+            if inp.name not in initializers:
+                in_strs.append(printable_value_info(inp))
+            else:
+                in_with_init_strs.append(printable_value_info(inp))
+        if in_strs:
+            content.append(prefix + ' '.join(header))
+            header = []
+            for line in in_strs:
+                content.append(prefix + '  ' + line)
+        header.append(")")
+
+        if in_with_init_strs:
+            header.append("optional inputs with matching initializers (")
+            content.append(prefix + ' '.join(header))
+            header = []
+            for line in in_with_init_strs:
+                content.append(prefix + '  ' + line)
+            header.append(")")
+
+        # from IR 4 onwards an initializer is not required to have a matching graph input
+        # so output the name, type and shape of those as well
+        if len(in_with_init_strs) < len(initializers):
+            graph_inputs = {i.name for i in graph.input}
+            init_strs = [printable_tensor_proto(i) for i in graph.initializer
+                         if i.name not in graph_inputs]
+            header.append("initializers (")
+            content.append(prefix + ' '.join(header))
+            header = []
+            for line in init_strs:
+                content.append(prefix + '  ' + line)
+            header.append(")")
+
+    header.append('{')
+    content.append(prefix + ' '.join(header))
+    graphs: List[GraphProto] = []
+    # body
+    for node in graph.node:
+        contents_subgraphs = printable_node(node, indent, subgraphs=True)
+        assert isinstance(contents_subgraphs[1], list)
+        content.append(contents_subgraphs[0])
+        graphs.extend(contents_subgraphs[1])
+    # tail
+    tail = ['return']
+    if len(graph.output):
+        tail.append(
+            ', '.join(['%{}'.format(out.name) for out in graph.output]))
+    content.append(indent + ' '.join(tail))
+    # closing bracket
+    content.append(prefix + '}')
+    for g in graphs:
+        content.append('\n' + printable_graph(g))
+    return '\n'.join(content)
+
+
+def strip_doc_string(proto: google.protobuf.message.Message) -> None:
+    """
+    Empties `doc_string` field on any nested protobuf messages
+    """
+    assert isinstance(proto, google.protobuf.message.Message)
+    for descriptor in proto.DESCRIPTOR.fields:
+        if descriptor.name == 'doc_string':
+            proto.ClearField(descriptor.name)
+        elif descriptor.type == descriptor.TYPE_MESSAGE:
+            if descriptor.label == descriptor.LABEL_REPEATED:
+                for x in getattr(proto, descriptor.name):
+                    strip_doc_string(x)
+            elif proto.HasField(descriptor.name):
+                strip_doc_string(getattr(proto, descriptor.name))
+
+
+def make_training_info(algorithm: GraphProto, algorithm_bindings: AssignmentBindingType, initialization: Optional[GraphProto], initialization_bindings: Optional[AssignmentBindingType]) -> TrainingInfoProto:
+    training_info = TrainingInfoProto()
+    training_info.algorithm.CopyFrom(algorithm)
+    for k, v in algorithm_bindings:
+        binding = training_info.update_binding.add()
+        binding.key = k
+        binding.value = v
+
+    if initialization:
+        training_info.initialization.CopyFrom(initialization)
+    if initialization_bindings:
+        for k, v in initialization_bindings:
+            binding = training_info.initialization_binding.add()
+            binding.key = k
+            binding.value = v
+
+    return training_info
+
+
+# For backwards compatibility
+def make_sequence_value_info(
+        name: Text,
+        elem_type: int,
+        shape: Optional[Sequence[Union[Text, int, None]]],
+        doc_string: Text = "",
+        elem_shape_denotation: Optional[List[Text]] = None,
+) -> ValueInfoProto:
+    """Makes a Sequence[Tensors] ValueInfoProto based on the data type and shape."""
+    warnings.warn(str("`onnx.helper.make_sequence_value_info` is a deprecated alias for `onnx.helper.make_tensor_sequence_value_info`. To silence this warning, please use `make_tensor_sequence_value_info` for `TensorProto` sequences. Deprecated in ONNX v1.10.0, `onnx.helper.make_sequence_value_info alias` will be removed in an upcoming release."), DeprecationWarning, stacklevel=2)
+    return make_tensor_sequence_value_info(name, elem_type, shape, doc_string, elem_shape_denotation)
diff --git a/third_party/onnx/onnx/hub.py b/third_party/onnx/onnx/hub.py
new file mode 100644
index 0000000000..a1608c3e9f
--- /dev/null
+++ b/third_party/onnx/onnx/hub.py
@@ -0,0 +1,259 @@
+# SPDX-License-Identifier: Apache-2.0
+"""ONNX Model Hub
+
+This implements the python client for the ONNX model hub.
+"""
+from os.path import join
+from urllib.request import urlopen
+from urllib.error import HTTPError
+import json
+import os
+import hashlib
+from io import BytesIO
+from typing import List, Optional, Dict, Any, Tuple, cast, Set, IO
+import onnx
+import sys
+
+if "ONNX_HOME" in os.environ:
+    _ONNX_HUB_DIR = join(os.environ["ONNX_HOME"], "hub")
+elif "XDG_CACHE_HOME" in os.environ:
+    _ONNX_HUB_DIR = join(os.environ["XDG_CACHE_HOME"], "onnx", "hub")
+else:
+    _ONNX_HUB_DIR = join(os.path.expanduser("~"), ".cache", "onnx", "hub")
+
+
+class ModelInfo(object):
+    """
+    A class to represent a model's property and metadata in the ONNX Hub.
+    It extracts model name, path, sha, tags, etc. from the passed in raw_model_info dict.
+
+    Attributes:
+        model: The name of the model.
+        model_path: The path to the model, relative to the model zoo (https://github.com/onnx/models/) repo root.
+        metadata: Additional metadata of the model, such as the size of the model, IO ports, etc.
+        model_sha: The SHA256 digest of the model file.
+        tags: A set of tags associated with the model.
+        opset: The opset version of the model.
+    """
+
+    def __init__(self, raw_model_info: Dict[str, Any]) -> None:
+        """
+        Parameters:
+            raw_model_info: A JSON dict containing the model info.
+        """
+        self.model = cast(str, raw_model_info["model"])
+
+        self.model_path = cast(str, raw_model_info["model_path"])
+        self.metadata: Dict[str, Any] = cast(Dict[str, Any], raw_model_info["metadata"])
+        self.model_sha: Optional[str] = None
+        if "model_sha" in self.metadata:
+            self.model_sha = cast(str, self.metadata["model_sha"])
+
+        self.tags: Set[str] = set()
+        if "tags" in self.metadata:
+            self.tags = set(cast(List[str], self.metadata["tags"]))
+
+        self.opset = cast(int, raw_model_info["opset_version"])
+        self.raw_model_info: Dict[str, Any] = raw_model_info
+
+    def __str__(self) -> str:
+        return "ModelInfo(model={}, opset={}, path={}, metadata={})".format(
+            self.model, self.opset, self.model_path, self.metadata
+        )
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+
+def set_dir(new_dir: str) -> None:
+    """
+    Sets the current ONNX hub cache location
+
+    :param new_dir: location of new model hub cache
+    """
+    global _ONNX_HUB_DIR
+    _ONNX_HUB_DIR = new_dir
+
+
+def get_dir() -> str:
+    """
+    Gets the current ONNX hub cache location
+
+    :return: The location of the ONNX hub model cache
+    """
+    return _ONNX_HUB_DIR
+
+
+def _parse_repo_info(repo: str) -> Tuple[str, str, str]:
+    """
+    Gets the repo owner, name and ref from a repo specification string.
+    """
+    repo_owner = repo.split("/")[0]
+    repo_name = repo.split("/")[1].split(":")[0]
+    if ":" in repo:
+        repo_ref = repo.split("/")[1].split(":")[1]
+    else:
+        repo_ref = "main"
+    return repo_owner, repo_name, repo_ref
+
+
+def _verify_repo_ref(repo: str) -> bool:
+    """
+    Verifies whether the given model repo can be trusted.
+    A model repo can be trusted if it matches onnx/models:main.
+    """
+    repo_owner, repo_name, repo_ref = _parse_repo_info(repo)
+    return (repo_owner == "onnx") and (repo_name == "models") and (repo_ref == "main")
+
+
+def _get_base_url(repo: str, lfs: bool = False) -> str:
+    """
+    Gets the base github url from a repo specification string
+
+    :param repo: The location of the model repo in format "user/repo[:branch]".
+        If no branch is found will default to "main"
+    :param lfs: whether the url is for downloading lfs models
+    :return: the base github url for downloading
+    """
+    repo_owner, repo_name, repo_ref = _parse_repo_info(repo)
+
+    if lfs:
+        return "https://media.githubusercontent.com/media/{}/{}/{}/".format(repo_owner, repo_name, repo_ref)
+    else:
+        return "https://raw.githubusercontent.com/{}/{}/{}/".format(repo_owner, repo_name, repo_ref)
+
+
+def _download_file(url: str, file_name: str) -> None:
+    """
+    Downloads the file with specifed file_name from the url
+
+    :param url: a url of download link
+    :param file_name: a specified file name for the downloaded file
+    """
+    chunk_size = 16384  # 1024 * 16
+    with urlopen(url) as response, open(file_name, 'wb') as f:
+        # Loads processively with chuck_size for huge models
+        while True:
+            chunk = response.read(chunk_size)
+            if not chunk:
+                break
+            f.write(chunk)
+
+
+def list_models(
+    repo: str = "onnx/models:main", model: Optional[str] = None, tags: Optional[List[str]] = None
+) -> List[ModelInfo]:
+    """
+    Gets the list of model info consistent with a given name and tags
+
+    :param repo: The location of the model repo in format "user/repo[:branch]".
+        If no branch is found will default to "main"
+    :param model: The name of the model to search for. If `None`, will return all models with matching tags.
+    :param tags: A list of tags to filter models by. If `None`, will return all models with matching name.
+    :return: list of ModelInfo
+    """
+    base_url = _get_base_url(repo)
+    manifest_url = base_url + "ONNX_HUB_MANIFEST.json"
+    try:
+        with urlopen(manifest_url) as response:
+            manifest: List[ModelInfo] = [ModelInfo(info) for info in json.load(cast(IO[str], response))]
+    except HTTPError as e:
+        raise AssertionError("Could not find manifest at {}".format(manifest_url), e)
+
+    # Filter by model name first.
+    matching_models = manifest if model is None else [m for m in manifest if m.model.lower() == model.lower()]
+
+    # Filter by tags
+    if tags is None:
+        return matching_models
+    else:
+        canonical_tags = {t.lower() for t in tags}
+        matching_info_list: List[ModelInfo] = []
+        for m in matching_models:
+            model_tags = {t.lower() for t in m.tags}
+            if len(canonical_tags.intersection(model_tags)) > 0:
+                matching_info_list.append(m)
+        return matching_info_list
+
+
+def get_model_info(model: str, repo: str = "onnx/models:main", opset: Optional[int] = None) -> ModelInfo:
+    """
+    Gets the model info matching the given name and opset.
+
+    :param model: The name of the onnx model in the manifest. This field is case-sensitive
+    :param repo: The location of the model repo in format "user/repo[:branch]".
+        If no branch is found will default to "main"
+    :param opset: The opset of the model to get. The default of `None` will return the model with largest opset.
+    :return: ModelInfo
+    """
+    matching_models = list_models(repo, model)
+    if not matching_models:
+        raise AssertionError("No models found with name {}".format(model))
+
+    if opset is None:
+        selected_models = sorted(matching_models, key=lambda m: -m.opset)
+    else:
+        selected_models = [m for m in matching_models if m.opset == opset]
+        if len(selected_models) == 0:
+            valid_opsets = [m.opset for m in matching_models]
+            raise AssertionError("{} has no version with opset {}. Valid opsets: {}".format(model, opset, valid_opsets))
+    return selected_models[0]
+
+
+def load(
+    model: str,
+    repo: str = "onnx/models:main",
+    opset: Optional[int] = None,
+    force_reload: bool = False,
+    silent: bool = False,
+) -> Optional[onnx.ModelProto]:
+    """
+    Downloads a model by name from the onnx model hub
+
+    :param model: The name of the onnx model in the manifest. This field is case-sensitive
+    :param repo: The location of the model repo in format "user/repo[:branch]".
+        If no branch is found will default to "main"
+    :param opset: The opset of the model to download. The default of `None` automatically chooses the largest opset
+    :param force_reload: Whether to force the model to re-download even if its already found in the cache
+    :param silent: Whether to suppress the warning message if the repo is not trusted.
+    :return: ModelProto or None
+    """
+    selected_model = get_model_info(model, repo, opset)
+    local_model_path_arr = selected_model.model_path.split("/")
+    if selected_model.model_sha is not None:
+        local_model_path_arr[-1] = "{}_{}".format(selected_model.model_sha, local_model_path_arr[-1])
+    local_model_path = join(_ONNX_HUB_DIR, os.sep.join(local_model_path_arr))
+
+    if force_reload or not os.path.exists(local_model_path):
+        if not _verify_repo_ref(repo) and not silent:
+            msg = (
+                'The model repo specification "{}" is not trusted and may'
+                " contain security vulnerabilities. Only continue if you trust this repo."
+            ).format(repo)
+
+            print(msg, file=sys.stderr)
+            print("Continue?[y/n]")
+            if input().lower() != "y":
+                return None
+
+        os.makedirs(os.path.dirname(local_model_path), exist_ok=True)
+        lfs_url = _get_base_url(repo, True)
+        print("Downloading {} to local path {}".format(model, local_model_path))
+        _download_file(lfs_url + selected_model.model_path, local_model_path)
+    else:
+        print("Using cached {} model from {}".format(model, local_model_path))
+
+    with open(local_model_path, "rb") as f:
+        model_bytes = f.read()
+
+    if selected_model.model_sha is not None:
+        downloaded_sha = hashlib.sha256(model_bytes).hexdigest()
+        if not downloaded_sha == selected_model.model_sha:
+            raise AssertionError(
+                (
+                    "The cached model has SHA256 {} while checksum should be {}. "
+                    + "The model in the hub may have been updated. Use force_reload to download the model from the model hub."
+                ).format(downloaded_sha, selected_model.model_sha)
+            )
+
+    return onnx.load(cast(IO[bytes], BytesIO(model_bytes)))
diff --git a/third_party/onnx/onnx/mapping.py b/third_party/onnx/onnx/mapping.py
new file mode 100644
index 0000000000..63d60993e7
--- /dev/null
+++ b/third_party/onnx/onnx/mapping.py
@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx import TensorProto, SequenceProto, OptionalProto
+import numpy as np  # type: ignore
+
+TENSOR_TYPE_TO_NP_TYPE = {
+    int(TensorProto.FLOAT): np.dtype('float32'),
+    int(TensorProto.UINT8): np.dtype('uint8'),
+    int(TensorProto.INT8): np.dtype('int8'),
+    int(TensorProto.UINT16): np.dtype('uint16'),
+    int(TensorProto.INT16): np.dtype('int16'),
+    int(TensorProto.INT32): np.dtype('int32'),
+    int(TensorProto.INT64): np.dtype('int64'),
+    int(TensorProto.BOOL): np.dtype('bool'),
+    int(TensorProto.FLOAT16): np.dtype('float16'),
+    int(TensorProto.BFLOAT16): np.dtype('float16'),  # native numpy does not support bfloat16
+    int(TensorProto.DOUBLE): np.dtype('float64'),
+    int(TensorProto.COMPLEX64): np.dtype('complex64'),
+    int(TensorProto.COMPLEX128): np.dtype('complex128'),
+    int(TensorProto.UINT32): np.dtype('uint32'),
+    int(TensorProto.UINT64): np.dtype('uint64'),
+    int(TensorProto.STRING): np.dtype('object')
+}
+
+# Currently native numpy does not support bfloat16 so TensorProto.BFLOAT16 is ignored for now
+# Numpy float16 array is only reversed to TensorProto.FLOAT16
+NP_TYPE_TO_TENSOR_TYPE = {v: k for k, v in TENSOR_TYPE_TO_NP_TYPE.items() if k != TensorProto.BFLOAT16}
+
+TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE = {
+    int(TensorProto.FLOAT): int(TensorProto.FLOAT),
+    int(TensorProto.UINT8): int(TensorProto.INT32),
+    int(TensorProto.INT8): int(TensorProto.INT32),
+    int(TensorProto.UINT16): int(TensorProto.INT32),
+    int(TensorProto.INT16): int(TensorProto.INT32),
+    int(TensorProto.INT32): int(TensorProto.INT32),
+    int(TensorProto.INT64): int(TensorProto.INT64),
+    int(TensorProto.BOOL): int(TensorProto.INT32),
+    int(TensorProto.FLOAT16): int(TensorProto.UINT16),
+    int(TensorProto.BFLOAT16): int(TensorProto.UINT16),
+    int(TensorProto.DOUBLE): int(TensorProto.DOUBLE),
+    int(TensorProto.COMPLEX64): int(TensorProto.FLOAT),
+    int(TensorProto.COMPLEX128): int(TensorProto.DOUBLE),
+    int(TensorProto.UINT32): int(TensorProto.UINT32),
+    int(TensorProto.UINT64): int(TensorProto.UINT64),
+    int(TensorProto.STRING): int(TensorProto.STRING),
+}
+
+STORAGE_TENSOR_TYPE_TO_FIELD = {
+    int(TensorProto.FLOAT): 'float_data',
+    int(TensorProto.INT32): 'int32_data',
+    int(TensorProto.INT64): 'int64_data',
+    int(TensorProto.UINT16): 'int32_data',
+    int(TensorProto.DOUBLE): 'double_data',
+    int(TensorProto.COMPLEX64): 'float_data',
+    int(TensorProto.COMPLEX128): 'double_data',
+    int(TensorProto.UINT32): 'uint64_data',
+    int(TensorProto.UINT64): 'uint64_data',
+    int(TensorProto.STRING): 'string_data',
+    int(TensorProto.BOOL): 'int32_data',
+}
+
+STORAGE_ELEMENT_TYPE_TO_FIELD = {
+    int(SequenceProto.TENSOR): 'tensor_values',
+    int(SequenceProto.SPARSE_TENSOR): 'sparse_tensor_values',
+    int(SequenceProto.SEQUENCE): 'sequence_values',
+    int(SequenceProto.MAP): 'map_values',
+    int(OptionalProto.OPTIONAL): 'optional_value'
+}
+
+OPTIONAL_ELEMENT_TYPE_TO_FIELD = {
+    int(OptionalProto.TENSOR): 'tensor_value',
+    int(OptionalProto.SPARSE_TENSOR): 'sparse_tensor_value',
+    int(OptionalProto.SEQUENCE): 'sequence_value',
+    int(OptionalProto.MAP): 'map_value',
+    int(OptionalProto.OPTIONAL): 'optional_value'
+}
diff --git a/third_party/onnx/onnx/numpy_helper.py b/third_party/onnx/onnx/numpy_helper.py
new file mode 100644
index 0000000000..620644439b
--- /dev/null
+++ b/third_party/onnx/onnx/numpy_helper.py
@@ -0,0 +1,362 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+
+import numpy as np  # type: ignore
+from onnx import TensorProto, MapProto, SequenceProto, OptionalProto
+from onnx import mapping, helper
+from onnx.external_data_helper import load_external_data_for_tensor, uses_external_data
+from typing import Sequence, Any, Optional, Text, List, Dict
+
+
+def combine_pairs_to_complex(fa: Sequence[int]) -> Sequence[np.complex64]:
+    return [complex(fa[i * 2], fa[i * 2 + 1]) for i in range(len(fa) // 2)]
+
+
+def to_array(tensor: TensorProto, base_dir: Text = "") -> np.ndarray:
+    """Converts a tensor def object to a numpy array.
+
+    Inputs:
+        tensor: a TensorProto object.
+        base_dir: if external tensor exists, base_dir can help to find the path to it
+    Returns:
+        arr: the converted array.
+    """
+    if tensor.HasField("segment"):
+        raise ValueError(
+            "Currently not supporting loading segments.")
+    if tensor.data_type == TensorProto.UNDEFINED:
+        raise TypeError("The element type in the input tensor is not defined.")
+
+    tensor_dtype = tensor.data_type
+    np_dtype = mapping.TENSOR_TYPE_TO_NP_TYPE[tensor_dtype]
+    storage_type = mapping.TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE[tensor_dtype]
+    storage_np_dtype = mapping.TENSOR_TYPE_TO_NP_TYPE[storage_type]
+    storage_field = mapping.STORAGE_TENSOR_TYPE_TO_FIELD[storage_type]
+    dims = tensor.dims
+
+    if tensor.data_type == TensorProto.STRING:
+        utf8_strings = getattr(tensor, storage_field)
+        ss = list(s.decode('utf-8') for s in utf8_strings)
+        return np.asarray(ss).astype(np_dtype).reshape(dims)
+
+    # Load raw data from external tensor if it exists
+    if uses_external_data(tensor):
+        load_external_data_for_tensor(tensor, base_dir)
+
+    if tensor.HasField("raw_data"):
+        # Raw_bytes support: using frombuffer.
+        if sys.byteorder == 'big':
+            # Convert endian from little to big
+            convert_endian(tensor)
+        return np.frombuffer(
+            tensor.raw_data,
+            dtype=np_dtype).reshape(dims)
+    else:
+        # float16/bfloat16 is stored as int32 (uint16 type); Need view to get the original value
+        if (tensor_dtype == TensorProto.FLOAT16
+                or tensor_dtype == TensorProto.BFLOAT16):
+            return (
+                np.asarray(
+                    tensor.int32_data,
+                    dtype=np.uint16)
+                .reshape(dims)
+                .view(np.float16))
+        data = getattr(tensor, storage_field)
+        if (tensor_dtype == TensorProto.COMPLEX64
+                or tensor_dtype == TensorProto.COMPLEX128):
+            data = combine_pairs_to_complex(data)
+
+        return (
+            np.asarray(
+                data,
+                dtype=storage_np_dtype)
+            .astype(np_dtype)
+            .reshape(dims)
+        )
+
+
+def from_array(arr: np.ndarray, name: Optional[Text] = None) -> TensorProto:
+    """Converts a numpy array to a tensor def.
+
+    Inputs:
+        arr: a numpy array.
+        name: (optional) the name of the tensor.
+    Returns:
+        TensorProto: the converted tensor def.
+    """
+    tensor = TensorProto()
+    tensor.dims.extend(arr.shape)
+    if name:
+        tensor.name = name
+
+    if arr.dtype == object:
+        # Special care for strings.
+        tensor.data_type = mapping.NP_TYPE_TO_TENSOR_TYPE[arr.dtype]
+        # TODO: Introduce full string support.
+        # We flatten the array in case there are 2-D arrays are specified
+        # We throw the error below if we have a 3-D array or some kind of other
+        # object. If you want more complex shapes then follow the below instructions.
+        # Unlike other types where the shape is automatically inferred from
+        # nested arrays of values, the only reliable way now to feed strings
+        # is to put them into a flat array then specify type astype(object)
+        # (otherwise all strings may have different types depending on their length)
+        # and then specify shape .reshape([x, y, z])
+        flat_array = arr.flatten()
+        for e in flat_array:
+            if isinstance(e, str):
+                tensor.string_data.append(e.encode('utf-8'))
+            elif isinstance(e, np.ndarray):
+                for s in e:
+                    if isinstance(s, str):
+                        tensor.string_data.append(s.encode('utf-8'))
+                    elif isinstance(s, bytes):
+                        tensor.string_data.append(s)
+            elif isinstance(e, bytes):
+                tensor.string_data.append(e)
+            else:
+                raise NotImplementedError(
+                    "Unrecognized object in the object array, expect a string, or array of bytes: ", str(type(e)))
+        return tensor
+
+    # For numerical types, directly use numpy raw bytes.
+    try:
+        dtype = mapping.NP_TYPE_TO_TENSOR_TYPE[arr.dtype]
+    except KeyError:
+        raise RuntimeError(
+            "Numpy data type not understood yet: {}".format(str(arr.dtype)))
+    tensor.data_type = dtype
+    tensor.raw_data = arr.tobytes()  # note: tobytes() is only after 1.9.
+    if sys.byteorder == 'big':
+        # Convert endian from big to little
+        convert_endian(tensor)
+
+    return tensor
+
+
+def to_list(sequence: SequenceProto) -> List[Any]:
+    """Converts a sequence def to a Python list.
+
+    Inputs:
+        sequence: a SequenceProto object.
+    Returns:
+        list: the converted list.
+    """
+    lst: List[Any] = []
+    elem_type = sequence.elem_type
+    value_field = mapping.STORAGE_ELEMENT_TYPE_TO_FIELD[elem_type]
+    values = getattr(sequence, value_field)
+    for value in values:
+        if elem_type == SequenceProto.TENSOR or elem_type == SequenceProto.SPARSE_TENSOR:
+            lst.append(to_array(value))
+        elif elem_type == SequenceProto.SEQUENCE:
+            lst.append(to_list(value))
+        elif elem_type == SequenceProto.MAP:
+            lst.append(to_dict(value))
+        else:
+            raise TypeError("The element type in the input sequence is not supported.")
+    return lst
+
+
+def from_list(lst: List[Any], name: Optional[Text] = None, dtype: Optional[int] = None) -> SequenceProto:
+    """Converts a list into a sequence def.
+
+    Inputs:
+        lst: a Python list
+        name: (optional) the name of the sequence.
+        dtype: (optional) type of element in the input list, used for specifying
+                          sequence values when converting an empty list.
+    Returns:
+        SequenceProto: the converted sequence def.
+    """
+    sequence = SequenceProto()
+    if name:
+        sequence.name = name
+
+    if dtype:
+        elem_type = dtype
+    elif len(lst) > 0:
+        first_elem = lst[0]
+        if isinstance(first_elem, dict):
+            elem_type = SequenceProto.MAP
+        elif isinstance(first_elem, list):
+            elem_type = SequenceProto.SEQUENCE
+        else:
+            elem_type = SequenceProto.TENSOR
+    else:
+        # if empty input list and no dtype specified
+        # choose sequence of tensors on default
+        elem_type = SequenceProto.TENSOR
+    sequence.elem_type = elem_type
+
+    if (len(lst) > 0) and not all(isinstance(elem, type(lst[0])) for elem in lst):
+        raise TypeError("The element type in the input list is not the same "
+                        "for all elements and therefore is not supported as a sequence.")
+
+    if elem_type == SequenceProto.TENSOR:
+        for tensor in lst:
+            sequence.tensor_values.extend([from_array(tensor)])
+    elif elem_type == SequenceProto.SEQUENCE:
+        for seq in lst:
+            sequence.sequence_values.extend([from_list(seq)])
+    elif elem_type == SequenceProto.MAP:
+        for map in lst:
+            sequence.map_values.extend([from_dict(map)])
+    else:
+        raise TypeError("The element type in the input list is not a tensor, "
+                        "sequence, or map and is not supported.")
+    return sequence
+
+
+def to_dict(map: MapProto) -> Dict[Any, Any]:
+    """Converts a map def to a Python dictionary.
+
+    Inputs:
+        map: a MapProto object.
+    Returns:
+        dict: the converted dictionary.
+    """
+    key_list: List[Any] = []
+    if map.key_type == TensorProto.STRING:
+        key_list = list(map.string_keys)
+    else:
+        key_list = list(map.keys)
+
+    value_list = to_list(map.values)
+    if len(key_list) != len(value_list):
+        raise IndexError("Length of keys and values for MapProto (map name: ",
+                        map.name,
+                        ") are not the same.")
+    dictionary = dict(zip(key_list, value_list))
+    return dictionary
+
+
+def from_dict(dict: Dict[Any, Any], name: Optional[Text] = None) -> MapProto:
+    """Converts a Python dictionary into a map def.
+
+    Inputs:
+        dict: Python dictionary
+        name: (optional) the name of the map.
+    Returns:
+        MapProto: the converted map def.
+    """
+    map = MapProto()
+    if name:
+        map.name = name
+    keys = list(dict.keys())
+    raw_key_type = np.array(keys[0]).dtype
+    key_type = mapping.NP_TYPE_TO_TENSOR_TYPE[raw_key_type]
+
+    valid_key_int_types = [TensorProto.INT8, TensorProto.INT16, TensorProto.INT32,
+                           TensorProto.INT64, TensorProto.UINT8, TensorProto.UINT16,
+                           TensorProto.UINT32, TensorProto.UINT64]
+
+    if not all(isinstance(key, raw_key_type) for key in keys):
+        raise TypeError("The key type in the input dictionary is not the same "
+                        "for all keys and therefore is not valid as a map.")
+
+    values = list(dict.values())
+    raw_value_type = type(values[0])
+    if not all(isinstance(val, raw_value_type) for val in values):
+        raise TypeError("The value type in the input dictionary is not the same "
+                        "for all values and therefore is not valid as a map.")
+
+    value_seq = from_list(values)
+
+    map.key_type = key_type
+    if key_type == TensorProto.STRING:
+        map.string_keys.extend(keys)
+    elif key_type in valid_key_int_types:
+        map.keys.extend(keys)
+    map.values.CopyFrom(value_seq)
+    return map
+
+
+def to_optional(optional: OptionalProto) -> Optional[Any]:
+    """Converts an optional def to a Python optional.
+
+    Inputs:
+        optional: an OptionalProto object.
+    Returns:
+        opt: the converted optional.
+    """
+    opt: Optional[Any] = None
+    elem_type = optional.elem_type
+    if elem_type == OptionalProto.UNDEFINED:
+        return opt
+    value_field = mapping.OPTIONAL_ELEMENT_TYPE_TO_FIELD[elem_type]
+    value = getattr(optional, value_field)
+    # TODO: create a map and replace conditional branches
+    if elem_type == OptionalProto.TENSOR or elem_type == OptionalProto.SPARSE_TENSOR:
+        opt = to_array(value)
+    elif elem_type == OptionalProto.SEQUENCE:
+        opt = to_list(value)
+    elif elem_type == OptionalProto.MAP:
+        opt = to_dict(value)
+    elif elem_type == OptionalProto.OPTIONAL:
+        return to_optional(value)
+    else:
+        raise TypeError("The element type in the input optional is not supported.")
+    return opt
+
+
+def from_optional(
+        opt: Optional[Any],
+        name: Optional[Text] = None,
+        dtype: Optional[int] = None
+) -> OptionalProto:
+    """Converts an optional value into a Optional def.
+
+    Inputs:
+        opt: a Python optional
+        name: (optional) the name of the optional.
+        dtype: (optional) type of element in the input, used for specifying
+                          optional values when converting empty none. dtype must
+                          be a valid OptionalProto.DataType value
+    Returns:
+        optional: the converted optional def.
+    """
+    # TODO: create a map and replace conditional branches
+    optional = OptionalProto()
+    if name:
+        optional.name = name
+
+    if dtype:
+        # dtype must be a valid OptionalProto.DataType
+        valid_dtypes = [v for v in OptionalProto.DataType.values()]
+        assert dtype in valid_dtypes
+        elem_type = dtype
+    elif isinstance(opt, dict):
+        elem_type = OptionalProto.MAP
+    elif isinstance(opt, list):
+        elem_type = OptionalProto.SEQUENCE
+    elif opt is None:
+        elem_type = OptionalProto.UNDEFINED
+    else:
+        elem_type = OptionalProto.TENSOR
+
+    optional.elem_type = elem_type
+
+    if opt is not None:
+        if elem_type == OptionalProto.TENSOR:
+            optional.tensor_value.CopyFrom(from_array(opt))
+        elif elem_type == OptionalProto.SEQUENCE:
+            optional.sequence_value.CopyFrom(from_list(opt))
+        elif elem_type == OptionalProto.MAP:
+            optional.map_value.CopyFrom(from_dict(opt))
+        else:
+            raise TypeError("The element type in the input is not a tensor, "
+                            "sequence, or map and is not supported.")
+    return optional
+
+
+def convert_endian(tensor: TensorProto) -> None:
+    """
+    Call to convert endianess of raw data in tensor.
+
+    Arguments:
+        tensor (TensorProto): TensorProto to be converted.
+    """
+    tensor_dtype = tensor.data_type
+    np_dtype = mapping.TENSOR_TYPE_TO_NP_TYPE[tensor_dtype]
+    tensor.raw_data = np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes()
diff --git a/third_party/onnx/onnx/onnx-data.in.proto b/third_party/onnx/onnx/onnx-data.in.proto
new file mode 100644
index 0000000000..7e247c873d
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-data.in.proto
@@ -0,0 +1,150 @@
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package {PACKAGE_NAME};
+// #if ONNX-ML
+import "onnx/onnx-ml.proto";
+// #else
+import "onnx/onnx.proto";
+// #endif
+
+// This file contains the proto definitions for MapProto and
+// SequenceProto. These protos are used to represent the data structures
+// of maps and sequence for use in test data or ModelProto.
+
+// Sequences
+//
+// Defines a dense, ordered, collection of elements that are of homogeneous types.
+// Sequences can be made out of tensors, maps, or sequences.
+//
+// If a sequence is made out of tensors, the tensors must have the same element
+// type (i.e. int32). In some cases, the tensors in a sequence can have different
+// shapes.  Whether the tensors can have different shapes or not depends on the
+// type/shape associated with the corresponding "ValueInfo". For example,
+// "Sequence<Tensor<float, [M,N]>" means that all tensors have same shape. However,
+// "Sequence<Tensor<float, [omitted,omitted]>" means they can have different
+// shapes (all of rank 2), where "omitted" means the corresponding dimension has
+// no symbolic/constant value. Finally, "Sequence<Tensor<float, omitted>>" means
+// that the different tensors can have different ranks, when the "shape" itself
+// is omitted from the tensor-type. For a more complete description, refer to
+// https://github.com/onnx/onnx/blob/main/docs/IR.md#static-tensor-shapes.
+//
+message SequenceProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element.
+  // This field MUST have a valid SequenceProto.DataType value
+  optional int32 elem_type = 2;
+
+  // For TensorProto values.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  repeated TensorProto tensor_values = 3;
+
+  // For SparseTensorProto values.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  repeated SparseTensorProto sparse_tensor_values = 4;
+
+  // For SequenceProto values, allowing sequences to be of themselves.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  repeated SequenceProto sequence_values = 5;
+
+  // For MapProto values.
+  // When this field is present, the elem_type field MUST be MAP.
+  repeated MapProto map_values = 6;
+
+  // For OptionalProto values.
+  // When this field is present, the elem_type field MUST be Optional.
+  repeated OptionalProto optional_values = 7;
+
+}
+
+
+// Maps
+//
+// Specifies an associative table, defined by keys and values.
+// MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32,
+// INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR,
+// SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain
+// the same throughout the instantiation of the MapProto.
+//
+message MapProto {
+
+  optional string name = 1;
+
+  // All MapProto data types must have the same length of keys and values.
+
+  // The data type of the key.
+  // This field MUST have a valid TensorProto.DataType value of
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING
+  optional int32 key_type = 2;
+
+  // Every element of keys has to be one of the following data types
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING.
+  // The integer cases are represented by the repeated int64 field keys below.
+  repeated int64 keys = 3;
+
+  // If keys are strings, they are represented by the repeated bytes field
+  // string_keys below.
+  repeated bytes string_keys = 4;
+
+  // MapProto values are represented in a SequenceProto of the same length as the
+  // repeated keys field and have to be one of the following data types
+  // TENSOR, SPARSE_TENSOR, MAP, SEQUENCE.
+  optional SequenceProto values = 5;
+}
+
+// Optional
+//
+//
+message OptionalProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element, identifies if the OptionalProto value
+  // is Tensor, Sparse Tensor, Sequence, Map, or Optional.
+  // The type of the optional value MUST match the elem_type specified.
+  // This field MUST have a valid OptionalProto.DataType value.
+  optional int32 elem_type = 2;
+
+  // For TensorProto value.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  optional TensorProto tensor_value = 3;
+
+  // For SparseTensorProto value.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  optional SparseTensorProto sparse_tensor_value = 4;
+
+  // For SequenceProto value.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  optional SequenceProto sequence_value = 5;
+
+  // For MapProto value.
+  // When this field is present, the elem_type field MUST be MAP.
+  optional MapProto map_value = 6;
+
+  // For OptionalProto value, allowing optional to be of itself (completeness)
+  // When this field is present, the elem_type field MUST be OPTIONAL.
+  optional OptionalProto optional_value = 7;
+
+}
diff --git a/third_party/onnx/onnx/onnx-data.proto b/third_party/onnx/onnx/onnx-data.proto
new file mode 100644
index 0000000000..95a3b35d65
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-data.proto
@@ -0,0 +1,155 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package onnx;
+import "onnx/onnx-ml.proto";
+
+// This file contains the proto definitions for MapProto and
+// SequenceProto. These protos are used to represent the data structures
+// of maps and sequence for use in test data or ModelProto.
+
+// Sequences
+//
+// Defines a dense, ordered, collection of elements that are of homogeneous types.
+// Sequences can be made out of tensors, maps, or sequences.
+//
+// If a sequence is made out of tensors, the tensors must have the same element
+// type (i.e. int32). In some cases, the tensors in a sequence can have different
+// shapes.  Whether the tensors can have different shapes or not depends on the
+// type/shape associated with the corresponding "ValueInfo". For example,
+// "Sequence<Tensor<float, [M,N]>" means that all tensors have same shape. However,
+// "Sequence<Tensor<float, [omitted,omitted]>" means they can have different
+// shapes (all of rank 2), where "omitted" means the corresponding dimension has
+// no symbolic/constant value. Finally, "Sequence<Tensor<float, omitted>>" means
+// that the different tensors can have different ranks, when the "shape" itself
+// is omitted from the tensor-type. For a more complete description, refer to
+// https://github.com/onnx/onnx/blob/main/docs/IR.md#static-tensor-shapes.
+//
+message SequenceProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element.
+  // This field MUST have a valid SequenceProto.DataType value
+  optional int32 elem_type = 2;
+
+  // For TensorProto values.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  repeated TensorProto tensor_values = 3;
+
+  // For SparseTensorProto values.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  repeated SparseTensorProto sparse_tensor_values = 4;
+
+  // For SequenceProto values, allowing sequences to be of themselves.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  repeated SequenceProto sequence_values = 5;
+
+  // For MapProto values.
+  // When this field is present, the elem_type field MUST be MAP.
+  repeated MapProto map_values = 6;
+
+  // For OptionalProto values.
+  // When this field is present, the elem_type field MUST be Optional.
+  repeated OptionalProto optional_values = 7;
+
+}
+
+
+// Maps
+//
+// Specifies an associative table, defined by keys and values.
+// MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32,
+// INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR,
+// SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain
+// the same throughout the instantiation of the MapProto.
+//
+message MapProto {
+
+  optional string name = 1;
+
+  // All MapProto data types must have the same length of keys and values.
+
+  // The data type of the key.
+  // This field MUST have a valid TensorProto.DataType value of
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING
+  optional int32 key_type = 2;
+
+  // Every element of keys has to be one of the following data types
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING.
+  // The integer cases are represented by the repeated int64 field keys below.
+  repeated int64 keys = 3;
+
+  // If keys are strings, they are represented by the repeated bytes field
+  // string_keys below.
+  repeated bytes string_keys = 4;
+
+  // MapProto values are represented in a SequenceProto of the same length as the
+  // repeated keys field and have to be one of the following data types
+  // TENSOR, SPARSE_TENSOR, MAP, SEQUENCE.
+  optional SequenceProto values = 5;
+}
+
+// Optional
+//
+//
+message OptionalProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element, identifies if the OptionalProto value
+  // is Tensor, Sparse Tensor, Sequence, Map, or Optional.
+  // The type of the optional value MUST match the elem_type specified.
+  // This field MUST have a valid OptionalProto.DataType value.
+  optional int32 elem_type = 2;
+
+  // For TensorProto value.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  optional TensorProto tensor_value = 3;
+
+  // For SparseTensorProto value.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  optional SparseTensorProto sparse_tensor_value = 4;
+
+  // For SequenceProto value.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  optional SequenceProto sequence_value = 5;
+
+  // For MapProto value.
+  // When this field is present, the elem_type field MUST be MAP.
+  optional MapProto map_value = 6;
+
+  // For OptionalProto value, allowing optional to be of itself (completeness)
+  // When this field is present, the elem_type field MUST be OPTIONAL.
+  optional OptionalProto optional_value = 7;
+
+}
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-data.proto3 b/third_party/onnx/onnx/onnx-data.proto3
new file mode 100644
index 0000000000..6175ef5323
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-data.proto3
@@ -0,0 +1,155 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto3";
+
+package onnx;
+import "onnx/onnx-ml.proto3";
+
+// This file contains the proto definitions for MapProto and
+// SequenceProto. These protos are used to represent the data structures
+// of maps and sequence for use in test data or ModelProto.
+
+// Sequences
+//
+// Defines a dense, ordered, collection of elements that are of homogeneous types.
+// Sequences can be made out of tensors, maps, or sequences.
+//
+// If a sequence is made out of tensors, the tensors must have the same element
+// type (i.e. int32). In some cases, the tensors in a sequence can have different
+// shapes.  Whether the tensors can have different shapes or not depends on the
+// type/shape associated with the corresponding "ValueInfo". For example,
+// "Sequence<Tensor<float, [M,N]>" means that all tensors have same shape. However,
+// "Sequence<Tensor<float, [omitted,omitted]>" means they can have different
+// shapes (all of rank 2), where "omitted" means the corresponding dimension has
+// no symbolic/constant value. Finally, "Sequence<Tensor<float, omitted>>" means
+// that the different tensors can have different ranks, when the "shape" itself
+// is omitted from the tensor-type. For a more complete description, refer to
+// https://github.com/onnx/onnx/blob/main/docs/IR.md#static-tensor-shapes.
+//
+message SequenceProto {
+
+  string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element.
+  // This field MUST have a valid SequenceProto.DataType value
+  int32 elem_type = 2;
+
+  // For TensorProto values.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  repeated TensorProto tensor_values = 3;
+
+  // For SparseTensorProto values.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  repeated SparseTensorProto sparse_tensor_values = 4;
+
+  // For SequenceProto values, allowing sequences to be of themselves.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  repeated SequenceProto sequence_values = 5;
+
+  // For MapProto values.
+  // When this field is present, the elem_type field MUST be MAP.
+  repeated MapProto map_values = 6;
+
+  // For OptionalProto values.
+  // When this field is present, the elem_type field MUST be Optional.
+  repeated OptionalProto optional_values = 7;
+
+}
+
+
+// Maps
+//
+// Specifies an associative table, defined by keys and values.
+// MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32,
+// INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR,
+// SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain
+// the same throughout the instantiation of the MapProto.
+//
+message MapProto {
+
+  string name = 1;
+
+  // All MapProto data types must have the same length of keys and values.
+
+  // The data type of the key.
+  // This field MUST have a valid TensorProto.DataType value of
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING
+  int32 key_type = 2;
+
+  // Every element of keys has to be one of the following data types
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING.
+  // The integer cases are represented by the repeated int64 field keys below.
+  repeated int64 keys = 3;
+
+  // If keys are strings, they are represented by the repeated bytes field
+  // string_keys below.
+  repeated bytes string_keys = 4;
+
+  // MapProto values are represented in a SequenceProto of the same length as the
+  // repeated keys field and have to be one of the following data types
+  // TENSOR, SPARSE_TENSOR, MAP, SEQUENCE.
+  SequenceProto values = 5;
+}
+
+// Optional
+//
+//
+message OptionalProto {
+
+  string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element, identifies if the OptionalProto value
+  // is Tensor, Sparse Tensor, Sequence, Map, or Optional.
+  // The type of the optional value MUST match the elem_type specified.
+  // This field MUST have a valid OptionalProto.DataType value.
+  int32 elem_type = 2;
+
+  // For TensorProto value.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  TensorProto tensor_value = 3;
+
+  // For SparseTensorProto value.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  SparseTensorProto sparse_tensor_value = 4;
+
+  // For SequenceProto value.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  SequenceProto sequence_value = 5;
+
+  // For MapProto value.
+  // When this field is present, the elem_type field MUST be MAP.
+  MapProto map_value = 6;
+
+  // For OptionalProto value, allowing optional to be of itself (completeness)
+  // When this field is present, the elem_type field MUST be OPTIONAL.
+  OptionalProto optional_value = 7;
+
+}
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-data_pb.h b/third_party/onnx/onnx/onnx-data_pb.h
new file mode 100644
index 0000000000..e919a13e1f
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-data_pb.h
@@ -0,0 +1,9 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/onnx_pb.h"
+#include "onnx/onnx-data.pb.h"
diff --git a/third_party/onnx/onnx/onnx-ml.proto b/third_party/onnx/onnx/onnx-ml.proto
new file mode 100644
index 0000000000..204d7f3d42
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-ml.proto
@@ -0,0 +1,834 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package onnx;
+
+// Overview
+//
+// ONNX is an open specification that is comprised of the following components:
+//
+// 1)  A definition of an extensible computation graph model.
+// 2)  Definitions of standard data types.
+// 3)  Definitions of built-in operators.
+//
+// This document describes the syntax of models and their computation graphs,
+// as well as the standard data types. Together, they are referred to as the ONNX
+// Intermediate Representation, or 'IR' for short.
+//
+// The normative semantic specification of the ONNX IR is found in docs/IR.md.
+// Definitions of the built-in neural network operators may be found in docs/Operators.md.
+// Definitions of the built-in classical machine learning operators may be found in
+// docs/Operators-ml.md.
+
+// Notes
+//
+// Protobuf compatibility
+//
+// To simplify framework compatibility, ONNX is defined using the subset of protobuf
+// that is compatible with both protobuf v2 and v3. This means that we do not use any
+// protobuf features that are only available in one of the two versions.
+//
+// Here are the most notable contortions we have to carry out to work around
+// these limitations:
+//
+//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
+//     of key-value pairs, where order does not matter and duplicates
+//     are not allowed.
+
+
+// Versioning
+//
+// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
+//
+// To be compatible with both proto2 and proto3, we will use a version number
+// that is not defined by the default value but an explicit enum number.
+enum Version {
+  // proto3 requires the first enum value to be zero.
+  // We add this just to appease the compiler.
+  _START_VERSION = 0;
+  // The version field is always serialized and we will use it to store the
+  // version that the  graph is generated from. This helps us set up version
+  // control.
+  // For the IR, we are using simple numbers starting with 0x00000001,
+  // which was the version we published on Oct 10, 2017.
+  IR_VERSION_2017_10_10 = 0x0000000000000001;
+
+  // IR_VERSION 2 published on Oct 30, 2017
+  // - Added type discriminator to AttributeProto to support proto3 users
+  IR_VERSION_2017_10_30 = 0x0000000000000002;
+
+  // IR VERSION 3 published on Nov 3, 2017
+  // - For operator versioning:
+  //    - Added new message OperatorSetIdProto
+  //    - Added opset_import in ModelProto
+  // - For vendor extensions, added domain in NodeProto
+  IR_VERSION_2017_11_3 = 0x0000000000000003;
+
+  // IR VERSION 4 published on Jan 22, 2019
+  // - Relax constraint that initializers should be a subset of graph inputs
+  // - Add type BFLOAT16
+  IR_VERSION_2019_1_22 = 0x0000000000000004;
+
+  // IR VERSION 5 published on March 18, 2019
+  // - Add message TensorAnnotation.
+  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
+  IR_VERSION_2019_3_18 = 0x0000000000000005;
+
+  // IR VERSION 6 published on Sep 19, 2019
+  // - Add support for sparse tensor constants stored in model.
+  //   - Add message SparseTensorProto
+  //   - Add sparse initializers
+  IR_VERSION_2019_9_19 = 0x0000000000000006;
+
+  // IR VERSION 7 published on May 8, 2020
+  // - Add support to allow function body graph to rely on multiple external opreator sets.
+  // - Add a list to promote inference graph's initializers to global and
+  //   mutable variables. Global variables are visible in all graphs of the
+  //   stored models.
+  // - Add message TrainingInfoProto to store initialization
+  //   method and training algorithm. The execution of TrainingInfoProto
+  //   can modify the values of mutable variables.
+  // - Implicitly add inference graph into each TrainingInfoProto's algorithm.
+  IR_VERSION_2020_5_8 = 0x0000000000000007;
+
+  // IR VERSION 8 published on <TBD>
+  // Introduce TypeProto.SparseTensor
+  // Introduce TypeProto.Optional
+  // Added a list of FunctionProtos local to the model
+  // Deprecated since_version and operator status from FunctionProto
+  IR_VERSION = 0x0000000000000008;
+
+}
+
+// Attributes
+//
+// A named attribute containing either singular float, integer, string, graph,
+// and tensor values, or repeated float, integer, string, graph, and tensor values.
+// An AttributeProto MUST contain the name field, and *only one* of the
+// following content fields, effectively enforcing a C/C++ union equivalent.
+message AttributeProto {
+
+  // Note: this enum is structurally identical to the OpSchema::AttrType
+  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
+  enum AttributeType {
+    UNDEFINED = 0;
+    FLOAT = 1;
+    INT = 2;
+    STRING = 3;
+    TENSOR = 4;
+    GRAPH = 5;
+    SPARSE_TENSOR = 11;
+    TYPE_PROTO = 13;
+
+    FLOATS = 6;
+    INTS = 7;
+    STRINGS = 8;
+    TENSORS = 9;
+    GRAPHS = 10;
+    SPARSE_TENSORS = 12;
+    TYPE_PROTOS = 14;
+  }
+
+  // The name field MUST be present for this version of the IR.
+  optional string name = 1;           // namespace Attribute
+
+  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
+  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
+  // in parent scope.
+  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
+  optional string ref_attr_name = 21;
+
+  // A human-readable documentation for this attribute. Markdown is allowed.
+  optional string doc_string = 13;
+
+  // The type field MUST be present for this version of the IR.
+  // For 0.0.1 versions of the IR, this field was not defined, and
+  // implementations needed to use has_field heuristics to determine
+  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
+  // field MUST be set and match the f|i|s|t|... field in use.  This
+  // change was made to accommodate proto3 implementations.
+  optional AttributeType type = 20;   // discriminator that indicates which field below is in use
+
+  // Exactly ONE of the following fields must be present for this version of the IR
+  optional float f = 2;               // float
+  optional int64 i = 3;               // int
+  optional bytes s = 4;               // UTF-8 string
+  optional TensorProto t = 5;         // tensor value
+  optional GraphProto g = 6;          // graph
+  optional SparseTensorProto sparse_tensor = 22;  // sparse tensor value
+  // Do not use field below, it's deprecated.
+  // optional ValueProto v = 12;         // value - subsumes everything but graph
+  optional TypeProto tp = 14;          // type proto
+
+  repeated float floats = 7;          // list of floats
+  repeated int64 ints = 8;            // list of ints
+  repeated bytes strings = 9;         // list of UTF-8 strings
+  repeated TensorProto tensors = 10;  // list of tensors
+  repeated GraphProto graphs = 11;    // list of graph
+  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
+  repeated TypeProto type_protos = 15;// list of type protos
+}
+
+// Defines information on value, including the name, the type, and
+// the shape of the value.
+message ValueInfoProto {
+  // This field MUST be present in this version of the IR.
+  optional string name = 1;     // namespace Value
+  // This field MUST be present in this version of the IR for
+  // inputs and outputs of the top-level graph.
+  optional TypeProto type = 2;
+  // A human-readable documentation for this value. Markdown is allowed.
+  optional string doc_string = 3;
+}
+
+// Nodes
+//
+// Computation graphs are made up of a DAG of nodes, which represent what is
+// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
+//
+// For example, it can be a node of type "Conv" that takes in an image, a filter
+// tensor and a bias tensor, and produces the convolved output.
+message NodeProto {
+  repeated string input = 1;    // namespace Value
+  repeated string output = 2;   // namespace Value
+
+  // An optional identifier for this node in a graph.
+  // This field MAY be absent in ths version of the IR.
+  optional string name = 3;     // namespace Node
+
+  // The symbolic identifier of the Operator to execute.
+  optional string op_type = 4;  // namespace Operator
+  // The domain of the OperatorSet that specifies the operator named by op_type.
+  optional string domain = 7;   // namespace Domain
+
+  // Additional named attributes.
+  repeated AttributeProto attribute = 5;
+
+  // A human-readable documentation for this node. Markdown is allowed.
+  optional string doc_string = 6;
+}
+
+// Training information
+// TrainingInfoProto stores information for training a model.
+// In particular, this defines two functionalities: an initialization-step
+// and a training-algorithm-step. Initialization resets the model
+// back to its original state as if no training has been performed.
+// Training algorithm improves the model based on input data.
+//
+// The semantics of the initialization-step is that the initializers
+// in ModelProto.graph and in TrainingInfoProto.algorithm are first
+// initialized as specified by the initializers in the graph, and then
+// updated by the "initialization_binding" in every instance in
+// ModelProto.training_info.
+//
+// The field "algorithm" defines a computation graph which represents a
+// training algorithm's step. After the execution of a
+// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
+// may be immediately updated. If the targeted training algorithm contains
+// consecutive update steps (such as block coordinate descent methods),
+// the user needs to create a TrainingInfoProto for each step.
+message TrainingInfoProto {
+  // This field describes a graph to compute the initial tensors
+  // upon starting the training process. Initialization graph has no input
+  // and can have multiple outputs. Usually, trainable tensors in neural
+  // networks are randomly initialized. To achieve that, for each tensor,
+  // the user can put a random number operator such as RandomNormal or
+  // RandomUniform in TrainingInfoProto.initialization.node and assign its
+  // random output to the specific tensor using "initialization_binding".
+  // This graph can also set the initializers in "algorithm" in the same
+  // TrainingInfoProto; a use case is resetting the number of training
+  // iteration to zero.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Thus, no initializer would be changed by default.
+  optional GraphProto initialization = 1;
+
+  // This field represents a training algorithm step. Given required inputs,
+  // it computes outputs to update initializers in its own or inference graph's
+  // initializer lists. In general, this field contains loss node, gradient node,
+  // optimizer node, increment of iteration count.
+  //
+  // An execution of the training algorithm step is performed by executing the
+  // graph obtained by combining the inference graph (namely "ModelProto.graph")
+  // and the "algorithm" graph. That is, the actual the actual
+  // input/initializer/output/node/value_info/sparse_initializer list of
+  // the training graph is the concatenation of
+  // "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
+  // and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
+  // in that order. This combined graph must satisfy the normal ONNX conditions.
+  // Now, let's provide a visualization of graph combination for clarity.
+  // Let the inference graph (i.e., "ModelProto.graph") be
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
+  // and the "algorithm" graph be
+  //    tensor_d -> Add -> tensor_e
+  // The combination process results
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
+  //
+  // Notice that an input of a node in the "algorithm" graph may reference the
+  // output of a node in the inference graph (but not the other way round). Also, inference
+  // node cannot reference inputs of "algorithm". With these restrictions, inference graph
+  // can always be run independently without training information.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Evaluating the default training step never
+  // update any initializers.
+  optional GraphProto algorithm = 2;
+
+  // This field specifies the bindings from the outputs of "initialization" to
+  // some initializers in "ModelProto.graph.initializer" and
+  // the "algorithm.initializer" in the same TrainingInfoProto.
+  // See "update_binding" below for details.
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "initialization".
+  repeated StringStringEntryProto initialization_binding = 3;
+
+  // Gradient-based training is usually an iterative procedure. In one gradient
+  // descent iteration, we apply
+  //
+  // x = x - r * g
+  //
+  // where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
+  // gradient of "x" with respect to a chosen loss. To avoid adding assignments
+  // into the training graph, we split the update equation into
+  //
+  // y = x - r * g
+  // x = y
+  //
+  // The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
+  // tell that "y" should be assigned to "x", the field "update_binding" may
+  // contain a key-value pair of strings, "x" (key of StringStringEntryProto)
+  // and "y" (value of StringStringEntryProto).
+  // For a neural network with multiple trainable (mutable) tensors, there can
+  // be multiple key-value pairs in "update_binding".
+  //
+  // The initializers appears as keys in "update_binding" are considered
+  // mutable variables. This implies some behaviors
+  // as described below.
+  //
+  //  1. We have only unique keys in all "update_binding"s so that two
+  //     variables may not have the same name. This ensures that one
+  //     variable is assigned up to once.
+  //  2. The keys must appear in names of "ModelProto.graph.initializer" or
+  //     "TrainingInfoProto.algorithm.initializer".
+  //  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
+  //  4. Mutable variables are initialized to the value specified by the
+  //     corresponding initializer, and then potentially updated by
+  //     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
+  //
+  // This field usually contains names of trainable tensors
+  // (in ModelProto.graph), optimizer states such as momentums in advanced
+  // stochastic gradient methods (in TrainingInfoProto.graph),
+  // and number of training iterations (in TrainingInfoProto.graph).
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "algorithm".
+  repeated StringStringEntryProto update_binding = 4;
+}
+
+// Models
+//
+// ModelProto is a top-level file/container format for bundling a ML model and
+// associating its computation graph with metadata.
+//
+// The semantics of the model are described by the associated GraphProto's.
+message ModelProto {
+  // The version of the IR this model targets. See Version enum above.
+  // This field MUST be present.
+  optional int64 ir_version = 1;
+
+  // The OperatorSets this model relies on.
+  // All ModelProtos MUST have at least one entry that
+  // specifies which version of the ONNX OperatorSet is
+  // being imported.
+  //
+  // All nodes in the ModelProto's graph will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets.
+  repeated OperatorSetIdProto opset_import = 8;
+
+  // The name of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_name = 2;
+
+  // The version of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_version = 3;
+
+  // Domain name of the model.
+  // We use reverse domain names as name space indicators. For example:
+  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
+  //
+  // Together with `model_version` and GraphProto.name, this forms the unique identity of
+  // the graph.
+  optional string domain = 4;
+
+  // The version of the graph encoded. See Version enum below.
+  optional int64 model_version = 5;
+
+  // A human-readable documentation for this model. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The parameterized graph that is evaluated to execute the model.
+  optional GraphProto graph = 7;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+
+  // Training-specific information. Sequentially executing all stored
+  // `TrainingInfoProto.algorithm`s and assigning their outputs following
+  // the corresponding `TrainingInfoProto.update_binding`s is one training
+  // iteration. Similarly, to initialize the model
+  // (as if training hasn't happened), the user should sequentially execute
+  // all stored `TrainingInfoProto.initialization`s and assigns their outputs
+  // using `TrainingInfoProto.initialization_binding`s.
+  //
+  // If this field is empty, the training behavior of the model is undefined.
+  repeated TrainingInfoProto training_info = 20;
+
+  // A list of function protos local to the model.
+  //
+  // Name of the function "FunctionProto.name" should be unique within the domain "FunctionProto.domain".
+  // In case of any conflicts the behavior (whether the model local functions are given higher priority,
+  // or standard opserator sets are given higher priotity or this is treated as error) is defined by 
+  // the runtimes.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto and other model local FunctionProtos. 
+  // Example, if same operator set say 'A' is imported by a FunctionProto and ModelProto 
+  // or by 2 FunctionProtos then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same for every node in the function body.
+  //
+  // One FunctionProto can reference other FunctionProto in the model, however, recursive reference
+  // is not allowed.
+  repeated FunctionProto functions = 25;
+};
+
+// StringStringEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message StringStringEntryProto {
+  optional string key = 1;
+  optional string value= 2;
+};
+
+message TensorAnnotation {
+  optional string tensor_name = 1;
+  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
+  // The keys used in the mapping below must be pre-defined in ONNX spec.
+  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
+  // quantization parameter keys.
+  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
+}
+
+
+
+// Graphs
+//
+// A graph defines the computational logic of a model and is comprised of a parameterized
+// list of nodes that form a directed acyclic graph based on their inputs and outputs.
+// This is the equivalent of the "network" or "graph" in many deep learning
+// frameworks.
+message GraphProto {
+  // The nodes in the graph, sorted topologically.
+  repeated NodeProto node = 1;
+
+  // The name of the graph.
+  optional string name = 2;   // namespace Graph
+
+  // A list of named tensor values, used to specify constant inputs of the graph.
+  // Each initializer (both TensorProto as well SparseTensorProto) MUST have a name.
+  // The name MUST be unique across both initializer and sparse_initializer,
+  // but the name MAY also appear in the input list.
+  repeated TensorProto initializer = 5;
+
+  // Initializers (see above) stored in sparse format.
+  repeated SparseTensorProto sparse_initializer = 15;
+
+  // A human-readable documentation for this graph. Markdown is allowed.
+  optional string doc_string = 10;
+
+  // The inputs and outputs of the graph.
+  repeated ValueInfoProto input = 11;
+  repeated ValueInfoProto output = 12;
+
+  // Information for the values in the graph. The ValueInfoProto.name's
+  // must be distinct. It is optional for a value to appear in value_info list.
+  repeated ValueInfoProto value_info = 13;
+
+  // This field carries information to indicate the mapping among a tensor and its
+  // quantization parameter tensors. For example:
+  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
+  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
+  repeated TensorAnnotation quantization_annotation = 14;
+
+  // DO NOT USE the following fields, they were deprecated from earlier versions.
+  // repeated string input = 3;
+  // repeated string output = 4;
+  // optional int64 ir_version = 6;
+  // optional int64 producer_version = 7;
+  // optional string producer_tag = 8;
+  // optional string domain = 9;
+}
+
+// Tensors
+//
+// A serialized tensor value.
+message TensorProto {
+  enum DataType {
+    UNDEFINED = 0;
+    // Basic types.
+    FLOAT = 1;   // float
+    UINT8 = 2;   // uint8_t
+    INT8 = 3;    // int8_t
+    UINT16 = 4;  // uint16_t
+    INT16 = 5;   // int16_t
+    INT32 = 6;   // int32_t
+    INT64 = 7;   // int64_t
+    STRING = 8;  // string
+    BOOL = 9;    // bool
+
+    // IEEE754 half-precision floating-point format (16 bits wide).
+    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
+    FLOAT16 = 10;
+
+    DOUBLE = 11;
+    UINT32 = 12;
+    UINT64 = 13;
+    COMPLEX64 = 14;     // complex with float32 real and imaginary components
+    COMPLEX128 = 15;    // complex with float64 real and imaginary components
+
+    // Non-IEEE floating-point format based on IEEE754 single-precision
+    // floating-point number truncated to 16 bits.
+    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
+    BFLOAT16 = 16;
+
+    // Future extensions go here.
+  }
+
+  // The shape of the tensor.
+  repeated int64 dims = 1;
+
+  // The data type of the tensor.
+  // This field MUST have a valid TensorProto.DataType value
+  optional int32 data_type = 2;
+
+  // For very large tensors, we may want to store them in chunks, in which
+  // case the following fields will specify the segment that is stored in
+  // the current TensorProto.
+  message Segment {
+    optional int64 begin = 1;
+    optional int64 end = 2;
+  }
+  optional Segment segment = 3;
+
+  // Tensor content must be organized in row-major order.
+  //
+  // Depending on the data_type field, exactly one of the fields below with
+  // name ending in _data is used to store the elements of the tensor.
+
+  // For float and complex64 values
+  // Complex64 tensors are encoded as a single array of floats,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
+  repeated float float_data = 4 [packed = true];
+
+  // For int32, uint8, int8, uint16, int16, bool, and float16 values
+  // float16 values must be bit-wise converted to an uint16_t prior
+  // to writing to the buffer.
+  // When this field is present, the data_type field MUST be
+  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
+  repeated int32 int32_data = 5 [packed = true];
+
+  // For strings.
+  // Each element of string_data is a UTF-8 encoded Unicode
+  // string. No trailing null, no leading BOM. The protobuf "string"
+  // scalar type is not used to match ML community conventions.
+  // When this field is present, the data_type field MUST be STRING
+  repeated bytes string_data = 6;
+
+  // For int64.
+  // When this field is present, the data_type field MUST be INT64
+  repeated int64 int64_data = 7 [packed = true];
+
+  // Optionally, a name for the tensor.
+  optional string name = 8; // namespace Value
+
+  // A human-readable documentation for this tensor. Markdown is allowed.
+  optional string doc_string = 12;
+
+  // Serializations can either use one of the fields above, or use this
+  // raw bytes field. The only exception is the string case, where one is
+  // required to store the content in the repeated bytes string_data field.
+  //
+  // When this raw_data field is used to store tensor value, elements MUST
+  // be stored in as fixed-width, little-endian order.
+  // Floating-point data types MUST be stored in IEEE 754 format.
+  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
+  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
+  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
+  //
+  // Note: the advantage of specific field rather than the raw_data field is
+  // that in some cases (e.g. int data), protobuf does a better packing via
+  // variable length storage, and may lead to smaller binary footprint.
+  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
+  optional bytes raw_data = 9;
+
+  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
+  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
+  // external_data stores key-value pairs describing data location. Recognized keys are:
+  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
+  //                           protobuf model was stored
+  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
+  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+  // - "length" (optional) - number of bytes containing data. Integer stored as string.
+  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
+  repeated StringStringEntryProto external_data = 13;
+
+  // Location of the data for this tensor. MUST be one of:
+  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
+  // - EXTERNAL - data stored in an external location as described by external_data field.
+  enum DataLocation {
+    DEFAULT = 0;
+    EXTERNAL = 1;
+  }
+
+  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
+  optional DataLocation data_location = 14;
+
+  // For double
+  // Complex128 tensors are encoded as a single array of doubles,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
+  repeated double double_data = 10 [packed = true];
+
+  // For uint64 and uint32 values
+  // When this field is present, the data_type field MUST be
+  // UINT32 or UINT64
+  repeated uint64 uint64_data = 11 [packed = true];
+}
+
+// A serialized sparse-tensor value
+message SparseTensorProto {
+  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
+  // The default-value is zero for numeric tensors, and empty-string for string tensors.
+  // values must have a non-empty name present which serves as a name for SparseTensorProto
+  // when used in sparse_initializer list.
+  optional TensorProto values = 1;
+
+  // The indices of the non-default values, which may be stored in one of two formats.
+  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
+  // corresponding to the j-th index of the i-th value (in the values tensor).
+  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
+  // must be the linearized-index of the i-th value (in the values tensor).
+  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
+  // using the shape provided below.
+  // The indices must appear in ascending order without duplication.
+  // In the first format, the ordering is lexicographic-ordering:
+  // e.g., index-value [1,4] must appear before [2,1]
+  optional TensorProto indices = 2;
+
+  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
+  repeated int64 dims = 3;
+}
+
+// Defines a tensor shape. A dimension can be either an integer value
+// or a symbolic variable. A symbolic variable represents an unknown
+// dimension.
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;   // namespace Shape
+    };
+    // Standard denotation can optionally be used to denote tensor
+    // dimensions with standard semantic descriptions to ensure
+    // that operations are applied to the correct axis of a tensor.
+    // Refer to https://github.com/onnx/onnx/blob/main/docs/DimensionDenotation.md#denotation-definition
+    // for pre-defined dimension denotations.
+    optional string denotation = 3;
+  };
+  repeated Dimension dim = 1;
+}
+
+// Types
+//
+// The standard ONNX data types.
+message TypeProto {
+
+  message Tensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+  // repeated T
+  message Sequence {
+    // The type and optional shape of each element of the sequence.
+    // This field MUST be present for this version of the IR.
+    optional TypeProto elem_type = 1;
+  };
+
+  // map<K,V>
+  message Map {
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
+    optional int32 key_type = 1;
+    // This field MUST be present for this version of the IR.
+    optional TypeProto value_type = 2;
+  };
+
+  // wrapper for Tensor, Sequence, or Map
+  message Optional {
+    // The type and optional shape of the element wrapped.
+    // This field MUST be present for this version of the IR.
+    // Possible values correspond to OptionalProto.DataType enum
+    optional TypeProto elem_type = 1;
+  };
+
+
+  message SparseTensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+
+  message Opaque {
+    // When missing, the domain is the same as the model's.
+    optional string domain = 1;
+    // The name is optional but significant when provided.
+    optional string name = 2;
+    // parameters that help defining the type
+    // DEPRECATED do not use.
+    // repeated TypeProto parameters = 3;
+  }
+
+
+  oneof value {
+    // The type of a tensor.
+    Tensor tensor_type = 1;
+
+    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
+    //        as input and output to graphs and nodes. These types are needed to naturally
+    //        support classical ML operators.  DNN operators SHOULD restrict their input
+    //        and output types to tensors.
+
+    // The type of a sequence.
+    Sequence sequence_type = 4;
+
+    // The type of a map.
+    Map map_type = 5;
+
+    // The type of an optional.
+    Optional optional_type = 9;
+
+
+    // Type of the sparse tensor
+    SparseTensor sparse_tensor_type = 8;
+
+
+    Opaque opaque_type = 7;
+
+  }
+
+  // An optional denotation can be used to denote the whole
+  // type with a standard semantic description as to what is
+  // stored inside. Refer to https://github.com/onnx/onnx/blob/main/docs/TypeDenotation.md#type-denotation-definition
+  // for pre-defined type denotations.
+  optional string denotation = 6;
+}
+
+// Operator Sets
+//
+// OperatorSets are uniquely identified by a (domain, opset_version) pair.
+message OperatorSetIdProto {
+  // The domain of the operator set being identified.
+  // The empty string ("") or absence of this field implies the operator
+  // set that is defined as part of the ONNX specification.
+  // This field MUST be present in this version of the IR when referring to any other operator set.
+  optional string domain = 1;
+
+  // The version of the operator set being identified.
+  // This field MUST be present in this version of the IR.
+  optional int64 version = 2;
+}
+
+// Operator/function status.
+enum OperatorStatus {
+    EXPERIMENTAL = 0;
+    STABLE = 1;
+}
+
+message FunctionProto {
+  // The name of the function, similar usage of op_type in OperatorProto.
+  // Combined with FunctionProto.domain, this forms the unique identity of
+  // the FunctionProto.
+  optional string name = 1;
+
+  // Deprecated since IR Version 8
+  // optional int64 since_version = 2;
+  reserved 2;
+  reserved "since_version";
+
+  // Deprecated since IR Version 8
+  // optional OperatorStatus status = 3;
+  reserved 3;
+  reserved "status";
+
+  // The inputs and outputs of the function.
+  repeated string input = 4;
+  repeated string output = 5;
+
+  // The attributes of the function.
+  repeated string attribute = 6;
+
+  // The nodes in the function.
+  repeated NodeProto node = 7;
+  // A human-readable documentation for this function. Markdown is allowed.
+  optional string doc_string = 8;
+
+  // The OperatorSets this function body (graph) relies on.
+  //
+  // All nodes in the function body (graph) will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets. This means at most one version can be relied
+  // for one domain.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto. Example, if same operator set say 'A' is imported by FunctionProto
+  // and ModelProto then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same.
+
+  repeated OperatorSetIdProto opset_import = 9;
+
+  // The domain which this function belongs to. Combined with FunctionProto.name, this forms the unique identity of
+  // the FunctionProto.
+  optional string domain = 10;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-ml.proto3 b/third_party/onnx/onnx/onnx-ml.proto3
new file mode 100644
index 0000000000..7310c9da4d
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-ml.proto3
@@ -0,0 +1,834 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto3";
+
+package onnx;
+
+// Overview
+//
+// ONNX is an open specification that is comprised of the following components:
+//
+// 1)  A definition of an extensible computation graph model.
+// 2)  Definitions of standard data types.
+// 3)  Definitions of built-in operators.
+//
+// This document describes the syntax of models and their computation graphs,
+// as well as the standard data types. Together, they are referred to as the ONNX
+// Intermediate Representation, or 'IR' for short.
+//
+// The normative semantic specification of the ONNX IR is found in docs/IR.md.
+// Definitions of the built-in neural network operators may be found in docs/Operators.md.
+// Definitions of the built-in classical machine learning operators may be found in
+// docs/Operators-ml.md.
+
+// Notes
+//
+// Protobuf compatibility
+//
+// To simplify framework compatibility, ONNX is defined using the subset of protobuf
+// that is compatible with both protobuf v2 and v3. This means that we do not use any
+// protobuf features that are only available in one of the two versions.
+//
+// Here are the most notable contortions we have to carry out to work around
+// these limitations:
+//
+//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
+//     of key-value pairs, where order does not matter and duplicates
+//     are not allowed.
+
+
+// Versioning
+//
+// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
+//
+// To be compatible with both proto2 and proto3, we will use a version number
+// that is not defined by the default value but an explicit enum number.
+enum Version {
+  // proto3 requires the first enum value to be zero.
+  // We add this just to appease the compiler.
+  _START_VERSION = 0;
+  // The version field is always serialized and we will use it to store the
+  // version that the  graph is generated from. This helps us set up version
+  // control.
+  // For the IR, we are using simple numbers starting with 0x00000001,
+  // which was the version we published on Oct 10, 2017.
+  IR_VERSION_2017_10_10 = 0x0000000000000001;
+
+  // IR_VERSION 2 published on Oct 30, 2017
+  // - Added type discriminator to AttributeProto to support proto3 users
+  IR_VERSION_2017_10_30 = 0x0000000000000002;
+
+  // IR VERSION 3 published on Nov 3, 2017
+  // - For operator versioning:
+  //    - Added new message OperatorSetIdProto
+  //    - Added opset_import in ModelProto
+  // - For vendor extensions, added domain in NodeProto
+  IR_VERSION_2017_11_3 = 0x0000000000000003;
+
+  // IR VERSION 4 published on Jan 22, 2019
+  // - Relax constraint that initializers should be a subset of graph inputs
+  // - Add type BFLOAT16
+  IR_VERSION_2019_1_22 = 0x0000000000000004;
+
+  // IR VERSION 5 published on March 18, 2019
+  // - Add message TensorAnnotation.
+  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
+  IR_VERSION_2019_3_18 = 0x0000000000000005;
+
+  // IR VERSION 6 published on Sep 19, 2019
+  // - Add support for sparse tensor constants stored in model.
+  //   - Add message SparseTensorProto
+  //   - Add sparse initializers
+  IR_VERSION_2019_9_19 = 0x0000000000000006;
+
+  // IR VERSION 7 published on May 8, 2020
+  // - Add support to allow function body graph to rely on multiple external opreator sets.
+  // - Add a list to promote inference graph's initializers to global and
+  //   mutable variables. Global variables are visible in all graphs of the
+  //   stored models.
+  // - Add message TrainingInfoProto to store initialization
+  //   method and training algorithm. The execution of TrainingInfoProto
+  //   can modify the values of mutable variables.
+  // - Implicitly add inference graph into each TrainingInfoProto's algorithm.
+  IR_VERSION_2020_5_8 = 0x0000000000000007;
+
+  // IR VERSION 8 published on <TBD>
+  // Introduce TypeProto.SparseTensor
+  // Introduce TypeProto.Optional
+  // Added a list of FunctionProtos local to the model
+  // Deprecated since_version and operator status from FunctionProto
+  IR_VERSION = 0x0000000000000008;
+
+}
+
+// Attributes
+//
+// A named attribute containing either singular float, integer, string, graph,
+// and tensor values, or repeated float, integer, string, graph, and tensor values.
+// An AttributeProto MUST contain the name field, and *only one* of the
+// following content fields, effectively enforcing a C/C++ union equivalent.
+message AttributeProto {
+
+  // Note: this enum is structurally identical to the OpSchema::AttrType
+  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
+  enum AttributeType {
+    UNDEFINED = 0;
+    FLOAT = 1;
+    INT = 2;
+    STRING = 3;
+    TENSOR = 4;
+    GRAPH = 5;
+    SPARSE_TENSOR = 11;
+    TYPE_PROTO = 13;
+
+    FLOATS = 6;
+    INTS = 7;
+    STRINGS = 8;
+    TENSORS = 9;
+    GRAPHS = 10;
+    SPARSE_TENSORS = 12;
+    TYPE_PROTOS = 14;
+  }
+
+  // The name field MUST be present for this version of the IR.
+  string name = 1;           // namespace Attribute
+
+  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
+  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
+  // in parent scope.
+  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
+  string ref_attr_name = 21;
+
+  // A human-readable documentation for this attribute. Markdown is allowed.
+  string doc_string = 13;
+
+  // The type field MUST be present for this version of the IR.
+  // For 0.0.1 versions of the IR, this field was not defined, and
+  // implementations needed to use has_field heuristics to determine
+  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
+  // field MUST be set and match the f|i|s|t|... field in use.  This
+  // change was made to accommodate proto3 implementations.
+  AttributeType type = 20;   // discriminator that indicates which field below is in use
+
+  // Exactly ONE of the following fields must be present for this version of the IR
+  float f = 2;               // float
+  int64 i = 3;               // int
+  bytes s = 4;               // UTF-8 string
+  TensorProto t = 5;         // tensor value
+  GraphProto g = 6;          // graph
+  SparseTensorProto sparse_tensor = 22;  // sparse tensor value
+  // Do not use field below, it's deprecated.
+  // optional ValueProto v = 12;         // value - subsumes everything but graph
+  TypeProto tp = 14;          // type proto
+
+  repeated float floats = 7;          // list of floats
+  repeated int64 ints = 8;            // list of ints
+  repeated bytes strings = 9;         // list of UTF-8 strings
+  repeated TensorProto tensors = 10;  // list of tensors
+  repeated GraphProto graphs = 11;    // list of graph
+  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
+  repeated TypeProto type_protos = 15;// list of type protos
+}
+
+// Defines information on value, including the name, the type, and
+// the shape of the value.
+message ValueInfoProto {
+  // This field MUST be present in this version of the IR.
+  string name = 1;     // namespace Value
+  // This field MUST be present in this version of the IR for
+  // inputs and outputs of the top-level graph.
+  TypeProto type = 2;
+  // A human-readable documentation for this value. Markdown is allowed.
+  string doc_string = 3;
+}
+
+// Nodes
+//
+// Computation graphs are made up of a DAG of nodes, which represent what is
+// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
+//
+// For example, it can be a node of type "Conv" that takes in an image, a filter
+// tensor and a bias tensor, and produces the convolved output.
+message NodeProto {
+  repeated string input = 1;    // namespace Value
+  repeated string output = 2;   // namespace Value
+
+  // An optional identifier for this node in a graph.
+  // This field MAY be absent in ths version of the IR.
+  string name = 3;     // namespace Node
+
+  // The symbolic identifier of the Operator to execute.
+  string op_type = 4;  // namespace Operator
+  // The domain of the OperatorSet that specifies the operator named by op_type.
+  string domain = 7;   // namespace Domain
+
+  // Additional named attributes.
+  repeated AttributeProto attribute = 5;
+
+  // A human-readable documentation for this node. Markdown is allowed.
+  string doc_string = 6;
+}
+
+// Training information
+// TrainingInfoProto stores information for training a model.
+// In particular, this defines two functionalities: an initialization-step
+// and a training-algorithm-step. Initialization resets the model
+// back to its original state as if no training has been performed.
+// Training algorithm improves the model based on input data.
+//
+// The semantics of the initialization-step is that the initializers
+// in ModelProto.graph and in TrainingInfoProto.algorithm are first
+// initialized as specified by the initializers in the graph, and then
+// updated by the "initialization_binding" in every instance in
+// ModelProto.training_info.
+//
+// The field "algorithm" defines a computation graph which represents a
+// training algorithm's step. After the execution of a
+// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
+// may be immediately updated. If the targeted training algorithm contains
+// consecutive update steps (such as block coordinate descent methods),
+// the user needs to create a TrainingInfoProto for each step.
+message TrainingInfoProto {
+  // This field describes a graph to compute the initial tensors
+  // upon starting the training process. Initialization graph has no input
+  // and can have multiple outputs. Usually, trainable tensors in neural
+  // networks are randomly initialized. To achieve that, for each tensor,
+  // the user can put a random number operator such as RandomNormal or
+  // RandomUniform in TrainingInfoProto.initialization.node and assign its
+  // random output to the specific tensor using "initialization_binding".
+  // This graph can also set the initializers in "algorithm" in the same
+  // TrainingInfoProto; a use case is resetting the number of training
+  // iteration to zero.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Thus, no initializer would be changed by default.
+  GraphProto initialization = 1;
+
+  // This field represents a training algorithm step. Given required inputs,
+  // it computes outputs to update initializers in its own or inference graph's
+  // initializer lists. In general, this field contains loss node, gradient node,
+  // optimizer node, increment of iteration count.
+  //
+  // An execution of the training algorithm step is performed by executing the
+  // graph obtained by combining the inference graph (namely "ModelProto.graph")
+  // and the "algorithm" graph. That is, the actual the actual
+  // input/initializer/output/node/value_info/sparse_initializer list of
+  // the training graph is the concatenation of
+  // "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
+  // and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
+  // in that order. This combined graph must satisfy the normal ONNX conditions.
+  // Now, let's provide a visualization of graph combination for clarity.
+  // Let the inference graph (i.e., "ModelProto.graph") be
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
+  // and the "algorithm" graph be
+  //    tensor_d -> Add -> tensor_e
+  // The combination process results
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
+  //
+  // Notice that an input of a node in the "algorithm" graph may reference the
+  // output of a node in the inference graph (but not the other way round). Also, inference
+  // node cannot reference inputs of "algorithm". With these restrictions, inference graph
+  // can always be run independently without training information.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Evaluating the default training step never
+  // update any initializers.
+  GraphProto algorithm = 2;
+
+  // This field specifies the bindings from the outputs of "initialization" to
+  // some initializers in "ModelProto.graph.initializer" and
+  // the "algorithm.initializer" in the same TrainingInfoProto.
+  // See "update_binding" below for details.
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "initialization".
+  repeated StringStringEntryProto initialization_binding = 3;
+
+  // Gradient-based training is usually an iterative procedure. In one gradient
+  // descent iteration, we apply
+  //
+  // x = x - r * g
+  //
+  // where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
+  // gradient of "x" with respect to a chosen loss. To avoid adding assignments
+  // into the training graph, we split the update equation into
+  //
+  // y = x - r * g
+  // x = y
+  //
+  // The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
+  // tell that "y" should be assigned to "x", the field "update_binding" may
+  // contain a key-value pair of strings, "x" (key of StringStringEntryProto)
+  // and "y" (value of StringStringEntryProto).
+  // For a neural network with multiple trainable (mutable) tensors, there can
+  // be multiple key-value pairs in "update_binding".
+  //
+  // The initializers appears as keys in "update_binding" are considered
+  // mutable variables. This implies some behaviors
+  // as described below.
+  //
+  //  1. We have only unique keys in all "update_binding"s so that two
+  //     variables may not have the same name. This ensures that one
+  //     variable is assigned up to once.
+  //  2. The keys must appear in names of "ModelProto.graph.initializer" or
+  //     "TrainingInfoProto.algorithm.initializer".
+  //  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
+  //  4. Mutable variables are initialized to the value specified by the
+  //     corresponding initializer, and then potentially updated by
+  //     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
+  //
+  // This field usually contains names of trainable tensors
+  // (in ModelProto.graph), optimizer states such as momentums in advanced
+  // stochastic gradient methods (in TrainingInfoProto.graph),
+  // and number of training iterations (in TrainingInfoProto.graph).
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "algorithm".
+  repeated StringStringEntryProto update_binding = 4;
+}
+
+// Models
+//
+// ModelProto is a top-level file/container format for bundling a ML model and
+// associating its computation graph with metadata.
+//
+// The semantics of the model are described by the associated GraphProto's.
+message ModelProto {
+  // The version of the IR this model targets. See Version enum above.
+  // This field MUST be present.
+  int64 ir_version = 1;
+
+  // The OperatorSets this model relies on.
+  // All ModelProtos MUST have at least one entry that
+  // specifies which version of the ONNX OperatorSet is
+  // being imported.
+  //
+  // All nodes in the ModelProto's graph will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets.
+  repeated OperatorSetIdProto opset_import = 8;
+
+  // The name of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  string producer_name = 2;
+
+  // The version of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  string producer_version = 3;
+
+  // Domain name of the model.
+  // We use reverse domain names as name space indicators. For example:
+  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
+  //
+  // Together with `model_version` and GraphProto.name, this forms the unique identity of
+  // the graph.
+  string domain = 4;
+
+  // The version of the graph encoded. See Version enum below.
+  int64 model_version = 5;
+
+  // A human-readable documentation for this model. Markdown is allowed.
+  string doc_string = 6;
+
+  // The parameterized graph that is evaluated to execute the model.
+  GraphProto graph = 7;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+
+  // Training-specific information. Sequentially executing all stored
+  // `TrainingInfoProto.algorithm`s and assigning their outputs following
+  // the corresponding `TrainingInfoProto.update_binding`s is one training
+  // iteration. Similarly, to initialize the model
+  // (as if training hasn't happened), the user should sequentially execute
+  // all stored `TrainingInfoProto.initialization`s and assigns their outputs
+  // using `TrainingInfoProto.initialization_binding`s.
+  //
+  // If this field is empty, the training behavior of the model is undefined.
+  repeated TrainingInfoProto training_info = 20;
+
+  // A list of function protos local to the model.
+  //
+  // Name of the function "FunctionProto.name" should be unique within the domain "FunctionProto.domain".
+  // In case of any conflicts the behavior (whether the model local functions are given higher priority,
+  // or standard opserator sets are given higher priotity or this is treated as error) is defined by 
+  // the runtimes.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto and other model local FunctionProtos. 
+  // Example, if same operator set say 'A' is imported by a FunctionProto and ModelProto 
+  // or by 2 FunctionProtos then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same for every node in the function body.
+  //
+  // One FunctionProto can reference other FunctionProto in the model, however, recursive reference
+  // is not allowed.
+  repeated FunctionProto functions = 25;
+};
+
+// StringStringEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message StringStringEntryProto {
+  string key = 1;
+  string value= 2;
+};
+
+message TensorAnnotation {
+  string tensor_name = 1;
+  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
+  // The keys used in the mapping below must be pre-defined in ONNX spec.
+  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
+  // quantization parameter keys.
+  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
+}
+
+
+
+// Graphs
+//
+// A graph defines the computational logic of a model and is comprised of a parameterized
+// list of nodes that form a directed acyclic graph based on their inputs and outputs.
+// This is the equivalent of the "network" or "graph" in many deep learning
+// frameworks.
+message GraphProto {
+  // The nodes in the graph, sorted topologically.
+  repeated NodeProto node = 1;
+
+  // The name of the graph.
+  string name = 2;   // namespace Graph
+
+  // A list of named tensor values, used to specify constant inputs of the graph.
+  // Each initializer (both TensorProto as well SparseTensorProto) MUST have a name.
+  // The name MUST be unique across both initializer and sparse_initializer,
+  // but the name MAY also appear in the input list.
+  repeated TensorProto initializer = 5;
+
+  // Initializers (see above) stored in sparse format.
+  repeated SparseTensorProto sparse_initializer = 15;
+
+  // A human-readable documentation for this graph. Markdown is allowed.
+  string doc_string = 10;
+
+  // The inputs and outputs of the graph.
+  repeated ValueInfoProto input = 11;
+  repeated ValueInfoProto output = 12;
+
+  // Information for the values in the graph. The ValueInfoProto.name's
+  // must be distinct. It is optional for a value to appear in value_info list.
+  repeated ValueInfoProto value_info = 13;
+
+  // This field carries information to indicate the mapping among a tensor and its
+  // quantization parameter tensors. For example:
+  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
+  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
+  repeated TensorAnnotation quantization_annotation = 14;
+
+  // DO NOT USE the following fields, they were deprecated from earlier versions.
+  // repeated string input = 3;
+  // repeated string output = 4;
+  // optional int64 ir_version = 6;
+  // optional int64 producer_version = 7;
+  // optional string producer_tag = 8;
+  // optional string domain = 9;
+}
+
+// Tensors
+//
+// A serialized tensor value.
+message TensorProto {
+  enum DataType {
+    UNDEFINED = 0;
+    // Basic types.
+    FLOAT = 1;   // float
+    UINT8 = 2;   // uint8_t
+    INT8 = 3;    // int8_t
+    UINT16 = 4;  // uint16_t
+    INT16 = 5;   // int16_t
+    INT32 = 6;   // int32_t
+    INT64 = 7;   // int64_t
+    STRING = 8;  // string
+    BOOL = 9;    // bool
+
+    // IEEE754 half-precision floating-point format (16 bits wide).
+    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
+    FLOAT16 = 10;
+
+    DOUBLE = 11;
+    UINT32 = 12;
+    UINT64 = 13;
+    COMPLEX64 = 14;     // complex with float32 real and imaginary components
+    COMPLEX128 = 15;    // complex with float64 real and imaginary components
+
+    // Non-IEEE floating-point format based on IEEE754 single-precision
+    // floating-point number truncated to 16 bits.
+    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
+    BFLOAT16 = 16;
+
+    // Future extensions go here.
+  }
+
+  // The shape of the tensor.
+  repeated int64 dims = 1;
+
+  // The data type of the tensor.
+  // This field MUST have a valid TensorProto.DataType value
+  int32 data_type = 2;
+
+  // For very large tensors, we may want to store them in chunks, in which
+  // case the following fields will specify the segment that is stored in
+  // the current TensorProto.
+  message Segment {
+    int64 begin = 1;
+    int64 end = 2;
+  }
+  Segment segment = 3;
+
+  // Tensor content must be organized in row-major order.
+  //
+  // Depending on the data_type field, exactly one of the fields below with
+  // name ending in _data is used to store the elements of the tensor.
+
+  // For float and complex64 values
+  // Complex64 tensors are encoded as a single array of floats,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
+  repeated float float_data = 4 [packed = true];
+
+  // For int32, uint8, int8, uint16, int16, bool, and float16 values
+  // float16 values must be bit-wise converted to an uint16_t prior
+  // to writing to the buffer.
+  // When this field is present, the data_type field MUST be
+  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
+  repeated int32 int32_data = 5 [packed = true];
+
+  // For strings.
+  // Each element of string_data is a UTF-8 encoded Unicode
+  // string. No trailing null, no leading BOM. The protobuf "string"
+  // scalar type is not used to match ML community conventions.
+  // When this field is present, the data_type field MUST be STRING
+  repeated bytes string_data = 6;
+
+  // For int64.
+  // When this field is present, the data_type field MUST be INT64
+  repeated int64 int64_data = 7 [packed = true];
+
+  // Optionally, a name for the tensor.
+  string name = 8; // namespace Value
+
+  // A human-readable documentation for this tensor. Markdown is allowed.
+  string doc_string = 12;
+
+  // Serializations can either use one of the fields above, or use this
+  // raw bytes field. The only exception is the string case, where one is
+  // required to store the content in the repeated bytes string_data field.
+  //
+  // When this raw_data field is used to store tensor value, elements MUST
+  // be stored in as fixed-width, little-endian order.
+  // Floating-point data types MUST be stored in IEEE 754 format.
+  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
+  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
+  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
+  //
+  // Note: the advantage of specific field rather than the raw_data field is
+  // that in some cases (e.g. int data), protobuf does a better packing via
+  // variable length storage, and may lead to smaller binary footprint.
+  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
+  bytes raw_data = 9;
+
+  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
+  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
+  // external_data stores key-value pairs describing data location. Recognized keys are:
+  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
+  //                           protobuf model was stored
+  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
+  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+  // - "length" (optional) - number of bytes containing data. Integer stored as string.
+  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
+  repeated StringStringEntryProto external_data = 13;
+
+  // Location of the data for this tensor. MUST be one of:
+  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
+  // - EXTERNAL - data stored in an external location as described by external_data field.
+  enum DataLocation {
+    DEFAULT = 0;
+    EXTERNAL = 1;
+  }
+
+  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
+  DataLocation data_location = 14;
+
+  // For double
+  // Complex128 tensors are encoded as a single array of doubles,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
+  repeated double double_data = 10 [packed = true];
+
+  // For uint64 and uint32 values
+  // When this field is present, the data_type field MUST be
+  // UINT32 or UINT64
+  repeated uint64 uint64_data = 11 [packed = true];
+}
+
+// A serialized sparse-tensor value
+message SparseTensorProto {
+  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
+  // The default-value is zero for numeric tensors, and empty-string for string tensors.
+  // values must have a non-empty name present which serves as a name for SparseTensorProto
+  // when used in sparse_initializer list.
+  TensorProto values = 1;
+
+  // The indices of the non-default values, which may be stored in one of two formats.
+  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
+  // corresponding to the j-th index of the i-th value (in the values tensor).
+  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
+  // must be the linearized-index of the i-th value (in the values tensor).
+  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
+  // using the shape provided below.
+  // The indices must appear in ascending order without duplication.
+  // In the first format, the ordering is lexicographic-ordering:
+  // e.g., index-value [1,4] must appear before [2,1]
+  TensorProto indices = 2;
+
+  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
+  repeated int64 dims = 3;
+}
+
+// Defines a tensor shape. A dimension can be either an integer value
+// or a symbolic variable. A symbolic variable represents an unknown
+// dimension.
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;   // namespace Shape
+    };
+    // Standard denotation can optionally be used to denote tensor
+    // dimensions with standard semantic descriptions to ensure
+    // that operations are applied to the correct axis of a tensor.
+    // Refer to https://github.com/onnx/onnx/blob/main/docs/DimensionDenotation.md#denotation-definition
+    // for pre-defined dimension denotations.
+    string denotation = 3;
+  };
+  repeated Dimension dim = 1;
+}
+
+// Types
+//
+// The standard ONNX data types.
+message TypeProto {
+
+  message Tensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    int32 elem_type = 1;
+    TensorShapeProto shape = 2;
+  }
+
+  // repeated T
+  message Sequence {
+    // The type and optional shape of each element of the sequence.
+    // This field MUST be present for this version of the IR.
+    TypeProto elem_type = 1;
+  };
+
+  // map<K,V>
+  message Map {
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
+    int32 key_type = 1;
+    // This field MUST be present for this version of the IR.
+    TypeProto value_type = 2;
+  };
+
+  // wrapper for Tensor, Sequence, or Map
+  message Optional {
+    // The type and optional shape of the element wrapped.
+    // This field MUST be present for this version of the IR.
+    // Possible values correspond to OptionalProto.DataType enum
+    TypeProto elem_type = 1;
+  };
+
+
+  message SparseTensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    int32 elem_type = 1;
+    TensorShapeProto shape = 2;
+  }
+
+
+  message Opaque {
+    // When missing, the domain is the same as the model's.
+    string domain = 1;
+    // The name is optional but significant when provided.
+    string name = 2;
+    // parameters that help defining the type
+    // DEPRECATED do not use.
+    // repeated TypeProto parameters = 3;
+  }
+
+
+  oneof value {
+    // The type of a tensor.
+    Tensor tensor_type = 1;
+
+    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
+    //        as input and output to graphs and nodes. These types are needed to naturally
+    //        support classical ML operators.  DNN operators SHOULD restrict their input
+    //        and output types to tensors.
+
+    // The type of a sequence.
+    Sequence sequence_type = 4;
+
+    // The type of a map.
+    Map map_type = 5;
+
+    // The type of an optional.
+    Optional optional_type = 9;
+
+
+    // Type of the sparse tensor
+    SparseTensor sparse_tensor_type = 8;
+
+
+    Opaque opaque_type = 7;
+
+  }
+
+  // An optional denotation can be used to denote the whole
+  // type with a standard semantic description as to what is
+  // stored inside. Refer to https://github.com/onnx/onnx/blob/main/docs/TypeDenotation.md#type-denotation-definition
+  // for pre-defined type denotations.
+  string denotation = 6;
+}
+
+// Operator Sets
+//
+// OperatorSets are uniquely identified by a (domain, opset_version) pair.
+message OperatorSetIdProto {
+  // The domain of the operator set being identified.
+  // The empty string ("") or absence of this field implies the operator
+  // set that is defined as part of the ONNX specification.
+  // This field MUST be present in this version of the IR when referring to any other operator set.
+  string domain = 1;
+
+  // The version of the operator set being identified.
+  // This field MUST be present in this version of the IR.
+  int64 version = 2;
+}
+
+// Operator/function status.
+enum OperatorStatus {
+    EXPERIMENTAL = 0;
+    STABLE = 1;
+}
+
+message FunctionProto {
+  // The name of the function, similar usage of op_type in OperatorProto.
+  // Combined with FunctionProto.domain, this forms the unique identity of
+  // the FunctionProto.
+  string name = 1;
+
+  // Deprecated since IR Version 8
+  // optional int64 since_version = 2;
+  reserved 2;
+  reserved "since_version";
+
+  // Deprecated since IR Version 8
+  // optional OperatorStatus status = 3;
+  reserved 3;
+  reserved "status";
+
+  // The inputs and outputs of the function.
+  repeated string input = 4;
+  repeated string output = 5;
+
+  // The attributes of the function.
+  repeated string attribute = 6;
+
+  // The nodes in the function.
+  repeated NodeProto node = 7;
+  // A human-readable documentation for this function. Markdown is allowed.
+  string doc_string = 8;
+
+  // The OperatorSets this function body (graph) relies on.
+  //
+  // All nodes in the function body (graph) will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets. This means at most one version can be relied
+  // for one domain.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto. Example, if same operator set say 'A' is imported by FunctionProto
+  // and ModelProto then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same.
+
+  repeated OperatorSetIdProto opset_import = 9;
+
+  // The domain which this function belongs to. Combined with FunctionProto.name, this forms the unique identity of
+  // the FunctionProto.
+  string domain = 10;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-operators-ml.proto b/third_party/onnx/onnx/onnx-operators-ml.proto
new file mode 100644
index 0000000000..c077f1efc7
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-operators-ml.proto
@@ -0,0 +1,136 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+syntax = "proto2";
+
+package onnx;
+import "onnx/onnx-ml.proto";
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  optional string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  optional int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  optional OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  optional string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distingushed byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  optional string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  optional int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  optional string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  optional int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-operators-ml.proto3 b/third_party/onnx/onnx/onnx-operators-ml.proto3
new file mode 100644
index 0000000000..f5c6350bf4
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-operators-ml.proto3
@@ -0,0 +1,136 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+syntax = "proto3";
+
+package onnx;
+import "onnx/onnx-ml.proto3";
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distingushed byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-operators.in.proto b/third_party/onnx/onnx/onnx-operators.in.proto
new file mode 100644
index 0000000000..7b3d0fb694
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-operators.in.proto
@@ -0,0 +1,131 @@
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+syntax = "proto2";
+
+package {PACKAGE_NAME};
+// #if ONNX-ML
+import "onnx/onnx-ml.proto";
+// #else
+import "onnx/onnx.proto";
+// #endif
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  optional string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  optional int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  optional OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  optional string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distingushed byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  optional string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  optional int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  optional string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  optional int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
diff --git a/third_party/onnx/onnx/onnx-operators.proto b/third_party/onnx/onnx/onnx-operators.proto
new file mode 100644
index 0000000000..d8c7af4d89
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-operators.proto
@@ -0,0 +1,136 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+syntax = "proto2";
+
+package onnx;
+import "onnx/onnx.proto";
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  optional string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  optional int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  optional OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  optional string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distingushed byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  optional string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  optional int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  optional string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  optional int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-operators.proto3 b/third_party/onnx/onnx/onnx-operators.proto3
new file mode 100644
index 0000000000..3cd256b0c2
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-operators.proto3
@@ -0,0 +1,136 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the MIT license.
+
+syntax = "proto3";
+
+package onnx;
+import "onnx/onnx.proto3";
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distingushed byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx-operators_pb.h b/third_party/onnx/onnx/onnx-operators_pb.h
new file mode 100644
index 0000000000..c0a7363666
--- /dev/null
+++ b/third_party/onnx/onnx/onnx-operators_pb.h
@@ -0,0 +1,13 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#pragma once
+
+#include "onnx/onnx_pb.h"
+#ifdef ONNX_ML
+#include "onnx/onnx-operators-ml.pb.h"
+#else
+#include "onnx/onnx-operators.pb.h"
+#endif
diff --git a/third_party/onnx/onnx/onnx.in.proto b/third_party/onnx/onnx/onnx.in.proto
new file mode 100644
index 0000000000..d626dee0de
--- /dev/null
+++ b/third_party/onnx/onnx/onnx.in.proto
@@ -0,0 +1,831 @@
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package {PACKAGE_NAME};
+
+// Overview
+//
+// ONNX is an open specification that is comprised of the following components:
+//
+// 1)  A definition of an extensible computation graph model.
+// 2)  Definitions of standard data types.
+// 3)  Definitions of built-in operators.
+//
+// This document describes the syntax of models and their computation graphs,
+// as well as the standard data types. Together, they are referred to as the ONNX
+// Intermediate Representation, or 'IR' for short.
+//
+// The normative semantic specification of the ONNX IR is found in docs/IR.md.
+// Definitions of the built-in neural network operators may be found in docs/Operators.md.
+// #if ONNX-ML
+// Definitions of the built-in classical machine learning operators may be found in
+// docs/Operators-ml.md.
+// #endif
+
+// Notes
+//
+// Protobuf compatibility
+//
+// To simplify framework compatibility, ONNX is defined using the subset of protobuf
+// that is compatible with both protobuf v2 and v3. This means that we do not use any
+// protobuf features that are only available in one of the two versions.
+//
+// Here are the most notable contortions we have to carry out to work around
+// these limitations:
+//
+//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
+//     of key-value pairs, where order does not matter and duplicates
+//     are not allowed.
+
+
+// Versioning
+//
+// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
+//
+// To be compatible with both proto2 and proto3, we will use a version number
+// that is not defined by the default value but an explicit enum number.
+enum Version {
+  // proto3 requires the first enum value to be zero.
+  // We add this just to appease the compiler.
+  _START_VERSION = 0;
+  // The version field is always serialized and we will use it to store the
+  // version that the  graph is generated from. This helps us set up version
+  // control.
+  // For the IR, we are using simple numbers starting with 0x00000001,
+  // which was the version we published on Oct 10, 2017.
+  IR_VERSION_2017_10_10 = 0x0000000000000001;
+
+  // IR_VERSION 2 published on Oct 30, 2017
+  // - Added type discriminator to AttributeProto to support proto3 users
+  IR_VERSION_2017_10_30 = 0x0000000000000002;
+
+  // IR VERSION 3 published on Nov 3, 2017
+  // - For operator versioning:
+  //    - Added new message OperatorSetIdProto
+  //    - Added opset_import in ModelProto
+  // - For vendor extensions, added domain in NodeProto
+  IR_VERSION_2017_11_3 = 0x0000000000000003;
+
+  // IR VERSION 4 published on Jan 22, 2019
+  // - Relax constraint that initializers should be a subset of graph inputs
+  // - Add type BFLOAT16
+  IR_VERSION_2019_1_22 = 0x0000000000000004;
+
+  // IR VERSION 5 published on March 18, 2019
+  // - Add message TensorAnnotation.
+  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
+  IR_VERSION_2019_3_18 = 0x0000000000000005;
+
+  // IR VERSION 6 published on Sep 19, 2019
+  // - Add support for sparse tensor constants stored in model.
+  //   - Add message SparseTensorProto
+  //   - Add sparse initializers
+  IR_VERSION_2019_9_19 = 0x0000000000000006;
+
+  // IR VERSION 7 published on May 8, 2020
+  // - Add support to allow function body graph to rely on multiple external opreator sets.
+  // - Add a list to promote inference graph's initializers to global and
+  //   mutable variables. Global variables are visible in all graphs of the
+  //   stored models.
+  // - Add message TrainingInfoProto to store initialization
+  //   method and training algorithm. The execution of TrainingInfoProto
+  //   can modify the values of mutable variables.
+  // - Implicitly add inference graph into each TrainingInfoProto's algorithm.
+  IR_VERSION_2020_5_8 = 0x0000000000000007;
+
+  // IR VERSION 8 published on <TBD>
+  // Introduce TypeProto.SparseTensor
+  // Introduce TypeProto.Optional
+  // Added a list of FunctionProtos local to the model
+  // Deprecated since_version and operator status from FunctionProto
+  IR_VERSION = 0x0000000000000008;
+
+}
+
+// Attributes
+//
+// A named attribute containing either singular float, integer, string, graph,
+// and tensor values, or repeated float, integer, string, graph, and tensor values.
+// An AttributeProto MUST contain the name field, and *only one* of the
+// following content fields, effectively enforcing a C/C++ union equivalent.
+message AttributeProto {
+
+  // Note: this enum is structurally identical to the OpSchema::AttrType
+  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
+  enum AttributeType {
+    UNDEFINED = 0;
+    FLOAT = 1;
+    INT = 2;
+    STRING = 3;
+    TENSOR = 4;
+    GRAPH = 5;
+    SPARSE_TENSOR = 11;
+    TYPE_PROTO = 13;
+
+    FLOATS = 6;
+    INTS = 7;
+    STRINGS = 8;
+    TENSORS = 9;
+    GRAPHS = 10;
+    SPARSE_TENSORS = 12;
+    TYPE_PROTOS = 14;
+  }
+
+  // The name field MUST be present for this version of the IR.
+  optional string name = 1;           // namespace Attribute
+
+  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
+  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
+  // in parent scope.
+  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
+  optional string ref_attr_name = 21;
+
+  // A human-readable documentation for this attribute. Markdown is allowed.
+  optional string doc_string = 13;
+
+  // The type field MUST be present for this version of the IR.
+  // For 0.0.1 versions of the IR, this field was not defined, and
+  // implementations needed to use has_field heuristics to determine
+  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
+  // field MUST be set and match the f|i|s|t|... field in use.  This
+  // change was made to accommodate proto3 implementations.
+  optional AttributeType type = 20;   // discriminator that indicates which field below is in use
+
+  // Exactly ONE of the following fields must be present for this version of the IR
+  optional float f = 2;               // float
+  optional int64 i = 3;               // int
+  optional bytes s = 4;               // UTF-8 string
+  optional TensorProto t = 5;         // tensor value
+  optional GraphProto g = 6;          // graph
+  optional SparseTensorProto sparse_tensor = 22;  // sparse tensor value
+  // Do not use field below, it's deprecated.
+  // optional ValueProto v = 12;         // value - subsumes everything but graph
+  optional TypeProto tp = 14;          // type proto
+
+  repeated float floats = 7;          // list of floats
+  repeated int64 ints = 8;            // list of ints
+  repeated bytes strings = 9;         // list of UTF-8 strings
+  repeated TensorProto tensors = 10;  // list of tensors
+  repeated GraphProto graphs = 11;    // list of graph
+  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
+  repeated TypeProto type_protos = 15;// list of type protos
+}
+
+// Defines information on value, including the name, the type, and
+// the shape of the value.
+message ValueInfoProto {
+  // This field MUST be present in this version of the IR.
+  optional string name = 1;     // namespace Value
+  // This field MUST be present in this version of the IR for
+  // inputs and outputs of the top-level graph.
+  optional TypeProto type = 2;
+  // A human-readable documentation for this value. Markdown is allowed.
+  optional string doc_string = 3;
+}
+
+// Nodes
+//
+// Computation graphs are made up of a DAG of nodes, which represent what is
+// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
+//
+// For example, it can be a node of type "Conv" that takes in an image, a filter
+// tensor and a bias tensor, and produces the convolved output.
+message NodeProto {
+  repeated string input = 1;    // namespace Value
+  repeated string output = 2;   // namespace Value
+
+  // An optional identifier for this node in a graph.
+  // This field MAY be absent in ths version of the IR.
+  optional string name = 3;     // namespace Node
+
+  // The symbolic identifier of the Operator to execute.
+  optional string op_type = 4;  // namespace Operator
+  // The domain of the OperatorSet that specifies the operator named by op_type.
+  optional string domain = 7;   // namespace Domain
+
+  // Additional named attributes.
+  repeated AttributeProto attribute = 5;
+
+  // A human-readable documentation for this node. Markdown is allowed.
+  optional string doc_string = 6;
+}
+
+// Training information
+// TrainingInfoProto stores information for training a model.
+// In particular, this defines two functionalities: an initialization-step
+// and a training-algorithm-step. Initialization resets the model
+// back to its original state as if no training has been performed.
+// Training algorithm improves the model based on input data.
+//
+// The semantics of the initialization-step is that the initializers
+// in ModelProto.graph and in TrainingInfoProto.algorithm are first
+// initialized as specified by the initializers in the graph, and then
+// updated by the "initialization_binding" in every instance in
+// ModelProto.training_info.
+//
+// The field "algorithm" defines a computation graph which represents a
+// training algorithm's step. After the execution of a
+// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
+// may be immediately updated. If the targeted training algorithm contains
+// consecutive update steps (such as block coordinate descent methods),
+// the user needs to create a TrainingInfoProto for each step.
+message TrainingInfoProto {
+  // This field describes a graph to compute the initial tensors
+  // upon starting the training process. Initialization graph has no input
+  // and can have multiple outputs. Usually, trainable tensors in neural
+  // networks are randomly initialized. To achieve that, for each tensor,
+  // the user can put a random number operator such as RandomNormal or
+  // RandomUniform in TrainingInfoProto.initialization.node and assign its
+  // random output to the specific tensor using "initialization_binding".
+  // This graph can also set the initializers in "algorithm" in the same
+  // TrainingInfoProto; a use case is resetting the number of training
+  // iteration to zero.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Thus, no initializer would be changed by default.
+  optional GraphProto initialization = 1;
+
+  // This field represents a training algorithm step. Given required inputs,
+  // it computes outputs to update initializers in its own or inference graph's
+  // initializer lists. In general, this field contains loss node, gradient node,
+  // optimizer node, increment of iteration count.
+  //
+  // An execution of the training algorithm step is performed by executing the
+  // graph obtained by combining the inference graph (namely "ModelProto.graph")
+  // and the "algorithm" graph. That is, the actual the actual
+  // input/initializer/output/node/value_info/sparse_initializer list of
+  // the training graph is the concatenation of
+  // "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
+  // and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
+  // in that order. This combined graph must satisfy the normal ONNX conditions.
+  // Now, let's provide a visualization of graph combination for clarity.
+  // Let the inference graph (i.e., "ModelProto.graph") be
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
+  // and the "algorithm" graph be
+  //    tensor_d -> Add -> tensor_e
+  // The combination process results
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
+  //
+  // Notice that an input of a node in the "algorithm" graph may reference the
+  // output of a node in the inference graph (but not the other way round). Also, inference
+  // node cannot reference inputs of "algorithm". With these restrictions, inference graph
+  // can always be run independently without training information.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Evaluating the default training step never
+  // update any initializers.
+  optional GraphProto algorithm = 2;
+
+  // This field specifies the bindings from the outputs of "initialization" to
+  // some initializers in "ModelProto.graph.initializer" and
+  // the "algorithm.initializer" in the same TrainingInfoProto.
+  // See "update_binding" below for details.
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "initialization".
+  repeated StringStringEntryProto initialization_binding = 3;
+
+  // Gradient-based training is usually an iterative procedure. In one gradient
+  // descent iteration, we apply
+  //
+  // x = x - r * g
+  //
+  // where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
+  // gradient of "x" with respect to a chosen loss. To avoid adding assignments
+  // into the training graph, we split the update equation into
+  //
+  // y = x - r * g
+  // x = y
+  //
+  // The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
+  // tell that "y" should be assigned to "x", the field "update_binding" may
+  // contain a key-value pair of strings, "x" (key of StringStringEntryProto)
+  // and "y" (value of StringStringEntryProto).
+  // For a neural network with multiple trainable (mutable) tensors, there can
+  // be multiple key-value pairs in "update_binding".
+  //
+  // The initializers appears as keys in "update_binding" are considered
+  // mutable variables. This implies some behaviors
+  // as described below.
+  //
+  //  1. We have only unique keys in all "update_binding"s so that two
+  //     variables may not have the same name. This ensures that one
+  //     variable is assigned up to once.
+  //  2. The keys must appear in names of "ModelProto.graph.initializer" or
+  //     "TrainingInfoProto.algorithm.initializer".
+  //  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
+  //  4. Mutable variables are initialized to the value specified by the
+  //     corresponding initializer, and then potentially updated by
+  //     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
+  //
+  // This field usually contains names of trainable tensors
+  // (in ModelProto.graph), optimizer states such as momentums in advanced
+  // stochastic gradient methods (in TrainingInfoProto.graph),
+  // and number of training iterations (in TrainingInfoProto.graph).
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "algorithm".
+  repeated StringStringEntryProto update_binding = 4;
+}
+
+// Models
+//
+// ModelProto is a top-level file/container format for bundling a ML model and
+// associating its computation graph with metadata.
+//
+// The semantics of the model are described by the associated GraphProto's.
+message ModelProto {
+  // The version of the IR this model targets. See Version enum above.
+  // This field MUST be present.
+  optional int64 ir_version = 1;
+
+  // The OperatorSets this model relies on.
+  // All ModelProtos MUST have at least one entry that
+  // specifies which version of the ONNX OperatorSet is
+  // being imported.
+  //
+  // All nodes in the ModelProto's graph will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets.
+  repeated OperatorSetIdProto opset_import = 8;
+
+  // The name of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_name = 2;
+
+  // The version of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_version = 3;
+
+  // Domain name of the model.
+  // We use reverse domain names as name space indicators. For example:
+  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
+  //
+  // Together with `model_version` and GraphProto.name, this forms the unique identity of
+  // the graph.
+  optional string domain = 4;
+
+  // The version of the graph encoded. See Version enum below.
+  optional int64 model_version = 5;
+
+  // A human-readable documentation for this model. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The parameterized graph that is evaluated to execute the model.
+  optional GraphProto graph = 7;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+
+  // Training-specific information. Sequentially executing all stored
+  // `TrainingInfoProto.algorithm`s and assigning their outputs following
+  // the corresponding `TrainingInfoProto.update_binding`s is one training
+  // iteration. Similarly, to initialize the model
+  // (as if training hasn't happened), the user should sequentially execute
+  // all stored `TrainingInfoProto.initialization`s and assigns their outputs
+  // using `TrainingInfoProto.initialization_binding`s.
+  //
+  // If this field is empty, the training behavior of the model is undefined.
+  repeated TrainingInfoProto training_info = 20;
+
+  // A list of function protos local to the model.
+  //
+  // Name of the function "FunctionProto.name" should be unique within the domain "FunctionProto.domain".
+  // In case of any conflicts the behavior (whether the model local functions are given higher priority,
+  // or standard opserator sets are given higher priotity or this is treated as error) is defined by 
+  // the runtimes.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto and other model local FunctionProtos. 
+  // Example, if same operator set say 'A' is imported by a FunctionProto and ModelProto 
+  // or by 2 FunctionProtos then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same for every node in the function body.
+  //
+  // One FunctionProto can reference other FunctionProto in the model, however, recursive reference
+  // is not allowed.
+  repeated FunctionProto functions = 25;
+};
+
+// StringStringEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message StringStringEntryProto {
+  optional string key = 1;
+  optional string value= 2;
+};
+
+message TensorAnnotation {
+  optional string tensor_name = 1;
+  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
+  // The keys used in the mapping below must be pre-defined in ONNX spec.
+  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
+  // quantization parameter keys.
+  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
+}
+
+
+
+// Graphs
+//
+// A graph defines the computational logic of a model and is comprised of a parameterized
+// list of nodes that form a directed acyclic graph based on their inputs and outputs.
+// This is the equivalent of the "network" or "graph" in many deep learning
+// frameworks.
+message GraphProto {
+  // The nodes in the graph, sorted topologically.
+  repeated NodeProto node = 1;
+
+  // The name of the graph.
+  optional string name = 2;   // namespace Graph
+
+  // A list of named tensor values, used to specify constant inputs of the graph.
+  // Each initializer (both TensorProto as well SparseTensorProto) MUST have a name.
+  // The name MUST be unique across both initializer and sparse_initializer,
+  // but the name MAY also appear in the input list.
+  repeated TensorProto initializer = 5;
+
+  // Initializers (see above) stored in sparse format.
+  repeated SparseTensorProto sparse_initializer = 15;
+
+  // A human-readable documentation for this graph. Markdown is allowed.
+  optional string doc_string = 10;
+
+  // The inputs and outputs of the graph.
+  repeated ValueInfoProto input = 11;
+  repeated ValueInfoProto output = 12;
+
+  // Information for the values in the graph. The ValueInfoProto.name's
+  // must be distinct. It is optional for a value to appear in value_info list.
+  repeated ValueInfoProto value_info = 13;
+
+  // This field carries information to indicate the mapping among a tensor and its
+  // quantization parameter tensors. For example:
+  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
+  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
+  repeated TensorAnnotation quantization_annotation = 14;
+
+  // DO NOT USE the following fields, they were deprecated from earlier versions.
+  // repeated string input = 3;
+  // repeated string output = 4;
+  // optional int64 ir_version = 6;
+  // optional int64 producer_version = 7;
+  // optional string producer_tag = 8;
+  // optional string domain = 9;
+}
+
+// Tensors
+//
+// A serialized tensor value.
+message TensorProto {
+  enum DataType {
+    UNDEFINED = 0;
+    // Basic types.
+    FLOAT = 1;   // float
+    UINT8 = 2;   // uint8_t
+    INT8 = 3;    // int8_t
+    UINT16 = 4;  // uint16_t
+    INT16 = 5;   // int16_t
+    INT32 = 6;   // int32_t
+    INT64 = 7;   // int64_t
+    STRING = 8;  // string
+    BOOL = 9;    // bool
+
+    // IEEE754 half-precision floating-point format (16 bits wide).
+    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
+    FLOAT16 = 10;
+
+    DOUBLE = 11;
+    UINT32 = 12;
+    UINT64 = 13;
+    COMPLEX64 = 14;     // complex with float32 real and imaginary components
+    COMPLEX128 = 15;    // complex with float64 real and imaginary components
+
+    // Non-IEEE floating-point format based on IEEE754 single-precision
+    // floating-point number truncated to 16 bits.
+    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
+    BFLOAT16 = 16;
+
+    // Future extensions go here.
+  }
+
+  // The shape of the tensor.
+  repeated int64 dims = 1;
+
+  // The data type of the tensor.
+  // This field MUST have a valid TensorProto.DataType value
+  optional int32 data_type = 2;
+
+  // For very large tensors, we may want to store them in chunks, in which
+  // case the following fields will specify the segment that is stored in
+  // the current TensorProto.
+  message Segment {
+    optional int64 begin = 1;
+    optional int64 end = 2;
+  }
+  optional Segment segment = 3;
+
+  // Tensor content must be organized in row-major order.
+  //
+  // Depending on the data_type field, exactly one of the fields below with
+  // name ending in _data is used to store the elements of the tensor.
+
+  // For float and complex64 values
+  // Complex64 tensors are encoded as a single array of floats,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
+  repeated float float_data = 4 [packed = true];
+
+  // For int32, uint8, int8, uint16, int16, bool, and float16 values
+  // float16 values must be bit-wise converted to an uint16_t prior
+  // to writing to the buffer.
+  // When this field is present, the data_type field MUST be
+  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
+  repeated int32 int32_data = 5 [packed = true];
+
+  // For strings.
+  // Each element of string_data is a UTF-8 encoded Unicode
+  // string. No trailing null, no leading BOM. The protobuf "string"
+  // scalar type is not used to match ML community conventions.
+  // When this field is present, the data_type field MUST be STRING
+  repeated bytes string_data = 6;
+
+  // For int64.
+  // When this field is present, the data_type field MUST be INT64
+  repeated int64 int64_data = 7 [packed = true];
+
+  // Optionally, a name for the tensor.
+  optional string name = 8; // namespace Value
+
+  // A human-readable documentation for this tensor. Markdown is allowed.
+  optional string doc_string = 12;
+
+  // Serializations can either use one of the fields above, or use this
+  // raw bytes field. The only exception is the string case, where one is
+  // required to store the content in the repeated bytes string_data field.
+  //
+  // When this raw_data field is used to store tensor value, elements MUST
+  // be stored in as fixed-width, little-endian order.
+  // Floating-point data types MUST be stored in IEEE 754 format.
+  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
+  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
+  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
+  //
+  // Note: the advantage of specific field rather than the raw_data field is
+  // that in some cases (e.g. int data), protobuf does a better packing via
+  // variable length storage, and may lead to smaller binary footprint.
+  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
+  optional bytes raw_data = 9;
+
+  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
+  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
+  // external_data stores key-value pairs describing data location. Recognized keys are:
+  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
+  //                           protobuf model was stored
+  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
+  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+  // - "length" (optional) - number of bytes containing data. Integer stored as string.
+  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
+  repeated StringStringEntryProto external_data = 13;
+
+  // Location of the data for this tensor. MUST be one of:
+  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
+  // - EXTERNAL - data stored in an external location as described by external_data field.
+  enum DataLocation {
+    DEFAULT = 0;
+    EXTERNAL = 1;
+  }
+
+  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
+  optional DataLocation data_location = 14;
+
+  // For double
+  // Complex128 tensors are encoded as a single array of doubles,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
+  repeated double double_data = 10 [packed = true];
+
+  // For uint64 and uint32 values
+  // When this field is present, the data_type field MUST be
+  // UINT32 or UINT64
+  repeated uint64 uint64_data = 11 [packed = true];
+}
+
+// A serialized sparse-tensor value
+message SparseTensorProto {
+  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
+  // The default-value is zero for numeric tensors, and empty-string for string tensors.
+  // values must have a non-empty name present which serves as a name for SparseTensorProto
+  // when used in sparse_initializer list.
+  optional TensorProto values = 1;
+
+  // The indices of the non-default values, which may be stored in one of two formats.
+  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
+  // corresponding to the j-th index of the i-th value (in the values tensor).
+  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
+  // must be the linearized-index of the i-th value (in the values tensor).
+  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
+  // using the shape provided below.
+  // The indices must appear in ascending order without duplication.
+  // In the first format, the ordering is lexicographic-ordering:
+  // e.g., index-value [1,4] must appear before [2,1]
+  optional TensorProto indices = 2;
+
+  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
+  repeated int64 dims = 3;
+}
+
+// Defines a tensor shape. A dimension can be either an integer value
+// or a symbolic variable. A symbolic variable represents an unknown
+// dimension.
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;   // namespace Shape
+    };
+    // Standard denotation can optionally be used to denote tensor
+    // dimensions with standard semantic descriptions to ensure
+    // that operations are applied to the correct axis of a tensor.
+    // Refer to https://github.com/onnx/onnx/blob/main/docs/DimensionDenotation.md#denotation-definition
+    // for pre-defined dimension denotations.
+    optional string denotation = 3;
+  };
+  repeated Dimension dim = 1;
+}
+
+// Types
+//
+// The standard ONNX data types.
+message TypeProto {
+
+  message Tensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+  // repeated T
+  message Sequence {
+    // The type and optional shape of each element of the sequence.
+    // This field MUST be present for this version of the IR.
+    optional TypeProto elem_type = 1;
+  };
+
+  // map<K,V>
+  message Map {
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
+    optional int32 key_type = 1;
+    // This field MUST be present for this version of the IR.
+    optional TypeProto value_type = 2;
+  };
+
+  // wrapper for Tensor, Sequence, or Map
+  message Optional {
+    // The type and optional shape of the element wrapped.
+    // This field MUST be present for this version of the IR.
+    // Possible values correspond to OptionalProto.DataType enum
+    optional TypeProto elem_type = 1;
+  };
+
+
+  message SparseTensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+// #if ONNX-ML
+
+  message Opaque {
+    // When missing, the domain is the same as the model's.
+    optional string domain = 1;
+    // The name is optional but significant when provided.
+    optional string name = 2;
+    // parameters that help defining the type
+    // DEPRECATED do not use.
+    // repeated TypeProto parameters = 3;
+  }
+
+// #endif
+
+  oneof value {
+    // The type of a tensor.
+    Tensor tensor_type = 1;
+
+    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
+    //        as input and output to graphs and nodes. These types are needed to naturally
+    //        support classical ML operators.  DNN operators SHOULD restrict their input
+    //        and output types to tensors.
+
+    // The type of a sequence.
+    Sequence sequence_type = 4;
+
+    // The type of a map.
+    Map map_type = 5;
+
+    // The type of an optional.
+    Optional optional_type = 9;
+
+
+    // Type of the sparse tensor
+    SparseTensor sparse_tensor_type = 8;
+
+// #if ONNX-ML
+
+    Opaque opaque_type = 7;
+
+// #endif
+  }
+
+  // An optional denotation can be used to denote the whole
+  // type with a standard semantic description as to what is
+  // stored inside. Refer to https://github.com/onnx/onnx/blob/main/docs/TypeDenotation.md#type-denotation-definition
+  // for pre-defined type denotations.
+  optional string denotation = 6;
+}
+
+// Operator Sets
+//
+// OperatorSets are uniquely identified by a (domain, opset_version) pair.
+message OperatorSetIdProto {
+  // The domain of the operator set being identified.
+  // The empty string ("") or absence of this field implies the operator
+  // set that is defined as part of the ONNX specification.
+  // This field MUST be present in this version of the IR when referring to any other operator set.
+  optional string domain = 1;
+
+  // The version of the operator set being identified.
+  // This field MUST be present in this version of the IR.
+  optional int64 version = 2;
+}
+
+// Operator/function status.
+enum OperatorStatus {
+    EXPERIMENTAL = 0;
+    STABLE = 1;
+}
+
+message FunctionProto {
+  // The name of the function, similar usage of op_type in OperatorProto.
+  // Combined with FunctionProto.domain, this forms the unique identity of
+  // the FunctionProto.
+  optional string name = 1;
+
+  // Deprecated since IR Version 8
+  // optional int64 since_version = 2;
+  reserved 2;
+  reserved "since_version";
+
+  // Deprecated since IR Version 8
+  // optional OperatorStatus status = 3;
+  reserved 3;
+  reserved "status";
+
+  // The inputs and outputs of the function.
+  repeated string input = 4;
+  repeated string output = 5;
+
+  // The attributes of the function.
+  repeated string attribute = 6;
+
+  // The nodes in the function.
+  repeated NodeProto node = 7;
+  // A human-readable documentation for this function. Markdown is allowed.
+  optional string doc_string = 8;
+
+  // The OperatorSets this function body (graph) relies on.
+  //
+  // All nodes in the function body (graph) will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets. This means at most one version can be relied
+  // for one domain.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto. Example, if same operator set say 'A' is imported by FunctionProto
+  // and ModelProto then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same.
+
+  repeated OperatorSetIdProto opset_import = 9;
+
+  // The domain which this function belongs to. Combined with FunctionProto.name, this forms the unique identity of
+  // the FunctionProto.
+  optional string domain = 10;
+}
+
diff --git a/third_party/onnx/onnx/onnx.proto b/third_party/onnx/onnx/onnx.proto
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+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package onnx;
+
+// Overview
+//
+// ONNX is an open specification that is comprised of the following components:
+//
+// 1)  A definition of an extensible computation graph model.
+// 2)  Definitions of standard data types.
+// 3)  Definitions of built-in operators.
+//
+// This document describes the syntax of models and their computation graphs,
+// as well as the standard data types. Together, they are referred to as the ONNX
+// Intermediate Representation, or 'IR' for short.
+//
+// The normative semantic specification of the ONNX IR is found in docs/IR.md.
+// Definitions of the built-in neural network operators may be found in docs/Operators.md.
+
+// Notes
+//
+// Protobuf compatibility
+//
+// To simplify framework compatibility, ONNX is defined using the subset of protobuf
+// that is compatible with both protobuf v2 and v3. This means that we do not use any
+// protobuf features that are only available in one of the two versions.
+//
+// Here are the most notable contortions we have to carry out to work around
+// these limitations:
+//
+//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
+//     of key-value pairs, where order does not matter and duplicates
+//     are not allowed.
+
+
+// Versioning
+//
+// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
+//
+// To be compatible with both proto2 and proto3, we will use a version number
+// that is not defined by the default value but an explicit enum number.
+enum Version {
+  // proto3 requires the first enum value to be zero.
+  // We add this just to appease the compiler.
+  _START_VERSION = 0;
+  // The version field is always serialized and we will use it to store the
+  // version that the  graph is generated from. This helps us set up version
+  // control.
+  // For the IR, we are using simple numbers starting with 0x00000001,
+  // which was the version we published on Oct 10, 2017.
+  IR_VERSION_2017_10_10 = 0x0000000000000001;
+
+  // IR_VERSION 2 published on Oct 30, 2017
+  // - Added type discriminator to AttributeProto to support proto3 users
+  IR_VERSION_2017_10_30 = 0x0000000000000002;
+
+  // IR VERSION 3 published on Nov 3, 2017
+  // - For operator versioning:
+  //    - Added new message OperatorSetIdProto
+  //    - Added opset_import in ModelProto
+  // - For vendor extensions, added domain in NodeProto
+  IR_VERSION_2017_11_3 = 0x0000000000000003;
+
+  // IR VERSION 4 published on Jan 22, 2019
+  // - Relax constraint that initializers should be a subset of graph inputs
+  // - Add type BFLOAT16
+  IR_VERSION_2019_1_22 = 0x0000000000000004;
+
+  // IR VERSION 5 published on March 18, 2019
+  // - Add message TensorAnnotation.
+  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
+  IR_VERSION_2019_3_18 = 0x0000000000000005;
+
+  // IR VERSION 6 published on Sep 19, 2019
+  // - Add support for sparse tensor constants stored in model.
+  //   - Add message SparseTensorProto
+  //   - Add sparse initializers
+  IR_VERSION_2019_9_19 = 0x0000000000000006;
+
+  // IR VERSION 7 published on May 8, 2020
+  // - Add support to allow function body graph to rely on multiple external opreator sets.
+  // - Add a list to promote inference graph's initializers to global and
+  //   mutable variables. Global variables are visible in all graphs of the
+  //   stored models.
+  // - Add message TrainingInfoProto to store initialization
+  //   method and training algorithm. The execution of TrainingInfoProto
+  //   can modify the values of mutable variables.
+  // - Implicitly add inference graph into each TrainingInfoProto's algorithm.
+  IR_VERSION_2020_5_8 = 0x0000000000000007;
+
+  // IR VERSION 8 published on <TBD>
+  // Introduce TypeProto.SparseTensor
+  // Introduce TypeProto.Optional
+  // Added a list of FunctionProtos local to the model
+  // Deprecated since_version and operator status from FunctionProto
+  IR_VERSION = 0x0000000000000008;
+
+}
+
+// Attributes
+//
+// A named attribute containing either singular float, integer, string, graph,
+// and tensor values, or repeated float, integer, string, graph, and tensor values.
+// An AttributeProto MUST contain the name field, and *only one* of the
+// following content fields, effectively enforcing a C/C++ union equivalent.
+message AttributeProto {
+
+  // Note: this enum is structurally identical to the OpSchema::AttrType
+  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
+  enum AttributeType {
+    UNDEFINED = 0;
+    FLOAT = 1;
+    INT = 2;
+    STRING = 3;
+    TENSOR = 4;
+    GRAPH = 5;
+    SPARSE_TENSOR = 11;
+    TYPE_PROTO = 13;
+
+    FLOATS = 6;
+    INTS = 7;
+    STRINGS = 8;
+    TENSORS = 9;
+    GRAPHS = 10;
+    SPARSE_TENSORS = 12;
+    TYPE_PROTOS = 14;
+  }
+
+  // The name field MUST be present for this version of the IR.
+  optional string name = 1;           // namespace Attribute
+
+  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
+  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
+  // in parent scope.
+  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
+  optional string ref_attr_name = 21;
+
+  // A human-readable documentation for this attribute. Markdown is allowed.
+  optional string doc_string = 13;
+
+  // The type field MUST be present for this version of the IR.
+  // For 0.0.1 versions of the IR, this field was not defined, and
+  // implementations needed to use has_field heuristics to determine
+  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
+  // field MUST be set and match the f|i|s|t|... field in use.  This
+  // change was made to accommodate proto3 implementations.
+  optional AttributeType type = 20;   // discriminator that indicates which field below is in use
+
+  // Exactly ONE of the following fields must be present for this version of the IR
+  optional float f = 2;               // float
+  optional int64 i = 3;               // int
+  optional bytes s = 4;               // UTF-8 string
+  optional TensorProto t = 5;         // tensor value
+  optional GraphProto g = 6;          // graph
+  optional SparseTensorProto sparse_tensor = 22;  // sparse tensor value
+  // Do not use field below, it's deprecated.
+  // optional ValueProto v = 12;         // value - subsumes everything but graph
+  optional TypeProto tp = 14;          // type proto
+
+  repeated float floats = 7;          // list of floats
+  repeated int64 ints = 8;            // list of ints
+  repeated bytes strings = 9;         // list of UTF-8 strings
+  repeated TensorProto tensors = 10;  // list of tensors
+  repeated GraphProto graphs = 11;    // list of graph
+  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
+  repeated TypeProto type_protos = 15;// list of type protos
+}
+
+// Defines information on value, including the name, the type, and
+// the shape of the value.
+message ValueInfoProto {
+  // This field MUST be present in this version of the IR.
+  optional string name = 1;     // namespace Value
+  // This field MUST be present in this version of the IR for
+  // inputs and outputs of the top-level graph.
+  optional TypeProto type = 2;
+  // A human-readable documentation for this value. Markdown is allowed.
+  optional string doc_string = 3;
+}
+
+// Nodes
+//
+// Computation graphs are made up of a DAG of nodes, which represent what is
+// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
+//
+// For example, it can be a node of type "Conv" that takes in an image, a filter
+// tensor and a bias tensor, and produces the convolved output.
+message NodeProto {
+  repeated string input = 1;    // namespace Value
+  repeated string output = 2;   // namespace Value
+
+  // An optional identifier for this node in a graph.
+  // This field MAY be absent in ths version of the IR.
+  optional string name = 3;     // namespace Node
+
+  // The symbolic identifier of the Operator to execute.
+  optional string op_type = 4;  // namespace Operator
+  // The domain of the OperatorSet that specifies the operator named by op_type.
+  optional string domain = 7;   // namespace Domain
+
+  // Additional named attributes.
+  repeated AttributeProto attribute = 5;
+
+  // A human-readable documentation for this node. Markdown is allowed.
+  optional string doc_string = 6;
+}
+
+// Training information
+// TrainingInfoProto stores information for training a model.
+// In particular, this defines two functionalities: an initialization-step
+// and a training-algorithm-step. Initialization resets the model
+// back to its original state as if no training has been performed.
+// Training algorithm improves the model based on input data.
+//
+// The semantics of the initialization-step is that the initializers
+// in ModelProto.graph and in TrainingInfoProto.algorithm are first
+// initialized as specified by the initializers in the graph, and then
+// updated by the "initialization_binding" in every instance in
+// ModelProto.training_info.
+//
+// The field "algorithm" defines a computation graph which represents a
+// training algorithm's step. After the execution of a
+// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
+// may be immediately updated. If the targeted training algorithm contains
+// consecutive update steps (such as block coordinate descent methods),
+// the user needs to create a TrainingInfoProto for each step.
+message TrainingInfoProto {
+  // This field describes a graph to compute the initial tensors
+  // upon starting the training process. Initialization graph has no input
+  // and can have multiple outputs. Usually, trainable tensors in neural
+  // networks are randomly initialized. To achieve that, for each tensor,
+  // the user can put a random number operator such as RandomNormal or
+  // RandomUniform in TrainingInfoProto.initialization.node and assign its
+  // random output to the specific tensor using "initialization_binding".
+  // This graph can also set the initializers in "algorithm" in the same
+  // TrainingInfoProto; a use case is resetting the number of training
+  // iteration to zero.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Thus, no initializer would be changed by default.
+  optional GraphProto initialization = 1;
+
+  // This field represents a training algorithm step. Given required inputs,
+  // it computes outputs to update initializers in its own or inference graph's
+  // initializer lists. In general, this field contains loss node, gradient node,
+  // optimizer node, increment of iteration count.
+  //
+  // An execution of the training algorithm step is performed by executing the
+  // graph obtained by combining the inference graph (namely "ModelProto.graph")
+  // and the "algorithm" graph. That is, the actual the actual
+  // input/initializer/output/node/value_info/sparse_initializer list of
+  // the training graph is the concatenation of
+  // "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
+  // and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
+  // in that order. This combined graph must satisfy the normal ONNX conditions.
+  // Now, let's provide a visualization of graph combination for clarity.
+  // Let the inference graph (i.e., "ModelProto.graph") be
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
+  // and the "algorithm" graph be
+  //    tensor_d -> Add -> tensor_e
+  // The combination process results
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
+  //
+  // Notice that an input of a node in the "algorithm" graph may reference the
+  // output of a node in the inference graph (but not the other way round). Also, inference
+  // node cannot reference inputs of "algorithm". With these restrictions, inference graph
+  // can always be run independently without training information.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Evaluating the default training step never
+  // update any initializers.
+  optional GraphProto algorithm = 2;
+
+  // This field specifies the bindings from the outputs of "initialization" to
+  // some initializers in "ModelProto.graph.initializer" and
+  // the "algorithm.initializer" in the same TrainingInfoProto.
+  // See "update_binding" below for details.
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "initialization".
+  repeated StringStringEntryProto initialization_binding = 3;
+
+  // Gradient-based training is usually an iterative procedure. In one gradient
+  // descent iteration, we apply
+  //
+  // x = x - r * g
+  //
+  // where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
+  // gradient of "x" with respect to a chosen loss. To avoid adding assignments
+  // into the training graph, we split the update equation into
+  //
+  // y = x - r * g
+  // x = y
+  //
+  // The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
+  // tell that "y" should be assigned to "x", the field "update_binding" may
+  // contain a key-value pair of strings, "x" (key of StringStringEntryProto)
+  // and "y" (value of StringStringEntryProto).
+  // For a neural network with multiple trainable (mutable) tensors, there can
+  // be multiple key-value pairs in "update_binding".
+  //
+  // The initializers appears as keys in "update_binding" are considered
+  // mutable variables. This implies some behaviors
+  // as described below.
+  //
+  //  1. We have only unique keys in all "update_binding"s so that two
+  //     variables may not have the same name. This ensures that one
+  //     variable is assigned up to once.
+  //  2. The keys must appear in names of "ModelProto.graph.initializer" or
+  //     "TrainingInfoProto.algorithm.initializer".
+  //  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
+  //  4. Mutable variables are initialized to the value specified by the
+  //     corresponding initializer, and then potentially updated by
+  //     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
+  //
+  // This field usually contains names of trainable tensors
+  // (in ModelProto.graph), optimizer states such as momentums in advanced
+  // stochastic gradient methods (in TrainingInfoProto.graph),
+  // and number of training iterations (in TrainingInfoProto.graph).
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "algorithm".
+  repeated StringStringEntryProto update_binding = 4;
+}
+
+// Models
+//
+// ModelProto is a top-level file/container format for bundling a ML model and
+// associating its computation graph with metadata.
+//
+// The semantics of the model are described by the associated GraphProto's.
+message ModelProto {
+  // The version of the IR this model targets. See Version enum above.
+  // This field MUST be present.
+  optional int64 ir_version = 1;
+
+  // The OperatorSets this model relies on.
+  // All ModelProtos MUST have at least one entry that
+  // specifies which version of the ONNX OperatorSet is
+  // being imported.
+  //
+  // All nodes in the ModelProto's graph will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets.
+  repeated OperatorSetIdProto opset_import = 8;
+
+  // The name of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_name = 2;
+
+  // The version of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_version = 3;
+
+  // Domain name of the model.
+  // We use reverse domain names as name space indicators. For example:
+  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
+  //
+  // Together with `model_version` and GraphProto.name, this forms the unique identity of
+  // the graph.
+  optional string domain = 4;
+
+  // The version of the graph encoded. See Version enum below.
+  optional int64 model_version = 5;
+
+  // A human-readable documentation for this model. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The parameterized graph that is evaluated to execute the model.
+  optional GraphProto graph = 7;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+
+  // Training-specific information. Sequentially executing all stored
+  // `TrainingInfoProto.algorithm`s and assigning their outputs following
+  // the corresponding `TrainingInfoProto.update_binding`s is one training
+  // iteration. Similarly, to initialize the model
+  // (as if training hasn't happened), the user should sequentially execute
+  // all stored `TrainingInfoProto.initialization`s and assigns their outputs
+  // using `TrainingInfoProto.initialization_binding`s.
+  //
+  // If this field is empty, the training behavior of the model is undefined.
+  repeated TrainingInfoProto training_info = 20;
+
+  // A list of function protos local to the model.
+  //
+  // Name of the function "FunctionProto.name" should be unique within the domain "FunctionProto.domain".
+  // In case of any conflicts the behavior (whether the model local functions are given higher priority,
+  // or standard opserator sets are given higher priotity or this is treated as error) is defined by 
+  // the runtimes.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto and other model local FunctionProtos. 
+  // Example, if same operator set say 'A' is imported by a FunctionProto and ModelProto 
+  // or by 2 FunctionProtos then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same for every node in the function body.
+  //
+  // One FunctionProto can reference other FunctionProto in the model, however, recursive reference
+  // is not allowed.
+  repeated FunctionProto functions = 25;
+};
+
+// StringStringEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message StringStringEntryProto {
+  optional string key = 1;
+  optional string value= 2;
+};
+
+message TensorAnnotation {
+  optional string tensor_name = 1;
+  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
+  // The keys used in the mapping below must be pre-defined in ONNX spec.
+  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
+  // quantization parameter keys.
+  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
+}
+
+
+
+// Graphs
+//
+// A graph defines the computational logic of a model and is comprised of a parameterized
+// list of nodes that form a directed acyclic graph based on their inputs and outputs.
+// This is the equivalent of the "network" or "graph" in many deep learning
+// frameworks.
+message GraphProto {
+  // The nodes in the graph, sorted topologically.
+  repeated NodeProto node = 1;
+
+  // The name of the graph.
+  optional string name = 2;   // namespace Graph
+
+  // A list of named tensor values, used to specify constant inputs of the graph.
+  // Each initializer (both TensorProto as well SparseTensorProto) MUST have a name.
+  // The name MUST be unique across both initializer and sparse_initializer,
+  // but the name MAY also appear in the input list.
+  repeated TensorProto initializer = 5;
+
+  // Initializers (see above) stored in sparse format.
+  repeated SparseTensorProto sparse_initializer = 15;
+
+  // A human-readable documentation for this graph. Markdown is allowed.
+  optional string doc_string = 10;
+
+  // The inputs and outputs of the graph.
+  repeated ValueInfoProto input = 11;
+  repeated ValueInfoProto output = 12;
+
+  // Information for the values in the graph. The ValueInfoProto.name's
+  // must be distinct. It is optional for a value to appear in value_info list.
+  repeated ValueInfoProto value_info = 13;
+
+  // This field carries information to indicate the mapping among a tensor and its
+  // quantization parameter tensors. For example:
+  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
+  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
+  repeated TensorAnnotation quantization_annotation = 14;
+
+  // DO NOT USE the following fields, they were deprecated from earlier versions.
+  // repeated string input = 3;
+  // repeated string output = 4;
+  // optional int64 ir_version = 6;
+  // optional int64 producer_version = 7;
+  // optional string producer_tag = 8;
+  // optional string domain = 9;
+}
+
+// Tensors
+//
+// A serialized tensor value.
+message TensorProto {
+  enum DataType {
+    UNDEFINED = 0;
+    // Basic types.
+    FLOAT = 1;   // float
+    UINT8 = 2;   // uint8_t
+    INT8 = 3;    // int8_t
+    UINT16 = 4;  // uint16_t
+    INT16 = 5;   // int16_t
+    INT32 = 6;   // int32_t
+    INT64 = 7;   // int64_t
+    STRING = 8;  // string
+    BOOL = 9;    // bool
+
+    // IEEE754 half-precision floating-point format (16 bits wide).
+    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
+    FLOAT16 = 10;
+
+    DOUBLE = 11;
+    UINT32 = 12;
+    UINT64 = 13;
+    COMPLEX64 = 14;     // complex with float32 real and imaginary components
+    COMPLEX128 = 15;    // complex with float64 real and imaginary components
+
+    // Non-IEEE floating-point format based on IEEE754 single-precision
+    // floating-point number truncated to 16 bits.
+    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
+    BFLOAT16 = 16;
+
+    // Future extensions go here.
+  }
+
+  // The shape of the tensor.
+  repeated int64 dims = 1;
+
+  // The data type of the tensor.
+  // This field MUST have a valid TensorProto.DataType value
+  optional int32 data_type = 2;
+
+  // For very large tensors, we may want to store them in chunks, in which
+  // case the following fields will specify the segment that is stored in
+  // the current TensorProto.
+  message Segment {
+    optional int64 begin = 1;
+    optional int64 end = 2;
+  }
+  optional Segment segment = 3;
+
+  // Tensor content must be organized in row-major order.
+  //
+  // Depending on the data_type field, exactly one of the fields below with
+  // name ending in _data is used to store the elements of the tensor.
+
+  // For float and complex64 values
+  // Complex64 tensors are encoded as a single array of floats,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
+  repeated float float_data = 4 [packed = true];
+
+  // For int32, uint8, int8, uint16, int16, bool, and float16 values
+  // float16 values must be bit-wise converted to an uint16_t prior
+  // to writing to the buffer.
+  // When this field is present, the data_type field MUST be
+  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
+  repeated int32 int32_data = 5 [packed = true];
+
+  // For strings.
+  // Each element of string_data is a UTF-8 encoded Unicode
+  // string. No trailing null, no leading BOM. The protobuf "string"
+  // scalar type is not used to match ML community conventions.
+  // When this field is present, the data_type field MUST be STRING
+  repeated bytes string_data = 6;
+
+  // For int64.
+  // When this field is present, the data_type field MUST be INT64
+  repeated int64 int64_data = 7 [packed = true];
+
+  // Optionally, a name for the tensor.
+  optional string name = 8; // namespace Value
+
+  // A human-readable documentation for this tensor. Markdown is allowed.
+  optional string doc_string = 12;
+
+  // Serializations can either use one of the fields above, or use this
+  // raw bytes field. The only exception is the string case, where one is
+  // required to store the content in the repeated bytes string_data field.
+  //
+  // When this raw_data field is used to store tensor value, elements MUST
+  // be stored in as fixed-width, little-endian order.
+  // Floating-point data types MUST be stored in IEEE 754 format.
+  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
+  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
+  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
+  //
+  // Note: the advantage of specific field rather than the raw_data field is
+  // that in some cases (e.g. int data), protobuf does a better packing via
+  // variable length storage, and may lead to smaller binary footprint.
+  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
+  optional bytes raw_data = 9;
+
+  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
+  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
+  // external_data stores key-value pairs describing data location. Recognized keys are:
+  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
+  //                           protobuf model was stored
+  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
+  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+  // - "length" (optional) - number of bytes containing data. Integer stored as string.
+  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
+  repeated StringStringEntryProto external_data = 13;
+
+  // Location of the data for this tensor. MUST be one of:
+  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
+  // - EXTERNAL - data stored in an external location as described by external_data field.
+  enum DataLocation {
+    DEFAULT = 0;
+    EXTERNAL = 1;
+  }
+
+  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
+  optional DataLocation data_location = 14;
+
+  // For double
+  // Complex128 tensors are encoded as a single array of doubles,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
+  repeated double double_data = 10 [packed = true];
+
+  // For uint64 and uint32 values
+  // When this field is present, the data_type field MUST be
+  // UINT32 or UINT64
+  repeated uint64 uint64_data = 11 [packed = true];
+}
+
+// A serialized sparse-tensor value
+message SparseTensorProto {
+  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
+  // The default-value is zero for numeric tensors, and empty-string for string tensors.
+  // values must have a non-empty name present which serves as a name for SparseTensorProto
+  // when used in sparse_initializer list.
+  optional TensorProto values = 1;
+
+  // The indices of the non-default values, which may be stored in one of two formats.
+  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
+  // corresponding to the j-th index of the i-th value (in the values tensor).
+  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
+  // must be the linearized-index of the i-th value (in the values tensor).
+  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
+  // using the shape provided below.
+  // The indices must appear in ascending order without duplication.
+  // In the first format, the ordering is lexicographic-ordering:
+  // e.g., index-value [1,4] must appear before [2,1]
+  optional TensorProto indices = 2;
+
+  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
+  repeated int64 dims = 3;
+}
+
+// Defines a tensor shape. A dimension can be either an integer value
+// or a symbolic variable. A symbolic variable represents an unknown
+// dimension.
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;   // namespace Shape
+    };
+    // Standard denotation can optionally be used to denote tensor
+    // dimensions with standard semantic descriptions to ensure
+    // that operations are applied to the correct axis of a tensor.
+    // Refer to https://github.com/onnx/onnx/blob/main/docs/DimensionDenotation.md#denotation-definition
+    // for pre-defined dimension denotations.
+    optional string denotation = 3;
+  };
+  repeated Dimension dim = 1;
+}
+
+// Types
+//
+// The standard ONNX data types.
+message TypeProto {
+
+  message Tensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+  // repeated T
+  message Sequence {
+    // The type and optional shape of each element of the sequence.
+    // This field MUST be present for this version of the IR.
+    optional TypeProto elem_type = 1;
+  };
+
+  // map<K,V>
+  message Map {
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
+    optional int32 key_type = 1;
+    // This field MUST be present for this version of the IR.
+    optional TypeProto value_type = 2;
+  };
+
+  // wrapper for Tensor, Sequence, or Map
+  message Optional {
+    // The type and optional shape of the element wrapped.
+    // This field MUST be present for this version of the IR.
+    // Possible values correspond to OptionalProto.DataType enum
+    optional TypeProto elem_type = 1;
+  };
+
+
+  message SparseTensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+
+  oneof value {
+    // The type of a tensor.
+    Tensor tensor_type = 1;
+
+    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
+    //        as input and output to graphs and nodes. These types are needed to naturally
+    //        support classical ML operators.  DNN operators SHOULD restrict their input
+    //        and output types to tensors.
+
+    // The type of a sequence.
+    Sequence sequence_type = 4;
+
+    // The type of a map.
+    Map map_type = 5;
+
+    // The type of an optional.
+    Optional optional_type = 9;
+
+
+    // Type of the sparse tensor
+    SparseTensor sparse_tensor_type = 8;
+
+  }
+
+  // An optional denotation can be used to denote the whole
+  // type with a standard semantic description as to what is
+  // stored inside. Refer to https://github.com/onnx/onnx/blob/main/docs/TypeDenotation.md#type-denotation-definition
+  // for pre-defined type denotations.
+  optional string denotation = 6;
+}
+
+// Operator Sets
+//
+// OperatorSets are uniquely identified by a (domain, opset_version) pair.
+message OperatorSetIdProto {
+  // The domain of the operator set being identified.
+  // The empty string ("") or absence of this field implies the operator
+  // set that is defined as part of the ONNX specification.
+  // This field MUST be present in this version of the IR when referring to any other operator set.
+  optional string domain = 1;
+
+  // The version of the operator set being identified.
+  // This field MUST be present in this version of the IR.
+  optional int64 version = 2;
+}
+
+// Operator/function status.
+enum OperatorStatus {
+    EXPERIMENTAL = 0;
+    STABLE = 1;
+}
+
+message FunctionProto {
+  // The name of the function, similar usage of op_type in OperatorProto.
+  // Combined with FunctionProto.domain, this forms the unique identity of
+  // the FunctionProto.
+  optional string name = 1;
+
+  // Deprecated since IR Version 8
+  // optional int64 since_version = 2;
+  reserved 2;
+  reserved "since_version";
+
+  // Deprecated since IR Version 8
+  // optional OperatorStatus status = 3;
+  reserved 3;
+  reserved "status";
+
+  // The inputs and outputs of the function.
+  repeated string input = 4;
+  repeated string output = 5;
+
+  // The attributes of the function.
+  repeated string attribute = 6;
+
+  // The nodes in the function.
+  repeated NodeProto node = 7;
+  // A human-readable documentation for this function. Markdown is allowed.
+  optional string doc_string = 8;
+
+  // The OperatorSets this function body (graph) relies on.
+  //
+  // All nodes in the function body (graph) will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets. This means at most one version can be relied
+  // for one domain.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto. Example, if same operator set say 'A' is imported by FunctionProto
+  // and ModelProto then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same.
+
+  repeated OperatorSetIdProto opset_import = 9;
+
+  // The domain which this function belongs to. Combined with FunctionProto.name, this forms the unique identity of
+  // the FunctionProto.
+  optional string domain = 10;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx.proto3 b/third_party/onnx/onnx/onnx.proto3
new file mode 100644
index 0000000000..0cb484fe49
--- /dev/null
+++ b/third_party/onnx/onnx/onnx.proto3
@@ -0,0 +1,818 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto3";
+
+package onnx;
+
+// Overview
+//
+// ONNX is an open specification that is comprised of the following components:
+//
+// 1)  A definition of an extensible computation graph model.
+// 2)  Definitions of standard data types.
+// 3)  Definitions of built-in operators.
+//
+// This document describes the syntax of models and their computation graphs,
+// as well as the standard data types. Together, they are referred to as the ONNX
+// Intermediate Representation, or 'IR' for short.
+//
+// The normative semantic specification of the ONNX IR is found in docs/IR.md.
+// Definitions of the built-in neural network operators may be found in docs/Operators.md.
+
+// Notes
+//
+// Protobuf compatibility
+//
+// To simplify framework compatibility, ONNX is defined using the subset of protobuf
+// that is compatible with both protobuf v2 and v3. This means that we do not use any
+// protobuf features that are only available in one of the two versions.
+//
+// Here are the most notable contortions we have to carry out to work around
+// these limitations:
+//
+//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
+//     of key-value pairs, where order does not matter and duplicates
+//     are not allowed.
+
+
+// Versioning
+//
+// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
+//
+// To be compatible with both proto2 and proto3, we will use a version number
+// that is not defined by the default value but an explicit enum number.
+enum Version {
+  // proto3 requires the first enum value to be zero.
+  // We add this just to appease the compiler.
+  _START_VERSION = 0;
+  // The version field is always serialized and we will use it to store the
+  // version that the  graph is generated from. This helps us set up version
+  // control.
+  // For the IR, we are using simple numbers starting with 0x00000001,
+  // which was the version we published on Oct 10, 2017.
+  IR_VERSION_2017_10_10 = 0x0000000000000001;
+
+  // IR_VERSION 2 published on Oct 30, 2017
+  // - Added type discriminator to AttributeProto to support proto3 users
+  IR_VERSION_2017_10_30 = 0x0000000000000002;
+
+  // IR VERSION 3 published on Nov 3, 2017
+  // - For operator versioning:
+  //    - Added new message OperatorSetIdProto
+  //    - Added opset_import in ModelProto
+  // - For vendor extensions, added domain in NodeProto
+  IR_VERSION_2017_11_3 = 0x0000000000000003;
+
+  // IR VERSION 4 published on Jan 22, 2019
+  // - Relax constraint that initializers should be a subset of graph inputs
+  // - Add type BFLOAT16
+  IR_VERSION_2019_1_22 = 0x0000000000000004;
+
+  // IR VERSION 5 published on March 18, 2019
+  // - Add message TensorAnnotation.
+  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
+  IR_VERSION_2019_3_18 = 0x0000000000000005;
+
+  // IR VERSION 6 published on Sep 19, 2019
+  // - Add support for sparse tensor constants stored in model.
+  //   - Add message SparseTensorProto
+  //   - Add sparse initializers
+  IR_VERSION_2019_9_19 = 0x0000000000000006;
+
+  // IR VERSION 7 published on May 8, 2020
+  // - Add support to allow function body graph to rely on multiple external opreator sets.
+  // - Add a list to promote inference graph's initializers to global and
+  //   mutable variables. Global variables are visible in all graphs of the
+  //   stored models.
+  // - Add message TrainingInfoProto to store initialization
+  //   method and training algorithm. The execution of TrainingInfoProto
+  //   can modify the values of mutable variables.
+  // - Implicitly add inference graph into each TrainingInfoProto's algorithm.
+  IR_VERSION_2020_5_8 = 0x0000000000000007;
+
+  // IR VERSION 8 published on <TBD>
+  // Introduce TypeProto.SparseTensor
+  // Introduce TypeProto.Optional
+  // Added a list of FunctionProtos local to the model
+  // Deprecated since_version and operator status from FunctionProto
+  IR_VERSION = 0x0000000000000008;
+
+}
+
+// Attributes
+//
+// A named attribute containing either singular float, integer, string, graph,
+// and tensor values, or repeated float, integer, string, graph, and tensor values.
+// An AttributeProto MUST contain the name field, and *only one* of the
+// following content fields, effectively enforcing a C/C++ union equivalent.
+message AttributeProto {
+
+  // Note: this enum is structurally identical to the OpSchema::AttrType
+  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
+  enum AttributeType {
+    UNDEFINED = 0;
+    FLOAT = 1;
+    INT = 2;
+    STRING = 3;
+    TENSOR = 4;
+    GRAPH = 5;
+    SPARSE_TENSOR = 11;
+    TYPE_PROTO = 13;
+
+    FLOATS = 6;
+    INTS = 7;
+    STRINGS = 8;
+    TENSORS = 9;
+    GRAPHS = 10;
+    SPARSE_TENSORS = 12;
+    TYPE_PROTOS = 14;
+  }
+
+  // The name field MUST be present for this version of the IR.
+  string name = 1;           // namespace Attribute
+
+  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
+  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
+  // in parent scope.
+  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
+  string ref_attr_name = 21;
+
+  // A human-readable documentation for this attribute. Markdown is allowed.
+  string doc_string = 13;
+
+  // The type field MUST be present for this version of the IR.
+  // For 0.0.1 versions of the IR, this field was not defined, and
+  // implementations needed to use has_field heuristics to determine
+  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
+  // field MUST be set and match the f|i|s|t|... field in use.  This
+  // change was made to accommodate proto3 implementations.
+  AttributeType type = 20;   // discriminator that indicates which field below is in use
+
+  // Exactly ONE of the following fields must be present for this version of the IR
+  float f = 2;               // float
+  int64 i = 3;               // int
+  bytes s = 4;               // UTF-8 string
+  TensorProto t = 5;         // tensor value
+  GraphProto g = 6;          // graph
+  SparseTensorProto sparse_tensor = 22;  // sparse tensor value
+  // Do not use field below, it's deprecated.
+  // optional ValueProto v = 12;         // value - subsumes everything but graph
+  TypeProto tp = 14;          // type proto
+
+  repeated float floats = 7;          // list of floats
+  repeated int64 ints = 8;            // list of ints
+  repeated bytes strings = 9;         // list of UTF-8 strings
+  repeated TensorProto tensors = 10;  // list of tensors
+  repeated GraphProto graphs = 11;    // list of graph
+  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
+  repeated TypeProto type_protos = 15;// list of type protos
+}
+
+// Defines information on value, including the name, the type, and
+// the shape of the value.
+message ValueInfoProto {
+  // This field MUST be present in this version of the IR.
+  string name = 1;     // namespace Value
+  // This field MUST be present in this version of the IR for
+  // inputs and outputs of the top-level graph.
+  TypeProto type = 2;
+  // A human-readable documentation for this value. Markdown is allowed.
+  string doc_string = 3;
+}
+
+// Nodes
+//
+// Computation graphs are made up of a DAG of nodes, which represent what is
+// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
+//
+// For example, it can be a node of type "Conv" that takes in an image, a filter
+// tensor and a bias tensor, and produces the convolved output.
+message NodeProto {
+  repeated string input = 1;    // namespace Value
+  repeated string output = 2;   // namespace Value
+
+  // An optional identifier for this node in a graph.
+  // This field MAY be absent in ths version of the IR.
+  string name = 3;     // namespace Node
+
+  // The symbolic identifier of the Operator to execute.
+  string op_type = 4;  // namespace Operator
+  // The domain of the OperatorSet that specifies the operator named by op_type.
+  string domain = 7;   // namespace Domain
+
+  // Additional named attributes.
+  repeated AttributeProto attribute = 5;
+
+  // A human-readable documentation for this node. Markdown is allowed.
+  string doc_string = 6;
+}
+
+// Training information
+// TrainingInfoProto stores information for training a model.
+// In particular, this defines two functionalities: an initialization-step
+// and a training-algorithm-step. Initialization resets the model
+// back to its original state as if no training has been performed.
+// Training algorithm improves the model based on input data.
+//
+// The semantics of the initialization-step is that the initializers
+// in ModelProto.graph and in TrainingInfoProto.algorithm are first
+// initialized as specified by the initializers in the graph, and then
+// updated by the "initialization_binding" in every instance in
+// ModelProto.training_info.
+//
+// The field "algorithm" defines a computation graph which represents a
+// training algorithm's step. After the execution of a
+// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
+// may be immediately updated. If the targeted training algorithm contains
+// consecutive update steps (such as block coordinate descent methods),
+// the user needs to create a TrainingInfoProto for each step.
+message TrainingInfoProto {
+  // This field describes a graph to compute the initial tensors
+  // upon starting the training process. Initialization graph has no input
+  // and can have multiple outputs. Usually, trainable tensors in neural
+  // networks are randomly initialized. To achieve that, for each tensor,
+  // the user can put a random number operator such as RandomNormal or
+  // RandomUniform in TrainingInfoProto.initialization.node and assign its
+  // random output to the specific tensor using "initialization_binding".
+  // This graph can also set the initializers in "algorithm" in the same
+  // TrainingInfoProto; a use case is resetting the number of training
+  // iteration to zero.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Thus, no initializer would be changed by default.
+  GraphProto initialization = 1;
+
+  // This field represents a training algorithm step. Given required inputs,
+  // it computes outputs to update initializers in its own or inference graph's
+  // initializer lists. In general, this field contains loss node, gradient node,
+  // optimizer node, increment of iteration count.
+  //
+  // An execution of the training algorithm step is performed by executing the
+  // graph obtained by combining the inference graph (namely "ModelProto.graph")
+  // and the "algorithm" graph. That is, the actual the actual
+  // input/initializer/output/node/value_info/sparse_initializer list of
+  // the training graph is the concatenation of
+  // "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
+  // and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
+  // in that order. This combined graph must satisfy the normal ONNX conditions.
+  // Now, let's provide a visualization of graph combination for clarity.
+  // Let the inference graph (i.e., "ModelProto.graph") be
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
+  // and the "algorithm" graph be
+  //    tensor_d -> Add -> tensor_e
+  // The combination process results
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
+  //
+  // Notice that an input of a node in the "algorithm" graph may reference the
+  // output of a node in the inference graph (but not the other way round). Also, inference
+  // node cannot reference inputs of "algorithm". With these restrictions, inference graph
+  // can always be run independently without training information.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Evaluating the default training step never
+  // update any initializers.
+  GraphProto algorithm = 2;
+
+  // This field specifies the bindings from the outputs of "initialization" to
+  // some initializers in "ModelProto.graph.initializer" and
+  // the "algorithm.initializer" in the same TrainingInfoProto.
+  // See "update_binding" below for details.
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "initialization".
+  repeated StringStringEntryProto initialization_binding = 3;
+
+  // Gradient-based training is usually an iterative procedure. In one gradient
+  // descent iteration, we apply
+  //
+  // x = x - r * g
+  //
+  // where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
+  // gradient of "x" with respect to a chosen loss. To avoid adding assignments
+  // into the training graph, we split the update equation into
+  //
+  // y = x - r * g
+  // x = y
+  //
+  // The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
+  // tell that "y" should be assigned to "x", the field "update_binding" may
+  // contain a key-value pair of strings, "x" (key of StringStringEntryProto)
+  // and "y" (value of StringStringEntryProto).
+  // For a neural network with multiple trainable (mutable) tensors, there can
+  // be multiple key-value pairs in "update_binding".
+  //
+  // The initializers appears as keys in "update_binding" are considered
+  // mutable variables. This implies some behaviors
+  // as described below.
+  //
+  //  1. We have only unique keys in all "update_binding"s so that two
+  //     variables may not have the same name. This ensures that one
+  //     variable is assigned up to once.
+  //  2. The keys must appear in names of "ModelProto.graph.initializer" or
+  //     "TrainingInfoProto.algorithm.initializer".
+  //  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
+  //  4. Mutable variables are initialized to the value specified by the
+  //     corresponding initializer, and then potentially updated by
+  //     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
+  //
+  // This field usually contains names of trainable tensors
+  // (in ModelProto.graph), optimizer states such as momentums in advanced
+  // stochastic gradient methods (in TrainingInfoProto.graph),
+  // and number of training iterations (in TrainingInfoProto.graph).
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "algorithm".
+  repeated StringStringEntryProto update_binding = 4;
+}
+
+// Models
+//
+// ModelProto is a top-level file/container format for bundling a ML model and
+// associating its computation graph with metadata.
+//
+// The semantics of the model are described by the associated GraphProto's.
+message ModelProto {
+  // The version of the IR this model targets. See Version enum above.
+  // This field MUST be present.
+  int64 ir_version = 1;
+
+  // The OperatorSets this model relies on.
+  // All ModelProtos MUST have at least one entry that
+  // specifies which version of the ONNX OperatorSet is
+  // being imported.
+  //
+  // All nodes in the ModelProto's graph will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets.
+  repeated OperatorSetIdProto opset_import = 8;
+
+  // The name of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  string producer_name = 2;
+
+  // The version of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  string producer_version = 3;
+
+  // Domain name of the model.
+  // We use reverse domain names as name space indicators. For example:
+  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
+  //
+  // Together with `model_version` and GraphProto.name, this forms the unique identity of
+  // the graph.
+  string domain = 4;
+
+  // The version of the graph encoded. See Version enum below.
+  int64 model_version = 5;
+
+  // A human-readable documentation for this model. Markdown is allowed.
+  string doc_string = 6;
+
+  // The parameterized graph that is evaluated to execute the model.
+  GraphProto graph = 7;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+
+  // Training-specific information. Sequentially executing all stored
+  // `TrainingInfoProto.algorithm`s and assigning their outputs following
+  // the corresponding `TrainingInfoProto.update_binding`s is one training
+  // iteration. Similarly, to initialize the model
+  // (as if training hasn't happened), the user should sequentially execute
+  // all stored `TrainingInfoProto.initialization`s and assigns their outputs
+  // using `TrainingInfoProto.initialization_binding`s.
+  //
+  // If this field is empty, the training behavior of the model is undefined.
+  repeated TrainingInfoProto training_info = 20;
+
+  // A list of function protos local to the model.
+  //
+  // Name of the function "FunctionProto.name" should be unique within the domain "FunctionProto.domain".
+  // In case of any conflicts the behavior (whether the model local functions are given higher priority,
+  // or standard opserator sets are given higher priotity or this is treated as error) is defined by 
+  // the runtimes.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto and other model local FunctionProtos. 
+  // Example, if same operator set say 'A' is imported by a FunctionProto and ModelProto 
+  // or by 2 FunctionProtos then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same for every node in the function body.
+  //
+  // One FunctionProto can reference other FunctionProto in the model, however, recursive reference
+  // is not allowed.
+  repeated FunctionProto functions = 25;
+};
+
+// StringStringEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message StringStringEntryProto {
+  string key = 1;
+  string value= 2;
+};
+
+message TensorAnnotation {
+  string tensor_name = 1;
+  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
+  // The keys used in the mapping below must be pre-defined in ONNX spec.
+  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
+  // quantization parameter keys.
+  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
+}
+
+
+
+// Graphs
+//
+// A graph defines the computational logic of a model and is comprised of a parameterized
+// list of nodes that form a directed acyclic graph based on their inputs and outputs.
+// This is the equivalent of the "network" or "graph" in many deep learning
+// frameworks.
+message GraphProto {
+  // The nodes in the graph, sorted topologically.
+  repeated NodeProto node = 1;
+
+  // The name of the graph.
+  string name = 2;   // namespace Graph
+
+  // A list of named tensor values, used to specify constant inputs of the graph.
+  // Each initializer (both TensorProto as well SparseTensorProto) MUST have a name.
+  // The name MUST be unique across both initializer and sparse_initializer,
+  // but the name MAY also appear in the input list.
+  repeated TensorProto initializer = 5;
+
+  // Initializers (see above) stored in sparse format.
+  repeated SparseTensorProto sparse_initializer = 15;
+
+  // A human-readable documentation for this graph. Markdown is allowed.
+  string doc_string = 10;
+
+  // The inputs and outputs of the graph.
+  repeated ValueInfoProto input = 11;
+  repeated ValueInfoProto output = 12;
+
+  // Information for the values in the graph. The ValueInfoProto.name's
+  // must be distinct. It is optional for a value to appear in value_info list.
+  repeated ValueInfoProto value_info = 13;
+
+  // This field carries information to indicate the mapping among a tensor and its
+  // quantization parameter tensors. For example:
+  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
+  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
+  repeated TensorAnnotation quantization_annotation = 14;
+
+  // DO NOT USE the following fields, they were deprecated from earlier versions.
+  // repeated string input = 3;
+  // repeated string output = 4;
+  // optional int64 ir_version = 6;
+  // optional int64 producer_version = 7;
+  // optional string producer_tag = 8;
+  // optional string domain = 9;
+}
+
+// Tensors
+//
+// A serialized tensor value.
+message TensorProto {
+  enum DataType {
+    UNDEFINED = 0;
+    // Basic types.
+    FLOAT = 1;   // float
+    UINT8 = 2;   // uint8_t
+    INT8 = 3;    // int8_t
+    UINT16 = 4;  // uint16_t
+    INT16 = 5;   // int16_t
+    INT32 = 6;   // int32_t
+    INT64 = 7;   // int64_t
+    STRING = 8;  // string
+    BOOL = 9;    // bool
+
+    // IEEE754 half-precision floating-point format (16 bits wide).
+    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
+    FLOAT16 = 10;
+
+    DOUBLE = 11;
+    UINT32 = 12;
+    UINT64 = 13;
+    COMPLEX64 = 14;     // complex with float32 real and imaginary components
+    COMPLEX128 = 15;    // complex with float64 real and imaginary components
+
+    // Non-IEEE floating-point format based on IEEE754 single-precision
+    // floating-point number truncated to 16 bits.
+    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
+    BFLOAT16 = 16;
+
+    // Future extensions go here.
+  }
+
+  // The shape of the tensor.
+  repeated int64 dims = 1;
+
+  // The data type of the tensor.
+  // This field MUST have a valid TensorProto.DataType value
+  int32 data_type = 2;
+
+  // For very large tensors, we may want to store them in chunks, in which
+  // case the following fields will specify the segment that is stored in
+  // the current TensorProto.
+  message Segment {
+    int64 begin = 1;
+    int64 end = 2;
+  }
+  Segment segment = 3;
+
+  // Tensor content must be organized in row-major order.
+  //
+  // Depending on the data_type field, exactly one of the fields below with
+  // name ending in _data is used to store the elements of the tensor.
+
+  // For float and complex64 values
+  // Complex64 tensors are encoded as a single array of floats,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
+  repeated float float_data = 4 [packed = true];
+
+  // For int32, uint8, int8, uint16, int16, bool, and float16 values
+  // float16 values must be bit-wise converted to an uint16_t prior
+  // to writing to the buffer.
+  // When this field is present, the data_type field MUST be
+  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
+  repeated int32 int32_data = 5 [packed = true];
+
+  // For strings.
+  // Each element of string_data is a UTF-8 encoded Unicode
+  // string. No trailing null, no leading BOM. The protobuf "string"
+  // scalar type is not used to match ML community conventions.
+  // When this field is present, the data_type field MUST be STRING
+  repeated bytes string_data = 6;
+
+  // For int64.
+  // When this field is present, the data_type field MUST be INT64
+  repeated int64 int64_data = 7 [packed = true];
+
+  // Optionally, a name for the tensor.
+  string name = 8; // namespace Value
+
+  // A human-readable documentation for this tensor. Markdown is allowed.
+  string doc_string = 12;
+
+  // Serializations can either use one of the fields above, or use this
+  // raw bytes field. The only exception is the string case, where one is
+  // required to store the content in the repeated bytes string_data field.
+  //
+  // When this raw_data field is used to store tensor value, elements MUST
+  // be stored in as fixed-width, little-endian order.
+  // Floating-point data types MUST be stored in IEEE 754 format.
+  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
+  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
+  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
+  //
+  // Note: the advantage of specific field rather than the raw_data field is
+  // that in some cases (e.g. int data), protobuf does a better packing via
+  // variable length storage, and may lead to smaller binary footprint.
+  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
+  bytes raw_data = 9;
+
+  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
+  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
+  // external_data stores key-value pairs describing data location. Recognized keys are:
+  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
+  //                           protobuf model was stored
+  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
+  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+  // - "length" (optional) - number of bytes containing data. Integer stored as string.
+  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
+  repeated StringStringEntryProto external_data = 13;
+
+  // Location of the data for this tensor. MUST be one of:
+  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
+  // - EXTERNAL - data stored in an external location as described by external_data field.
+  enum DataLocation {
+    DEFAULT = 0;
+    EXTERNAL = 1;
+  }
+
+  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
+  DataLocation data_location = 14;
+
+  // For double
+  // Complex128 tensors are encoded as a single array of doubles,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
+  repeated double double_data = 10 [packed = true];
+
+  // For uint64 and uint32 values
+  // When this field is present, the data_type field MUST be
+  // UINT32 or UINT64
+  repeated uint64 uint64_data = 11 [packed = true];
+}
+
+// A serialized sparse-tensor value
+message SparseTensorProto {
+  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
+  // The default-value is zero for numeric tensors, and empty-string for string tensors.
+  // values must have a non-empty name present which serves as a name for SparseTensorProto
+  // when used in sparse_initializer list.
+  TensorProto values = 1;
+
+  // The indices of the non-default values, which may be stored in one of two formats.
+  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
+  // corresponding to the j-th index of the i-th value (in the values tensor).
+  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
+  // must be the linearized-index of the i-th value (in the values tensor).
+  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
+  // using the shape provided below.
+  // The indices must appear in ascending order without duplication.
+  // In the first format, the ordering is lexicographic-ordering:
+  // e.g., index-value [1,4] must appear before [2,1]
+  TensorProto indices = 2;
+
+  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
+  repeated int64 dims = 3;
+}
+
+// Defines a tensor shape. A dimension can be either an integer value
+// or a symbolic variable. A symbolic variable represents an unknown
+// dimension.
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;   // namespace Shape
+    };
+    // Standard denotation can optionally be used to denote tensor
+    // dimensions with standard semantic descriptions to ensure
+    // that operations are applied to the correct axis of a tensor.
+    // Refer to https://github.com/onnx/onnx/blob/main/docs/DimensionDenotation.md#denotation-definition
+    // for pre-defined dimension denotations.
+    string denotation = 3;
+  };
+  repeated Dimension dim = 1;
+}
+
+// Types
+//
+// The standard ONNX data types.
+message TypeProto {
+
+  message Tensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    int32 elem_type = 1;
+    TensorShapeProto shape = 2;
+  }
+
+  // repeated T
+  message Sequence {
+    // The type and optional shape of each element of the sequence.
+    // This field MUST be present for this version of the IR.
+    TypeProto elem_type = 1;
+  };
+
+  // map<K,V>
+  message Map {
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
+    int32 key_type = 1;
+    // This field MUST be present for this version of the IR.
+    TypeProto value_type = 2;
+  };
+
+  // wrapper for Tensor, Sequence, or Map
+  message Optional {
+    // The type and optional shape of the element wrapped.
+    // This field MUST be present for this version of the IR.
+    // Possible values correspond to OptionalProto.DataType enum
+    TypeProto elem_type = 1;
+  };
+
+
+  message SparseTensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    int32 elem_type = 1;
+    TensorShapeProto shape = 2;
+  }
+
+
+  oneof value {
+    // The type of a tensor.
+    Tensor tensor_type = 1;
+
+    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
+    //        as input and output to graphs and nodes. These types are needed to naturally
+    //        support classical ML operators.  DNN operators SHOULD restrict their input
+    //        and output types to tensors.
+
+    // The type of a sequence.
+    Sequence sequence_type = 4;
+
+    // The type of a map.
+    Map map_type = 5;
+
+    // The type of an optional.
+    Optional optional_type = 9;
+
+
+    // Type of the sparse tensor
+    SparseTensor sparse_tensor_type = 8;
+
+  }
+
+  // An optional denotation can be used to denote the whole
+  // type with a standard semantic description as to what is
+  // stored inside. Refer to https://github.com/onnx/onnx/blob/main/docs/TypeDenotation.md#type-denotation-definition
+  // for pre-defined type denotations.
+  string denotation = 6;
+}
+
+// Operator Sets
+//
+// OperatorSets are uniquely identified by a (domain, opset_version) pair.
+message OperatorSetIdProto {
+  // The domain of the operator set being identified.
+  // The empty string ("") or absence of this field implies the operator
+  // set that is defined as part of the ONNX specification.
+  // This field MUST be present in this version of the IR when referring to any other operator set.
+  string domain = 1;
+
+  // The version of the operator set being identified.
+  // This field MUST be present in this version of the IR.
+  int64 version = 2;
+}
+
+// Operator/function status.
+enum OperatorStatus {
+    EXPERIMENTAL = 0;
+    STABLE = 1;
+}
+
+message FunctionProto {
+  // The name of the function, similar usage of op_type in OperatorProto.
+  // Combined with FunctionProto.domain, this forms the unique identity of
+  // the FunctionProto.
+  string name = 1;
+
+  // Deprecated since IR Version 8
+  // optional int64 since_version = 2;
+  reserved 2;
+  reserved "since_version";
+
+  // Deprecated since IR Version 8
+  // optional OperatorStatus status = 3;
+  reserved 3;
+  reserved "status";
+
+  // The inputs and outputs of the function.
+  repeated string input = 4;
+  repeated string output = 5;
+
+  // The attributes of the function.
+  repeated string attribute = 6;
+
+  // The nodes in the function.
+  repeated NodeProto node = 7;
+  // A human-readable documentation for this function. Markdown is allowed.
+  string doc_string = 8;
+
+  // The OperatorSets this function body (graph) relies on.
+  //
+  // All nodes in the function body (graph) will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets. This means at most one version can be relied
+  // for one domain.
+  // 
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto. Example, if same operator set say 'A' is imported by FunctionProto
+  // and ModelProto then versions for the operator set may be different but, 
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same.
+
+  repeated OperatorSetIdProto opset_import = 9;
+
+  // The domain which this function belongs to. Combined with FunctionProto.name, this forms the unique identity of
+  // the FunctionProto.
+  string domain = 10;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/third_party/onnx/onnx/onnx_cpp2py_export/__init__.pyi b/third_party/onnx/onnx/onnx_cpp2py_export/__init__.pyi
new file mode 100644
index 0000000000..232bc30387
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_cpp2py_export/__init__.pyi
@@ -0,0 +1,5 @@
+# This is __init__.pyi, not __init__.py
+# This directory is only considered for typing information, not for actual
+# module implementations.
+
+ONNX_ML : bool = ...
diff --git a/third_party/onnx/onnx/onnx_cpp2py_export/checker.pyi b/third_party/onnx/onnx/onnx_cpp2py_export/checker.pyi
new file mode 100644
index 0000000000..94e76d8281
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_cpp2py_export/checker.pyi
@@ -0,0 +1,20 @@
+from typing import Dict, Text, Union
+
+
+class CheckerContext(object):
+    ir_version: int = ...
+    opset_imports: Dict[Text, int] = ...
+
+
+class ValidationError(Exception):
+    ...
+
+
+def check_value_info(bytes: bytes, checker_context: CheckerContext) -> None: ...
+def check_tensor(bytes: bytes, checker_context: CheckerContext) -> None: ...
+def check_sparse_tensor(bytes: bytes, checker_context: CheckerContext) -> None: ...
+def check_attribute(bytes: bytes, checker_context: CheckerContext) -> None: ...
+def check_node(bytes: bytes, checker_context: CheckerContext) -> None: ...
+def check_graph(bytes: bytes, checker_context: CheckerContext) -> None: ...
+def check_model(bytes: bytes) -> None: ...
+def check_model_path(path: Text) -> None: ...
diff --git a/third_party/onnx/onnx/onnx_cpp2py_export/defs.pyi b/third_party/onnx/onnx/onnx_cpp2py_export/defs.pyi
new file mode 100644
index 0000000000..009a85561c
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_cpp2py_export/defs.pyi
@@ -0,0 +1,131 @@
+from typing import Text, Dict, Tuple, Sequence, Optional, Set, overload
+from onnx import AttributeProto
+
+class SchemaError(Exception):
+    ...
+
+class OpSchema(object):
+    @property
+    def file(self) -> Text: ...
+    @property
+    def line(self) -> int: ...
+    @property
+    def support_level(self) -> SupportType: ...
+    @property
+    def doc(self) -> Optional[Text]: ...
+    @property
+    def since_version(self) -> int: ...
+    @property
+    def deprecated(self) -> bool: ...
+    @property
+    def domain(self) -> Text: ...
+    @property
+    def name(self) -> Text: ...
+    @property
+    def min_input(self) -> int: ...
+    @property
+    def max_input(self) -> int: ...
+    @property
+    def min_output(self) -> int: ...
+    @property
+    def max_output(self) -> int: ...
+    @property
+    def attributes(self) -> Dict[Text, Attribute]: ...
+    @property
+    def inputs(self) -> Sequence[FormalParameter]: ...
+    @property
+    def outputs(self) -> Sequence[FormalParameter]: ...
+    @property
+    def type_constraints(self) -> Sequence[TypeConstraintParam]: ...
+    @property
+    def has_type_and_shape_inference_function(self) -> bool: ...
+    @property
+    def has_data_propagation_function(self) -> bool: ...
+
+    @staticmethod
+    def is_infinite(v: int) -> bool: ...
+
+    def consumed(self, schema: OpSchema, i: int) -> Tuple[UseType, int]: ...
+
+    class TypeConstraintParam(object):
+        @property
+        def type_param_str(self) -> Text: ...
+        @property
+        def description(self) -> Text: ...
+        @property
+        def allowed_type_strs(self) -> Sequence[Text]: ...
+
+    class FormalParameterOption(object):
+        Single: 'OpSchema.FormalParameterOption' = ...
+        Optional: 'OpSchema.FormalParameterOption' = ...
+        Variadic: 'OpSchema.FormalParameterOption' = ...
+
+    class DifferentiationCategory(object):
+        Unknown: 'OpSchema.DifferentiationCategory' = ...
+        Differentiable: 'OpSchema.DifferentiationCategory' = ...
+        NonDifferentiable: 'OpSchema.DifferentiationCategory' = ...
+
+    class FormalParameter(object):
+        @property
+        def name(self) -> Text: ...
+        @property
+        def types(self) -> Set[Text]: ...
+        @property
+        def typeStr(self) -> Text: ...
+        @property
+        def description(self) -> Text: ...
+        @property
+        def option(self) -> OpSchema.FormalParameterOption: ...
+        @property
+        def isHomogeneous(self) -> bool: ...
+        @property
+        def differentiationCategory(self) -> OpSchema.DifferentiationCategory: ...
+
+    class AttrType(object):
+        FLOAT: 'OpSchema.AttrType' = ...
+        INT: 'OpSchema.AttrType' = ...
+        STRING: 'OpSchema.AttrType' = ...
+        TENSOR: 'OpSchema.AttrType' = ...
+        GRAPH: 'OpSchema.AttrType' = ...
+        SPARSE_TENSOR: 'OpSchema.AttrType' = ...
+        TYPE_PROTO: 'OpSchema.AttrType' = ...
+        FLOATS: 'OpSchema.AttrType' = ...
+        INTS: 'OpSchema.AttrType' = ...
+        STRINGS: 'OpSchema.AttrType' = ...
+        TENSORS: 'OpSchema.AttrType' = ...
+        GRAPHS: 'OpSchema.AttrType' = ...
+        SPARSE_TENSORS: 'OpSchema.AttrType' = ...
+        TYPE_PROTOS: 'OpSchema.AttrType' = ...
+
+    class Attribute(object):
+        @property
+        def name(self) -> Text: ...
+        @property
+        def description(self) -> Text: ...
+        @property
+        def type(self) -> OpSchema.AttrType: ...
+        @property
+        def default_value(self) -> AttributeProto: ...
+        @property
+        def required(self) -> bool: ...
+
+
+    class SupportType(int):
+        COMMON: 'OpSchema.SupportType' = ...
+        EXPERIMENTAL: 'OpSchema.SupportType' = ...
+
+
+    class UseType(object):
+        DEFAULT: 'OpSchema.UseType' = ...
+        CONSUME_ALLOWED: 'OpSchema.UseType' = ...
+        CONSUME_ENFORCED: 'OpSchema.UseType' = ...
+
+
+def has_schema(op_type: Text) -> bool: ...
+def schema_version_map() -> Dict[Text, Tuple[int, int]]: ...
+@overload
+def get_schema(op_type: Text, max_inclusive_version: int, domain: Text = "") -> OpSchema: ...
+@overload
+def get_schema(op_type: Text, domain: Text = "") -> OpSchema: ...
+def get_all_schemas() -> Sequence[OpSchema]: ...
+def get_all_schemas_with_history() -> Sequence[OpSchema]: ...
diff --git a/third_party/onnx/onnx/onnx_cpp2py_export/parser.pyi b/third_party/onnx/onnx/onnx_cpp2py_export/parser.pyi
new file mode 100644
index 0000000000..617f67a9c0
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_cpp2py_export/parser.pyi
@@ -0,0 +1,9 @@
+from typing import Text, Tuple
+
+
+def parse_model(model: Text) -> Tuple[bool, bytes, bytes]:
+    ...
+
+
+def parse_graph(graph: Text) -> Tuple[bool, bytes, bytes]:
+    ...
diff --git a/third_party/onnx/onnx/onnx_cpp2py_export/shape_inference.pyi b/third_party/onnx/onnx/onnx_cpp2py_export/shape_inference.pyi
new file mode 100644
index 0000000000..ccdafb64a0
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_cpp2py_export/shape_inference.pyi
@@ -0,0 +1,9 @@
+from typing import Text
+
+
+class InferenceError(Exception):
+    ...
+
+def infer_shapes(b: bytes, check_type: bool, strict_mode: bool, data_prop: bool) -> bytes: ...
+
+def infer_shapes_path(model_path: Text, output_path: Text, check_type: bool, strict_mode: bool, data_prop: bool) -> None: ...
diff --git a/third_party/onnx/onnx/onnx_cpp2py_export/version_converter.pyi b/third_party/onnx/onnx/onnx_cpp2py_export/version_converter.pyi
new file mode 100644
index 0000000000..b78b6e6701
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_cpp2py_export/version_converter.pyi
@@ -0,0 +1,7 @@
+from typing import Sequence, Text
+
+class ConvertError(Exception):
+    ...
+
+# Where the first bytes are a serialized ModelProto
+def convert_version(bytes: bytes, target: int) -> bytes: ...
diff --git a/third_party/onnx/onnx/onnx_pb.h b/third_party/onnx/onnx/onnx_pb.h
new file mode 100644
index 0000000000..1073e272aa
--- /dev/null
+++ b/third_party/onnx/onnx/onnx_pb.h
@@ -0,0 +1,57 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+
+#ifndef ONNX_ONNX_PB_H
+#define ONNX_ONNX_PB_H
+
+// Defines ONNX_EXPORT and ONNX_IMPORT. On Windows, this corresponds to
+// different declarations (dllexport and dllimport). On Linux/Mac, it just
+// resolves to the same "default visibility" setting.
+#if defined(_MSC_VER)
+#if defined(ONNX_BUILD_SHARED_LIBS) || defined(ONNX_BUILD_MAIN_LIB)
+#define ONNX_EXPORT __declspec(dllexport)
+#define ONNX_IMPORT __declspec(dllimport)
+#else
+#define ONNX_EXPORT
+#define ONNX_IMPORT
+#endif
+#else
+#if defined(__GNUC__)
+#define ONNX_EXPORT __attribute__((__visibility__("default")))
+#else
+#define ONNX_EXPORT
+#endif
+#define ONNX_IMPORT ONNX_EXPORT
+#endif
+
+// ONNX_API is a macro that, depends on whether you are building the
+// main ONNX library or not, resolves to either ONNX_EXPORT or
+// ONNX_IMPORT.
+//
+// This is used in e.g. ONNX's protobuf files: when building the main library,
+// it is defined as ONNX_EXPORT to fix a Windows global-variable-in-dll
+// issue, and for anyone dependent on ONNX it will be defined as
+// ONNX_IMPORT. ONNX_BUILD_MAIN_LIB can also be set when being built
+// statically if ONNX is being linked into a shared library that wants
+// to export the ONNX APIs and classes.
+//
+// More details on Windows dllimport / dllexport can be found at
+// https://msdn.microsoft.com/en-us/library/3y1sfaz2.aspx
+//
+// This solution is similar to
+// https://github.com/pytorch/pytorch/blob/master/caffe2/core/common.h
+#if defined(ONNX_BUILD_SHARED_LIBS) || defined(ONNX_BUILD_MAIN_LIB)
+#define ONNX_API ONNX_EXPORT
+#else
+#define ONNX_API ONNX_IMPORT
+#endif
+
+#ifdef ONNX_ML
+#include "onnx/onnx-ml.pb.h"
+#else
+#include "onnx/onnx.pb.h"
+#endif
+
+#endif // ! ONNX_ONNX_PB_H
diff --git a/third_party/onnx/onnx/onnxifi.h b/third_party/onnx/onnx/onnxifi.h
new file mode 100644
index 0000000000..51b079bd5b
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi.h
@@ -0,0 +1,2007 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#ifndef ONNXIFI_H
+#define ONNXIFI_H 1
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(_WIN32) && defined(_M_IX86)
+/* Windows x86 */
+#define ONNXIFI_ABI __stdcall
+#elif defined(__i386__)
+/* Linux x86 */
+#define ONNXIFI_ABI __attribute__((__cdecl__))
+#else
+#define ONNXIFI_ABI
+#endif
+
+#ifndef ONNXIFI_PUBLIC
+#if defined(__ELF__)
+#define ONNXIFI_PUBLIC __attribute__((__visibility__("default")))
+#elif defined(__MACH__)
+#define ONNXIFI_PUBLIC __attribute__((__visibility__("default")))
+#elif defined(_WIN32) && defined(__GNUC__)
+#ifdef ONNXIFI_BUILD_LIBRARY
+#define ONNXIFI_PUBLIC __attribute__((__dllexport__))
+#else
+#define ONNXIFI_PUBLIC __attribute__((__dllimport__))
+#endif
+#elif defined(_WIN32)
+#ifdef ONNXIFI_BUILD_LIBRARY
+#define ONNXIFI_PUBLIC __declspec(dllexport)
+#else
+#define ONNXIFI_PUBLIC __declspec(dllimport)
+#endif
+#else
+#define ONNXIFI_PUBLIC
+#endif
+#endif
+
+#ifndef ONNXIFI_CHECK_RESULT
+  #if defined(__GNUC__) && (__GNUC__ >= 4)
+    #define ONNXIFI_CHECK_RESULT __attribute__((__warn_unused_result__))
+  #elif defined(_MSC_VER) && (_MSC_VER >= 1700)
+    #define ONNXIFI_CHECK_RESULT _Check_return_
+  #else
+    #define ONNXIFI_CHECK_RESULT
+  #endif
+#endif
+
+#include <stddef.h>
+
+#if !defined(ONNXIFI_NO_STDINT_H)
+#if defined(_MSC_VER) && (_MSC_VER < 1600)
+typedef signed __int8 int8_t;
+typedef unsigned __int8 uint8_t;
+typedef signed __int16 int16_t;
+typedef unsigned __int16 uint16_t;
+typedef signed __int32 int32_t;
+typedef unsigned __int32 uint32_t;
+typedef signed __int64 int64_t;
+typedef unsigned __int64 uint64_t;
+#else
+#include <stdint.h>
+#endif
+#endif /* !defined(ONNXIFI_NO_STDINT_H) */
+
+/**
+ * Opaque ONNXIFI backend ID.
+ *
+ * ONNXIFI backend is a combination of software layer and hardware device used
+ * to run an ONNX graph. Backend ID uniquely identifies a backend for the life-
+ * time of the process (i.e. no two hardware devices, software layers, or
+ * combinations of both can have the same backend ID). Backend ID stays valid
+ * even if the hardware device used by the backend disconnects from the system.
+ */
+typedef void* onnxBackendID;
+/**
+ * Opaque ONNXIFI backend handle.
+ * ONNXIFI backend is a combination of software layer and hardware device used
+ * to run an ONNX graph.
+ */
+typedef void* onnxBackend;
+/** Opaque ONNXIFI graph handle. */
+typedef void* onnxGraph;
+/** Opaque ONNXIFI even handle. */
+typedef void* onnxEvent;
+
+/** Return code for ONNXIFI functions */
+typedef int32_t onnxStatus;
+/**
+ * Type for enumeration values.
+ *
+ * The low 32 bits are reserved for standardized ONNXIFI values.
+ * The high 32 bits are reserved for vendor-specific extensions. Applications
+ * must check for specific vendor extensions before interpreting these bits.
+ */
+typedef uint64_t onnxEnum;
+/**
+ * Type for bit fields.
+ *
+ * The low 32 bits are reserved for standardized ONNXIFI values.
+ * The high 32 bits are reserved for vendor-specific extensions. Applications
+ * must check for specific vendor extensions before interpreting these bits.
+ */
+typedef uint64_t onnxBitfield;
+/**
+ * Type for pointers or handles for memory buffers.
+ * This type is intended to work not only for CPU-addressable memory, but also
+ * for device memory. uint64_t ensures the API can accommodate Vulkan buffers.
+ */
+typedef uint64_t onnxPointer;
+
+#define ONNXIFI_STATUS_SUCCESS 0x0000
+#define ONNXIFI_STATUS_FALLBACK 0x0001
+#define ONNXIFI_STATUS_INVALID_ID 0x0101
+#define ONNXIFI_STATUS_INVALID_SIZE 0x0102
+#define ONNXIFI_STATUS_INVALID_POINTER 0x0103
+#define ONNXIFI_STATUS_INVALID_PROTOBUF 0x0104
+#define ONNXIFI_STATUS_INVALID_MODEL 0x0105
+#define ONNXIFI_STATUS_INVALID_BACKEND 0x0106
+#define ONNXIFI_STATUS_INVALID_GRAPH 0x0107
+#define ONNXIFI_STATUS_INVALID_EVENT 0x0108
+#define ONNXIFI_STATUS_INVALID_STATE 0x0109
+#define ONNXIFI_STATUS_INVALID_NAME 0x010A
+#define ONNXIFI_STATUS_INVALID_SHAPE 0x010B
+#define ONNXIFI_STATUS_INVALID_DATATYPE 0x010C
+#define ONNXIFI_STATUS_INVALID_MEMORY_TYPE 0x010D
+#define ONNXIFI_STATUS_INVALID_MEMORY_LOCATION 0x010E
+#define ONNXIFI_STATUS_INVALID_FENCE_TYPE 0x010F
+#define ONNXIFI_STATUS_INVALID_PROPERTY 0x0110
+#define ONNXIFI_STATUS_UNSUPPORTED_TAG 0x0201
+#define ONNXIFI_STATUS_UNSUPPORTED_VERSION 0x0202
+#define ONNXIFI_STATUS_UNSUPPORTED_OPERATOR 0x0203
+#define ONNXIFI_STATUS_UNSUPPORTED_ATTRIBUTE 0x0204
+#define ONNXIFI_STATUS_UNSUPPORTED_SHAPE 0x0205
+#define ONNXIFI_STATUS_UNSUPPORTED_DATATYPE 0x0206
+#define ONNXIFI_STATUS_UNSUPPORTED_MEMORY_TYPE 0x0207
+#define ONNXIFI_STATUS_UNSUPPORTED_FENCE_TYPE 0x0208
+#define ONNXIFI_STATUS_UNSUPPORTED_PROPERTY 0x0209
+#define ONNXIFI_STATUS_UNIDENTIFIED_NAME 0x0301
+#define ONNXIFI_STATUS_MISMATCHING_SHAPE 0x0302
+#define ONNXIFI_STATUS_MISMATCHING_DATATYPE 0x0303
+#define ONNXIFI_STATUS_NO_SYSTEM_MEMORY 0x0401
+#define ONNXIFI_STATUS_NO_DEVICE_MEMORY 0x0402
+#define ONNXIFI_STATUS_NO_SYSTEM_RESOURCES 0x0403
+#define ONNXIFI_STATUS_NO_DEVICE_RESOURCES 0x0404
+#define ONNXIFI_STATUS_BACKEND_UNAVAILABLE 0x0405
+#define ONNXIFI_STATUS_INTERNAL_ERROR 0x0406
+
+/**
+ * State of an ONNXIFI event object.
+ *
+ * Possible values:
+ *     ONNXIFI_EVENT_STATE_INVALID
+ *     ONNXIFI_EVENT_STATE_NONSIGNALLED
+ *     ONNXIFI_EVENT_STATE_SIGNALLED
+ */
+typedef int32_t onnxEventState;
+
+/**
+ * State for an invalid onnxEvent.
+ */
+#define ONNXIFI_EVENT_STATE_INVALID 0
+/**
+ * Non-signalled onnxEvent state.
+ * onnxInitEvent creates events in non-signalled state.
+ */
+#define ONNXIFI_EVENT_STATE_NONSIGNALLED 0x16BD
+/**
+ * Signalled onnxEvent state.
+ * onnxSignalEvent changes event state to signalled.
+ */
+#define ONNXIFI_EVENT_STATE_SIGNALLED 0x3395
+
+/** Special-purpose accelerator for neural network */
+#define ONNXIFI_DEVICE_TYPE_NPU 0x01
+/** Digital signal processor */
+#define ONNXIFI_DEVICE_TYPE_DSP 0x02
+/** Graphics accelerator */
+#define ONNXIFI_DEVICE_TYPE_GPU 0x04
+/** General-purpose central processor */
+#define ONNXIFI_DEVICE_TYPE_CPU 0x08
+/** Field-programmable gate array */
+#define ONNXIFI_DEVICE_TYPE_FPGA 0x10
+/**
+ * Heterogeneous backend whichinternally arbitrates or distributes work between
+ * multiple device types.
+ */
+#define ONNXIFI_DEVICE_TYPE_HETEROGENEOUS 0x20
+
+/**
+ * The backend supports multi-threaded access to ONNXIFI backend, graph, and
+ * event objects. E.g. onnxInitGraph can be called on a different thread than
+ * onnxInitBackend.
+ *
+ * If this capability it not indicated, ONNXIFI backend, graph, and event
+ * objects that relate to the backend must always be used on the same thread
+ * where the backend object was initialized.
+ */
+#define ONNXIFI_CAPABILITY_THREAD_SAFE 0x01
+/**
+ * The backend supports ONNX graphs with symbolic variables in the outer
+ * shape dimension (batch size), using TensorShapeProto.dim_param for
+ * ModelProto.graph.input.type.shape or ModelProto.graph.output.type.shape.
+ *
+ * The exact numerical value of the  of all input and output tensors must be specified
+ * in the onnxSetGraphIO call(s).
+ */
+#define ONNXIFI_CAPABILITY_SYMBOLIC_BATCH_SIZE 0x02
+/**
+ * The backend supports ONNX graphs with symbolic variables in the all
+ * shape dimensions, using TensorShapeProto.dim_param for
+ * ModelProto.graph.input.type.shape or ModelProto.graph.output.type.shape.
+ *
+ * Backends with this capability also MUST support
+ * ONNXIFI_CAPABILITY_SYMBOLIC_BATCH_SIZE capability.
+ *
+ * The exact numerical shape of all input and output tensors must be specified
+ * in the onnxSetGraphIO call(s).
+ */
+#define ONNXIFI_CAPABILITY_SYMBOLIC_SIZE_TENSORS 0x04
+/**
+ * The backend supports ONNX graphs with data-dependent outer shape dimension
+ * (batch size) of graph outputs. The ONNX graph would specify unknown outer
+ * shape dimension (batch size) using symbolic variables, so this capability
+ * requires ONNXIFI_CAPABILITY_SYMBOLIC_BATCH_SIZE support.
+ *
+ * For outputs with data-dependent outer shape dimension (batch size) the value
+ * specified in onnxSetGraphIO call is interpreted as the upper limit. The exact
+ * numerical batch size of the output can be retrieved by attaching a Shape
+ * operator to the tensor with data-dependent shape and reading its output
+ * through ONNXIFI.
+ */
+#define ONNXIFI_CAPABILITY_VARIABLE_BATCH_SIZE 0x08
+/**
+ * The backend supports ONNX graphs with data-dependent output shapes.
+ * The ONNX graph would specify unknown output shapes using symbolic variables,
+ * so this capability requires ONNXIFI_CAPABILITY_SYMBOLIC_SIZE_TENSORS support.
+ *
+ * Backends with this capability also MUST support
+ * ONNXIFI_CAPABILITY_VARIABLE_BATCH_SIZE capability.
+ *
+ * For outputs with data-dependent shapes the shape specified in onnxSetGraphIO
+ * call is interpreted as the upper limit. The exact numerical shape of the
+ * output can be retrieved by attaching a Shape operator to the tensor with
+ * data-dependent shape and reading its output through ONNXIFI.
+ */
+#define ONNXIFI_CAPABILITY_VARIABLE_SIZE_OUTPUTS 0x10
+/**
+ * The backend uses a hot-pluggable device, and can be disconnected at any time.
+ *
+ * If the underlying device disconnects from the system, subsequent operations
+ * with the backend, or objects created on the backend, will fail with
+ * ONNXIFI_STATUS_BACKEND_UNAVAILABLE status code.
+ */
+#define ONNXIFI_CAPABILITY_HOT_PLUGGABLE 0x20
+
+/**
+ * Type of the backend information.
+ *
+ * Possible values:
+ *     ONNXIFI_BACKEND_ONNXIFI_VERSION
+ *     ONNXIFI_BACKEND_NAME
+ *     ONNXIFI_BACKEND_VENDOR
+ *     ONNXIFI_BACKEND_VERSION
+ *     ONNXIFI_BACKEND_EXTENSIONS
+ *     ONNXIFI_BACKEND_DEVICE
+ *     ONNXIFI_BACKEND_DEVICE_TYPE
+ *     ONNXIFI_BACKEND_ONNX_IR_VERSION
+ *     ONNXIFI_BACKEND_OPSET_VERSION
+ *     ONNXIFI_BACKEND_CAPABILITIES
+ *     ONNXIFI_BACKEND_INIT_PROPERTIES
+ *     ONNXIFI_BACKEND_MEMORY_TYPES
+ *     ONNXIFI_BACKEND_GRAPH_INIT_PROPERTIES
+ *     ONNXIFI_BACKEND_SYNCHRONIZATION_TYPES
+ *     ONNXIFI_BACKEND_MEMORY_SIZE
+ *     ONNXIFI_BACKEND_MAX_GRAPH_SIZE
+ *     ONNXIFI_BACKEND_MAX_GRAPH_COUNT
+ *     ONNXIFI_BACKEND_MACS_FP32
+ *     ONNXIFI_BACKEND_MACS_FP16
+ *     ONNXIFI_BACKEND_MEMORY_BANDWIDTH
+ *     ONNXIFI_BACKEND_CPU_MEMORY_READ_BANDWIDTH
+ *     ONNXIFI_BACKEND_CPU_MEMORY_WRITE_BANDWIDTH
+ *     ONNXIFI_BACKEND_PCI_BUS_ID
+ *     ONNXIFI_BACKEND_PCI_DEVICE_ID
+ *     ONNXIFI_BACKEND_PCI_DOMAIN_ID
+ *     ONNXIFI_BACKEND_DIRECTX_ID
+ *     ONNXIFI_BACKEND_CUDA_INDEX
+ *     ONNXIFI_BACKEND_OPENCL_PLATFORM_ID
+ *     ONNXIFI_BACKEND_OPENCL_DEVICE_ID
+ */
+typedef int32_t onnxBackendInfo;
+
+/**
+ * Major and minor version of ONNXIFI specification implemented by the backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: uint64_t.
+ *      The high 32 bits specify the major version.
+ *      The low 32 bits specify the minor version.
+ *
+ * Possible values:
+ *      UINT64_C(0x0000000100000000) for ONNXIFI 1.0
+ */
+#define ONNXIFI_BACKEND_ONNXIFI_VERSION 0
+
+/**
+ * Marketing name of the backend (excluding the vendor name).
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * This string MUST be in UTF-8 encoding and NOT locale-sensitive.
+ *
+ * Value type: char[], e.g.:
+ *    "Caffe2"
+ *    "Glow"
+ */
+#define ONNXIFI_BACKEND_NAME 1
+
+/**
+ * Name of the backend vendor.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * This string MUST be in UTF-8 encoding and NOT locale-sensitive.
+ *
+ * Value type: char[], e.g.:
+ *    "Facebook"
+ *    "Marat Dukhan"
+ */
+#define ONNXIFI_BACKEND_VENDOR 2
+
+/**
+ * Version of the backend software. Exact format is vendor-specific, but MUST be
+ * unique for the software release.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * This string MUST be in US-ASCII encoding and NOT locale-sensitive.
+ *
+ * Value type: char[], e.g.:
+ *    "1.2.3"
+ *    "1.2.3.0"
+ *    "1.2.3-db3a4439d233276e25681fb4735b7f8e674dda65"
+ */
+#define ONNXIFI_BACKEND_VERSION 3
+
+/**
+ * Space-separated list of vendor- or device-specific extensions supported on
+ * this backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * This string MUST be in US-ASCII encoding and NOT locale-sensitive.
+ *
+ * Value type: char[], e.g.:
+ *    ""
+ *    "onnx_clone_graph"
+ *    "onnx_clone_graph fb_maskrcnn"
+ */
+#define ONNXIFI_BACKEND_EXTENSIONS 4
+
+/**
+ * Descriptive name of the device (i.e. CPU, GPU, DSP, or NPU model).
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * This string MUST be in UTF-8 encoding and NOT locale-sensitive.
+ *
+ * Value type: char[], e.g.:
+ *    "nnDuino 123"
+ */
+#define ONNXIFI_BACKEND_DEVICE 5
+
+/**
+ * Type of the device.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: onnxEnum.
+ * Possible values:
+ *      ONNXIFI_DEVICE_TYPE_NPU
+ *      ONNXIFI_DEVICE_TYPE_DSP
+ *      ONNXIFI_DEVICE_TYPE_GPU
+ *      ONNXIFI_DEVICE_TYPE_CPU
+ *      ONNXIFI_DEVICE_TYPE_FPGA
+ *      ONNXIFI_DEVICE_TYPE_HETEROGENEOUS
+ */
+#define ONNXIFI_BACKEND_DEVICE_TYPE 6
+
+/**
+ * List of supported ONNX IR versions.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: char[], e.g.:
+ *    "3" (IR version in ONNX 1.0)
+ *
+ * Possible values: space-separated list of supported ONNX IR versions,
+ *     represented as decimal integers. ONNX IR versions must match values
+ *     in ONNX Version enum.
+ */
+#define ONNXIFI_BACKEND_ONNX_IR_VERSION 7
+
+/**
+ * List of supported operator set domains and maximum supported operator set
+ * version for each domain.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: char[], e.g.:
+ *    "ai.onnx:1" (only operators in version 1 of default ONNX operator set)
+ *    "ai.onnx:7" (operators up to version 7 of default ONNX operator set)
+ *    "org.pytorch:2 ai.onnx:6 ai.facebook:1"
+ *
+ * Possible values: space-separated list of domain:max_version pairs where
+ *     domain corresponds to OperatorSetIdProto.domain and max_version
+ *     corresponds to the maximum value of OperatorSetIdProto.version supported
+ *     by the backend for this domain. The backend MUST support all previous
+ *     operator set versions as well.
+ */
+#define ONNXIFI_BACKEND_OPSET_VERSION 8
+
+/**
+ * Optional features supported by the backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: onnxBitfield.
+ * Possible values: any combination of the following flags:
+ *      ONNXIFI_CAPABILITY_THREAD_SAFE
+ *      ONNXIFI_CAPABILITY_SYMBOLIC_BATCH_SIZE
+ *      ONNXIFI_CAPABILITY_SYMBOLIC_SIZE_TENSORS
+ *      ONNXIFI_CAPABILITY_VARIABLE_BATCH_SIZE
+ *      ONNXIFI_CAPABILITY_VARIABLE_SIZE_OUTPUTS
+ *      ONNXIFI_CAPABILITY_HOT_PLUGGABLE
+ *      or any vendor-specific flags in the high 32 bits of the bit field.
+ */
+#define ONNXIFI_BACKEND_CAPABILITIES 10
+
+/**
+ * Auxiliary initialization properties supported by the backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: onnxBitfield.
+ * Possible values: any combination of vendor-specific flags in high 32 bits of
+ * the bit field.
+ */
+#define ONNXIFI_BACKEND_INIT_PROPERTIES 11
+
+/**
+ * Memory types supported for graph inputs and outputs.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: onnxBitfield.
+ * Possible values are any combination of the following flags:
+ *     ONNXIFI_MEMORY_TYPE_CPU (always supported)
+ *     ONNXIFI_MEMORY_TYPE_CUDA_BUFFER
+ *     ONNXIFI_MEMORY_TYPE_OPENCL_BUFFER
+ *     ONNXIFI_MEMORY_TYPE_OPENGLES_TEXTURE_2D
+ *     ONNXIFI_MEMORY_TYPE_D3D_RESOURCE
+ *     or any vendor-specific flags in the high 32 bits of the bit field.
+ */
+#define ONNXIFI_BACKEND_MEMORY_TYPES 12
+
+/**
+ * Auxiliary initialization properties supported by graphs on the backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: onnxBitfield.
+ * Possible values: any combination of vendor-specific flags in high 32 bits of
+ * the bit field.
+ */
+#define ONNXIFI_BACKEND_GRAPH_INIT_PROPERTIES 13
+
+/**
+ * Memory synchronization primitives supported for graph inputs and outputs.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Possible values are any combination of the following flags:
+ *     ONNXIFI_SYNCHRONIZATION_EVENT    (onnxEvent, always supported)
+ *     ONNXIFI_SYNCHRONIZATION_IMPLICIT
+ *     or any vendor-specific flags in the high 32 bits of the bit field.
+ */
+#define ONNXIFI_BACKEND_SYNCHRONIZATION_TYPES 14
+
+/**
+ * Maximum amount of memory, in bytes, available to the use by the backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_MEMORY_SIZE 20
+
+/**
+ * Maximum size of network parameters, in bytes.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_MAX_GRAPH_SIZE 21
+
+/**
+ * Maximum number of independent network graphs supported by the backend.
+ *
+ * Since ONNXIFI 1.0, backends MUST support this information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_MAX_GRAPH_COUNT 22
+
+/**
+ * Number of FP32 multiply-accumulate operations per second delivered by the
+ * backend.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ * If the backend does not support FP32 computation, the value MUST be 0.
+ */
+#define ONNXIFI_BACKEND_MACS_FP32 30
+
+/**
+ * Number of FP16 multiply-accumulate operations per second delivered by the
+ * backend.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ * If the backend does not support FP16 computation, the value MUST be 0.
+ */
+#define ONNXIFI_BACKEND_MACS_FP16 31
+
+/**
+ * Bandwidth, in bytes per second, of the global memory specific to the backend
+ * device.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_MEMORY_BANDWIDTH 35
+
+/**
+ * Bandwidth, in bytes per second, of transferring data from cacheable
+ * CPU-allocated memory to the backend device.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_CPU_MEMORY_READ_BANDWIDTH 36
+
+/**
+ * Bandwidth, in bytes per second, of transferring data to cacheable
+ * CPU-allocated memory from the backend device.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_CPU_MEMORY_WRITE_BANDWIDTH 37
+
+/**
+ * PCI bus ID of the backend device.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_PCI_BUS_ID 40
+
+/**
+ * PCI device ID of the backend device.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_PCI_DEVICE_ID 41
+
+/**
+ * PCI domain/function ID of the backend device.
+ *
+ * Since ONNXIFI 1.0, backends are recommended, but not required to support this
+ * information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_PCI_DOMAIN_ID 42
+
+/**
+ * DirectX ID of the backend device.
+ *
+ * This is the value that would be returned by ID3D12Device::GetAdapterLuid()
+ * for the hardware device used by the backend.
+ *
+ * Since ONNXIFI 1.0, DXGI-based backends are recommended, but not required to
+ * support this information query.
+ *
+ * Value type: LUID (8 bytes).
+ */
+#define ONNXIFI_BACKEND_DIRECTX_ID 43
+
+/**
+ * CUDA index of the backend device.
+ *
+ * Since ONNXIFI 1.0, CUDA-based backends are recommended, but not required to
+ * support this information query.
+ *
+ * Value type: uint64_t.
+ */
+#define ONNXIFI_BACKEND_CUDA_INDEX 44
+
+/**
+ * OpenCL platform ID for the backend device.
+ * This platform ID is guaranteed to remain valid for the lifetime of ONNXIFI
+ * objects related to the same ONNXIFI backend (backend ID, backend, graph,
+ * event).
+ *
+ * Since ONNXIFI 1.0, OpenCL-based backends are recommended, but not required to
+ * support this information query.
+ *
+ * Value type: cl_platform_id.
+ */
+#define ONNXIFI_BACKEND_OPENCL_PLATFORM_ID 45
+
+/**
+ * OpenCL device ID for the backend device.
+ * This device ID is guaranteed to remain valid for the lifetime of ONNXIFI
+ * objects related to the same ONNXIFI backend (backend ID, backend, graph,
+ * event).
+ *
+ * Since ONNXIFI 1.0, OpenCL-based backends are recommended, but not required to
+ * support this information query.
+ *
+ * Value type: cl_device_id.
+ */
+#define ONNXIFI_BACKEND_OPENCL_DEVICE_ID 46
+
+/* Note: the data type values match ONNX TensorProto.DataType enum */
+#define ONNXIFI_DATATYPE_UNDEFINED 0
+#define ONNXIFI_DATATYPE_FLOAT16 10
+#define ONNXIFI_DATATYPE_FLOAT32 1
+#define ONNXIFI_DATATYPE_FLOAT64 11
+#define ONNXIFI_DATATYPE_INT8 3
+#define ONNXIFI_DATATYPE_INT16 5
+#define ONNXIFI_DATATYPE_INT32 6
+#define ONNXIFI_DATATYPE_INT64 7
+#define ONNXIFI_DATATYPE_UINT8 2
+#define ONNXIFI_DATATYPE_UINT16 4
+#define ONNXIFI_DATATYPE_UINT32 12
+#define ONNXIFI_DATATYPE_UINT64 13
+#define ONNXIFI_DATATYPE_COMPLEX64 14
+#define ONNXIFI_DATATYPE_COMPLEX128 15
+#define ONNXIFI_DATATYPE_BFLOAT16 16
+
+/** Cacheable CPU memory */
+#define ONNXIFI_MEMORY_TYPE_CPU 0
+/** CUDA memory buffer (allocated via cudaMalloc/cuMalloc).  */
+#define ONNXIFI_MEMORY_TYPE_CUDA_BUFFER 1
+/** OpenCL cl_mem object for a buffer or sub-buffer. */
+#define ONNXIFI_MEMORY_TYPE_OPENCL_BUFFER 2
+/** OpenGL ES 2.0+ 2D Texture. */
+#define ONNXIFI_MEMORY_TYPE_OPENGLES_TEXTURE_2D 4
+/** Direct3D resource. */
+#define ONNXIFI_MEMORY_TYPE_D3D_RESOURCE 8
+
+/**
+ * Terminates the list of auxiliary backend initialization properties passed to
+ * onnxInitBackend.
+ */
+#define ONNXIFI_BACKEND_PROPERTY_NONE 0
+/**
+ * Optimization target for graphs initialized on the backend.
+ *
+ * Possible values:
+ *     ONNXIFI_OPTIMIZATION_HIGH_THROUGHPUT
+ *     ONNXIFI_OPTIMIZATION_LOW_LATENCY
+ *     ONNXIFI_OPTIMIZATION_LOW_POWER
+ *     ONNXIFI_OPTIMIZATION_LOW_DELAY
+ */
+#define ONNXIFI_BACKEND_PROPERTY_OPTIMIZATION 1
+/**
+ * Logging verbosity level for the backend.
+ *
+ * If this property is not specified during initialization, the backend should
+ * assume ONNXIFI_LOG_LEVEL_WARNING logging verbosity level.
+ *
+ * Possible values:
+ *     ONNXIFI_LOG_LEVEL_ERROR
+ *     ONNXIFI_LOG_LEVEL_WARNING
+ *     ONNXIFI_LOG_LEVEL_INFO
+ *     ONNXIFI_LOG_LEVEL_DEBUG
+ */
+#define ONNXIFI_BACKEND_PROPERTY_LOG_LEVEL 2
+/**
+ * CUDA stream to be used by the backend.
+ * CUDA stream must be created on the CUDA device used by the ONNXIFI backend.
+ * Users can query which CUDA device is used by the ONNXIFI backend by calling
+ * onnxGetBackendInfo with ONNXIFI_BACKEND_CUDA_INDEX info type.
+ *
+ * If this property is not specified during initialization, the backend can
+ * create a new CUDA stream for the device, or use a default CUDA stream.
+ *
+ * Possible values: cudaStream_t or CUstream object, cast to uint64_t.
+ */
+#define ONNXIFI_BACKEND_CUDA_STREAM 4
+/**
+ * OpenCL context to be used by the backend.
+ * The context must be created with the OpenCL device ID and the OpenCL platform
+ * ID used by the ONNXIFI backend. Users can query which OpenCL device ID and
+ * OpenCL platform ID are used by the ONNXIFI backend by calling
+ * onnxGetBackendInfo with ONNXIFI_BACKEND_OPENCL_PLATFORM_ID
+ * and ONNXIFI_BACKEND_OPENCL_DEVICE_ID info types.
+ *
+ * If this property is not specified during initialization, the backend will
+ * create a new OpenCL context for the device.
+ *
+ * Possible values: cl_context object, cast to uint64_t.
+ */
+#define ONNXIFI_BACKEND_OPENCL_CONTEXT 8
+
+/**
+ * Terminates the list of auxiliary graph initialization properties passed to
+ * onnxInitGraph.
+ */
+#define ONNXIFI_GRAPH_PROPERTY_NONE 0
+
+/**
+ * Optimize graph representation and compilation for highest throughput.
+ */
+#define ONNXIFI_OPTIMIZATION_HIGH_THROUGHPUT 0
+
+/**
+ * Optimize graph representation and compilation for lowest latency.
+ */
+#define ONNXIFI_OPTIMIZATION_LOW_LATENCY 1
+
+/**
+ * Optimize graph representation and compilation for lowest power consumption.
+ */
+#define ONNXIFI_OPTIMIZATION_LOW_POWER 2
+
+/**
+ * Optimize graph representation and compilation for lowest delay until first
+ * result.
+ */
+#define ONNXIFI_OPTIMIZATION_LOW_DELAY 3
+
+/**
+ * Log events which caused a failure in an ONNXIFI function call.
+ */
+#define ONNXIFI_LOG_LEVEL_ERROR 4
+
+/**
+ * Log events in ONNXIFI_LOG_LEVEL_ERROR and events which caused
+ * a performance, accuracy, or quality of service degradation in a backend.
+ * Enabling this logging level SHOULD NOT have a measurable effect on
+ * performance.
+ */
+#define ONNXIFI_LOG_LEVEL_WARNING 3
+
+/**
+ * Log events in ONNXIFI_LOG_LEVEL_WARNING and high-level status information
+ * about operation of a backend. Enabling this logging level MAY cause a small
+ * degradation in performance.
+ */
+#define ONNXIFI_LOG_LEVEL_INFO 2
+
+/**
+ * Log events in ONNXIFI_LOG_LEVEL_INFO and detailed status information about
+ * operations of a backend. Enabling this logging level MAY cause a serious
+ * degradation in performance.
+ */
+#define ONNXIFI_LOG_LEVEL_DEBUG 1
+
+/**
+ * Tag for version 1 of tensor descriptor structure (onnxTensorDescriptorV1).
+ *
+ * The tag is unique for this version. If ONNXIFI introduce a new version of
+ * the tensor descriptor structure in the future, it will get a new tag value.
+ */
+#define ONNXIFI_TAG_TENSOR_DESCRIPTOR_V1 0x43DFBF69
+
+typedef struct onnxTensorDescriptorV1 {
+  /**
+   * 32-bit tag needed to distinguish different versions of a tensor descriptor
+   * structure. In the onnxTensorDescriptorV1 structure, the tag MUST be set to
+   * ONNXIFI_TAG_TENSOR_DESCRIPTOR_V1. If ONNXIFI introduce a new version of the
+   * tensor descriptor structure in the future, it WILL have 32-bit tag with a
+   * different value as the first member of the structure.
+   *
+   * ONNXIFI implementations MUST validate tag before accessing any other
+   * members of the structure.
+   */
+  int32_t tag;
+  /**
+   * Name of the blob corresponding to this tensor in the ONNX model. The name
+   * must exactly match the ValueInfoProto.name of one of the
+   * ModelProto.graph.input or ModelProto.graph.output
+   */
+  const char* name;
+  /**
+   * Data type of the elements in the tensor.
+   *
+   * Possible values:
+   *     ONNXIFI_DATATYPE_FLOAT16
+   *     ONNXIFI_DATATYPE_FLOAT32
+   *     ONNXIFI_DATATYPE_FLOAT64
+   *     ONNXIFI_DATATYPE_INT8
+   *     ONNXIFI_DATATYPE_INT16
+   *     ONNXIFI_DATATYPE_INT32
+   *     ONNXIFI_DATATYPE_INT64
+   *     ONNXIFI_DATATYPE_UINT8
+   *     ONNXIFI_DATATYPE_UINT16
+   *     ONNXIFI_DATATYPE_UINT32
+   *     ONNXIFI_DATATYPE_UINT64
+   *     ONNXIFI_DATATYPE_COMPLEX64
+   *     ONNXIFI_DATATYPE_COMPLEX128
+   */
+  onnxEnum dataType;
+  /**
+   * Type of memory that stores the tensor.
+   *
+   * ONNXIFI_MEMORY_TYPE_CPU memory type is always supported by the backend, but
+   * other memory types are optional. The use MUST call onnxGetBackendInfo with
+   * ONNXIFI_BACKEND_MEMORY_TYPES to check if a particular memory type is
+   * supported before using it.
+   *
+   * If the memory type is different than ONNXIFI_MEMORY_TYPE_CPU, it must be
+   * allocated on the same device as the backend.
+   *
+   * Possible values:
+   *     ONNXIFI_MEMORY_TYPE_CPU                 (always supported)
+   *     ONNXIFI_MEMORY_TYPE_CUDA_BUFFER         (support is optional)
+   *     ONNXIFI_MEMORY_TYPE_OPENCL_BUFFER       (support is optional)
+   *     ONNXIFI_MEMORY_TYPE_OPENGLES_TEXTURE_2D (support is optional)
+   *     ONNXIFI_MEMORY_TYPE_D3D_RESOURCE        (support is optional)
+   */
+  onnxEnum memoryType;
+  /**
+   * Number of dimensions in the tensor.
+   * For a scalar, the number of dimensions is 0.
+   */
+  uint32_t dimensions;
+  /**
+   * Dimensions of the tensor.
+   * For a scalar, this pointer can be NULL.
+   */
+  const uint64_t* shape;
+  /**
+   * Pointers to tensor data.
+   *
+   * Interpretation depends on memoryType:
+   *   - ONNXIFI_MEMORY_TYPE_CPU: buffer is a valid pointer to CPU memory.
+   *   - ONNXIFI_MEMORY_TYPE_CUDA_BUFFER: buffer is a valid pointer to CUDA
+   *     device memory, allocated via cudaMalloc or cuMalloc. CUDA device memory
+   *     must be allocated on the same device as the backend.
+   *   - ONNXIFI_MEMORY_TYPE_OPENCL_BUFFER: buffer is a cl_mem handle for an
+   *     OpenCL buffer or a sub-buffer. cl_mem object must be allocated on the
+   *     same OpenCL context as the backend. The context must be specified via
+   *     ONNXIFI_BACKEND_OPENCL_CONTEXT initialization property to
+   *     onnxInitBackend.
+   *   - ONNXIFI_MEMORY_TYPE_OPENGLES_TEXTURE_2D: buffer is a name of a 2D
+   *     texture created by glGenTextures. The texture must be allocated on the
+   *     same device as the backend.
+   *   - ONNXIFI_MEMORY_TYPE_D3D_RESOURCE: TBD
+   */
+  onnxPointer buffer;
+} onnxTensorDescriptorV1;
+
+/**
+ * Synchronization using ONNXIFI event object (onnxEvent).
+ */
+#define ONNXIFI_SYNCHRONIZATION_EVENT 0
+/**
+ * Implicit synchronization of inputs and outputs access with the caller.
+ * The details are backend-specific, and may involve extra parameters passed
+ * during backend initialization.
+ *
+ * Examples:
+ *  - CUDA-based backends could implicitly synchronize with the caller through
+ *    the use of the same CUDA stream.
+ *  - OpenCL-based backends could implicitly synchronize with the caller through
+ *    the use of the same in-order OpenCL command queue.
+ */
+#define ONNXIFI_SYNCHRONIZATION_IMPLICIT 2
+
+/**
+ * Tag for version 1 of memory fence structure (onnxMemoryFenceV1).
+ *
+ * The tag is unique for this version. If ONNXIFI introduce a new version of
+ * the memory fence structure in the future, it will get a new tag value.
+ */
+#define ONNXIFI_TAG_MEMORY_FENCE_V1 0x23E08AAB
+
+typedef struct onnxMemoryFenceV1 {
+  /**
+   * 32-bit tag needed to distinguish different versions of a memory fence
+   * structure. In the onnxMemoryFenceV1 structure, the tag MUST be set to
+   * ONNXIFI_TAG_MEMORY_FENCE_V1. If ONNXIFI introduce a new version of the
+   * memory fence structure in the future, it WILL have 32-bit tag with a
+   * different value as the first member of the structure.
+   *
+   * ONNXIFI implementations MUST validate tag before accessing any other
+   * members of the structure.
+   */
+  int32_t tag;
+  /**
+   * Type of memory synchronization primitive.
+   *
+   * Possible values:
+   *      ONNXIFI_SYNCHRONIZATION_EVENT    (onnxEvent, always supported)
+   *      ONNXIFI_SYNCHRONIZATION_IMPLICIT
+   */
+  onnxEnum type;
+  union {
+    /**
+     * Handle for a single-shot ONNXIFI event used as a synchronization
+     * primitive. Event for the input fence must be created by the caller to
+     * onnxRunGraph. Event for the output fence is created by implementation of
+     * onnxRunGraph, and stored into the output memory fence structure before
+     * onnxRunGraph returns.
+     */
+    onnxEvent event;
+  };
+} onnxMemoryFenceV1;
+
+/* Function pointer declarations for dynamic loading */
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxGetBackendIDsFunction)(
+    onnxBackendID* backendIDs,
+    size_t* numBackends);
+typedef onnxStatus
+  (ONNXIFI_ABI* onnxReleaseBackendIDFunction)(
+    onnxBackendID backendID);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxGetBackendInfoFunction)(
+    onnxBackendID backendID,
+    onnxBackendInfo infoType,
+    void* infoValue,
+    size_t* infoValueSize);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxGetBackendCompatibilityFunction)(
+    onnxBackendID backendID,
+    size_t onnxModelSize,
+    const void* onnxModel);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxInitBackendFunction)(
+    onnxBackendID backendID,
+    const uint64_t* auxPropertiesList,
+    onnxBackend* backend);
+typedef onnxStatus
+  (ONNXIFI_ABI* onnxReleaseBackendFunction)(
+    onnxBackend backend);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxInitEventFunction)(
+    onnxBackend backend,
+    onnxEvent* event);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxSignalEventFunction)(
+    onnxEvent event);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxGetEventStateFunction)(
+    onnxEvent event,
+    onnxEventState* state);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxWaitEventFunction)(
+    onnxEvent event);
+typedef onnxStatus
+  (ONNXIFI_ABI* onnxReleaseEventFunction)(
+    onnxEvent event);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxInitGraphFunction)(
+    onnxBackend backend,
+    const uint64_t* auxPropertiesList,
+    size_t onnxModelSize,
+    const void* onnxModel,
+    uint32_t weightsCount,
+    const onnxTensorDescriptorV1* weightDescriptors,
+    onnxGraph* graph);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxSetGraphIOFunction)(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors);
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxRunGraphFunction)(
+    onnxGraph graph,
+    const onnxMemoryFenceV1* inputFence,
+    onnxMemoryFenceV1* outputFence);
+typedef onnxStatus
+  (ONNXIFI_ABI* onnxReleaseGraphFunction)(
+    onnxGraph graph);
+
+/**
+ * Get stable IDs of available backends on the system.
+ *
+ * ONNXIFI backend is a combination of software layer and hardware device used
+ * to run an ONNX graph. The same software layer may expose multiple backends
+ * (e.g. one ONNXIFI backend for each GPU in the system, or one ONNXIFI backend
+ * for GPU and another for CPU, both implemented in the same software). Backends
+ * implemented in the same software, but targeting different devices (e.g.
+ * "MyNN" for CPU and "MyNN" for GPU) have different backend IDs.
+ *
+ * Note that some (hot-pluggable) backends can be connected and disconnected at
+ * any time, and thus subsequent calls to this function may return different
+ * number or set of backend IDs. The returned IDs, however, stay valid even if
+ * the hardware device used by the backend disconnects from the system.
+ *
+ * To avoid resource leak, the backend ID MUST be released through a call to
+ * onnxReleaseBackendID when it is no longer needed.
+ *
+ * @param backendIDs[out] - pointer to the memory location where the backend IDs
+ *                          will be returned. If the pointer is NULL, it is
+ *                          ignored, and the function returns only the number
+ *                          of backend IDs through numBackendIDs pointer.
+ * @param numBackendIDs[in,out] - pointer to a variable specifying number of
+ *                                available backends. On function entry, the
+ *                                variable MUST contain the capacity, in number
+ *                                of backend IDs, of the memory buffer specified
+ *                                by backendIDs. For successful completion, this
+ *                                capacity must be at least as large as the
+ *                                number of available backends. If the function
+ *                                completes with either ONNXIFI_STATUS_SUCCESS
+ *                                or ONNXIFI_STATUS_FALLBACK status codes, the
+ *                                number of backend IDs written into backendIDs
+ *                                buffer is stored in the variable specified by
+ *                                this pointer.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded, and backend IDs
+ *                                are stored in the location specified by
+ *                                backendIDs, and the number of the backends
+ *                                is stored in the location specified by
+ *                                numBackends.
+ * @retval ONNXIFI_STATUS_FALLBACK The function call completed, but the
+ *                                 backend IDs were not stored in the
+ *                                 location specified by backendIDs, either
+ *                                 because it is NULL, or because the size of
+ *                                 the memory buffer is insufficient to store
+ *                                 all available backend IDs. The number of
+ *                                 available backends is stored in the
+ *                                 location specified by numBackends.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        numBackends is NULL.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed because the
+ *                                         system failed to allocate memory
+ *                                         to store backend ID information.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxGetBackendIDs(
+    onnxBackendID* backendIDs,
+    size_t* numBackends);
+
+/**
+ * Deinitialize ONNXIFI backend IDs and release associated resources.
+ *
+ * The user MUST deinitialize all objects created with this backend ID
+ * (onnxBackend, onnxGraph, onnxEvent) before calling this function to
+ * deinitialize the backend ID.
+ *
+ * @param backendID - backend ID returned by onnxGetBackendIDs.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the resources
+ *                                associated to the backend ID were released to
+ *                                the operating system.
+ * @retval ONNXIFI_STATUS_INVALID_ID The function call failed because backendID
+ *                                   is not an ONNXIFI backend ID.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseBackendID(
+    onnxBackendID backendID);
+
+/**
+ * Query high-level information about the backend and its target device.
+ *
+ * ONNXIFI backend is a combination of software layer and hardware device used
+ * to run an ONNX graph. The same software layer may expose multiple backends
+ * (e.g. one ONNXIFI backend for each GPU in the system, or one ONNXIFI backend
+ * for GPU and another for CPU, both implemented in the same software).
+ *
+ * The content, data type, and availability of information provided by this
+ * function depends on infoType value as specified below:
+ *
+ *         infoType value                           data type      support
+ *     ONNXIFI_BACKEND_ONNXIFI_VERSION               uint64_t     required
+ *     ONNXIFI_BACKEND_NAME                           char[]      required
+ *     ONNXIFI_BACKEND_VENDOR                         char[]      required
+ *     ONNXIFI_BACKEND_VERSION                        char[]      required
+ *     ONNXIFI_BACKEND_EXTENSIONS                     char[]      required
+ *     ONNXIFI_BACKEND_DEVICE                         char[]      required
+ *     ONNXIFI_BACKEND_DEVICE_TYPE                   onnxEnum     required
+ *     ONNXIFI_BACKEND_ONNX_IR_VERSION                char[]      required
+ *     ONNXIFI_BACKEND_OPSET_VERSION                  char[]      required
+ *     ONNXIFI_BACKEND_CAPABILITIES                onnxBitfield   required
+ *     ONNXIFI_BACKEND_INIT_PROPERTIES             onnxBitfield   required
+ *     ONNXIFI_BACKEND_MEMORY_TYPES                onnxBitfield   required
+ *     ONNXIFI_BACKEND_GRAPH_INIT_PROPERTIES       onnxBitfield   required
+ *     ONNXIFI_BACKEND_SYNCHRONIZATION_TYPES       onnxBitfield   required
+ *     ONNXIFI_BACKEND_MEMORY_SIZE                   uint64_t     required
+ *     ONNXIFI_BACKEND_MAX_GRAPH_SIZE                uint64_t     required
+ *     ONNXIFI_BACKEND_MAX_GRAPH_COUNT               uint64_t     required
+ *     ONNXIFI_BACKEND_MACS_FP32                     uint64_t     optional
+ *     ONNXIFI_BACKEND_MACS_FP16                     uint64_t     optional
+ *     ONNXIFI_BACKEND_MEMORY_BANDWIDTH              uint64_t     optional
+ *     ONNXIFI_BACKEND_CPU_MEMORY_READ_BANDWIDTH     uint64_t     optional
+ *     ONNXIFI_BACKEND_CPU_MEMORY_WRITE_BANDWIDTH    uint64_t     optional
+ *     ONNXIFI_BACKEND_PCI_BUS_ID                    uint64_t     optional
+ *     ONNXIFI_BACKEND_PCI_DEVICE_ID                 uint64_t     optional
+ *     ONNXIFI_BACKEND_PCI_DOMAIN_ID                 uint64_t     optional
+ *     ONNXIFI_BACKEND_DIRECTX_ID                      LUID       optional
+ *     ONNXIFI_BACKEND_CUDA_INDEX                    uint64_t     optional
+ *     ONNXIFI_BACKEND_OPENCL_PLATFORM_ID         cl_platform_id  optional
+ *     ONNXIFI_BACKEND_OPENCL_DEVICE_ID            cl_device_id   optional
+ *
+ * @param backendID - ID of the backend to query.
+ * @param infoType - type of the backend information to query. Must be one of
+ *                   the ONNXIFI_BACKEND_* constants. If this value is not
+ *                   supported by the backend, the function will fail with
+ *                   ONNXIFI_STATUS_UNSUPPORTED_ATTRIBUTE.
+ * @param infoValue[out] - pointer to the memory location where the backend
+ *                         information value will be returned. If the pointer is
+ *                         NULL, is it ignored.
+ * @param infoValueSize[in,out] - pointer to a variable specifying size, in
+ *                                bytes, of the information value. On function
+ *                                entry, the variable MUST contain the size of
+ *                                the memory buffer specified by infoValue.
+ *                                For successful completion, this size must be
+ *                                at least as large as the queried value. If the
+ *                                function completes with either
+ *                                ONNXIFI_STATUS_SUCCESS or
+ *                                ONNXIFI_STATUS_FALLBACK status codes, the
+ *                                actual size of the value queried in the call
+ *                                is stored in the variable specified by this
+ *                                pointer.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded, and requested
+ *                                value is stored in the location specified by
+ *                                infoValue, and the actual size of the
+ *                                requested value is stored in the location
+ *                                specified by infoValueSize.
+ * @retval ONNXIFI_STATUS_FALLBACK The function call completed, but the
+ *                                 requested value was not stored in the
+ *                                 location specified by infoValue, either
+ *                                 because it is NULL, or because the size of
+ *                                 the memory buffer is insufficient for the
+ *                                 value. The actual size of the requested value
+ *                                 is stored in the location specified by
+ *                                 infoValueSize.
+ * @retval ONNXIFI_STATUS_INVALID_ID The function call failed because backendID
+ *                                   is not an ONNXIFI backend ID.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        infoValueSize is NULL.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_ATTRIBUTE The function call failed because
+ *                                              the value of infoType is not
+ *                                              supported by the backend.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxGetBackendInfo(
+    onnxBackendID backendID,
+    onnxBackendInfo infoType,
+    void* infoValue,
+    size_t* infoValueSize);
+
+/**
+ * Query if an ONNX model graph is compatible with the backend.
+ *
+ * Model graph is passed as a serialized ModelProto message, where types and
+ * dimensions of all inputs (including static weights) and outputs are specified
+ * through ModelProto.graph.input and ModelProto.graph.output messages. If the
+ * backend supports ONNXIFI_CAPABILITY_SYMBOLIC_SIZE_TENSORS, some of the shape
+ * dimensions can be symbolic. If the backend supports
+ * ONNXIFI_CAPABILITY_SYMBOLIC_BATCH_SIZE, the outer shape dimension can be
+ * symbolic. In these cases, the validation of symbolic dimension should be
+ * deferred until graph inputs and outputs are specified in onnxSetGraphIO.
+ *
+ * Commonly, the serialized ModelProto message passed to this function would
+ * not include the static weights (ModelProto.graph.initializer is empty), and
+ * the backend implementation MUST NOT rely on the weights to determine if the
+ * graph is supported.
+ *
+ * An important use-case is a ModelProto containing only a single NodeProto in
+ * ModelProto.graph.node, which happens when a high-level framework checks
+ * operators one-by-one to find a connected subgraph that can be offloaded to
+ * the backend. Backend implementations SHOULD optimize performance for this
+ * use-case.
+ *
+ * @param backend - ID of the backend to query.
+ * @param onnxModelSize - size of the serialized ONNX ModelProto message,
+ *                        in bytes.
+ * @param[in] onnxModel - pointer to serialized ONNX ModelProto message
+ *                        representing the model graph.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the model
+ *                                graph can efficiently run on the backend.
+ * @retval ONNXIFI_STATUS_FALLBACK The function call succeeded and the model
+ *                                 graph can run on the backend through some
+ *                                 emulation layer with some efficiency loss. If
+ *                                 a backend decomposes this operator into
+ *                                 multiple sub-operators, it should return this
+ *                                 code. E.g. if a backend does not natively
+ *                                 support grouped or depthwise convolution, but
+ *                                 can execute it as multiple unit-group
+ *                                 convolution operators, it must returns this
+ *                                 code.
+ * @retval ONNXIFI_STATUS_INVALID_ID The function call failed because backendID
+ *                                   is not an ONNXIFI backend ID.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        onnxModel is NULL.
+ * @retval ONNXIFI_STATUS_INVALID_SIZE The function call failed because
+ *                                     onnxModelSize is 0.
+ * @retval ONNXIFI_STATUS_INVALID_PROTOBUF The function call failed because it
+ *                                         couldn't parse the serialized
+ *                                         protobuf as an ONNX ModelProto
+ *                                         message.
+ * @retval ONNXIFI_STATUS_INVALID_MODEL The function call failed because the
+ *                                      parsed ModelProto message does not
+ *                                      satisfy ONNX requirements and
+ *                                      constraints.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_VERSION The function call failed because
+ *                                            the ONNX IR version or operator
+ *                                            version is not supported by the
+ *                                            backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_OPERATOR The function call failed because
+ *                                             one of the operators in the model
+ *                                             graph is not supported by the
+ *                                             backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_ATTRIBUTE The function call failed because
+ *                                              the backend does not support the
+ *                                              particular AttributeProto
+ *                                              values in one of the operators.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_SHAPE The function call failed because the
+ *                                          backend does not support the
+ *                                          tensor shapes in an input or output
+ *                                          of one of the operators. The
+ *                                          problematic tensor shapes could be
+ *                                          directly specified through
+ *                                          ValueInfoProto in GraphProto.input,
+ *                                          GraphProto.output, or
+ *                                          GraphProto.value_info, through
+ *                                          TensorProto in
+ *                                          GraphProto.initializer, or inferred
+ *                                          from the inputs by the backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_DATATYPE The function call failed because
+ *                                             the backend does not support the
+ *                                             data types in an input or output
+ *                                             of one of the operators. The
+ *                                             problematic data types could be
+ *                                             directly specified through
+ *                                             ValueInfoProto in
+ *                                             GraphProto.input,
+ *                                             GraphProto.output, or
+ *                                             GraphProto.value_info, through
+ *                                             TensorProto in
+ *                                             GraphProto.initializer, or
+ *                                             inferred from the inputs by the
+ *                                             backend.
+ * @retval ONNXIFI_STATUS_MISMATCHING_SHAPE The function call failed because
+ *                                          output or intermediate shapes
+ *                                          specified in the ONNX model graph do
+ *                                          not match the shapes inferred from
+ *                                          input shapes.
+ * @retval ONNXIFI_STATUS_MISMATCHING_DATATYPE The function call failed because
+ *                                             output or intermediate data types
+ *                                             specified in the ONNX model graph
+ *                                             do not match the data types
+ *                                             inferred from graph inputs.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed because the
+ *                                         backend could not allocate enough
+ *                                         system memory to parse and analyze
+ *                                         the model graph.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxGetBackendCompatibility(
+    onnxBackendID backendID,
+    size_t onnxModelSize,
+    const void* onnxModel);
+
+/**
+ * Initialize an ONNXIFI backend.
+ *
+ * ONNXIFI backend is a combination of software layer and hardware device used
+ * to run an ONNXIFI graph. The same software layer may expose multiple backends
+ * (e.g. one ONNXIFI backend for each GPU in the system, or one ONNXIFI backend
+ * for GPU and another for CPU, both implemented in the same software).
+ *
+ * @param backendID - ID of the backend to initialize.
+ * @param[in] auxPropertiesList - optional list of backend initialization
+ *                                properties, terminated by
+ *                                ONNXIFI_BACKEND_PROPERTY_NONE entry. Can be
+ *                                NULL or empty.
+ * @param[out] backend - pointer to an opaque handle for the initialized ONNXIFI
+ *                       backend. If the function fails, the handle is
+ *                       initialized to NULL.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the backend
+ *                                was successfully initialized.
+ * @retval ONNXIFI_STATUS_INVALID_ID The function call failed because backendID
+ *                                   is not an ONNXIFI backend ID.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        backend pointer is NULL.
+ * @retval ONNXIFI_STATUS_INVALID_PROPERTY The function call failed because one
+ *                                         of the backend initialization
+ *                                         property values is invalid.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_PROPERTY The function call failed because
+ *                                             backend does not recognize one
+ *                                             of the initialization
+ *                                             property IDs.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed due to
+ *                                         insufficient system memory to
+ *                                         initialize backend.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_RESOURCES The function call failed due to
+ *                                            insufficient non-memory system
+ *                                            resources (e.g. file handles) to
+ *                                            initialize the backend.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_MEMORY The function call failed due to
+ *                                         insufficient backend-specific memory
+ *                                         to initialize the backend.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_RESOURCES The function call failed due to
+ *                                            insufficient non-memory
+ *                                            backend-specific resources (e.g.
+ *                                            command queues) to initialize the
+ *                                            backend.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxInitBackend(
+    onnxBackendID backendID,
+    const uint64_t* auxPropertiesList,
+    onnxBackend* backend);
+
+/**
+ * Deinitialize an ONNXIFI backend and release associated resources.
+ *
+ * The user MUST deinitialize all objects created on this backend (onnxGraph,
+ * onnxEvent) before calling this function to deinitialize the backend.
+ *
+ * @param backend - ONNXIFI backend handle created by onnxInitBackend.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the backend
+ *                                resources were released to the operating
+ *                                system.
+ * @retval ONNXIFI_STATUS_INVALID_BACKEND The function call failed because
+ *                                        backend is not an ONNXIFI backend
+ *                                        handle.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseBackend(
+    onnxBackend backend);
+
+/**
+ * Initialize a single-shot ONNXIFI event.
+ *
+ * The newly created event is in non-signalled state.
+ *
+ * @param backend - backend handle created by onnxInitBackend. This backend
+ *                  would be used to initialize the event.
+ * @param[out] event - pointer to the opaque handle for the created ONNXIFI
+ *                     event. If the function fails, the handle is initialized
+ *                     to NULL.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the event
+ *                                was successfully initialized.
+ * @retval ONNXIFI_STATUS_INVALID_BACKEND The function call failed because
+ *                                        backend is not an ONNXIFI backend
+ *                                        handle.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        event pointer is NULL.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed due to
+ *                                         insufficient system memory to
+ *                                         initialize the event.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_RESOURCES The function call failed due to
+ *                                            insufficient non-memory system
+ *                                            resources (e.g. file handles) to
+ *                                            initialize the event.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_MEMORY The function call failed due to
+ *                                         insufficient backend-specific memory
+ *                                         to initialize the event.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_RESOURCES The function call failed due to
+ *                                            insufficient non-memory
+ *                                            backend-specific resources (e.g.
+ *                                            command queues) to initialize the
+ *                                            event.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxInitEvent(
+    onnxBackend backend,
+    onnxEvent* event);
+
+/**
+ * Change the state of an ONNXIFI event to signalled.
+ *
+ * @param event - event handle created by onnxInitEvent. While it is technically
+ *                possible to use this function for output memory fence event
+ *                created by onnxRunGraph, users SHOULD NOT do that.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the event
+ *                                was changed to signalled state.
+ * @retval ONNXIFI_STATUS_INVALID_EVENT The function call failed because event
+ *                                      is not an ONNXIFI event handle.
+ * @retval ONNXIFI_STATUS_INVALID_STATE The function call failed because event
+ *                                      is already in the signalled state.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxSignalEvent(
+    onnxEvent event);
+
+/**
+ * Query ONNXIFI event state without blocking.
+ *
+ * @param event - event handle created by onnxRunGraph. While it is technically
+ *                possible to use this function to events created by
+ *                onnxInitEvent, this is not the intended use-case.
+ * @param[out] state - pointer to the variable that will store the state of the
+ *                     event. If the function fails, the variable is initialized
+ *                     to ONNXIFI_EVENT_STATE_INVALID.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the state
+ *                                variable was initialized to either
+ *                                ONNXIFI_EVENT_STATE_SIGNALLED or
+ *                                ONNXIFI_EVENT_STATE_NONSIGNALLED according
+ *                                to the state of the event.
+ * @retval ONNXIFI_STATUS_INVALID_EVENT The function call failed because event
+ *                                      is not an ONNXIFI event handle.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because state
+ *                                        pointer is NULL.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxGetEventState(
+    onnxEvent event,
+    onnxEventState* state);
+
+/**
+ * Wait until an ONNXIFI event transitions to signalled state.
+ *
+ * @param event - event handle created by onnxRunGraph. While it is technically
+ *                possible to use this function to events created by
+ *                onnxInitEvent, this is not the intended use-case.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the function
+ *                                returned because event transitioned to
+ *                                signalled state.
+ * @retval ONNXIFI_STATUS_INVALID_EVENT The function call failed because event
+ *                                      is not an ONNXIFI event handle.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxWaitEvent(
+    onnxEvent event);
+
+/**
+ * Deinitialize an ONNXIFI event and release associated resources.
+ *
+ * @param event - event handle created by either onnxInitEvent or onnxRunGraph.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the event
+ *                                resources were released to the operating
+ *                                system.
+ * @retval ONNXIFI_STATUS_INVALID_EVENT The function call failed because event
+ *                                      is not an ONNXIFI event handle.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseEvent(
+    onnxEvent event);
+
+/**
+ * Parse an ONNXIFI graph and convert it for a particular backend.
+ *
+ * Model graph is passed as a serialized ModelProto message, where types and
+ * dimensions of all inputs (including static weights) and outputs are specified
+ * through ModelProto.graph.input and ModelProto.graph.output messages. If the
+ * backend supports ONNXIFI_CAPABILITY_SYMBOLIC_SIZE_TENSORS, some of the shape
+ * dimensions can be symbolic. If the backend supports
+ * ONNXIFI_CAPABILITY_SYMBOLIC_BATCH_SIZE, the outer shape dimension can be
+ * symbolic. In these cases, their validation should be deferred until a later
+ * call to onnxSetGraphIO.
+ *
+ * Values of all static weights of the graph must be specified either in
+ * ModelProto.graph.initializer, or through the weightDescriptors parameters,
+ * but not through any combination of the two methods. If the caller creates the
+ * graph on the fly, it SHOULD pass weights through weightDescriptors as it
+ * involves less overhead.
+ *
+ * Blobs and operators in this graph are independent of the blobs and operators
+ * of other graphs on the same backend.
+ *
+ * @param backend - backend handle created by onnxInitBackend. This backend
+ *                  would be used to setup and run the model graph.
+ * @param[in] auxPropertiesList - optional list of graph initialization
+ *                                properties, terminated by
+ *                                ONNXIFI_GRAPH_PROPERTY_NONE entry. Can be
+ *                                NULL or empty.
+ * @param onnxModelSize - size of the serialized ONNX ModelProto message,
+ *                        in bytes.
+ * @param[in] onnxModel - pointer to serialized ONNX ModelProto message
+ *                        representing the model graph. The backend MUST not
+ *                        assume that the serialized ModelProto message is
+ *                        present at this address after the function returns.
+ * @param weightsCount - number of weights specified in this function call
+ *                       through tensor descriptors. Alternatively, the weights
+ *                       can be specified in ModelProto.graph.initializer.
+ *                       If weightsCount is non-zero, weightDescriptors must be
+ *                       non-NULL.
+ * @param[in] weightDescriptors - descriptors of static input tensors for the
+ *                                graph. Elements of this array provide location
+ *                                for blobs identified by ValueInfoProto.name
+ *                                listed in ModelProto.graph.input of the ONNX
+ *                                graph. If this parameter is non-NULL,
+ *                                all static inputs must be specified through
+ *                                the tensor descriptors, and the
+ *                                ModelProto.graph.initilizer list must be
+ *                                empty. The tensor descriptors
+ *                                must use ONNXIFI_MEMORY_TYPE_CPU memory type,
+ *                                and the backend must copy the values of the
+ *                                tensors and all metadata, including shape,
+ *                                into its own memory before the function
+ *                                returns.
+ * @param[out] graph - pointer to the opaque handle for the created ONNXIFI
+ *                     graph. If the function fails, and this pointer is
+ *                     non-NULL, the handle is initialized to NULL.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the model
+ *                                graph was successfully initialized on the
+ *                                backend.
+ * @retval ONNXIFI_STATUS_FALLBACK The function call succeeded and the model
+ *                                 graph was initialized for the backend through
+ *                                 an emulation layer with substantial
+ *                                 efficiency loss. If a backend decomposes an
+ *                                 operator into multiple sub-operators, it
+ *                                 MUST return this code. E.g. if a backend
+ *                                 does not natively support grouped or
+ *                                 depthwise convolution, but can execute it as
+ *                                 multiple unit-group convolution operators, it
+ *                                 should return this code.
+ * @retval ONNXIFI_STATUS_INVALID_BACKEND The function call failed because
+ *                                        backend is not an ONNXIFI backend
+ *                                        handle.
+ * @retval ONNXIFI_STATUS_INVALID_PROPERTY The function call failed because one
+ *                                         of the graph initialization property
+ *                                         values is invalid.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        onnxModel or graph pointer is NULL, or
+ *                                        weightDescriptors pointer is NULL
+ *                                        while weightsCount is non-zero.
+ * @retval ONNXIFI_STATUS_INVALID_SIZE The function call failed because
+ *                                     onnxModelSize is 0.
+ * @retval ONNXIFI_STATUS_INVALID_PROTOBUF The function call failed because it
+ *                                         couldn't parse the serialized
+ *                                         protobuf as an ONNX ModelProto
+ *                                         message.
+ * @retval ONNXIFI_STATUS_INVALID_MODEL The function call failed because the
+ *                                      parsed ModelProto message does not
+ *                                      satisfy ONNX requirements and
+ *                                      constraints.
+ * @retval ONNXIFI_STATUS_INVALID_SHAPE The function call failed because one of
+ *                                      the shape dimensions in
+ *                                      weightDescriptors is 0.
+ * @retval ONNXIFI_STATUS_INVALID_DATATYPE The function call failed because
+ *                                         one of the data types in
+ *                                         weightDescriptors is unknown to the
+ *                                         backend.
+ * @retval ONNXIFI_STATUS_INVALID_MEMORY_TYPE The function call failed because
+ *                                            one of the memory types in
+ *                                            weightDescriptors is unknown to
+ *                                            the backend.
+ * @retval ONNXIFI_STATUS_INVALID_MEMORY_LOCATION The function call failed
+ *                                                because one of the memory
+ *                                                locations in weightDescriptors
+ *                                                is invalid (NULL pointer).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_PROPERTY The function call failed because
+ *                                             backend does not recognize one
+ *                                             of the graph initialization
+ *                                             property IDs.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_VERSION The function call failed because
+ *                                            the ONNX IR version or operator
+ *                                            version is not supported by the
+ *                                            backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_OPERATOR The function call failed because
+ *                                             one of the operators in the model
+ *                                             graph is not supported by the
+ *                                             backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_ATTRIBUTE The function call failed because
+ *                                              the backend does not support the
+ *                                              particular AttributeProto
+ *                                              values in one of the operators.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_SHAPE The function call failed because the
+ *                                          backend does not support the
+ *                                          tensor shapes in an input or
+ *                                          output of one of the operators.
+ *                                          The problematic tensor shapes could
+ *                                          be directly specified through
+ *                                          ValueInfoProto in GraphProto.input,
+ *                                          GraphProto.output, or
+ &                                          GraphProto.value_info, through
+ *                                          TensorProto in
+ *                                          GraphProto.initializer, through
+ *                                          weightDescriptors argument,
+ *                                          or inferred from the inputs by the
+ *                                          backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_DATATYPE The function call failed because
+ *                                             the backend does not support the
+ *                                             data types in an input or output
+ *                                             of one of the operators. The
+ *                                             problematic data types could be
+ *                                             directly specified through
+ *                                             ValueInfoProto in
+ *                                             GraphProto.input,
+ *                                             GraphProto.output, or
+ *                                             GraphProto.value_info, through
+ *                                             TensorProto in
+ *                                             GraphProto.initializer, through
+ *                                             weightDescriptors argument,
+ *                                             or inferred from the inputs by
+ *                                             the backend.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_MEMORY_TYPE The function call failed
+ *                                                because one of the memory
+ *                                                types in weightDescriptors is
+ *                                                different from
+ *                                                ONNXIFI_MEMORY_TYPE_CPU.
+ * @retval ONNXIFI_STATUS_MISMATCHING_SHAPE The function call failed because
+ *                                          the shapes specified in weight
+ *                                          descriptors do not match the shapes
+ *                                          specified in the ONNX model graph,
+ *                                          or output or intermediate shapes
+ *                                          specified in the ONNX model graph do
+ *                                          not match the shapes inferred from
+ *                                          input shapes.
+ * @retval ONNXIFI_STATUS_MISMATCHING_DATATYPE The function call failed because
+ *                                             data types specified in weight
+ *                                             descriptors do not match the data
+ *                                             types specified in ONNX model
+ *                                             graph, or output or intermediate
+ *                                             data types specified in the ONNX
+ *                                             model graph do not match the data
+ *                                             types inferred from graph inputs.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed because the
+ *                                         backend could not allocate enough
+ *                                         system memory to parse, analyze, and
+ *                                         initialize the model graph.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_RESOURCES The function call failed due to
+ *                                            insufficient non-memory system
+ *                                            resources (e.g. file handles) to
+ *                                            initialize the graph.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_MEMORY The function call failed due to
+ *                                         insufficient backend-specific memory
+ *                                         to initialize the graph.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_RESOURCES The function call failed due to
+ *                                            insufficient non-memory
+ *                                            backend-specific resources (e.g.
+ *                                            command queues) to initialize the
+ *                                            graph.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       implementation experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxInitGraph(
+    onnxBackend backend,
+    const uint64_t* auxPropertiesList,
+    size_t onnxModelSize,
+    const void* onnxModel,
+    uint32_t weightsCount,
+    const onnxTensorDescriptorV1* weightDescriptors,
+    onnxGraph* graph);
+
+/**
+ * Set locations for inputs and outputs of an ONNXIFI graph.
+ *
+ * The caller MUST ensure that the memory buffers specified for input and output
+ * tensors remain accessible until all in-flight graph executions which use
+ * specified buffer locations complete AND
+ * - Either a next call to onnxSetGraphIO specifies different buffer locations
+ * - Or the graph is deinitialized via onnxReleaseGraph
+ * The caller can invalidate other data in tensor descriptors, including shape,
+ * once the function returns.
+ *
+ * Calls to onnxRunGraph WILL use input and output locations specified in the
+ * preceding onnxSetGraphIO on the same graph. Asynchronous graph executions
+ * that were in-flight before onnxSetGraphIO call will continue to use buffer
+ * locations that were current when these graph executions started. An ONNXIFI
+ * implementation MAY block inside onnxSetGraphIO until all in-flight graph
+ * executions that started before the call complete.
+ *
+ * If a call to onnxSetGraphIO fails, it invalidates input and output locations
+ * for the graph, and a subsequent call to onnxRunGraph will fail with
+ * ONNXIFI_STATUS_UNIDENTIFIED_NAME.
+ *
+ * @param graph - graph handle created by onnxInitGraph.
+ * @param inputsCount - number of elements in the inputDescriptors array.
+ * @param[in] inputDescriptors - descriptors of input tensors for the graph.
+ *                               Elements of this array must provide a location
+ *                               for each ValueInfoProto.name listed in
+ *                               ModelProto.graph.input of the ONNX graph.
+ *                               If inputsCount is non-zero, inputDescriptors
+ *                               pointer must be non-NULL.
+ * @param outputsCount - number of elements in the outputDescriptors array.
+ *                       Must be greater than zero.
+ * @param[in] outputDescriptors - descriptors of output tensors for the graph.
+ *                                outputDescriptors pointer must be non-NULL.
+ *                                Elements of this array must provide a location
+ *                                for each ValueInfoProto.name listed in
+ *                                ModelProto.graph.output of the ONNX graph.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the all graph
+ *                                inputs and outputs were matched to a memory
+ *                                location.
+ * @retval ONNXIFI_STATUS_INVALID_GRAPH The function call failed because
+ *                                      graph is not an ONNXIFI graph handle.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        outputDescriptors pointer is NULL or
+ *                                        inputDescriptors pointer is NULL while
+ *                                        inputsCount is non-zero.
+ * @retval ONNXIFI_STATUS_INVALID_NAME The function call failed because one of
+ *                                     the names in tensor descriptors doesn't
+ *                                     match blob name in ModelProto.graph.input
+ *                                     or ModelProto.graph.output, or the same
+ *                                     name appears in more than one tensor
+ *                                     descriptor.
+ * @retval ONNXIFI_STATUS_INVALID_SHAPE The function call failed because one of
+ *                                      the shape dimensions is 0.
+ * @retval ONNXIFI_STATUS_INVALID_DATATYPE The function call failed because
+ *                                         one of the data types in
+ *                                         inputDescriptors or outputDescriptors
+ *                                         is unknown to the backend.
+ * @retval ONNXIFI_STATUS_INVALID_MEMORY_TYPE The function call failed because
+ *                                            one of the memory types in
+ *                                            inputDescriptors or
+ *                                            outputDescriptors is unknown to
+ *                                            the backend.
+ * @retval ONNXIFI_STATUS_INVALID_MEMORY_LOCATION The function call failed
+ *                                                because one of the memory
+ *                                                locations in inputDescriptors
+ *                                                or outputDescriptors is not
+ *                                                valid for the specified
+ *                                                memory type (e.g. NULL pointer
+ *                                                for ONNXIFI_MEMORY_TYPE_CPU).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_TAG The function call failed because one
+ *                                        of the tags in inputDescriptors or
+ *                                        outputDescriptors is unknown to the
+ *                                        backend (tag does not match
+ *                                        ONNXIFI_TAG_TENSOR_DESCRIPTOR_V1).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_SHAPE The function call failed because the
+ *                                          backend does not support the
+ *                                          tensor shapes in an input or output
+ *                                          of one of the operators. The
+ *                                          problematic tensor shapes could be
+ *                                          directly specified through
+ *                                          inputDescriptors or
+ *                                          outputDescriptors argument,
+ *                                          or inferred from the inputs by the
+ *                                          backend. This error code can be
+ *                                          returned when the backend supports
+ *                                          variable-size inputs and outputs,
+ *                                          and the problematic tensor shape was
+ *                                          provided in the ValueInfoProto as a
+ *                                          symbolic variable.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_MEMORY_TYPE The function call failed
+ *                                                because the backend does not
+ *                                                support one of the memory
+ *                                                types in inputDescriptors or
+ *                                                outputDescriptors.
+ * @retval ONNXIFI_STATUS_UNIDENTIFIED_NAME The function call failed because one
+ *                                          of the ValueInfoProto.name value in
+ *                                          ModelProto.graph.input or
+ *                                          ModelProto.graph.output doesn't have
+ *                                          a match in the inputDescriptors or
+ *                                          outputDescriptors.
+ * @retval ONNXIFI_STATUS_MISMATCHING_SHAPE The function call failed because
+ *                                          the shapes specified through
+ *                                          inputDescriptors or
+ *                                          outputDescriptors argument are
+ *                                          inconsistent with the shapes
+ *                                          specified in the ONNX model graph.
+ * @retval ONNXIFI_STATUS_MISMATCHING_DATATYPE The function call failed because
+ *                                             data types specified through
+ *                                             inputDescriptors or
+ *                                             outputDescriptors argument are
+ *                                             inconsistent with the data types
+ *                                             specified in the ONNX model
+ *                                             graph.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed because the
+ *                                         backend could not allocate enough
+ *                                         system memory to parse, analyze, and
+ *                                         initialize the tensor locations.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_RESOURCES The function call failed due to
+ *                                            insufficient non-memory system
+ *                                            resources (e.g. file handles) to
+ *                                            initialize the tensor locations.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_MEMORY The function call failed due to
+ *                                         insufficient backend-specific memory
+ *                                         to initialize the tensor locations.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_RESOURCES The function call failed due to
+ *                                            insufficient non-memory
+ *                                            backend-specific resources (e.g.
+ *                                            command queues) to initialize the
+ *                                            tensor locations.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxSetGraphIO(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors);
+
+/**
+ * Asynchronously execute operations in an ONNXIFI graph using pre-specified
+ * locations for inputs and outputs.
+ *
+ * This function operates asynchronously: it doesn't require that the locations
+ * for graph inputs graph inputs hold valid values before the function is
+ * called, and doesn't guarantee that the locations for graph outputs hold
+ * valid values when the function returns. Instead, two synchronization
+ * primitives are used to signal to the backend when inputs are ready to use,
+ * and to signal to the caller when outputs are ready to use. The only
+ * synchronization primitive that is always available is onnxEvent
+ * (ONNXIFI_SYNCHRONIZATION_EVENT memory fence type). If a backend supports
+ * additional types of synchronization primitives, it must indicate them in
+ * ONNXIFI_BACKEND_SYNCHRONIZATION_TYPES information query.
+ *
+ * The caller must successfully specify locations of input and output tensors
+ * for the graph through onnxSetGraphIO before calling this function.
+ *
+ * @param graph - graph handle created by onnxInitGraph.
+ * @param[in] inputFence - synchronization primitive that signals when graph
+ *                         inputs are ready to use by the backend. The
+ *                         synchronization primitive always must be initialized
+ *                         by the caller.
+ * @param[out] outputFence - synchronization primitive that signals when graph
+ *                           outputs are ready to use by the caller. The type
+ *                           of the synchronization primitive always must be
+ *                           initialized by the caller. The type of the
+ *                           synchronization primitive determines whether it
+ *                           is initialized by the user before the call or by
+ *                           the backend as a result of this call. Single-shot
+ *                           synchronizatiom objects are initialized as a result
+ *                           of the call. Reusable synchronization objects are
+ *                           generally initialized by the user prior to the
+ *                           call.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the all graph
+ *                                inputs and outputs were matched to a memory
+ *                                location.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        inputFence or outputFence pointer is
+ *                                        NULL.
+ * @retval ONNXIFI_STATUS_INVALID_GRAPH The function call failed because
+ *                                      graph is not an ONNXIFI graph handle.
+ * @retval ONNXIFI_STATUS_INVALID_FENCE_TYPE The function call failed because
+ *                                           the type of synchronization
+ *                                           primitive specified in inputFence
+ *                                           or outputFence is unknown to the
+ *                                           backend.
+ * @retval ONNXIFI_STATUS_INVALID_EVENT The function call failed because
+ *                                      the memory synchronization primitive
+ *                                      specified in inputFence or outputFence
+ *                                      is not valid (e.g. NULL onnxEvent).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_TAG The function call failed because a tag
+ *                                        in inputFence or outputFence is
+ *                                        unknown to the backend (tag does not
+ *                                        match ONNXIFI_TAG_MEMORY_FENCE_V1).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_FENCE_TYPE The function call failed
+ *                                               because the backend does not
+ *                                               support the type of
+ *                                               synchronization primitive
+ *                                               specified in inputFence or
+ *                                               outputFence.
+ * @retval ONNXIFI_STATUS_UNIDENTIFIED_NAME The function call failed because
+ *                                          some of the ValueInfoProto.name
+ *                                          value in ModelProto.graph.input or
+ *                                          ModelProto.graph.output were not
+ *                                          specified in a call to
+ *                                          onnxSetGraphIO.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed because the
+ *                                         backend could not allocate enough
+ *                                         system memory to execute the model
+ *                                         graph.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_RESOURCES The function call failed due to
+ *                                            insufficient non-memory system
+ *                                            resources (e.g. file handles) to
+ *                                            execute the model graph.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_MEMORY The function call failed due to
+ *                                         insufficient backend-specific memory
+ *                                         to execute the graph.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_RESOURCES The function call failed due to
+ *                                            insufficient non-memory
+ *                                            backend-specific resources (e.g.
+ *                                            command queues) to execute the
+ *                                            graph.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+  onnxRunGraph(
+    onnxGraph graph,
+    const onnxMemoryFenceV1* inputFence,
+    onnxMemoryFenceV1* outputFence);
+
+/**
+ * Deinitialize an ONNXIFI graph and release associated resources.
+ *
+ * If there are in-flight asynchronous inference operations on this graph,
+ * the function MUST block until all outstanding operations complete.
+ *
+ * @param graph - graph handle created by onnxInitGraph.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the graph
+ *                                resources were released to the operating
+ *                                system.
+ * @retval ONNXIFI_STATUS_INVALID_GRAPH The function call failed because graph
+ *                                      is not an ONNXIFI graph handle.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       graph backend experienced an
+ *                                       unrecovered internal error.
+ */
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseGraph(
+    onnxGraph graph);
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /* !defined(ONNXIFI_H) */
diff --git a/third_party/onnx/onnx/onnxifi_dummy.c b/third_party/onnx/onnx/onnxifi_dummy.c
new file mode 100644
index 0000000000..af2bca4828
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_dummy.c
@@ -0,0 +1,200 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+/*
+ * Dummy implementation of ONNX backend interface for manual test.
+ * Prints the name of the called function and backend name on each call.
+ */
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+
+#include "onnx/onnxifi.h"
+#include "onnx/onnxifi_ext.h"
+
+/*
+ * ONNXIFI Functions
+ */
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxGetBackendIDs(onnxBackendID* backendIDs, size_t* numBackends) {
+  if (backendIDs == NULL || numBackends == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *numBackends = 1;
+  *backendIDs = 0;
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxReleaseBackendID(onnxBackendID backendID) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxGetBackendInfo(
+    onnxBackendID backendID,
+    onnxBackendInfo infoType,
+    void* infoValue,
+    size_t* infoValueSize) {
+  if (infoValueSize == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  if (infoValue != NULL) {
+    *infoValueSize = *infoValueSize > 10 ? 10 : *infoValueSize;
+    memset(infoValue, 0, *infoValueSize);
+  } else {
+    *infoValueSize = sizeof(uint64_t);
+  }
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxGetBackendCompatibility(
+    onnxBackendID backendID,
+    size_t onnxModelSize,
+    const void* onnxModel) {
+  if (onnxModel == NULL && onnxModelSize != 0){
+	return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxInitBackend(
+    onnxBackendID backendID,
+    const uint64_t* auxPropertiesList,
+    onnxBackend* backend) {
+  if (backend == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *backend = NULL;
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxReleaseBackend(onnxBackend backend) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxInitEvent(onnxBackend backend, onnxEvent* event) {
+  if (event == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *event = NULL;
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxSignalEvent(onnxEvent event) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxWaitEvent(onnxEvent event) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxReleaseEvent(onnxEvent event) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxInitGraph(
+    onnxBackend backend,
+    const uint64_t* auxPropertiesList,
+    size_t onnxModelSize,
+    const void* onnxModel,
+    uint32_t weightCount,
+    const onnxTensorDescriptorV1* weightDescriptors,
+    onnxGraph* graph) {
+  if (graph == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *graph = NULL;
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxSetGraphIO(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxRunGraph(
+    onnxGraph graph,
+    const onnxMemoryFenceV1* inputFence,
+    onnxMemoryFenceV1* outputFence) {
+  if (outputFence == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  outputFence->type = ONNXIFI_SYNCHRONIZATION_EVENT;
+  outputFence->event = NULL;
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxGetEventState(onnxEvent event, onnxEventState* state) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxReleaseGraph(onnxGraph graph) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+/*
+ * ONNXIFI Extension Functions
+ */
+
+/*
+ * This is the function list and the number of functions in onnxifi_ext
+ * we have in this backend. It should be a subset of ALL_EXT_FUNCTION_LIST
+ * in onnxifi_ext.h
+ */
+const int extension_function_number = 2;
+const char* extension_function_list[] = {"onnxGetExtensionFunctionAddress",
+                                         "onnxSetIOAndRunGraph"};
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxGetExtensionFunctionAddress(
+    onnxBackendID backendID,
+    const char* name,
+    onnxExtensionFunctionPointer* function) {
+  if (name == NULL || function == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *function = NULL;
+  int i;
+  for (i = 0; i < extension_function_number; i++) {
+    /* target function found */
+    if (strcmp(name, extension_function_list[i]) == 0) {
+      switch (i) {
+        case 0:
+          *function = (void *)&onnxGetExtensionFunctionAddress;
+          break;
+        case 1:
+          *function = (void *)&onnxSetIOAndRunGraph;
+          break;
+      }
+    }
+  }
+
+  if (*function == NULL) {
+    return ONNXIFI_STATUS_UNIDENTIFIED_NAME;
+  }
+  return ONNXIFI_STATUS_SUCCESS;
+}
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxSetIOAndRunGraph(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors,
+    onnxMemoryFenceV1* outputFence) {
+  return ONNXIFI_STATUS_SUCCESS;
+}
diff --git a/third_party/onnx/onnx/onnxifi_ext.h b/third_party/onnx/onnx/onnxifi_ext.h
new file mode 100644
index 0000000000..a6ad95e0ff
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_ext.h
@@ -0,0 +1,230 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#ifndef ONNXIFI_EXT_H
+#define ONNXIFI_EXT_H 1
+
+#include "onnx/onnxifi.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * Generic ONNXIFI extension function pointer.
+ *
+ * The caller should convert this generic function pointer to the function
+ * pointer specific for an extension function type.
+ */
+typedef onnxStatus (ONNXIFI_ABI* onnxExtensionFunctionPointer)(void);
+
+/* Function pointer declarations for dynamic loading */
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxGetExtensionFunctionAddressFunction)(
+    onnxBackendID backendID,
+    const char* name,
+    onnxExtensionFunctionPointer* function);
+
+/**
+ * Query function pointer for an ONNXIFI extension function.
+ *
+ * The returned function pointer is specific to the provided backend ID, and
+ * MUST NOT be used with objects created for other backend IDs.
+ *
+ * This function is a part of onnx_extension_function extension. Backends which
+ * implement this function MUST list "onnx_extension_function" in the result of
+ * onnxGetBackendInfo with ONNXIFI_BACKEND_EXTENSIONS information type.
+ *
+ * @param backendID - ID of the backend to query for extension function.
+ * @param[in] name - name of the extension function to query.
+ * @param[out] function - pointer to a generic function pointer for an ONNXIFI
+ *                        extension function. If the function fails, the
+ *                        function pointer is initialized to NULL. The caller
+ *                        MUST cast this function pointer to the type specific
+ *                        for the extension function before use.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the extension
+ *                                function pointer is stored in the location
+ *                                specified by function argument.
+ * @retval ONNXIFI_STATUS_INVALID_ID The function call failed because backendID
+ *                                   is not an ONNXIFI backend ID.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        name or function argument is NULL.
+ * @retval ONNXIFI_STATUS_UNIDENTIFIED_NAME The function call failed because
+ *                                          the backend does not implement
+ *                                          the function identified by the name.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ */
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI
+onnxGetExtensionFunctionAddress(
+    onnxBackendID backendID,
+    const char* name,
+    onnxExtensionFunctionPointer* function);
+
+/* Extension function pointer declarations for dynamic loading */
+typedef ONNXIFI_CHECK_RESULT onnxStatus
+  (ONNXIFI_ABI* onnxSetIOAndRunGraphFunction)(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors,
+    onnxMemoryFenceV1* outputFence);
+
+/**
+ * A combination of onnxSetIO and onnxRunGraph, functionally equals to first run
+ * onnxSetIO(graph, inputsCount, inputDescriptors, outputsCount,
+ * outputDescriptors), then run onnxRunGraph(graph, inputFence, outputFence)
+ * with an internal inputFence.
+ *
+ * As two separate functions, it is difficult to do atomic evaluation.
+ * Therefore, we would like to unify this process and make it evaluable.
+ *
+ * @param graph - graph handle created by onnxInitGraph.
+ * @param inputsCount - number of elements in the inputDescriptors array.
+ * @param[in] inputDescriptors - descriptors of input tensors for the graph.
+ *                               Elements of this array must provide a location
+ *                               for each ValueInfoProto.name listed in
+ *                               ModelProto.graph.input of the ONNX graph.
+ *                               If inputsCount is non-zero, inputDescriptors
+ *                               pointer must be non-NULL.
+ * @param outputsCount - number of elements in the outputDescriptors array.
+ *                       Must be greater than zero.
+ * @param[in] outputDescriptors - descriptors of output tensors for the graph.
+ *                                outputDescriptors pointer must be non-NULL.
+ *                                Elements of this array must provide a location
+ *                                for each ValueInfoProto.name listed in
+ *                                ModelProto.graph.output of the ONNX graph.
+ * @param[out] outputFence - synchronization primitive that signals when graph
+ *                           outputs are ready to use by the caller. The type
+ *                           of the synchronization primitive always must be
+ *                           initialized by the caller. The type of the
+ *                           synchronization primitive determines whether it
+ *                           is initialized by the user before the call or by
+ *                           the backend as a result of this call. Single-shot
+ *                           synchronizatiom objects are initialized as a result
+ *                           of the call. Reusable synchronization objects are
+ *                           generally initialized by the user prior to the
+ *                           call.
+ *
+ * @retval ONNXIFI_STATUS_SUCCESS The function call succeeded and the all graph
+ *                                inputs and outputs were matched to a memory
+ *                                location.
+ * @retval ONNXIFI_STATUS_INVALID_GRAPH The function call failed because
+ *                                      graph is not an ONNXIFI graph handle.
+ * @retval ONNXIFI_STATUS_INVALID_POINTER The function call failed because
+ *                                        outputDescriptors pointer is NULL or
+ *                                        inputDescriptors pointer is NULL while
+ *                                        inputsCount is non-zero.
+ * @retval ONNXIFI_STATUS_INVALID_NAME The function call failed because one of
+ *                                     the names in tensor descriptors doesn't
+ *                                     match blob name in ModelProto.graph.input
+ *                                     or ModelProto.graph.output, or the same
+ *                                     name appears in more than one tensor
+ *                                     descriptor.
+ * @retval ONNXIFI_STATUS_INVALID_SHAPE The function call failed because one of
+ *                                      the shape dimensions is 0.
+ * @retval ONNXIFI_STATUS_INVALID_DATATYPE The function call failed because
+ *                                         one of the data types in
+ *                                         inputDescriptors or outputDescriptors
+ *                                         is unknown to the backend.
+ * @retval ONNXIFI_STATUS_INVALID_MEMORY_TYPE The function call failed because
+ *                                            one of the memory types in
+ *                                            inputDescriptors or
+ *                                            outputDescriptors is unknown to
+ *                                            the backend.
+ * @retval ONNXIFI_STATUS_INVALID_MEMORY_LOCATION The function call failed
+ *                                                because one of the memory
+ *                                                locations in inputDescriptors
+ *                                                or outputDescriptors is not
+ *                                                valid for the specified
+ *                                                memory type (e.g. NULL pointer
+ *                                                for ONNXIFI_MEMORY_TYPE_CPU).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_TAG The function call failed because one
+ *                                        of the tags in inputDescriptors or
+ *                                        outputDescriptors is unknown to the
+ *                                        backend (tag does not match
+ *                                        ONNXIFI_TAG_TENSOR_DESCRIPTOR_V1).
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_SHAPE The function call failed because the
+ *                                          backend does not support the
+ *                                          tensor shapes in an input or output
+ *                                          of one of the operators. The
+ *                                          problematic tensor shapes could be
+ *                                          directly specified through
+ *                                          inputDescriptors or
+ *                                          outputDescriptors argument,
+ *                                          or inferred from the inputs by the
+ *                                          backend. This error code can be
+ *                                          returned when the backend supports
+ *                                          variable-size inputs and outputs,
+ *                                          and the problematic tensor shape was
+ *                                          provided in the ValueInfoProto as a
+ *                                          symbolic variable.
+ * @retval ONNXIFI_STATUS_UNSUPPORTED_MEMORY_TYPE The function call failed
+ *                                                because the backend does not
+ *                                                support one of the memory
+ *                                                types in inputDescriptors or
+ *                                                outputDescriptors.
+ * @retval ONNXIFI_STATUS_UNIDENTIFIED_NAME The function call failed because one
+ *                                          of the ValueInfoProto.name value in
+ *                                          ModelProto.graph.input or
+ *                                          ModelProto.graph.output doesn't have
+ *                                          a match in the inputDescriptors or
+ *                                          outputDescriptors.
+ * @retval ONNXIFI_STATUS_MISMATCHING_SHAPE The function call failed because
+ *                                          the shapes specified through
+ *                                          inputDescriptors or
+ *                                          outputDescriptors argument are
+ *                                          inconsistent with the shapes
+ *                                          specified in the ONNX model graph.
+ * @retval ONNXIFI_STATUS_MISMATCHING_DATATYPE The function call failed because
+ *                                             data types specified through
+ *                                             inputDescriptors or
+ *                                             outputDescriptors argument are
+ *                                             inconsistent with the data types
+ *                                             specified in the ONNX model
+ *                                             graph.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_MEMORY The function call failed because the
+ *                                         backend could not allocate enough
+ *                                         system memory to parse, analyze, and
+ *                                         initialize the tensor locations.
+ * @retval ONNXIFI_STATUS_NO_SYSTEM_RESOURCES The function call failed due to
+ *                                            insufficient non-memory system
+ *                                            resources (e.g. file handles) to
+ *                                            initialize the tensor locations.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_MEMORY The function call failed due to
+ *                                         insufficient backend-specific memory
+ *                                         to initialize the tensor locations.
+ * @retval ONNXIFI_STATUS_NO_DEVICE_RESOURCES The function call failed due to
+ *                                            insufficient non-memory
+ *                                            backend-specific resources (e.g.
+ *                                            command queues) to initialize the
+ *                                            tensor locations.
+ * @retval ONNXIFI_STATUS_BACKEND_UNAVAILABLE The function call failed because
+ *                                            the backend was disconnected or
+ *                                            uninstalled from the system.
+ * @retval ONNXIFI_STATUS_INTERNAL_ERROR The function call failed because the
+ *                                       backend experienced an unrecovered
+ *                                       internal error.
+ */
+
+ONNXIFI_PUBLIC ONNXIFI_CHECK_RESULT onnxStatus ONNXIFI_ABI onnxSetIOAndRunGraph(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors,
+    onnxMemoryFenceV1* outputFence);
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /* !defined(ONNXIFI_EXT_H) */
diff --git a/third_party/onnx/onnx/onnxifi_loader.c b/third_party/onnx/onnx/onnxifi_loader.c
new file mode 100644
index 0000000000..7759f06838
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_loader.c
@@ -0,0 +1,216 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#ifdef _WIN32
+#include <windows.h>
+#else
+#include <stdlib.h>
+#include <string.h>
+#include <stdio.h>
+#include <dlfcn.h>
+#endif
+
+#include "onnx/onnxifi_loader.h"
+
+/* ONNXIFI_LOADER_LOGGING macro enables/disables logging. Its OFF by default. */
+#ifndef ONNXIFI_LOADER_LOGGING
+#define ONNXIFI_LOADER_LOGGING 0
+#endif
+
+#if ONNXIFI_LOADER_LOGGING
+#if defined(__ANDROID__)
+#include <android/log.h>
+/* Tag used for logging on Android */
+#define ONNXIFI_LOADER_ANDROID_LOG_TAG "ONNXIFI-LOADER"
+#else
+#include <stdio.h>
+#endif
+#endif
+
+#if defined(__APPLE__)
+#define ONNXIFI_LIBRARY_NAME "libonnxifi.dylib"
+#elif defined(_WIN32)
+#define ONNXIFI_LIBRARY_NAME L"onnxifi.dll"
+#else
+#define ONNXIFI_LIBRARY_NAME "libonnxifi.so"
+#endif
+
+/* Order must match declaration order in onnxifi_library structure */
+static const char onnxifi_function_names[] =
+    "onnxGetBackendIDs\0"
+    "onnxReleaseBackendID\0"
+    "onnxGetBackendInfo\0"
+    "onnxGetBackendCompatibility\0"
+    "onnxInitBackend\0"
+    "onnxReleaseBackend\0"
+    "onnxInitEvent\0"
+    "onnxSignalEvent\0"
+    "onnxGetEventState\0"
+    "onnxWaitEvent\0"
+    "onnxReleaseEvent\0"
+    "onnxInitGraph\0"
+    "onnxSetGraphIO\0"
+    "onnxRunGraph\0"
+    "onnxReleaseGraph\0"
+#ifdef ONNXIFI_ENABLE_EXT
+    "onnxGetExtensionFunctionAddress\0"
+#endif
+    ;
+
+int ONNXIFI_ABI onnxifi_load(
+  uint32_t flags,
+#ifdef _WIN32
+  const wchar_t* path,
+#else
+  const char* path,
+#endif
+  struct onnxifi_library* onnx)
+{
+  size_t i;
+  const char* function_name;
+
+  if (onnx == NULL) {
+    return 0;
+  }
+
+#ifdef _WIN32
+  ZeroMemory(onnx, sizeof(struct onnxifi_library));
+#else
+  memset(onnx, 0, sizeof(struct onnxifi_library));
+#endif
+  if (!(flags & ONNXIFI_LOADER_FLAG_VERSION_1_0)) {
+    /* Unknown ONNXIFI version requested */
+    return 0;
+  }
+
+  if (path == NULL) {
+    path = ONNXIFI_LIBRARY_NAME;
+  }
+
+#ifdef _WIN32
+  onnx->handle = (void*) LoadLibraryExW(path, NULL, LOAD_LIBRARY_SEARCH_DEFAULT_DIRS);
+#else
+  /* Clear libdl error state */
+  dlerror();
+  onnx->handle = dlopen(path, RTLD_NOW | RTLD_LOCAL);
+#endif
+  if (onnx->handle == NULL) {
+#if ONNXIFI_LOADER_LOGGING
+#if defined(__ANDROID__)
+    __android_log_print(
+      ANDROID_LOG_ERROR,
+      ONNXIFI_LOADER_ANDROID_LOG_TAG,
+      "failed to load %s: %s",
+      path, dlerror());
+#elif defined(_WIN32)
+    fprintf(
+      stderr,
+      "Error: failed to load %S: error %u\n",
+      path, (unsigned long) GetLastError());
+#else
+    fprintf(stderr, "Error: failed to load %s: %s\n",
+      path, dlerror());
+#endif
+#endif /* ONNXIFI_LOADER_LOGGING */
+
+    goto failed;
+  }
+
+  function_name = onnxifi_function_names;
+  for (i = 0; i < ONNXIFI_LOADER_FUNCTION_COUNT; i++) {
+#ifdef _WIN32
+    onnx->functions[i] = GetProcAddress((HMODULE) onnx->handle, function_name);
+#else
+    onnx->functions[i] = dlsym(onnx->handle, function_name);
+#endif
+
+    if (onnx->functions[i] == NULL) {
+#if ONNXIFI_LOADER_LOGGING
+#if defined(__ANDROID__)
+      __android_log_print(
+        ANDROID_LOG_ERROR,
+        ONNXIFI_LOADER_ANDROID_LOG_TAG,
+        "failed to find function %s in %s: %s",
+        function_name,
+        ONNXIFI_LIBRARY_NAME,
+        dlerror());
+#elif defined(_WIN32)
+      fprintf(
+        stderr,
+        "Error: failed to find function %s in %s: error %u\n",
+        function_name,
+        ONNXIFI_LIBRARY_NAME,
+        (unsigned long) GetLastError());
+#else
+      fprintf(
+        stderr,
+        "Error: failed to find function %s in %s: %s\n",
+        function_name,
+        ONNXIFI_LIBRARY_NAME,
+        dlerror());
+#endif
+#endif /* ONNXIFI_LOADER_LOGGING */
+
+      goto failed;
+    }
+#ifdef _WIN32
+    function_name += lstrlenA(function_name);
+#else
+    function_name += strlen(function_name);
+#endif
+    /* Skip null-terminator */
+    function_name += 1;
+  }
+
+  onnx->flags = flags & ONNXIFI_LOADER_FLAG_VERSION_MASK;
+  return 1;
+
+failed:
+  onnxifi_unload(onnx);
+  return 0;
+}
+
+void ONNXIFI_ABI onnxifi_unload(struct onnxifi_library* onnx) {
+  if (onnx != NULL) {
+    if (onnx->handle != NULL) {
+#ifdef _WIN32
+      if (FreeLibrary((HMODULE) onnx->handle) == FALSE) {
+#if ONNXIFI_LOADER_LOGGING
+        fprintf(
+          stderr,
+          "Error: failed to unload library %s: error %u\n",
+          ONNXIFI_LIBRARY_NAME,
+          (unsigned long) GetLastError());
+#endif /* ONNXIFI_LOADER_LOGGING */
+      }
+#else /* !defined(_WIN32) */
+      /* Clear libdl error state */
+      dlerror();
+      if (dlclose(onnx->handle) != 0) {
+#if ONNXIFI_LOADER_LOGGING
+#if defined(__ANDROID__)
+        __android_log_print(
+          ANDROID_LOG_ERROR,
+          ONNXIFI_LOADER_ANDROID_LOG_TAG,
+          "failed to unload %s: %s",
+          ONNXIFI_LIBRARY_NAME,
+          dlerror());
+#else
+        fprintf(
+          stderr,
+          "Error: failed to unload %s: %s\n",
+          ONNXIFI_LIBRARY_NAME,
+          dlerror());
+#endif
+#endif /* ONNXIFI_LOADER_LOGGING */
+      }
+#endif /* !defined(_WIN32) */
+    }
+#ifdef _WIN32
+    ZeroMemory(onnx, sizeof(struct onnxifi_library));
+#else
+    memset(onnx, 0, sizeof(struct onnxifi_library));
+#endif
+  }
+}
diff --git a/third_party/onnx/onnx/onnxifi_loader.h b/third_party/onnx/onnx/onnxifi_loader.h
new file mode 100644
index 0000000000..c9d77103ff
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_loader.h
@@ -0,0 +1,113 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#ifndef ONNXIFI_LOADER_H
+#define ONNXIFI_LOADER_H 1
+
+#include "onnx/onnxifi.h"
+#include "onnx/onnxifi_ext.h"
+
+#ifdef ONNXIFI_ENABLE_EXT
+#define ONNXIFI_LOADER_FUNCTION_COUNT 16
+#else
+#define ONNXIFI_LOADER_FUNCTION_COUNT 15
+#endif
+
+#define ONNXIFI_LOADER_FLAG_VERSION_MASK 0xFF
+#define ONNXIFI_LOADER_FLAG_VERSION_1_0 0x01
+
+#ifndef ONNXIFI_HIDDEN
+#if defined(__ELF__)
+#define ONNXIFI_HIDDEN __attribute__((__visibility__("hidden")))
+#elif defined(__MACH__)
+#define ONNXIFI_HIDDEN __attribute__((__visibility__("hidden")))
+#else
+#define ONNXIFI_HIDDEN
+#endif
+#endif /* !defined(ONNXIFI_HIDDEN) */
+
+struct onnxifi_library {
+  /*
+   * Opaque handle for the loaded ONNXIFI library.
+   *
+   * Note: this is the value returned from LoadLibraryW (on Windows), or
+   * dlopen (on other operating systems and environments).
+   */
+  void* handle;
+  /*
+   * Options used for dynamic loading of the ONNXIFI library and its API
+   * functions. These are the options passed in flags parameter to onnxifi_load.
+   */
+  uint32_t flags;
+  union {
+    struct {
+      onnxGetBackendIDsFunction onnxGetBackendIDs;
+      onnxReleaseBackendIDFunction onnxReleaseBackendID;
+      onnxGetBackendInfoFunction onnxGetBackendInfo;
+      onnxGetBackendCompatibilityFunction onnxGetBackendCompatibility;
+      onnxInitBackendFunction onnxInitBackend;
+      onnxReleaseBackendFunction onnxReleaseBackend;
+      onnxInitEventFunction onnxInitEvent;
+      onnxSignalEventFunction onnxSignalEvent;
+      onnxGetEventStateFunction onnxGetEventState;
+      onnxWaitEventFunction onnxWaitEvent;
+      onnxReleaseEventFunction onnxReleaseEvent;
+      onnxInitGraphFunction onnxInitGraph;
+      onnxSetGraphIOFunction onnxSetGraphIO;
+      onnxRunGraphFunction onnxRunGraph;
+      onnxReleaseGraphFunction onnxReleaseGraph;
+#ifdef ONNXIFI_ENABLE_EXT
+      onnxGetExtensionFunctionAddressFunction onnxGetExtensionFunctionAddress;
+#endif
+    };
+    void* functions[ONNXIFI_LOADER_FUNCTION_COUNT];
+  };
+};
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * Dynamically load the ONNXIFI library.
+ *
+ * @param flags - options for dynamic loading of the ONNXIFI library.
+ *                The ONNXIFI_LOADER_FLAG_VERSION_MASK part of the flags
+ *                specifies which ONNX API function should be loaded. The only
+ *                currently supported value is ONNXIFI_LOADER_FLAG_VERSION_1_0.
+ * @param path - optional path to the ONNXIFI library to load.
+ *               If this argument is null, the default path is used.
+ * @param[out] library - the structure representing the dynamic library and
+ *                       its API functions. On success, this structure will
+ *                       be initialized with valid pointers to implentation.
+ *                       On failure, this structure will be zero-initialized.
+ *
+ * @return Non-zero if the function succeeds, or zero if the function fails.
+ */
+ONNXIFI_HIDDEN int ONNXIFI_ABI onnxifi_load(
+  uint32_t flags,
+#ifdef _WIN32
+  const wchar_t* path,
+#else
+  const char* path,
+#endif
+  struct onnxifi_library* library);
+
+/**
+ * Unload the dynamically loaded ONNXIFI library.
+ *
+ * @param[in,out] library - the structure representing the dynamic library and
+ *                          its API functions. If this structure is
+ *                          zero-initialized, the function does nothing.
+ *                          The function zero-initialized the structure before
+ *                          returning.
+ */
+ONNXIFI_HIDDEN void ONNXIFI_ABI onnxifi_unload(
+  struct onnxifi_library* library);
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /* !defined(ONNXIFI_LOADER_H) */
diff --git a/third_party/onnx/onnx/onnxifi_utils.cc b/third_party/onnx/onnx/onnxifi_utils.cc
new file mode 100644
index 0000000000..fa5a29a807
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_utils.cc
@@ -0,0 +1,27 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnxifi_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace testing {
+
+onnxTensorDescriptorV1 ProtoToOnnxTensorDescriptor(
+    const ONNX_NAMESPACE::TensorProto& proto_tensor,
+    std::vector<std::vector<uint64_t>>& shape_pool) {
+  onnxTensorDescriptorV1 onnx_tensor;
+  onnx_tensor.tag = ONNXIFI_TAG_TENSOR_DESCRIPTOR_V1;
+  onnx_tensor.name = proto_tensor.name().c_str();
+  onnx_tensor.dataType = proto_tensor.data_type();
+  onnx_tensor.memoryType = ONNXIFI_MEMORY_TYPE_CPU;
+  std::vector<uint64_t> shape_values(
+      proto_tensor.dims().begin(), proto_tensor.dims().end());
+  shape_pool.emplace_back(std::move(shape_values));
+  onnx_tensor.dimensions = (uint32_t)shape_pool.back().size();
+  onnx_tensor.shape = shape_pool.back().data();
+  onnx_tensor.buffer = (onnxPointer)proto_tensor.raw_data().data();
+  return onnx_tensor;
+}
+} // namespace testing
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/onnxifi_utils.h b/third_party/onnx/onnx/onnxifi_utils.h
new file mode 100644
index 0000000000..66a5a59220
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_utils.h
@@ -0,0 +1,19 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <sstream>
+#include <string>
+
+#include "onnx/onnx_pb.h"
+#include "onnx/onnxifi.h"
+#include "onnx/proto_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace testing {
+
+onnxTensorDescriptorV1 ProtoToOnnxTensorDescriptor(
+    const ONNX_NAMESPACE::TensorProto& proto_tensor,
+    std::vector<std::vector<uint64_t>>& shape_pool);
+} // namespace testing
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/onnxifi_wrapper.c b/third_party/onnx/onnx/onnxifi_wrapper.c
new file mode 100644
index 0000000000..736868cd7a
--- /dev/null
+++ b/third_party/onnx/onnx/onnxifi_wrapper.c
@@ -0,0 +1,987 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+/*
+ * ONNX wrapper api discovers vendor-specific implementations installed in
+ * the system and exposes them under a single interface.
+ *
+ *
+ *
+ */
+
+#include <stdlib.h>
+#include <stdint.h>
+#include <stdbool.h>
+#include <assert.h>
+#include <stdio.h>
+#ifdef _WIN32
+  #include <windows.h>
+#else
+  #include <string.h>
+  #include <pthread.h>
+  #include <dirent.h>
+  #include <errno.h>
+#endif
+
+#include <onnx/onnxifi.h>
+#include <onnx/onnxifi_loader.h>
+
+#if defined(_WIN32)
+#define ONNXIFI_FILENAME_WILDCARD L"\\onnxifi-*.dll"
+#define ONNXIFI_FILENAME_WILDCARD_LENGTH 14
+#elif defined(__APPLE__)
+/* Minimum filename: "libonnxifi-?.dylib" */
+#define ONNXIFI_FILENAME_MIN 18
+#define ONNXIFI_FILENAME_PREFIX "libonnxifi-"
+#define ONNXIFI_FILENAME_SUFFIX ".dylib"
+#else
+/* Minimum filename: "libonnxifi-?.so" */
+#define ONNXIFI_FILENAME_MIN 15
+#define ONNXIFI_FILENAME_PREFIX "libonnxifi-"
+#define ONNXIFI_FILENAME_SUFFIX ".so"
+#endif
+
+#define ONNXIFI_BACKEND_ID_MAGIC UINT32_C(0x2EDD3764)
+#define ONNXIFI_BACKEND_MAGIC    UINT32_C(0x4B9B2902)
+#define ONNXIFI_GRAPH_MAGIC      UINT32_C(0xD9ACFACD)
+#define ONNXIFI_EVENT_MAGIC      UINT32_C(0x18C1D735)
+
+struct onnxifi_backend_id_wrapper {
+  uint32_t magic;
+  onnxBackendID backend_id;
+  struct onnxifi_library* library;
+};
+
+struct onnxifi_backend_wrapper {
+  uint32_t magic;
+  onnxBackend backend;
+  struct onnxifi_library* library;
+};
+
+struct onnxifi_graph_wrapper {
+  uint32_t magic;
+  onnxGraph graph;
+  struct onnxifi_library* library;
+};
+
+struct onnxifi_event_wrapper {
+  uint32_t magic;
+  onnxEvent event;
+  struct onnxifi_library* library;
+};
+
+static struct onnxifi_library* libraries = NULL;
+static uint32_t num_libraries = 0;
+
+#ifdef _WIN32
+static INIT_ONCE init_guard = INIT_ONCE_STATIC_INIT;
+
+static BOOL CALLBACK load_all_windows_backends(
+  PINIT_ONCE init_once,
+  PVOID parameter,
+  PVOID* context)
+{
+  WCHAR* onnxifi_library_wildcard = NULL;
+  HANDLE find_file_handle = INVALID_HANDLE_VALUE;
+  WIN32_FIND_DATAW find_file_data;
+
+  UINT system_directory_path_length = GetSystemDirectoryW(NULL, 0);
+  if (system_directory_path_length == 0) {
+    fprintf(stderr, "Error: failed to get system directory path: %u\n",
+      (unsigned int) GetLastError());
+    goto cleanup;
+  }
+
+  onnxifi_library_wildcard = malloc(sizeof(WCHAR) *
+    (system_directory_path_length + ONNXIFI_FILENAME_WILDCARD_LENGTH + 1));
+  if (onnxifi_library_wildcard == NULL) {
+    fprintf(stderr,
+      "Error: failed to allocate %Iu bytes for ONNXIFI path\n",
+      sizeof(WCHAR) *
+      (system_directory_path_length + ONNXIFI_FILENAME_WILDCARD_LENGTH + 1));
+    goto cleanup;
+  }
+
+  if (GetSystemDirectoryW(
+      onnxifi_library_wildcard, system_directory_path_length) == 0)
+  {
+    fprintf(stderr, "Error: failed to get system directory path: %u\n",
+      (unsigned int) GetLastError());
+    goto cleanup;
+  }
+
+  memcpy(onnxifi_library_wildcard + system_directory_path_length,
+    ONNXIFI_FILENAME_WILDCARD,
+    sizeof(WCHAR) * (ONNXIFI_FILENAME_WILDCARD_LENGTH + 1));
+
+  find_file_handle = FindFirstFileW(onnxifi_library_wildcard, &find_file_data);
+  if (find_file_handle == INVALID_HANDLE_VALUE) {
+    const DWORD error = GetLastError();
+    if (error != ERROR_FILE_NOT_FOUND) {
+      fprintf(stderr,
+        "Error: failed to list ONNXIFI libraries %S: error %u\n",
+        onnxifi_library_wildcard, (unsigned int) error);
+    }
+    goto cleanup;
+  }
+
+  for (;;) {
+    struct onnxifi_library library;
+    if (!onnxifi_load(ONNXIFI_LOADER_FLAG_VERSION_1_0,
+        find_file_data.cFileName, &library))
+    {
+      fprintf(stderr, "Error: failed to load library %S\n",
+        find_file_data.cFileName);
+      continue;
+    }
+
+    struct onnxifi_library* new_libraries =
+      realloc(libraries, (num_libraries + 1) * sizeof(struct onnxifi_library));
+    if (new_libraries == NULL) {
+      fprintf(stderr, "Error: failed to allocate space for library %S\n",
+        find_file_data.cFileName);
+      onnxifi_unload(&library);
+      continue;
+    }
+
+    /* All actions for the new library succeeded, commit changes */
+    libraries = new_libraries;
+    memcpy(&libraries[num_libraries], &library, sizeof(library));
+    num_libraries++;
+
+    if (FindNextFileW(find_file_handle, &find_file_data) != FALSE) {
+      const DWORD error = GetLastError();
+      if (error != ERROR_NO_MORE_FILES) {
+        fprintf(stderr,
+          "Error: failed to some of ONNXIFI libraries %S: error %u\n",
+          onnxifi_library_wildcard, (unsigned int) error);
+      }
+      break;
+    }
+  }
+
+cleanup:
+  if (find_file_handle != INVALID_HANDLE_VALUE) {
+    FindClose(find_file_handle);
+  }
+  free(onnxifi_library_wildcard);
+  return TRUE;
+}
+#else
+static pthread_once_t init_guard = PTHREAD_ONCE_INIT;
+
+#ifndef ONNXIFI_SEARCH_DIR
+#ifdef __APPLE__
+#define ONNXIFI_SEARCH_DIR "/opt/onnx/lib/"
+#else
+#define ONNXIFI_SEARCH_DIR "/usr/lib/"
+#endif
+#endif
+
+/* Finds filename in a null-terminated file path */
+static inline const char* find_filename(const char* filepath) {
+  const char* filename_separator = strrchr(filepath, '/');
+  if (filename_separator == NULL) {
+    return filepath;
+  } else {
+    return filename_separator + 1;
+  }
+}
+
+static inline bool is_onnxifi_path(const char* filepath) {
+  const char* filename = find_filename(filepath);
+
+  const size_t filename_length = strlen(filename);
+  if (filename_length < ONNXIFI_FILENAME_MIN) {
+    /* Filename too short */
+    return false;
+  }
+  const char* filename_end = filename + filename_length;
+
+  /* Expected filename structure: <prefix><backend><suffix> */
+  if (memcmp(filename,
+      ONNXIFI_FILENAME_PREFIX,
+      strlen(ONNXIFI_FILENAME_PREFIX)) != 0)
+  {
+    /* Prefix mismatch */
+    return false;
+  }
+
+  const char* suffix = filename_end - strlen(ONNXIFI_FILENAME_SUFFIX);
+  if (memcmp(suffix,
+      ONNXIFI_FILENAME_SUFFIX,
+      strlen(ONNXIFI_FILENAME_SUFFIX)) != 0)
+  {
+    /* Suffix mismatch */
+    return false;
+  }
+
+  return true;
+}
+
+static void load_all_posix_backends(void) {
+  DIR* directory = opendir(ONNXIFI_SEARCH_DIR);
+  if (directory == NULL) {
+    fprintf(stderr, "Error: failed to open directory %s: %s\n",
+      ONNXIFI_SEARCH_DIR, strerror(errno));
+    return;
+  }
+
+  for (;;) {
+    /* Required to distinguish between error and end of directory in readdir */
+    errno = 0;
+
+    struct dirent* entry = readdir(directory);
+    if (entry == NULL) {
+      if (errno != 0) {
+        fprintf(stderr, "Error: failed to read directory %s: %s\n",
+          ONNXIFI_SEARCH_DIR, strerror(errno));
+      }
+      goto cleanup;
+    }
+
+    if (!is_onnxifi_path(entry->d_name)) {
+      continue;
+    }
+
+    struct onnxifi_library library;
+    if (!onnxifi_load(ONNXIFI_LOADER_FLAG_VERSION_1_0, entry->d_name, &library))
+    {
+      fprintf(stderr, "Error: failed to load library %s\n", entry->d_name);
+      continue;
+    }
+
+    struct onnxifi_library* new_libraries =
+      realloc(libraries, (num_libraries + 1) * sizeof(struct onnxifi_library));
+    if (new_libraries == NULL) {
+      fprintf(stderr, "Error: failed to allocate space for library %s\n",
+        entry->d_name);
+      onnxifi_unload(&library);
+      continue;
+    }
+
+    /* All actions for the new library succeeded, commit changes */
+    libraries = new_libraries;
+    memcpy(&libraries[num_libraries], &library, sizeof(library));
+    num_libraries++;
+  }
+
+cleanup:
+  if (directory != NULL) {
+    if (closedir(directory) != 0) {
+      /* Error */
+      fprintf(stderr, "Warning: failed to close directory %s: %s\n",
+        ONNXIFI_SEARCH_DIR, strerror(errno));
+    }
+  }
+}
+#endif
+
+/* Platform-independent wrapper for init_once */
+static void load_all_libraries_once(void) {
+#ifdef _WIN32
+  InitOnceExecuteOnce(&init_guard, load_all_windows_backends, NULL, NULL);
+#else
+  pthread_once(&init_guard, load_all_posix_backends);
+#endif
+}
+
+static onnxStatus wrap_backend_ids(
+  struct onnxifi_library* library,
+  size_t num_backends,
+  onnxBackendID* backend_ids)
+{
+  size_t num_wrapped_backends = 0;
+  for (; num_wrapped_backends < num_backends; num_wrapped_backends++) {
+    struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+      (struct onnxifi_backend_id_wrapper*)
+        malloc(sizeof(struct onnxifi_backend_id_wrapper));
+    if (backend_id_wrapper == NULL) {
+      goto cleanup;
+    }
+
+    backend_id_wrapper->magic      = ONNXIFI_BACKEND_ID_MAGIC;
+    backend_id_wrapper->backend_id = backend_ids[num_wrapped_backends];
+    backend_id_wrapper->library    = library;
+
+    /* Replace backend ID with its wrapper */
+    backend_ids[num_wrapped_backends] = (onnxBackendID) backend_id_wrapper;
+  }
+  return ONNXIFI_STATUS_SUCCESS;
+
+cleanup:
+  /* Unwrap all the backends */
+  for (size_t i = 0; i < num_wrapped_backends; i++) {
+    struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+      (struct onnxifi_backend_id_wrapper*) backend_ids[i];
+    assert(backend_id_wrapper->magic == ONNXIFI_BACKEND_ID_MAGIC);
+
+    /* Replace wrapper with the wrapped backend ID */
+    backend_ids[i] = backend_id_wrapper->backend_id;
+
+    /* Safety precaution to avoid use-after-free bugs */
+    memset(backend_id_wrapper, 0, sizeof(struct onnxifi_backend_id_wrapper));
+    free(backend_id_wrapper);
+  }
+  return ONNXIFI_STATUS_NO_SYSTEM_MEMORY;
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxGetBackendIDs(
+    onnxBackendID* backendIDs,
+    size_t* numBackends)
+{
+  load_all_libraries_once();
+  onnxStatus status;
+
+  /* Number of backend IDs requested to be stored by the caller */
+  const size_t num_expected_ids = (backendIDs == NULL) ? 0 : *numBackends;
+  /* Number of backend IDs in the system */
+  size_t num_available_ids = 0;
+  /* Number of backend IDs wrapped and ready to return */
+  size_t num_wrapped_ids = 0;
+  onnxBackendID* backend_ids = NULL;
+  if (num_expected_ids != 0) {
+    backend_ids = malloc(num_expected_ids * sizeof(onnxBackendID));
+    if (backend_ids == NULL) {
+      status = ONNXIFI_STATUS_NO_SYSTEM_MEMORY;
+      goto error;
+    } else {
+      /* Safety precaution to avoid dangling pointer bugs */
+      memset(backend_ids, 0, num_expected_ids * sizeof(onnxBackendID));
+    }
+  }
+
+  for (size_t l = 0; l < num_libraries; l++) {
+    if (num_expected_ids > num_available_ids) {
+      /* Query and wrap backend IDs from ONNXIFI library */
+      const size_t max_library_ids = num_expected_ids - num_available_ids;
+      size_t num_library_ids = max_library_ids;
+      status = libraries[l].onnxGetBackendIDs(
+        &backend_ids[num_available_ids],
+        &num_library_ids);
+      const size_t num_stored_ids =
+        (num_library_ids < max_library_ids) ? num_library_ids : max_library_ids;
+      switch (status) {
+        case ONNXIFI_STATUS_SUCCESS:
+        case ONNXIFI_STATUS_FALLBACK:
+          status = wrap_backend_ids(
+            &libraries[l],
+            num_stored_ids,
+            &backend_ids[num_available_ids]);
+          if (status != ONNXIFI_STATUS_SUCCESS) {
+            /* Release unwrapped backends for this library */
+            for (size_t i = 0; i < num_stored_ids; i++) {
+              (void) libraries[l].onnxReleaseBackendID(backend_ids[i]);
+
+              /* Safety precaution to avoid use-after-free bugs */
+              backend_ids[i] = NULL;
+            }
+            /* Release wrapped backends for other libraries */
+            goto error;
+          }
+
+          num_wrapped_ids += num_stored_ids;
+          num_available_ids += num_library_ids;
+          break;
+        default:
+          /* Release wrapped backends for other libraries */
+          goto error;
+      }
+    } else {
+      /* Not enough space in user-provided buffer: only count the backend IDs */
+      size_t num_library_ids = 0;
+      status = libraries[l].onnxGetBackendIDs(NULL, &num_library_ids);
+      if (status != ONNXIFI_STATUS_FALLBACK) {
+        /* Release wrapped backends for other libraries */
+        goto error;
+      }
+
+      num_available_ids += num_library_ids;
+    }
+  }
+
+  /*
+   * Successful return:
+   * - Copy backend IDs to user-provided memory (if applicable)
+   * - Store number of backend IDs to user-provided variable
+   * - Return success or fallback codes
+   */
+  if (backendIDs == NULL) {
+    *numBackends = num_available_ids;
+    return ONNXIFI_STATUS_FALLBACK;
+  } else {
+    memcpy(backendIDs, backend_ids, num_wrapped_ids * sizeof(onnxBackendID));
+    free(backend_ids);
+    *numBackends = num_available_ids;
+    return num_available_ids <= num_expected_ids ?
+      ONNXIFI_STATUS_SUCCESS : ONNXIFI_STATUS_FALLBACK;
+  }
+
+error:
+  /*
+   * Error return:
+   * - Release all wrapped backend IDs.
+   * - Deallocate wrapper structures.
+   */
+  for (size_t i = 0; i < num_wrapped_ids; i++) {
+    struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+      (struct onnxifi_backend_id_wrapper*) backend_ids[i];
+    assert(backend_id_wrapper->magic == ONNXIFI_BACKEND_ID_MAGIC);
+
+    (void) backend_id_wrapper->library->onnxReleaseBackendID(
+      backend_id_wrapper->backend_id);
+
+    /* Safety precaution to avoid use-after-free bugs */
+    memset(backend_id_wrapper, 0, sizeof(struct onnxifi_backend_id_wrapper));
+    free(backend_id_wrapper);
+  }
+  free(backend_ids);
+  return status;
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseBackendID(
+    onnxBackendID backendID)
+{
+  if (backendID == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+    (struct onnxifi_backend_id_wrapper*) backendID;
+  if (backend_id_wrapper->magic != ONNXIFI_BACKEND_ID_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+  if (backend_id_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  /* Call onnxReleaseBackendID with unwrapped backend ID */
+  const onnxStatus status =
+    backend_id_wrapper->library->onnxReleaseBackendID(
+      backend_id_wrapper->backend_id);
+
+  /*
+   * Note: ReleaseBackendID either succeeded, or failed with internal error.
+   * Either way, it is not safe to use the backend ID again.
+   */
+  memset(backend_id_wrapper, 0, sizeof(struct onnxifi_backend_id_wrapper));
+  free(backend_id_wrapper);
+
+  return status;
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxGetBackendInfo(
+    onnxBackendID backendID,
+    onnxBackendInfo infoType,
+    void* infoValue,
+    size_t* infoValueSize)
+{
+  if (backendID == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  const struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+    (const struct onnxifi_backend_id_wrapper*) backendID;
+  if (backend_id_wrapper->magic != ONNXIFI_BACKEND_ID_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+  if (backend_id_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  /* Call onnxGetBackendInfo with unwrapped backend ID */
+  return backend_id_wrapper->library->onnxGetBackendInfo(
+    backend_id_wrapper->backend_id,
+    infoType,
+    infoValue,
+    infoValueSize);
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxGetBackendCompatibility(
+    onnxBackendID backendID,
+    size_t onnxModelSize,
+    const void* onnxModel)
+{
+  if (backendID == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  const struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+    (const struct onnxifi_backend_id_wrapper*) backendID;
+  if (backend_id_wrapper->magic != ONNXIFI_BACKEND_ID_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+  if (backend_id_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  /* Call onnxGetBackendCompatibility with unwrapped backend ID */
+  return backend_id_wrapper->library->onnxGetBackendCompatibility(
+    backend_id_wrapper->backend_id,
+    onnxModelSize,
+    onnxModel);
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxInitBackend(
+    onnxBackendID backendID,
+    const uint64_t* auxPropertiesList,
+    onnxBackend* backend)
+{
+  if (backend == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *backend = NULL;
+
+  if (backendID == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  const struct onnxifi_backend_id_wrapper* backend_id_wrapper =
+    (const struct onnxifi_backend_id_wrapper*) backendID;
+  if (backend_id_wrapper->magic != ONNXIFI_BACKEND_ID_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+  if (backend_id_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_ID;
+  }
+
+  struct onnxifi_backend_wrapper* backend_wrapper =
+    (struct onnxifi_backend_wrapper*)
+      malloc(sizeof(struct onnxifi_backend_wrapper));
+  if (backend_wrapper == NULL) {
+    return ONNXIFI_STATUS_NO_SYSTEM_MEMORY;
+  }
+  memset(backend_wrapper, 0, sizeof(struct onnxifi_backend_wrapper));
+
+  /* Call onnxInitBackend with unwrapped backend ID */
+  const onnxStatus status = backend_id_wrapper->library->onnxInitBackend(
+    backend_id_wrapper->backend_id,
+    auxPropertiesList,
+    &backend_wrapper->backend);
+  if (status == ONNXIFI_STATUS_SUCCESS) {
+    /* Success, return wrapped graph */
+    backend_wrapper->magic = ONNXIFI_BACKEND_MAGIC;
+    backend_wrapper->library = backend_id_wrapper->library;
+    *backend = (onnxBackend) backend_wrapper;
+    return ONNXIFI_STATUS_SUCCESS;
+  } else {
+    /* Failure, release allocated memory */
+    free(backend_wrapper);
+    return status;
+  }
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseBackend(
+    onnxBackend backend)
+{
+  if (backend == NULL) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+
+  struct onnxifi_backend_wrapper* backend_wrapper =
+    (struct onnxifi_backend_wrapper*) backend;
+  if (backend_wrapper->magic != ONNXIFI_BACKEND_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+  if (backend_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+
+  /* Call onnxReleaseBackend with unwrapped backend handle */
+  const onnxStatus status = backend_wrapper->library->onnxReleaseBackend(
+    backend_wrapper->backend);
+
+  /*
+   * Note: ReleaseBackend either succeeded, or failed with internal error.
+   * Either way, it is not safe to use the backend handle again.
+   */
+  memset(backend_wrapper, 0, sizeof(struct onnxifi_backend_wrapper));
+  free(backend_wrapper);
+
+  return status;
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxInitEvent(
+    onnxBackend backend,
+    onnxEvent* event)
+{
+  if (event == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *event = NULL;
+
+  if (backend == NULL) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+
+  const struct onnxifi_backend_wrapper* backend_wrapper =
+    (const struct onnxifi_backend_wrapper*) backend;
+  if (backend_wrapper->magic != ONNXIFI_BACKEND_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+  if (backend_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+
+  struct onnxifi_event_wrapper* event_wrapper =
+    (struct onnxifi_event_wrapper*)
+      malloc(sizeof(struct onnxifi_event_wrapper));
+  if (event_wrapper == NULL) {
+    return ONNXIFI_STATUS_NO_SYSTEM_MEMORY;
+  }
+  memset(event_wrapper, 0, sizeof(struct onnxifi_event_wrapper));
+
+  /* Call onnxInitEvent with unwrapped backend handle */
+  const onnxStatus status = backend_wrapper->library->onnxInitEvent(
+    backend_wrapper->backend,
+    &event_wrapper->event);
+  if (status == ONNXIFI_STATUS_SUCCESS) {
+    /* Success, return wrapped graph */
+    event_wrapper->magic = ONNXIFI_EVENT_MAGIC;
+    event_wrapper->library = backend_wrapper->library;
+    *event = (onnxEvent) event_wrapper;
+    return ONNXIFI_STATUS_SUCCESS;
+  } else {
+    /* Failure, release allocated memory */
+    free(event_wrapper);
+    return status;
+  }
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxSignalEvent(
+    onnxEvent event)
+{
+  if (event == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  const struct onnxifi_event_wrapper* event_wrapper =
+    (const struct onnxifi_event_wrapper*) event;
+  if (event_wrapper->magic != ONNXIFI_EVENT_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+  if (event_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  /* Call onnxSignalEvent with unwrapped backend handle */
+  return event_wrapper->library->onnxSignalEvent(
+    event_wrapper->event);
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxGetEventState(
+    onnxEvent event,
+    onnxEventState* state)
+{
+  if (state == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *state = ONNXIFI_EVENT_STATE_INVALID;
+
+  if (event == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  const struct onnxifi_event_wrapper* event_wrapper =
+    (const struct onnxifi_event_wrapper*) event;
+  if (event_wrapper->magic != ONNXIFI_EVENT_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+  if (event_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  /* Call onnxGetEventState with unwrapped backend handle */
+  return event_wrapper->library->onnxGetEventState(
+    event_wrapper->event, state);
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxWaitEvent(
+    onnxEvent event)
+{
+  if (event == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  const struct onnxifi_event_wrapper* event_wrapper =
+    (const struct onnxifi_event_wrapper*) event;
+  if (event_wrapper->magic != ONNXIFI_EVENT_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+  if (event_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  /* Call onnxWaitEvent with unwrapped backend handle */
+  return event_wrapper->library->onnxWaitEvent(
+    event_wrapper->event);
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxReleaseEvent(
+    onnxEvent event)
+{
+  if (event == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  struct onnxifi_event_wrapper* event_wrapper =
+    (struct onnxifi_event_wrapper*) event;
+  if (event_wrapper->magic != ONNXIFI_EVENT_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+  if (event_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_EVENT;
+  }
+
+  /* Call onnxReleaseEvent with unwrapped event handle */
+  const onnxStatus status = event_wrapper->library->onnxReleaseEvent(
+    event_wrapper->event);
+
+  /*
+   * Note: ReleaseEvent either succeeded, or failed with internal error.
+   * Either way, it is not safe to use the event handle again.
+   */
+  memset(event_wrapper, 0, sizeof(struct onnxifi_event_wrapper));
+  free(event_wrapper);
+
+  return status;
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI onnxInitGraph(
+    onnxBackend backend,
+    const uint64_t* auxPropertiesList,
+    size_t onnxModelSize,
+    const void* onnxModel,
+    uint32_t weightsCount,
+    const onnxTensorDescriptorV1* weightDescriptors,
+    onnxGraph* graph)
+{
+  if (graph == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  *graph = NULL;
+
+  if (backend == NULL) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+
+  struct onnxifi_backend_wrapper* backend_wrapper =
+    (struct onnxifi_backend_wrapper*) backend;
+  if (backend_wrapper->magic != ONNXIFI_BACKEND_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+  if (backend_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_BACKEND;
+  }
+
+  struct onnxifi_graph_wrapper* graph_wrapper =
+    (struct onnxifi_graph_wrapper*)
+      malloc(sizeof(struct onnxifi_graph_wrapper));
+  if (graph_wrapper == NULL) {
+    return ONNXIFI_STATUS_NO_SYSTEM_MEMORY;
+  }
+  memset(graph_wrapper, 0, sizeof(struct onnxifi_graph_wrapper));
+
+  /* Call onnxInitGraph with unwrapped backend handle */
+  const onnxStatus status = backend_wrapper->library->onnxInitGraph(
+    backend_wrapper->backend,
+    auxPropertiesList,
+    onnxModelSize,
+    onnxModel,
+    weightsCount,
+    weightDescriptors,
+    &graph_wrapper->graph);
+  switch (status) {
+    case ONNXIFI_STATUS_SUCCESS:
+    case ONNXIFI_STATUS_FALLBACK:
+      /* Success, return wrapped graph */
+      graph_wrapper->magic = ONNXIFI_GRAPH_MAGIC;
+      graph_wrapper->library = backend_wrapper->library;
+      *graph = (onnxGraph) graph_wrapper;
+      return status;
+    default:
+      /* Failure, release allocated memory */
+      free(graph_wrapper);
+      return status;
+  }
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI onnxSetGraphIO(
+    onnxGraph graph,
+    uint32_t inputsCount,
+    const onnxTensorDescriptorV1* inputDescriptors,
+    uint32_t outputsCount,
+    const onnxTensorDescriptorV1* outputDescriptors)
+{
+  if (graph == NULL) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+
+  struct onnxifi_graph_wrapper* graph_wrapper =
+    (struct onnxifi_graph_wrapper*) graph;
+  if (graph_wrapper->magic != ONNXIFI_GRAPH_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+  if (graph_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+
+  /* Call onnxSetGraphIO with unwrapped graph handle */
+  return graph_wrapper->library->onnxSetGraphIO(
+    graph_wrapper->graph,
+    inputsCount,
+    inputDescriptors,
+    outputsCount,
+    outputDescriptors);
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI
+  onnxRunGraph(
+    onnxGraph graph,
+    const onnxMemoryFenceV1* inputFence,
+    onnxMemoryFenceV1* outputFence)
+{
+  if (graph == NULL) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+  if (inputFence == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  if (outputFence == NULL) {
+    return ONNXIFI_STATUS_INVALID_POINTER;
+  }
+  switch (inputFence->tag) {
+    case ONNXIFI_TAG_MEMORY_FENCE_V1:
+      break;
+    default:
+      return ONNXIFI_STATUS_UNSUPPORTED_TAG;
+  }
+  switch (outputFence->tag) {
+    case ONNXIFI_TAG_MEMORY_FENCE_V1:
+      break;
+    default:
+      return ONNXIFI_STATUS_UNSUPPORTED_TAG;
+  }
+
+  struct onnxifi_graph_wrapper* graph_wrapper =
+    (struct onnxifi_graph_wrapper*) graph;
+  if (graph_wrapper->magic != ONNXIFI_GRAPH_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+  if (graph_wrapper->library == NULL) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+
+  const onnxMemoryFenceV1* input_fence_ptr = inputFence;
+  onnxMemoryFenceV1 input_fence_wrapper;
+  switch (inputFence->type) {
+    case ONNXIFI_SYNCHRONIZATION_EVENT:
+    {
+      if (inputFence->event == NULL) {
+        return ONNXIFI_STATUS_INVALID_EVENT;
+      }
+      const struct onnxifi_event_wrapper* event_wrapper =
+        (const struct onnxifi_event_wrapper*) inputFence->event;
+      if (event_wrapper->magic != ONNXIFI_EVENT_MAGIC) {
+        return ONNXIFI_STATUS_INVALID_EVENT;
+      }
+      if (event_wrapper->library != graph_wrapper->library) {
+        return ONNXIFI_STATUS_INVALID_EVENT;
+      }
+
+      /* Initialize wrapper for input fence */
+      input_fence_wrapper.tag = ONNXIFI_TAG_MEMORY_FENCE_V1;
+      input_fence_wrapper.type = ONNXIFI_SYNCHRONIZATION_EVENT;
+      input_fence_wrapper.event = event_wrapper->event;
+      input_fence_ptr = &input_fence_wrapper;
+      break;
+    }
+    default:
+      /* Pass inputFence as is */
+      break;
+  }
+
+  onnxMemoryFenceV1* output_fence_ptr = outputFence;
+  onnxMemoryFenceV1 output_fence_wrapper;
+  struct onnxifi_event_wrapper* output_event = NULL;
+  switch (outputFence->type) {
+    case ONNXIFI_SYNCHRONIZATION_EVENT:
+    {
+      /* Initialize wrapper for output fence */
+      output_fence_wrapper.tag = ONNXIFI_TAG_MEMORY_FENCE_V1;
+      output_fence_wrapper.type = ONNXIFI_SYNCHRONIZATION_EVENT;
+      /* event will be populated by onnxRunGraph */
+      output_fence_wrapper.event = NULL;
+      output_fence_ptr = &output_fence_wrapper;
+
+      /*
+       * Pre-allocate memory for output event wrapper.
+       * This must be done before onnxRunGraph, so in case allocation fails,
+       * the function call has no side-effects.
+       */
+      output_event = malloc(sizeof(struct onnxifi_event_wrapper));
+      if (output_event == NULL) {
+        return ONNXIFI_STATUS_NO_SYSTEM_MEMORY;
+      }
+      memset(output_event, 0, sizeof(struct onnxifi_event_wrapper));
+    }
+  }
+
+  const onnxStatus status = graph_wrapper->library->onnxRunGraph(
+    graph_wrapper->graph,
+    input_fence_ptr,
+    output_fence_ptr);
+
+  if (status == ONNXIFI_STATUS_SUCCESS) {
+    /* Wrap output event, if needed */
+    if (output_event != NULL) {
+      output_event->magic = ONNXIFI_EVENT_MAGIC;
+      output_event->event = output_fence_wrapper.event;
+      output_event->library = graph_wrapper->library;
+      outputFence->event = (onnxEvent) output_event;
+    }
+  } else {
+    /* Deallocate output event wrapper, if needed */
+    free(output_event);
+  }
+
+  return status;
+}
+
+ONNXIFI_PUBLIC onnxStatus ONNXIFI_ABI onnxReleaseGraph(
+    onnxGraph graph)
+{
+  if (graph == NULL) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+
+  struct onnxifi_graph_wrapper* graph_wrapper =
+    (struct onnxifi_graph_wrapper*) graph;
+  if (graph_wrapper->magic != ONNXIFI_GRAPH_MAGIC) {
+    return ONNXIFI_STATUS_INVALID_GRAPH;
+  }
+
+  const onnxStatus status =
+    graph_wrapper->library->onnxReleaseGraph(graph_wrapper->graph);
+
+  /*
+   * Note: ReleaseGraph either succeeded, or failed with internal error.
+   * Either way, it is not safe to use the graph handle again.
+   */
+  memset(graph_wrapper, 0, sizeof(struct onnxifi_graph_wrapper));
+  free(graph_wrapper);
+
+  return status;
+}
diff --git a/third_party/onnx/onnx/parser.py b/third_party/onnx/onnx/parser.py
new file mode 100644
index 0000000000..271c09a45b
--- /dev/null
+++ b/third_party/onnx/onnx/parser.py
@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+import onnx
+import onnx.onnx_cpp2py_export.parser as C
+from typing import Text
+
+
+class ParseError(Exception):
+    pass
+
+
+def parse_model(model_text: Text) -> onnx.ModelProto:
+    """Parse a string to build a ModelProto.
+
+    Arguments:
+        model_text (string): formatted string
+    Returns:
+        ModelProto
+    """
+    (success, msg, model_proto_str) = C.parse_model(model_text)
+    if success:
+        return onnx.load_from_string(model_proto_str)
+    else:
+        raise ParseError(msg)
+
+
+def parse_graph(graph_text: Text) -> onnx.GraphProto:
+    """Parse a string to build a GraphProto.
+
+    Arguments:
+        graph_text (string): formatted string
+    Returns:
+        GraphProto
+    """
+    (success, msg, graph_proto_str) = C.parse_graph(graph_text)
+    if success:
+        G = onnx.GraphProto()
+        G.ParseFromString(graph_proto_str)
+        return G
+    else:
+        raise ParseError(msg)
diff --git a/third_party/onnx/onnx/proto_utils.h b/third_party/onnx/onnx/proto_utils.h
new file mode 100644
index 0000000000..b9064b0950
--- /dev/null
+++ b/third_party/onnx/onnx/proto_utils.h
@@ -0,0 +1,64 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <google/protobuf/io/coded_stream.h>
+#include <google/protobuf/io/zero_copy_stream_impl_lite.h>
+
+#include "onnx/onnx_pb.h"
+
+#ifdef ONNX_USE_LITE_PROTO
+#include <google/protobuf/message_lite.h>
+#else // ONNX_USE_LITE_PROTO
+#include <google/protobuf/message.h>
+#endif  // !ONNX_USE_LITE_PROTO
+
+namespace ONNX_NAMESPACE {
+
+#ifdef ONNX_USE_LITE_PROTO
+using ::google::protobuf::MessageLite;
+inline std::string ProtoDebugString(const MessageLite& proto) {
+  // Since the MessageLite interface does not support reflection, there is very
+  // little information that this and similar methods can provide.
+  // But when using lite proto this is the best we can provide.
+  return proto.ShortDebugString();
+}
+#else
+using ::google::protobuf::Message;
+inline std::string ProtoDebugString(const Message& proto) {
+  return proto.ShortDebugString();
+}
+#endif
+
+template <typename Proto>
+bool ParseProtoFromBytes(Proto* proto, const char* buffer, size_t length) {
+  ::google::protobuf::io::ArrayInputStream input_stream(buffer, static_cast<int>(length));
+  ::google::protobuf::io::CodedInputStream coded_stream(&input_stream);
+  int total_bytes_limit = (2048LL << 20) - 1;
+#if GOOGLE_PROTOBUF_VERSION >= 3011000
+    // Only take one parameter since protobuf 3.11
+    coded_stream.SetTotalBytesLimit(total_bytes_limit);
+#else
+    // Total bytes hard limit / warning limit are set to 2GB and 512MB respectively.
+    coded_stream.SetTotalBytesLimit(total_bytes_limit, 512LL << 20);
+#endif
+
+  return proto->ParseFromCodedStream(&coded_stream);
+}
+
+template<typename T> inline std::vector<T> RetrieveValues(const AttributeProto& attr);
+template<> inline std::vector<int64_t> RetrieveValues(const AttributeProto& attr) {
+  return {attr.ints().begin(), attr.ints().end()};
+}
+
+template<> inline std::vector<std::string> RetrieveValues(const AttributeProto& attr) {
+  return { attr.strings().begin(), attr.strings().end() };
+}
+
+template<> inline std::vector<float> RetrieveValues(const AttributeProto& attr) {
+  return { attr.floats().begin(), attr.floats().end() };
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/py_utils.h b/third_party/onnx/onnx/py_utils.h
new file mode 100644
index 0000000000..0d8901e95b
--- /dev/null
+++ b/third_party/onnx/onnx/py_utils.h
@@ -0,0 +1,22 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+#include "onnx/proto_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace py = pybind11;
+
+template <typename Proto>
+bool ParseProtoFromPyBytes(Proto* proto, const py::bytes& bytes) {
+  // Get the buffer from Python bytes object
+  char* buffer = nullptr;
+  Py_ssize_t length;
+  PyBytes_AsStringAndSize(bytes.ptr(), &buffer, &length);
+
+  return ParseProtoFromBytes(proto, buffer, length);
+}
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/shape_inference.py b/third_party/onnx/onnx/shape_inference.py
new file mode 100644
index 0000000000..00a3d0567f
--- /dev/null
+++ b/third_party/onnx/onnx/shape_inference.py
@@ -0,0 +1,63 @@
+# SPDX-License-Identifier: Apache-2.0
+"""onnx shape inference. Shape inference is not guaranteed to be
+complete.
+
+"""
+
+import onnx
+import onnx.onnx_cpp2py_export.shape_inference as C
+from onnx import ModelProto
+from typing import Text, Union
+
+
+def infer_shapes(model: Union[ModelProto, bytes], check_type: bool = False, strict_mode: bool = False, data_prop: bool = False) -> ModelProto:
+    """Apply shape inference to the provided ModelProto.
+
+    Inferred shapes are added to the value_info field of the graph.
+
+    If the inferred values conflict with values already provided in the
+    graph, that means that the provided values are invalid (or there is a
+    bug in shape inference), and the result is unspecified.
+
+    Arguments:
+        model (Union[ModelProto, Text, bytes], bool, bool, bool) -> ModelProto
+        check_type (bool): Checks the type-equality for input and output
+        strict_mode (bool): Stricter shape inference, it will throw errors if any;
+            Otherwise, simply stop if any error
+        data_prop (bool): Enables data propagation for limited operators to perform shape computation
+
+    Returns:
+        (ModelProto) model with inferred shape information
+    """
+    if isinstance(model, (ModelProto, bytes)):
+        model_str = model if isinstance(model, bytes) else model.SerializeToString()
+        inferred_model_str = C.infer_shapes(model_str, check_type, strict_mode, data_prop)
+        return onnx.load_from_string(inferred_model_str)
+    elif isinstance(model, str):
+        raise TypeError('infer_shapes only accepts ModelProto or bytes,'
+                        'you can use infer_shapes_path for the model path (String).')
+    else:
+        raise TypeError('infer_shapes only accepts ModelProto or bytes, '
+                         'incorrect type: {}'.format(type(model)))
+
+
+def infer_shapes_path(model_path: Text, output_path: Text = '', check_type: bool = False, strict_mode: bool = False, data_prop: bool = False) -> None:
+    """
+    Take model path for shape_inference same as infer_shape; it support >2GB models
+    Directly output the inferred model to the output_path; Default is the original model path
+    """
+    if isinstance(model_path, ModelProto):
+        raise TypeError('infer_shapes_path only accepts model Path (String),'
+                        'you can use infer_shapes for the ModelProto.')
+    # Directly output the inferred model into the specified path, return nothing
+    elif isinstance(model_path, str):
+        # If output_path is not defined, default output_path would be the original model path
+        if output_path == '':
+            output_path = model_path
+        C.infer_shapes_path(model_path, output_path, check_type, strict_mode, data_prop)
+    else:
+        raise TypeError('infer_shapes_path only accepts model path (String), '
+                         'incorrect type: {}'.format(type(model_path)))
+
+
+InferenceError = C.InferenceError
diff --git a/third_party/onnx/onnx/shape_inference/implementation.cc b/third_party/onnx/onnx/shape_inference/implementation.cc
new file mode 100644
index 0000000000..8cb0d421b3
--- /dev/null
+++ b/third_party/onnx/onnx/shape_inference/implementation.cc
@@ -0,0 +1,837 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/shape_inference/implementation.h"
+#include <fstream>
+#include <list>
+#include "onnx/checker.h"
+#include "onnx/defs/data_type_utils.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace shape_inference {
+namespace {
+
+std::string GetValueCaseString(const TypeProto& type) {
+  switch (type.value_case()) {
+    case TypeProto::ValueCase::kTensorType:
+      return "tensor_type";
+    case TypeProto::ValueCase::kSequenceType:
+      return "sequence_type";
+    case TypeProto::ValueCase::kMapType:
+      return "map_type";
+    case TypeProto::ValueCase::kOptionalType:
+      return "optional_type";
+#ifdef ONNX_ML
+    case TypeProto::ValueCase::kOpaqueType:
+      return "opaque_type";
+#endif
+    case TypeProto::ValueCase::kSparseTensorType:
+      return "sparse_tensor_type";
+    case TypeProto::ValueCase::VALUE_NOT_SET:
+      return "NOT_SET";
+    default:
+      return ONNX_NAMESPACE::to_string(type.value_case());
+  }
+}
+
+std::string GetElemTypeString(const TypeProto_Tensor& type) {
+#ifndef ONNX_USE_LITE_PROTO
+  const std::string type_str = TensorProto::DataType_Name(static_cast<TensorProto_DataType>(type.elem_type()));
+  if (!type_str.empty()) {
+    return type_str;
+  }
+#endif
+  return ONNX_NAMESPACE::to_string(type.elem_type());
+}
+
+std::string GetElemTypeString(const TypeProto_SparseTensor& type) {
+#ifndef ONNX_USE_LITE_PROTO
+  const std::string type_str = TensorProto::DataType_Name(static_cast<TensorProto_DataType>(type.elem_type()));
+  if (!type_str.empty()) {
+    return type_str;
+  }
+#endif
+  return ONNX_NAMESPACE::to_string(type.elem_type());
+}
+
+} // namespace
+
+template <class T>
+void CheckTensorShapesAndTypes(const T& inferred_type, const T& existing_type) {
+  if (inferred_type.elem_type() != TensorProto::UNDEFINED && existing_type.elem_type() != TensorProto::UNDEFINED &&
+      existing_type.elem_type() != inferred_type.elem_type()) {
+    std::stringstream ss;
+    ss << "Inferred elem type differs from existing elem type: (" << GetElemTypeString(inferred_type) << ") vs ("
+       << GetElemTypeString(existing_type) << ")";
+    fail_type_inference(ss.str());
+  }
+
+  if (!inferred_type.has_shape() || !existing_type.has_shape()) {
+    return;
+  }
+
+  if (inferred_type.shape().dim_size() != existing_type.shape().dim_size()) {
+    std::stringstream ss;
+    ss << "Inferred shape and existing shape differ in rank: (" << inferred_type.shape().dim_size() << ") vs ("
+       << existing_type.shape().dim_size() << ")";
+    fail_shape_inference(ss.str());
+  }
+
+  for (int i = 0; i < inferred_type.shape().dim_size(); ++i) {
+    const auto& inferred_dim = inferred_type.shape().dim(i);
+    const auto& existing_dim = existing_type.shape().dim(i);
+    if (inferred_dim.has_dim_value() && existing_dim.has_dim_value() &&
+        inferred_dim.dim_value() != existing_dim.dim_value()) {
+      std::stringstream ss;
+      ss << "Inferred shape and existing shape differ in dimension " << i << ": (" << inferred_dim.dim_value()
+         << ") vs (" << existing_dim.dim_value() << ")";
+      fail_shape_inference(ss.str());
+    }
+  }
+}
+
+void checkShapesAndTypes(const TypeProto& inferred_type, const TypeProto& existing_type) {
+  const auto inferred_value_case = inferred_type.value_case();
+  const auto existing_value_case = existing_type.value_case();
+  if (inferred_value_case == TypeProto::ValueCase::VALUE_NOT_SET ||
+      existing_value_case == TypeProto::ValueCase::VALUE_NOT_SET) {
+    // nothing to check; will assign inferredType to undefined existingType
+    return;
+  }
+  if (inferred_value_case != existing_value_case) {
+    fail_type_inference(
+        "type case mismatch. existing=",
+        GetValueCaseString(existing_type),
+        " inferred=",
+        GetValueCaseString(inferred_type));
+  }
+
+  if (inferred_value_case == TypeProto::kTensorType && existing_value_case == TypeProto::kTensorType) {
+    CheckTensorShapesAndTypes(inferred_type.tensor_type(), existing_type.tensor_type());
+  } else if (
+      inferred_value_case == TypeProto::kSparseTensorType && existing_value_case == TypeProto::kSparseTensorType) {
+    CheckTensorShapesAndTypes(inferred_type.sparse_tensor_type(), existing_type.sparse_tensor_type());
+  } else if (inferred_value_case == TypeProto::kSequenceType && existing_value_case == TypeProto::kSequenceType) {
+    checkShapesAndTypes(inferred_type.sequence_type().elem_type(), existing_type.sequence_type().elem_type());
+  } else if (inferred_value_case == TypeProto::kOptionalType && existing_value_case == TypeProto::kOptionalType) {
+    checkShapesAndTypes(inferred_type.optional_type().elem_type(), existing_type.optional_type().elem_type());
+  } else if (
+      inferred_value_case == TypeProto::TypeProto::kMapType && existing_value_case == TypeProto::TypeProto::kMapType) {
+    if (inferred_type.map_type().key_type() != existing_type.map_type().key_type()) {
+      fail_type_inference(
+          "key type mismatch from MapProto. existing=",
+          Utils::DataTypeUtils::ToDataTypeString(existing_type.map_type().key_type()),
+          " inferred=",
+          Utils::DataTypeUtils::ToDataTypeString(inferred_type.map_type().key_type()));
+    }
+    checkShapesAndTypes(inferred_type.map_type().value_type(), existing_type.map_type().value_type());
+  } else {
+    fail_type_inference("type case unsupported. existing=", existing_value_case, " inferred=", inferred_value_case);
+  }
+}
+
+void mergeShapesAndTypes(const TypeProto_Tensor& inferred_type, TypeProto_Tensor* existing_type) {
+  if (existing_type->elem_type() == TensorProto::UNDEFINED) {
+    existing_type->set_elem_type(inferred_type.elem_type());
+  }
+
+  if (!inferred_type.has_shape()) {
+    return;
+  }
+
+  if (!existing_type->has_shape()) {
+    *existing_type->mutable_shape() = inferred_type.shape();
+    return;
+  }
+
+  for (int i = 0; i < inferred_type.shape().dim_size(); ++i) {
+    const auto& inferred_dim = inferred_type.shape().dim(i);
+    auto* existing_dim = existing_type->mutable_shape()->mutable_dim(i);
+    if ((!existing_dim->has_dim_value() && !existing_dim->has_dim_param()) || inferred_dim.has_dim_value()) {
+      *existing_dim = inferred_dim;
+    }
+  }
+}
+
+void mergeShapesAndTypes(const TypeProto_SparseTensor& inferred_type, TypeProto_SparseTensor* existing_type) {
+  if (existing_type->elem_type() == TensorProto::UNDEFINED) {
+    existing_type->set_elem_type(inferred_type.elem_type());
+  }
+
+  if (!inferred_type.has_shape()) {
+    return;
+  }
+
+  if (!existing_type->has_shape()) {
+    *existing_type->mutable_shape() = inferred_type.shape();
+    return;
+  }
+
+  for (int i = 0; i < inferred_type.shape().dim_size(); ++i) {
+    const auto& inferred_dim = inferred_type.shape().dim(i);
+    auto* existing_dim = existing_type->mutable_shape()->mutable_dim(i);
+    if ((!existing_dim->has_dim_value() && !existing_dim->has_dim_param()) || inferred_dim.has_dim_value()) {
+      *existing_dim = inferred_dim;
+    }
+  }
+}
+
+void mergeShapesAndTypes(const TypeProto& inferred_type, TypeProto* existing_type) {
+  // Check before merge
+  checkShapesAndTypes(inferred_type, *existing_type);
+  const auto inferred_val_case = inferred_type.value_case();
+  if (inferred_val_case == TypeProto::kTensorType) {
+    mergeShapesAndTypes(inferred_type.tensor_type(), existing_type->mutable_tensor_type());
+  } else if (inferred_val_case == TypeProto::kSparseTensorType) {
+    mergeShapesAndTypes(inferred_type.sparse_tensor_type(), existing_type->mutable_sparse_tensor_type());
+  } else if (inferred_val_case == TypeProto::kSequenceType) {
+    mergeShapesAndTypes(
+        inferred_type.sequence_type().elem_type(), existing_type->mutable_sequence_type()->mutable_elem_type());
+  } else if (inferred_val_case == TypeProto::kOptionalType) {
+    mergeShapesAndTypes(
+        inferred_type.optional_type().elem_type(), existing_type->mutable_optional_type()->mutable_elem_type());
+  } else if (inferred_val_case == TypeProto::kMapType) {
+    mergeShapesAndTypes(inferred_type.map_type().value_type(), existing_type->mutable_map_type()->mutable_value_type());
+  }
+}
+
+// TypeProto_Tensor or TypeProto_SparseTensor
+template <typename TensorTypeProto>
+void GenerateSymbolicShape(TensorTypeProto* inferred_type, SymbolTable& symbol_table) {
+  if (!inferred_type->has_shape()) {
+    return;
+  }
+  for (int i = 0; i < inferred_type->shape().dim_size(); ++i) {
+    // set a symbol if it doesn't have dim_value and dim_param
+    auto* dim = inferred_type->mutable_shape()->mutable_dim(i);
+    if (!dim->has_dim_value() && !dim->has_dim_param()) {
+      dim->set_dim_param(symbol_table.createNew("unk__"));
+    }
+  }
+}
+
+void MaterializeSymbolicShape(TypeProto* inferred_type, SymbolTable& symbol_table) {
+  const auto inferred_val_case = inferred_type->value_case();
+  if (inferred_val_case == TypeProto::ValueCase::VALUE_NOT_SET) {
+    return;
+  }
+
+  if (inferred_val_case == TypeProto::kTensorType) {
+    GenerateSymbolicShape(inferred_type->mutable_tensor_type(), symbol_table);
+  } else if (inferred_val_case == TypeProto::kSparseTensorType) {
+    GenerateSymbolicShape(inferred_type->mutable_sparse_tensor_type(), symbol_table);
+  } else if (inferred_val_case == TypeProto::kSequenceType) {
+    MaterializeSymbolicShape(inferred_type->mutable_sequence_type()->mutable_elem_type(), symbol_table);
+  } else if (inferred_val_case == TypeProto::kOptionalType) {
+    MaterializeSymbolicShape(inferred_type->mutable_optional_type()->mutable_elem_type(), symbol_table);
+  } else {
+    fail_shape_inference("type case unsupported for symbolic shape inference. inferred=", inferred_val_case);
+  }
+}
+
+std::string GetModelLocalFunctionsMapIdentifier(const std::string& domain, const std::string& func_name) {
+  return domain + ":" + func_name;
+}
+
+class ShapeInferenceImplBase {
+ public:
+  void updateType(const std::string& name, TypeProto* inferred_type) {
+    if (inferred_type->value_case() == TypeProto::ValueCase::VALUE_NOT_SET) {
+      return;
+    }
+
+    if (symbol_table) {
+      MaterializeSymbolicShape(inferred_type, *symbol_table);
+    }
+
+    // Find any pre-existing type and shape info. If there is such,
+    // then check for compatibility with the inferred
+    // information. Otherwise, initialize it in an empty state.
+    auto iter = value_types_by_name.find(name);
+    TypeProto* existing_type = nullptr;
+    if (iter != value_types_by_name.end()) {
+      existing_type = iter->second;
+    } else {
+      // Create a new value_info if defined type does not exist
+      auto vi = g.add_value_info(); // TODO: clean this up
+      vi->set_name(name);
+      existing_type = vi->mutable_type();
+      // For undefined output type, update both value_info and output for now
+      // Update existing output with undefined type: assign inferred type to it
+      iter = undefined_value_types_by_name.find(name);
+      if (iter != undefined_value_types_by_name.end()) {
+        *iter->second = *inferred_type;
+      }
+    }
+
+    // TODO: cleanup this by merging with previous if-else
+    // Now we can merge pre-existing and inferred info
+    mergeShapesAndTypes(*inferred_type, existing_type);
+
+    // Make merged info available to further inference.
+    value_types_by_name[name] = existing_type;
+  }
+
+  void updateType(ValueInfoProto& valueInfo) {
+    if (valueInfo.has_type()) {
+      value_types_by_name[valueInfo.name()] = valueInfo.mutable_type();
+    } else {
+      undefined_value_types_by_name[valueInfo.name()] = valueInfo.mutable_type();
+    }
+  }
+
+  void preprocess(NodeProto& n) {
+    if (checker::check_is_experimental_op(n.op_type())) {
+      has_experimental_op = true;
+    } else if (n.op_type() == "Constant" && n.output().size() == 1) {
+      for (const auto& attr : n.attribute()) {
+        if (attr.name() == "value") {
+          if (attr.type() == AttributeProto::TENSOR && attr.has_t()) {
+            input_data_by_name[n.output(0)] = &attr.t();
+          } else if (attr.type() == AttributeProto::SPARSE_TENSOR && attr.has_sparse_tensor()) {
+            input_sparse_data_by_name[n.output(0)] = &attr.sparse_tensor();
+          }
+        }
+      }
+    }
+  }
+
+  void process(NodeProto& n) {
+    // Resolve domain for node
+    auto dit = opset_imports.find(n.domain());
+    if (dit == opset_imports.end()) {
+      fail_type_inference(
+          "Cannot infer type and shape for node name ",
+          n.name(),
+          ". No opset import for domain",
+          n.domain(),
+          " optype ",
+          n.op_type());
+    }
+    auto domain_version = dit->second;
+    const auto schema = schema_registry->GetSchema(n.op_type(), domain_version, n.domain());
+    InferenceContextImpl ctx(
+        n,
+        value_types_by_name,
+        input_data_by_name,
+        input_sparse_data_by_name,
+        &generated_shape_data_by_name,
+        &graph_inference_context);
+
+    ONNX_TRY {
+      if (schema) {
+        if (schema->has_type_and_shape_inference_function()) {
+          schema->GetTypeAndShapeInferenceFunction()(ctx);
+        } else if (schema->HasFunction()) {
+          InferShapeForFunctionNode(
+              *(schema->GetFunction()),
+              schema_registry,
+              ctx,
+              options,
+              model_local_functions_map,
+              symbol_table,
+              &generated_shape_data_by_name);
+        } else {
+          // Continue with inference for remaining nodes
+          return;
+        }
+      } else if (model_local_functions_map.size() > 0) {
+        auto iter = model_local_functions_map.find(GetModelLocalFunctionsMapIdentifier(n.domain(), n.op_type()));
+        if (iter != model_local_functions_map.end()) {
+          InferShapeForFunctionNode(
+              *(iter->second),
+              schema_registry,
+              ctx,
+              options,
+              model_local_functions_map,
+              symbol_table,
+              &generated_shape_data_by_name);
+        } else {
+          has_unsupported_op = true;
+          return;
+        }
+      } else {
+        has_unsupported_op = true;
+        return;
+      }
+    }
+    ONNX_CATCH(const ONNX_NAMESPACE::InferenceError& ex) {
+      ONNX_HANDLE_EXCEPTION([&]() {
+        // onnx does not support unsupported/experimental operators
+        // so it won't consider it as an error
+        if (!has_unsupported_op && !has_experimental_op) {
+          inference_errors.push_back(GetErrorWithNodeInfo(n, ex));
+        }
+      });
+      // Continue with inference for remaining nodes
+      return;
+    }
+
+    ONNX_TRY {
+      // check the type-equality for input and output
+      if (options.check_type && schema) {
+        schema->CheckInputOutputType(ctx);
+      }
+
+      for (int i = 0; i < n.output_size(); ++i) {
+        // skip type and shape propagation for missing optional outputs.
+        if (!n.output(i).empty())
+          updateType(n.output(i), ctx.getOutputType(i));
+      }
+
+      // If data propagation is enabled, propagate shape data if it exists.
+      if (options.enable_data_propagation && schema && schema->has_data_propagation_function()) {
+        DataPropagationContextImpl data_propagation_ctx(
+            n, value_types_by_name, input_data_by_name, generated_shape_data_by_name);
+        schema->GetDataPropagationFunction()(data_propagation_ctx);
+      }
+    }
+    ONNX_CATCH(const std::runtime_error& err) {
+      ONNX_HANDLE_EXCEPTION([&]() { fail_shape_inference(GetErrorWithNodeInfo(n, err)); });
+    }
+  }
+
+  // TypeProto_Tensor or TypeProto_SparseTensor
+  template <typename T>
+  void processInitializer(
+      const std::string& name,
+      const T& tensorValue,
+      TypeProto& initializer_type,
+      std::unordered_map<std::string, const T*>& map) {
+    map[name] = &tensorValue;
+    auto iter = value_types_by_name.find(name);
+    // If it already exists in input, check input and initializer is sync
+    // use shape info from input (input has priority over initializer)
+    if (iter != value_types_by_name.end()) {
+      checkShapesAndTypes(initializer_type, *iter->second);
+      // CheckTensorShapesAndTypes(*initializer_tensor_type, *iter->second->mutable_tensor_type());
+    }
+    // Support IR>=4: some tensors can only exist in initializer and not in input
+    // So shape_inference should make use of initializer shapes
+    // Store initializer shape info in value_info as well
+    else if (ir_version >= 4) {
+      initializer_type_list.push_back(std::move(initializer_type));
+      value_types_by_name[name] = &initializer_type_list.back();
+    }
+  }
+
+  void process(GraphProto& graph) {
+    for (auto& vi : *graph.mutable_value_info()) {
+      updateType(vi);
+    }
+    for (auto& vi : *graph.mutable_input()) {
+      updateType(vi);
+    }
+    for (auto& vi : *graph.mutable_output()) {
+      updateType(vi);
+    }
+    for (const auto& tp : graph.initializer()) {
+      TypeProto initializer_type;
+      TypeProto_Tensor* initializer_tensor_type = initializer_type.mutable_tensor_type();
+      initializer_tensor_type->set_elem_type(tp.data_type());
+      // set the shape according to the initializer shape info
+      auto* shape = initializer_tensor_type->mutable_shape();
+      for (int i = 0; i < tp.dims_size(); ++i) {
+        shape->add_dim()->set_dim_value(tp.dims(i));
+      }
+      processInitializer(tp.name(), tp, initializer_type, input_data_by_name);
+    }
+    for (const auto& tp : graph.sparse_initializer()) {
+      TypeProto initializer_type;
+      auto* initializer_sparse_tensor_type = initializer_type.mutable_sparse_tensor_type();
+      initializer_sparse_tensor_type->set_elem_type(tp.values().data_type());
+      // set the shape according to the initializer shape info
+      auto* shape = initializer_sparse_tensor_type->mutable_shape();
+      for (int i = 0; i < tp.dims_size(); ++i) {
+        shape->add_dim()->set_dim_value(tp.dims(i));
+      }
+      processInitializer(tp.values().name(), tp, initializer_type, input_sparse_data_by_name);
+    }
+    // Collect data from constant nodes and check if any experimental ops exist
+    for (auto& n : *graph.mutable_node()) {
+      preprocess(n); // process constant node
+    }
+    for (auto& n : *graph.mutable_node()) {
+      process(n);
+    }
+    // Throw shape inference error if any. Error mode right now only supports 0 and 1.
+    // When set to 0, any node level shape inference errors are not thrown. This is to support backward compatiblity
+    // with 1.7 and earlier releases. When set to 1 it will throw all exceptions.
+    // TODO: Add a more granular way for exception handling.
+    if (options.error_mode > 0 && !inference_errors.empty()) {
+      std::string full_errors = "Shape inference error(s): ";
+      for (const std::string& error : inference_errors) {
+        full_errors += error + "\n";
+      }
+      fail_shape_inference(full_errors);
+    }
+  }
+
+
+ void process(const NodeProto& n, std::unordered_map<std::string, const AttributeProto*> attr_map) {
+    NodeProto copy_n(n);
+    // Add attribute information into the temporary node
+    copy_n.clear_attribute();
+    for (const auto& attr : n.attribute()) {
+      if (attr.has_ref_attr_name()) {
+        if (attr_map.count(attr.ref_attr_name())) {
+          auto copy_attr = *attr_map[attr.ref_attr_name()];
+          copy_attr.set_name(attr.name());
+          copy_n.add_attribute()->CopyFrom(copy_attr);
+        }
+      } else {
+        copy_n.add_attribute()->CopyFrom(attr);
+      }
+    }
+    process(copy_n);
+  }
+
+  void process(const FunctionProto& func_proto, InferenceContext& ctx) {
+    // Get a temporary tensor-shape map
+    const auto num_func_inputs = func_proto.input_size();
+    std::vector<TypeProto> types_cache(num_func_inputs);
+    for (int i = 0; i < num_func_inputs; ++i) {
+      if (ctx.getInputType(i) == nullptr) {
+        fail_type_inference("Input ", i, " type is missing.");
+      }
+      types_cache[i] = *ctx.getInputType(i); // TODO: investigate whether we can remove cache
+      value_types_by_name[func_proto.input().Get(i)] = &types_cache[i];
+    }
+
+    // Create a temporary initializer value map
+    for (int i = 0; i < static_cast<int>(ctx.getNumInputs()) && i < num_func_inputs; ++i) {
+      const TypeProto* type = ctx.getInputType(i);
+      if (type->value_case() == TypeProto::kTensorType && ctx.getInputData(i) != nullptr) {
+        input_data_by_name[func_proto.input().Get(i)] = ctx.getInputData(i);
+      } else if (type->value_case() == TypeProto::kSparseTensorType && ctx.getInputSparseData(i) != nullptr) {
+        input_sparse_data_by_name[func_proto.input().Get(i)] = ctx.getInputSparseData(i);
+      }
+    }
+
+    std::unordered_map<std::string, const AttributeProto*> attr_map;
+    for (auto& attr : func_proto.attribute()) {
+      if (ctx.getAttribute(attr) != nullptr) {
+        attr_map[attr] = ctx.getAttribute(attr);
+      }
+    }
+    for (auto& n : func_proto.node()) {
+      process(n, attr_map);
+    }
+
+    for (int i = 0; i < func_proto.output_size(); ++i) {
+      const std::string& output_name = func_proto.output().Get(i);
+      // Skip if no type inferred for the tensor
+      auto iter = value_types_by_name.find(output_name);
+      if (iter != value_types_by_name.cend()) {
+        // Copy the type info to ctx
+        // to pass back to main graph
+        auto type_proto = ctx.getOutputType(i);
+        type_proto->CopyFrom(*(iter->second));
+      }
+    }
+
+ }
+
+ public:
+  ShapeInferenceImplBase(
+      GraphProto* g_in,
+      const std::unordered_map<std::string, TypeProto*>& outer_scope_value_types_by_name_in,
+      const std::unordered_map<std::string, int>& opset_imports_in,
+      const ShapeInferenceOptions& options_in,
+      SymbolTable* symbol_table_in,
+      const ModelLocalFunctionsMap& model_local_functions_map_in,
+      const std::unordered_map<std::string, TensorShapeProto>& generated_shape_data_by_name_in,
+      const ISchemaRegistry* schema_registry_in = OpSchemaRegistry::Instance(),
+      const int ir_version_in = IR_VERSION // default the latest one
+      )
+      : g(*g_in),
+        value_types_by_name(outer_scope_value_types_by_name_in),
+        opset_imports(opset_imports_in),
+        options(options_in),
+        symbol_table(symbol_table_in),
+        model_local_functions_map(model_local_functions_map_in),
+        generated_shape_data_by_name(generated_shape_data_by_name_in),
+        schema_registry(schema_registry_in),
+        ir_version(ir_version_in),
+        graph_inference_context{
+            value_types_by_name,
+            opset_imports,
+            symbol_table,
+            schema_registry,
+            ir_version,
+            model_local_functions_map} {}
+
+ private:
+  GraphProto& g;
+  std::unordered_map<std::string, TypeProto*> value_types_by_name;
+  const std::unordered_map<std::string, int>& opset_imports;
+
+  const ShapeInferenceOptions& options;
+  SymbolTable* symbol_table;
+  const ModelLocalFunctionsMap& model_local_functions_map;
+  std::unordered_map<std::string, TensorShapeProto> generated_shape_data_by_name;
+  const ISchemaRegistry* schema_registry;
+  int ir_version;
+  GraphInferenceContext graph_inference_context;
+
+  std::unordered_map<std::string, TypeProto*> undefined_value_types_by_name;
+  std::unordered_map<std::string, const TensorProto*> input_data_by_name;
+  std::unordered_map<std::string, const SparseTensorProto*> input_sparse_data_by_name;
+
+  bool has_experimental_op = false;
+  bool has_unsupported_op = false;
+
+  std::vector<std::string> inference_errors;
+
+  std::list<TypeProto> initializer_type_list;
+};
+
+static void InferShapesImpl(
+    GraphProto* g,
+    const std::unordered_map<std::string, TypeProto*>& outer_scope_value_types_by_name,
+    const std::unordered_map<std::string, int>& opset_imports,
+    const ShapeInferenceOptions& options,
+    SymbolTable* symbol_table,
+    const ModelLocalFunctionsMap& model_local_functions_map,
+    const ISchemaRegistry* schema_registry = OpSchemaRegistry::Instance(),
+    const int ir_version = IR_VERSION // default the latest one
+) {
+  ShapeInferenceImplBase base(
+      g,
+      outer_scope_value_types_by_name,
+      opset_imports,
+      options,
+      symbol_table,
+      model_local_functions_map,
+      {},
+      schema_registry,
+      ir_version);
+  base.process(*g);
+}
+
+void InferShapes(
+    GraphProto* g,
+    const std::unordered_map<std::string, int>& opset_imports,
+    const ISchemaRegistry* schema_registry,
+    const ShapeInferenceOptions& options,
+    const std::unordered_map<std::string, const FunctionProto*>& model_local_functions) {
+  SymbolTableImpl symbol_table;
+  TraverseGraphsToAddExistingSymbols(*g, symbol_table);
+  InferShapesImpl(
+      g,
+      std::unordered_map<std::string, TypeProto*>(0),
+      opset_imports,
+      options,
+      &symbol_table,
+      model_local_functions,
+      schema_registry);
+}
+
+void InferShapes(ModelProto& m, const ISchemaRegistry* schema_registry, const ShapeInferenceOptions& options) {
+  std::unordered_map<std::string, int> opset_imports;
+  for (const auto& opset_import : m.opset_import()) {
+    opset_imports[opset_import.domain()] = static_cast<int>(opset_import.version());
+  }
+
+  ModelLocalFunctionsMap model_local_functions_by_id;
+  for (const auto& function_proto : m.functions()) {
+    model_local_functions_by_id.insert(
+        {GetModelLocalFunctionsMapIdentifier(function_proto.domain(), function_proto.name()), &function_proto});
+  }
+
+  auto* g = m.mutable_graph();
+  SymbolTableImpl symbol_table;
+  TraverseGraphsToAddExistingSymbols(*g, symbol_table);
+  InferShapesImpl(
+      g,
+      std::unordered_map<std::string, TypeProto*>(0),
+      opset_imports,
+      options,
+      &symbol_table,
+      model_local_functions_by_id,
+      schema_registry,
+      m.ir_version());
+}
+
+void InferShapes(
+    const std::string& model_path,
+    const std::string& save_path,
+    const ISchemaRegistry* schema_registry,
+    const ShapeInferenceOptions& options) {
+  ModelProto model;
+  std::fstream model_stream(model_path, std::ios::in | std::ios::binary);
+  if (!model_stream.good()) {
+    fail_check("Unable to open model file:", model_path, ". Please check if it is a valid file.");
+  }
+  std::string data{std::istreambuf_iterator<char>{model_stream}, std::istreambuf_iterator<char>{}};
+  if (!ParseProtoFromBytes(&model, data.c_str(), data.size())) {
+    fail_check(
+        "Unable to parse model from file:", model_path, ". Please check if it is a valid protobuf file of model.");
+  }
+  InferShapes(model, schema_registry, options);
+  // Save the inferred model to the original model path
+  // Use SerializeToString instead of SerializeToOstream due to LITE_PROTO
+  std::fstream output(save_path, std::ios::out | std::ios::trunc | std::ios::binary);
+  std::string model_string;
+  ONNX_TRY {
+    model.SerializeToString(&model_string);
+    output << model_string;
+  }
+  ONNX_CATCH(...) {
+    fail_check("Unable to save inferred model to the target path:", save_path);
+  }
+}
+
+// Infer shape for functions.
+void InferShapeForFunctionNode(
+    const FunctionProto& func_proto,
+    const std::unordered_map<std::string, int>& func_opset_imports,
+    const ISchemaRegistry* schema_registry,
+    InferenceContext& ctx,
+    const ShapeInferenceOptions& options,
+    const std::unordered_map<std::string, const FunctionProto*>& model_local_functions_map,
+    SymbolTable* symbol_table,
+    std::unordered_map<std::string, TensorShapeProto>* generated_shape_data_by_name) {
+  if (options.enable_data_propagation && generated_shape_data_by_name == nullptr) {
+    fail_shape_inference(
+        "Container for generated shape data cannot be nullptr when enable_data_propagation option is set.");
+  }
+
+  GraphProto g;
+
+  ShapeInferenceImplBase base(
+      &g,
+      {}, //outer_scope_value_types_by_name,
+      func_opset_imports,
+      options,
+      symbol_table,
+      model_local_functions_map,
+      *generated_shape_data_by_name,
+      schema_registry);
+  base.process(func_proto, ctx);
+}
+
+void InferShapeForFunctionNode(
+    const FunctionProto& function_proto,
+    const ISchemaRegistry* schema_registry,
+    InferenceContext& ctx,
+    const ShapeInferenceOptions& options,
+    const std::unordered_map<std::string, const FunctionProto*>& model_local_functions_map,
+    SymbolTable* symbol_table,
+    std::unordered_map<std::string, TensorShapeProto>* generated_shape_data_by_name) {
+  std::unordered_map<std::string, int> opset_imports;
+  for (const auto& opset_import : function_proto.opset_import()) {
+    opset_imports[opset_import.domain()] = static_cast<int>(opset_import.version());
+  }
+
+  InferShapeForFunctionNode(
+      function_proto,
+      opset_imports,
+      schema_registry,
+      ctx,
+      options,
+      model_local_functions_map,
+      symbol_table,
+      generated_shape_data_by_name);
+}
+
+std::vector<const TypeProto*> GraphInferencerImpl::doInferencing(
+    const std::vector<const TypeProto*>& input_types,
+    const std::vector<const TensorProto*>& input_data) {
+  SymbolTable* symbol_table = getSymbolTable();
+  int num_inputs = int(input_types.size());
+  std::unordered_set<std::string> initializer_name_set;
+  for (const auto& tp : g_->initializer()) {
+    initializer_name_set.insert(tp.name());
+  }
+
+  if (context_->ir_version >= 4) {
+    if (g_->input_size() != num_inputs) {
+      fail_shape_inference("Graph has ", g_->input_size(), " inputs but ", num_inputs, " were provided");
+    }
+    for (int i = 0; i < g_->input_size(); ++i) {
+      if (initializer_name_set.count(g_->input(i).name()) > 0) {
+        fail_shape_inference(
+            "Cannot use the same name as both a subgraph initializer and subgraph input: ", g_->input(i).name());
+      }
+    }
+  } else {
+    // IR < 4 requires all initializers to be optional inputs
+    // So the number of graph input can be larger than the number of node input
+    if (num_inputs > g_->input_size()) {
+      fail_shape_inference(
+          "Graph has ",
+          g_->input_size(),
+          " inputs but ",
+          num_inputs,
+          " were provided.",
+          "The number of graph input cannot be smaller than the number of node input");
+    } else if (num_inputs < g_->input_size()) {
+      for (int i = 0; i < g_->input_size(); ++i) {
+        if (i < num_inputs && initializer_name_set.count(g_->input(i).name()) > 0) {
+          fail_shape_inference("Graph initializer names must appear after the actual inputs: ", g_->input(i).name());
+        } else if (i >= num_inputs && initializer_name_set.count(g_->input(i).name()) == 0) {
+          // Further check whether the additional input is in initializers
+          fail_shape_inference("Cannot find missing input: ", g_->input(i).name(), "in initializers. ");
+        }
+      }
+    }
+  }
+
+  for (int i = 0, end = num_inputs; i < end; ++i) {
+    const TypeProto* inferred_input = input_types[i];
+
+    if (!inferred_input)
+      continue;
+
+    TypeProto* graph_input = g_->mutable_input(i)->mutable_type();
+    // Even if graphInput doesn't have defined type, it will assign inferredType to it
+    mergeShapesAndTypes(*inferred_input, graph_input);
+
+    if (symbol_table) {
+      MaterializeSymbolicShape(graph_input, *symbol_table);
+    }
+  }
+
+  // future: pass inputData into InferShapes either directly, or indirectly by
+  // updating initializers that match subgraph inputs.
+  (void)input_data;
+  ShapeInferenceOptions options{};
+  InferShapesImpl(
+      g_,
+      *context_->outer_scope_value_types_by_name, // never null
+      context_->opset_imports,
+      options,
+      symbol_table,
+      context_->model_local_functions,
+      context_->schema_registry);
+
+  std::vector<const TypeProto*> graph_output_types;
+  graph_output_types.reserve(g_->output().size());
+  for (const ValueInfoProto& output : g_->output()) {
+    graph_output_types.push_back(&output.type());
+  }
+
+  return graph_output_types;
+}
+
+std::string GetErrorWithNodeInfo(NodeProto n, std::runtime_error err) {
+  std::string op_name = n.has_name() ? (", node name: " + n.name()) : "";
+  return "(op_type:" + n.op_type() + op_name + "): " + err.what();
+}
+
+void TraverseGraphsToAddExistingSymbols(const GraphProto& g, SymbolTable& symbol_table) {
+  symbol_table.addFromGraph(g);
+  for (const auto& n : g.node()) {
+    for (auto& attr : n.attribute()) {
+      if (attr.has_g()) {
+        TraverseGraphsToAddExistingSymbols(attr.g(), symbol_table);
+      }
+    }
+  }
+}
+
+} // namespace shape_inference
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/shape_inference/implementation.h b/third_party/onnx/onnx/shape_inference/implementation.h
new file mode 100644
index 0000000000..df66bffe7e
--- /dev/null
+++ b/third_party/onnx/onnx/shape_inference/implementation.h
@@ -0,0 +1,495 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+#include "onnx/proto_utils.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace shape_inference {
+
+using ModelLocalFunctionsMap = std::unordered_map<std::string, const FunctionProto*>;
+
+class SymbolTableImpl : public SymbolTable {
+ public:
+  SymbolTableImpl() : index_(0){}
+
+  void addFromGraph(const GraphProto& g) {
+    AddExistingSymbolicDims(g.input());
+    AddExistingSymbolicDims(g.output());
+    AddExistingSymbolicDims(g.value_info());
+  }
+  // Creates a new unique symbol with the given prefix and adds it to the SymbolTable
+  // Returns the newly created symbol
+  std::string createNew(const std::string& symbol_prefix="unk__") {
+    std::string newSymbol;
+    do {
+      newSymbol = symbol_prefix + std::to_string(index_++);
+    } while(existing_symbols.count(newSymbol) > 0);
+    existing_symbols.insert(newSymbol);
+    return newSymbol;
+  }
+
+ private:
+  unsigned int index_;
+  std::unordered_set<std::string> existing_symbols;
+
+  // TypeProto_Tensor or TypeProto_SparseTensor
+  template <typename TensorTypeProto>
+  void AddExistingSymbolicDims(const TensorTypeProto& tensorType) {
+    if (tensorType.has_shape()) {
+      for (int i = 0; i < tensorType.shape().dim_size(); ++i) {
+        if (tensorType.shape().dim(i).has_dim_param()) {
+          existing_symbols.insert(tensorType.shape().dim(i).dim_param());
+        }
+      }
+    }
+  }
+
+  void AddExistingSymbolicDims(const TypeProto& typeProto) {
+      const auto val_case = typeProto.value_case();
+      switch (val_case) {
+        case TypeProto::kTensorType:
+          AddExistingSymbolicDims(typeProto.tensor_type());
+          break;
+        case TypeProto::kSparseTensorType:
+          AddExistingSymbolicDims(typeProto.sparse_tensor_type());
+          break;
+        case TypeProto::kSequenceType:
+          AddExistingSymbolicDims(typeProto.sequence_type().elem_type());
+          break;
+        case TypeProto::kOptionalType:
+          AddExistingSymbolicDims(typeProto.optional_type().elem_type());
+          break;
+        case TypeProto::kMapType:
+          AddExistingSymbolicDims(typeProto.map_type().value_type());
+          break;
+        default:
+          break;
+    }
+  }
+
+  void AddExistingSymbolicDims(const google::protobuf::RepeatedPtrField<ValueInfoProto>& protos) {
+    for (const auto& proto : protos) {
+      AddExistingSymbolicDims(proto.type());
+    }
+  }
+};
+
+struct GraphInferenceContext {
+  GraphInferenceContext(
+      const std::unordered_map<std::string, TypeProto*>&
+          outer_scope_value_types_by_name_in,
+      const std::unordered_map<std::string, int> opset_imports_in,
+      SymbolTable* symbol_table_in = nullptr,
+      const ISchemaRegistry* schema_registry_in = OpSchemaRegistry::Instance(),
+      const int ir_version_in = IR_VERSION,
+      const ModelLocalFunctionsMap& model_local_functions_in = {})
+      : outer_scope_value_types_by_name{&outer_scope_value_types_by_name_in},
+        opset_imports{opset_imports_in},
+        symbol_table{symbol_table_in},
+        schema_registry{schema_registry_in},
+        ir_version{ir_version_in},
+        model_local_functions{model_local_functions_in} {}
+
+  const std::unordered_map<std::string, TypeProto*>* outer_scope_value_types_by_name;
+  const std::unordered_map<std::string, int> opset_imports;
+  SymbolTable* symbol_table;
+  const ISchemaRegistry* schema_registry;
+  const int ir_version;
+  const ModelLocalFunctionsMap& model_local_functions;
+};
+
+class GraphInferencerImpl : public GraphInferencer {
+ public:
+  GraphInferencerImpl(GraphProto& g, GraphInferenceContext& context)
+      : g_{&g}, context_{&context} {}
+
+  std::vector<const TypeProto*> doInferencing(
+      const std::vector<const TypeProto*>& inputTypes,
+      const std::vector<const TensorProto*>& inputData) override;
+
+  SymbolTable* getSymbolTable() {
+    return context_->symbol_table;
+  }
+
+ private:
+  GraphProto* g_;
+  GraphInferenceContext* context_;
+};
+
+struct InferenceContextImpl : public InferenceContext {
+  InferenceContextImpl(
+      NodeProto& n,
+      const std::unordered_map<std::string, TypeProto*>& valueTypesByName,
+      const std::unordered_map<std::string, const TensorProto*>&
+          inputDataByName,
+      const std::unordered_map<std::string, const SparseTensorProto*>& 
+          inputSparseDataByName,
+      const std::unordered_map<std::string, TensorShapeProto>* generatedShapeData = nullptr,
+      GraphInferenceContext* graphInferenceContext = nullptr)
+      : graphInferenceContext_{graphInferenceContext} {
+    for (auto& attr : *n.mutable_attribute()) {
+      attributesByName_[attr.name()] = &attr;
+      if (attr.has_g()) {
+        // need a mutable GraphProto to run inferencing on this attribute
+        graphProtoAttributesByName_[attr.name()] = attr.mutable_g();
+      }
+    }
+
+    for (const auto& input : n.input()) {
+      auto valueTypesIter = valueTypesByName.find(input);
+      if (valueTypesIter != valueTypesByName.end()) {
+        allInputTypes_.push_back(valueTypesIter->second);
+      } else {
+        allInputTypes_.push_back(nullptr);
+      }
+
+      // input data can be in 1 of the 3 containers 
+      // inputDataByName - this is when input is TensorProto
+      // inputSparseDataByName - this is when input is SparseTesnorProto
+      // generatedShapeData - this is when input was geenrated as part of partial data propagation
+      const auto inputDataIter = inputDataByName.find(input);
+      if (inputDataIter != inputDataByName.cend()) {
+        allInputData_.push_back(inputDataIter->second);
+        allInputSparseData_.push_back(nullptr);
+        allShapeInputData_.push_back(nullptr);
+      } else {
+        allInputData_.push_back(nullptr);
+        const auto inputSparseDataIter = inputSparseDataByName.find(input);
+        if (inputSparseDataIter != inputSparseDataByName.cend()) {
+          allInputSparseData_.push_back(inputSparseDataIter->second);
+          allShapeInputData_.push_back(nullptr);
+        } else {
+          allInputSparseData_.push_back(nullptr);
+          if (generatedShapeData != nullptr) {
+            const auto inputShapeDataIter = generatedShapeData->find(input);
+            if (inputShapeDataIter != generatedShapeData->cend()) {
+              allShapeInputData_.push_back(&inputShapeDataIter->second);
+            } else {
+              allShapeInputData_.push_back(nullptr);
+            }
+          } else {
+            allShapeInputData_.push_back(nullptr);
+          }
+        }
+      }
+    }
+
+    allOutputTypes_.resize(n.output_size());
+  }
+
+  const AttributeProto* getAttribute(const std::string& name) const override {
+    auto iter = attributesByName_.find(name);
+    if (iter == attributesByName_.end()) {
+      return nullptr;
+    } else {
+      return iter->second;
+    }
+  }
+
+  size_t getNumInputs() const override {
+    return allInputTypes_.size();
+  }
+
+  const TypeProto* getInputType(size_t index) const override {
+    if (index >= allInputTypes_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    return allInputTypes_[index];
+  }
+
+  const TensorProto* getInputData(size_t index) const override {
+    if (index >= allInputData_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    return allInputData_[index];
+  }
+
+  const TensorShapeProto* getSymbolicInput(size_t index) const override {
+    if (index >= allShapeInputData_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+
+    return allShapeInputData_[index];
+  }
+
+  const SparseTensorProto* getInputSparseData(size_t index) const override {
+    if (index >= allInputSparseData_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    return allInputSparseData_[index];
+  }
+
+  size_t getNumOutputs() const override {
+    return allOutputTypes_.size();
+  }
+
+  TypeProto* getOutputType(size_t index) override {
+    if (index >= allOutputTypes_.size()) {
+      ONNX_THROW("Output " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    return &allOutputTypes_[index];
+  }
+
+  GraphInferencer* getGraphAttributeInferencer(
+      const std::string& attr_name) override {
+    if (!graphInferenceContext_) {
+      fail_type_inference(
+          "GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.");
+    }
+
+    GraphInferencer* inferencer = nullptr;
+
+    auto entry = graphAttributeInferencers_.find(attr_name);
+    if (entry == graphAttributeInferencers_.cend()) {
+      // create GraphInferencer instance
+      auto attrNameToGraphProto = graphProtoAttributesByName_.find(attr_name);
+      if (attrNameToGraphProto == graphProtoAttributesByName_.cend()) {
+        fail_type_inference(
+            "Attribute ", attr_name, " does not contain a graph.");
+      }
+
+      std::unique_ptr<GraphInferencer> new_inferencer{new GraphInferencerImpl(
+          *attrNameToGraphProto->second, *graphInferenceContext_)};
+
+      inferencer = new_inferencer.get();
+      graphAttributeInferencers_.emplace(attr_name, std::move(new_inferencer));
+    } else {
+      inferencer = entry->second.get();
+    }
+
+    return inferencer;
+  } 
+
+  std::vector<const TensorProto*> allInputData_;
+  std::vector<const SparseTensorProto*> allInputSparseData_;
+  std::vector<const TensorShapeProto*> allShapeInputData_;
+  std::unordered_map<std::string, const AttributeProto*> attributesByName_;
+  std::unordered_map<std::string, GraphProto*> graphProtoAttributesByName_;
+  std::vector<const TypeProto*> allInputTypes_;
+  std::vector<TypeProto> allOutputTypes_;
+  GraphInferenceContext* graphInferenceContext_;
+
+  // mutable as internal cache of GraphInferencer instances
+  mutable std::unordered_map<std::string, std::unique_ptr<GraphInferencer>>
+      graphAttributeInferencers_;
+};
+
+struct DataPropagationContextImpl : public DataPropagationContext {
+  DataPropagationContextImpl(
+      NodeProto& n,
+      const std::unordered_map<std::string, TypeProto*>& valueTypesByName,
+      const std::unordered_map<std::string, const TensorProto*>& inputDataByName,
+      std::unordered_map<std::string, TensorShapeProto>& generatedShapeData)
+      : generatedShapeData_(generatedShapeData) {
+    size_t input_idx = 0;
+  
+    for (auto& attr : *n.mutable_attribute()) {
+      attributesByName_[attr.name()] = &attr;
+    }
+
+    for (const auto& input : n.input()) {
+      inputIndexToNameMap_.insert({input_idx++, input});
+
+      auto valueTypesIter = valueTypesByName.find(input);
+      if (valueTypesIter != valueTypesByName.end()) {
+        allInputTypes_.push_back(valueTypesIter->second);
+      } else {
+        allInputTypes_.push_back(nullptr);
+      }
+
+      const auto inputDataIter = inputDataByName.find(input);
+      if (inputDataIter != inputDataByName.cend()) {
+        allInputData_.push_back(inputDataIter->second);
+      } else {
+        allInputData_.push_back(nullptr);
+      }
+    }
+
+    size_t output_idx = 0;
+    for (const auto& output : n.output()) {
+      outputIndexToNameMap_.insert({output_idx++, output});
+    }
+
+    allOutputTypes_.resize(n.output_size());
+  }
+
+  const AttributeProto* getAttribute(const std::string& name) const override {
+    auto iter = attributesByName_.find(name);
+    if (iter == attributesByName_.end()) {
+      return nullptr;
+    } else {
+      return iter->second;
+    }
+  }
+
+  size_t getNumInputs() const override {
+    return allInputTypes_.size();
+  }
+
+  const TypeProto* getInputType(size_t index) const override {
+    if (index >= allInputTypes_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    return allInputTypes_[index];
+  }
+
+  size_t getNumOutputs() const override {
+    return allOutputTypes_.size();
+  }
+
+  const TypeProto* getOutputType(size_t index) const override {
+    if (index >= allOutputTypes_.size()) {
+      ONNX_THROW("Output " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    return &allOutputTypes_[index];
+  }
+
+  // Convert integer vector into TensorShapeProto
+  template <typename INTEGER>
+  void vectorToTensorShapeProto(const std::vector<INTEGER>& input_vals, TensorShapeProto& converted_tsp) const {
+    for (unsigned int i = 0; i < input_vals.size(); ++i) {
+      converted_tsp.mutable_dim()->Add()->set_dim_value(input_vals[i]);
+    }
+  }
+
+  const TensorShapeProto* getInputData(size_t index) override {
+    if (index >= allInputData_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    const std::string input_name = inputIndexToNameMap_.at(index);
+    // Gets it from previous data propagation
+    auto iter = generatedShapeData_.find(input_name);
+    if (iter != generatedShapeData_.end()) {
+      return &iter->second;
+    }
+    // Otherwise, gets it from initializer if it exists
+    const auto* input_data = allInputData_[index];
+    // Only scalar (0D tensor) or 1D tensor can be converted for now
+    // TODO: It should support tensors with more dimension on demand
+    if (input_data != nullptr &&
+        (input_data->dims_size() == 0 || input_data->dims_size() == 1)) {
+        TensorShapeProto tsp;
+
+        if (input_data->data_type() == TensorProto_DataType_INT64) {
+          vectorToTensorShapeProto(ParseData<int64_t>(input_data), tsp);
+        } else if (input_data->data_type() == TensorProto_DataType_INT32) {
+          vectorToTensorShapeProto(ParseData<int32_t>(input_data), tsp);
+        } else {
+          // Only supports integer type to form a shape
+          return nullptr;
+        }
+
+        // Adds this TensorShapeProto from initializer into generatedShapeData
+        // for future use
+        auto result = generatedShapeData_.insert({input_name, std::move(tsp)});
+        if (result.second) {
+          return &(result.first->second);
+        }
+    }
+    return nullptr;
+  }
+
+  void addOutputData(size_t index, TensorShapeProto&& tsp) override {
+    if (index >= outputIndexToNameMap_.size()) {
+      ONNX_THROW("Input " + ONNX_NAMESPACE::to_string(index) + " is out of bounds.");
+    }
+    auto result = generatedShapeData_.insert({outputIndexToNameMap_.at(index), std::move(tsp)});
+    if (!result.second) {
+      fail_shape_inference("Data for input  " + ONNX_NAMESPACE::to_string(index) + " already exists.");
+    }
+  }
+
+  std::vector<const TensorProto*> allInputData_;
+  std::unordered_map<size_t, std::string> inputIndexToNameMap_;
+  std::unordered_map<size_t, std::string> outputIndexToNameMap_;
+  std::vector<const TypeProto*> allInputTypes_;
+  std::vector<TypeProto> allOutputTypes_;
+  std::unordered_map<std::string, TensorShapeProto>& generatedShapeData_;
+  std::unordered_map<std::string, const AttributeProto*> attributesByName_;
+};
+
+void checkShapesAndTypes(
+    const TypeProto_Sequence& inferredType,
+    const TypeProto_Sequence& existingType);
+
+void checkShapesAndTypes(
+    const TypeProto& inferredType,
+    const TypeProto& existingType);
+
+template <typename TensorTypeProto>
+void GenerateSymbolicShape(TensorTypeProto* inferredType, SymbolTable& symbolTable);
+
+void MaterializeSymbolicShape(TypeProto* inferredType, SymbolTable& symbolTable);
+
+void mergeShapesAndTypes(const TypeProto_Tensor& inferredType, TypeProto_Tensor* existingType);
+
+void mergeShapesAndTypes(const TypeProto_SparseTensor& inferredType, TypeProto_SparseTensor* existingType);
+
+void mergeShapesAndTypes(const TypeProto_Sequence& inferredType, TypeProto_Tensor* existingType);
+
+void mergeShapesAndTypes(const TypeProto& inferredType, TypeProto* existingType);
+
+void InferShapes(
+    ModelProto& m,
+    const ISchemaRegistry* schema_registry = OpSchemaRegistry::Instance(),
+    const ShapeInferenceOptions& options = {}
+    );
+
+///
+/// ModelLocalFunctionsMap is a map of function id -> model local function proto
+/// All the ONNX helper utilities expect the function id == <function_proto.domain>:<function_proto.name>
+///
+void InferShapes(
+    GraphProto* g,
+    const std::unordered_map<std::string, int>& opset_imports,
+    const ISchemaRegistry* schema_registry = OpSchemaRegistry::Instance(),
+    const ShapeInferenceOptions& options = {},
+    const ModelLocalFunctionsMap& in_model_functions = {}
+    );
+
+void InferShapes(
+    const std::string& model_path,
+    const std::string& save_path = "",
+    const ISchemaRegistry* schema_registry = OpSchemaRegistry::Instance(),
+    const ShapeInferenceOptions& options = {}
+    );
+
+///
+/// ModelLocalFunctionsMap is a map of function id -> model local function proto
+/// All the ONNX helper utilities expect the function id == <function_proto.domain>:<function_proto.name>
+///
+void InferShapeForFunctionNode(
+    const FunctionProto& func,
+    const ISchemaRegistry* schema_registry,
+    InferenceContext& ctx,
+    const ShapeInferenceOptions& options = {},
+    const ModelLocalFunctionsMap& model_local_functions_map = {},
+    SymbolTable* symbolTable = nullptr,
+    std::unordered_map<std::string, TensorShapeProto>* generatedShapeDataByName = nullptr);
+
+///
+/// ModelLocalFunctionsMap is a map of function id -> model local function proto
+/// All the ONNX helper utilities expect the function id == <function_proto.domain>:<function_proto.name>
+///
+void InferShapeForFunctionNode(
+    const FunctionProto& func_proto,
+    const std::unordered_map<std::string, int>& func_opset_imports,
+    const ISchemaRegistry* schema_registry,
+    InferenceContext& ctx,
+    const ShapeInferenceOptions& options = {},
+    const ModelLocalFunctionsMap& model_local_functions_map = {},
+    SymbolTable* symbolTable = nullptr,
+    std::unordered_map<std::string, TensorShapeProto>* generated_shape_data_by_name = nullptr);
+
+std::string GetErrorWithNodeInfo(NodeProto n, std::runtime_error err);
+
+void TraverseGraphsToAddExistingSymbols(const GraphProto& g, SymbolTable& symbolTable);
+
+} // namespace shape_inference
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/string_utils.h b/third_party/onnx/onnx/string_utils.h
new file mode 100644
index 0000000000..03bbedb8f1
--- /dev/null
+++ b/third_party/onnx/onnx/string_utils.h
@@ -0,0 +1,62 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <sstream>
+#include <string>
+
+namespace ONNX_NAMESPACE {
+
+#if defined(__ANDROID__)
+template <typename T>
+std::string to_string(T value) {
+  std::ostringstream os;
+  os << value;
+  return os.str();
+}
+
+inline int stoi(const std::string& str) {
+  std::stringstream ss;
+  int n = 0;
+  ss << str;
+  ss >> n;
+  return n;
+}
+
+#else
+using std::stoi;
+using std::to_string;
+#endif // defined(__ANDROID__)
+
+inline void MakeStringInternal(std::stringstream& /*ss*/) {}
+
+template <typename T>
+inline void MakeStringInternal(std::stringstream& ss, const T& t) {
+  ss << t;
+}
+
+template <typename T, typename... Args>
+inline void
+MakeStringInternal(std::stringstream& ss, const T& t, const Args&... args) {
+  MakeStringInternal(ss, t);
+  MakeStringInternal(ss, args...);
+}
+
+template <typename... Args>
+std::string MakeString(const Args&... args) {
+  std::stringstream ss;
+  MakeStringInternal(ss, args...);
+  return std::string(ss.str());
+}
+
+// Specializations for already-a-string types.
+template <>
+inline std::string MakeString(const std::string& str) {
+  return str;
+}
+inline std::string MakeString(const char* c_str) {
+  return std::string(c_str);
+}
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/__init__.pyi b/third_party/onnx/onnx/test/__init__.pyi
new file mode 100644
index 0000000000..d731072e31
--- /dev/null
+++ b/third_party/onnx/onnx/test/__init__.pyi
@@ -0,0 +1,2 @@
+# This file needs to be here to enable mypy type checking for this folder.
+# It doesn't make this a python module for installation purposes as a __init__.pyi would.
diff --git a/third_party/onnx/onnx/test/automatic_upgrade_test.py b/third_party/onnx/onnx/test/automatic_upgrade_test.py
new file mode 100644
index 0000000000..c05b01ef41
--- /dev/null
+++ b/third_party/onnx/onnx/test/automatic_upgrade_test.py
@@ -0,0 +1,1097 @@
+# SPDX-License-Identifier: Apache-2.0
+import onnx
+from onnx import helper, TensorProto, shape_inference, version_converter, ValueInfoProto
+from typing import Text, List, Dict, Any, Union, Callable, Optional, cast
+import string
+import numpy as np  # type: ignore
+import unittest
+
+#####################################################################################
+# Every test creates a model containing a single operator from the lowest possible
+# opset version, upgrades it to the most recent opset version and then runs checker +
+# shape inference on the upgraded model.
+####################################################################################
+
+latest_opset = onnx.defs.onnx_opset_version()
+tested_ops = []
+
+
+class TestAutomaticUpgrade(unittest.TestCase):
+
+    def _test_op_upgrade(
+        self,
+        op: Text,
+        from_opset: int,
+        input_shapes: List[Union[List[Optional[int]], Text]] = [[3, 4, 5]],
+        output_shapes: List[List[Optional[int]]] = [[3, 4, 5]],
+        input_types: Union[List[Any], None] = None,
+        output_types: Union[List[Any], None] = None,
+        initializer: List[Any] = [],
+        attrs: Dict[Text, Any] = {},
+        seq_inputs: List[int] = [],
+        seq_outputs: List[int] = [],
+        optional_inputs: List[int] = [],
+        optional_outputs: List[int] = []
+    ) -> None:
+        global tested_ops
+        tested_ops.append(op)
+
+        n_inputs = len(input_shapes)
+        letters = list(string.ascii_lowercase)[:n_inputs]
+        input_names = [
+            letter if shape != '' else '' for (letter, shape) in zip(letters, input_shapes)
+        ]
+        if input_types is None:
+            input_types = [TensorProto.FLOAT] * n_inputs
+        is_sequence = [0 if id not in seq_inputs else 1 for id in range(n_inputs)]
+        is_optional = [0 if id not in optional_inputs else 1 for id in range(n_inputs)]
+        # turn empty strings into [0] to ease type analysis, even though those entries
+        # will be ignored
+        input_shapes_cast = cast(List[List[int]],
+                [[0] if isinstance(shape, str) else shape for shape in input_shapes]
+        )
+        inputs: List[ValueInfoProto] = []
+        for (name, ttype, shape, is_seq, is_opt) in \
+                zip(input_names, input_types, input_shapes_cast, is_sequence, is_optional):
+            if name != '':
+                if is_seq:
+                    inputs += [helper.make_tensor_sequence_value_info(name, ttype, shape)]
+                elif is_opt:
+                    type_proto = helper.make_tensor_type_proto(ttype, shape)
+                    optional_type_proto = helper.make_optional_type_proto(type_proto)
+                    inputs += [helper.make_value_info(name, optional_type_proto)]
+                else:
+                    inputs += [helper.make_tensor_value_info(name, ttype, shape)]
+
+        n_outputs = len(output_shapes)
+        output_names = list(string.ascii_lowercase)[n_inputs:n_inputs + n_outputs]
+        if output_types is None:
+            output_types = [TensorProto.FLOAT] * n_outputs
+        is_sequence = [0 if id not in seq_outputs else 1 for id in range(n_outputs)]
+        is_optional = [0 if id not in optional_outputs else 1 for id in range(n_outputs)]
+        output_shapes_cast = cast(List[List[int]],
+                [[0] if isinstance(shape, str) else shape for shape in output_shapes]
+        )
+        outputs: List[ValueInfoProto] = []
+        for (name, ttype, shape, is_seq, is_opt) in \
+                zip(output_names, output_types, output_shapes_cast, is_sequence, is_optional):
+            if is_seq:
+                outputs += [helper.make_tensor_sequence_value_info(name, ttype, shape)]
+            elif is_opt:
+                type_proto = helper.make_tensor_type_proto(ttype, shape)
+                optional_type_proto = helper.make_optional_type_proto(type_proto)
+                outputs += [helper.make_value_info(name, optional_type_proto)]
+            else:
+                outputs += [helper.make_tensor_value_info(name, ttype, shape)]
+
+        node = helper.make_node(op, input_names, output_names, **attrs)
+        graph = helper.make_graph([node], op, inputs, outputs, initializer)
+        original = helper.make_model(
+            graph,
+            producer_name='test',
+            opset_imports=[helper.make_opsetid('', from_opset)]
+        )
+        onnx.checker.check_model(original)
+        shape_inference.infer_shapes(original, strict_mode=True)
+
+        converted = version_converter.convert_version(original, latest_opset)
+        onnx.checker.check_model(converted)
+        shape_inference.infer_shapes(converted, strict_mode=True)
+
+    def test_Abs(self) -> None:
+        self._test_op_upgrade('Abs', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Acosh(self) -> None:
+        self._test_op_upgrade('Acosh', 9)
+
+    def test_Acos(self) -> None:
+        self._test_op_upgrade('Acos', 7)
+
+    def test_And(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('And', 7, [[2, 3], [2, 3]], [[2, 3]],
+            [TensorProto.BOOL, TensorProto.BOOL], [TensorProto.BOOL]
+        )
+
+    def test_Asinh(self) -> None:
+        self._test_op_upgrade('Asinh', 9)
+
+    def test_Atanh(self) -> None:
+        self._test_op_upgrade('Atanh', 9)
+
+    def test_Add_1(self) -> None:
+        self._test_op_upgrade('Add', 1,
+            [[3, 4, 5], [3, 4, 5]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_Add_2(self) -> None:
+        self._test_op_upgrade('Add', 1, [[3, 4, 5], [5]],
+            attrs={'consumed_inputs': [0], 'broadcast': 1}
+        )
+
+    def test_Add_3(self) -> None:
+        self._test_op_upgrade('Add', 1, [[3, 4, 5], [3]],
+            attrs={'consumed_inputs': [0], 'broadcast': 1, 'axis': 0}
+        )
+
+    def test_ArgMax_1(self) -> None:
+        self._test_op_upgrade('ArgMax', 7, [[2, 3, 4]], [[1, 3, 4]],
+            output_types=[TensorProto.INT64]
+        )
+
+    def test_ArgMax_2(self) -> None:
+        self._test_op_upgrade('ArgMax', 7, [[2, 3, 4]], [[2, 1, 4]],
+            output_types=[TensorProto.INT64],
+            attrs={'axis': 1}
+        )
+
+    def test_ArgMin_1(self) -> None:
+        self._test_op_upgrade('ArgMin', 7, [[2, 3, 4]], [[1, 3, 4]],
+            output_types=[TensorProto.INT64]
+        )
+
+    def test_ArgMin_2(self) -> None:
+        self._test_op_upgrade('ArgMin', 7, [[2, 3, 4]], [[2, 1, 4]],
+            output_types=[TensorProto.INT64],
+            attrs={'axis': 1}
+        )
+
+    def test_Asin(self) -> None:
+        self._test_op_upgrade('Asin', 7)
+
+    def test_Atan(self) -> None:
+        self._test_op_upgrade('Atan', 7)
+
+    def test_AveragePool(self) -> None:
+        self._test_op_upgrade('AveragePool', 1, [[1, 1, 5, 5]], [[1, 1, 4, 4]],
+            attrs={'kernel_shape': [2, 2]}
+        )
+
+    def test_Bernoulli(self) -> None:
+        self._test_op_upgrade('Bernoulli', 15)
+
+    def test_BitShift(self) -> None:
+        self._test_op_upgrade('BitShift', 11, [[2, 3], [2, 3]], [[2, 3]],
+            [TensorProto.UINT8, TensorProto.UINT8], [TensorProto.UINT8],
+            attrs={'direction': 'RIGHT'}
+        )
+
+    def test_BatchNormalization_1(self) -> None:
+        self._test_op_upgrade('BatchNormalization', 1, [[1, 3], [3], [3], [3], [3]], [[1, 3]],
+            attrs={'consumed_inputs': [1, 1], 'is_test': 1, 'spatial': 1}
+        )
+
+    def test_BatchNormalization_2(self) -> None:
+        self._test_op_upgrade('BatchNormalization', 14,
+            [[1, 3], [3], [3], [3], [3]], [[1, 3], [3], [3]],
+            attrs={'training_mode': 1}
+        )
+
+    def test_Cast(self) -> None:
+        # 5->6 adapter is missing
+        self._test_op_upgrade('Cast', 6, [[2, 3]], [[2, 3]], [TensorProto.INT64], attrs={'to': 1})
+
+    def test_Ceil(self) -> None:
+        self._test_op_upgrade('Ceil', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Celu(self) -> None:
+        self._test_op_upgrade('Celu', 12)
+
+    def test_Clip_1(self) -> None:
+        self._test_op_upgrade('Clip', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Clip_2(self) -> None:
+        self._test_op_upgrade('Clip', 1, attrs={'consumed_inputs': [0], 'min': -1.4})
+
+    def test_Clip_3(self) -> None:
+        self._test_op_upgrade('Clip', 1, attrs={'consumed_inputs': [0], 'max': 2.6})
+
+    def test_Clip_4(self) -> None:
+        self._test_op_upgrade('Clip', 1, attrs={'consumed_inputs': [0], 'min': -1.4, 'max': 2.6})
+
+    def test_Compress(self) -> None:
+        self._test_op_upgrade('Compress', 9, [[6, 7], [3]], [[3]],
+            [TensorProto.FLOAT, TensorProto.BOOL], [TensorProto.FLOAT]
+        )
+
+    def test_Concat(self) -> None:
+        self._test_op_upgrade('Concat', 1, [[2, 3], [2, 4]], [[2, 7]])
+
+    def test_constant(self) -> None:
+        value = helper.make_tensor(
+            'Value',
+            TensorProto.FLOAT,
+            dims=[3, 4, 5],
+            vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(),
+            raw=True
+        )
+        self._test_op_upgrade('Constant', 1, [], attrs={'value': value})
+
+    def test_ConstantOfShape(self) -> None:
+        self._test_op_upgrade('ConstantOfShape', 9, [[3]])
+
+    def test_Conv_1(self) -> None:
+        self._test_op_upgrade('Conv', 1, [[1, 3, 5, 5], [4, 3, 2, 2], [4]], [[1, 4, 4, 4]])
+
+    def test_Conv_2(self) -> None:
+        self._test_op_upgrade('Conv', 1, [[1, 3, 5, 5], [4, 3, 2, 2], [4]], [[1, 4, 4, 4]])
+
+    def test_Conv_3(self) -> None:
+        self._test_op_upgrade('Conv', 1, [[1, 3, 5, 5], [4, 1, 2, 2], [4]], [[1, 4, 3, 7]],
+            attrs={'dilations': [1, 2], 'group': 3, 'pads': [0, 1, 2, 3], 'strides': [2, 1]})
+
+    def test_Convinteger(self) -> None:
+        self._test_op_upgrade('ConvInteger', 10, [[1, 3, 5, 5], [4, 3, 2, 2], [4]], [[1, 4, 4, 4]],
+            [TensorProto.UINT8, TensorProto.UINT8, TensorProto.UINT8], [TensorProto.INT32]
+        )
+
+    def test_ConvTranspose(self) -> None:
+        self._test_op_upgrade('ConvTranspose', 1, [[1, 1, 5, 5], [1, 1, 3, 3]], [[1, 1, 7, 7]])
+
+    def test_Cosh(self) -> None:
+        self._test_op_upgrade('Cosh', 9)
+
+    def test_Cos(self) -> None:
+        self._test_op_upgrade('Cos', 7)
+
+    def test_Cumsum(self) -> None:
+        self._test_op_upgrade('CumSum', 11, [[3, 4, 5], []], [[3, 4, 5]],
+            [TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_DepthToSpace(self) -> None:
+        self._test_op_upgrade('DepthToSpace', 1, [[1, 8, 3, 3]], [[1, 2, 6, 6]],
+            attrs={'blocksize': 2}
+        )
+
+    def test_DequantizeLinear(self) -> None:
+        self._test_op_upgrade('DequantizeLinear', 10, [[2, 3], [], []], [[2, 3]],
+            [TensorProto.INT8, TensorProto.FLOAT, TensorProto.INT8]
+        )
+
+    def test_Det_1(self) -> None:
+        self._test_op_upgrade('Det', 11, [[3, 5, 5]], [[3]])
+
+    def test_Det_2(self) -> None:
+        self._test_op_upgrade('Det', 11, [[5, 5]], [[]])
+
+    def test_DynamicQuantizeLinear(self) -> None:
+        self._test_op_upgrade('DynamicQuantizeLinear', 11, [[3, 4, 5]], [[3, 4, 5], [], []],
+            output_types=[TensorProto.UINT8, TensorProto.FLOAT, TensorProto.UINT8]
+        )
+
+    def test_Div(self) -> None:
+        self._test_op_upgrade('Div', 1, [[3, 4, 5], [3, 1, 5]], attrs={'consumed_inputs': [0]})
+
+    def test_Dropout(self) -> None:
+        self._test_op_upgrade('Dropout', 1, attrs={'consumed_inputs': [0], 'is_test': 1})
+
+    def test_Einsum_1(self) -> None:
+        self._test_op_upgrade('Einsum', 12, [[3, 4, 5], [3, 5, 6]], [[3, 4, 6]],
+            attrs={'equation': 'bij, bjk -> bik'}
+        )
+
+    def test_Einsum_2(self) -> None:
+        self._test_op_upgrade('Einsum', 12, [[4, 5]], [[5, 4]],
+            attrs={'equation': 'ij->ji'}
+        )
+
+    def test_Elu(self) -> None:
+        self._test_op_upgrade('Elu', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Equal(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('Equal', 7, [[2, 3], [2, 3]], [[2, 3]], output_types=[TensorProto.BOOL])
+
+    def test_Erf(self) -> None:
+        self._test_op_upgrade('Erf', 9)
+
+    def test_Exp(self) -> None:
+        self._test_op_upgrade('Exp', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Expand(self) -> None:
+        shape = helper.make_tensor(
+            'b',
+            TensorProto.INT64,
+            dims=[4],
+            vals=np.array([5, 2, 6, 4])
+        )
+        self._test_op_upgrade('Expand', 8, [[2, 1, 4], [4]], [[5, 2, 6, 4]],
+            [TensorProto.FLOAT, TensorProto.INT64],
+            initializer=[shape]
+        )
+
+    def test_EyeLike(self) -> None:
+        self._test_op_upgrade('EyeLike', 9, [[4, 5]], [[4, 5]])
+
+    def test_Flatten(self) -> None:
+        self._test_op_upgrade('Flatten', 1, [[3, 4, 5]], [[3, 20]], attrs={'axis': 1})
+
+    def test_Floor(self) -> None:
+        self._test_op_upgrade('Floor', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Gather(self) -> None:
+        self._test_op_upgrade('Gather', 1, [[3, 4, 5], [6, 7]], [[6, 7, 4, 5]],
+            [TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_GatherElements(self) -> None:
+        self._test_op_upgrade('GatherElements', 11, [[3, 4, 5], [6, 7]], [[6, 7]],
+            [TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_GatherND(self) -> None:
+        self._test_op_upgrade('GatherND', 11, [[1, 2, 3], [1, 2, 3]], [[1, 2]])
+
+    def test_Gemm(self) -> None:
+        self._test_op_upgrade('Gemm', 1, [[5, 4], [4, 3], [3]], [[5, 3]])
+
+    def test_GlobalAveragePool(self) -> None:
+        self._test_op_upgrade('GlobalAveragePool', 1, [[1, 3, 10, 10]], [[1, 3, 1, 1]])
+
+    def test_GlobalMaxPool(self) -> None:
+        self._test_op_upgrade('GlobalMaxPool', 1, [[1, 3, 10, 10]], [[1, 3, 1, 1]])
+
+    def test_GlobalLpPool(self) -> None:
+        # 1->2 adapter is missing
+        self._test_op_upgrade('GlobalLpPool', 2, [[1, 3, 10, 10]], [[1, 3, 1, 1]])
+
+    def test_Greater(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('Greater', 7, [[2, 3], [2, 3]], [[2, 3]],
+            output_types=[TensorProto.BOOL]
+        )
+
+    def test_GreaterOrEqual(self) -> None:
+        self._test_op_upgrade('GreaterOrEqual', 12, [[2, 3], [2, 3]], [[2, 3]],
+            output_types=[TensorProto.BOOL]
+        )
+
+    def test_GridSample(self) -> None:
+        self._test_op_upgrade('GridSample', 16, [[1, 1, 3, 3], [1, 3, 3, 2]], [[1, 1, 3, 3]],
+            input_types=[TensorProto.FLOAT, TensorProto.FLOAT],
+            output_types=[TensorProto.FLOAT],
+            attrs={'mode': 'nearest', 'padding_mode': 'border', 'align_corners': 1},
+        )
+
+    def test_GRU_1(self) -> None:
+        # 2->3, 6->7 adapters are missing
+        self._test_op_upgrade('GRU', 7,
+            [[5, 3, 4], [1, 18, 4], [1, 18, 4]], [[5, 1, 3, 6], [1, 3, 6]],
+            attrs={'hidden_size': 6}
+        )
+
+    def test_GRU_2(self) -> None:
+        # 2->3, 6->7 adapters are missing
+        self._test_op_upgrade('GRU', 7,
+            [[5, 3, 4], [2, 18, 4], [2, 18, 4]], [[5, 2, 3, 6], [2, 3, 6]],
+            attrs={'hidden_size': 6, 'direction': 'bidirectional'}
+        )
+
+    def test_GRU_3(self) -> None:
+        # 2->3, 6->7 adapters are missing
+        self._test_op_upgrade('GRU', 7,
+            [[5, 3, 4], [1, 18, 4], [1, 18, 4], [1, 24], [5], [1, 5, 6]],
+            [[5, 1, 3, 6], [1, 3, 6]],
+            [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            attrs={'hidden_size': 6}
+        )
+
+    def test_HardSigmoid(self) -> None:
+        self._test_op_upgrade('HardSigmoid', 1, attrs={'consumed_inputs': [0]})
+
+    def test_HardSwish(self) -> None:
+        self._test_op_upgrade('HardSwish', 14)
+
+    def test_Hardmax(self) -> None:
+        self._test_op_upgrade('Hardmax', 1)
+
+    def test_Identity(self) -> None:
+        self._test_op_upgrade('Identity', 1)
+
+    def test_If(self) -> None:
+        sub_output = [helper.make_tensor_value_info('out', TensorProto.FLOAT, [3, 4, 5])]
+        then_tensor = helper.make_tensor(
+            'Value',
+            TensorProto.FLOAT,
+            dims=[3, 4, 5],
+            vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(), raw=True
+        )
+        then_node = helper.make_node('Constant', [], ['out'], value=then_tensor)
+        then_graph = helper.make_graph([then_node], 'then_graph', [], sub_output, [])
+        else_tensor = helper.make_tensor(
+            'Value',
+            TensorProto.FLOAT,
+            dims=[3, 4, 5],
+            vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(), raw=True
+        )
+        else_node = helper.make_node('Constant', [], ['out'], value=else_tensor)
+        else_graph = helper.make_graph([else_node], 'else_graph', [], sub_output, [])
+        self._test_op_upgrade('If', 1, [[0]], [[3, 4, 5]], [TensorProto.BOOL],
+            attrs={'then_branch': then_graph, 'else_branch': else_graph}
+        )
+
+    def test_InstanceNormalization(self) -> None:
+        self._test_op_upgrade('InstanceNormalization', 1, [[1, 3], [3], [3]], [[1, 3]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_IsInf(self) -> None:
+        self._test_op_upgrade('IsInf', 10, [[2, 3]], [[2, 3]], output_types=[TensorProto.BOOL])
+
+    def test_IsNaN(self) -> None:
+        self._test_op_upgrade('IsNaN', 9, [[2, 3]], [[2, 3]], output_types=[TensorProto.BOOL])
+
+    def test_LeakyRelu(self) -> None:
+        self._test_op_upgrade('LeakyRelu', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Less(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('Less', 7, [[2, 3], [2, 3]], [[2, 3]], output_types=[TensorProto.BOOL])
+
+    def test_LessOrEqual(self) -> None:
+        self._test_op_upgrade('LessOrEqual', 12, [[2, 3], [2, 3]], [[2, 3]],
+            output_types=[TensorProto.BOOL]
+        )
+
+    def test_Log(self) -> None:
+        self._test_op_upgrade('Log', 1, attrs={'consumed_inputs': [0]})
+
+    def test_LogSoftmax(self) -> None:
+        self._test_op_upgrade('LogSoftmax', 1)
+
+    def test_Loop_1(self) -> None:
+        iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+        cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+        x_in = onnx.helper.make_tensor_value_info('x_in', onnx.TensorProto.FLOAT, [1])
+        cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+        x_out = onnx.helper.make_tensor_value_info('x_out', onnx.TensorProto.FLOAT, [1])
+        x_scan = onnx.helper.make_tensor_value_info('x_scan', onnx.TensorProto.FLOAT, [1])
+        const = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['one'],
+            value=onnx.helper.make_tensor(
+                name='value',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=[1],
+                vals=np.array([1]).astype(np.float32).astype(float),
+            )
+        )
+        add = onnx.helper.make_node(
+            'Add',
+            inputs=['x_in', 'one'],
+            outputs=['x_out']
+        )
+        id_1 = onnx.helper.make_node(
+            'Identity',
+            inputs=['x_out'],
+            outputs=['x_scan']
+        )
+        id_2 = onnx.helper.make_node(
+            'Identity',
+            inputs=['cond_in'],
+            outputs=['cond_out']
+        )
+        loop_body = onnx.helper.make_graph(
+            [const, add, id_1, id_2],
+            'loop_body',
+            [iter_count, cond_in, x_in],
+            [cond_out, x_out, x_scan]
+        )
+        self._test_op_upgrade('Loop', 1, [[], '', [1]], [[1], [5, 1]],
+            [TensorProto.INT64, TensorProto.BOOL, TensorProto.FLOAT],
+            attrs={'body': loop_body}
+        )
+
+    def test_Loop_2(self) -> None:
+        iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+        cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+        x_in = onnx.helper.make_tensor_value_info('x_in', onnx.TensorProto.FLOAT, [2, 1])
+        cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+        x_out = onnx.helper.make_tensor_value_info('x_out', onnx.TensorProto.FLOAT, [2, 1])
+        squeeze = onnx.helper.make_node(
+            'Squeeze',
+            inputs=['x_in'],
+            outputs=['squeeze_out'],
+            axes=[1]
+        )
+        unsqueeze = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['squeeze_out'],
+            outputs=['x_out'],
+            axes=[1]
+        )
+        identity = onnx.helper.make_node(
+            'Identity',
+            inputs=['cond_in'],
+            outputs=['cond_out']
+        )
+        loop_body = onnx.helper.make_graph(
+            [squeeze, unsqueeze, identity],
+            'loop_body',
+            [iter_count, cond_in, x_in],
+            [cond_out, x_out]
+        )
+        self._test_op_upgrade('Loop', 12, [[], '', [2, 1]], [[2, 1]],
+            [TensorProto.INT64, TensorProto.BOOL, TensorProto.FLOAT],
+            attrs={'body': loop_body}
+        )
+
+    def test_LpNormalization(self) -> None:
+        self._test_op_upgrade('LpNormalization', 1)
+
+    def test_LpPool(self) -> None:
+        # 1->2 adapter is missing
+        self._test_op_upgrade('LpPool', 2, [[1, 1, 5, 5]], [[1, 1, 4, 4]],
+            attrs={'kernel_shape': [2, 2]}
+        )
+
+    def test_LRN_1(self) -> None:
+        self._test_op_upgrade('LRN', 1, attrs={'size': 3})
+
+    def test_LRN_2(self) -> None:
+        self._test_op_upgrade('LRN', 1, [[2, 3, 4, 5]], [[2, 3, 4, 5]],
+            attrs={'size': 3}
+        )
+
+    def test_LSTM_1(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('LSTM', 7,
+            [[5, 3, 4], [1, 24, 4], [1, 24, 4]],
+            [[5, 1, 3, 6], [1, 3, 6], [1, 3, 6]],
+            attrs={'hidden_size': 6}
+        )
+
+    def test_LSTM_2(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('LSTM', 7,
+            [[5, 3, 4], [2, 24, 4], [2, 24, 4]],
+            [[5, 2, 3, 6], [2, 3, 6], [2, 3, 6]],
+            attrs={'hidden_size': 6, 'direction': 'bidirectional'}
+        )
+
+    def test_LSTM_3(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('LSTM', 7,
+            [[5, 3, 4], [1, 24, 4], [1, 24, 4], [1, 48], [5], [1, 5, 6], [1, 5, 6], [1, 18]],
+            [[5, 1, 3, 6], [1, 3, 6], [1, 3, 6]],
+            [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT],
+            attrs={'hidden_size': 6}
+        )
+
+    def test_MatMul_1(self) -> None:
+        self._test_op_upgrade('MatMul', 1, [[2, 3], [3, 4]], [[2, 4]])
+
+    def test_MatMul_2(self) -> None:
+        self._test_op_upgrade('MatMul', 1, [[5, 2, 3], [5, 3, 4]], [[5, 2, 4]])
+
+    def test_MatMulInteger_1(self) -> None:
+        self._test_op_upgrade('MatMulInteger', 10, [[2, 3], [3, 4]], [[2, 4]],
+            [TensorProto.INT8, TensorProto.INT8], [TensorProto.INT32]
+        )
+
+    def test_MatMulInteger_2(self) -> None:
+        self._test_op_upgrade('MatMulInteger', 10, [[2, 3], [3, 4], [], []], [[2, 4]],
+            [TensorProto.INT8, TensorProto.INT8, TensorProto.INT8, TensorProto.INT8],
+            [TensorProto.INT32]
+        )
+
+    def test_MatMulInteger_3(self) -> None:
+        self._test_op_upgrade('MatMulInteger', 10, [[2, 3], [3, 4], [2], [4]], [[2, 4]],
+            [TensorProto.INT8, TensorProto.INT8, TensorProto.INT8, TensorProto.INT8],
+            [TensorProto.INT32]
+        )
+
+    def test_Max(self) -> None:
+        self._test_op_upgrade('Max', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_MaxPool_1(self) -> None:
+        self._test_op_upgrade('MaxPool', 1, [[1, 1, 5, 5]], [[1, 1, 4, 4]],
+            attrs={'kernel_shape': [2, 2]}
+        )
+
+    def test_MaxPool_2(self) -> None:
+        self._test_op_upgrade('MaxPool', 8, [[1, 1, 5, 5]], [[1, 1, 4, 4], [1, 1, 4, 4]],
+            output_types=[TensorProto.FLOAT, TensorProto.INT64],
+            attrs={'kernel_shape': [2, 2]}
+        )
+
+    def test_MaxRoiPool(self) -> None:
+        self._test_op_upgrade('MaxRoiPool', 1, [[2, 3, 20, 20], [4, 5]], [[4, 3, 3, 3]],
+            attrs={'pooled_shape': [3, 3]}
+        )
+
+    def test_MaxUnpool(self) -> None:
+        self._test_op_upgrade('MaxUnpool', 9, [[1, 1, 5, 5], [1, 1, 5, 5]], [[1, 1, 6, 6]],
+            [TensorProto.FLOAT, TensorProto.INT64],
+            attrs={'kernel_shape': [2, 2]}
+        )
+
+    def test_Mean(self) -> None:
+        self._test_op_upgrade('Mean', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_MeanVarianceNormalization(self) -> None:
+        self._test_op_upgrade('MeanVarianceNormalization', 9, attrs={'axes': [1, 2]})
+
+    def test_Min(self) -> None:
+        self._test_op_upgrade('Min', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_Mod_1(self) -> None:
+        self._test_op_upgrade('Mod', 10, [[2, 3], [2, 3]], [[2, 3]])
+
+    def test_Mod_2(self) -> None:
+        self._test_op_upgrade('Mod', 10, [[2, 3], [2, 3]], [[2, 3]], attrs={'fmod': 1})
+
+    def test_Mul(self) -> None:
+        self._test_op_upgrade('Mul', 1, [[2, 3, 4], [2, 1, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_Multinomial(self) -> None:
+        self._test_op_upgrade('Multinomial', 7, [[3, 5]], [[3, 7]],
+            output_types=[TensorProto.INT32],
+            attrs={'sample_size': 7}
+        )
+
+    def test_Neg(self) -> None:
+        self._test_op_upgrade('Neg', 1, attrs={'consumed_inputs': [0]})
+
+    def test_NegativeLogLikelihoodLoss_1(self) -> None:
+        self._test_op_upgrade('NegativeLogLikelihoodLoss', 12, [[3, 4, 5], [3, 5]], [[]],
+            [TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_NegativeLogLikelihoodLoss_2(self) -> None:
+        self._test_op_upgrade('NegativeLogLikelihoodLoss', 12, [[3, 4, 5], [3, 5], [4]], [[]],
+            [TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT]
+        )
+
+    def test_NonMaxSuppression(self) -> None:
+        self._test_op_upgrade('NonMaxSuppression', 10, [[2, 3, 4], [3, 5, 6]], [[2, 3]],
+            output_types=[TensorProto.INT64]
+        )
+
+    def test_NonZero(self) -> None:
+        self._test_op_upgrade('NonZero', 9, [[3, 3]], [[2, 4]], output_types=[TensorProto.INT64])
+
+    def test_Not(self) -> None:
+        self._test_op_upgrade('Not', 1, [[2, 3]], [[2, 3]], [TensorProto.BOOL], [TensorProto.BOOL])
+
+    def test_OneHot(self) -> None:
+        self._test_op_upgrade('OneHot', 9, [[3, 4, 5], [], [2]], [[3, 4, 5, 6]])
+
+    def test_Or(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('Or', 7, [[2, 3], [2, 3]], [[2, 3]],
+            [TensorProto.BOOL, TensorProto.BOOL], [TensorProto.BOOL]
+        )
+
+    def test_Pad(self) -> None:
+        # 1->2 adapter is missing
+        self._test_op_upgrade('Pad', 2, [[3, 4]], [[5, 8]],
+            attrs={'pads': [1, 2, 1, 2], 'value': 1.5}
+        )
+
+    def test_Pow(self) -> None:
+        self._test_op_upgrade('Pow', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]])
+
+    def test_PRelu(self) -> None:
+        self._test_op_upgrade('PRelu', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_QLinearConv(self) -> None:
+        self._test_op_upgrade('QLinearConv', 10,
+            [[1, 3, 5, 5], [], [], [4, 3, 2, 2], [], [], [], []], [[1, 4, 4, 4]]
+        )
+
+    def test_QLinearMatMul(self) -> None:
+        self._test_op_upgrade('QLinearMatMul', 10, [[2, 3], [], [], [3, 4], [], [], [], []], [[2, 4]])
+
+    def test_QuantizeLinear(self) -> None:
+        self._test_op_upgrade('QuantizeLinear', 10, [[3, 4, 5], [], []], [[3, 4, 5]],
+            [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.UINT8], [TensorProto.UINT8]
+        )
+
+    def test_RandomNormal(self) -> None:
+        self._test_op_upgrade('RandomNormal', 1, [], [[3, 4, 5]], attrs={'shape': [3, 4, 5]})
+
+    def test_RandomNormalLike(self) -> None:
+        like = helper.make_tensor(
+            'a',
+            TensorProto.FLOAT,
+            dims=[3, 4, 5],
+            vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(), raw=True
+        )
+        self._test_op_upgrade('RandomNormalLike', 1, [[3, 4, 5]], [[3, 4, 5]],
+            initializer=[like]
+        )
+
+    def test_RandomUniform(self) -> None:
+        self._test_op_upgrade('RandomUniform', 1, [], [[3, 4, 5]], attrs={'shape': [3, 4, 5]})
+
+    def test_RandomUniformLike(self) -> None:
+        like = helper.make_tensor(
+            'a',
+            TensorProto.FLOAT,
+            dims=[3, 4, 5],
+            vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(), raw=True
+        )
+        self._test_op_upgrade('RandomUniformLike', 1, [[3, 4, 5]], [[3, 4, 5]],
+            initializer=[like]
+        )
+
+    def test_Range(self) -> None:
+        start = helper.make_tensor('a', TensorProto.FLOAT, dims=[], vals=np.array([0]))
+        end = helper.make_tensor('b', TensorProto.FLOAT, dims=[], vals=np.array([12]))
+        step = helper.make_tensor('c', TensorProto.FLOAT, dims=[], vals=np.array([2]))
+        self._test_op_upgrade('Range', 11, [[], [], []], [[6]],
+            initializer=[start, end, step]
+        )
+
+    def test_Reciprocal(self) -> None:
+        self._test_op_upgrade('Reciprocal', 1, attrs={'consumed_inputs': [0]})
+
+    def test_ReduceL1(self) -> None:
+        self._test_op_upgrade('ReduceL1', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceL2(self) -> None:
+        self._test_op_upgrade('ReduceL2', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceLogSum(self) -> None:
+        self._test_op_upgrade('ReduceLogSum', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceLogSumExp(self) -> None:
+        self._test_op_upgrade('ReduceLogSumExp', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceMean(self) -> None:
+        self._test_op_upgrade('ReduceMean', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceMax(self) -> None:
+        self._test_op_upgrade('ReduceMax', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceMin(self) -> None:
+        self._test_op_upgrade('ReduceMin', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceProd(self) -> None:
+        self._test_op_upgrade('ReduceProd', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceSum(self) -> None:
+        self._test_op_upgrade('ReduceSum', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_ReduceSumSquare(self) -> None:
+        self._test_op_upgrade('ReduceSumSquare', 1, [[3, 4, 5]], [[1, 1, 1]])
+
+    def test_Relu(self) -> None:
+        self._test_op_upgrade('Relu', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Reshape(self) -> None:
+        self._test_op_upgrade('Reshape', 1, [[3, 4, 5]], [[3, 10, 2]],
+            attrs={'consumed_inputs': [0], 'shape': [3, 10, 2]}
+        )
+
+    def test_Resize(self) -> None:
+        self._test_op_upgrade('Resize', 10, [[3, 4, 5], [3]], [[3, 8, 15]])
+
+    def test_ReverseSequence(self) -> None:
+        self._test_op_upgrade('ReverseSequence', 10, [[3, 4, 5], [4]], [[3, 4, 5]],
+            [TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_RNN_1(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('RNN', 7, [[5, 3, 4], [1, 6, 4], [1, 6, 4]], [[5, 1, 3, 6], [1, 3, 6]],
+            attrs={'hidden_size': 6}
+        )
+
+    def test_RNN_2(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('RNN', 7, [[5, 3, 4], [2, 6, 4], [2, 6, 4]], [[5, 2, 3, 6], [2, 3, 6]],
+            attrs={'hidden_size': 6, 'direction': 'bidirectional'}
+        )
+
+    def test_RNN_3(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('RNN', 7,
+            [[5, 3, 4], [1, 6, 4], [1, 6, 4], [1, 12], [5], [1, 5, 6]],
+            [[5, 1, 3, 6], [1, 3, 6]],
+            [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            attrs={'hidden_size': 6}
+        )
+
+    def test_RoiAlign_1(self) -> None:
+        self._test_op_upgrade('RoiAlign', 10, [[2, 3, 20, 20], [10, 4], [10]], [[10, 3, 1, 1]],
+            [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_RoiAlign_2(self) -> None:
+        self._test_op_upgrade('RoiAlign', 16, [[2, 3, 20, 20], [10, 4], [10]], [[10, 3, 1, 1]],
+            [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64],
+            attrs={'coordinate_transformation_mode': 'half_pixel'}
+        )
+
+    def test_Round(self) -> None:
+        self._test_op_upgrade('Round', 11)
+
+    def test_Scatter(self) -> None:
+        self._test_op_upgrade('Scatter', 9, [[2, 3], [1, 2], [1, 2]], [[2, 3]],
+            [TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            [TensorProto.FLOAT]
+        )
+
+    def test_ScatterElements_1(self) -> None:
+        self._test_op_upgrade('ScatterElements', 11, [[2, 3], [1, 2], [1, 2]], [[2, 3]],
+            [TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            [TensorProto.FLOAT]
+        )
+
+    def test_ScatterElements_2(self) -> None:
+        self._test_op_upgrade('ScatterElements', 16, [[2, 3], [1, 2], [1, 2]], [[2, 3]],
+            [TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            [TensorProto.FLOAT],
+            attrs={'reduction': 'add'}
+        )
+
+    def test_ScatterND_1(self) -> None:
+        self._test_op_upgrade('ScatterND', 11, [[2, 3], [1, 2], [1, 2]], [[2, 3]],
+            [TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            [TensorProto.FLOAT]
+        )
+
+    def test_ScatterND_2(self) -> None:
+        self._test_op_upgrade('ScatterND', 16, [[2, 3], [1, 2], [1, 2]], [[2, 3]],
+            [TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
+            [TensorProto.FLOAT],
+            attrs={'reduction': 'mul'}
+        )
+
+    def test_Scan(self) -> None:
+        sum_in = onnx.helper.make_tensor_value_info('sum_in', onnx.TensorProto.FLOAT, [2])
+        next_in = onnx.helper.make_tensor_value_info('next_in', onnx.TensorProto.FLOAT, [2])
+        sum_out = onnx.helper.make_tensor_value_info('sum_out', onnx.TensorProto.FLOAT, [2])
+        scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [2])
+        add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['sum_in', 'next_in'],
+            outputs=['sum_out']
+        )
+        id_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['sum_out'],
+            outputs=['scan_out']
+        )
+        body = onnx.helper.make_graph(
+            [add_node, id_node],
+            'scan_body',
+            [sum_in, next_in],
+            [sum_out, scan_out]
+        )
+        self._test_op_upgrade('Scan', 8, ['', [1, 2], [1, 3, 2]], [[1, 2], [1, 3, 2]],
+            attrs={'body': body, 'num_scan_inputs': 1}
+        )
+
+    def test_Selu(self) -> None:
+        self._test_op_upgrade('Selu', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Shape(self) -> None:
+        self._test_op_upgrade('Shape', 1, [[3, 4, 5]], [[3]], output_types=[TensorProto.INT64])
+
+    def test_Shrink(self) -> None:
+        self._test_op_upgrade('Shrink', 9)
+
+    def test_Sigmoid(self) -> None:
+        self._test_op_upgrade('Sigmoid', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Sign(self) -> None:
+        self._test_op_upgrade('Sign', 9)
+
+    def test_Sinh(self) -> None:
+        self._test_op_upgrade('Sinh', 9)
+
+    def test_Sin(self) -> None:
+        self._test_op_upgrade('Sin', 7)
+
+    def test_Size(self) -> None:
+        self._test_op_upgrade('Size', 1, [[3, 4, 5]], [[]], output_types=[TensorProto.INT64])
+
+    def test_Slice(self) -> None:
+        self._test_op_upgrade('Slice', 1, [[3, 4, 5]], [[3, 2, 2]],
+            attrs={'axes': [1, 2], 'starts': [0, 1], 'ends': [2, 3]}
+        )
+
+    def test_Softmax_0(self) -> None:
+        self._test_op_upgrade('Softmax', 1, attrs={'axis': 0})
+
+    def test_Softmax_1(self) -> None:
+        self._test_op_upgrade('Softmax', 1, attrs={'axis': 1})
+
+    def test_Softmax_2(self) -> None:
+        self._test_op_upgrade('Softmax', 1, attrs={'axis': 2})
+
+    def test_Softmax_3(self) -> None:
+        self._test_op_upgrade('Softmax', 1, attrs={'axis': -1})
+
+    def test_Softmax_4(self) -> None:
+        self._test_op_upgrade('Softmax', 1, attrs={'axis': -2})
+
+    def test_Softmax_5(self) -> None:
+        self._test_op_upgrade('Softmax', 1, attrs={'axis': -3})
+
+    def test_Softplus(self) -> None:
+        self._test_op_upgrade('Softplus', 1)
+
+    def test_Softsign(self) -> None:
+        self._test_op_upgrade('Softsign', 1)
+
+    def test_SoftmaxCrossEntropyLoss(self) -> None:
+        self._test_op_upgrade('SoftmaxCrossEntropyLoss', 12, [[3, 4, 5, 6], [3, 6]], [[]],
+            [TensorProto.FLOAT, TensorProto.INT64]
+        )
+
+    def test_SpaceToDepth(self) -> None:
+        self._test_op_upgrade('SpaceToDepth', 1, [[1, 3, 8, 8]], [[1, 12, 4, 4]],
+            attrs={'blocksize': 2}
+        )
+
+    def test_Split(self) -> None:
+        # 1->2 adapter is missing
+        self._test_op_upgrade('Split', 2, [[3, 4, 7]], [[3, 4, 2], [3, 4, 1], [3, 4, 4]],
+            attrs={'axis': 2, 'split': [2, 1, 4]}
+        )
+
+    def test_Sqrt(self) -> None:
+        self._test_op_upgrade('Sqrt', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Squeeze(self) -> None:
+        self._test_op_upgrade('Squeeze', 1, [[2, 1, 3, 4, 1]], [[2, 3, 4]])
+
+    def test_StringNormalizer(self) -> None:
+        self._test_op_upgrade('StringNormalizer', 10, [[1, 3]], [[1, 3]],
+            [TensorProto.STRING], [TensorProto.STRING],
+            attrs={'case_change_action': 'LOWER'}
+        )
+
+    def test_Sub(self) -> None:
+        self._test_op_upgrade('Sub', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_Sum(self) -> None:
+        self._test_op_upgrade('Sum', 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]],
+            attrs={'consumed_inputs': [0]}
+        )
+
+    def test_Tanh(self) -> None:
+        self._test_op_upgrade('Tanh', 1, attrs={'consumed_inputs': [0]})
+
+    def test_Tan(self) -> None:
+        self._test_op_upgrade('Tan', 7)
+
+    def test_TfIdfVectorizer(self) -> None:
+        self._test_op_upgrade('TfIdfVectorizer', 9, [[3]], [[5]],
+            attrs={'max_gram_length': 3, 'max_skip_count': 1, 'min_gram_length': 2, 'mode': 'TFIDF', 'ngram_counts': [0, 20], 'ngram_indexes': [3, 4]}
+        )
+
+    def test_ThresholdedRelu(self) -> None:
+        self._test_op_upgrade('ThresholdedRelu', 10)
+
+    def test_Tile(self) -> None:
+        # 5->6 adapter is missing
+        repeats = helper.make_tensor('b', TensorProto.INT64, dims=[3], vals=np.array([1, 2, 3]))
+        self._test_op_upgrade('Tile', 6, [[3, 4, 5], [3]], [[3, 8, 15]],
+            [TensorProto.FLOAT, TensorProto.INT64],
+            initializer=[repeats]
+        )
+
+    def test_TopK(self) -> None:
+        self._test_op_upgrade('TopK', 1, [[3, 4, 5]], [[3, 4, 2], [3, 4, 2]],
+            output_types=[TensorProto.FLOAT, TensorProto.INT64],
+            attrs={'k': 2}
+        )
+
+    def test_Transpose(self) -> None:
+        self._test_op_upgrade('Transpose', 1, [[1, 2, 5, 3, 7]], [[1, 7, 5, 2, 3]],
+            attrs={'perm': [0, 4, 2, 1, 3]}
+        )
+
+    def test_Trilu(self) -> None:
+        self._test_op_upgrade('Trilu', 14)
+
+    def test_Unique_1(self) -> None:
+        self._test_op_upgrade('Unique', 11, [[3, 4, 5]], [[None]])
+
+    def test_Unique_2(self) -> None:
+        self._test_op_upgrade('Unique', 11, [[3, 4, 5]], [[3, None, 5]],
+            attrs={'axis': 1}
+        )
+
+    def test_Unsqueeze(self) -> None:
+        self._test_op_upgrade('Unsqueeze', 1, [[3, 4, 5]], [[3, 4, 1, 5]], attrs={'axes': [2]})
+
+    def test_Upsample(self) -> None:
+        self._test_op_upgrade('Upsample', 1, [[1, 3, 4, 5]], [[1, 3, 6, 10]],
+            attrs={'width_scale': 2., 'height_scale': 1.5}
+        )
+
+    def test_Where(self) -> None:
+        self._test_op_upgrade('Where', 9, [[2, 3], [2, 3], [2, 3]], [[2, 3]],
+            [TensorProto.BOOL, TensorProto.FLOAT, TensorProto.FLOAT]
+        )
+
+    def test_Xor(self) -> None:
+        # 6->7 adapter is missing
+        self._test_op_upgrade('Xor', 7, [[2, 3], [2, 3]], [[2, 3]],
+            [TensorProto.BOOL, TensorProto.BOOL], [TensorProto.BOOL]
+        )
+
+    def test_CastLike(self) -> None:
+        self._test_op_upgrade('CastLike', 15,
+            [[2, 3, 4], [2, 1, 4]],
+            [[2, 3, 4]],
+            input_types=[TensorProto.FLOAT, TensorProto.FLOAT16],
+            output_types=[TensorProto.FLOAT16])
+
+    def test_LayerNormalization(self) -> None:
+        self._test_op_upgrade('LayerNormalization', 17,
+                              [[2, 3, 4, 5], [4, 5], [4, 5]],
+                              [[2, 3, 4, 5]],
+                              input_types=[TensorProto.FLOAT,
+                                           TensorProto.FLOAT, TensorProto.FLOAT],
+                              output_types=[TensorProto.FLOAT],
+                              attrs={'axis': 2})
+
+    def test_ops_tested(self) -> None:
+        all_schemas = onnx.defs.get_all_schemas()
+        all_op_names = [schema.name for schema in all_schemas if schema.domain == '']
+        excluded_ops = [
+            # Sequence-based and Optional-based ops disabled because
+            # the version converter doesn't play nicely with sequences
+            'ConcatFromSequence',
+            'SequenceAt',
+            'SequenceConstruct',
+            'SequenceEmpty',
+            'SequenceErase',
+            'SequenceInsert',
+            'SequenceLength',
+            'SequenceMap',
+            'SplitToSequence',
+            'Optional',
+            'OptionalGetElement',
+            "OptionalHasElement"
+        ]
+        all_op_names = [op for op in all_op_names if op not in excluded_ops]
+
+        untested_ops = set(all_op_names) - set(tested_ops)
+        assert len(untested_ops) == 0
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/basic_test.py b/third_party/onnx/onnx/test/basic_test.py
new file mode 100644
index 0000000000..9c7d9d8b67
--- /dev/null
+++ b/third_party/onnx/onnx/test/basic_test.py
@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx import AttributeProto, NodeProto, GraphProto, ModelProto, TensorProto, IR_VERSION
+
+import io
+import onnx
+import os
+import tempfile
+import unittest
+
+from onnx import helper
+
+
+class TestBasicFunctions(unittest.TestCase):
+
+    def _simple_model(self) -> ModelProto:
+        # Create a ModelProto.
+        model = ModelProto()
+        model.ir_version = IR_VERSION
+        return model
+
+    def _simple_tensor(self) -> TensorProto:
+        # Create a TensorProto.
+        tensor = helper.make_tensor(
+            name='test-tensor',
+            data_type=TensorProto.FLOAT,
+            dims=(2, 3, 4),
+            vals=[x + 0.5 for x in range(24)]
+        )
+        return tensor
+
+    def test_save_and_load_model(self) -> None:
+        proto = self._simple_model()
+        cls = ModelProto
+        proto_string = onnx._serialize(proto)
+
+        # Test if input is string
+        loaded_proto = onnx.load_model_from_string(proto_string)
+        self.assertTrue(proto == loaded_proto)
+
+        # Test if input has a read function
+        f = io.BytesIO()
+        onnx.save_model(proto_string, f)
+        f = io.BytesIO(f.getvalue())
+        loaded_proto = onnx.load_model(f, cls)
+        self.assertTrue(proto == loaded_proto)
+
+        # Test if input is a file name
+        try:
+            fi = tempfile.NamedTemporaryFile(delete=False)
+            onnx.save_model(proto, fi)
+            fi.close()
+
+            loaded_proto = onnx.load_model(fi.name, cls)
+            self.assertTrue(proto == loaded_proto)
+        finally:
+            os.remove(fi.name)
+
+    def test_save_and_load_tensor(self) -> None:
+        proto = self._simple_tensor()
+        cls = TensorProto
+        proto_string = onnx._serialize(proto)
+
+        # Test if input is string
+        loaded_proto = onnx.load_tensor_from_string(proto_string)
+        self.assertTrue(proto == loaded_proto)
+
+        # Test if input has a read function
+        f = io.BytesIO()
+        onnx.save_tensor(loaded_proto, f)
+        f = io.BytesIO(f.getvalue())
+        loaded_proto = onnx.load_tensor(f, cls)
+        self.assertTrue(proto == loaded_proto)
+
+        # Test if input is a file name
+        try:
+            tfile = tempfile.NamedTemporaryFile(delete=False)
+            onnx.save_tensor(proto, tfile)
+            tfile.close()
+
+            loaded_proto = onnx.load_tensor(tfile.name, cls)
+            self.assertTrue(proto == loaded_proto)
+        finally:
+            os.remove(tfile.name)
+
+    def test_existence(self) -> None:
+        try:
+            AttributeProto
+            NodeProto
+            GraphProto
+            ModelProto
+        except Exception as e:
+            self.fail(
+                'Did not find proper onnx protobufs. Error is: {}'
+                .format(e))
+
+    def test_version_exists(self) -> None:
+        model = ModelProto()
+        # When we create it, graph should not have a version string.
+        self.assertFalse(model.HasField('ir_version'))
+        # We should touch the version so it is annotated with the current
+        # ir version of the running ONNX
+        model.ir_version = IR_VERSION
+        model_string = model.SerializeToString()
+        model.ParseFromString(model_string)
+        self.assertTrue(model.HasField('ir_version'))
+        # Check if the version is correct.
+        self.assertEqual(model.ir_version, IR_VERSION)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/checker_test.py b/third_party/onnx/onnx/test/checker_test.py
new file mode 100644
index 0000000000..7c74830777
--- /dev/null
+++ b/third_party/onnx/onnx/test/checker_test.py
@@ -0,0 +1,783 @@
+# SPDX-License-Identifier: Apache-2.0
+import unittest
+
+from typing import List, Sequence, Text
+import numpy as np  # type: ignore
+
+from onnx import checker, helper, numpy_helper, shape_inference
+from onnx import TensorProto, GraphProto, SparseTensorProto
+import onnx.onnx_cpp2py_export.checker as C
+import onnx.defs
+
+
+class TestChecker(unittest.TestCase):
+    @property
+    def _sample_float_tensor(self) -> TensorProto:
+        np_array = np.random.randn(2, 3).astype(np.float32)
+        return helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT,
+            dims=(2, 3),
+            vals=np_array.reshape(6).tolist()
+        )
+
+    def make_sparse(self,
+                    shape: Sequence[int],
+                    values: Sequence[int],
+                    indices_shape: Sequence[int],
+                    indices: Sequence[int],
+                    name: Text = 'spval'
+                    ) -> SparseTensorProto:
+        sparse = SparseTensorProto()
+        sparse.dims.extend(shape)
+        nnz = len(values)
+
+        sparse.values.CopyFrom(helper.make_tensor(name, TensorProto.INT64, (nnz,), values))
+        sparse.indices.CopyFrom(helper.make_tensor('spind', TensorProto.INT64, indices_shape, indices))
+        return sparse
+
+    def test_check_node(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+
+        checker.check_node(node)
+
+    def test_check_node_input_marked_optional(self) -> None:
+        # GivenTensorFill's input is marked optional, hence it is used in this test.
+        node = helper.make_node(
+            "GivenTensorFill", [], ["Y"], name="test")
+        checker.check_node(node)
+
+        # Explicitly pass the empty string as optional
+        node = helper.make_node(
+            "GivenTensorFill", [""], ["Y"], name="test")
+
+        # Input of RELU is not optional
+        node = helper.make_node(
+            "Relu", [""], ["Y"], name="test")
+        self.assertRaises(checker.ValidationError, checker.check_node, node)
+
+    def test_check_graph_ir_version_3(self) -> None:
+        ctx = C.CheckerContext()
+        ctx.ir_version = 3
+        ctx.opset_imports = {'': onnx.defs.onnx_opset_version()}
+
+        def check_ir_version_3(g: GraphProto) -> None:
+            checker.check_graph(g, ctx)
+
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        check_ir_version_3(graph)
+
+        graph.initializer.extend([self._sample_float_tensor])
+
+        graph.initializer[0].name = 'no-exist'
+
+        self.assertRaises(checker.ValidationError, check_ir_version_3, graph)
+
+        graph.initializer[0].name = 'X'
+        check_ir_version_3(graph)
+
+    def test_check_graph(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        checker.check_graph(graph)
+
+        graph.initializer.extend([self._sample_float_tensor])
+
+        graph.initializer[0].name = 'no-exist'
+        checker.check_graph(graph)
+
+        graph.initializer[0].name = 'X'
+        checker.check_graph(graph)
+
+    def test_check_graph_types(self) -> None:
+        # This is for https://github.com/onnx/onnx/issues/3849.
+        # It confirms that type checking is performed
+        # when checker.check_model is called with full_check=True
+
+        node_div = helper.make_node(
+            "Div", ["X", "Y"], ["Z"], name="test_div")
+        node_identity = helper.make_node(
+            "Identity", ["Z"], ["W"], name="test_identity")
+
+        graph = helper.make_graph(
+            [node_div, node_identity],
+            "test",
+            [
+                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
+                # intentionally use a BOOL type which is not supported by the Div op.
+                helper.make_tensor_value_info("Y", TensorProto.BOOL, [1, 2])],
+            [helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 2])])
+
+        model = helper.make_model(graph, producer_name='test')
+
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+        checker.check_graph(graph)
+
+        graph = helper.make_graph(
+            [node_div, node_identity],
+            "test",
+            [
+                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
+                # intentionally use a Int32 type which is in conflict with Div's other input X.
+                helper.make_tensor_value_info("Y", TensorProto.INT32, [1, 2])],
+            [helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 2])])
+
+        model = helper.make_model(graph, producer_name='test')
+
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+        checker.check_graph(graph)
+
+    def test_check_graph_empty_initializer_name(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        checker.check_graph(graph)
+
+        # Supply no name for the initializer
+        graph.initializer.extend([self._sample_float_tensor])
+        graph.initializer[0].name = ''
+        self.assertRaises(checker.ValidationError, checker.check_graph, graph)
+
+    def test_check_graph_empty_sparse_initializer_name(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        checker.check_graph(graph)
+
+        # Supply no name for the sparse_initializer
+        sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81], '')
+        graph.sparse_initializer.extend([sparse])
+        self.assertRaises(checker.ValidationError, checker.check_graph, graph)
+
+    def test_check_graph_duplicate_init_names(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        checker.check_graph(graph)
+
+        graph.initializer.extend([self._sample_float_tensor])
+        graph.initializer[0].name = 'X'
+
+        # Add sparse initializer with the same name as above
+        sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81], 'X')
+        graph.sparse_initializer.extend([sparse])
+        self.assertRaises(checker.ValidationError, checker.check_graph, graph)
+
+    def test_check_graph_optional_input(self) -> None:
+        # GivenTensorFill's input is marked optional, hence it is used in this test.
+        node = helper.make_node(
+            "GivenTensorFill", [""], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        checker.check_graph(graph)
+
+    def test_check_graph_ssa(self) -> None:
+        relu1 = helper.make_node(
+            "Relu", ["X"], ["Z"], name="relu1")
+        relu2 = helper.make_node(
+            "Relu", ["Y"], ["Z"], name="relu2")
+
+        graph = helper.make_graph(
+            [relu1, relu2],
+            "test",
+            inputs=[
+                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
+                helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])
+            ],
+            outputs=[
+                helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])
+            ]
+        )
+        self.assertRaises(checker.ValidationError, checker.check_graph, graph)
+
+    def test_check_graph_topologically_sorted(self) -> None:
+        n1 = helper.make_node(
+            "Scale", ["X"], ["Y"], scale=2., name="n1")
+        n2 = helper.make_node(
+            "Scale", ["Y"], ["Z"], scale=3., name="n2")
+
+        graph = helper.make_graph(
+            [n2, n1],
+            "test",
+            inputs=[
+                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])
+            ],
+            outputs=[
+                helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])
+            ]
+        )
+        self.assertRaises(checker.ValidationError, checker.check_graph, graph)
+
+    def test_check_model(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        model = helper.make_model(graph, producer_name='test')
+
+        checker.check_model(model)
+
+    def test_check_serialized_model(self) -> None:
+        node = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        model = helper.make_model(graph, producer_name='test')
+
+        checker.check_model(model.SerializeToString())
+
+    def test_check_old_model(self) -> None:
+        node = helper.make_node(
+            "Pad", ["X"], ["Y"], paddings=(0, 0, 0, 0))
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        onnx_id = helper.make_opsetid("", 1)
+        model = helper.make_model(graph, producer_name='test', opset_imports=[onnx_id])
+
+        checker.check_model(model)
+
+    def test_check_tensor(self) -> None:
+        tensor = self._sample_float_tensor
+        checker.check_tensor(tensor)
+
+        tensor.raw_data = np.random.randn(2, 3).astype(np.float32).tobytes()
+        self.assertRaises(checker.ValidationError, checker.check_tensor, tensor)
+
+    def test_check_string_tensor(self) -> None:
+        tensor = TensorProto()
+        tensor.data_type = TensorProto.STRING
+        tensor.dims.append(1)
+        tensor.string_data.append('Test'.encode('utf-8'))
+        checker.check_tensor(tensor)
+
+        del tensor.string_data[:]
+        tensor.raw_data = 'Test'.encode('utf-8')
+        # string data should not be stored in raw_data field
+        self.assertRaises(checker.ValidationError, checker.check_tensor, tensor)
+
+    def test_check_tensor_mismatched_field(self) -> None:
+        tensor = self._sample_float_tensor
+        tensor.data_type = TensorProto.INT32
+        self.assertRaises(checker.ValidationError, checker.check_tensor, tensor)
+
+    def test_nested_graph(self) -> None:
+        n1 = helper.make_node(
+            "Scale", ["X"], ["Y"], scale=2., name="n1")
+        n2 = helper.make_node(
+            "Scale", ["Y"], ["Z"], scale=3., name="n2")
+
+        graph = helper.make_graph(
+            [n1, n2],
+            "nested",
+            inputs=[],
+            outputs=[
+                helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])
+            ]
+        )
+
+        i1 = helper.make_node(
+            "If", ["cond"], ["Z"], then_branch=graph, else_branch=graph)
+
+        graph = helper.make_graph(
+            [i1],
+            "test",
+            inputs=[
+                helper.make_tensor_value_info("cond", TensorProto.BOOL, [1]),
+                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])
+            ],
+            outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
+        )
+
+        checker.check_graph(graph)
+
+    def test_nested_graph_without_subgraph_input_shape(self) -> None:
+        n1 = helper.make_node(
+            "Scale", ["X"], ["Y"], scale=2., name="n1")
+        n2 = helper.make_node(
+            "Scale", ["Y"], ["Z"], scale=3., name="n2")
+
+        input_x = onnx.ValueInfoProto()
+        input_x.name = "X"
+        graph = helper.make_graph(
+            [n1, n2],
+            "nested",
+            inputs=[],
+            outputs=[
+                helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])
+            ]
+        )
+
+        i1 = helper.make_node(
+            "If", ["cond"], ["Z"], then_branch=graph, else_branch=graph)
+
+        graph = helper.make_graph(
+            [i1],
+            "test",
+            inputs=[
+                helper.make_tensor_value_info("cond", TensorProto.BOOL, [1]),
+                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])
+            ],
+            outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
+        )
+
+        checker.check_graph(graph)
+
+    @property
+    def _sample_0_elem_tensor(self) -> TensorProto:
+        np_array = np.random.randn(0, 3).astype(np.float32)
+        return helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT,
+            dims=(0, 3),
+            vals=np_array.reshape(0).tolist()
+        )
+
+    def test_check_tensor_zero_elem(self) -> None:
+        tensor = self._sample_0_elem_tensor
+        checker.check_tensor(tensor)
+
+    def test_check_removed_experimental_op(self) -> None:
+        node = helper.make_node(
+            "ConstantFill", [], ["Y"], name="test", shape=[1, 2])
+        checker.check_node(node)
+
+    def test_skip_schema_check_on_non_standard_domain(self) -> None:
+        node = helper.make_node(
+            "NonExistOp", ["X"], ["Y"], name="test", domain="test.domain")
+        graph = helper.make_graph(
+            [node],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        onnx_id = helper.make_opsetid("test.domain", 1)
+        model = helper.make_model(graph, producer_name='test',
+                                  opset_imports=[onnx_id])
+        checker.check_model(model)
+
+    def test_check_sparse_tensor(self) -> None:
+        sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81])
+        checker.check_sparse_tensor(sparse)
+
+    def test_check_sparse_tensor_invalid_index(self) -> None:
+        # index value 181 is out-of-range
+        sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 181])
+        self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
+
+    def test_check_sparse_tensor_unordered(self) -> None:
+        # index values are not in sorted order
+        sparse = self.make_sparse([100], [13, 17, 19], [3], [27, 9, 81])
+        self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
+
+    def test_check_sparse_tensor_coo_format(self) -> None:
+        sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 2], [0, 9, 2, 7, 8, 1])
+        checker.check_sparse_tensor(sparse)
+
+    def test_check_sparse_tensor_coo_format_invalid_index(self) -> None:
+        sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 2], [0, 9, 0, 27, 8, 1])
+        self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
+
+    def test_check_sparse_tensor_coo_format_invalid_shape(self) -> None:
+        sparse = self.make_sparse([10, 10], [13, 17, 19], [2, 3], [0, 9, 2, 7, 8, 1])
+        self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
+
+    def test_check_sparse_tensor_coo_format_invalid_dim2(self) -> None:
+        sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 1], [0, 1, 2])
+        self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
+
+    def test_check_sparse_matmul(self) -> None:
+        M = 5
+        N = 10
+        # Create ValueInfoProto for input X of shape [N]
+        X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [N])
+        # Create a [M,N] sparse-matrix constant C
+        sparse_tensor = self.make_sparse([M, N], [2, 3, 1], [3], [3, 11, 37])
+        node1 = helper.make_node('Constant', [], ['C'], sparse_value=sparse_tensor)
+        # Create ValueInfoProto for output Y of shape [M]
+        Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [M])
+        # Compute Y = C X
+        node2 = helper.make_node('MatMul', ['C', 'X'], ['Y'])
+        # create graph
+        graph = helper.make_graph([node1, node2], "sparse_matmul", [X], [Y])
+        # check graph
+        checker.check_graph(graph)
+
+    def test_check_model_unsupported_input_type(self) -> None:
+        N = 10
+        X = helper.make_tensor_value_info('X', TensorProto.BOOL, [N])
+        Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [N])
+        Z = helper.make_tensor_value_info('Z', TensorProto.FLOAT, [N])
+        onnx_id = helper.make_opsetid("", 6)
+        node = helper.make_node('Add', ['X', 'Y'], ['Z'])
+        graph = helper.make_graph([node], "test_add_input", [X, Y], [Z])
+        model = helper.make_model(graph, producer_name='test', opset_imports=[onnx_id])
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+    def test_check_model_inconsistent_type(self) -> None:
+        N = 10
+        X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [N])
+        Y = helper.make_tensor_value_info('Y', TensorProto.INT32, [N])
+        Z = helper.make_tensor_value_info('Z', TensorProto.FLOAT, [N])
+        onnx_id = helper.make_opsetid("", 6)
+        node = helper.make_node('Add', ['X', 'Y'], ['Z'])
+        graph = helper.make_graph([node], "test_add_input", [X, Y], [Z])
+        model = helper.make_model(graph, producer_name='test', opset_imports=[onnx_id])
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+    def test_check_model_unsupported_output_type(self) -> None:
+        N = 10
+        X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [N])
+        Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [N])
+        Z = helper.make_tensor_value_info('Z', TensorProto.BOOL, [N])
+        onnx_id = helper.make_opsetid("", 6)
+        node = helper.make_node('Add', ['X', 'Y'], ['Z'])
+        graph = helper.make_graph([node], "test_add_input", [X, Y], [Z])
+        model = helper.make_model(graph, producer_name='test', opset_imports=[onnx_id])
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+    def test_loop_with_same_initializer_input_below_ir4(self) -> None:
+        # This is for testing IR<4: tensors must exist both in initializer and input
+        # shape_inference should allow different number of graph input and node input for Loop
+        # Comes from a tf2onnx model
+
+        model = helper.make_model(
+            opset_imports=[helper.make_operatorsetid('', 8)],
+            ir_version=3,
+            graph=helper.make_graph(
+                name='test-loop',
+                inputs=[
+                    helper.make_tensor_value_info('input_0', TensorProto.INT32, shape=[1]),
+                    helper.make_tensor_value_info('while_maximum_iterations_0', TensorProto.INT64, shape=[]),
+                    helper.make_tensor_value_info('const_fold_opt__18', TensorProto.INT64, shape=[1]),
+                    helper.make_tensor_value_info('const_fold_opt__17', TensorProto.FLOAT, shape=[]),
+                    helper.make_tensor_value_info('Const_0', TensorProto.INT32, shape=[1]),
+                ],
+                outputs=[helper.make_tensor_value_info('output_0', TensorProto.INT32, shape=[1])],
+                initializer=[
+                    numpy_helper.from_array(np.array(9223372036854775807, dtype=np.int64), name='while_maximum_iterations_0'),
+                    numpy_helper.from_array(np.array([-1], dtype=np.int64), name='const_fold_opt__18'),
+                    numpy_helper.from_array(np.array(10.0, dtype=np.float32), name='const_fold_opt__17'),
+                    numpy_helper.from_array(np.array([1], dtype=np.int32), name='Const_0'),
+                ],
+                nodes=[
+                    helper.make_node('Cast', inputs=['input_0'], outputs=['while_cond_158_while_Less__13_0'], name='while_cond_158_while_Less__13', domain='', to=TensorProto.FLOAT),
+                    helper.make_node('Less', inputs=['while_cond_158_while_Less__13_0', 'const_fold_opt__17'], outputs=['while_cond_158_while_Less_0'], name='while_cond_158_while_Less', domain=''),
+                    helper.make_node('Squeeze', inputs=['while_cond_158_while_Less_0'], outputs=['while_cond_158_while_Squeeze_0'], name='while_cond_158_while_Squeeze', domain=''),
+                    helper.make_node(
+                        'Loop',
+                        inputs=['while_maximum_iterations_0', 'while_cond_158_while_Squeeze_0', 'input_0', 'Const_0'],
+                        outputs=['while_loop_0', 'while_loop_1'],
+                        name='while_loop',
+                        body=helper.make_graph(
+                            name='while_body',
+                            inputs=[
+                                helper.make_tensor_value_info('while_while_loop_counter_0', TensorProto.INT64, shape=[]),
+                                helper.make_tensor_value_info('cond__15_0', TensorProto.BOOL, shape=[]),
+                                helper.make_tensor_value_info('while_placeholder_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('while_add_const_0_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('const_fold_opt__19', TensorProto.FLOAT, shape=[]),
+                            ],
+                            outputs=[
+                                helper.make_tensor_value_info('cond___while_Identity_graph_outputs_Identity__3_0', TensorProto.BOOL, shape=[]),
+                                helper.make_tensor_value_info('while_Identity_2_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('while_add_const_0_0', TensorProto.INT32, shape=[1]),
+                            ],
+                            initializer=[numpy_helper.from_array(np.array(10.0, dtype=np.float32), name='const_fold_opt__19')],
+                            nodes=[
+                                helper.make_node('Add', inputs=['while_placeholder_0', 'while_add_const_0_0'], outputs=['while_Identity_2_0'], name='while_Add'),
+                                helper.make_node('Cast', inputs=['while_Identity_2_0'], outputs=['cond___while_Less__13_0'], name='cond___while_Less__13', domain='', to=TensorProto.FLOAT),
+                                helper.make_node('Less', inputs=['cond___while_Less__13_0', 'const_fold_opt__19'], outputs=['cond___while_Less_0'], name='cond___while_Less', domain=''),
+                                helper.make_node('Squeeze', inputs=['cond___while_Less_0'], outputs=['cond___while_Identity_graph_outputs_Identity__3_0'], name='cond___while_Squeeze', domain=''),
+                            ],
+                        ),
+                    ),
+                    helper.make_node('Unsqueeze', inputs=['while_loop_0'], outputs=['Reshape_tensor_0'], name='Reshape_tensor', axes=[0]),
+                    helper.make_node('Reshape', inputs=['Reshape_tensor_0', 'const_fold_opt__18'], outputs=['output_0'], name='Reshape'),
+                ],
+            ),
+        )
+        # Should not throw an error
+        checker.check_model(model, full_check=True)
+
+    def test_loop_with_different_initializer_input_below_ir4(self) -> None:
+        # This is for testing IR<4: tensors must exist both in initializer and input
+        # Testing an optional input which does not exist in initializers
+        # Checker should throw an error said the missing input is not in initializers
+
+        model = helper.make_model(
+            opset_imports=[helper.make_operatorsetid('', 8)],
+            ir_version=3,
+            graph=helper.make_graph(
+                name='test-loop',
+                inputs=[
+                    helper.make_tensor_value_info('input_0', TensorProto.INT32, shape=[1]),
+                    helper.make_tensor_value_info('while_maximum_iterations_0', TensorProto.INT64, shape=[]),
+                    helper.make_tensor_value_info('const_fold_opt__18', TensorProto.INT64, shape=[1]),
+                    helper.make_tensor_value_info('const_fold_opt__17', TensorProto.FLOAT, shape=[]),
+                    helper.make_tensor_value_info('Const_0', TensorProto.INT32, shape=[1]),
+                ],
+                outputs=[helper.make_tensor_value_info('output_0', TensorProto.INT32, shape=[1])],
+                initializer=[
+                    numpy_helper.from_array(np.array(9223372036854775807, dtype=np.int64), name='while_maximum_iterations_0'),
+                    numpy_helper.from_array(np.array([-1], dtype=np.int64), name='const_fold_opt__18'),
+                    numpy_helper.from_array(np.array(10.0, dtype=np.float32), name='const_fold_opt__17'),
+                    numpy_helper.from_array(np.array([1], dtype=np.int32), name='Const_0'),
+                ],
+                nodes=[
+                    helper.make_node('Cast', inputs=['input_0'], outputs=['while_cond_158_while_Less__13_0'], name='while_cond_158_while_Less__13', domain='', to=TensorProto.FLOAT),
+                    helper.make_node('Less', inputs=['while_cond_158_while_Less__13_0', 'const_fold_opt__17'], outputs=['while_cond_158_while_Less_0'], name='while_cond_158_while_Less', domain=''),
+                    helper.make_node('Squeeze', inputs=['while_cond_158_while_Less_0'], outputs=['while_cond_158_while_Squeeze_0'], name='while_cond_158_while_Squeeze', domain=''),
+                    helper.make_node(
+                        'Loop',
+                        inputs=['while_maximum_iterations_0', 'while_cond_158_while_Squeeze_0', 'input_0', 'Const_0'],
+                        outputs=['while_loop_0', 'while_loop_1'],
+                        name='while_loop',
+                        body=helper.make_graph(
+                            name='while_body',
+                            inputs=[
+                                helper.make_tensor_value_info('while_while_loop_counter_0', TensorProto.INT64, shape=[]),
+                                helper.make_tensor_value_info('cond__15_0', TensorProto.BOOL, shape=[]),
+                                helper.make_tensor_value_info('while_placeholder_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('while_add_const_0_0', TensorProto.INT32, shape=[1]),
+                                # The following input cannot be found in initializer and checker should throw an error
+                                helper.make_tensor_value_info('const_fold_opt__18', TensorProto.FLOAT, shape=[]),
+                            ],
+                            outputs=[
+                                helper.make_tensor_value_info('cond___while_Identity_graph_outputs_Identity__3_0', TensorProto.BOOL, shape=[]),
+                                helper.make_tensor_value_info('while_Identity_2_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('while_add_const_0_0', TensorProto.INT32, shape=[1]),
+                            ],
+                            initializer=[],
+                            nodes=[
+                                helper.make_node('Add', inputs=['while_placeholder_0', 'while_add_const_0_0'], outputs=['while_Identity_2_0'], name='while_Add'),
+                                helper.make_node('Cast', inputs=['while_Identity_2_0'], outputs=['cond___while_Less__13_0'], name='cond___while_Less__13', domain='', to=TensorProto.FLOAT)
+                            ],
+                        ),
+                    ),
+                    helper.make_node('Unsqueeze', inputs=['while_loop_0'], outputs=['Reshape_tensor_0'], name='Reshape_tensor', axes=[0]),
+                    helper.make_node('Reshape', inputs=['Reshape_tensor_0', 'const_fold_opt__18'], outputs=['output_0'], name='Reshape'),
+                ],
+            ),
+        )
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+    def test_loop_with_same_initializer_input_above_ir4(self) -> None:
+        # This is for testing IR>=4:
+        # Cannot use the same name as both a subgraph initializer and subgraph input
+
+        model = helper.make_model(
+            opset_imports=[helper.make_operatorsetid('', 11)],
+            ir_version=6,
+            graph=helper.make_graph(
+                name='test-loop',
+                inputs=[
+                    helper.make_tensor_value_info('input_0', TensorProto.INT32, shape=[1]),
+                    helper.make_tensor_value_info('while_maximum_iterations_0', TensorProto.INT64, shape=[]),
+                    helper.make_tensor_value_info('const_fold_opt__18', TensorProto.INT64, shape=[1]),
+                    helper.make_tensor_value_info('const_fold_opt__17', TensorProto.FLOAT, shape=[]),
+                    helper.make_tensor_value_info('Const_0', TensorProto.INT32, shape=[1]),
+                ],
+                outputs=[helper.make_tensor_value_info('output_0', TensorProto.INT32, shape=[1])],
+                initializer=[
+                    numpy_helper.from_array(np.array(9223372036854775807, dtype=np.int64), name='while_maximum_iterations_0'),
+                    numpy_helper.from_array(np.array([-1], dtype=np.int64), name='const_fold_opt__18'),
+                    numpy_helper.from_array(np.array(10.0, dtype=np.float32), name='const_fold_opt__17'),
+                    numpy_helper.from_array(np.array([1], dtype=np.int32), name='Const_0'),
+                ],
+                nodes=[
+                    helper.make_node('Cast', inputs=['input_0'], outputs=['while_cond_158_while_Less__13_0'], name='while_cond_158_while_Less__13', domain='', to=TensorProto.FLOAT),
+                    helper.make_node('Less', inputs=['while_cond_158_while_Less__13_0', 'const_fold_opt__17'], outputs=['while_cond_158_while_Less_0'], name='while_cond_158_while_Less', domain=''),
+                    helper.make_node('Squeeze', inputs=['while_cond_158_while_Less_0'], outputs=['while_cond_158_while_Squeeze_0'], name='while_cond_158_while_Squeeze', domain=''),
+                    helper.make_node(
+                        'Loop',
+                        inputs=['while_maximum_iterations_0', 'while_cond_158_while_Squeeze_0', 'input_0', 'Const_0'],
+                        outputs=['while_loop_0', 'while_loop_1'],
+                        name='while_loop',
+                        body=helper.make_graph(
+                            name='while_body',
+                            inputs=[
+                                helper.make_tensor_value_info('while_while_loop_counter_0', TensorProto.INT64, shape=[]),
+                                helper.make_tensor_value_info('cond__15_0', TensorProto.BOOL, shape=[]),
+                                helper.make_tensor_value_info('while_placeholder_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('while_add_const_0_0', TensorProto.INT32, shape=[1]),
+                            ],
+                            outputs=[
+                                helper.make_tensor_value_info('cond___while_Identity_graph_outputs_Identity__3_0', TensorProto.BOOL, shape=[]),
+                                helper.make_tensor_value_info('while_Identity_2_0', TensorProto.INT32, shape=[1]),
+                                helper.make_tensor_value_info('while_add_const_0_0', TensorProto.INT32, shape=[1]),
+                            ],
+                            # Cannot use the same name as both a subgraph initializer and subgraph input: while_while_loop_counter_0
+                            initializer=[numpy_helper.from_array(np.array(10, dtype=np.int64), name='while_while_loop_counter_0')],
+                            nodes=[
+                                helper.make_node('Add', inputs=['while_placeholder_0', 'while_add_const_0_0'], outputs=['while_Identity_2_0'], name='while_Add'),
+                                helper.make_node('Cast', inputs=['while_Identity_2_0'], outputs=['cond___while_Less__13_0'], name='cond___while_Less__13', domain='', to=TensorProto.FLOAT),
+                                helper.make_node('Less', inputs=['cond___while_Less__13_0', 'while_while_loop_counter_0'], outputs=['cond___while_Less_0'], name='cond___while_Less', domain=''),
+                                helper.make_node('Squeeze', inputs=['cond___while_Less_0'], outputs=['cond___while_Identity_graph_outputs_Identity__3_0'], name='cond___while_Squeeze', domain=''),
+                            ],
+                        ),
+                    ),
+                    helper.make_node('Unsqueeze', inputs=['while_loop_0'], outputs=['Reshape_tensor_0'], name='Reshape_tensor', axes=[0]),
+                    helper.make_node('Reshape', inputs=['Reshape_tensor_0', 'const_fold_opt__18'], outputs=['output_0'], name='Reshape'),
+                ],
+            ),
+        )
+        self.assertRaises(shape_inference.InferenceError, checker.check_model, model, True)
+
+    def _contruct_loop_model(self, inputs_list: List[Text], outputs_list: List[Text]) -> onnx.ModelProto:
+        y_in = onnx.helper.make_tensor_value_info('y_in', onnx.TensorProto.FLOAT, [1])
+        y_out = onnx.helper.make_tensor_value_info('y_out', onnx.TensorProto.FLOAT, [1])
+        scan_out = onnx.helper.make_tensor_value_info('scan_out', onnx.TensorProto.FLOAT, [1])
+        cond_in = onnx.helper.make_tensor_value_info('cond_in', onnx.TensorProto.BOOL, [])
+        cond_out = onnx.helper.make_tensor_value_info('cond_out', onnx.TensorProto.BOOL, [])
+        iter_count = onnx.helper.make_tensor_value_info('iter_count', onnx.TensorProto.INT64, [])
+
+        x_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['x'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_x',
+                data_type=onnx.TensorProto.FLOAT,
+                dims=[5],
+                vals=[1., 2., 3., 4., 5.],
+            )
+        )
+
+        one_const_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['one'],
+            value=onnx.helper.make_tensor(
+                name='const_tensor_one',
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1]
+            )
+        )
+
+        i_add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['iter_count', 'one'],
+            outputs=['end']
+        )
+
+        start_unsqueeze_node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['iter_count'],
+            outputs=['slice_start'],
+            axes=[0]
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            'Unsqueeze',
+            inputs=['end'],
+            outputs=['slice_end'],
+            axes=[0]
+        )
+
+        slice_node = onnx.helper.make_node(
+            'Slice',
+            inputs=['x', 'slice_start', 'slice_end'],
+            outputs=['slice_out']
+        )
+
+        y_add_node = onnx.helper.make_node(
+            'Add',
+            inputs=['y_in', 'slice_out'],
+            outputs=['y_out']
+        )
+
+        identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['cond_in'],
+            outputs=['cond_out']
+        )
+
+        scan_identity_node = onnx.helper.make_node(
+            'Identity',
+            inputs=['y_out'],
+            outputs=['scan_out']
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [identity_node, x_const_node, one_const_node, i_add_node,
+             start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node,
+             scan_identity_node],
+            'loop_body',
+            [iter_count, cond_in, y_in],
+            [cond_out, y_out, scan_out]
+        )
+
+        node = onnx.helper.make_node(
+            'Loop',
+            inputs=inputs_list,
+            outputs=outputs_list,
+            body=loop_body
+        )
+
+        model = helper.make_model(
+            opset_imports=[onnx.helper.make_opsetid("", 11)],
+            graph=helper.make_graph(
+                name='test-loop',
+                inputs=[
+                    helper.make_tensor_value_info('trip_count', TensorProto.INT64, shape=[5]),
+                    helper.make_tensor_value_info('cond', TensorProto.BOOL, shape=[1]),
+                    helper.make_tensor_value_info('y', TensorProto.FLOAT, shape=[1]),
+                ],
+                outputs=[
+                    helper.make_tensor_value_info('cond', TensorProto.FLOAT, shape=[13]),
+                    helper.make_tensor_value_info('res_scan', TensorProto.FLOAT, shape=[5, 1])
+                ],
+                nodes=[node],
+            ),
+        )
+        return model
+
+    def test_loop_with_empty_input(self) -> None:
+        model = self._contruct_loop_model(['trip_count', 'cond', ''], ['res_y', 'res_scan'])
+        self.assertRaises(checker.ValidationError, checker.check_model, model)
+
+    def test_loop_with_empty_output(self) -> None:
+        model = self._contruct_loop_model(['trip_count', 'cond', 'y'], ['', 'res_scan'])
+        self.assertRaises(checker.ValidationError, checker.check_model, model)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/compose_test.py b/third_party/onnx/onnx/test/compose_test.py
new file mode 100644
index 0000000000..7c5f5f7bf8
--- /dev/null
+++ b/third_party/onnx/onnx/test/compose_test.py
@@ -0,0 +1,799 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+import unittest
+
+from typing import Text, List, Optional, Tuple, Callable
+
+from onnx import helper, parser, checker, compose, version_converter, \
+    ModelProto, GraphProto, ValueInfoProto, TensorProto, SparseTensorProto, \
+    FunctionProto, NodeProto
+
+
+def _load_model(m_def: Text) -> ModelProto:
+    '''
+    Parses a model from a string representation, including checking the model for correctness
+    '''
+    m = parser.parse_model(m_def)
+    checker.check_model(m)
+    return m
+
+
+def _prefixed(prefix: Text, s: Text) -> Text:
+    '''
+    Prefixes a string (if not empty)
+    '''
+    return prefix + s if len(s) > 0 else s
+
+
+def _get_shape(value_info: ValueInfoProto) -> List[int]:
+    '''
+    Returns a list of integers representing the shape of the provided ValueInfoProto
+    '''
+    return [value_info.type.tensor_type.shape.dim[d].dim_value
+            for d in range(len(value_info.type.tensor_type.shape.dim))]
+
+
+def _make_sparse_tensor(name: Text) -> SparseTensorProto:
+    dense_shape = [3, 3]
+    linear_indices = [2, 3, 5]
+    sparse_values = [1.7, 0.4, 0.9]
+    values_tensor = helper.make_tensor(
+        name=name + "_values", data_type=TensorProto.FLOAT,
+        dims=[len(sparse_values)],
+        vals=np.array(sparse_values).astype(np.float32), raw=False)
+
+    indices_tensor = helper.make_tensor(
+        name=name + "_idx", data_type=TensorProto.INT64,
+        dims=[len(linear_indices)],
+        vals=np.array(linear_indices).astype(np.int64), raw=False)
+    return helper.make_sparse_tensor(values_tensor, indices_tensor, dense_shape)
+
+
+m1_def = '''
+    <
+        ir_version: 7,
+        opset_import: [ "": 10, "com.microsoft": 1]
+    >
+    agraph (float[N, M] A0, float[N, M] A1) => (float[N, M] B00, float[N, M] B10, float[N, M] B20)
+    {
+        B00 = Add(A0, A1)
+        B10 = Sub(A0, A1)
+        B20 = Mul(A0, A1)
+    }
+    '''
+
+m2_def = '''
+    <
+        ir_version: 7,
+        opset_import: [ "": 10, "com.microsoft": 1]
+    >
+    agraph (float[N, M] B01, float[N, M] B11, float[N, M] B21) => (float[N, M] D0)
+    {
+        C0 = Add(B01, B11)
+        C1 = Sub(B11, B21)
+        M1 = Mul(C0, C1)
+    }
+    '''
+
+
+class TestComposeFunctions(unittest.TestCase):
+    def _test_merge_models(
+        self,
+        m1def: Text,
+        m2def: Text,
+        io_map: List[Tuple[Text, Text]],
+        check_expectations: Callable[[GraphProto, GraphProto, GraphProto], None],
+        inputs: Optional[List[Text]] = None,
+        outputs: Optional[List[Text]] = None,
+        prefix1: Optional[Text] = None,
+        prefix2: Optional[Text] = None
+    ) -> None:
+        m1, m2 = _load_model(m1def), _load_model(m2def)
+        g3 = compose.merge_graphs(
+            m1.graph, m2.graph, io_map=io_map,
+            inputs=inputs, outputs=outputs,
+            prefix1=prefix1, prefix2=prefix2,
+        )
+        checker.check_graph(g3)
+        check_expectations(m1.graph, m2.graph, g3)
+        m3 = compose.merge_models(
+            m1, m2, io_map=io_map,
+            inputs=inputs, outputs=outputs,
+            prefix1=prefix1, prefix2=prefix2,
+        )
+        checker.check_model(m3)
+        check_expectations(m1.graph, m2.graph, m3.graph)
+
+    def test_case_connect_all_no_name_collision(self) -> None:
+        '''
+        Tests a simple scenario where two models without overlapping names are merged by
+        connecting all the outputs in the first models to all the inputs in the second model
+        '''
+        def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
+            self.assertEqual(g3.input, g1.input)
+            self.assertEqual(g3.output, g2.output)
+            self.assertEqual(['Add', 'Sub', 'Mul', 'Add', 'Sub', 'Mul'],
+                             [item.op_type for item in g3.node])
+        io_map = [("B00", "B01"), ("B10", "B11"), ("B20", "B21")]
+        self._test_merge_models(m1_def, m2_def, io_map, check_expectations)
+
+    def test_case_connect_same_output_twice(self) -> None:
+        '''
+        Tests a scenario where we merge two models by connecting a single output in the first model
+        to all the inputs in the second
+        '''
+        def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
+            self.assertEqual(g3.input, g1.input)
+            self.assertEqual(['B10', 'B20', 'D0'], [elem.name for elem in g3.output])
+            self.assertEqual(['Add', 'Sub', 'Mul', 'Add', 'Sub', 'Mul'],
+                             [item.op_type for item in g3.node])
+        io_map = [("B00", "B01"), ("B00", "B11"), ("B00", "B21")]
+        self._test_merge_models(m1_def, m2_def, io_map, check_expectations)
+
+    def test_case_connect_same_output_drop_outputs(self) -> None:
+        '''
+        Tests a scenario where we merge two models by connecting a single output in the first model
+        to all the inputs in the second, while dropping the rest of the outputs in the first model
+        '''
+        def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
+            self.assertEqual(g3.input, g1.input)
+            self.assertEqual(['D0'], [elem.name for elem in g3.output])
+            self.assertEqual(['Add', 'Add', 'Sub', 'Mul'], [item.op_type for item in g3.node])
+        io_map = [("B00", "B01"), ("B00", "B11"), ("B00", "B21")]
+        outputs = ['D0']
+        self._test_merge_models(m1_def, m2_def, io_map, check_expectations, outputs=outputs)
+
+    def test_case_connect_same_input_output_name(self) -> None:
+        '''
+        Tests a scenario where we merge two models, where the inputs/outputs connected
+        are named exactly the same
+        '''
+
+        m1_def = '''
+            <
+                ir_version: 7,
+                opset_import: [ "": 10]
+            >
+            agraph (float[N, M] A) => (float[N, M] B)
+            {
+                B = Add(A, A)
+            }
+            '''
+        m2_def = '''
+            <
+                ir_version: 7,
+                opset_import: [ "": 10]
+            >
+            agraph (float[N, M] B) => (float[N, M] C)
+            {
+                C = Add(B, B)
+            }
+            '''
+        io_map = [("B", "B")]
+
+        def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
+            self.assertEqual(['A'], [elem.name for elem in g3.input])
+            self.assertEqual(['C'], [elem.name for elem in g3.output])
+        self._test_merge_models(m1_def, m2_def, io_map, check_expectations)
+
+    def test_case_drop_inputs_outputs(self) -> None:
+        '''
+        Tests a scenario where we merge two models, not including some of the inputs/outputs
+        '''
+
+        m1_def = '''
+            <
+                ir_version: 7,
+                opset_import: [ "": 10]
+            >
+            agraph (float[N] A0, float[N] B0) => (float[N] A1, float[N] B1)
+            {
+                A1 = Add(A0, A0)
+                B1 = Sub(B0, B0)
+            }
+            '''
+        m2_def = '''
+            <
+                ir_version: 7,
+                opset_import: [ "": 10]
+            >
+            agraph (float[N] A2, float[N] B2) => (float[N] A3, float[N] B3)
+            {
+                A3 = Add(A2, A2)
+                B3 = Sub(B2, B2)
+            }
+            '''
+        io_map = [("A1", "B2")]
+
+        def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
+            self.assertEqual(['A0'], [elem.name for elem in g3.input])
+            self.assertEqual(['B3'], [elem.name for elem in g3.output])
+            self.assertEqual(['Add', 'Sub'], [elem.op_type for elem in g3.node])
+
+        inputs = ['A0']
+        outputs = ['B3']
+        self._test_merge_models(
+            m1_def, m2_def, io_map, check_expectations, inputs=inputs, outputs=outputs)
+
+    def test_case_name_collision_prefix(self) -> None:
+        '''
+        Tests a scenario where we merge two models that have name collisions, but they
+        are avoided by prefixing the models model.
+        '''
+
+        m1_def = '''
+            <
+                ir_version: 7,
+                opset_import: [ "": 10]
+            >
+            agraph (float[N] A, float[N] B) => (float[N] C)
+            {
+                C = Add(A, B)
+            }
+            '''
+        io_map = [("C", "A")]
+
+        def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
+            self.assertEqual(['m1/A', 'm1/B', 'm2/B'], [elem.name for elem in g3.input])
+            self.assertEqual(['m2/C'], [elem.name for elem in g3.output])
+            self.assertEqual(['Add', 'Add'], [elem.op_type for elem in g3.node])
+
+        self._test_merge_models(
+            m1_def, m1_def, io_map, check_expectations, prefix1='m1/', prefix2='m2/')
+
+    def test_case_connect_partially_no_name_collision(self) -> None:
+        '''
+        Tests a scenario where two models without overlapping names are merged by
+        connecting some outputs from the first model to some inputs in the second.
+        The remaining inputs/outputs should be present in the combined model
+        '''
+        def check_expectations(g1: GraphProto, g2: GraphProto, g4: GraphProto) -> None:
+            # B20 <-> B21 not connected. They should still be present
+            # in the inputs and outputs of the combined graph
+            self.assertEqual(['A0', 'A1', 'B21'], [elem.name for elem in g4.input])
+            self.assertEqual(['B20', 'D0'], [elem.name for elem in g4.output])
+        io_map = [("B00", "B01"), ("B10", "B11")]
+        self._test_merge_models(m1_def, m2_def, io_map, check_expectations)
+
+    def test_merge_models_with_metadata_props(self) -> None:
+        m1 = _load_model(m1_def)
+        helper.set_model_props(m1, {'p1': 'v1', 'p2': 'v2'})
+
+        m2 = _load_model(m2_def)
+        helper.set_model_props(m2, {'p3': 'v3', 'p4': 'v4'})
+
+        io_map = [("B00", "B01")]
+        m3 = compose.merge_models(m1, m2, io_map=io_map)
+        assert len(m3.metadata_props) == 4
+
+        # Overlap, but same value
+        helper.set_model_props(m2, {'p1': 'v1', 'p4': 'v4'})
+        m3 = compose.merge_models(m1, m2, io_map=io_map)
+        assert len(m3.metadata_props) == 3
+
+        # Same keys but not same value. Error
+        helper.set_model_props(m2, {'p1': 'v5', 'p4': 'v4'})
+        self.assertRaises(ValueError,
+                          compose.merge_models, m1, m2, io_map=io_map)
+
+    def test_error_wrong_input_output_name(self) -> None:
+        '''
+        Tests that providing a non existing output/input name in the io_map argument produces an error.
+        '''
+        m1, m2 = _load_model(m1_def), _load_model(m2_def)
+
+        self.assertRaises(ValueError,
+                          compose.merge_models, m1, m2,
+                          io_map=[("wrong_outname", "B01"), ("B10", "B11"), ("B20", "B21")])
+
+        # Wrong output name
+        self.assertRaises(ValueError,
+                          compose.merge_models, m1, m2,
+                          io_map=[("B00", "wrong_input"), ("B10", "B11"), ("B20", "B21")])
+
+    def test_error_ir_version_mismatch(self) -> None:
+        m1 = _load_model('''
+    <
+        ir_version: 7,
+        opset_import: [ "": 13]
+    >
+    agraph (float[N, M] X0) => (float[N, M] Y0)
+    {
+        Y0 = Add(X0, X0)
+    }
+    ''')
+
+        m2 = _load_model('''
+    <
+        ir_version: 6,
+        opset_import: [ "": 13]
+    >
+    agraph (float[N, M] X1) => (float[N, M] Y1)
+    {
+        Y1 = Add(X1, X1)
+    }
+    ''')
+        # Wrong IR version name
+        self.assertRaises(ValueError,
+                          compose.merge_models, m1, m2,
+                          io_map=[("Y0", "X1")])
+
+    def test_error_opset_import_mismatch(self) -> None:
+        '''
+        Tests that providing models with different operator set imported produces an error
+        '''
+        m1, m2 = _load_model(m1_def), _load_model(m2_def)
+        m1 = helper.make_model(m1.graph, producer_name='test',
+                               opset_imports=[helper.make_opsetid("", 10)])
+        m2 = helper.make_model(m2.graph, producer_name='test',
+                               opset_imports=[helper.make_opsetid("", 15)])
+
+        io_map = [("B00", "B01"), ("B10", "B11"), ("B20", "B21")]
+        self.assertRaises(ValueError,
+                          compose.merge_models, m1, m2, io_map)
+
+        # Converting to the same Operator set version, should work
+        m1 = version_converter.convert_version(m1, 15)
+        m3 = compose.merge_models(m1, m2, io_map=io_map)
+        checker.check_model(m3)
+
+    def _test_add_prefix(self,
+                         rename_nodes: bool = False, rename_edges: bool = False,
+                         rename_inputs: bool = False, rename_outputs: bool = False,
+                         rename_initializers: bool = False, rename_value_infos: bool = False,
+                         inplace: bool = False) -> None:
+        m1 = _load_model(m1_def)
+
+        prefix = 'pre/'
+
+        if inplace:
+            m2 = ModelProto()
+            m2.CopyFrom(m1)
+            compose.add_prefix(m2, prefix,
+                               rename_nodes=rename_nodes,
+                               rename_edges=rename_edges,
+                               rename_inputs=rename_inputs,
+                               rename_outputs=rename_outputs,
+                               rename_initializers=rename_initializers,
+                               rename_value_infos=rename_value_infos,
+                               inplace=True)
+        else:
+            m2 = compose.add_prefix(m1, prefix,
+                                    rename_nodes=rename_nodes,
+                                    rename_edges=rename_edges,
+                                    rename_inputs=rename_inputs,
+                                    rename_outputs=rename_outputs,
+                                    rename_initializers=rename_initializers,
+                                    rename_value_infos=rename_value_infos)
+        g_in = m1.graph
+        g_out = m2.graph
+
+        if rename_edges or rename_inputs or rename_outputs or rename_initializers or rename_value_infos:
+            name_mapping = {}
+
+            # Rename inputs/outputs/edges. Propagate name changes from and to edges
+            if rename_edges:
+                for n in g_in.node:
+                    for e in n.input:
+                        name_mapping[e] = _prefixed(prefix, e)
+                    for e in n.output:
+                        name_mapping[e] = _prefixed(prefix, e)
+            else:
+                if rename_inputs:
+                    for elem in g_in.input:
+                        name_mapping[elem.name] = _prefixed(prefix, elem.name)
+                if rename_outputs:
+                    for elem in g_in.output:
+                        name_mapping[elem.name] = _prefixed(prefix, elem.name)
+
+            if rename_initializers:
+                for init in g_in.initializer:
+                    name_mapping[init.name] = _prefixed(prefix, init.name)
+                for sparse_init in g_in.sparse_initializer:
+                    name_mapping[sparse_init.values.name] = \
+                        _prefixed(prefix, sparse_init.values.name)
+                    name_mapping[sparse_init.indices.name] = \
+                        _prefixed(prefix, sparse_init.indices.name)
+
+            if rename_value_infos:
+                for value_info in g_in.output:
+                    name_mapping[value_info.name] = _prefixed(prefix, value_info.name)
+
+            for n1, n0 in zip(g_out.node, g_in.node):
+                for e1, e0 in zip(n1.input, n0.input):
+                    self.assertEqual(name_mapping.get(e0, e0), e1)
+                for e1, e0 in zip(n1.output, n0.output):
+                    self.assertEqual(name_mapping.get(e0, e0), e1)
+            for i1, i0 in zip(g_out.input, g_in.input):
+                self.assertEqual(name_mapping.get(i0.name, i0.name), i1.name)
+            for o1, o0 in zip(g_out.output, g_in.output):
+                self.assertEqual(name_mapping.get(o0.name, o0.name), o1.name)
+
+            for init1, init0 in zip(g_out.initializer, g_in.initializer):
+                self.assertEqual(name_mapping.get(
+                    init0.name, init0.name), init1.name)
+
+            for sparse_init1, sparse_init0 in zip(g_out.sparse_initializer, g_in.sparse_initializer):
+                self.assertEqual(name_mapping.get(
+                    sparse_init0.values.name, sparse_init0.values.name), sparse_init1.values.name)
+                self.assertEqual(name_mapping.get(
+                    sparse_init0.indices.name, sparse_init0.indices.name), sparse_init1.indices.name)
+
+            for vi1, vi0 in zip(g_out.value_info, g_in.value_info):
+                self.assertEqual(name_mapping.get(vi0.name, vi0.name), vi1.name)
+
+            if rename_nodes:
+                for n1, n0 in zip(g_out.node, g_in.node):
+                    self.assertEqual(_prefixed(prefix, n0.name), n1.name)
+
+    def test_add_prefix_nodes(self) -> None:
+        '''
+        Tests renaming nodes only
+        '''
+        self._test_add_prefix(rename_nodes=True)
+
+    def test_add_prefix_edges(self) -> None:
+        '''
+        Tests prefixing nodes edges. This will also rename inputs/outputs, since the names are shared
+        '''
+        self._test_add_prefix(rename_edges=True)
+
+    def test_add_prefix_inputs(self) -> None:
+        '''
+        Tests prefixing graph inputs only. Relevant node edges should be renamed as well
+        '''
+        self._test_add_prefix(rename_inputs=True)
+
+    def test_add_prefix_outputs(self) -> None:
+        '''
+        Tests prefixing graph outputs only. Relevant node edges should be renamed as well
+        '''
+        self._test_add_prefix(rename_outputs=True)
+
+    def test_add_prefix_all(self) -> None:
+        '''
+        Tests prefixing all names in the graph
+        '''
+        self._test_add_prefix(True, True, True, True, True, True)
+
+    def test_add_prefix_inplace(self) -> None:
+        '''
+        Tests prefixing inplace
+        '''
+        self._test_add_prefix(inplace=True)
+
+    def test_expand_out_dim(self) -> None:
+        '''
+        Tests expanding output dimensions. The resulting graph should have the same output names,
+        but with one more dimension at the specified index.
+        '''
+        m1 = _load_model(m1_def)
+
+        def _check_model(m1: ModelProto, m2: ModelProto, dim_idx: int) -> None:
+            for out_g2, out_g1 in zip(m2.graph.output, m1.graph.output):
+                self.assertEqual(out_g2.name, out_g1.name)
+                self.assertEqual(out_g2.type.tensor_type.elem_type,
+                                 out_g1.type.tensor_type.elem_type)
+                expected_out_shape = _get_shape(out_g1)
+                expected_out_shape.insert(dim_idx, 1)
+                self.assertEqual(_get_shape(out_g2), expected_out_shape)
+
+        for dim_idx in [0, 2, -1, -3]:
+            m2 = compose.expand_out_dim(m1, dim_idx)
+            _check_model(m1, m2, dim_idx)
+
+        # Test inplace
+        m2 = ModelProto()
+        m2.CopyFrom(m1)
+        dim_idx = 0
+        compose.expand_out_dim(m2, dim_idx, inplace=True)
+        _check_model(m1, m2, dim_idx)
+
+    def _test_overlapping_names(
+        self,
+        inputs0: List[Text] = ['i0', 'i1'],
+        inputs1: List[Text] = ['i2', 'i3'],
+        outputs0: List[Text] = ['o0', 'o1'],
+        outputs1: List[Text] = ['o2', 'o3'],
+        value_info0: List[Text] = ['v0', 'v1'],
+        value_info1: List[Text] = ['v2', 'v3'],
+        initializer0: List[Text] = ['init0', 'init1'],
+        initializer1: List[Text] = ['init2', 'init3'],
+        sparse_initializer0: List[Text] = ['sparse_init0', 'sparse_init1'],
+        sparse_initializer1: List[Text] = ['sparse_init2', 'sparse_init3'],
+    ) -> None:
+        n0 = [helper.make_node('Identity', inputs=[inputs0[i]], outputs=[outputs0[i]])
+              for i in range(len(inputs0))]
+        i0 = [helper.make_tensor_value_info(inputs0[i], TensorProto.FLOAT, [])
+              for i in range(len(inputs0))]
+        o0 = [helper.make_tensor_value_info(outputs0[i], TensorProto.FLOAT, [])
+              for i in range(len(outputs0))]
+        vi0 = [helper.make_tensor_value_info(value_info0[i], TensorProto.FLOAT, [])
+               for i in range(len(value_info0))]
+        init0 = [helper.make_tensor(name=initializer0[i], data_type=TensorProto.INT64, dims=(), vals=[1])
+                 for i in range(len(initializer0))]
+
+        sparse_init0 = [_make_sparse_tensor(
+            sparse_initializer0[i]) for i in range(len(sparse_initializer0))]
+
+        n1 = [helper.make_node('Identity', inputs=[inputs1[i]], outputs=[outputs1[i]])
+              for i in range(len(inputs1))]
+        i1 = [helper.make_tensor_value_info(inputs1[i], TensorProto.FLOAT, [])
+              for i in range(len(inputs1))]
+        o1 = [helper.make_tensor_value_info(outputs1[i], TensorProto.FLOAT, [])
+              for i in range(len(outputs1))]
+        vi1 = [helper.make_tensor_value_info(value_info1[i], TensorProto.FLOAT, [])
+               for i in range(len(value_info1))]
+        init1 = [helper.make_tensor(name=initializer1[i], data_type=TensorProto.INT64, dims=(), vals=[1])
+                 for i in range(len(initializer1))]
+        sparse_init1 = [_make_sparse_tensor(sparse_initializer1[i])
+                        for i in range(len(sparse_initializer1))]
+
+        ops = [helper.make_opsetid("", 10)]
+        m0 = helper.make_model(
+            helper.make_graph(
+                nodes=n0, name='g0', inputs=i0, outputs=o0, value_info=vi0,
+                initializer=init0, sparse_initializer=sparse_init0),
+            producer_name='test',
+            opset_imports=ops)
+        m1 = helper.make_model(
+            helper.make_graph(
+                nodes=n1, name='g1', inputs=i1, outputs=o1, value_info=vi1,
+                initializer=init1, sparse_initializer=sparse_init1),
+            producer_name='test',
+            opset_imports=ops)
+
+        overlap = compose.check_overlapping_names(m0.graph, m1.graph)
+        i = 0
+
+        overlapping_inputs = list(set(inputs0) & set(inputs1))
+        overlapping_outputs = list(set(outputs0) & set(outputs1))
+        overlapping_edges = list(set(overlapping_inputs + overlapping_outputs))
+        if len(overlapping_edges) > 0:
+            self.assertEqual(overlap[i], ('edge', overlapping_edges))
+            i += 1
+
+        overlapping_vis = list(set(value_info0) & set(value_info1))
+        if len(overlapping_vis) > 0:
+            self.assertEqual(overlap[i], ('value_info', overlapping_vis))
+            i += 1
+
+        overlapping_init = list(set(initializer0) & set(initializer1))
+        if len(overlapping_init) > 0:
+            self.assertEqual(overlap[i], ('initializer', overlapping_init))
+            i += 1
+
+        overlapping_sparse_init = list(set(sparse_initializer0) & set(sparse_initializer1))
+        if len(overlapping_sparse_init) > 0:
+            expected_overlap = []
+            for overlapping_name in overlapping_sparse_init:
+                expected_overlap.append(overlapping_name + '_values')
+                expected_overlap.append(overlapping_name + '_idx')
+            self.assertEqual(overlap[i], ('sparse_initializer', expected_overlap))
+            i += 1
+
+        m0_new = compose.add_prefix(m0, prefix='g0/')
+        overlap = compose.check_overlapping_names(m0_new.graph, m1.graph)
+        self.assertEqual(0, len(overlap))
+
+    def test_overlapping_input_names(self) -> None:
+        '''
+        Tests error checking when the name of the inputs overlaps
+        '''
+        self._test_overlapping_names(
+            inputs0=['i0', 'i1'], inputs1=['i1', 'i2'])
+
+    def test_overlapping_output_names(self) -> None:
+        '''
+        Tests error checking when the name of the output overlaps
+        '''
+        self._test_overlapping_names(
+            outputs0=['o0', 'o1'], outputs1=['o1', 'o2'])
+
+    def test_overlapping_value_info_names(self) -> None:
+        '''
+        Tests error checking when the name of value_info entries overlaps
+        '''
+        self._test_overlapping_names(
+            value_info0=['vi0', 'vi1'], value_info1=['vi1', 'vi2'])
+
+    def test_overlapping_initializer_names(self) -> None:
+        '''
+        Tests error checking when the name of initializer entries overlaps
+        '''
+        self._test_overlapping_names(
+            initializer0=['init0', 'init1'], initializer1=['init1', 'init2'])
+
+    def test_overlapping_sparse_initializer_names(self) -> None:
+        '''
+        Tests error checking when the name of sparse_initializer entries overlaps
+        '''
+        self._test_overlapping_names(
+            sparse_initializer0=['sparse_init0', 'sparse_init1'],
+            sparse_initializer1=['sparse_init1', 'sparse_init2'])
+
+    def test_overlapping_function_names(self) -> None:
+        '''
+        Tests error checking when the name of local function entries overlaps
+        '''
+        ops = [
+            helper.make_opsetid("", 10),
+            helper.make_opsetid("local", 10)
+        ]
+
+        def _make_function(
+            domain: Text,
+            fname: Text,
+            inputs: List[Text],
+            outputs: List[Text],
+            nodes: List[NodeProto],
+        ) -> FunctionProto:
+            f = FunctionProto()
+            f.domain = domain
+            f.name = fname
+            f.input.extend(inputs)
+            f.output.extend(outputs)
+            f.node.extend(nodes)
+            f.opset_import.extend(ops)
+            return f
+
+        ops = [
+            helper.make_opsetid("", 10),
+            helper.make_opsetid("local", 10)
+        ]
+
+        g = GraphProto()
+        g.input.extend([
+            helper.make_tensor_value_info('x0', TensorProto.FLOAT, []),
+            helper.make_tensor_value_info('x1', TensorProto.FLOAT, [])
+        ])
+        g.output.extend([
+            helper.make_tensor_value_info('y', TensorProto.FLOAT, []),
+        ])
+        g.node.extend([
+            helper.make_node(
+                'f1', domain='local', inputs=['x0', 'x1'], outputs=['y'])
+        ])
+
+        g1 = GraphProto()
+        g1.CopyFrom(g)
+        g1.name = 'g1'
+        m1 = helper.make_model(g1, producer_name='test', opset_imports=ops)
+        m1.functions.extend([
+            _make_function(
+                'local', 'f1', ['x0', 'x1'], ['y'],
+                [helper.make_node('Add', inputs=['x0', 'x1'], outputs=['y'])]
+            )
+        ])
+        checker.check_model(m1)
+
+        g2 = GraphProto()
+        g2.CopyFrom(g)
+        g2.name = 'g2'
+        m2 = helper.make_model(g2, producer_name='test', opset_imports=ops)
+        m2.functions.extend([
+            _make_function(
+                'local', 'f1', ['x0', 'x1'], ['y'],
+                [helper.make_node('Mul', inputs=['x0', 'x1'], outputs=['y'])]
+            )
+        ])
+        checker.check_model(m2)
+
+        m = compose.merge_models(
+            m1, m2,
+            io_map=[('y', 'x0'), ('y', 'x1')],
+            prefix1='m1/', prefix2='m2/'
+        )
+        checker.check_model(m)
+
+        nodes = [n.op_type for n in m.graph.node]
+        self.assertEqual(['m1/f1', 'm2/f1'], nodes)
+
+        functions = [f.name for f in m.functions]
+        self.assertEqual(['m1/f1', 'm2/f1'], functions)
+
+        g3 = GraphProto()
+        g3.CopyFrom(g)
+        g3.name = 'g3'
+        g3.node[0].op_type = 'f2'
+        m3 = helper.make_model(g3, producer_name='test', opset_imports=ops)
+        m3.functions.extend([
+            _make_function(
+                'local', 'f1', ['x0', 'x1'], ['y'],
+                [
+                    helper.make_node('Add', inputs=['x0', 'x1'], outputs=['y0']),
+                    helper.make_node('Mul', inputs=['x0', 'x1'], outputs=['y1']),
+                    helper.make_node('Add', inputs=['y0', 'y1'], outputs=['y'])
+                ]
+            ),
+            _make_function(
+                'local', 'f2', ['x0', 'x1'], ['y'],
+                [
+                    helper.make_node('f1', domain='local', inputs=['x0', 'x1'], outputs=['y0']),
+                    helper.make_node('Mul', inputs=['x0', 'x1'], outputs=['y1']),
+                    helper.make_node('Add', inputs=['y0', 'y1'], outputs=['y'])
+                ]
+            )
+        ])
+        checker.check_model(m3)
+
+        m = compose.merge_models(
+            m1, m3,
+            io_map=[('y', 'x0'), ('y', 'x1')],
+            prefix1='m1/', prefix2='m3/'
+        )
+        checker.check_model(m)
+
+        nodes = [n.op_type for n in m.graph.node]
+        self.assertEqual(['m1/f1', 'm3/f2'], nodes)
+
+        functions = [f.name for f in m.functions]
+        self.assertEqual(['m1/f1', 'm3/f1', 'm3/f2'], functions)
+
+        self.assertEqual(
+            ['Add'], [n.op_type for n in m.functions[0].node])
+        self.assertEqual(
+            ['Add', 'Mul', 'Add'], [n.op_type for n in m.functions[1].node])
+        self.assertEqual(
+            ['m3/f1', 'Mul', 'Add'], [n.op_type for n in m.functions[2].node])
+
+    def test_merge_drop_unnecessary_initializers_and_value_info(self) -> None:
+        '''
+        Tests automatic removal of initializers when merging graphs
+        '''
+        ops = [helper.make_opsetid("", 10)]
+
+        g = GraphProto()
+        g.input.extend([helper.make_tensor_value_info('x', TensorProto.FLOAT, [])])
+        g.output.extend([helper.make_tensor_value_info('y', TensorProto.FLOAT, [])])
+        g.node.extend([helper.make_node('Identity', inputs=['x'], outputs=['y'])])
+
+        g1 = GraphProto()
+        g1.CopyFrom(g)
+        g1.name = 'g1'
+        m1 = helper.make_model(g1, producer_name='test', opset_imports=ops)
+        checker.check_model(m1)
+
+        g2 = GraphProto()
+        g2.CopyFrom(g)
+        g2.name = 'g2'
+        g2.initializer.extend(
+            [helper.make_tensor(name='x', data_type=TensorProto.FLOAT, dims=(), vals=[0])]
+        )
+        m2 = helper.make_model(g2, producer_name='test', opset_imports=ops)
+        checker.check_model(m2)
+
+        g3 = GraphProto()
+        g3.CopyFrom(g)
+        g3.name = 'g3'
+        g3.sparse_initializer.extend(
+            [_make_sparse_tensor('x')]
+        )
+        m3 = helper.make_model(g3, producer_name='test', opset_imports=ops)
+        checker.check_model(m3)
+
+        g4 = GraphProto()
+        g4.CopyFrom(g)
+        g4.name = 'g3'
+        g4.value_info.extend(
+            [helper.make_tensor_value_info('x', TensorProto.FLOAT, [])]
+        )
+        m4 = helper.make_model(g4, producer_name='test', opset_imports=ops)
+        checker.check_model(m4)
+
+        # Initializer 'x' from m1 is removed, because there is no longer an input with that name
+        out_m1 = compose.merge_models(m1, m2, prefix1='m1/', io_map=[('y', 'x')])
+        self.assertEqual(0, len(out_m1.graph.initializer))
+
+        # Sparse initializer 'x' from m1 is removed, because there is no longer an input with that name
+        out_m2 = compose.merge_models(m1, m3, prefix1='m1/', io_map=[('y', 'x')])
+        self.assertEqual(0, len(out_m2.graph.initializer))
+
+        # Value info 'x' from m1 is removed, because there is no longer an input with that name
+        out_m3 = compose.merge_models(m1, m4, prefix1='m1/', io_map=[('y', 'x')])
+        self.assertEqual(0, len(out_m3.graph.value_info))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/cpp/data_propagation_test.cc b/third_party/onnx/onnx/test/cpp/data_propagation_test.cc
new file mode 100644
index 0000000000..3a9f484bf7
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/data_propagation_test.cc
@@ -0,0 +1,399 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+#include "onnx/checker.h"
+#include "onnx/defs/parser.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/shape_inference.h"
+#include "onnx/onnx_pb.h"
+
+#include "onnx/shape_inference/implementation.h"
+
+using namespace ONNX_NAMESPACE::shape_inference;
+
+namespace ONNX_NAMESPACE {
+
+namespace Test {
+
+inline bool CompareShape(
+    const TensorShapeProto& inferredShape,
+    const TensorShapeProto& expectedShape,
+    bool checkSameParam = false) {
+  EXPECT_TRUE(inferredShape.dim_size() == expectedShape.dim_size()) << "Dim size for inferred and expected shape is different.";
+
+  for (int i = 0; i < inferredShape.dim_size(); i++) {
+    EXPECT_TRUE(
+        (inferredShape.dim(i).has_dim_value() == expectedShape.dim(i).has_dim_value()) &&
+        (inferredShape.dim(i).has_dim_param() == expectedShape.dim(i).has_dim_param()))
+        << "Inferred and expected dim values are different.";
+
+    EXPECT_TRUE(
+        inferredShape.dim(i).has_dim_value() ? inferredShape.dim(i).dim_value() == expectedShape.dim(i).dim_value()
+            : checkSameParam                 ? inferredShape.dim(i).dim_param() == expectedShape.dim(i).dim_param()
+                                             : true)
+        << "Inferred and expected dims are different.";
+  }
+
+  return true;
+}
+
+ TensorShapeProto RunDataPropagation(const char* graphCode, int domainVersion = 15) {
+  // Parses the graph from graphCode
+  GraphProto graph;
+  OnnxParser parser(graphCode);
+  auto status = parser.Parse(graph);
+  EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
+  EXPECT_TRUE(parser.EndOfInput()) << "Extra unparsed input unexpected.";
+
+  // Constructs name to TypeProto map from value_info, input, output
+  std::unordered_map<std::string, TypeProto*> valueTypesByName;
+  for (auto& vi : *graph.mutable_value_info()) {
+    if (vi.has_type()) {
+      valueTypesByName[vi.name()] = vi.mutable_type();
+    }
+  }
+  for (auto& vi : *graph.mutable_input()) {
+    if (vi.has_type()) {
+      valueTypesByName[vi.name()] = vi.mutable_type();
+    }
+  }
+  for (auto& vi : *graph.mutable_output()) {
+    if (vi.has_type()) {
+      valueTypesByName[vi.name()] = vi.mutable_type();
+    }
+  }
+
+  // Constructs name to TensorProto map from initializer
+  std::unordered_map<std::string, const TensorProto*> inputDataByName;
+  for (const auto& tp : graph.initializer()) {
+    inputDataByName[tp.name()] = &tp;
+  }
+  // Collects data from constant nodes
+  for (const auto& n : graph.node()) {
+    if (n.op_type() != "Constant" || n.output().size() != 1) {
+      continue;
+    }
+    for (const auto& attr : n.attribute()) {
+      if (attr.name() == "value") {
+        if (attr.type() == AttributeProto::TENSOR && attr.has_t()) {
+          inputDataByName[n.output(0)] = &attr.t();
+        }
+      }
+    }
+  }
+
+  // Runs data propagation on each node
+  std::unordered_map<std::string, TensorShapeProto> generatedShapeDataByName;
+  auto* schemaRegistry = OpSchemaRegistry::Instance();
+  TensorShapeProto inferredShape;
+  for (auto n: graph.node()) {
+    // No need to run data propagation on Constant
+    if (n.op_type() == "Constant") {
+      continue;
+    }
+    DataPropagationContextImpl dataPropagationCtx(
+        n, valueTypesByName, inputDataByName, generatedShapeDataByName);
+    const auto schema = schemaRegistry->GetSchema(n.op_type(), domainVersion, n.domain());
+    EXPECT_TRUE(schema->has_data_propagation_function());
+    schema->GetDataPropagationFunction()(dataPropagationCtx);
+  }
+
+  // Assuming the graph being tested only has 1 output. 
+  // If this ever changes then fixes are required here.
+  const auto inputShapeDataIter = generatedShapeDataByName.find(graph.output()[0].name());
+  EXPECT_TRUE(inputShapeDataIter != generatedShapeDataByName.cend());
+
+  inferredShape.CopyFrom(inputShapeDataIter->second);
+
+  // Returns the partial shape data for output
+  return inferredShape;
+ }
+
+TEST(DataPropagationImplTest, ShapeTest) {
+  const char* code = R"ONNX(
+agraph (int32[7,4,1] x) => (int32[3] y)
+{
+    xs = Shape(x)
+    y = Cast<to = 7>(xs)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(7);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(1);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SymbolicShapeTest) {
+  const char* code = R"ONNX(
+agraph (int32[N,3,256,256] x) => (int32[4] y)
+{
+    xs = Shape(x)
+    y = Cast<to = 7>(xs)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_param("N");
+  expected_tsp.mutable_dim()->Add()->set_dim_value(3);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(256);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(256);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp, true));
+}
+
+TEST(DataPropagationImplTest, CastTest) {
+  const char* code = R"ONNX(
+agraph (int32[2,5] x) => (int32[2] y)
+{
+    xs = Shape(x)
+    y = Cast<to = 7>(xs)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(2);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SqueezeTest) {
+  const char* code = R"ONNX(
+agraph (int32[2,5] x) => (int32[2] z)
+{
+    xs = Shape(x)
+    y = Squeeze(xs)
+    z = Cast<to = 7>(y)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(2);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, UnsqueezeTest) {
+  const char* code = R"ONNX(
+agraph (int32[2,5] x) => (int32[1,2] w)
+{
+    xs = Shape(x)
+    axis = Constant<value = int64[1] {1}>()
+    z = Unsqueeze(xs, axis)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(2);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SizeTest) {
+  const char* code = R"ONNX(
+agraph (int64[1] x) => (int32[1] w)
+<int64[3] init = {2,3,5}>
+{
+    z = Size(init)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(3);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, AddTest) {
+  const char* code = R"ONNX(
+agraph (int32[2,4,5] x, int32[2,4,5] y) => (int32[3] w)
+{
+    xs = Shape(x)
+    ys = Shape(y)
+    z = Add(xs, ys)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(8);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(10);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, AddSymbolicShapeTest) {
+  const char* code = R"ONNX(
+agraph (int32[2,4,5] x, int32[2,4,M] y) => (int32[3] w)
+{
+    xs = Shape(x)
+    ys = Shape(y)
+    z = Add(xs, ys)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  // Add({2,4,5}, {2,4,M}) = {4,8,?}
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(8);
+  // Not computable so do not set value or param
+  expected_tsp.mutable_dim()->Add();
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SubTest) {
+  const char* code = R"ONNX(
+agraph (int32[10,11,6] x, int32[5] y) => (int32[3] w)
+{
+    xs = Shape(x)
+    ys = Shape(y)
+    z = Sub(xs, ys)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(6);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(1);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, MulTest) {
+  const char* code = R"ONNX(
+agraph (int32[2] x, int32[5,1,7] y) => (int32[3] w)
+{
+    xs = Shape(x)
+    ys = Shape(y)
+    z = Mul(xs, ys)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(10);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(2);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(14);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, ConcatTest) {
+  const char* code = R"ONNX(
+agraph (int32[1,2] x, int32[3,4] y) => (int32[4] w)
+{
+    xs = Shape(x)
+    ys = Shape(y)
+    z = Concat<axis = 0>(xs, ys)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(1);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(2);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(3);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, GatherTest) {
+  const char* code = R"ONNX(
+agraph (int32[1,2,3,4,5,6] x) => (int32[3] w)
+{
+    xs = Shape(x)
+    indices = Constant<value = int64[3] {0,3,5}>()
+    z = Gather<axis = 0>(xs, indices)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(1);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(6);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, GatherNegativeIndicesTest) {
+  const char* code = R"ONNX(
+agraph (int32[1,2,3,4,5,6] x) => (int32[2] w)
+{
+    xs = Shape(x)
+    indices = Constant<value = int64[2] {-2,-1}>()
+    z = Gather<axis = 0>(xs, indices)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(6);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SliceTest) {
+  const char* code = R"ONNX(
+agraph (int32[1,2,3,4,5,6,7,8] x) => (int32[2] w)
+{
+    xs = Shape(x)
+    starts = Constant<value = int64[1] {1}>()
+    ends = Constant<value = int64[1] {7}>()
+    axes = Constant<value = int64[1] {0}>()
+    steps = Constant<value = int64[1] {3}>()
+    z = Slice(xs, starts, ends, axes, steps)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(2);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SliceDefaultAxesAndStepTest) {
+  const char* code = R"ONNX(
+agraph (int32[1,2,3,4,5,6,7,8] x) => (int32[3] w)
+{
+    xs = Shape(x)
+    starts = Constant<value = int64[1] {2}>()
+    ends = Constant<value = int64[1] {5}>()
+    z = Slice(xs, starts, ends)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(3);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(5);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+TEST(DataPropagationImplTest, SliceNegativeStartEndStepTest) {
+  const char* code = R"ONNX(
+agraph (int32[1,2,3,4,5,6,7,8] x) => (int32[3] w)
+{
+    xs = Shape(x)
+    starts = Constant<value = int64[1] {-3}>()
+    ends = Constant<value = int64[1] {-7}>()
+    axes = Constant<value = int64[1] {0}>()
+    steps = Constant<value = int64[1] {-2}>()
+    z = Slice(xs, starts, ends, axes, steps)
+    w = Cast<to = 7>(z)
+}
+)ONNX";
+  TensorShapeProto expected_tsp;
+  expected_tsp.mutable_dim()->Add()->set_dim_value(6);
+  expected_tsp.mutable_dim()->Add()->set_dim_value(4);
+  const auto propagated_tsp = RunDataPropagation(code);
+  EXPECT_TRUE(CompareShape(propagated_tsp, expected_tsp));
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/function_context_test.cc b/third_party/onnx/onnx/test/cpp/function_context_test.cc
new file mode 100644
index 0000000000..18e0030e54
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/function_context_test.cc
@@ -0,0 +1,183 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+#include "onnx/checker.h"
+#include "onnx/common/constants.h"
+#include "onnx/defs/function.h"
+#include "onnx/defs/schema.h"
+
+using namespace ONNX_NAMESPACE::checker;
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+
+// Utilities. TODO: Turn them into reusable ONNX utilities for use by
+
+TensorProto ToTensor(double value, TensorProto_DataType elem_type) {
+  TensorProto t;
+  t.set_data_type(elem_type);
+  switch (elem_type) {
+    case TensorProto_DataType::TensorProto_DataType_FLOAT:
+      t.add_float_data((float)value);
+      break;
+    case TensorProto_DataType::TensorProto_DataType_DOUBLE:
+      t.add_double_data(value);
+      break;
+    // case TensorProto_DataType::TensorProto_DataType_FLOAT16:
+    //   t.add_int32_data(onnxruntime::math::floatToHalf((float)value));
+    //   break;
+    default:
+      assert(false);
+  }
+
+  return t;
+}
+
+void BuildNodes(FunctionProto& functionProto, const std::vector<FunctionBodyHelper::NodeDef>& node_defs) {
+  for (size_t i = 0; i < node_defs.size(); i++) {
+    const FunctionBodyHelper::NodeDef& node = node_defs[i];
+    auto* np = functionProto.add_node();
+
+    np->set_op_type(node.op_type);
+    for (const auto& inp : node.inputs) {
+      np->add_input(inp);
+    }
+    for (const auto& o : node.outputs) {
+      np->add_output(o);
+    }
+    for (const auto& attr : node.attributes) {
+      *(np->add_attribute()) = attr.proto;
+    }
+  }
+}
+
+bool BuildFunctionProto(
+    FunctionProto& functionProto,
+    const OpSchema& schema,
+    const std::vector<FunctionBodyHelper::NodeDef>& node_defs) {
+  BuildNodes(functionProto, node_defs);
+  schema.BuildFunction(functionProto);
+  return true;
+}
+
+// A monomorphic context-dependent function test-case.
+static bool
+BuildFloatFunctionBody(const FunctionBodyBuildContext& ctx, const OpSchema& schema, FunctionProto& functionProto) {
+  // Create a scalar-tensor constant 2.0 of float type:
+  auto two_as_tensor = ToTensor(2.0, TensorProto_DataType::TensorProto_DataType_FLOAT);
+
+  std::vector<FunctionBodyHelper::NodeDef> body{// nodes: {outputs, op, inputs, attributes}
+                                                {{"Two"}, "Constant", {}, {{"value", two_as_tensor}}},
+                                                {{"Y"}, "Mul", {"X", "Two"}}};
+
+  return BuildFunctionProto(functionProto, schema, body);
+}
+
+void RegisterCustomFuncFloatSchema() {
+  ONNX_NAMESPACE::OpSchema schema;
+  schema.SetName("CustomFuncFloat")
+      .SetDomain(ONNX_DOMAIN)
+      .SinceVersion(12)
+      .SetDoc("This operator returns an output tensor that is twice the input tensor.")
+      .Input(0, "X", "Input tensor", "T", OpSchema::Single)
+      .Output(0, "Y", "Output tensor", "T", OpSchema::Single)
+      .TypeConstraint("T", {"tensor(float)"}, "Type of the input and output values")
+      .SetContextDependentFunctionBodyBuilder(BuildFloatFunctionBody);
+  ONNX_NAMESPACE::OpSchemaRegistry::OpSchemaRegisterOnce unused(schema);
+  (void)unused;
+}
+
+// Test for Context dependant function without type context
+TEST(FunctionAPITest, ContextDependentFunctionTest) {
+  RegisterCustomFuncFloatSchema();
+
+  const auto* schema = OpSchemaRegistry::Schema("CustomFuncFloat", 12, ONNX_DOMAIN);
+  EXPECT_TRUE(schema);
+  EXPECT_FALSE(schema->HasFunction());
+  EXPECT_TRUE(schema->HasContextDependentFunction());
+
+  NodeProto nodeProto;
+  nodeProto.set_op_type("CustomFuncFloat");
+  nodeProto.add_input("X");
+  nodeProto.add_output("Y");
+
+  FunctionBodyBuildContextImpl ctx(nodeProto);
+  FunctionProto fnProto;
+  EXPECT_TRUE(schema->BuildContextDependentFunction(ctx, fnProto));
+  EXPECT_EQ(fnProto.node_size(), 2);
+
+  LexicalScopeContext lexicalScope;
+  CheckerContext checkerCtx;
+  std::unordered_map<std::string, int> opset_imports({{ONNX_DOMAIN, 12}});
+  checkerCtx.set_opset_imports(opset_imports);
+  checkerCtx.set_ir_version(7);
+  check_function(fnProto, checkerCtx, lexicalScope);
+}
+
+// A polymorphic context-dependent function test-case.
+
+static bool
+BuildFunctionBody(const FunctionBodyBuildContext& ctx, const OpSchema& schema, FunctionProto& functionProto) {
+  // Create a scalar-tensor constant 2.0 of input-type:
+  auto* tp = ctx.getInputType(0);
+  if ((tp == nullptr) || (!tp->has_tensor_type()))
+    return false;
+  auto elem_type = (TensorProto_DataType)tp->tensor_type().elem_type();
+  auto two_as_tensor = ToTensor(2.0, elem_type);
+
+  std::vector<FunctionBodyHelper::NodeDef> body{// nodes: {outputs, op, inputs, attributes}
+                                                {{"Two"}, "Constant", {}, {{"value", two_as_tensor}}},
+                                                {{"Y"}, "Mul", {"X", "Two"}}};
+
+  return BuildFunctionProto(functionProto, schema, body);
+}
+
+void RegisterCustomFunctionSchema() {
+  ONNX_NAMESPACE::OpSchema schema;
+  schema.SetName("CustomFunction")
+      .SetDomain(ONNX_DOMAIN)
+      .SinceVersion(12)
+      .SetDoc("This operator returns an output tensor that is twice the input tensor.")
+      .Input(0, "X", "Input tensor", "T", OpSchema::Single)
+      .Output(0, "Y", "Output tensor", "T", OpSchema::Single)
+      .TypeConstraint("T", {"tensor(float)", "tensor(double)"}, "Type of the input and output values")
+      .SetContextDependentFunctionBodyBuilder(BuildFunctionBody);
+  ONNX_NAMESPACE::OpSchemaRegistry::OpSchemaRegisterOnce unused(schema);
+  (void)unused;
+}
+
+// Test for Context dependant function with type context
+TEST(FunctionAPITest, TypeContextTest) {
+  RegisterCustomFunctionSchema();
+
+  const auto* schema = OpSchemaRegistry::Schema("CustomFunction", 12, ONNX_DOMAIN);
+  EXPECT_TRUE(schema);
+  EXPECT_FALSE(schema->HasFunction());
+  EXPECT_TRUE(schema->HasContextDependentFunction());
+
+  NodeProto nodeProto;
+  nodeProto.set_op_type("CustomFunction");
+  nodeProto.add_input("X");
+  nodeProto.add_output("Y");
+
+  TypeProto floatTypeProto;
+  floatTypeProto.mutable_tensor_type()->set_elem_type(TensorProto_DataType::TensorProto_DataType_FLOAT);
+
+  FunctionBodyBuildContextImpl ctx(nodeProto, {floatTypeProto});
+  FunctionProto fnProto;
+  EXPECT_TRUE(schema->BuildContextDependentFunction(ctx, fnProto));
+  EXPECT_EQ(fnProto.node_size(), 2);
+
+  LexicalScopeContext lexicalScope;
+  CheckerContext checkerCtx;
+  std::unordered_map<std::string, int> opset_imports({{ONNX_DOMAIN, 12}});
+  checkerCtx.set_opset_imports(opset_imports);
+  checkerCtx.set_ir_version(7);
+  check_function(fnProto, checkerCtx, lexicalScope);
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/function_get_test.cc b/third_party/onnx/onnx/test/cpp/function_get_test.cc
new file mode 100644
index 0000000000..461ad74df2
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/function_get_test.cc
@@ -0,0 +1,22 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+#include "onnx/common/constants.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+
+TEST(FunctionAPITest, Get_Function_op_With_Version) {
+  const auto* schema = OpSchemaRegistry::Schema("MeanVarianceNormalization", 9, "");
+  EXPECT_TRUE(schema);
+  EXPECT_TRUE(schema->HasFunction());
+  auto func = schema->GetFunction();
+  EXPECT_EQ(func->name(), "MeanVarianceNormalization");
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/function_verify_test.cc b/third_party/onnx/onnx/test/cpp/function_verify_test.cc
new file mode 100644
index 0000000000..f1a13ce14d
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/function_verify_test.cc
@@ -0,0 +1,359 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include <set>
+#include "gtest/gtest.h"
+#include "onnx/checker.h"
+#include "onnx/common/constants.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/parser.h"
+#include "onnx/onnx-operators_pb.h"
+#include "onnx/onnx_pb.h"
+#include "onnx/shape_inference/implementation.h"
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+using namespace checker;
+using TENSOR_TYPES_MAP = std::unordered_map<std::string, std::vector<std::string>>;
+
+void VerifyTypeConstraint(const OpSchema& function_op, const FunctionProto* function_proto, int& counter) {
+  // This is a simple partial type-checker for a function-body.
+  // TODO: Revisit to make the type-checker more complete.
+  TENSOR_TYPES_MAP tc_map;
+  std::set<std::string> primitive_types(OpSchema::all_tensor_types().begin(), OpSchema::all_tensor_types().end());
+  for (const auto& input : function_op.inputs()) {
+    std::string name = input.GetName();
+    auto& tvec = tc_map[name];
+    for (const auto& t : input.GetTypes()) {
+      tvec.emplace_back(*t);
+    }
+  }
+
+  for (const auto& output : function_op.outputs()) {
+    std::string name = output.GetName();
+    auto& tvec = tc_map[name];
+    for (const auto& t : output.GetTypes()) {
+      tvec.emplace_back(*t);
+    }
+  }
+
+  for (auto& node : function_proto->node()) {
+    std::string op_type = node.op_type();
+    const OpSchema* schema = OpSchemaRegistry::Schema(op_type, function_op.since_version(), function_op.domain());
+
+    // Check that the types of actual inputs, if known, are legal as per schema
+    // of called op:
+    auto num_formal_inputs = static_cast<size_t>(schema->inputs().size());
+    auto num_actual_inputs = static_cast<size_t>(node.input_size());
+
+    for (size_t i = 0; i < num_actual_inputs; ++i) {
+      auto actual_param_name = node.input(static_cast<int>(i));
+      auto iter = tc_map.find(actual_param_name);
+      if (iter != tc_map.end()) {
+        // if i >= num_formal_inputs, it is a variadic parameter corresponding
+        // to the last formal parameter.
+        auto formal_i = std::min(i, num_formal_inputs - 1);
+        const auto& types = schema->inputs().at(formal_i).GetTypes();
+        std::unordered_set<std::string> allowed_types;
+        for (auto& s : types) {
+          allowed_types.insert(*s);
+        }
+        for (auto& actual_type : iter->second) {
+          if (allowed_types.find(actual_type) == allowed_types.end()) {
+            fail_check(
+                "Input type " + actual_type + " of parameter " + actual_param_name + " of function " +
+                function_op.Name() + " is not allowed by operator " + op_type);
+          }
+        }
+      }
+    }
+
+    // No simple check exists for outputs: we need to integrate type inference
+    // to identify the possible output types and verify that they are included
+    // in the function-schema.
+  }
+
+  ++counter;
+}
+
+void VerifyFunction(const OpSchema& op, const FunctionProto* function_proto, int& counter) {
+  // Verify function proto is valid
+  if (!function_proto) {
+    fail_check("Cannot get function body for op '", op.Name(), "'");
+  }
+  CheckerContext ctx;
+  std::unordered_map<std::string, int> op_set;
+  auto version_range = OpSchemaRegistry::DomainToVersionRange::Instance().Map().at(op.domain());
+  if (op.since_version() > version_range.second || op.since_version() < version_range.first) {
+    fail_check("Invalid function version in function op '", op.Name(), "'");
+  }
+
+  if (function_proto->opset_import_size() > 0) {
+    for (const auto& opset_import : function_proto->opset_import()) {
+      op_set.insert({opset_import.domain(), opset_import.version()});
+    }
+  } else {
+    op_set.insert({op.domain(), op.since_version()});
+  }
+
+  ctx.set_opset_imports(op_set);
+  ctx.set_is_main_graph(false);
+  LexicalScopeContext lex_ctx;
+  ONNX_TRY {
+    check_function(*function_proto, ctx, lex_ctx);
+  }
+  ONNX_CATCH(ValidationError & ex) {
+    ONNX_HANDLE_EXCEPTION([&]() { fail_check(ex.what()); });
+  }
+
+  // Verify function op has compatible Type constraints defined in
+  // op and function body.
+  VerifyTypeConstraint(op, function_proto, counter);
+}
+
+// Verify registered ops with function body has compatible
+// definition on TypeConstraints between ops and function body
+TEST(FunctionVerification, VerifyFunctionOps) {
+  const std::vector<OpSchema> schemas = OpSchemaRegistry::get_all_schemas();
+  int function_counter = 0, verified_counter = 0;
+  for (const auto s : schemas) {
+    if (!s.HasFunction())
+      continue;
+    // Skip test for functions with known errors that need to be fixed:
+    // Range currently permits int16 parameters, but the operator Sub, called
+    // from the body of Range does not yet support int16 parameter.
+    if (s.Name() == "Range")
+      continue;
+    ONNX_TRY {
+      ++function_counter;
+      auto function_body = s.GetFunction();
+      VerifyFunction(s, function_body, verified_counter);
+    }
+    ONNX_CATCH(ONNX_NAMESPACE::checker::ValidationError e) {
+      ONNX_HANDLE_EXCEPTION([&]() { FAIL() << e.what(); });
+    }
+  }
+  std::cerr << "[          ] Verified " << verified_counter << "/" << function_counter << " Functions." << std::endl;
+}
+
+// Verify that FunctionExpandHelper obtains missing default attributes
+// from schema and adds them to ops in expanded subgraph.
+TEST(FunctionVerification, VerifyFunctionExpandHelper) {
+  GraphProto graph;
+  NodeProto* new_node = graph.add_node();
+  new_node->set_op_type("MeanVarianceNormalization");
+
+  const auto* schema = OpSchemaRegistry::Schema("MeanVarianceNormalization", 9, "");
+  const FunctionProto* func = schema->GetFunction();
+  const auto default_axes_attribute = schema->attributes().at("axes").default_value;
+
+  FunctionExpandHelper(*new_node, *func, graph);
+
+  for (const auto& node : graph.node()) {
+    if (node.op_type() == "ReduceMean") {
+      auto attr = node.attribute(0);
+      EXPECT_EQ(attr.name(), "axes");
+      EXPECT_EQ(attr.ints().size(), default_axes_attribute.ints().size());
+
+      for (int i = 0; i < default_axes_attribute.ints().size(); ++i) {
+        EXPECT_EQ(attr.ints(i), default_axes_attribute.ints(i));
+      }
+      return;
+    }
+  }
+  FAIL() << "During expanding MeanVarianceNormalization function, "
+         << "the default attribute `axes` has not been assigned to ReduceMean op.";
+}
+
+void RegisterFunctionSchema() {
+  ONNX_NAMESPACE::OpSchema function_schema;
+  function_schema.SetName("DynamicQuantizeLinear_Fake")
+      .SetDomain(AI_ONNX_ML_DOMAIN)
+      .SinceVersion(2)
+      .SetDoc("Test Op")
+      .Input(0, "x", "Input tensor", "T1")
+      .Output(0, "y", "Quantized output tensor", "T2")
+      .Output(
+          1, "y_scale", "Output scale. It's a scalar, which means a per-tensor/layer quantization.", "tensor(float)")
+      .Output(2, "y_zero_point", "Output zero point. It's a scalar, which means a per-tensor/layer quantization.", "T2")
+      .TypeConstraint("T1", {"tensor(float)"}, "Constrain 'x' to float tensor.")
+      .TypeConstraint("T2", {"tensor(uint8)"}, "Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.")
+      .FunctionBody(
+          FunctionBodyHelper::BuildNodes(
+              {// nodes: {outputs, op, inputs, attributes}
+               FunctionBodyHelper::Const<float>("Q_Min", 0.f),
+               FunctionBodyHelper::Const<float>("Q_Max", 255.f),
+               {{"X_Min"}, "ReduceMin", {"x"}, {MakeAttribute("keepdims", int64_t(0))}},
+               {{"X_Min_Adjusted"}, "Min", {"X_Min", "Q_Min"}},
+               {{"X_Max"}, "ReduceMax", {"x"}, {MakeAttribute("keepdims", int64_t(0))}},
+               {{"X_Max_Adjusted"}, "Max", {"X_Max", "Q_Min"}},
+               {{"X_Range"}, "Sub", {"X_Max_Adjusted", "X_Min_Adjusted"}},
+               {{"Scale"}, "Div", {"X_Range", "Q_Max"}},
+               {{"Min_Scaled"}, "Div", {"X_Min_Adjusted", "Scale"}},
+               {{"Initial_ZeroPoint_FP"}, "Sub", {"Q_Min", "Min_Scaled"}},
+               {{"Clipped_ZeroPoint_FP"}, "Clip", {"Initial_ZeroPoint_FP", "Q_Min", "Q_Max"}},
+               {{"Rounded_ZeroPoint_FP"}, "Round", {"Clipped_ZeroPoint_FP"}},
+               {{"Zeropoint"}, "Cast", {"Rounded_ZeroPoint_FP"}, {MakeAttribute("to", int64_t(2))}},
+               {{"y_scale"}, "Identity", {"Scale"}},
+               {{"y_zero_point"}, "Identity", {"Zeropoint"}},
+               {{"y"}, "QuantizeLinear", {"x", "Scale", "Zeropoint"}}}),
+          []() {
+            std::vector<OperatorSetIdProto> operator_sets(2);
+            auto& onnx_opset = operator_sets[0];
+            onnx_opset.set_domain("");
+            onnx_opset.set_version(13);
+
+            auto& test_opset = operator_sets[1];
+            test_opset.set_domain(AI_ONNX_ML_DOMAIN);
+            test_opset.set_version(2);
+
+            return operator_sets;
+          }());
+  ONNX_NAMESPACE::OpSchemaRegistry::OpSchemaRegisterOnce unused(function_schema);
+  (void)unused;
+}
+
+TEST(FunctionVerification, VerifyFunctionBodyWithMultipleDomains) {
+  RegisterFunctionSchema();
+
+  const auto* schema = OpSchemaRegistry::Schema("DynamicQuantizeLinear_Fake", 2, AI_ONNX_ML_DOMAIN);
+  EXPECT_TRUE(schema);
+  EXPECT_TRUE(schema->HasFunction());
+  EXPECT_FALSE(schema->HasContextDependentFunction());
+
+  const FunctionProto* fnProto = schema->GetFunction();
+  EXPECT_EQ(fnProto->node_size(), 16);
+
+  LexicalScopeContext lexicalScope;
+  CheckerContext checkerCtx;
+  std::unordered_map<std::string, int> opset_imports({{AI_ONNX_ML_DOMAIN, 2}, {"", 13}});
+  checkerCtx.set_opset_imports(opset_imports);
+  checkerCtx.set_ir_version(7);
+  check_function(*fnProto, checkerCtx, lexicalScope);
+}
+
+TEST(FunctionVerification, VerifyModelLocalFunctions) {
+  const char* code = R"ONNX(
+<
+  ir_version: 8,
+  opset_import: [ "" : 13, "custom_domain_1" : 1, "custom_domain_2" : 1],
+  producer_name: "FunctionProtoTest",
+  producer_version: "1.0",
+  model_version: 1,
+  doc_string: "A test model for model local functions."
+>
+agraph (float[N] x) => (uint8[N] out)
+{
+    o1, o2 = custom_domain_1.bar(x)
+    o3 = Add(o1, o2)
+    o4 = custom_domain_2.foo(o3)
+    out = Identity(o4)
+}
+
+<
+  domain: "custom_domain_1",
+  opset_import: [ "" : 13],
+  doc_string: "Test function proto"
+>
+bar (x) => (o1, o2) {
+      o1 = Identity (x)
+      o2 = Identity (o1)
+}
+
+<
+  domain: "custom_domain_2",
+  opset_import: [ "" : 13],
+  doc_string: "Test function proto"
+>
+foo (x) => (y) {
+      Q_Min = Constant <value = float[1] {0.0}> ()
+      Q_Max = Constant <value = float[1] {255.0}> ()
+      X_Min = ReduceMin <keepdims = 0> (x)
+      X_Max = ReduceMax <keepdims = 0> (x)
+      X_Range = Sub (X_Max, X_Min)
+      Scale = Div (X_Range, Q_Max)
+      ZeroPoint_FP = Sub (Q_Min, Scale)
+      Zeropoint = Cast <to = 2> (ZeroPoint_FP)
+      y = QuantizeLinear (x, Scale, Zeropoint)
+}
+)ONNX";
+
+  ModelProto model;
+  auto status = OnnxParser::Parse(model, code);
+  EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
+  check_model(model);
+
+  ShapeInferenceOptions options{true, 1, true};
+  ONNX_NAMESPACE::shape_inference::InferShapes(model, OpSchemaRegistry::Instance(), options);
+}
+
+TEST(FunctionVerification, VerifyNestedModelLocalFunctions) {
+  const char* code = R"ONNX(
+<
+  ir_version: 8,
+  opset_import: [ "" : 13, "custom_domain_1" : 1, "custom_domain_2" : 1],
+  producer_name: "FunctionProtoTest",
+  producer_version: "1.0",
+  model_version: 1,
+  doc_string: "A test model for model local functions."
+>
+agraph (float[N] x) => (uint8[N] out)
+{
+    o1, o2 = custom_domain_1.bar(x)
+    o3 = Add(o1, o2)
+    o4 = custom_domain_2.foo(o3)
+    out = Identity(o4)
+}
+
+<
+  domain: "custom_domain_1",
+  opset_import: [ "" : 13],
+  doc_string: "Test function proto"
+>
+bar (x) => (o1, o2) {
+      o1 = Identity (x)
+      o2 = Identity (o1)
+}
+
+<
+  domain: "custom_domain_2",
+  opset_import: [ "" : 13, "custom_domain_3" : 1],
+  doc_string: "Test function proto"
+>
+foo (x) => (o4) {
+      o1 = custom_domain_3.foo (x)
+      o4 = Identity (o1)
+}
+
+<
+  domain: "custom_domain_3",
+  opset_import: [ "" : 13],
+  doc_string: "Test function proto"
+>
+foo (x) => (y) {
+      Q_Min = Constant <value = float[1] {0.0}> ()
+      Q_Max = Constant <value = float[1] {255.0}> ()
+      X_Min = ReduceMin <keepdims = 0> (x)
+      X_Max = ReduceMax <keepdims = 0> (x)
+      X_Range = Sub (X_Max, X_Min)
+      Scale = Div (X_Range, Q_Max)
+      ZeroPoint_FP = Sub (Q_Min, Scale)
+      Zeropoint = Cast <to = 2> (ZeroPoint_FP)
+      y = QuantizeLinear (x, Scale, Zeropoint)
+}
+)ONNX";
+
+  ModelProto model;
+  auto status = OnnxParser::Parse(model, code);
+  EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
+
+  check_model(model);
+
+  ShapeInferenceOptions options{true, 1, true};
+  ONNX_NAMESPACE::shape_inference::InferShapes(model, OpSchemaRegistry::Instance(), options);
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/onnxifi_backend_test.cc b/third_party/onnx/onnx/test/cpp/onnxifi_backend_test.cc
new file mode 100644
index 0000000000..dd05ae29ec
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/onnxifi_backend_test.cc
@@ -0,0 +1,116 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+
+#include "gtest/gtest.h"
+#include "onnx/onnxifi_loader.h"
+#include "onnx/onnxifi.h"
+
+#if defined(__APPLE__)
+#define ONNXIFI_DUMMY_LIBRARY "libonnxifi_dummy.dylib"
+#elif defined(_WIN32)
+#define ONNXIFI_DUMMY_LIBRARY L"onnxifi_dummy.dll"
+#else
+#define ONNXIFI_DUMMY_LIBRARY "libonnxifi_dummy.so"
+#endif
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+TEST(OnnxifiLoadTest, OnnxifiDummyBackend) {
+#define EXPECT_EQ_OSS(X) EXPECT_EQ(X, ONNXIFI_STATUS_SUCCESS)
+  onnxifi_library dummy_backend;
+  EXPECT_TRUE(onnxifi_load(1, ONNXIFI_DUMMY_LIBRARY, &dummy_backend));
+
+  onnxBackendID backendID;
+  onnxBackend backend;
+  onnxEvent event;
+  onnxGraph graph;
+
+  // Testing onnxGetBackendIDs
+  size_t numBackends = -1;
+
+  EXPECT_EQ_OSS(dummy_backend.onnxGetBackendIDs(&backendID, &numBackends));
+  EXPECT_EQ(numBackends, 1);
+
+  // Testing onnxReleaseBackendID
+  EXPECT_EQ_OSS(dummy_backend.onnxReleaseBackendID(backendID));
+
+  // Testing onnxGetBackendInfo
+  onnxBackendInfo infoType = 0;
+  char infoValue[11] = "abc";
+  size_t infoValueSize = 3;
+
+  EXPECT_EQ_OSS(dummy_backend.onnxGetBackendInfo(
+      backendID, infoType, infoValue, &infoValueSize));
+  EXPECT_EQ(infoValue[0], 0);
+
+  // Testing onnxGetBackendCompatibility
+  char onnxModel[] = "";
+  size_t onnxModelSize = 0;
+  EXPECT_EQ_OSS(dummy_backend.onnxGetBackendCompatibility(
+      backendID, onnxModelSize, onnxModel));
+
+  // Testing onnxInitBackend
+  const uint64_t backendProperties[] = {
+      ONNXIFI_BACKEND_PROPERTY_NONE
+  };
+  EXPECT_EQ_OSS(
+      dummy_backend.onnxInitBackend(backendID, backendProperties, &backend));
+
+  // Testing onnxReleaseBackend
+  EXPECT_EQ_OSS(dummy_backend.onnxReleaseBackend(backend));
+
+  // Testing onnxInitEvent
+  EXPECT_EQ_OSS(dummy_backend.onnxInitEvent(backend, &event));
+
+  // Testing onnxSignalEvent
+  EXPECT_EQ_OSS(dummy_backend.onnxSignalEvent(event));
+
+  // Testing onnxWaitEvent
+  EXPECT_EQ_OSS(dummy_backend.onnxWaitEvent(event));
+
+  // Testing onnxReleaseEvent
+  EXPECT_EQ_OSS(dummy_backend.onnxReleaseEvent(event));
+
+  // Testing onnxInitGraph
+  uint32_t weightCount = 1;
+  onnxTensorDescriptorV1 weightDescriptors;
+  const uint64_t graphProperties[] = {
+      ONNXIFI_GRAPH_PROPERTY_NONE
+  };
+  EXPECT_EQ_OSS(dummy_backend.onnxInitGraph(
+      backend,
+      graphProperties,
+      onnxModelSize,
+      onnxModel,
+      weightCount,
+      &weightDescriptors,
+      &graph));
+
+  // Testing onnxSetGraphIO
+  uint32_t inputsCount = 1;
+  onnxTensorDescriptorV1 inputDescriptors;
+  uint32_t outputsCount = 1;
+  onnxTensorDescriptorV1 outputDescriptors;
+  EXPECT_EQ_OSS(dummy_backend.onnxSetGraphIO(
+      graph, inputsCount, &inputDescriptors, outputsCount, &outputDescriptors));
+
+  // Testing onnxRunGraph
+  onnxMemoryFenceV1 inputFence, outputFence;
+  EXPECT_EQ_OSS(dummy_backend.onnxRunGraph(graph, &inputFence, &outputFence));
+  EXPECT_EQ(outputFence.type, ONNXIFI_SYNCHRONIZATION_EVENT);
+
+  // Testing onnxGetEventState
+  onnxEventState state;
+  EXPECT_EQ_OSS(dummy_backend.onnxGetEventState(event, &state));
+
+  // Testing onnxReleaseGraph
+  EXPECT_EQ_OSS(dummy_backend.onnxReleaseGraph(graph));
+
+#undef EXPECT_EQ_OSS
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/op_reg_test.cc b/third_party/onnx/onnx/test/cpp/op_reg_test.cc
new file mode 100644
index 0000000000..98deb35e15
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/op_reg_test.cc
@@ -0,0 +1,25 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+#include "onnx/defs/schema.h"
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+TEST(OpRegistrationTest, GemmOp) {
+  auto opSchema = OpSchemaRegistry::Schema("Gemm");
+  EXPECT_TRUE(nullptr != opSchema);
+  size_t input_size = opSchema->inputs().size();
+  EXPECT_EQ(input_size, 3);
+  EXPECT_EQ(opSchema->inputs()[0].GetTypes(), opSchema->outputs()[0].GetTypes());
+  size_t attr_size = opSchema->attributes().size();
+  EXPECT_EQ(attr_size, 4);
+  EXPECT_NE(opSchema->attributes().count("alpha"), 0);
+  EXPECT_EQ(opSchema->attributes().at("alpha").type, AttributeProto_AttributeType_FLOAT);
+  EXPECT_NE(opSchema->attributes().count("beta"), 0);
+  EXPECT_EQ(opSchema->attributes().at("beta").type, AttributeProto_AttributeType_FLOAT);
+}
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/parser_test.cc b/third_party/onnx/onnx/test/cpp/parser_test.cc
new file mode 100644
index 0000000000..16963bda4d
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/parser_test.cc
@@ -0,0 +1,460 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "gtest/gtest.h"
+
+#include "onnx/checker.h"
+#include "onnx/defs/parser.h"
+#include "onnx/defs/printer.h"
+
+using namespace ONNX_NAMESPACE;
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+
+template <typename T>
+static void Parse(T& parsedData, const char* input) {
+  OnnxParser parser(input);
+  auto status = parser.Parse(parsedData);
+  EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
+  EXPECT_TRUE(parser.EndOfInput()) << "Extra unparsed input unexpected.";
+}
+
+template <typename T>
+static void ExpectParseFailure(T& parsedData, const char* input) {
+  auto status = OnnxParser::Parse(parsedData, input);
+  EXPECT_FALSE(status.IsOK());
+}
+
+static void CheckModel(const char* code) {
+  ModelProto model;
+  Parse(model, code);
+
+  checker::check_model(model);
+}
+
+TEST(ParserTest, TypeTest) {
+  TypeProto type;
+
+  // 1-dimensional tensor type with symbolic dimension:
+  Parse(type, "float[N]");
+  EXPECT_TRUE(type.has_tensor_type());
+  int float_type = static_cast<int>(TensorProto_DataType::TensorProto_DataType_FLOAT);
+  int int32_type = static_cast<int>(TensorProto_DataType::TensorProto_DataType_INT32);
+  EXPECT_EQ(type.tensor_type().elem_type(), float_type);
+  EXPECT_TRUE(type.tensor_type().has_shape());
+  EXPECT_EQ(type.tensor_type().shape().dim_size(), 1);
+  EXPECT_EQ(type.tensor_type().shape().dim(0).dim_param(), "N");
+
+  // scalar type:
+  Parse(type, "float");
+  EXPECT_TRUE(type.has_tensor_type());
+  EXPECT_EQ(type.tensor_type().elem_type(), float_type);
+  EXPECT_TRUE(type.tensor_type().has_shape());
+  EXPECT_EQ(type.tensor_type().shape().dim_size(), 0);
+
+  // tensor type with unknown rank:
+  Parse(type, "float[]");
+  EXPECT_TRUE(type.has_tensor_type());
+  EXPECT_EQ(type.tensor_type().elem_type(), float_type);
+  EXPECT_FALSE(type.tensor_type().has_shape());
+
+  // 3-dimensional tensor
+  Parse(type, "float[N,M,K]");
+  EXPECT_EQ(type.tensor_type().shape().dim_size(), 3);
+
+  // Unspecified dimension (neither symbolic nor constant)
+  Parse(type, "float[N,?,K]");
+  EXPECT_FALSE(type.tensor_type().shape().dim(1).has_dim_param());
+  EXPECT_FALSE(type.tensor_type().shape().dim(1).has_dim_value());
+
+  // sequence type:
+  Parse(type, "seq(float[])");
+  EXPECT_TRUE(type.has_sequence_type());
+  auto& elttype = type.sequence_type().elem_type();
+  EXPECT_TRUE(elttype.has_tensor_type());
+  EXPECT_EQ(elttype.tensor_type().elem_type(), float_type);
+  EXPECT_FALSE(elttype.tensor_type().has_shape());
+
+  // optional type:
+  Parse(type, "optional(float)");
+  EXPECT_TRUE(type.has_optional_type());
+  auto& optelttype = type.optional_type().elem_type();
+  EXPECT_TRUE(optelttype.has_tensor_type());
+  EXPECT_EQ(optelttype.tensor_type().elem_type(), float_type);
+  EXPECT_TRUE(optelttype.tensor_type().has_shape());
+
+  // optional type:
+  Parse(type, "sparse_tensor(float[1000])");
+  EXPECT_TRUE(type.has_sparse_tensor_type());
+  EXPECT_EQ(type.sparse_tensor_type().elem_type(), float_type);
+  EXPECT_EQ(type.sparse_tensor_type().shape().dim_size(), 1);
+
+  // map type:
+  Parse(type, "map(int32, float[N])");
+  EXPECT_TRUE(type.has_map_type());
+  EXPECT_EQ(type.map_type().key_type(), int32_type);
+  auto& valtype = type.map_type().value_type();
+  EXPECT_TRUE(valtype.has_tensor_type());
+  EXPECT_EQ(valtype.tensor_type().elem_type(), float_type);
+  EXPECT_EQ(valtype.tensor_type().shape().dim_size(), 1);
+}
+
+TEST(ParserTest, TensorProtoTest) {
+  TensorProto tensorProto;
+
+  // Concrete tensor-type with numeric dimensions expected:
+  ExpectParseFailure(tensorProto, "int32[] {1, 2, 3, 4, 5}");
+
+  // Symbolic dimensions are not allowed.
+  ExpectParseFailure(tensorProto, "int32[N] {1, 2, 3, 4, 5}");
+
+  Parse(tensorProto, "int32[5] {1, 2, 3, 4, 5}");
+
+  Parse(tensorProto, "int32[5] T {1, 2, 3, 4, 5}");
+  EXPECT_EQ(tensorProto.name(), "T");
+
+  Parse(tensorProto, "float[5] {1, 2.0, 3.1, 4, 5.5}");
+
+  Parse(tensorProto, "float[5] {1e1, 2.0e-1, 3.1E-1, 4E+1, 5.5e-10}");
+
+  Parse(tensorProto, "string[2] { \"Hello\", \"World\" }");
+}
+
+TEST(ParserTest, AttributeTest) {
+  AttributeProto attr;
+
+  Parse(attr, "x = 2");
+  EXPECT_EQ(attr.name(), "x");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_INT);
+  EXPECT_EQ(attr.i(), 2);
+
+  Parse(attr, "x = 0.625");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_FLOAT);
+  EXPECT_FLOAT_EQ(attr.f(), 0.625);
+
+  Parse(attr, "x = [2, 4, 6]");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_INTS);
+  EXPECT_EQ(attr.ints_size(), 3);
+
+  Parse(attr, "x = [0.125, 0.625]");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_FLOATS);
+  EXPECT_EQ(attr.floats_size(), 2);
+
+  Parse(attr, "x = float[3] {2.1, 4.1, 6.1}");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_TENSOR);
+
+  Parse(attr, "x = \"astring\"");
+  EXPECT_EQ(attr.name(), "x");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_STRING);
+  EXPECT_EQ(attr.s(), "astring");
+
+  Parse(attr, "x = [\"abc\", \"def\"]");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_STRINGS);
+
+  Parse(attr, "x : ints = @xyz");
+  EXPECT_EQ(attr.ref_attr_name(), "xyz");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_INTS);
+
+  Parse(attr, R"ONNX(
+    body = somegraph (float[N] y, float[N] z) => (float[N] w)
+      {
+        x = foo(y, z)
+        w = bar(x, y)
+      }
+)ONNX");
+  EXPECT_EQ(attr.type(), AttributeProto_AttributeType::AttributeProto_AttributeType_GRAPH);
+  EXPECT_EQ(attr.g().node_size(), 2);
+}
+
+TEST(ParserTest, AttrListTest) {
+  const char* code = R"ONNX(
+<
+    x = 2,
+    w = 3
+>
+)ONNX";
+
+  AttrList attributes;
+  Parse(attributes, code);
+  EXPECT_EQ(attributes.size(), 2);
+  EXPECT_EQ(attributes.Get(0).name(), "x");
+  EXPECT_EQ(attributes.Get(1).name(), "w");
+}
+
+TEST(ParserTest, NodeTest) {
+  const char* code = "x = foo(y, z)";
+  NodeProto n;
+  Parse(n, code);
+
+  EXPECT_EQ(n.input_size(), 2);
+  EXPECT_EQ(n.input(0), "y");
+  EXPECT_EQ(n.input(1), "z");
+  EXPECT_EQ(n.output_size(), 1);
+  EXPECT_EQ(n.output(0), "x");
+  EXPECT_EQ(n.op_type(), "foo");
+
+  NodeList nl;
+  Parse(nl, R"ONNX(
+      {
+       sub_result = Sub(limit, start)
+       sub_result_casted = Cast<to = 1>(sub_result)
+       delta_casted = Cast<to = 1>(delta)
+       div_result = Div(sub_result_casted, delta_casted)
+       ceil_result = Ceil(div_result)
+       ceil_result_relu = Relu(ceil_result)
+       ceil_result_relu_int = Cast<to = 7>(ceil_result_relu)
+       ceil_result_relu_bool = Cast<to = 9>(ceil_result_relu)
+       variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start)
+       }
+       )ONNX");
+}
+
+TEST(ParserTest, QualifiedOpNameTest) {
+  const char* code = "x = com.example.foo(y, z)";
+  NodeProto n;
+  Parse(n, code);
+
+  EXPECT_EQ(n.domain(), "com.example");
+  EXPECT_EQ(n.op_type(), "foo");
+}
+
+TEST(ParserTest, NodeListTest) {
+  const char* code = R"ONNX(
+{
+    x = foo(y, z)
+    w = bar(x, y)
+}
+)ONNX";
+
+  GraphProto graph;
+  Parse(*graph.mutable_node(), code);
+
+  EXPECT_EQ(graph.node_size(), 2);
+  EXPECT_EQ(graph.node(0).op_type(), "foo");
+  EXPECT_EQ(graph.node(1).op_type(), "bar");
+}
+
+TEST(ParserTest, NodeAttrTest1) {
+  const char* code = "x = foo <a = 100, b = 200.5, c = \"astring\"> (y, z)";
+  NodeProto n;
+  Parse(n, code);
+
+  EXPECT_EQ(n.attribute_size(), 3);
+  EXPECT_EQ(n.attribute(0).name(), "a");
+  EXPECT_EQ(n.attribute(1).name(), "b");
+  EXPECT_EQ(n.attribute(2).name(), "c");
+}
+
+TEST(ParserTest, NodeAttrTest2) {
+  const char* code = "x = foo <d = [5, 10], e = [0.55, 0.66], f = [\"str1\", \"str2\"]> (y, z)";
+  NodeProto n;
+  Parse(n, code);
+  EXPECT_EQ(n.attribute_size(), 3);
+}
+
+TEST(ParserTest, GraphTest) {
+  const char* code = R"ONNX(
+agraph (float[N] y, float[N] z) => (float[N] w)
+<float[2] w1 = {1.0, 2.0}, float[3] w2 = {4.0, 5.0, 6.0}, float[N] x>
+{
+    # This is a comment.
+    x = foo(y, z, w1) # More comments.
+    w = bar(x, y, w2)
+}
+)ONNX";
+
+  GraphProto graph;
+  Parse(graph, code);
+
+  EXPECT_EQ(graph.name(), "agraph");
+  EXPECT_EQ(graph.input_size(), 2);
+  EXPECT_EQ(graph.output_size(), 1);
+  EXPECT_EQ(graph.node_size(), 2);
+  EXPECT_EQ(graph.initializer_size(), 2);
+  EXPECT_EQ(graph.value_info_size(), 1);
+}
+
+TEST(ParserTest, GraphPartialTypeTest) {
+  const char* code = R"ONNX(
+agraph (float[N] y, z) => (float[N] w)
+{
+    x = foo(y, z)
+    w = bar(x, y)
+}
+)ONNX";
+
+  GraphProto graph;
+  Parse(graph, code);
+
+  EXPECT_EQ(graph.name(), "agraph");
+  EXPECT_EQ(graph.input_size(), 2);
+  EXPECT_EQ(graph.output_size(), 1);
+}
+
+TEST(ParserTest, FunctionTest) {
+  const char* code = R"ONNX(
+<
+  opset_import: [ "" : 10 ],
+  domain: "ai.onnx.ml",
+  doc_string: "A function test case."
+>
+f (y, z) => (w)
+{
+    x = Add(y, z)
+    w = Mul(x, y)
+}
+)ONNX";
+
+  FunctionProto fp;
+  Parse(fp, code);
+
+  EXPECT_EQ(fp.name(), "f");
+  EXPECT_EQ(fp.input_size(), 2);
+  EXPECT_EQ(fp.output_size(), 1);
+  EXPECT_EQ(fp.node_size(), 2);
+  EXPECT_EQ(fp.attribute_size(), 0);
+  EXPECT_EQ(fp.opset_import_size(), 1);
+}
+
+TEST(ParserTest, InitializerTest) {
+  const char* code = R"ONNX(
+agraph (float y = {1.0}, float[N] z) => (float[N] w)
+<float[2] w1 = {1.0, 2.0}, float[3] w2 = {4.0, 5.0, 6.0}, float[N] x>
+{
+    x = foo(y, z, w1)
+    w = bar(x, y, w2)
+}
+)ONNX";
+
+  GraphProto graph;
+  Parse(graph, code);
+
+  EXPECT_EQ(graph.input_size(), 2);
+  EXPECT_EQ(graph.output_size(), 1);
+  EXPECT_EQ(graph.initializer_size(), 3); // y, w1, w2
+  EXPECT_EQ(graph.value_info_size(), 1); // x
+}
+
+TEST(ParserTest, IfNodeTest) {
+  const char* code = R"ONNX(
+z = If (b) <
+    then_branch = g1 () => (float[N] z_then)
+      {
+        z_then = foo(y)
+      },
+    else_branch = g2 () => (float[N] z_else)
+      {
+        z_else = bar(x)
+      }
+    >
+)ONNX";
+
+  NodeProto node;
+  Parse(node, code);
+  EXPECT_EQ(node.input_size(), 1);
+  EXPECT_EQ(node.output_size(), 1);
+  EXPECT_EQ(node.attribute_size(), 2);
+}
+
+TEST(ParserTest, ModelTest) {
+  const char* code = R"ONNX(
+<
+  ir_version: 7,
+  opset_import: [ "ai.onnx.ml" : 10 ],
+  producer_name: "ParserTest",
+  producer_version: "1.0",
+  domain: "ai.onnx.ml",
+  model_version: 1,
+  doc_string: "A parser test case model.",
+  metadata_props: [ "somekey" : "somevalue", "key2" : "value2" ]
+>
+agraph (float[N] y, float[N] z) => (float[N] w)
+{
+    x = foo(y, z)
+    w = bar(x, y)
+}
+)ONNX";
+
+  ModelProto model;
+  Parse(model, code);
+
+  EXPECT_EQ(model.graph().input_size(), 2);
+  EXPECT_EQ(model.graph().output_size(), 1);
+  EXPECT_EQ(model.graph().node_size(), 2);
+}
+
+TEST(ParserTest, ModelCheckTest) {
+  const char* code = R"ONNX(
+<
+  ir_version: 7,
+  opset_import: [ "" : 10 ]
+>
+agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
+{
+    T = MatMul(X, W)
+    S = Add(T, B)
+    C = Softmax(S)
+}
+)ONNX";
+
+  CheckModel(code);
+}
+
+TEST(ParserTest, IfModelTest) {
+  const char* code = R"ONNX(
+<
+  ir_version: 7,
+  opset_import: [ "" : 13 ]
+>
+iftest (bool b, float[128] X, float[128] Y) => (float[128] Z)
+{
+  Z = If (b) <
+      then_branch = g1 () => (float[128] z_then) { z_then = Identity(X) },
+      else_branch = g2 () => (float[128] z_else) { z_else = Identity(Y) }
+      >
+}
+)ONNX";
+
+  CheckModel(code);
+}
+
+TEST(ParserTest, FunModelTest) {
+  const char* code = R"ONNX(
+<
+  ir_version: 8,
+  opset_import: [ "" : 10, "local" : 1 ]
+>
+agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
+{
+  T = local.foo (X, W, B)
+  C = local.square(T)
+}
+
+<
+  opset_import: [ "" : 10 ],
+  domain: "local",
+  doc_string: "Function foo."
+>
+foo (x, w, b) => (c) {
+  T = MatMul(x, w)
+  S = Add(T, b)
+  c = Softmax(S)
+}
+
+<
+  opset_import: [ "" : 10 ],
+  domain: "local",
+  doc_string: "Function square."
+>
+square (x) => (y) {
+  y = Mul (x, x)
+}
+)ONNX";
+
+  CheckModel(code);
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/test/cpp/schema_registration_test.cc b/third_party/onnx/onnx/test/cpp/schema_registration_test.cc
new file mode 100644
index 0000000000..ea8e27f6b4
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/schema_registration_test.cc
@@ -0,0 +1,43 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+#include "onnx/defs/operator_sets.h"
+#include "onnx/defs/schema.h"
+
+using namespace ONNX_NAMESPACE;
+
+namespace ONNX_NAMESPACE {
+namespace Test {
+
+// By default ONNX registers all opset versions and selective schema loading cannot be tested
+// So this test is run only when static registration is disabled
+TEST(SchemaRegistrationTest, RegisterSpecifiedOpsetSchemaVersion) {
+#ifdef __ONNX_DISABLE_STATIC_REGISTRATION
+    EXPECT_TRUE(OpSchemaRegistry::Instance()->GetLoadedSchemaVersion() == -1);
+    RegisterOnnxOperatorSetSchema(13);
+    EXPECT_TRUE(OpSchemaRegistry::Instance()->GetLoadedSchemaVersion() == 13);
+
+    auto opSchema = OpSchemaRegistry::Schema("Add");
+    EXPECT_NE(nullptr, opSchema);
+    EXPECT_EQ(opSchema->SinceVersion(), 13);
+
+    // Should not find opset 12
+    opSchema = OpSchemaRegistry::Schema("Add", 12);
+    EXPECT_EQ(nullptr, opSchema);
+
+    // Should not find opset 14
+    opSchema = OpSchemaRegistry::Schema("Trilu");
+    EXPECT_EQ(nullptr, opSchema);
+
+    // Acos-7 is the latest Acos before specified 13
+    opSchema = OpSchemaRegistry::Schema("Acos");
+    EXPECT_NE(nullptr, opSchema);
+    EXPECT_EQ(opSchema->SinceVersion(), 7);
+#endif
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/shape_inference_test.cc b/third_party/onnx/onnx/test/cpp/shape_inference_test.cc
new file mode 100644
index 0000000000..02674269a4
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/shape_inference_test.cc
@@ -0,0 +1,620 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/shape_inference.h"
+#include "onnx/onnx_pb.h"
+#include "onnx/defs/parser.h"
+#include "onnx/shape_inference/implementation.h"
+
+using namespace ONNX_NAMESPACE::shape_inference;
+
+namespace ONNX_NAMESPACE {
+// onnx/defs/controlflow/old.cc
+void ScanInferenceFunctionOpset8(InferenceContext& ctx);
+// onnx/defs/controlflow/defs.cc
+void ScanInferenceFunction(InferenceContext& ctx);
+
+namespace Test {
+
+template<class Type>
+void CreateDims(Type& proto, int num_dims) {
+  auto mutable_shape = proto.mutable_shape();
+  mutable_shape->clear_dim();
+
+  for (int i = 0; i < num_dims; ++i)
+    mutable_shape->add_dim();
+}
+
+template <class Type>
+void SetDimValues(
+    Type& proto,
+    const std::vector<int>& values) {
+  auto* mutable_shape = proto.mutable_shape();
+  EXPECT_TRUE(mutable_shape->dim_size() == values.size());
+
+  int idx = 0;
+  for (auto value : values) {
+    auto mutable_dim = mutable_shape->mutable_dim(idx++);
+    if (value != -1)
+      mutable_dim->set_dim_value(value);
+  }
+}
+
+template <class Type>
+void SetDimParams(
+    Type& proto,
+    const std::vector<const std::string*>& values) {
+  auto mutable_shape = proto.mutable_shape();
+  EXPECT_TRUE(mutable_shape->dim_size() == values.size());
+
+  int idx = 0;
+  for (auto value : values) {
+    auto mutable_dim = mutable_shape->mutable_dim(idx++);
+    if (value)
+      mutable_dim->set_dim_param(*value);
+  }
+}
+
+template <class Type>
+void Dump(const Type& t) {
+  auto& s_shape = t.shape();
+  auto num_dims = s_shape.dim_size();
+  std::cout << num_dims << " dims. ";
+  for (int i = 0; i < num_dims; ++i) {
+    auto x = s_shape.dim(0);
+    auto y = x.has_dim_value();
+    auto z = x.has_dim_param();
+
+    std::cout << "Dim " << i << " Value:"
+              << (y ? ONNX_NAMESPACE::to_string(x.dim_value()) : "<unset>")
+              << ", Param:" << (z ? x.dim_param() : "<unset>") << "\n";
+  }
+};
+
+TEST(ShapeInferenceTest, mergeShapeInfo_HasShape) {
+  // source has shape, target doesn't
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 1);
+    SetDimValues(source, {1});
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim_size() == 1 && shape.dim(0).dim_value() == 1);
+  }
+
+  // source has no shape, target does
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(target, 1);
+    SetDimValues(target, {1});
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim_size() == 1 && shape.dim(0).dim_value() == 1);
+  }
+  // source has shape, target doesn't
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+
+    CreateDims(source, 1);
+    SetDimValues(source, {1});
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim_size() == 1 && shape.dim(0).dim_value() == 1);
+  }
+
+  // source has no shape, target does
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+
+    CreateDims(target, 1);
+    SetDimValues(target, {1});
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim_size() == 1 && shape.dim(0).dim_value() == 1);
+  }
+}
+TEST(ShapeInferenceTest, mergeShapeInfo_PreferValueOverParam) {
+  std::string param = "A";
+
+  // source has value, target has param. prefer value
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 1);
+    SetDimValues(source, {1});
+
+    CreateDims(target, 1);
+    SetDimParams(target, {&param});
+
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim_size() == 1 && shape.dim(0).dim_value() == 1);
+  }
+
+  // source has param, target has value.
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 1);
+    SetDimParams(source, {&param});
+
+    CreateDims(target, 1);
+    SetDimValues(target, {1});
+
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim_size() == 1 && shape.dim(0).dim_value() == 1);
+  }
+}
+
+TEST(ShapeInferenceTest, mergeShapeInfo_CombineShapes) {
+  // merge from both sides, preferring real value over -1
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {-1, 2});
+
+    CreateDims(target, 2);
+    SetDimValues(target, {1, -1});
+
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim(0).dim_value() == 1 && shape.dim(1).dim_value() == 2);
+  }
+
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {-1, 2});
+
+    CreateDims(target, 2);
+    SetDimValues(target, {1, -1});
+
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim(0).dim_value() == 1 && shape.dim(1).dim_value() == 2);
+  }
+
+
+  // prefer value over param,
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {-1, 2});
+
+    CreateDims(target, 2);
+    SetDimValues(target, {1, 0});
+    // replace second dim with a param. the value from the source should be
+    // preferred
+    const std::string param = "A";
+    target.mutable_shape()->mutable_dim(1)->set_dim_param(param);
+
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim(0).dim_value() == 1 && shape.dim(1).dim_value() == 2);
+  }
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {-1, 2});
+
+    CreateDims(target, 2);
+    SetDimValues(target, {1, 0});
+    // replace second dim with a param. the value from the source should be
+    // preferred
+    const std::string param = "A";
+    target.mutable_shape()->mutable_dim(1)->set_dim_param(param);
+
+    mergeInShapeInfo(source, target);
+
+    Dump(target);
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim(0).dim_value() == 1 && shape.dim(1).dim_value() == 2);
+  }
+}
+
+TEST(ShapeInferenceTest, mergeShapeInfo_Mismatches) {
+#ifndef ONNX_NO_EXCEPTIONS
+  // mismatched num dims
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {-1, 2});
+
+    CreateDims(target, 3);
+    SetDimValues(target, {1, -1, 1});
+
+    EXPECT_THROW(
+        mergeInShapeInfo(source, target), ONNX_NAMESPACE::InferenceError);
+  }
+
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {-1, 2});
+
+    CreateDims(target, 3);
+    SetDimValues(target, {1, -1, 1});
+
+    EXPECT_THROW(mergeInShapeInfo(source, target), ONNX_NAMESPACE::InferenceError);
+  }
+
+
+  // mismatched dim values
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {2, 2});
+
+    CreateDims(target, 2);
+    SetDimValues(target, {2, 1});
+
+    EXPECT_THROW(
+        mergeInShapeInfo(source, target), ONNX_NAMESPACE::InferenceError);
+  }
+
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+
+    CreateDims(source, 2);
+    SetDimValues(source, {2, 2});
+
+    CreateDims(target, 2);
+    SetDimValues(target, {2, 1});
+
+    EXPECT_THROW(mergeInShapeInfo(source, target), ONNX_NAMESPACE::InferenceError);
+  }
+#endif
+  // mismatched param value. prefer target
+  {
+    TypeProto_Tensor source;
+    TypeProto_Tensor target;
+    const std::string param_a = "A";
+    const std::string param_b = "B";
+
+    CreateDims(source, 1);
+    SetDimParams(source, {&param_a});
+
+    CreateDims(target, 1);
+    SetDimParams(target, {&param_b});
+
+    mergeInShapeInfo(source, target);
+
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim(0).dim_param() == "B");
+  }
+  {
+    TypeProto_SparseTensor source;
+    TypeProto_SparseTensor target;
+    const std::string param_a = "A";
+    const std::string param_b = "B";
+
+    CreateDims(source, 1);
+    SetDimParams(source, {&param_a});
+
+    CreateDims(target, 1);
+    SetDimParams(target, {&param_b});
+
+    mergeInShapeInfo(source, target);
+
+    auto& shape = target.shape();
+    EXPECT_TRUE(shape.dim(0).dim_param() == "B");
+  }
+}
+
+// Check subgraph inferencing via GraphInferencer using a Scan
+static void doInferencingTest(bool use_scan_opset8) {
+  auto* schemaRegistry = OpSchemaRegistry::Instance();
+  GraphProto subgraph;
+
+  // simple tensor without shape info
+  TypeProto simple_tensor_no_shape;
+  auto* tensor_type = simple_tensor_no_shape.mutable_tensor_type();
+  tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
+
+  // simple tensor with shape info
+  TypeProto simple_tensor = simple_tensor_no_shape;
+  simple_tensor.mutable_tensor_type()
+      ->mutable_shape()
+      ->add_dim()
+      ->set_dim_value(2);
+
+  // setup simple graph that can be used with Scan containing two Identity
+  // nodes. one for the loop state variable. one for the scan output.
+  {
+    NodeProto loop_state_identity;
+    loop_state_identity.set_name("loop_state_identity");
+    loop_state_identity.set_domain(ONNX_DOMAIN);
+    loop_state_identity.set_op_type("Identity");
+    loop_state_identity.set_doc_string("loop state identity");
+    loop_state_identity.add_input("loop_state_in");
+    loop_state_identity.add_output("loop_state_out");
+
+    *subgraph.add_node() = loop_state_identity;
+
+    NodeProto scan_in_out_identity;
+    scan_in_out_identity.set_name("scan_in_out_identity");
+    scan_in_out_identity.set_domain(ONNX_DOMAIN);
+    scan_in_out_identity.set_op_type("Identity");
+    scan_in_out_identity.set_doc_string("scan identity");
+    scan_in_out_identity.add_input("scan_in");
+    scan_in_out_identity.add_output("scan_out");
+    *subgraph.add_node() = scan_in_out_identity;
+
+    ValueInfoProto loop_state_in;
+    loop_state_in.set_name("loop_state_in");
+    *loop_state_in.mutable_type() = simple_tensor;
+    *subgraph.add_input() = loop_state_in;
+
+    ValueInfoProto scan_in;
+    scan_in.set_name("scan_in");
+    *scan_in.mutable_type() = simple_tensor;
+    *subgraph.add_input() = scan_in;
+
+    ValueInfoProto loop_state_out = loop_state_in;
+    loop_state_out.set_name("loop_state_out");
+    *loop_state_out.mutable_type() = simple_tensor_no_shape;
+    *subgraph.add_output() = loop_state_out;
+
+    ValueInfoProto scan_state_out = scan_in;
+    scan_state_out.set_name("scan_out");
+    *scan_state_out.mutable_type() = simple_tensor_no_shape;
+    *subgraph.add_output() = scan_state_out;
+  }
+
+  std::unordered_map<std::string, int> opset_imports;
+  opset_imports[ONNX_DOMAIN] = 8; // Scan is v8
+
+  const std::unordered_map<std::string, TypeProto*> outer_scope_value_types;
+  SymbolTableImpl symbolTable;
+  symbolTable.addFromGraph(subgraph);
+  GraphInferenceContext graphInfCtx(outer_scope_value_types, opset_imports, &symbolTable);
+  GraphInferencerImpl graphInferencer(subgraph, graphInfCtx);
+
+  // loop_state_in and scan_in are the two inputs.
+  // order in subgraphInputTypes matches their order as graph inputs.
+  std::vector<const TypeProto*> subgraphInputTypes = {&simple_tensor,
+                                                      &simple_tensor};
+
+  std::vector<const TensorProto*> subgraphInputData = {};
+  auto output =
+      graphInferencer.doInferencing(subgraphInputTypes, subgraphInputData);
+
+  // check the subgraph outputs had their shape inferred when we called
+  // doInferencing directly
+  EXPECT_TRUE(output.size() == 2);
+
+  auto checkType = [](const TypeProto& type, const TypeProto_Tensor& expect) {
+    auto checkDims = [](const TensorShapeProto& l, const TensorShapeProto& r) {
+      EXPECT_TRUE(l.dim_size() == r.dim_size());
+
+      for (int i = 0, end = l.dim_size(); i < end; ++i) {
+        // if (l.dim().Get(i).dim_value() != r.dim().Get(i).dim_value())
+        //  break;
+        EXPECT_TRUE(l.dim().Get(i).dim_value() == r.dim().Get(i).dim_value());
+      }
+    };
+
+    EXPECT_TRUE(type.has_tensor_type());
+    EXPECT_TRUE(type.tensor_type().elem_type() == expect.elem_type());
+    checkDims(type.tensor_type().shape(), expect.shape());
+  };
+
+  checkType(*output[0], simple_tensor.tensor_type());
+  checkType(*output[1], simple_tensor.tensor_type());
+
+  // setup Scan node to test subgraph inferencing works as expected when called
+  // from the operators type/shape inferencing function
+  NodeProto scan;
+  {
+    AttributeProto num_scan_inputs;
+    num_scan_inputs.set_name("num_scan_inputs");
+    num_scan_inputs.set_i(1);
+
+    AttributeProto body;
+    body.set_name("body");
+    *body.mutable_g() = subgraph;
+
+    *scan.add_attribute() = num_scan_inputs;
+    *scan.add_attribute() = body;
+
+    scan.set_name("Scan");
+    scan.set_domain(ONNX_DOMAIN);
+    scan.set_doc_string("Scan node");
+    scan.set_op_type("Scan");
+    if (use_scan_opset8)
+      scan.add_input(""); // optional sequence lens
+    scan.add_input("loop_state_start");
+    scan.add_input("scan_op_in");
+    scan.add_output("loop_state_final");
+    scan.add_output("scan_op_out");
+  }
+
+  TypeProto loop_state_in_tensor = simple_tensor_no_shape;
+  auto* shape = loop_state_in_tensor.mutable_tensor_type()->mutable_shape();
+  if (use_scan_opset8)
+    shape->add_dim()->set_dim_value(1); // batch size
+  shape->add_dim()->set_dim_value(2); // input size. must match subgraph
+
+  TypeProto loop_state_out_tensor = loop_state_in_tensor; // should be unchanged
+
+  TypeProto scan_in_tensor = simple_tensor_no_shape;
+  shape = scan_in_tensor.mutable_tensor_type()->mutable_shape();
+  if (use_scan_opset8)
+    shape->add_dim()->set_dim_value(1); // batch size
+  shape->add_dim()->set_dim_value(1); // sequence length
+  shape->add_dim()->set_dim_value(2); // input size. must match subgraph
+
+  TypeProto scan_out_tensor = scan_in_tensor; // should be unchanged
+
+  std::unordered_map<std::string, TypeProto*> valueTypesByName;
+  valueTypesByName["loop_state_start"] = &loop_state_in_tensor;
+  valueTypesByName["scan_op_in"] = &scan_in_tensor;
+
+  InferenceContextImpl ctx(scan, valueTypesByName, {}, {}, {}, & graphInfCtx);
+  if (use_scan_opset8)
+    ScanInferenceFunctionOpset8(ctx);
+  else
+    ScanInferenceFunction(ctx);
+
+  EXPECT_TRUE(ctx.getNumOutputs() == 2);
+  checkType(*ctx.getOutputType(0), loop_state_out_tensor.tensor_type());
+  checkType(*ctx.getOutputType(1), scan_out_tensor.tensor_type());
+}
+
+// Check subgraph inferencing via GraphInferencer using a Scan (from opset 8)
+TEST(GraphInferencerImplTest, Scan8_BasicTest) {
+  doInferencingTest(true);
+}
+
+// Check subgraph inferencing via GraphInferencer using a Scan (from opset 9)
+TEST(GraphInferencerImplTest, Scan9_BasicTest) {
+  doInferencingTest(false);
+}
+
+void RunReshapeShapeInfTest(const char* modelStr, TensorShapeProto& expectedShape) {
+  ModelProto model;
+  OnnxParser parser(modelStr);
+  auto status = parser.Parse(model);
+  EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
+  EXPECT_TRUE(parser.EndOfInput()) << "Extra unparsed input unexpected.";
+
+  ShapeInferenceOptions options{true, 1, true};
+  ONNX_NAMESPACE::shape_inference::InferShapes(model, ONNX_NAMESPACE::OpSchemaRegistry::Instance(), options);
+
+  const auto inferredShape = model.graph().output()[0].type().tensor_type().shape();
+  EXPECT_TRUE(inferredShape.dim_size() == expectedShape.dim_size());
+
+  for (int i = 0; i < inferredShape.dim_size(); i++) {
+    EXPECT_TRUE(
+        (inferredShape.dim(i).has_dim_value() && expectedShape.dim(i).has_dim_value()) ||
+        (inferredShape.dim(i).has_dim_param() && expectedShape.dim(i).has_dim_param()));
+
+    EXPECT_TRUE(
+        inferredShape.dim(i).has_dim_value() ? inferredShape.dim(i).dim_value() == expectedShape.dim(i).dim_value()
+                                             : inferredShape.dim(i).dim_param() == expectedShape.dim(i).dim_param());
+  }
+
+}
+TEST(ShapeInferenceTest, ReshapeTestWithShapeAsSymInput) {
+  const char* modelStr = R"ONNX(
+<
+  ir_version: 8,
+  opset_import: [ "" : 15],
+  producer_name: "DataPropagationTest",
+  producer_version: "1.0",
+  model_version: 1,
+  doc_string: "A test model for data propagation."
+>
+agraph (float[batch_size, 256, 768, 3] x, float[batch_size, 196608] m) => (float[?, ?, ?] z)
+{
+    y = Shape<start = 0, end = 3>(x)
+    z = Reshape(m, y)
+}
+)ONNX";
+
+  TensorShapeProto expectedShape;
+  expectedShape.mutable_dim()->Add()->set_dim_param("batch_size");
+  expectedShape.mutable_dim()->Add()->set_dim_value(256);
+  expectedShape.mutable_dim()->Add()->set_dim_value(768);
+
+  RunReshapeShapeInfTest(modelStr, expectedShape);
+}
+
+TEST(ShapeInferenceTest, ReshapeTestWithShapeAsInitializer) {
+  const char* modelStr = R"ONNX(
+<
+  ir_version: 8,
+  opset_import: [ "" : 15],
+  producer_name: "DataPropagationTest",
+  producer_version: "1.0",
+  model_version: 1,
+  doc_string: "A test model for data propagation."
+>
+agraph (float[1, 196608] m) => (float[?, ?, ?] z)
+<int64[3] shape = {1, 768, 256}>
+{
+    z = Reshape(m, shape)
+}
+)ONNX";
+
+  TensorShapeProto expectedShape;
+  expectedShape.mutable_dim()->Add()->set_dim_value(1);
+  expectedShape.mutable_dim()->Add()->set_dim_value(768);
+  expectedShape.mutable_dim()->Add()->set_dim_value(256);
+
+  RunReshapeShapeInfTest(modelStr, expectedShape);
+}
+
+TEST(ShapeInferenceTest, ReshapeTestWithShapeAsInitializer1) {
+  const char* modelStr = R"ONNX(
+<
+  ir_version: 8,
+  opset_import: [ "" : 15],
+  producer_name: "DataPropagationTest",
+  producer_version: "1.0",
+  model_version: 1,
+  doc_string: "A test model for data propagation."
+>
+agraph (float[1, 196608] m) => (float[?, ?, ?] z)
+<int64[3] shape = {1, -1, 256}>
+{
+    z = Reshape(m, shape)
+}
+)ONNX";
+
+  TensorShapeProto expectedShape;
+  expectedShape.mutable_dim()->Add()->set_dim_value(1);
+  expectedShape.mutable_dim()->Add()->set_dim_value(768);
+  expectedShape.mutable_dim()->Add()->set_dim_value(256);
+
+  RunReshapeShapeInfTest(modelStr, expectedShape);
+}
+
+} // namespace Test
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/test/cpp/test_main.cc b/third_party/onnx/onnx/test/cpp/test_main.cc
new file mode 100644
index 0000000000..fad5732dc2
--- /dev/null
+++ b/third_party/onnx/onnx/test/cpp/test_main.cc
@@ -0,0 +1,13 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <iostream>
+#include "gtest/gtest.h"
+
+GTEST_API_ int main(int argc, char** argv)
+{
+    std::cout << "Running main() from test_main.cc" << std::endl;
+    ::testing::InitGoogleTest(&argc, argv);
+    return RUN_ALL_TESTS();
+}
diff --git a/third_party/onnx/onnx/test/elu_test.py b/third_party/onnx/onnx/test/elu_test.py
new file mode 100644
index 0000000000..1403ca41d2
--- /dev/null
+++ b/third_party/onnx/onnx/test/elu_test.py
@@ -0,0 +1,17 @@
+# SPDX-License-Identifier: Apache-2.0
+import unittest
+
+from onnx import defs, checker, helper
+
+
+class TestRelu(unittest.TestCase):
+
+    def test_elu(self) -> None:
+        self.assertTrue(defs.has('Elu'))
+        node_def = helper.make_node(
+            'Elu', ['X'], ['Y'], alpha=1.0)
+        checker.check_node(node_def)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/function_test.py b/third_party/onnx/onnx/test/function_test.py
new file mode 100644
index 0000000000..5ad5c37351
--- /dev/null
+++ b/third_party/onnx/onnx/test/function_test.py
@@ -0,0 +1,167 @@
+import unittest
+
+import onnx
+from onnx import checker, parser, utils
+
+
+class TestFunction(unittest.TestCase):
+    def _verify_function_set(self, extracted_model, function_set, func_domain):  # type: ignore
+        checker.check_model(extracted_model)
+        self.assertEqual(len(extracted_model.functions), len(function_set))
+        for function in function_set:
+            self.assertIsNotNone(
+                next((f for f in extracted_model.functions
+                if f.name == function and f.domain == func_domain), None))
+
+    def test_extract_model_with_local_function(self) -> None:
+        r'''
+        #   1. build a model with graph below. extract models with output combinations
+        #   2. validate extracted models' local functions
+        #
+        # model graph:
+        #      i0                    i1                 i2
+        #      |   __________________|__________________/_________
+        #      |  |                  |             |   /          |
+        #      |  |                  |             |  /           |
+        #   func_add        func_identity          add         identity
+        #    |  ___\___________\____________________|_________    |
+        #    | |    \           \                   |  _______|___|
+        #    | |     \           \                  | |       |   |
+        #    add     function_nested_identity_add   add     function_nested_identity_add
+        #     |                 |                    |              |
+        #     |                 |                    |              |
+        #   o_func_add      o_all_func0           o_no_func     o_all_func1
+        #
+        # where function_nested_identity_add is a function that is defined with functions:
+        #       a               b
+        #       |               |
+        #   func_identity   func_identity
+        #             \       /
+        #             func_add
+        #                |
+        #                c
+        #
+        '''
+
+        # function common
+        func_domain = 'local'
+        func_opset_imports = [onnx.helper.make_opsetid("", 14)]
+        func_nested_opset_imports = [
+            onnx.helper.make_opsetid("", 14), onnx.helper.make_opsetid(func_domain, 1)]
+
+        # add function
+        func_add_name = 'func_add'
+        func_add_inputs = ['a', 'b']
+        func_add_outputs = ['c']
+        func_add_nodes = [onnx.helper.make_node('Add', ['a', 'b'], ['c'])]
+        func_add = onnx.helper.make_function(
+            func_domain,
+            func_add_name,
+            func_add_inputs,
+            func_add_outputs,
+            func_add_nodes,
+            func_opset_imports)
+
+        # identity function
+        func_identity_name = 'func_identity'
+        func_identity_inputs = ['a']
+        func_identity_outputs = ['b']
+        func_identity_nodes = [onnx.helper.make_node('Identity', ['a'], ['b'])]
+        func_identity = onnx.helper.make_function(
+            func_domain,
+            func_identity_name,
+            func_identity_inputs,
+            func_identity_outputs,
+            func_identity_nodes,
+            func_opset_imports)
+
+        # nested identity/add function
+        func_nested_identity_add_name = 'func_nested_identity_add'
+        func_nested_identity_add_inputs = ['a', 'b']
+        func_nested_identity_add_outputs = ['c']
+        func_nested_identity_add_nodes = [
+            onnx.helper.make_node('func_identity', ['a'], ['a1'], domain=func_domain),
+            onnx.helper.make_node('func_identity', ['b'], ['b1'], domain=func_domain),
+            onnx.helper.make_node('func_add', ['a1', 'b1'], ['c'], domain=func_domain)]
+        func_nested_identity_add = onnx.helper.make_function(
+            func_domain,
+            func_nested_identity_add_name,
+            func_nested_identity_add_inputs,
+            func_nested_identity_add_outputs,
+            func_nested_identity_add_nodes,
+            func_nested_opset_imports)
+
+        # create graph nodes
+        node_func_add = onnx.helper.make_node(func_add_name, ['i0', 'i1'], ['t0'], domain=func_domain)
+        node_add0 = onnx.helper.make_node('Add', ['i1', 'i2'], ['t2'])
+        node_add1 = onnx.helper.make_node('Add', ['t0', 't2'], ['o_func_add'])
+        node_func_identity = onnx.helper.make_node(func_identity_name, ['i1'], ['t1'], domain=func_domain)
+        node_identity = onnx.helper.make_node('Identity', ['i1'], ['t3'])
+        node_add2 = onnx.helper.make_node('Add', ['t3', 't2'], ['o_no_func'])
+        node_func_nested0 = onnx.helper.make_node(
+            func_nested_identity_add_name,
+            ['t0', 't1'],
+            ['o_all_func0'],
+            domain=func_domain)
+        node_func_nested1 = onnx.helper.make_node(
+            func_nested_identity_add_name,
+            ['t3', 't2'],
+            ['o_all_func1'],
+            domain=func_domain)
+
+        graph_name = 'graph_with_imbedded_functions'
+        ir_version = 8
+        opset_imports = [onnx.helper.make_opsetid("", 14), onnx.helper.make_opsetid("local", 1)]
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=2, shape=[5])
+
+        graph = onnx.helper.make_graph(
+            [node_func_add, node_add0, node_add1, node_func_identity, node_identity,
+            node_func_nested0, node_func_nested1, node_add2],
+            graph_name,
+            [
+                onnx.helper.make_value_info(name='i0', type_proto=tensor_type_proto),
+                onnx.helper.make_value_info(name='i1', type_proto=tensor_type_proto),
+                onnx.helper.make_value_info(name='i2', type_proto=tensor_type_proto)],
+            [
+                onnx.helper.make_value_info(name='o_no_func', type_proto=tensor_type_proto),
+                onnx.helper.make_value_info(name='o_func_add', type_proto=tensor_type_proto),
+                onnx.helper.make_value_info(name='o_all_func0', type_proto=tensor_type_proto),
+                onnx.helper.make_value_info(name='o_all_func1', type_proto=tensor_type_proto)])
+
+        meta = {
+            'ir_version': ir_version,
+            'opset_imports': opset_imports,
+            'producer_name': 'test_extract_model_with_local_function',
+            'functions': [func_identity, func_add, func_nested_identity_add],
+        }
+        model = onnx.helper.make_model(graph, **meta)
+
+        checker.check_model(model)
+        extracted_with_no_funcion = utils.Extractor(model).extract_model(['i0', 'i1', 'i2'], ['o_no_func'])
+        self._verify_function_set(extracted_with_no_funcion, {}, func_domain)
+
+        extracted_with_add_funcion = utils.Extractor(model).extract_model(['i0', 'i1', 'i2'], ['o_func_add'])
+        self._verify_function_set(extracted_with_add_funcion, {func_add_name}, func_domain)
+
+        extracted_with_o_all_funcion0 = utils.Extractor(model).extract_model(['i0', 'i1', 'i2'], ['o_all_func0'])
+        self._verify_function_set(
+            extracted_with_o_all_funcion0,
+            {func_add_name, func_identity_name, func_nested_identity_add_name},
+            func_domain)
+
+        extracted_with_o_all_funcion1 = utils.Extractor(model).extract_model(['i0', 'i1', 'i2'], ['o_all_func1'])
+        self._verify_function_set(
+            extracted_with_o_all_funcion1,
+            {func_add_name, func_identity_name, func_nested_identity_add_name},
+            func_domain)
+
+        extracted_with_o_all_funcion2 = utils.Extractor(model).extract_model(
+            ['i0', 'i1', 'i2'], ['o_no_func', 'o_func_add', 'o_all_func0', 'o_all_func1'])
+        self._verify_function_set(
+            extracted_with_o_all_funcion2,
+            {func_add_name, func_identity_name, func_nested_identity_add_name},
+            func_domain)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/helper_test.py b/third_party/onnx/onnx/test/helper_test.py
new file mode 100644
index 0000000000..bf99a41e0c
--- /dev/null
+++ b/third_party/onnx/onnx/test/helper_test.py
@@ -0,0 +1,608 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import random
+
+import numpy as np  # type: ignore
+
+from onnx import helper, defs, numpy_helper, checker
+from onnx import AttributeProto, TensorProto, GraphProto, ModelProto, OptionalProto, TypeProto, SequenceProto
+from typing import Text, Any, List, Tuple
+
+import unittest
+
+
+class TestHelperAttributeFunctions(unittest.TestCase):
+
+    def test_attr_float(self) -> None:
+        # float
+        attr = helper.make_attribute("float", 1.)
+        self.assertEqual(attr.name, "float")
+        self.assertEqual(attr.f, 1.)
+        checker.check_attribute(attr)
+        # float with scientific
+        attr = helper.make_attribute("float", 1e10)
+        self.assertEqual(attr.name, "float")
+        self.assertEqual(attr.f, 1e10)
+        checker.check_attribute(attr)
+
+    def test_attr_int(self) -> None:
+        # integer
+        attr = helper.make_attribute("int", 3)
+        self.assertEqual(attr.name, "int")
+        self.assertEqual(attr.i, 3)
+        checker.check_attribute(attr)
+        # long integer
+        attr = helper.make_attribute("int", 5)
+        self.assertEqual(attr.name, "int")
+        self.assertEqual(attr.i, 5)
+        checker.check_attribute(attr)
+        # octinteger
+        attr = helper.make_attribute("int", 0o1701)
+        self.assertEqual(attr.name, "int")
+        self.assertEqual(attr.i, 0o1701)
+        checker.check_attribute(attr)
+        # hexinteger
+        attr = helper.make_attribute("int", 0x1701)
+        self.assertEqual(attr.name, "int")
+        self.assertEqual(attr.i, 0x1701)
+        checker.check_attribute(attr)
+
+    def test_attr_doc_string(self) -> None:
+        attr = helper.make_attribute("a", "value")
+        self.assertEqual(attr.name, "a")
+        self.assertEqual(attr.doc_string, "")
+        attr = helper.make_attribute("a", "value", "doc")
+        self.assertEqual(attr.name, "a")
+        self.assertEqual(attr.doc_string, "doc")
+
+    def test_attr_string(self) -> None:
+        # bytes
+        attr = helper.make_attribute("str", b"test")
+        self.assertEqual(attr.name, "str")
+        self.assertEqual(attr.s, b"test")
+        checker.check_attribute(attr)
+        # unspecified
+        attr = helper.make_attribute("str", "test")
+        self.assertEqual(attr.name, "str")
+        self.assertEqual(attr.s, b"test")
+        checker.check_attribute(attr)
+        # unicode
+        attr = helper.make_attribute("str", u"test")
+        self.assertEqual(attr.name, "str")
+        self.assertEqual(attr.s, b"test")
+        checker.check_attribute(attr)
+        # empty str
+        attr = helper.make_attribute("str", "")
+        self.assertEqual(attr.name, "str")
+        self.assertEqual(helper.get_attribute_value(attr), b"")
+        checker.check_attribute(attr)
+
+    def test_attr_repeated_float(self) -> None:
+        attr = helper.make_attribute("floats", [1.0, 2.0])
+        self.assertEqual(attr.name, "floats")
+        self.assertEqual(list(attr.floats), [1.0, 2.0])
+        checker.check_attribute(attr)
+
+    def test_attr_repeated_int(self) -> None:
+        attr = helper.make_attribute("ints", [1, 2])
+        self.assertEqual(attr.name, "ints")
+        self.assertEqual(list(attr.ints), [1, 2])
+        checker.check_attribute(attr)
+
+    def test_attr_repeated_mixed_floats_and_ints(self) -> None:
+        attr = helper.make_attribute("mixed", [1, 2, 3.0, 4.5])
+        self.assertEqual(attr.name, "mixed")
+        self.assertEqual(list(attr.floats), [1.0, 2.0, 3.0, 4.5])
+        checker.check_attribute(attr)
+
+    def test_attr_repeated_str(self) -> None:
+        attr = helper.make_attribute("strings", ["str1", "str2"])
+        self.assertEqual(attr.name, "strings")
+        self.assertEqual(list(attr.strings), [b"str1", b"str2"])
+        checker.check_attribute(attr)
+
+    def test_attr_repeated_tensor_proto(self) -> None:
+        tensors = [
+            helper.make_tensor(
+                name='a',
+                data_type=TensorProto.FLOAT,
+                dims=(1,),
+                vals=np.ones(1)
+            ),
+            helper.make_tensor(
+                name='b',
+                data_type=TensorProto.FLOAT,
+                dims=(1,),
+                vals=np.ones(1)
+            )]
+        attr = helper.make_attribute("tensors", tensors)
+        self.assertEqual(attr.name, "tensors")
+        self.assertEqual(list(attr.tensors), tensors)
+        checker.check_attribute(attr)
+
+    def test_attr_sparse_tensor_proto(self) -> None:
+        dense_shape = [3, 3]
+        sparse_values = [1.764052391052246, 0.40015721321105957, 0.978738009929657]
+        values_tensor = helper.make_tensor(name='sparse_values', data_type=TensorProto.FLOAT,
+                                        dims=[len(sparse_values)],
+                                        vals=np.array(sparse_values).astype(np.float32), raw=False)
+
+        linear_indicies = [2, 3, 5]
+        indicies_tensor = helper.make_tensor(name='indicies', data_type=TensorProto.INT64,
+                                        dims=[len(linear_indicies)],
+                                        vals=np.array(linear_indicies).astype(np.int64), raw=False)
+        sparse_tensor = helper.make_sparse_tensor(values_tensor, indicies_tensor, dense_shape)
+
+        attr = helper.make_attribute("sparse_attr", sparse_tensor)
+        self.assertEqual(attr.name, "sparse_attr")
+        checker.check_sparse_tensor(helper.get_attribute_value(attr))
+        checker.check_attribute(attr)
+
+    def test_attr_sparse_tensor_repeated_protos(self) -> None:
+        dense_shape = [3, 3]
+        sparse_values = [1.764052391052246, 0.40015721321105957, 0.978738009929657]
+        values_tensor = helper.make_tensor(name='sparse_values', data_type=TensorProto.FLOAT,
+                                        dims=[len(sparse_values)],
+                                        vals=np.array(sparse_values).astype(np.float32), raw=False)
+
+        linear_indicies = [2, 3, 5]
+        indicies_tensor = helper.make_tensor(name='indicies', data_type=TensorProto.INT64,
+                                        dims=[len(linear_indicies)],
+                                        vals=np.array(linear_indicies).astype(np.int64), raw=False)
+        sparse_tensor = helper.make_sparse_tensor(values_tensor, indicies_tensor, dense_shape)
+
+        repeated_sparse = [sparse_tensor, sparse_tensor]
+        attr = helper.make_attribute("sparse_attrs", repeated_sparse)
+        self.assertEqual(attr.name, "sparse_attrs")
+        checker.check_attribute(attr)
+        for s in helper.get_attribute_value(attr):
+            checker.check_sparse_tensor(s)
+
+    def test_attr_repeated_graph_proto(self) -> None:
+        graphs = [GraphProto(), GraphProto()]
+        graphs[0].name = "a"
+        graphs[1].name = "b"
+        attr = helper.make_attribute("graphs", graphs)
+        self.assertEqual(attr.name, "graphs")
+        self.assertEqual(list(attr.graphs), graphs)
+        checker.check_attribute(attr)
+
+    def test_attr_type_proto(self) -> None:
+        # type_proto
+        type = TypeProto()
+        attr = helper.make_attribute("type_proto", type)
+        self.assertEqual(attr.name, "type_proto")
+        self.assertEqual(attr.tp, type)
+        self.assertEqual(attr.type, AttributeProto.TYPE_PROTO)
+        # type_protos
+        types = [TypeProto(), TypeProto()]
+        attr = helper.make_attribute("type_protos", types)
+
+        self.assertEqual(attr.name, "type_protos")
+        self.assertEqual(list(attr.type_protos), types)
+        self.assertEqual(attr.type, AttributeProto.TYPE_PROTOS)
+
+    def test_is_attr_legal(self) -> None:
+        # no name, no field
+        attr = AttributeProto()
+        self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
+        # name, but no field
+        attr = AttributeProto()
+        attr.name = "test"
+        self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
+        # name, with two fields
+        attr = AttributeProto()
+        attr.name = "test"
+        attr.f = 1.0
+        attr.i = 2
+        self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
+
+    def test_is_attr_legal_verbose(self) -> None:
+
+        def _set(attr: AttributeProto, type: AttributeProto.AttributeType, var: Text, value: Any) -> None:
+            setattr(attr, var, value)
+            setattr(attr, 'type', type)
+
+        def _extend(attr: AttributeProto, type: AttributeProto.AttributeType, var: List[Any], value: Any) -> None:
+            var.extend(value)
+            setattr(attr, 'type', type)
+
+        SET_ATTR = [
+            (lambda attr: _set(attr, AttributeProto.FLOAT, "f", 1.0)),
+            (lambda attr: _set(attr, AttributeProto.INT, "i", 1)),
+            (lambda attr: _set(attr, AttributeProto.STRING, "s", b"str")),
+            (lambda attr: _extend(attr, AttributeProto.FLOATS, attr.floats, [1.0, 2.0])),
+            (lambda attr: _extend(attr, AttributeProto.INTS, attr.ints, [1, 2])),
+            (lambda attr: _extend(attr, AttributeProto.STRINGS, attr.strings, [b"a", b"b"])),
+        ]
+        # Randomly set one field, and the result should be legal.
+        for _i in range(100):
+            attr = AttributeProto()
+            attr.name = "test"
+            random.choice(SET_ATTR)(attr)
+            checker.check_attribute(attr)
+        # Randomly set two fields, and then ensure helper function catches it.
+        for _i in range(100):
+            attr = AttributeProto()
+            attr.name = "test"
+            for func in random.sample(SET_ATTR, 2):
+                func(attr)
+            self.assertRaises(checker.ValidationError,
+                              checker.check_attribute,
+                              attr)
+
+
+class TestHelperNodeFunctions(unittest.TestCase):
+
+    def test_node_no_arg(self) -> None:
+        self.assertTrue(defs.has("Relu"))
+        node_def = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test")
+        self.assertEqual(node_def.op_type, "Relu")
+        self.assertEqual(node_def.name, "test")
+        self.assertEqual(list(node_def.input), ["X"])
+        self.assertEqual(list(node_def.output), ["Y"])
+
+    def test_attr_doc_string(self) -> None:
+        node_def = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test", doc_string="doc")
+        self.assertEqual(node_def.doc_string, "doc")
+
+    def test_node_with_arg(self) -> None:
+        self.assertTrue(defs.has("Relu"))
+        # Note: Relu actually does not need an arg, but let's
+        # test it.
+        node_def = helper.make_node(
+            "Relu", ["X"], ["Y"],
+            arg_value=1)
+        self.assertEqual(node_def.op_type, "Relu")
+        self.assertEqual(list(node_def.input), ["X"])
+        self.assertEqual(list(node_def.output), ["Y"])
+        self.assertEqual(len(node_def.attribute), 1)
+        self.assertEqual(
+            node_def.attribute[0],
+            helper.make_attribute("arg_value", 1))
+
+    def test_node_domain(self) -> None:
+        node_def = helper.make_node(
+            "Relu", ["X"], ["Y"], name="test", doc_string="doc", domain="test.domain")
+        self.assertEqual(node_def.domain, "test.domain")
+
+    def test_graph(self) -> None:
+        node_def1 = helper.make_node(
+            "Relu", ["X"], ["Y"])
+        node_def2 = helper.make_node(
+            "Add", ["X", "Y"], ["Z"])
+        value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])]
+        graph = helper.make_graph(
+            [node_def1, node_def2],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
+            doc_string=None,
+            value_info=value_info,
+        )
+        self.assertEqual(graph.name, "test")
+        self.assertEqual(len(graph.node), 2)
+        self.assertEqual(graph.node[0], node_def1)
+        self.assertEqual(graph.node[1], node_def2)
+        self.assertEqual(graph.doc_string, "")
+        self.assertEqual(graph.value_info[0], value_info[0])
+
+    def test_graph_docstring(self) -> None:
+        graph = helper.make_graph([], "my graph", [], [], None, "my docs")
+        self.assertEqual(graph.name, "my graph")
+        self.assertEqual(graph.doc_string, "my docs")
+
+    def test_model(self) -> None:
+        node_def = helper.make_node(
+            "Relu", ["X"], ["Y"])
+        graph_def = helper.make_graph(
+            [node_def],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])])
+        self.assertRaises(AttributeError, helper.make_model, graph_def, xxx=1)
+        model_def = helper.make_model(graph_def, producer_name='test')
+        self.assertEqual(model_def.producer_name, 'test')
+
+    def test_model_docstring(self) -> None:
+        graph = helper.make_graph([], "my graph", [], [])
+        model_def = helper.make_model(graph, doc_string='test')
+        # models may have their own documentation, but don't have a name
+        # their name is the domain-qualified name of the underlying graph.
+        self.assertFalse(hasattr(model_def, "name"))
+        self.assertEqual(model_def.doc_string, 'test')
+
+    def test_model_metadata_props(self) -> None:
+        graph = helper.make_graph([], "my graph", [], [])
+        model_def = helper.make_model(graph, doc_string='test')
+        helper.set_model_props(model_def, {'Title': 'my graph', 'Keywords': 'test;graph'})
+        checker.check_model(model_def)
+        helper.set_model_props(model_def, {'Title': 'my graph', 'Keywords': 'test;graph'})
+        checker.check_model(model_def)  # helper replaces, so no dupe
+
+        dupe = model_def.metadata_props.add()
+        dupe.key = 'Title'
+        dupe.value = 'Other'
+        self.assertRaises(checker.ValidationError, checker.check_model, model_def)
+
+    def test_model_irversion(self) -> None:
+        def mk_model(opset_versions: List[Tuple[Text, int]]) -> ModelProto:
+            graph = helper.make_graph([], "my graph", [], [])
+            return helper.make_model_gen_version(
+                graph,
+                opset_imports=[helper.make_opsetid(*pair) for pair in opset_versions])
+
+        def test(opset_versions: List[Tuple[Text, int]], ir_version: int) -> None:
+            model = mk_model(opset_versions)
+            self.assertEqual(model.ir_version, ir_version)
+        # opset version 9 requires minimum ir_version 4
+        test([("", 9)], 4)
+        test([("", 10)], 5)
+        test([("", 11)], 6)
+        test([("", 12)], 7)
+        test([("", 13)], 7)
+        test([("", 14)], 7)
+        test([("", 15)], 8)
+        test([("", 16)], 8)
+        # standard opset can be referred to using empty-string or "ai.onnx"
+        test([("ai.onnx", 9)], 4)
+        test([("ai.onnx.ml", 2)], 6)
+        test([("ai.onnx.ml", 3)], 8)
+        test([("ai.onnx.training", 1)], 7)
+        # helper should pick *max* IR version required from all opsets specified.
+        test([("", 10), ("ai.onnx.ml", 2)], 6)
+        self.assertRaises(ValueError, mk_model, [("", 100)])
+
+
+class TestHelperTensorFunctions(unittest.TestCase):
+
+    def test_make_tensor(self) -> None:
+        np_array = np.random.randn(2, 3).astype(np.float32)
+
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT,
+            dims=(2, 3),
+            vals=np_array
+        )
+        self.assertEqual(tensor.name, 'test')
+        np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
+
+        # use raw_data field to store the data
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT,
+            dims=(2, 3),
+            vals=np_array.tobytes(),
+            raw=True,
+        )
+        np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
+
+        string_list = list(s.encode('utf-8') for s in ['Amy', 'Billy', 'Cindy', 'David'])
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.STRING,
+            dims=(2, 2),
+            vals=string_list,
+            raw=False
+        )
+        self.assertEqual(string_list, list(tensor.string_data))
+
+    def test_make_float16_tensor(self) -> None:
+        np_array = np.random.randn(2, 3).astype(np.float16)
+
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT16,
+            dims=np_array.shape,
+            vals=np_array
+        )
+        self.assertEqual(tensor.name, 'test')
+        np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
+
+    def test_make_float16_tensor_with_raw(self) -> None:
+        np_array = np.random.randn(2, 3).astype(np.float16)
+
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT16,
+            dims=np_array.shape,
+            vals=np_array.view(dtype=np.uint16).flatten().tobytes(),
+            raw=True
+        )
+        self.assertEqual(tensor.name, 'test')
+        np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
+
+    def test_make_bfloat16_tensor(self) -> None:
+        np_array = np.random.randn(8, 7).astype(np.float16)
+
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.BFLOAT16,
+            dims=np_array.shape,
+            vals=np_array
+        )
+        self.assertEqual(tensor.name, 'test')
+        np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
+
+    def test_make_bfloat16_tensor_with_raw(self) -> None:
+        np_array = np.random.randn(8, 7).astype(np.float16)
+
+        tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.BFLOAT16,
+            dims=np_array.shape,
+            vals=np_array.view(dtype=np.uint16).flatten().tobytes(),
+            raw=True
+        )
+        self.assertEqual(tensor.name, 'test')
+        np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
+
+    def test_make_sparse_tensor(self) -> None:
+        values = [1.1, 2.2, 3.3, 4.4, 5.5]
+        values_tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT,
+            dims=(5, ),
+            vals=values
+        )
+        indices = [1, 3, 5, 7, 9]
+        indices_tensor = helper.make_tensor(
+            name='test_indices',
+            data_type=TensorProto.INT64,
+            dims=(5, ),
+            vals=indices
+        )
+        dense_shape = [10]
+        sparse = helper.make_sparse_tensor(values_tensor, indices_tensor, dense_shape)
+        self.assertEqual(sparse.values, values_tensor)
+        self.assertEqual(sparse.indices, indices_tensor)
+        self.assertEqual(sparse.dims, dense_shape)
+
+    def test_make_tensor_value_info(self) -> None:
+        vi = helper.make_tensor_value_info('X', TensorProto.FLOAT, (2, 4))
+        checker.check_value_info(vi)
+
+        # scalar value
+        vi = helper.make_tensor_value_info('Y', TensorProto.FLOAT, ())
+        checker.check_value_info(vi)
+
+    def test_make_sparse_tensor_value_info(self) -> None:
+        vi = helper.make_sparse_tensor_value_info('X', TensorProto.FLOAT, (2, 3))
+        checker.check_value_info(vi)
+
+        # scalar value
+        vi = helper.make_sparse_tensor_value_info('Y', TensorProto.FLOAT, ())
+        checker.check_value_info(vi)
+
+
+class TestHelperOptionalAndSequenceFunctions(unittest.TestCase):
+    def test_make_optional(self) -> None:
+        values = [1.1, 2.2, 3.3, 4.4, 5.5]
+        values_tensor = helper.make_tensor(
+            name='test',
+            data_type=TensorProto.FLOAT,
+            dims=(5,),
+            vals=values
+        )
+        optional = helper.make_optional(
+            name='test',
+            elem_type=OptionalProto.TENSOR,
+            value=values_tensor
+        )
+        self.assertEqual(optional.name, 'test')
+        self.assertEqual(optional.elem_type, OptionalProto.TENSOR)
+        self.assertEqual(optional.tensor_value, values_tensor)
+
+        # Test Sequence
+        values_sequence = helper.make_sequence(
+            name='test',
+            elem_type=SequenceProto.TENSOR,
+            values=[values_tensor, values_tensor]
+        )
+        optional = helper.make_optional(
+            name='test',
+            elem_type=OptionalProto.SEQUENCE,
+            value=values_sequence
+        )
+        self.assertEqual(optional.name, 'test')
+        self.assertEqual(optional.elem_type, OptionalProto.SEQUENCE)
+        self.assertEqual(optional.sequence_value, values_sequence)
+
+        # Test None
+        optional_none = helper.make_optional(
+            name='test',
+            elem_type=OptionalProto.UNDEFINED,
+            value=None
+        )
+        self.assertEqual(optional_none.name, 'test')
+        self.assertEqual(optional_none.elem_type, OptionalProto.UNDEFINED)
+        self.assertFalse(optional_none.HasField('tensor_value'))
+
+    def test_make_optional_value_info(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=2, shape=[5])
+        tensor_val_into = helper.make_value_info(
+            name='test',
+            type_proto=tensor_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='test',
+            type_proto=optional_type_proto)
+
+        self.assertEqual(optional_val_info.name, 'test')
+        self.assertTrue(optional_val_info.type.optional_type)
+        self.assertEqual(optional_val_info.type.optional_type.elem_type, tensor_val_into.type)
+
+        # Test Sequence
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='test',
+            type_proto=optional_type_proto)
+
+        self.assertEqual(optional_val_info.name, 'test')
+        self.assertTrue(optional_val_info.type.optional_type)
+        sequence_value_info = helper.make_value_info(
+            name='test',
+            type_proto=tensor_type_proto)
+        self.assertEqual(optional_val_info.type.optional_type.elem_type.sequence_type.elem_type, sequence_value_info.type)
+
+    def test_make_seuence_value_info(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=2, shape=None)
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        sequence_val_info = helper.make_value_info(
+            name='test',
+            type_proto=sequence_type_proto
+        )
+        sequence_val_info_prim = helper.make_tensor_sequence_value_info(
+            name='test',
+            elem_type=2,
+            shape=None)
+
+        self.assertEqual(sequence_val_info, sequence_val_info_prim)
+
+
+class TestPrintableGraph(unittest.TestCase):
+
+    def test_initializer_with_matching_graph_input(self) -> None:
+        add = helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])
+        value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1])]
+
+        graph = helper.make_graph(
+            [add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1]),
+             helper.make_tensor_value_info("Y_Initializer", TensorProto.FLOAT, [1])],  # inputs
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1])],  # outputs
+            [helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])],  # initializers
+            doc_string=None,
+            value_info=value_info
+        )
+
+        graph_str = helper.printable_graph(graph)
+        self.assertTrue(''') optional inputs with matching initializers (
+  %Y_Initializer[FLOAT, 1]''' in graph_str, graph_str)
+
+    def test_initializer_no_matching_graph_input(self) -> None:
+        add = helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])
+        value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1])]
+
+        graph = helper.make_graph(
+            [add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1])],  # inputs
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1])],  # outputs
+            [helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])],  # initializers
+            doc_string=None,
+            value_info=value_info
+        )
+
+        graph_str = helper.printable_graph(graph)
+        self.assertTrue(''') initializers (
+  %Y_Initializer[FLOAT, 1]''' in graph_str, graph_str)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/hub_test.py b/third_party/onnx/onnx/test/hub_test.py
new file mode 100644
index 0000000000..768d1bc23f
--- /dev/null
+++ b/third_party/onnx/onnx/test/hub_test.py
@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+import unittest
+import onnx.hub as hub
+from onnx import ModelProto
+import glob
+from os.path import join
+import pytest  # type: ignore
+import os
+
+
+@pytest.mark.skipif(
+    'TEST_HUB' not in os.environ or not os.environ['TEST_HUB'],
+    reason="Conserving Git LFS quota"
+)
+class TestModelHub(unittest.TestCase):
+    def setUp(self) -> None:
+        self.name = "MNIST"
+        self.repo = "onnx/models:main"
+        self.opset = 7
+
+    def test_force_reload(self) -> None:
+        model = hub.load(self.name, self.repo, force_reload=True)
+        self.assertIsInstance(model, ModelProto)
+
+        cached_files = list(glob.glob(join(hub.get_dir(), "**", "*.onnx"), recursive=True))
+        self.assertGreaterEqual(len(cached_files), 1)
+
+    def test_listing_models(self) -> None:
+        model_info_list_1 = hub.list_models(self.repo, model="mnist", tags=["vision"])
+        model_info_list_2 = hub.list_models(self.repo, tags=["vision"])
+        model_info_list_3 = hub.list_models(self.repo)
+
+        self.assertGreater(len(model_info_list_1), 1)
+        self.assertGreater(len(model_info_list_2), len(model_info_list_1))
+        self.assertGreater(len(model_info_list_3), len(model_info_list_2))
+
+    def test_basic_usage(self) -> None:
+        model = hub.load(self.name, self.repo)
+        self.assertIsInstance(model, ModelProto)
+
+        cached_files = list(glob.glob(join(hub.get_dir(), "**", "*.onnx"), recursive=True))
+        self.assertGreaterEqual(len(cached_files), 1)
+
+    def test_custom_cache(self) -> None:
+        old_cache = hub.get_dir()
+        new_cache = join(old_cache, "custom")
+        hub.set_dir(new_cache)
+
+        model = hub.load(self.name, self.repo)
+        self.assertIsInstance(model, ModelProto)
+
+        cached_files = list(glob.glob(join(new_cache, "**", "*.onnx"), recursive=True))
+        self.assertGreaterEqual(len(cached_files), 1)
+
+        hub.set_dir(old_cache)
+
+    def test_download_with_opset(self) -> None:
+        model = hub.load(self.name, self.repo, opset=8)
+        self.assertIsInstance(model, ModelProto)
+
+    def test_opset_error(self) -> None:
+        self.assertRaises(AssertionError, lambda: hub.load(self.name, self.repo, opset=-1))
+
+    def test_manifest_not_found(self) -> None:
+        self.assertRaises(AssertionError, lambda: hub.load(self.name, "onnx/models:unknown", silent=True))
+
+    def test_verify_repo_ref(self) -> None:
+        # Not trusted repo:
+        verified = hub._verify_repo_ref("mhamilton723/models")
+        self.assertFalse(verified)
+
+        # Not trusted repo:
+        verified = hub._verify_repo_ref("onnx/models:unknown")
+        self.assertFalse(verified)
+
+        # Trusted repo:
+        verified = hub._verify_repo_ref(self.repo)
+        self.assertTrue(verified)
+
+    def test_get_model_info(self) -> None:
+        hub.get_model_info("mnist", self.repo, opset=8)
+        hub.get_model_info("mnist", self.repo)
+        self.assertRaises(AssertionError, lambda: hub.get_model_info("mnist", self.repo, opset=-1))
diff --git a/third_party/onnx/onnx/test/numpy_helper_test.py b/third_party/onnx/onnx/test/numpy_helper_test.py
new file mode 100644
index 0000000000..8110daa816
--- /dev/null
+++ b/third_party/onnx/onnx/test/numpy_helper_test.py
@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np  # type: ignore
+
+from onnx import numpy_helper
+
+import unittest
+
+
+class TestNumpyHelper(unittest.TestCase):
+
+    def _test_numpy_helper_float_type(self, dtype: np.number) -> None:
+        a = np.random.rand(13, 37).astype(dtype)
+        tensor_def = numpy_helper.from_array(a, "test")
+        self.assertEqual(tensor_def.name, "test")
+        a_recover = numpy_helper.to_array(tensor_def)
+        np.testing.assert_equal(a, a_recover)
+
+    def _test_numpy_helper_int_type(self, dtype: np.number) -> None:
+        a = np.random.randint(
+            np.iinfo(dtype).min,
+            np.iinfo(dtype).max,
+            dtype=dtype,
+            size=(13, 37))
+        tensor_def = numpy_helper.from_array(a, "test")
+        self.assertEqual(tensor_def.name, "test")
+        a_recover = numpy_helper.to_array(tensor_def)
+        np.testing.assert_equal(a, a_recover)
+
+    def test_float(self) -> None:
+        self._test_numpy_helper_float_type(np.float32)
+
+    def test_uint8(self) -> None:
+        self._test_numpy_helper_int_type(np.uint8)
+
+    def test_int8(self) -> None:
+        self._test_numpy_helper_int_type(np.int8)
+
+    def test_uint16(self) -> None:
+        self._test_numpy_helper_int_type(np.uint16)
+
+    def test_int16(self) -> None:
+        self._test_numpy_helper_int_type(np.int16)
+
+    def test_int32(self) -> None:
+        self._test_numpy_helper_int_type(np.int32)
+
+    def test_int64(self) -> None:
+        self._test_numpy_helper_int_type(np.int64)
+
+    def test_string(self) -> None:
+        a = np.array(['Amy', 'Billy', 'Cindy', 'David']).astype(object)
+        tensor_def = numpy_helper.from_array(a, "test")
+        self.assertEqual(tensor_def.name, "test")
+        a_recover = numpy_helper.to_array(tensor_def)
+        np.testing.assert_equal(a, a_recover)
+
+    def test_bool(self) -> None:
+        a = np.random.randint(2, size=(13, 37)).astype(bool)
+        tensor_def = numpy_helper.from_array(a, "test")
+        self.assertEqual(tensor_def.name, "test")
+        a_recover = numpy_helper.to_array(tensor_def)
+        np.testing.assert_equal(a, a_recover)
+
+    def test_float16(self) -> None:
+        self._test_numpy_helper_float_type(np.float32)
+
+    def test_complex64(self) -> None:
+        self._test_numpy_helper_float_type(np.complex64)
+
+    def test_complex128(self) -> None:
+        self._test_numpy_helper_float_type(np.complex128)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/parser_test.py b/third_party/onnx/onnx/test/parser_test.py
new file mode 100644
index 0000000000..d76c84a7d4
--- /dev/null
+++ b/third_party/onnx/onnx/test/parser_test.py
@@ -0,0 +1,68 @@
+# SPDX-License-Identifier: Apache-2.0
+import onnx
+import unittest
+from onnx import helper, parser, GraphProto
+
+
+class TestBasicFunctions(unittest.TestCase):
+    def check_graph(self, graph: GraphProto) -> None:
+        self.assertTrue(len(graph.node) == 3)
+        self.assertTrue(graph.node[0].op_type == "MatMul")
+        self.assertTrue(graph.node[1].op_type == "Add")
+        self.assertTrue(graph.node[2].op_type == "Softmax")
+
+    def test_parse_graph(self) -> None:
+        input = '''
+           agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
+           {
+              T = MatMul(X, W)
+              S = Add(T, B)
+              C = Softmax(S)
+           }
+           '''
+        graph = onnx.parser.parse_graph(input)
+        self.check_graph(graph)
+
+    def test_parse_model(self) -> None:
+        input = '''
+           <
+             ir_version: 7,
+             opset_import: [ "" : 10, "com.microsoft": 1]
+           >
+           agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
+           {
+              T = MatMul(X, W)
+              S = Add(T, B)
+              C = Softmax(S)
+           }
+           '''
+        model = onnx.parser.parse_model(input)
+        self.assertTrue(model.ir_version == 7)
+        self.assertTrue(len(model.opset_import) == 2)
+        self.check_graph(model.graph)
+
+    def test_parse_graph_error(self) -> None:
+        input = '''
+           agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
+           {
+              T = MatMul[X, W]
+              S = Add(T, B)
+              C = Softmax(S)
+           }
+           '''
+        self.assertRaises(onnx.parser.ParseError, lambda: onnx.parser.parse_graph(input))
+
+    def test_parse_model_error(self) -> None:
+        input = '''
+           <
+             ir_version: 7,
+             opset_import: [ "" : 10   "com.microsoft": 1]
+           >
+           agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
+           {
+              T = MatMul(X, W)
+              S = Add(T, B)
+              C = Softmax(S)
+           }
+           '''
+        self.assertRaises(onnx.parser.ParseError, lambda: onnx.parser.parse_model(input))
diff --git a/third_party/onnx/onnx/test/relu_test.py b/third_party/onnx/onnx/test/relu_test.py
new file mode 100644
index 0000000000..744e66e54f
--- /dev/null
+++ b/third_party/onnx/onnx/test/relu_test.py
@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+import unittest
+
+from onnx import defs, helper
+
+
+class TestRelu(unittest.TestCase):
+
+    def test_relu(self) -> None:
+        self.assertTrue(defs.has('Relu'))
+        helper.make_node(
+            'Relu', ['X'], ['Y'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/schema_test.py b/third_party/onnx/onnx/test/schema_test.py
new file mode 100644
index 0000000000..2e0d0397a3
--- /dev/null
+++ b/third_party/onnx/onnx/test/schema_test.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+import unittest
+
+from onnx import defs, AttributeProto
+
+
+class TestSchema(unittest.TestCase):
+
+    def test_get_schema(self) -> None:
+        defs.get_schema("Relu")
+
+    def test_typecheck(self) -> None:
+        defs.get_schema("Conv")
+
+    def test_attr_default_value(self) -> None:
+        v = defs.get_schema(
+            "BatchNormalization").attributes['epsilon'].default_value
+        self.assertEqual(type(v), AttributeProto)
+        self.assertEqual(v.type, AttributeProto.FLOAT)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/shape_inference_test.py b/third_party/onnx/onnx/test/shape_inference_test.py
new file mode 100644
index 0000000000..ae5f562eba
--- /dev/null
+++ b/third_party/onnx/onnx/test/shape_inference_test.py
@@ -0,0 +1,4180 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from onnx import checker, helper, numpy_helper, TensorProto, NodeProto, GraphProto, ValueInfoProto, ModelProto, ONNX_ML, SparseTensorProto, TypeProto
+from onnx.defs import ONNX_DOMAIN, ONNX_ML_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN
+from onnx.helper import make_node, make_tensor, make_tensor_value_info, make_empty_tensor_value_info, make_opsetid, make_tensor_sequence_value_info
+from typing import Sequence, Union, Text, Tuple, Type, List, Any, Optional
+import onnx.shape_inference
+import unittest
+import os
+import numpy as np  # type: ignore
+
+
+class TestShapeInference(unittest.TestCase):
+    def _make_graph(self,
+                    seed_values: Sequence[Union[Text, Tuple[Text, TensorProto.DataType, Any]]],
+                    nodes: List[NodeProto],
+                    value_info: List[ValueInfoProto],
+                    initializer: Optional[Sequence[TensorProto]] = None
+                    ) -> GraphProto:
+        if initializer is None:
+            initializer = []
+        names_in_initializer = set(x.name for x in initializer)
+        input_value_infos = []
+        # If the starting values are not also initializers,
+        # introduce the starting values as the output of reshape,
+        # so that the sizes are guaranteed to be unknown
+        for seed_value in seed_values:
+            if isinstance(seed_value, tuple):
+                seed_name, proto_type = seed_value[:2]
+                seed_value_info = make_tensor_value_info(*seed_value)
+            else:
+                seed_name, proto_type = seed_value, TensorProto.UNDEFINED
+                seed_value_info = make_empty_tensor_value_info(seed_value)
+            if seed_name in names_in_initializer:
+                input_value_infos.append(seed_value_info)
+            else:
+                value_info.append(seed_value_info)
+                input_value_infos.append(make_tensor_value_info('SEED_' + seed_name, proto_type, ()))
+                input_value_infos.append(make_tensor_value_info('UNKNOWN_SHAPE_' + seed_name, TensorProto.INT64, ()))
+                nodes[:0] = [make_node("Reshape", ['SEED_' + seed_name, 'UNKNOWN_SHAPE_' + seed_name], [seed_name])]
+        return helper.make_graph(nodes, "test", input_value_infos, [], initializer=initializer, value_info=value_info)
+
+    def _inferred(self, graph: GraphProto, **kwargs: Any) -> ModelProto:
+        kwargs[str('producer_name')] = 'onnx-test'
+        data_prop = kwargs.pop('data_prop', False)
+        orig_model = helper.make_model(graph, **kwargs)
+        inferred_model = onnx.shape_inference.infer_shapes(orig_model, strict_mode=True, data_prop=data_prop)
+        checker.check_model(inferred_model)
+        return inferred_model
+
+    def _assert_inferred(self, graph: GraphProto, vis: List[ValueInfoProto], **kwargs: Any) -> None:
+        names_in_vis = set(x.name for x in vis)
+        vis = list(x for x in graph.value_info if x.name not in names_in_vis) + vis
+        inferred_model = self._inferred(graph, **kwargs)
+        inferred_vis = list(inferred_model.graph.value_info)
+        vis = list(sorted(vis, key=lambda x: x.name))
+        inferred_vis = list(sorted(inferred_vis, key=lambda x: x.name))
+        assert len(vis) == len(inferred_vis)
+        for i in range(len(vis)):
+            self._compare_value_infos(vis[i].type, inferred_vis[i].type)
+
+    def _compare_value_infos(self, vi_type: TypeProto, inferred_vi_type: TypeProto) -> None:
+        if vi_type.HasField('tensor_type'):
+            assert inferred_vi_type.HasField('tensor_type')
+            assert vi_type.tensor_type.HasField('elem_type')
+            assert inferred_vi_type.tensor_type.HasField('elem_type')
+            assert vi_type.tensor_type.elem_type == inferred_vi_type.tensor_type.elem_type
+            assert vi_type.tensor_type.HasField('shape') == inferred_vi_type.tensor_type.HasField('shape')
+            if vi_type.tensor_type.HasField('shape'):
+                for dim_i in range(len(vi_type.tensor_type.shape.dim)):
+                    dim = vi_type.tensor_type.shape.dim[dim_i]
+                    inferred_dim = inferred_vi_type.tensor_type.shape.dim[dim_i]
+                    # if it is a symbolic shape, make sure the inferred symbol has generated (dim_param)
+                    if dim.dim_param:
+                        assert dim.dim_param == inferred_dim.dim_param, '\n%s\n%s\n' % (vi_type, inferred_vi_type)
+                    else:
+                        assert dim.dim_value == inferred_dim.dim_value, '\n%s\n%s\n' % (vi_type, inferred_vi_type)
+        elif vi_type.HasField('sequence_type'):
+            assert inferred_vi_type.HasField('sequence_type')
+            vi = vi_type.sequence_type.elem_type
+            inferred_vi = inferred_vi_type.sequence_type.elem_type
+            self._compare_value_infos(vi, inferred_vi)
+        elif vi_type.HasField('optional_type'):
+            assert inferred_vi_type.HasField('optional_type')
+            vi = vi_type.optional_type.elem_type
+            inferred_vi = inferred_vi_type.optional_type.elem_type
+            self._compare_value_infos(vi, inferred_vi)
+        else:
+            raise NotImplementedError(
+                "Unrecognized value info type in _compare_value_infos: ", str(vi_type))
+
+    def test_empty_graph(self) -> None:
+        graph = self._make_graph(
+            ['y'],
+            [], [])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def _identity_prop(self, op: Text, **kwargs: Any) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5))],
+            [make_node(op, 'x', 'y', **kwargs)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (30, 4, 5))])
+
+    def test_transpose(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))])
+
+    def test_transpose_preexisting(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+            [make_tensor_value_info("Y", TensorProto.FLOAT, None)])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))])
+
+    def test_transpose_partial(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+            [make_tensor_value_info("Y", TensorProto.UNDEFINED, (3, "a", "b"))])  # type: ignore
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))])
+
+    def test_transpose_preexisting_incorrect_shape(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+            [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5))])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def test_transpose_preexisting_incorrect_type(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+            [make_tensor_value_info("Y", TensorProto.STRING, (3, 2, 4))])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def test_transpose_incorrect_repeated_perm(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 1])],
+            [])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def _make_matmul_test_all_dims_known(self, shape1: Sequence[int], shape2: Sequence[int]) -> None:
+        expected_out_shape = np.matmul(np.arange(np.product(shape1)).reshape(shape1),
+                                       np.arange(np.product(shape2)).reshape(shape2)).shape
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, shape1),
+             ('y', TensorProto.FLOAT, shape2)],
+            [make_node('MatMul', ['x', 'y'], ['z'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, expected_out_shape)])
+
+    def test_matmul_all_dims_known(self) -> None:
+        self._make_matmul_test_all_dims_known((2,), (2,))
+
+        self._make_matmul_test_all_dims_known((4, 2), (2, 4))
+        self._make_matmul_test_all_dims_known((5, 2), (2, 4))
+        self._make_matmul_test_all_dims_known((5, 2), (2, 1))
+        self._make_matmul_test_all_dims_known((1, 2), (2, 3))
+        self._make_matmul_test_all_dims_known((2,), (2, 3))
+        self._make_matmul_test_all_dims_known((4, 2), (2,))
+        self._make_matmul_test_all_dims_known((1, 4, 2), (3, 2, 3))
+        self._make_matmul_test_all_dims_known((3, 4, 2), (3, 2, 3))
+        self._make_matmul_test_all_dims_known((5, 1, 4, 2), (1, 3, 2, 3))
+        self._make_matmul_test_all_dims_known((4, 2), (3, 2, 3))
+
+    def _make_matmul_test_allow_unknown(self, shape1: Any, shape2: Any, expected_out_shape: Any) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, shape1),
+             ('y', TensorProto.FLOAT, shape2)],
+            [make_node('MatMul', ['x', 'y'], ['z'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, expected_out_shape)])
+
+    def test_matmul_allow_unknown(self) -> None:
+        self._make_matmul_test_allow_unknown((None,), (None,), ())
+        self._make_matmul_test_allow_unknown((3,), (None,), ())
+        self._make_matmul_test_allow_unknown((2,), (2, "a"), ("a",))
+        self._make_matmul_test_allow_unknown((4, 2), (2, "a"), (4, "a"))
+        self._make_matmul_test_allow_unknown((4, None), (2, "a"), (4, "a"))
+        self._make_matmul_test_allow_unknown((4, None), (None, "a"), (4, "a"))
+        self._make_matmul_test_allow_unknown((1, 4, 2), ("a", 2, 5), ("a", 4, 5))
+        self._make_matmul_test_allow_unknown((1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5))
+        self._make_matmul_test_allow_unknown((3,), None, None)
+        self._make_matmul_test_allow_unknown(None, None, None)
+
+    def test_cast(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Cast", ["x"], ["y"], to=TensorProto.UINT8)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.UINT8, (2, 4, 3))])
+
+    def test_cast_like(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 4, 3)), ("t", TensorProto.FLOAT16, ("N",))],
+            [make_node("CastLike", ["x", "t"], ["y"])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT16, (2, 4, 3))])
+
+    def test_concat(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 4, 3)),
+             ("y", TensorProto.FLOAT, (7, 4, 3))],
+            [make_node("Concat", ['x', 'y'], ['z'], axis=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (9, 4, 3))])
+
+    def test_concat_missing_shape(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 4, 3)),
+             "y",
+             ("z", TensorProto.FLOAT, (None, None, None))],
+            [make_node("Concat", ['x', 'y', 'z'], ['out'], axis=0)],
+            [])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def test_concat_3d_axis_2(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 2, 2)),
+             ('y', TensorProto.FLOAT, (2, 2, 2))],
+            [make_node('Concat', ['x', 'y'], ['z'], axis=2)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 2, 4))])
+
+    def test_concat_param(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, ("a", 2)),
+             ("y", TensorProto.FLOAT, ("a", 3))],
+            [make_node("Concat", ['x', 'y'], ['z'], axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ("a", 5))])
+
+    def test_concat_param_single_input(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, ("a", 2))],
+            [make_node("Concat", ['x'], ['z'], axis=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ("a", 2))])
+
+    def test_reshape_dynamic_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (2, 4, 3)),
+             ('shape', TensorProto.INT64, (2,))],
+            [make_node("Reshape", ['x', 'shape'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, None)])
+
+    def test_reshape_static_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (2, 4, 3)),
+             ('shape', TensorProto.INT64, (2,))],
+            [make_node("Reshape", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (2,), (3, 8))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (3, 8))])
+
+    def test_reshape_static_shape_inferred(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (2, 4, 3)),
+             ('shape', TensorProto.INT64, (3,))],
+            [make_node("Reshape", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (3,), (0, 3, -1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (2, 3, 4))])
+
+    def test_reshape_static_shape_zero(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (1, 1, 1)),
+             ('shape', TensorProto.INT64, (3,))],
+            [make_node("Reshape", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (3,), (0, 1, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (1, 1, 1))])
+
+    def test_reshape_static_shape_allowzero(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (1, 0, 0)),
+             ('shape', TensorProto.INT64, (3,))],
+            [make_node("Reshape", ['x', 'shape'], ['y'], allowzero=1)],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (3,), (0, 1, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (0, 1, 1))])
+
+    def test_reshape_static_shape_constant(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (2, 4, 3))],
+            [make_node("Constant", [], ['shape'],
+                       value=make_tensor('shape', TensorProto.INT64, (2,), (3, 8))),
+             make_node("Reshape", ['x', 'shape'], ['y'])],
+            [])
+        self._assert_inferred(graph, [
+            make_tensor_value_info('shape', TensorProto.INT64, (2,)),
+            make_tensor_value_info('y', TensorProto.UINT8, (3, 8))])
+
+    def test_upsample(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (2, 4, 3, 5)),
+             ('scales', TensorProto.FLOAT, (4,))],
+            [make_node("Upsample", ['x', 'scales'], ['y'])],
+            [],
+            initializer=[make_tensor('scales', TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (2, 4, 3, 9))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)])
+
+    def test_upsample_raw_data(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (2, 4, 3, 5)),
+             ('scales', TensorProto.FLOAT, (4,))],
+            [make_node("Upsample", ['x', 'scales'], ['y'])],
+            [],
+            initializer=[make_tensor('scales', TensorProto.FLOAT, (4,),
+                                     vals=np.array([1.0, 1.1, 1.3, 1.9], dtype='<f4').tobytes(), raw=True)])  # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (2, 4, 3, 9))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)])
+
+    def test_upsample_raw_data_v7(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (1, 3, 4, 5))],
+            [make_node("Upsample", ['x'], ['y'], scales=[2.0, 1.1, 2.3, 1.9])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (2, 3, 9, 9))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 7)])
+
+    def test_expand(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (3, 1)),
+             ('shape', TensorProto.INT64, (3,))],
+            [make_node("Expand", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (3,), (2, 1, 6))])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (2, 3, 6))])
+
+    def test_expand_scalar_input(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, ()),
+             ('shape', TensorProto.INT64, (2,))],
+            [make_node("Expand", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (2,), (4, 8))])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (4, 8))])
+
+    def test_expand_raw_data(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (3, 1)),
+             ('shape', TensorProto.INT64, (2,))],
+            [make_node("Expand", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[make_tensor('shape', TensorProto.INT64, (2,),
+                                     vals=np.array([3, 4], dtype='<i8').tobytes(), raw=True)])  # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (3, 4))])
+
+    def test_expand_symbolic_input(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (3, 1, 2)),
+             ('y', TensorProto.INT32, (1, 4, 2))],
+            [make_node("Shape", ['y'], ['shape']),
+             make_node("Expand", ['x', 'shape'], ['z'])],
+            [])
+        self._assert_inferred(graph, [
+            make_tensor_value_info('shape', TensorProto.INT64, (3,)),
+            make_tensor_value_info('z', TensorProto.INT32, (3, 4, 2))],
+            data_prop=True)
+
+    def test_expand_dynamic_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (1, 2, None)),
+             ('shape', TensorProto.INT64, (3,))],
+            [make_node("Expand", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (None, 2, None))])
+
+    def test_expand_symbolic_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (1, 2, None)),
+             ('shape', TensorProto.INT64, ('unk__0',))],
+            [make_node("Expand", ['x', 'shape'], ['y'])],
+            [],
+            initializer=[])
+        # if giving a symbolic shape, Expand should not infer any shape or rank inference
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, None)])
+
+    def test_resize_size(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (2, 4, 3, 5)),
+             ('roi', TensorProto.FLOAT, (8,)),
+             ('scales', TensorProto.FLOAT, (4,)),
+             ('sizes', TensorProto.INT64, (4,))],
+            [make_node("Resize", ['x', 'roi', 'scales', 'sizes'], ['y'])],
+            [],
+            initializer=[make_tensor('sizes', TensorProto.INT64, (4,), (3, 5, 6, 7))])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (3, 5, 6, 7))])
+
+    def test_resize_scale(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (2, 4, 3, 5)),
+             ('roi', TensorProto.FLOAT, (8,)),
+             ('scales', TensorProto.FLOAT, (4,))],
+            [make_node("Resize", ['x', 'roi', 'scales'], ['y'])],
+            [],
+            initializer=[make_tensor('scales', TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (2, 4, 3, 9))])
+
+    def test_resize_scale_raw_data(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (1, 3, 4, 5)),
+             ('roi', TensorProto.FLOAT, (8,)),
+             ('scales', TensorProto.FLOAT, (4,))],
+            [make_node("Resize", ['x', 'roi', 'scales'], ['y'])],
+            [],
+            initializer=[make_tensor('scales', TensorProto.FLOAT, (4,),
+                                     vals=np.array([2.0, 1.1, 2.3, 1.9], dtype='<f4').tobytes(), raw=True)])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.INT32, (2, 3, 9, 9))])
+
+    def test_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Shape", ['x'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (3,))])
+
+    def test_shape_start_1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Shape", ['x'], ['y'], start=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (2,))])
+
+    def test_shape_end_1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Shape", ['x'], ['y'], end=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1,))])
+
+    def test_shape_negative_start(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Shape", ['x'], ['y'], start=-1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1,))])
+
+    def test_shape_clip1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Shape", ['x'], ['y'], start=-5)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (3,))])
+
+    def test_shape_clip2(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Shape", ['x'], ['y'], end=10)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (3,))])
+
+    def test_size(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4, 3))],
+            [make_node("Size", ['x'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, ())])
+
+    def test_gather(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 3)),
+             ('i', TensorProto.INT64, (2,))],
+            [make_node("Gather", ['x', 'i'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3))])  # type: ignore
+
+    def test_gather_axis1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 3, 5)),
+             ('i', TensorProto.INT64, (1, 2))],
+            [make_node("Gather", ['x', 'i'], ['y'], axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 1, 2, 5))])  # type: ignore
+
+    def test_gather_into_scalar(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3,)),
+             ('i', TensorProto.INT64, ())],
+            [make_node("Gather", ['x', 'i'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ())])
+
+    def test_gather_elements(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 2)),
+             ('i', TensorProto.INT64, (2, 2))],
+            [make_node("GatherElements", ['x', 'i'], ['y'], axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))])  # type: ignore
+
+    def test_gather_elements_axis0(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 3)),
+             ('i', TensorProto.INT64, (2, 3))],
+            [make_node("GatherElements", ['x', 'i'], ['y'], axis=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3))])  # type: ignore
+
+    def test_scatter(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 3)),
+             ('i', TensorProto.INT64, (2, 3)),
+             ('u', TensorProto.FLOAT, (2, 3))],
+            [make_node("Scatter", ['x', 'i', 'u'], ['y'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.FLOAT, (3, 3))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)])  # type: ignore
+
+    def test_scatter_axis1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, 5)),
+             ('i', TensorProto.INT64, (1, 2)),
+             ('u', TensorProto.FLOAT, (1, 2))],
+            [make_node("Scatter", ['x', 'i', 'u'], ['y'], axis=1)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.FLOAT, (1, 5))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)])  # type: ignore
+
+    def test_scatter_elements(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 3)),
+             ('i', TensorProto.INT64, (2, 3)),
+             ('u', TensorProto.FLOAT, (2, 3))],
+            [make_node("ScatterElements", ['x', 'i', 'u'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 3))])  # type: ignore
+
+    def test_scatter_elements_axis1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, 5)),
+             ('i', TensorProto.INT64, (1, 2)),
+             ('u', TensorProto.FLOAT, (1, 2))],
+            [make_node("ScatterElements", ['x', 'i', 'u'], ['y'], axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 5))])  # type: ignore
+
+    def test_scatternd(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('indices', TensorProto.INT64, (3, 3, 2)),
+             ('updates', TensorProto.FLOAT, (3, 3, 6))],
+            [make_node("ScatterND", ['x', 'indices', 'updates'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 5, 6))])  # type: ignore
+
+    def test_scatternd_noshape(self) -> None:
+        # The shape of 'x_reshaped' cannot be inferred, since it is the output of a dynamic reshape.
+        # Thus the shape of 'y' is also None.
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('indices', TensorProto.INT64, (3, 3, 2)),
+             ('updates', TensorProto.FLOAT, (3, 3, 6)),
+             ('shape', TensorProto.INT64, (2,))],
+            [make_node("Reshape", ['x', 'shape'], ['x_reshaped']),
+             make_node("ScatterND", ['x_reshaped', 'indices', 'updates'], ['y'])],
+            [])
+        self._assert_inferred(graph, [
+            make_tensor_value_info('x_reshaped', TensorProto.FLOAT, None),
+            make_tensor_value_info('y', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_squeeze(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, 3, 1, 1, 2, 1)),
+             ('axes', TensorProto.INT64, (4,))],
+            [make_node('Squeeze', ['x', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('axes', TensorProto.INT64, (4,), (0, 2, 3, 5))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 2))])
+
+    def test_unsqueeze_regular(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)),
+             ('axes', TensorProto.INT64, (4,))],
+            [make_node('Unsqueeze', ['x', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('axes', TensorProto.INT64, (4,), (0, 1, 3, 5))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 1, 3, 1, 2, 1))])
+
+    def test_unsqueeze_unsorted_axes(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5)),
+             ('axes', TensorProto.INT64, (2,))],
+            [make_node('Unsqueeze', ['x', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('axes', TensorProto.INT64, (2,), (4, 0))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 3, 4, 5, 1))])
+
+    def test_unsqueeze_negative_axes(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5)),
+             ('axes', TensorProto.INT64, (2,))],
+            [make_node('Unsqueeze', ['x', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('axes', TensorProto.INT64, (2,), (0, -1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 3, 4, 5, 1))])
+
+    def test_slice_without_input_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (1,)), ('ends', TensorProto.INT64, (1,))],
+            [make_node('Slice', ['x', 'starts', 'ends'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, None)])
+
+    def test_slice_with_input_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2, )), ('ends', TensorProto.INT64, (2, ))],
+            [make_node('Slice', ['x', 'starts', 'ends'], ['y'])],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2, ),
+                                      vals=np.array([1, 0], dtype='<i8').tobytes(), raw=True),  # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+                         make_tensor('ends', TensorProto.INT64, (2, ), (2, 2))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 2))])
+
+    def test_slice_with_input_shape_containing_dim_params(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, 'a', 1)),
+             ('starts', TensorProto.INT64, (3,)),
+             ('ends', TensorProto.INT64, (3,))],
+            [make_node('Slice', ['x', 'starts', 'ends'], ['y'])],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (3,), (0, 0, 0)),
+                            make_tensor('ends', TensorProto.INT64, (3,), (1, 1, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, None, 1))])  # type: ignore
+
+    def test_slice_with_input_shape_steps(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 6, 7)),
+             ('starts', TensorProto.INT64, (3,)),
+             ('ends', TensorProto.INT64, (3,)),
+             ('axes', TensorProto.INT64, (None)),
+             ('steps', TensorProto.INT64, (3,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], ['y'])],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (3,), (1, 0, 0)),
+                         make_tensor('ends', TensorProto.INT64, (3,), (2, 6, 6)),
+                         make_tensor('steps', TensorProto.INT64, (3,), (1, 4, 3))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 2, 2))])
+
+    def test_slice_with_input_shape_axes(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 6, 2)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,)),
+             ('steps', TensorProto.INT64, (None))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], ['y'])],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)),
+                         make_tensor('ends', TensorProto.INT64, (2,), (2, 2)),
+                         make_tensor('axes', TensorProto.INT64, (2,), (0, 2))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 6, 2))])
+
+    def test_slice_unsorted_axes(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)),
+                         make_tensor('ends', TensorProto.INT64, (2,), (2, 2)),
+                         make_tensor('axes', TensorProto.INT64, (2,), (1, 0))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 1))])  # can handle unsorted axes
+
+    def test_slice_giant_number(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)),
+                         make_tensor('ends', TensorProto.INT64, (2,), (200, 22000)),
+                         make_tensor('axes', TensorProto.INT64, (2,), (0, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))])
+
+    def test_slice_giant_step(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,)),
+             ('steps', TensorProto.INT64, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)),
+                         make_tensor('ends', TensorProto.INT64, (2,), (200, 200)),
+                         make_tensor('axes', TensorProto.INT64, (2,), (0, 1)),
+                         make_tensor('steps', TensorProto.INT64, (2,), (1, 200))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 1))])
+
+    def test_slice_negative_end(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)),
+                         make_tensor('ends', TensorProto.INT64, (2,), (200, -1)),  # negative end means begin from end of a dimension (here end = 2 - 1 = 1)
+                         make_tensor('axes', TensorProto.INT64, (2,), (0, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 1))])  # type: ignore
+
+    def test_slice_negative_start(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 2)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, -2)),  # negative start means begin from end of a dimension (here end = 2 - 2 = 0)
+                         make_tensor('ends', TensorProto.INT64, (2,), (200, 3)),
+                         make_tensor('axes', TensorProto.INT64, (2,), (0, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))])  # type: ignore
+
+    def test_slice_negative_step(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4)),
+             ('starts', TensorProto.INT64, (2,)),
+             ('ends', TensorProto.INT64, (2,)),
+             ('axes', TensorProto.INT64, (2,)),
+             ('steps', TensorProto.INT64, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 4)),  # 4 will be clamped to 3 since we are negative stepping
+                         make_tensor('ends', TensorProto.INT64, (2,), (200, 0)),
+                         make_tensor('axes', TensorProto.INT64, (2,), (0, 1)),
+                         make_tensor('steps', TensorProto.INT64, (2,), (1, -1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3))])  # type: ignore
+
+    def test_slice_variable_copy(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ("a", 2)),
+             ('starts', TensorProto.INT64, (1,)),
+             ('ends', TensorProto.INT64, (1,)),
+             ('axes', TensorProto.INT64, (1,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT64, (1,), (1,)),
+                         make_tensor('ends', TensorProto.INT64, (1,), (200,)),
+                         make_tensor('axes', TensorProto.INT64, (1,), (1,))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ("a", 1))])  # type: ignore
+
+    def test_slice_variable_input_types(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.DOUBLE, (3, 2)),
+             ('starts', TensorProto.INT32, (2,)),
+             ('ends', TensorProto.INT32, (2,)),
+             ('axes', TensorProto.INT32, (2,))],
+            [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')],
+            [],
+            initializer=[make_tensor('starts', TensorProto.INT32, (2,), (1, 0)),
+                         make_tensor('ends', TensorProto.INT32, (2,), (200, 22000)),
+                         make_tensor('axes', TensorProto.INT32, (2,), (0, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.DOUBLE, (2, 2))])
+
+    def test_conv(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)),
+             ('y', TensorProto.FLOAT, (5, 4, 2, 4, 3))],
+            [make_node('Conv', ['x', 'y'], 'z', pads=[0, 1, 1, 0, 0, 1], dilations=[1, 2, 2], strides=[1, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (3, 5, 4, 1, 3))])
+
+    def test_conv_1d_simple(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y', TensorProto.FLOAT, (50, 4, 2))],
+            [make_node('Conv', ['x', 'y'], 'z', dilations=[1])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 4))])
+
+    def test_conv_dilations(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 8, 8, 8)),
+             ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))],
+            [make_node('Conv', ['x', 'y'], 'z', dilations=[1, 2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 6, 4, 2))])
+
+    def test_conv_strides(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 8, 8, 8)),
+             ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))],
+            [make_node('Conv', ['x', 'y'], 'z', strides=[1, 2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 6, 3, 2))])
+
+    def test_conv_pads(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)),
+             ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))],
+            [make_node('Conv', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 6, 6, 6))])
+
+    def test_conv_auto_pad(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)),
+             ('y', TensorProto.FLOAT, (50, 4, 4, 3, 2))],
+            [make_node('Conv', ['x', 'y'], 'z', auto_pad='SAME_UPPER')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 7, 6, 4))])
+
+    def test_conv_auto_pads(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)),
+             ('y', TensorProto.FLOAT, (50, 4, 4, 3, 2))],
+            [make_node('Conv', ['x', 'y'], 'z', auto_pad='SAME_UPPER', strides=[2, 2, 1])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 4, 3, 4))])
+
+    def test_conv_auto_pad_dilation(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 65, 64, 63)),
+             ('y', TensorProto.FLOAT, (50, 4, 4, 3, 2))],
+            [make_node('Conv', ['x', 'y'], 'z', auto_pad='SAME_UPPER', dilations=[2, 3, 4])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 65, 64, 63))])
+
+    def test_conv_group(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 8, 8, 8)),
+             ('y', TensorProto.FLOAT, (4, 1, 8, 8, 8))],
+            [make_node('Conv', ['x', 'y'], 'z', group=4)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 1, 1, 1))])
+
+    def test_conv_only_one_pos(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y', TensorProto.FLOAT, (50, 4, 5))],
+            [make_node('Conv', ['x', 'y'], 'z', strides=[2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 1))])
+
+    def test_conv_partial_missing_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, None, 6, 4)),
+             ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))],
+            [make_node('Conv', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, None, 6, 6))])  # type: ignore
+
+    def test_conv_partial_missing_weight_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)),
+             ('y', TensorProto.FLOAT, (50, 4, None, 3, 3))],
+            [make_node('Conv', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, None)])
+
+    def test_average_pool_auto_pads(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4))],
+            [make_node('AveragePool', ['x'], 'z', auto_pad='SAME_UPPER', kernel_shape=[4, 3, 2], strides=[2, 2, 1])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 4, 3, 4))])
+
+    def test_relu(self) -> None:
+        self._identity_prop('Relu')
+
+    def test_identity(self) -> None:
+        self._identity_prop('Identity')
+
+    def test_identity_sequence(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 5, 4))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('Identity', ['in_sequence'], ['output_sequence'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 4)),  # type: ignore
+             make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, None, 4))])  # type: ignore
+
+    def test_identity_optional(self) -> None:
+        graph = self._make_graph(
+            [('in_tensor', TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('Optional', ['in_tensor'], ['in_optional']),
+             make_node('Identity', ['in_optional'], ['output_optional'])],
+            [])
+        tensor_type_proto = helper.make_tensor_type_proto(TensorProto.FLOAT, (2, 3, 4))
+        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
+        self._assert_inferred(
+            graph,
+            [helper.make_value_info('in_optional', optional_type_proto),  # type: ignore
+             helper.make_value_info('output_optional', optional_type_proto)])  # type: ignore
+
+    def test_identity_optional_sequence(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 5, 4))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('Optional', ['in_sequence'], ['in_optional']),
+             make_node('Identity', ['in_optional'], ['output_optional'])],
+            [])
+        tensor_type_proto = helper.make_tensor_type_proto(TensorProto.FLOAT, (2, None, 4))
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
+        self._assert_inferred(
+            graph,
+            [helper.make_value_info('in_sequence', sequence_type_proto),  # type: ignore
+             helper.make_value_info('in_optional', optional_type_proto),  # type: ignore
+             helper.make_value_info('output_optional', optional_type_proto)])  # type: ignore
+
+    def test_add(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y', TensorProto.FLOAT, (30, 4, 5))],
+            [make_node('Add', ['x', 'y'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 5))])
+
+    def test_pow(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y', TensorProto.FLOAT, (30, 4, 5))],
+            [make_node('Pow', ['x', 'y'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 5))])
+
+    def test_bitshift(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT32, (2, 3, 1)),
+             ('y', TensorProto.UINT32, (2, 3, 1))],
+            [make_node('BitShift', ['x', 'y'], 'z', direction="RIGHT")],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.UINT32, (2, 3, 1))])
+
+    def test_bitshift_broadcast_to_first(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT32, (16, 4, 1)),
+             ('y', TensorProto.UINT32, (1,))],
+            [make_node('BitShift', ['x', 'y'], 'z', direction="RIGHT")],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.UINT32, (16, 4, 1))])
+
+    def test_bitshift_broadcast_to_second(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT32, (1,)),
+             ('y', TensorProto.UINT32, (2, 3, 1))],
+            [make_node('BitShift', ['x', 'y'], 'z', direction="RIGHT")],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.UINT32, (2, 3, 1))])
+
+    def test_sum_single(self) -> None:
+        self._identity_prop('Sum')
+
+    def test_sum_multi(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y', TensorProto.FLOAT, (30, 4, 5)),
+             ('z', TensorProto.FLOAT, (30, 4, 5))],
+            [make_node('Sum', ['x', 'y', 'z'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (30, 4, 5))])
+
+    def test_sum_multi_broadcasting(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 1, 5)),
+             ('y', TensorProto.FLOAT, ("a", 4, 1)),
+             ('z', TensorProto.FLOAT, (4, "b"))],
+            [make_node('Sum', ['x', 'y', 'z'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (30, 4, 5))])
+
+    def test_sum_broadcasting_param(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ("a", 1, 5)),
+             ('y', TensorProto.FLOAT, ("a", 4, 1))],
+            [make_node('Sum', ['x', 'y'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, ("a", 4, 5))])
+
+    def test_random_normal(self) -> None:
+        graph = self._make_graph(
+            [],
+            [make_node('RandomNormal', [], ['out'], dtype=TensorProto.DOUBLE, shape=(3, 4, 5))],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.DOUBLE, (3, 4, 5))])
+
+    def test_random_normal_like(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('RandomNormalLike', ['X'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (2, 3, 4))])
+
+    def test_random_normal_like_with_dtype(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('RandomNormalLike', ['X'], ['out'], dtype=TensorProto.DOUBLE,)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.DOUBLE, (2, 3, 4))])
+
+    def test_bernoulli(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4))],
+            [make_node('Bernoulli', ['x'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4))])  # type: ignore
+
+    def test_bernoulli_with_dtype(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('Bernoulli', ['x'], ['out'], dtype=TensorProto.DOUBLE,)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.DOUBLE, (2, 3, 4))])  # type: ignore
+
+    def _logical_binary_op(self, op: Text, input_type: TensorProto.DataType) -> None:
+        graph = self._make_graph(
+            [('x', input_type, (30, 4, 5)),
+             ('y', input_type, (30, 4, 5))],
+            [make_node(op, ['x', 'y'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.BOOL, (30, 4, 5))])
+
+    def _logical_binary_op_with_broadcasting(self, op: Text, input_type: TensorProto.DataType) -> None:
+        graph = self._make_graph(
+            [('x', input_type, (1, 5)),
+             ('y', input_type, (30, 4, 5))],
+            [make_node(op, ['x', 'y'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.BOOL, (30, 4, 5))])
+
+    def test_logical_and(self) -> None:
+        self._logical_binary_op('And', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('And', TensorProto.BOOL)
+
+    def test_logical_or(self) -> None:
+        self._logical_binary_op('Or', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('Or', TensorProto.BOOL)
+
+    def test_logical_xor(self) -> None:
+        self._logical_binary_op('Xor', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('Xor', TensorProto.BOOL)
+
+    def test_greater(self) -> None:
+        self._logical_binary_op('Greater', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('Greater', TensorProto.BOOL)
+
+    def test_less(self) -> None:
+        self._logical_binary_op('Less', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('Less', TensorProto.BOOL)
+
+    def test_equal(self) -> None:
+        self._logical_binary_op('Equal', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('Equal', TensorProto.BOOL)
+
+    def test_logical_not(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.BOOL, (30, 4, 5))],
+            [make_node('Not', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.BOOL, (30, 4, 5))])
+
+    def test_less_or_equal(self) -> None:
+        self._logical_binary_op('LessOrEqual', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('LessOrEqual', TensorProto.BOOL)
+
+    def test_greater_or_equal(self) -> None:
+        self._logical_binary_op('GreaterOrEqual', TensorProto.BOOL)
+        self._logical_binary_op_with_broadcasting('GreaterOrEqual', TensorProto.BOOL)
+
+    def test_flatten(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3, 4, 5))],
+            [make_node('Flatten', ['x'], ['z'], axis=2)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (6, 20))])
+
+    def test_flatten_default_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3, 4, 5))],
+            [make_node('Flatten', ['x'], ['z'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 60))])
+
+    def test_flatten_zero_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3, 4, 5))],
+            [make_node('Flatten', ['x'], ['z'], axis=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (1, 120))])
+
+    def test_flatten_unknown_dim(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 'N', 4, 5))],
+            [make_node('Flatten', ['x'], ['z'], axis=2)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, 20))])  # type: ignore
+
+    def test_space_to_depth(self) -> None:
+        b = 10
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3, 100, 100))],
+            [make_node('SpaceToDepth', ['x'], ['z'], blocksize=b)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 300, 10, 10))])
+
+    def test_space_to_depth_unknown_dim(self) -> None:
+        b = 10
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 'N', 100, 100))],
+            [make_node('SpaceToDepth', ['x'], ['z'], blocksize=b)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, None, 10, 10))])  # type: ignore
+
+    def test_depth_to_space(self) -> None:
+        b = 10
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 300, 10, 10))],
+            [make_node('DepthToSpace', ['x'], ['z'], blocksize=b, mode='DCR')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 3, 100, 100))])
+
+    def _rnn_forward(self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (seqlen, batchsize, inpsize)),
+             ('w', TensorProto.FLOAT, (1, hiddensize, inpsize)),
+             ('r', TensorProto.FLOAT, (1, hiddensize, hiddensize))],
+            [make_node('RNN', ['x', 'w', 'r'], ['all', 'last'], hidden_size=hiddensize)],
+            [])
+        self._assert_inferred(graph, [
+            make_tensor_value_info('all', TensorProto.FLOAT, (seqlen, 1, batchsize, hiddensize)),
+            make_tensor_value_info('last', TensorProto.FLOAT, (1, batchsize, hiddensize))])
+
+    def test_rnn_forward(self) -> None:
+        self._rnn_forward(64, 32, 10, 4)
+
+    def _rnn_bidirectional(self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (seqlen, batchsize, inpsize)),
+             ('w', TensorProto.FLOAT, (2, hiddensize, inpsize)),
+             ('r', TensorProto.FLOAT, (2, hiddensize, hiddensize))],
+            [make_node('RNN', ['x', 'w', 'r'], ['all', 'last'], hidden_size=hiddensize,
+                direction="bidirectional")],
+            [])
+        self._assert_inferred(graph, [
+            make_tensor_value_info('all', TensorProto.FLOAT, (seqlen, 2, batchsize, hiddensize)),
+            make_tensor_value_info('last', TensorProto.FLOAT, (2, batchsize, hiddensize))])
+
+    def test_rnn_layout(self) -> None:
+        self._rnn_layout(64, 32, 10, 4)
+        self._rnn_layout(64, 32, 10, 4, 'bidirectional')
+
+    def _rnn_layout(self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int, direction: Text = 'forward') -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (batchsize, seqlen, inpsize)),
+             ('w', TensorProto.FLOAT, (1, hiddensize, inpsize)),
+             ('r', TensorProto.FLOAT, (1, hiddensize, hiddensize))],
+            [make_node('RNN', ['x', 'w', 'r'], ['all', 'last'], hidden_size=hiddensize,
+                layout=1, direction=direction)],
+            [])
+        if(direction == 'bidirectional'):
+            num_directions = 2
+        else:
+            num_directions = 1
+        self._assert_inferred(graph, [
+            make_tensor_value_info('all', TensorProto.FLOAT, (batchsize, seqlen, num_directions, hiddensize)),
+            make_tensor_value_info('last', TensorProto.FLOAT, (batchsize, num_directions, hiddensize))])
+
+    def test_rnn_bidirectional(self) -> None:
+        self._rnn_bidirectional(64, 32, 10, 4)
+
+    def _lstm_forward(self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (seqlen, batchsize, inpsize)),
+             ('w', TensorProto.FLOAT, (1, 4 * hiddensize, inpsize)),
+             ('r', TensorProto.FLOAT, (1, 4 * hiddensize, hiddensize))],
+            [make_node('LSTM', ['x', 'w', 'r'], ['all', 'hidden', 'last'], hidden_size=hiddensize)],
+            [])
+        self._assert_inferred(graph, [
+            make_tensor_value_info('all', TensorProto.FLOAT, (seqlen, 1, batchsize, hiddensize)),
+            make_tensor_value_info('hidden', TensorProto.FLOAT, (1, batchsize, hiddensize)),
+            make_tensor_value_info('last', TensorProto.FLOAT, (1, batchsize, hiddensize))])
+
+    def test_lstm_forward(self) -> None:
+        self._lstm_forward(64, 32, 10, 4)
+
+    def test_topk_default_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 10))],
+            [make_node('TopK', ['x', 'k'], ['y', 'z'])],
+            [],
+            initializer=[make_tensor('k', TensorProto.INT64, (1,), (2,))])
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5, 2)),
+                               make_tensor_value_info('z', TensorProto.INT64, (3, 4, 5, 2))])
+
+    def test_topk(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 10))],
+            [make_node('TopK', ['x', 'k'], ['y', 'z'], axis=2)],
+            [],
+            initializer=[make_tensor('k', TensorProto.INT64, (1,), (2,))])
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 2, 10)),
+                               make_tensor_value_info('z', TensorProto.INT64, (3, 4, 2, 10))])
+
+    def test_topk_raw_data(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 10))],
+            [make_node('TopK', ['x', 'k'], ['y', 'z'], axis=2)],
+            [],
+            initializer=[make_tensor('k', TensorProto.INT64, (1,),
+                                      vals=np.array([3], dtype='<i8').tobytes(), raw=True)])  # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 3, 10)),
+                               make_tensor_value_info('z', TensorProto.INT64, (3, 4, 3, 10))])
+
+    def test_topk_missing_k_value_output_rank_check(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 10)),
+            ('k', TensorProto.INT64, (1,))],
+            [make_node('TopK', ['x', 'k'], ['y', 'z'], axis=2)],
+            [])
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('y', TensorProto.FLOAT, (None, None, None, None)),  # type: ignore
+                               make_tensor_value_info('z', TensorProto.INT64, (None, None, None, None))])  # type: ignore
+
+    def test_gemm(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (7, 5)),
+             ('y', TensorProto.FLOAT, (5, 11)),
+             ('z', TensorProto.FLOAT, None)],
+            [make_node('Gemm', ['x', 'y', 'z'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))])
+
+    def test_gemm_transA(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 7)),
+             ('y', TensorProto.FLOAT, (5, 11)),
+             ('z', TensorProto.FLOAT, None)],
+            [make_node('Gemm', ['x', 'y', 'z'], ['out'], transA=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))])
+
+    def test_gemm_transB(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (7, 5)),
+             ('y', TensorProto.FLOAT, (11, 5)),
+             ('z', TensorProto.FLOAT, None)],
+            [make_node('Gemm', ['x', 'y', 'z'], ['out'], transB=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))])
+
+    def test_gemm_transA_and_transB(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 7)),
+             ('y', TensorProto.FLOAT, (11, 5)),
+             ('z', TensorProto.FLOAT, None)],
+            [make_node('Gemm', ['x', 'y', 'z'], ['out'], transA=1, transB=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))])
+
+    def test_gemm_no_bias(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (13, 7)),
+             ('y', TensorProto.FLOAT, (7, 17))],
+            [make_node('Gemm', ['x', 'y'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (13, 17))])
+
+    def test_reduce_op_shape_2_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ReduceL1', 'x', 'y', axes=(1, 2), keepdims=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (24,))])
+
+    def test_reduce_op_shape_keep_dims(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ReduceL1', 'x', 'y', axes=(1, 2), keepdims=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (24, 1, 1))])
+
+    def test_reduce_op_shape_default_value(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ReduceL1', 'x', 'y')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 1, 1))])
+
+    def test_reduce_op_shape_no_axes_do_not_keep_dims(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ReduceL1', 'x', 'y', keepdims=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, tuple())])
+
+    def test_reduce_op_shape_negative_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ReduceL1', 'x', 'y', axes=(-1, -2))],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (24, 1, 1))])
+
+    def test_argmax_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ArgMax', 'x', 'y', axis=1, keepdims=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (24, 1, 11))])
+
+    def test_argmax_shape_keepdims(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ArgMax', 'x', 'y', axis=0, keepdims=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (4, 11))])
+
+    def test_argmax_shape_default_value(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ArgMax', 'x', 'y')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1, 4, 11))])
+
+    def test_argmax_shape_negative_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (24, 4, 11))],
+            [make_node('ArgMax', 'x', 'y', axis=-2)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (24, 1, 11))])
+
+    def test_dropout(self) -> None:
+        graph = self._make_graph(
+            [('data', TensorProto.FLOAT, (3, 4, 5,)),
+             ('ratio', TensorProto.FLOAT, ())],
+            [make_node('Dropout', ['data', 'ratio'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5,))])
+
+    def test_LRN(self) -> None:
+        self._identity_prop('LRN', alpha=0.5, beta=0.5, size=1)
+
+    def test_batch_norm(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)),
+             ('scale', TensorProto.FLOAT, (4,)),
+             ('b', TensorProto.FLOAT, (4,)),
+             ('mean', TensorProto.FLOAT, (4,)),
+             ('var', TensorProto.FLOAT, (4,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'mean', 'var'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))])
+
+    def test_batch_norm_rank1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (128,)),   # 1-dimensional permitted
+             ('scale', TensorProto.FLOAT, (1,)),
+             ('b', TensorProto.FLOAT, (1,)),
+             ('mean', TensorProto.FLOAT, (1,)),
+             ('var', TensorProto.FLOAT, (1,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'mean', 'var'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (128,))])
+
+    def test_batch_norm_invalid(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (128,)),
+             ('scale', TensorProto.FLOAT, (1, 2)),   # invalid rank
+             ('b', TensorProto.FLOAT, (1,)),
+             ('mean', TensorProto.FLOAT, (1,)),
+             ('var', TensorProto.FLOAT, (1,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'mean', 'var'], ['out'])],
+            [])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def test_split_negative_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4))],
+            [make_node('Split', ['x'], ['y', 'z'], axis=-1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2)),
+                                      make_tensor_value_info('z', TensorProto.FLOAT, (2, 2))])
+
+    def test_split_with_split_attribute(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 4)),
+             ('split', TensorProto.INT64, (2,))],
+            [make_node('Split', ['x', 'split'], ['y', 'z'], axis=1)],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT64, (2,), (3, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3)),
+                                      make_tensor_value_info('z', TensorProto.FLOAT, (2, 1))])
+
+    def test_split_with_split_attribute_unknown_split_dim(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 'a', 'b')),
+             ('split', TensorProto.INT64, (2,))],
+            [make_node('Split', ['x', 'split'], ['y', 'z'], axis=1)],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT64, (2,), (3, 1))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, None, 'b')),  # type: ignore
+                                      make_tensor_value_info('z', TensorProto.FLOAT, (2, None, 'b'))])  # type: ignore
+
+    def test_split_from_GLU(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 6, 7))],
+            [make_node('Split', ['x'], ['y', 'z'], axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (5, 3, 7)),
+                                      make_tensor_value_info('z', TensorProto.FLOAT, (5, 3, 7))])
+
+    def test_GLU_partial(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 6, 7))],
+            [make_node('Split', ['x'], ['y', 'z'], axis=1),
+             make_node('Sigmoid', ['z'], ['a'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (5, 3, 7)),
+                                      make_tensor_value_info('z', TensorProto.FLOAT, (5, 3, 7)),
+                                      make_tensor_value_info('a', TensorProto.FLOAT, (5, 3, 7))])
+
+    def test_GLU(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 6, 7))],
+            [make_node('Split', ['x'], ['y', 'z'], axis=1),
+             make_node('Sigmoid', ['z'], ['a']),
+             make_node('Mul', ['y', 'a'], ['b'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (5, 3, 7)),
+                                      make_tensor_value_info('z', TensorProto.FLOAT, (5, 3, 7)),
+                                      make_tensor_value_info('a', TensorProto.FLOAT, (5, 3, 7)),
+                                      make_tensor_value_info('b', TensorProto.FLOAT, (5, 3, 7))])
+
+    def test_softmax_2d(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5))],
+            [make_node('Softmax', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5))])
+
+    def test_softmax_3d(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6))],
+            [make_node('Softmax', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))])
+
+    def test_hardmax_2d(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5))],
+            [make_node('Hardmax', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5))])
+
+    def test_hardmax_3d(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6))],
+            [make_node('Hardmax', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))])
+
+    def test_logsoftmax_2d(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5))],
+            [make_node('LogSoftmax', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5))])
+
+    def test_logsoftmax_3d(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6))],
+            [make_node('LogSoftmax', ['x'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))])
+
+    def test_logsoftmax_3d_negative_axis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6))],
+            [make_node('LogSoftmax', ['x'], 'z', axis=-1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))])
+
+    def test_maxpool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))])
+
+    def test_maxpool_with_indices(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y", "Z"], kernel_shape=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3)),
+                                      make_tensor_value_info("Z", TensorProto.INT64, (5, 3, 3, 3))])
+
+    def test_maxpool_3D(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))])
+
+    def test_maxpool_with_padding(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))])
+
+    def test_maxpool_with_padding_and_stride(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2], strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))])
+
+    def test_maxpool_with_floor_mode(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (32, 288, 35, 35))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], ceil_mode=False)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (32, 288, 17, 17))])
+
+    def test_maxpool_with_ceil_mode(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (32, 288, 35, 35))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], ceil_mode=True)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (32, 288, 18, 18))])
+
+    def test_maxpool_ceil(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (1, 1, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 2, 2))])
+
+    def test_maxpool_with_dilations(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], dilations=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))])
+
+    def test_maxpool_with_same_upper_padding_and_stride(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_UPPER", kernel_shape=[2, 2], strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))])
+
+    def test_maxpool_with_same_upper_padding_and_stride_and_dilation(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_UPPER", kernel_shape=[2, 2], strides=[2, 2], dilations=[2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))])
+
+    def test_maxpool_with_same_upper_padding_and_stride_one(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_UPPER", kernel_shape=[2, 2], strides=[1, 1])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 4, 4))])
+
+    def test_maxpool_with_same_lower_padding_and_stride(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 9, 9))],
+            [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_LOWER", kernel_shape=[2, 2], strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 5, 5))])
+
+    def test_maxpool_with_same_lower_padding_and_stride_and_dilation(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 9, 9))],
+            [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_LOWER", kernel_shape=[2, 2], strides=[2, 2], dilations=[2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 5, 5))])
+
+    def test_maxpool_with_same_lower_padding_and_big_stride(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_LOWER", kernel_shape=[2, 2], strides=[4, 4])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))])
+
+    def test_averagepool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))])
+
+    def test_averagepool_3D(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))],
+            [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))])
+
+    def test_averagepool_with_padding(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))])
+
+    def test_averagepool_with_padding_and_stride(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2], strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))])
+
+    def test_averagepool_ceil(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (1, 1, 4, 4))],
+            [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 2, 2))])
+
+    def test_lppool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))])
+
+    def test_lppool_3D(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))],
+            [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))])
+
+    def test_lppool_with_padding(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))])
+
+    def test_lppool_with_padding_and_stride(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2], strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))])
+
+    def test_roipool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4)),
+            ("rois", TensorProto.INT64, (2, 5))],
+            [make_node("MaxRoiPool", ["X", "rois"], ["Y"], pooled_shape=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3, 2, 2))])
+
+    def test_lp_norm(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7))],
+            [make_node('LpNormalization', ['x'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))])
+
+    def test_instance_norm(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)),
+             ('scale', TensorProto.FLOAT, (4,)),
+             ('b', TensorProto.FLOAT, (4,))],
+            [make_node('InstanceNormalization', ['x', 'scale', 'b'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))])
+
+    def test_global_maxpool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("GlobalMaxPool", ["X"], ["Y"])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))])
+
+    def test_global_averagepool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("GlobalAveragePool", ["X"], ["Y"])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))])
+
+    def test_global_lppool(self) -> None:
+        graph = self._make_graph(
+            [("X", TensorProto.FLOAT, (5, 3, 4, 4))],
+            [make_node("GlobalLpPool", ["X"], ["Y"])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))])
+
+    def test_conv_transpose(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 33, 33))])
+
+    def test_conv_transpose_with_pads(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 30, 30))])
+
+    def test_conv_transpose_with_output_shape(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], output_shape=[36, 36])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 36, 36))])
+
+    def test_conv_transpose_with_kernel_shape(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, None, None))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', kernel_shape=[3, 3], strides=[2, 2], pads=[1, 1, 2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 30, 30))])
+
+    def test_conv_transpose_with_dilations(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], dilations=[3, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 34, 34))])
+
+    def test_conv_transpose_with_group(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], group=2)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 64, 30, 30))])
+
+    def test_conv_transpose_with_group_and_output_shape(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], group=2, output_shape=[36, 36])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 64, 36, 36))])
+
+    def test_conv_transpose_with_pads_and_auto_pads(self) -> None:
+        # This test should fail because pads cannot be used simultaneously with auto_pad
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (1, 1, 2, 2)),
+             ('W', TensorProto.FLOAT, (1, 1, 3, 3)),
+             ('B', TensorProto.FLOAT, (1, ))],
+            [make_node('ConvTranspose', ['X', 'W', 'B'], 'Y', auto_pad="SAME_UPPER", strides=[1, 1], pads=[0, 1, 1, 0])],
+            [])
+        self.assertRaises(onnx.shape_inference.InferenceError, onnx.shape_inference.infer_shapes, helper.make_model(graph), strict_mode=True)
+
+    def test_conv_transpose_auto_pads(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16)),
+             ('W', TensorProto.FLOAT, (48, 32, 3, 3))],
+            [make_node('ConvTranspose', ['X', 'W'], 'Y', auto_pad="SAME_UPPER", strides=[2, 2])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 32, 32))])
+
+    def test_mvn_function_output_shape(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16))],
+            [make_node('MeanVarianceNormalization', 'X', 'Y', axes=[0, 2, 3])],
+            []
+        )
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 48, 16, 16))])
+
+    def test_scan(self) -> None:
+        batch_size = 1
+        seq_len = 'sequence'
+        input_size = 2
+        loop_state_size = 3
+
+        # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match
+        # the GraphProto, but Scan knows nothing about the additional inputs.
+        input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None),
+                             make_tensor_value_info('input', TensorProto.UNDEFINED, None)]
+        output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.UNDEFINED, None)]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['loop_state_in'], ['loop_state_out']),
+             make_node('Identity', ['input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('loop_state_orig', TensorProto.FLOAT, (batch_size, loop_state_size)),
+             ('scan_input', TensorProto.FLOAT, (batch_size, seq_len, input_size))],
+            [make_node('Scan', ['', 'loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'],
+                       num_scan_inputs=1, body=subgraph)],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (batch_size, loop_state_size)),
+             make_tensor_value_info('scan_output', TensorProto.FLOAT, (batch_size, seq_len, input_size))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 8)])
+
+    def test_scan_opset9(self) -> None:
+        seq_len = 'sequence'
+        input_size = 2
+        loop_state_size = 3
+
+        # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match
+        # the GraphProto, but Scan knows nothing about the additional inputs.
+        input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None),
+                             make_tensor_value_info('input', TensorProto.UNDEFINED, None)]
+        output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.UNDEFINED, None)]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['loop_state_in'], ['loop_state_out']),
+             make_node('Identity', ['input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)),
+             ('scan_input', TensorProto.FLOAT, (seq_len, input_size))],
+            [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'],
+                       num_scan_inputs=1, body=subgraph)],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)),
+             make_tensor_value_info('scan_output', TensorProto.FLOAT, (seq_len, input_size))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)])
+
+    def test_scan_opset9_axes(self) -> None:
+        axis_0_len = 'axis0'
+        seq_len = 'sequence'
+        input_size = 2
+        loop_state_size = 3
+
+        # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match
+        # the GraphProto, but Scan knows nothing about the additional inputs.
+        input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None),
+                             make_tensor_value_info('input', TensorProto.UNDEFINED, None)]
+        output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.UNDEFINED, None)]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['loop_state_in'], ['loop_state_out']),
+             make_node('Identity', ['input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)),
+             ('scan_input', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))],
+            [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'],
+                       num_scan_inputs=1, body=subgraph, scan_input_axes=[1])],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)),
+             make_tensor_value_info('scan_output', TensorProto.FLOAT, (seq_len, axis_0_len, input_size))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)])
+
+    def test_scan_opset9_output_axes(self) -> None:
+        axis_0_len = 'axis0'
+        seq_len = 'sequence'
+        input_size = 2
+        loop_state_size = 3
+
+        input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None),
+                             make_tensor_value_info('input', TensorProto.UNDEFINED, None)]
+        output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.UNDEFINED, None)]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['loop_state_in'], ['loop_state_out']),
+             make_node('Identity', ['input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)),
+             ('scan_input', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))],
+            [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'],
+                       num_scan_inputs=1, body=subgraph, scan_input_axes=[1], scan_output_axes=[1])],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)),
+             make_tensor_value_info('scan_output', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)])
+
+    def test_scan_opset9_negative_axes(self) -> None:
+        axis_0_len = 'axis0'
+        seq_len = 'sequence'
+        input_size = 2
+        loop_state_size = 3
+
+        input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None),
+                             make_tensor_value_info('input', TensorProto.UNDEFINED, None)]
+        output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.UNDEFINED, None)]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['loop_state_in'], ['loop_state_out']),
+             make_node('Identity', ['input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)),
+             ('scan_input', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))],
+            [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'],
+                       num_scan_inputs=1, body=subgraph, scan_input_axes=[-2], scan_output_axes=[-2])],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)),
+             make_tensor_value_info('scan_output', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)])
+
+    def test_if_ver1(self) -> None:
+
+        # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
+        # subtracts.
+        # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
+        then_subgraph = helper.make_graph(
+            [make_node('Add', ['current_value', 'add_value'], ['then_output'])],
+            "then_subgraph",
+            [],  # no inputs
+            [make_tensor_value_info('then_output', TensorProto.UNDEFINED, None)],
+        )
+
+        else_subgraph = helper.make_graph(
+            [make_node('Sub', ['current_value', 'sub_value'], ['else_output'])],
+            "else_subgraph",
+            [],  # no inputs
+            [make_tensor_value_info('else_output', TensorProto.UNDEFINED, None)],
+        )
+
+        graph = self._make_graph(
+            [('cond', TensorProto.BOOL, (1,)),
+             ('current_value', TensorProto.FLOAT, (1,)),
+             ('add_value', TensorProto.FLOAT, (1,)),
+             ('sub_value', TensorProto.FLOAT, (1,))],
+            [make_node('If', ['cond'], ['if_output'],
+                       then_branch=then_subgraph, else_branch=else_subgraph)],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('if_output', TensorProto.FLOAT, (1,))],
+            opset_imports=[make_opsetid(ONNX_DOMAIN, 10)])
+
+    def test_if(self) -> None:
+
+        # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
+        # subtracts.
+        # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
+        then_subgraph = helper.make_graph(
+            [make_node('Add', ['current_value', 'add_value'], ['then_output'])],
+            "then_subgraph",
+            [],  # no inputs
+            [make_tensor_value_info('then_output', TensorProto.UNDEFINED, None)],
+        )
+
+        else_subgraph = helper.make_graph(
+            [make_node('Sub', ['current_value', 'sub_value'], ['else_output'])],
+            "else_subgraph",
+            [],  # no inputs
+            [make_tensor_value_info('else_output', TensorProto.UNDEFINED, None)],
+        )
+
+        graph = self._make_graph(
+            [('cond', TensorProto.BOOL, (1,)),
+             ('current_value', TensorProto.FLOAT, (1,)),
+             ('add_value', TensorProto.FLOAT, (1,)),
+             ('sub_value', TensorProto.FLOAT, (1,))],
+            [make_node('If', ['cond'], ['if_output'],
+                       then_branch=then_subgraph, else_branch=else_subgraph)],
+            []
+        )
+
+        self._assert_inferred(graph, [make_tensor_value_info('if_output', TensorProto.FLOAT, (1,))])
+
+    def test_if_with_different_shapes_in_then_else_branches(self) -> None:
+
+        # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
+        # subtracts.
+        # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
+        then_subgraph = helper.make_graph(
+            [make_node('Add', ['current_value', 'add_value'], ['then_output'])],
+            "then_subgraph",
+            [],  # no inputs
+            [make_tensor_value_info('then_output', TensorProto.UNDEFINED, (1,))],
+        )
+
+        else_subgraph = helper.make_graph(
+            [make_node('Sub', ['current_value', 'sub_value'], ['else_output'])],
+            "else_subgraph",
+            [],  # no inputs
+            [make_tensor_value_info('else_output', TensorProto.UNDEFINED, (5,))],
+        )
+
+        graph = self._make_graph(
+            [('cond', TensorProto.BOOL, (1,)),
+             ('current_value', TensorProto.FLOAT, (1,)),
+             ('add_value', TensorProto.FLOAT, (1,)),
+             ('sub_value', TensorProto.FLOAT, (5,))],
+            [make_node('If', ['cond'], ['if_output'],
+                       then_branch=then_subgraph, else_branch=else_subgraph)],
+            []
+        )
+
+        self._assert_inferred(graph, [make_tensor_value_info('if_output', TensorProto.FLOAT, (None,))])  # type: ignore
+
+    def test_if_with_different_optional_shapes_in_then_else_branches(self) -> None:
+        # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
+        # subtracts.
+        # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
+        then_tensor_proto = helper.make_tensor_type_proto(elem_type=TensorProto.UNDEFINED, shape=[1, ])
+        then_optional_type_proto = helper.make_optional_type_proto(then_tensor_proto)
+        then_optional_vi = helper.make_value_info('then_optional_output', then_optional_type_proto)
+        then_subgraph = helper.make_graph(
+            [make_node('Optional', ['then_tensor_value'], ['then_optional_output'])],
+            "then_subgraph",
+            [],  # no inputs
+            [then_optional_vi],
+        )
+
+        else_tensor_proto = helper.make_tensor_type_proto(elem_type=TensorProto.UNDEFINED, shape=[5, ])
+        else_optional_type_proto = helper.make_optional_type_proto(else_tensor_proto)
+        else_optional_vi = helper.make_value_info('else_optional_output', else_optional_type_proto)
+        else_subgraph = helper.make_graph(
+            [make_node('Optional', ['else_tensor_value'], ['else_optional_output'])],
+            "else_subgraph",
+            [],  # no inputs
+            [else_optional_vi],
+        )
+
+        graph = self._make_graph(
+            [('cond', TensorProto.BOOL, (1,)),
+             ('then_tensor_value', TensorProto.FLOAT, (1,)),
+             ('else_tensor_value', TensorProto.FLOAT, (5,))],
+            [make_node('If', ['cond'], ['if_output'],
+                       then_branch=then_subgraph, else_branch=else_subgraph)],
+            []
+        )
+
+        output_tensor_proto = helper.make_tensor_type_proto(elem_type=TensorProto.FLOAT, shape=(None, ))
+        output_optional_type_proto = helper.make_optional_type_proto(output_tensor_proto)
+        output_optional_vi = helper.make_value_info('if_output', output_optional_type_proto)
+        self._assert_inferred(graph, [output_optional_vi])  # type: ignore
+
+    def test_maxunpool_shape_without_output_shape(self) -> None:
+        graph = self._make_graph(
+            [('xT', TensorProto.FLOAT, (1, 1, 2, 2)),
+             ('xI', TensorProto.FLOAT, (1, 1, 2, 2))],
+            [make_node('MaxUnpool', ['xT', 'xI'], 'Y', kernel_shape=[2, 2], strides=[2, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (1, 1, 4, 4))])
+
+    def test_maxunpool_shape_with_output_shape(self) -> None:
+        graph = self._make_graph(
+            [('xT', TensorProto.FLOAT, (1, 1, 2, 2)),
+             ('xI', TensorProto.FLOAT, (1, 1, 2, 2)),
+             ('output_shape', TensorProto.FLOAT, (4, ))],
+            [make_node('MaxUnpool', ['xT', 'xI', 'output_shape'], 'Y', kernel_shape=[2, 2], strides=[2, 2])],
+            [make_tensor_value_info("Y", TensorProto.FLOAT, None)])
+        self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, None)])
+
+    def test_onehot_without_axis(self) -> None:
+        graph = self._make_graph(
+            [('indices', TensorProto.INT64, (2, 2)),
+             ('depth', TensorProto.INT64, ()),
+             ('values', TensorProto.FLOAT, (2, ))],
+            [make_node('OneHot', ['indices', 'depth', 'values'], 'Y')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (2, 2, None))])  # type: ignore
+
+    def test_onehot_with_axis(self) -> None:
+        graph = self._make_graph(
+            [('indices', TensorProto.INT64, (2, 3, 5)),
+             ('depth', TensorProto.INT64, (1, )),
+             ('values', TensorProto.FLOAT, (2, ))],
+            [make_node('OneHot', ['indices', 'depth', 'values'], 'Y', axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (2, None, 3, 5))])  # type: ignore
+
+    def test_loop(self) -> None:
+        # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
+        # this breaks the subgraph inferencing as it expects the number of inputs passed from Loop to match
+        # the GraphProto, but Loop knows nothing about the additional inputs.
+        input_value_infos = [make_tensor_value_info('iter_num_in', TensorProto.INT64, (1,)),
+                             make_tensor_value_info('cond_in', TensorProto.UNDEFINED, None),
+                             make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, ())]
+        output_value_infos = [make_tensor_value_info('cond_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.FLOAT, (3,))]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['cond_in'], ['cond_out']),
+             make_node('Identity', ['loop_state_in'], ['loop_state_out']),
+             make_node('Identity', ['outer_scope_input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('max_trip_count', TensorProto.INT64, (1,)),
+             ('cond_orig', TensorProto.FLOAT, (1,)),
+             ('loop_state_orig', TensorProto.FLOAT, (2,)),
+             ('outer_scope_input', TensorProto.FLOAT, (3,))],
+            [make_node('Loop', ['max_trip_count', 'cond_orig', 'loop_state_orig'], ['loop_state_final', 'loop_output'],
+                       body=subgraph)],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, None),  # shape may change between iterations
+             make_tensor_value_info('loop_output', TensorProto.FLOAT, (None, 3))])  # type: ignore
+
+    def test_loop_no_state(self) -> None:
+        input_value_infos = [make_tensor_value_info('iter_num_in', TensorProto.INT64, (1,)),
+                             make_tensor_value_info('cond_in', TensorProto.UNDEFINED, None)]
+        output_value_infos = [make_tensor_value_info('cond_out', TensorProto.UNDEFINED, None),
+                              make_tensor_value_info('output', TensorProto.FLOAT, (3,))]
+
+        subgraph = helper.make_graph(
+            [make_node('Identity', ['cond_in'], ['cond_out']),
+             make_node('Identity', ['outer_scope_input'], ['output'])],
+            "subgraph",
+            input_value_infos,
+            output_value_infos
+        )
+
+        graph = self._make_graph(
+            [('max_trip_count', TensorProto.INT64, (1,)),
+             ('cond_orig', TensorProto.FLOAT, (1,)),
+             ('outer_scope_input', TensorProto.FLOAT, (3,))],
+            [make_node('Loop', ['max_trip_count', 'cond_orig'], ['loop_output'],
+                       body=subgraph)],
+            []
+        )
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('loop_output', TensorProto.FLOAT, (None, 3))])  # type: ignore
+
+    def test_constantofshape_with_input_shape(self) -> None:
+        graph = self._make_graph([],
+            [make_node("Constant", [], ['shape'],
+                       value=make_tensor('shape', TensorProto.INT64, (3,), (3, 4, 5))),
+             make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.INT32, (1, ), (2, )))],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_value_info('shape', TensorProto.INT64, (3,)),
+             make_tensor_value_info('y', TensorProto.INT32, (3, 4, 5))])  # type: ignore
+
+    def test_constantofshape_without_input_shape(self) -> None:
+        graph = self._make_graph([('shape', TensorProto.INT64, (3, ))],
+            [make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.UINT8, (1, ), (2, )))],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_value_info('y', TensorProto.UINT8, (None, None, None))])  # type: ignore
+
+    def test_constantofshape_with_symbolic_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5))],
+            [make_node("Shape", ['x'], ['shape']),
+             make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.INT32, (1, ), (2, )))],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_value_info('shape', TensorProto.INT64, (3,)),
+             make_tensor_value_info('y', TensorProto.INT32, (3, 4, 5))], data_prop=True)  # type: ignore
+
+    def test_constantofshape_without_input_shape_scalar(self) -> None:
+        graph = self._make_graph([('shape', TensorProto.INT64, (0, ))],
+            [make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.UINT8, (1, ), (2, )))],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_value_info('y', TensorProto.UINT8, ())])  # type: ignore
+
+    def test_constantofshape_with_shape_zero(self) -> None:
+        graph = self._make_graph([],
+            [make_node("Constant", [], ['shape'],
+                       value=make_tensor('shape', TensorProto.INT64, (1,), (0,))),
+             make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.INT32, (1, ), (2, )))],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_value_info('shape', TensorProto.INT64, (1,)),
+             make_tensor_value_info('y', TensorProto.INT32, (0,))])  # type: ignore
+
+    def test_convinteger(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (3, 4, 5, 6, 7)),
+             ('y', TensorProto.UINT8, (5, 4, 2, 4, 3))],
+            [make_node('ConvInteger', ['x', 'y'], 'z', pads=[0, 1, 1, 0, 0, 1], dilations=[1, 2, 2], strides=[1, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (3, 5, 4, 1, 3))])
+
+    def test_convinetger_dilations(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 8, 8, 8)),
+             ('y', TensorProto.INT8, (50, 4, 3, 3, 3)),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('ConvInteger', ['x', 'y', 'x_zero_point', 'y_zero_point'], 'z', dilations=[1, 2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, 6, 4, 2))])
+
+    def test_convinteger_strides(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT8, (30, 4, 8, 8, 8)),
+             ('y', TensorProto.INT8, (50, 4, 3, 3, 3)),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('ConvInteger', ['x', 'y', 'x_zero_point', 'y_zero_point'], 'z', strides=[1, 2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, 6, 3, 2))])
+
+    def test_convineteger_pads(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)),
+             ('y', TensorProto.INT8, (50, 4, 3, 3, 3))],
+            [make_node('ConvInteger', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, 6, 6, 6))])
+
+    def test_convineteger_group(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT8, (30, 4, 8, 8, 8)),
+             ('y', TensorProto.INT8, (4, 1, 8, 8, 8))],
+            [make_node('ConvInteger', ['x', 'y'], 'z', group=4)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 4, 1, 1, 1))])
+
+    def test_convineteger_partial_missing_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, None, 6, 4)),
+             ('y', TensorProto.UINT8, (50, 4, 3, 3, 3)),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('ConvInteger', ['x', 'y', 'x_zero_point', 'y_zero_point'], 'z', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, None, 6, 6))])  # type: ignore
+
+    def test_convineteger_partial_missing_weight_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)),
+             ('y', TensorProto.UINT8, (50, 4, None, 3, 3))],
+            [make_node('ConvInteger', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, None)])
+
+    def test_qlinearconv(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (3, 4, 5, 6, 7)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('w', TensorProto.UINT8, (5, 4, 2, 4, 3)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.UINT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[0, 1, 1, 0, 0, 1], dilations=[1, 2, 2], strides=[1, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (3, 5, 4, 1, 3))])
+
+    def test_qlinearconv_dilations(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 8, 8, 8)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('w', TensorProto.UINT8, (50, 4, 3, 3, 3)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.UINT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', dilations=[1, 2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 50, 6, 4, 2))])
+
+    def test_qlinearconv_strides(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT8, (30, 4, 8, 8, 8)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.INT8, ()),
+             ('w', TensorProto.INT8, (50, 4, 3, 3, 3)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.INT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.INT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', strides=[1, 2, 3])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT8, (30, 50, 6, 3, 2))])
+
+    def test_qlinearconv_pads(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('w', TensorProto.INT8, (50, 4, 3, 3, 3)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.INT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 50, 6, 6, 6))])
+
+    def test_qlinearconv_group(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT8, (30, 4, 8, 8, 8)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.INT8, ()),
+             ('w', TensorProto.INT8, (4, 1, 8, 8, 8)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.INT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.INT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', group=4)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT8, (30, 4, 1, 1, 1))])
+
+    def test_qlinearconv_partial_missing_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, None, 6, 4)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('w', TensorProto.UINT8, (50, 4, 3, 3, 3)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.UINT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 50, None, 6, 6))])  # type: ignore
+
+    def test_qlinearconv_partial_missing_weight_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.UINT8, ()),
+             ('w', TensorProto.UINT8, (50, 4, None, 3, 3)),
+             ('w_scale', TensorProto.FLOAT, ()),
+             ('w_zero_point', TensorProto.UINT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[1, 1, 2, 0, 1, 2])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, None)])
+
+    def _make_qlinearmatmul_test(self, shape1: Sequence[int], shape2: Sequence[int]) -> None:
+        expected_out_shape = np.matmul(np.arange(np.product(shape1)).reshape(shape1),
+                                       np.arange(np.product(shape2)).reshape(shape2)).shape
+        graph = self._make_graph(
+            [('a', TensorProto.UINT8, shape1),
+             ('a_scale', TensorProto.FLOAT, ()),
+             ('a_zero_point', TensorProto.UINT8, ()),
+             ('b', TensorProto.UINT8, shape2),
+             ('b_scale', TensorProto.FLOAT, ()),
+             ('b_zero_point', TensorProto.UINT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearMatMul', ['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, expected_out_shape)])
+
+    def test_qlinearmatmul(self) -> None:
+        self._make_qlinearmatmul_test((3,), (3,))
+        self._make_qlinearmatmul_test((4, 2), (2, 4))
+        self._make_qlinearmatmul_test((2,), (2, 3))
+        self._make_qlinearmatmul_test((4, 2), (2,))
+        self._make_qlinearmatmul_test((5, 1, 4, 2), (1, 3, 2, 3))
+        self._make_qlinearmatmul_test((4, 2), (3, 2, 3))
+
+    def _make_qlinearmatmul_test_allow_unknown(self, shape1: Any, shape2: Any, expected_out_shape: Any) -> None:
+        graph = self._make_graph(
+            [('a', TensorProto.UINT8, shape1),
+             ('a_scale', TensorProto.FLOAT, ()),
+             ('a_zero_point', TensorProto.UINT8, ()),
+             ('b', TensorProto.UINT8, shape2),
+             ('b_scale', TensorProto.FLOAT, ()),
+             ('b_zero_point', TensorProto.UINT8, ()),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QLinearMatMul', ['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, expected_out_shape)])
+
+    def test_qlinearmatmul_allow_unknown(self) -> None:
+        self._make_qlinearmatmul_test_allow_unknown((None,), (None,), ())
+        self._make_qlinearmatmul_test_allow_unknown((3,), (None,), ())
+        self._make_qlinearmatmul_test_allow_unknown((2,), (2, "a"), ("a",))
+        self._make_qlinearmatmul_test_allow_unknown((4, 2), (2, "a"), (4, "a"))
+        self._make_qlinearmatmul_test_allow_unknown((4, None), (2, "a"), (4, "a"))
+        self._make_qlinearmatmul_test_allow_unknown((4, None), (None, "a"), (4, "a"))
+        self._make_qlinearmatmul_test_allow_unknown((1, 4, 2), ("a", 2, 5), ("a", 4, 5))
+        self._make_qlinearmatmul_test_allow_unknown((1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5))
+        self._make_qlinearmatmul_test_allow_unknown(None, ("a", 2, 5), None)
+        self._make_qlinearmatmul_test_allow_unknown(None, None, None)
+
+    def _make_matmulinteger_test(self, shape1: Sequence[int], shape2: Sequence[int]) -> None:
+        expected_out_shape = np.matmul(np.arange(np.product(shape1)).reshape(shape1),
+                                       np.arange(np.product(shape2)).reshape(shape2)).shape
+        graph = self._make_graph(
+            [('A', TensorProto.UINT8, shape1),
+             ('B', TensorProto.UINT8, shape2),
+             ('a_zero_point', TensorProto.UINT8, ()),
+             ('b_zero_point', TensorProto.UINT8, ())],
+            [make_node('MatMulInteger', ['A', 'B', 'a_zero_point', 'b_zero_point'], ['Y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.INT32, expected_out_shape)])
+
+    def test_matmulinteger(self) -> None:
+        self._make_matmulinteger_test((2,), (2,))
+        self._make_matmulinteger_test((1, 2), (2, 3))
+        self._make_matmulinteger_test((2,), (2, 3))
+        self._make_matmulinteger_test((4, 2), (2,))
+        self._make_matmulinteger_test((5, 1, 4, 2), (1, 3, 2, 3))
+        self._make_matmulinteger_test((4, 2), (3, 2, 3))
+
+    def test_quantizelinear(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y_scale', TensorProto.FLOAT, ()),
+             ('y_zero_point', TensorProto.UINT8, ())],
+            [make_node('QuantizeLinear', ['x', 'y_scale', 'y_zero_point'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 4, 5))])
+
+    def test_quantizelinear_default_zp(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y_scale', TensorProto.FLOAT, ())],
+            [make_node('QuantizeLinear', ['x', 'y_scale'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 4, 5))])
+
+    def test_quantizelinear_optional_input(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5)),
+             ('y_scale', TensorProto.FLOAT, ())],
+            [make_node('QuantizeLinear', ['x', 'y_scale', ''], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 4, 5))])
+
+    def test_dequantizelinear(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.UINT8, (30, 4, 5)),
+             ('x_scale', TensorProto.FLOAT, ()),
+             ('x_zero_point', TensorProto.UINT8, ())],
+            [make_node('DequantizeLinear', ['x', 'x_scale', 'x_zero_point'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (30, 4, 5))])
+
+    def test_dynamicquantizelinear(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (30, 4, 5))],
+            [make_node('DynamicQuantizeLinear', ['x'], ['y', 'y_scale', 'y_zero_point'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 4, 5)),
+                                      make_tensor_value_info('y_scale', TensorProto.FLOAT, ()),
+                                      make_tensor_value_info('y_zero_point', TensorProto.UINT8, ())])
+
+    def test_reversesequence(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('sequence_lens', TensorProto.INT64, (5,))],
+            [make_node('ReverseSequence', ['x', 'sequence_lens'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 5, 6))])
+
+    def test_unique_without_axis(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (2, 4, 2))],
+            [make_node('Unique', ['X'], ['Y', 'indices', 'inverse_indices', 'counts'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (None,)),  # type: ignore
+                                      make_tensor_value_info('indices', TensorProto.INT64, (None,)),  # type: ignore
+                                      make_tensor_value_info('inverse_indices', TensorProto.INT64, (None,)),  # type: ignore
+                                      make_tensor_value_info('counts', TensorProto.INT64, (None,))])  # type: ignore
+
+    def test_unique_with_axis(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (2, 4, 2))],
+            [make_node('Unique', ['X'], ['Y', 'indices', 'inverse_indices', 'counts'], axis=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (2, None, 2)),  # type: ignore
+                                      make_tensor_value_info('indices', TensorProto.INT64, (None,)),  # type: ignore
+                                      make_tensor_value_info('inverse_indices', TensorProto.INT64, (None,)),  # type: ignore
+                                      make_tensor_value_info('counts', TensorProto.INT64, (None,))])  # type: ignore
+
+    def test_det(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (3, 3))],
+            [make_node('Det', ['X'], ['Y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, ())])
+
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (4, 5, 6, 7, 7))],
+            [make_node('Det', ['X'], ['Y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (4, 5, 6))])
+
+    def test_tile(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('repeats', TensorProto.INT64, (3,))],
+            [make_node('Tile', ['x', 'repeats'], ['y'])],
+            [],
+            initializer=[make_tensor('repeats', TensorProto.INT64, (3,), (1, 2, 3))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 10, 18))])
+
+    def test_tile_raw_input_data(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('repeats', TensorProto.INT64, (3,))],
+            [make_node('Tile', ['x', 'repeats'], ['y'])],
+            [],
+            initializer=[make_tensor('repeats', TensorProto.INT64, (3,),
+                                     vals=np.array([1, 2, 3], dtype='<i8').tobytes(), raw=True)])  # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 10, 18))])
+
+    def test_tile_rank_inference(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('repeats', TensorProto.INT64, (3,))],
+            [make_node('Tile', ['x', 'repeats'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None, None))])  # type: ignore
+
+    def test_linearclassifier_1D_input(self) -> None:
+        if ONNX_ML:
+            graph = self._make_graph(
+                [('x', TensorProto.FLOAT, (5,))],
+                [make_node('LinearClassifier', ['x'], ['y', 'z'], domain=ONNX_ML_DOMAIN, coefficients=[0.0008, -0.0008], intercepts=[2.0, 2.0], classlabels_ints=[1, 2])],
+                [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1,)),
+                                          make_tensor_value_info('z', TensorProto.FLOAT, (1, 2))],
+                                          opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 11)])
+
+    def test_linearclassifier_2D_input(self) -> None:
+        if ONNX_ML:
+            graph = self._make_graph(
+                [('x', TensorProto.FLOAT, (4, 5))],
+                [make_node('LinearClassifier', ['x'], ['y', 'z'], domain=ONNX_ML_DOMAIN, coefficients=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], intercepts=[2.0, 2.0, 3.0], classlabels_ints=[1, 2, 3])],
+                [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (4,)),
+                                          make_tensor_value_info('z', TensorProto.FLOAT, (4, 3))],
+                                          opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 11)])
+
+    def test_roialign_symbolic(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')),
+             ('rois', TensorProto.FLOAT, ('num_rois', 4)),
+             ('batch_indices', TensorProto.INT64, ('num_rois',))],
+            [make_node('RoiAlign', ['x', 'rois', 'batch_indices'], ['y'], output_height=10, output_width=5)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ('num_rois', 'C', 10, 5))])  # type: ignore
+
+    def test_roialign_symbolic_defaults(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')),
+             ('rois', TensorProto.FLOAT, ('num_rois', 4)),
+             ('batch_indices', TensorProto.INT64, ('num_rois',))],
+            [make_node('RoiAlign', ['x', 'rois', 'batch_indices'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ('num_rois', 'C', 1, 1))])  # type: ignore
+
+    def test_roialign_num_rois(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')),
+             ('rois', TensorProto.FLOAT, ('num_rois', 4)),
+             ('batch_indices', TensorProto.INT64, (15,))],
+            [make_node('RoiAlign', ['x', 'rois', 'batch_indices'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (15, 'C', 1, 1))])  # type: ignore
+
+    def test_label_encoder_string_int64(self) -> None:
+        if ONNX_ML:
+            string_list = ['A', 'm', 'y']
+            float_list = [94.17, 36.00]
+            int64_list = [12, 28, 86]
+            graph = self._make_graph(
+                [('x', TensorProto.STRING, (6, 1))],
+                [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN,
+                           keys_strings=string_list, values_int64s=int64_list)], [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (6, 1))],
+                                          opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)])
+
+            graph = self._make_graph(
+                [('x', TensorProto.INT64, (2, 3))],
+                [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN,
+                           keys_int64s=int64_list, values_strings=string_list)], [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, (2, 3))],
+                                  opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)])
+
+            graph = self._make_graph(
+                [('x', TensorProto.FLOAT, (2,))],
+                [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN,
+                           keys_floats=float_list, values_int64s=int64_list)], [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (2,))],
+                                  opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)])
+
+            graph = self._make_graph(
+                [('x', TensorProto.INT64, (8,))],
+                [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN,
+                           keys_int64s=int64_list, values_floats=float_list)], [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (8,))],
+                                  opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)])
+
+            graph = self._make_graph(
+                [('x', TensorProto.FLOAT, ())],
+                [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN,
+                           keys_floats=float_list, values_strings=string_list)], [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, ())],
+                                  opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)])
+
+            graph = self._make_graph(
+                [('x', TensorProto.STRING, (1, 2))],
+                [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN,
+                           keys_strings=string_list, values_floats=float_list)], [])
+            self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 2))],
+                                  opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)])
+
+    def make_sparse(self,
+                    shape: Sequence[int],
+                    values: Sequence[int],
+                    indices_shape: Sequence[int],
+                    indices: Sequence[int]
+                    ) -> SparseTensorProto:
+        sparse = SparseTensorProto()
+        sparse.dims.extend(shape)
+        nnz = len(values)
+        sparse.values.CopyFrom(helper.make_tensor('spval', TensorProto.INT64, (nnz,), values))
+        sparse.indices.CopyFrom(helper.make_tensor('spind', TensorProto.INT64, indices_shape, indices))
+        return sparse
+
+    def test_constant_sparse(self) -> None:
+        y_shape = [100]
+        y_value = self.make_sparse(y_shape, [13, 17, 19], [3], [9, 27, 81])
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], sparse_value=y_value)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, y_shape)])  # type: ignore
+
+    def test_constant_value_int(self) -> None:
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], value_int=42)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, [])])
+
+    def test_constant_value_ints(self) -> None:
+        value_ints = [1, 2, 3]
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], value_ints=value_ints)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, [len(value_ints)])])
+
+    def test_constant_value_float(self) -> None:
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], value_float=1.42)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, [])])
+
+    def test_constant_value_floats(self) -> None:
+        value_floats = [1.0, 1.1, 1.2]
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], value_floats=value_floats)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, [len(value_floats)])])
+
+    def test_constant_value_string(self) -> None:
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], value_string="String value")],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, [])])
+
+    def test_constant_value_strings(self) -> None:
+        value_strings = ["o", "n", "n", "x"]
+        graph = self._make_graph(
+            [],
+            [make_node('Constant', [], ['y'], value_strings=value_strings)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, [len(value_strings)])])
+
+    def test_range(self) -> None:
+        graph = self._make_graph(
+            [('start', TensorProto.FLOAT, ()),
+             ('limit', TensorProto.FLOAT, ()),
+             ('delta', TensorProto.FLOAT, ())],
+            [make_node('Range', ['start', 'limit', 'delta'], ['output'])],
+            [],
+            initializer=[make_tensor('start', TensorProto.FLOAT, (), (1,)),
+                         make_tensor('limit', TensorProto.FLOAT, (), (5,)),
+                         make_tensor('delta', TensorProto.FLOAT, (), (2,))])
+        self._assert_inferred(graph, [make_tensor_value_info('output', TensorProto.FLOAT, (2,))])
+
+    def test_range_rank_inference(self) -> None:
+        graph = self._make_graph(
+            [('start', TensorProto.INT32, ()),
+             ('limit', TensorProto.INT32, ()),
+             ('delta', TensorProto.INT32, ())],
+            [make_node('Range', ['start', 'limit', 'delta'], ['output'])],
+            [],
+            initializer=[make_tensor('start', TensorProto.INT32, (), (1,)),
+                         make_tensor('limit', TensorProto.INT32, (), (5,))])  # Missing 'delta' initializer
+        self._assert_inferred(graph, [make_tensor_value_info('output', TensorProto.INT32, (None,))])  # type: ignore
+
+    def test_gathernd(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (4, 5, 6)),
+             ('indices', TensorProto.INT64, (2,))],
+            [make_node('GatherND', ['x', 'indices'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (6,))])
+
+    def test_gathernd_batchdim_1(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 2, 2)),
+             ('indices', TensorProto.INT64, (2, 1))],
+            [make_node('GatherND', ['x', 'indices'], ['y'], batch_dims=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))])
+
+    def test_cumsum(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3)),
+             ('axis', TensorProto.FLOAT, (1,))],
+            [make_node('CumSum', ['x', 'axis'], 'z')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 3))])
+
+    def test_nonmaxsuppression(self) -> None:
+        graph = self._make_graph(
+            [('boxes', TensorProto.FLOAT, (1, 3, 4)),
+             ('scores', TensorProto.FLOAT, (1, 5, 3))],
+            [make_node('NonMaxSuppression', ['boxes', 'scores'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (None, 3))])  # type: ignore
+
+    def test_sequence_empty(self) -> None:
+        graph = self._make_graph(
+            [],
+            [make_node('SequenceEmpty', [], ['output'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_sequence_value_info('output', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_sequence_construct(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['output_sequence'])],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))])  # type: ignore
+
+    def test_sequence_construct_one_input(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('SequenceConstruct', ['input1'], ['output_sequence'])],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))])  # type: ignore
+
+    def test_sequence_construct_diff_rank(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3)),
+             ('input3', TensorProto.FLOAT, (2, 3))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['output_sequence'])],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_sequence_construct_diff_dim_size(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 5)),
+             ('input3', TensorProto.FLOAT, (2, 3, 6))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['output_sequence'])],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, None))])  # type: ignore
+
+    def test_sequence_insert(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4)),
+             ('input4', TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceInsert', ['in_sequence', 'input4'], ['output_sequence'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)),
+             make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))])  # type: ignore
+
+    def test_sequence_insert_diff_rank(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4)),
+             ('input4', TensorProto.FLOAT, (2, 3))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceInsert', ['in_sequence', 'input4'], ['output_sequence'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)),
+             make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_sequence_insert_diff_shape(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 5, 4)),
+             ('input4', TensorProto.FLOAT, (2, 5, 2))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceInsert', ['in_sequence', 'input4'], ['output_sequence'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 4)),  # type: ignore
+             make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, None, None))])  # type: ignore
+
+    def test_sequence_at(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4)),
+             ('ind', TensorProto.INT64, ())],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceAt', ['in_sequence', 'ind'], ['output'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)),
+             make_tensor_value_info('output', TensorProto.FLOAT, (2, 3, 4))])  # type: ignore
+
+    def test_sequence_at_unknown_shape(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4)),
+             ('ind', TensorProto.INT64, ())],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceAt', ['in_sequence', 'ind'], ['output'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, None),
+             make_tensor_value_info('output', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_sequence_at_unknown_dim_size(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 5)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4)),
+             ('ind', TensorProto.INT64, ())],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceAt', ['in_sequence', 'ind'], ['output'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, None)),  # type: ignore
+             make_tensor_value_info('output', TensorProto.FLOAT, (2, 3, None))])  # type: ignore
+
+    def test_sequence_erase(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4)),
+             ('input2', TensorProto.FLOAT, (2, 3, 4)),
+             ('input3', TensorProto.FLOAT, (2, 3, 4)),
+             ('ind', TensorProto.INT64, ())],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceErase', ['in_sequence', 'ind'], ['output_sequence'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)),
+             make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))])  # type: ignore
+
+    def test_sequence_erase_diff_dim_size(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 5, 'x')),
+             ('ind', TensorProto.INT64, ())],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceErase', ['in_sequence', 'ind'], ['output_sequence'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')),  # type: ignore
+             make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, None, 'x'))])  # type: ignore
+
+    def test_sequence_length(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceLength', ['in_sequence'], ['len'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')),
+             make_tensor_value_info('len', TensorProto.INT64, ())])  # type: ignore
+
+    def test_split_to_sequence(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4)),
+             ('split', TensorProto.INT32, (2,))],
+            [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT32, (2,), (3, 3))])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (3, 4))])  # type: ignore
+
+    def test_split_to_sequence_scalar(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4)),
+             ('split', TensorProto.INT32, ())],
+            [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT32, (), (2, ))])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 4))])  # type: ignore
+
+    def test_split_to_sequence_keepdims(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4))],
+            [make_node('SplitToSequence', ['input'], ['output_sequence'], keepdims=1)],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (1, 4))])  # type: ignore
+
+    def test_split_to_sequence_not_keepdims(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4))],
+            [make_node('SplitToSequence', ['input'], ['output_sequence'], keepdims=0)],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (4, ))])  # type: ignore
+
+    def test_split_to_sequence_ignore_keepdims(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4)),
+             ('split', TensorProto.INT32, (2,))],
+            [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'], keepdims=0)],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT32, (2,), (3, 3))])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (3, 4))])  # type: ignore
+
+    def test_split_to_sequence_axis(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4))],
+            [make_node('SplitToSequence', ['input'], ['output_sequence'], axis=1)],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (6, 1))])  # type: ignore
+
+    def test_split_to_sequence_neg_axis(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4))],
+            [make_node('SplitToSequence', ['input'], ['output_sequence'], axis=-2)],
+            [])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (1, 4))])  # type: ignore
+
+    def test_split_to_sequence_split_sizes(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4)),
+             ('split', TensorProto.INT32, (3,))],
+            [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT32, (3,), (2, 1, 3))])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (None, 4))])  # type: ignore
+
+    def test_split_to_sequence_non_divisible(self) -> None:
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (6, 4)),
+             ('split', TensorProto.INT32, ())],
+            [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])],
+            [],
+            initializer=[make_tensor('split', TensorProto.INT32, (), (4, ))])
+        self._assert_inferred(graph,
+            [make_tensor_sequence_value_info('output_sequence', TensorProto.FLOAT, (None, 4))])  # type: ignore
+
+    def test_concat_from_sequence(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=0)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')),
+             make_tensor_value_info('out', TensorProto.FLOAT, (None, 3, 'x'))])  # type: ignore
+
+    def test_concat_from_sequence_unknown_shape(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 3)),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=0)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, None),
+             make_tensor_value_info('out', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_concat_from_sequence_unknown_dim_size(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 4, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=0)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')),  # type: ignore
+             make_tensor_value_info('out', TensorProto.FLOAT, (None, None, 'x'))])  # type: ignore
+
+    def test_concat_from_sequence_axis(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 4, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=2)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')),  # type: ignore
+             make_tensor_value_info('out', TensorProto.FLOAT, (2, None, None))])  # type: ignore
+
+    def test_concat_from_sequence_neg_axis(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 4, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=-3)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')),  # type: ignore
+             make_tensor_value_info('out', TensorProto.FLOAT, (None, None, 'x'))])  # type: ignore
+
+    def test_concat_from_sequence_new_axis(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=2, new_axis=1)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')),
+             make_tensor_value_info('out', TensorProto.FLOAT, (2, 3, None, 'x'))])  # type: ignore
+
+    def test_concat_from_sequence_neg_new_axis(self) -> None:
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input2', TensorProto.FLOAT, (2, 3, 'x')),
+             ('input3', TensorProto.FLOAT, (2, 3, 'x'))],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=-1, new_axis=1)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')),
+             make_tensor_value_info('out', TensorProto.FLOAT, (2, 3, 'x', None))])  # type: ignore
+
+    def test_adagrad(self) -> None:
+        graph = self._make_graph(
+            [('R', TensorProto.FLOAT, ()),  # scalar's shape is ()
+             ('T', TensorProto.INT64, ()),  # scalar's shape is ()
+             ('X', TensorProto.FLOAT, (1, 2)),
+             ('G', TensorProto.FLOAT, (1, 2)),
+             ('H', TensorProto.FLOAT, (1, 2))],
+            [make_node('Adagrad', ['R', 'T', 'X', 'G', 'H'], ['X_new', 'H_new'],
+                       domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)],
+            [])
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('X_new', TensorProto.FLOAT, (1, 2)),
+             make_tensor_value_info('H_new', TensorProto.FLOAT, (1, 2))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    def test_adagrad_multiple(self) -> None:
+        graph = self._make_graph(
+            [('R', TensorProto.FLOAT, ()),  # scalar's shape is ()
+             ('T', TensorProto.INT64, ()),  # scalar's shape is ()
+             ('X1', TensorProto.FLOAT, (1, 2)),
+             ('X2', TensorProto.FLOAT, (3, 4)),
+             ('G1', TensorProto.FLOAT, (1, 2)),
+             ('G2', TensorProto.FLOAT, (3, 4)),
+             ('H1', TensorProto.FLOAT, (1, 2)),
+             ('H2', TensorProto.FLOAT, (3, 4))],
+            [make_node('Adagrad', ['R', 'T', 'X1', 'X2', 'G1', 'G2', 'H1', 'H2'],
+                       ['X1_new', 'X2_new', 'H1_new', 'H2_new'],
+                       domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)],
+            [])
+
+        self._assert_inferred(graph,
+            [make_tensor_value_info('X1_new', TensorProto.FLOAT, (1, 2)),
+             make_tensor_value_info('X2_new', TensorProto.FLOAT, (3, 4)),
+             make_tensor_value_info('H1_new', TensorProto.FLOAT, (1, 2)),
+             make_tensor_value_info('H2_new', TensorProto.FLOAT, (3, 4))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    def test_momentum(self) -> None:
+        graph = self._make_graph(
+            [('R', TensorProto.FLOAT, ()),  # scalar's shape is ()
+             ('T', TensorProto.INT64, ()),  # scalar's shape is ()
+             ('X', TensorProto.FLOAT, (1, 2)),
+             ('G', TensorProto.FLOAT, (1, 2)),
+             ('V', TensorProto.FLOAT, (1, 2))],
+            [make_node('Momentum', ['R', 'T', 'X', 'G', 'V'], ['X_new', 'V_new'],
+             alpha=0.9, beta=1.0, norm_coefficient=0.02, mode='standard',
+             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('X_new', TensorProto.FLOAT, (1, 2)),
+             make_tensor_value_info('V_new', TensorProto.FLOAT, (1, 2))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    def test_momentum_multiple(self) -> None:
+        graph = self._make_graph(
+            [('R', TensorProto.FLOAT, ()),  # scalar's shape is ()
+             ('T', TensorProto.INT64, ()),  # scalar's shape is ()
+             ('X1', TensorProto.FLOAT, (1, 2)),
+             ('X2', TensorProto.FLOAT, (3, 4)),
+             ('G1', TensorProto.FLOAT, (1, 2)),
+             ('G2', TensorProto.FLOAT, (3, 4)),
+             ('V1', TensorProto.FLOAT, (1, 2)),
+             ('V2', TensorProto.FLOAT, (3, 4))],
+            [make_node('Momentum', ['R', 'T', 'X1', 'X2', 'G1', 'G2', 'V1', 'V2'],
+             ['X1_new', 'X2_new', 'V1_new', 'V2_new'],
+             alpha=0.9, beta=1.0, norm_coefficient=0.02, mode='nesterov',
+             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)],
+            [])
+
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('X1_new', TensorProto.FLOAT, (1, 2)),
+             make_tensor_value_info('X2_new', TensorProto.FLOAT, (3, 4)),
+             make_tensor_value_info('V1_new', TensorProto.FLOAT, (1, 2)),
+             make_tensor_value_info('V2_new', TensorProto.FLOAT, (3, 4))],
+            opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)])
+
+    def test_adam(self) -> None:
+        graph = self._make_graph(
+            [('R', TensorProto.FLOAT, ()),  # scalar's shape is ()
+             ('T', TensorProto.INT64, ()),  # scalar's shape is ()
+             ('X', TensorProto.FLOAT, (1, 2)),
+             ('G', TensorProto.FLOAT, (1, 2)),
+             ('V', TensorProto.FLOAT, (1, 2)),
+             ('H', TensorProto.FLOAT, (1, 2))],
+            [make_node('Adam', ['R', 'T', 'X', 'G', 'V', 'H'], ['X_new', 'V_new', 'H_new'],
+             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+             alpha=0.9, beta=1.0, norm_coefficient=0.02)],
+            [])
+
+        infos = [make_tensor_value_info('X_new', TensorProto.FLOAT, (1, 2)),
+                 make_tensor_value_info('V_new', TensorProto.FLOAT, (1, 2)),
+                 make_tensor_value_info('H_new', TensorProto.FLOAT, (1, 2))]
+
+        self._assert_inferred(
+            graph,
+            infos,
+            opset_imports=[make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 12)])
+
+    def test_adam_multiple(self) -> None:
+        graph = self._make_graph(
+            [('R', TensorProto.FLOAT, ()),  # scalar's shape is ()
+             ('T', TensorProto.INT64, ()),  # scalar's shape is ()
+             ('X1', TensorProto.FLOAT, (1, 2)),
+             ('X2', TensorProto.FLOAT, (3, 4)),
+             ('G1', TensorProto.FLOAT, (1, 2)),
+             ('G2', TensorProto.FLOAT, (3, 4)),
+             ('V1', TensorProto.FLOAT, (1, 2)),
+             ('V2', TensorProto.FLOAT, (3, 4)),
+             ('H1', TensorProto.FLOAT, (1, 2)),
+             ('H2', TensorProto.FLOAT, (3, 4))],
+            [make_node('Adam', ['R', 'T', 'X1', 'X2', 'G1', 'G2', 'V1', 'V2', 'H1', 'H2'],
+             ['X1_new', 'X2_new', 'V1_new', 'V2_new', 'H1_new', 'H2_new'],
+             domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+             alpha=0.9, beta=1.0, norm_coefficient=0.02)],
+            [])
+
+        infos = [make_tensor_value_info('X1_new', TensorProto.FLOAT, (1, 2)),
+                 make_tensor_value_info('X2_new', TensorProto.FLOAT, (3, 4)),
+                 make_tensor_value_info('V1_new', TensorProto.FLOAT, (1, 2)),
+                 make_tensor_value_info('V2_new', TensorProto.FLOAT, (3, 4)),
+                 make_tensor_value_info('H1_new', TensorProto.FLOAT, (1, 2)),
+                 make_tensor_value_info('H2_new', TensorProto.FLOAT, (3, 4))]
+
+        self._assert_inferred(
+            graph,
+            infos,
+            opset_imports=[make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 12)])
+
+    def test_pad_opset10(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, None, 2))],
+            [make_node('Pad', 'x', 'y', pads=[1, 3, 1, 1, 0, 1])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, None, 4))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)])  # type: ignore
+
+    def test_constant_pad_2d_opset10(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3, 4, 4))],
+            [make_node('Pad', 'x', 'y', pads=[0, 0, 3, 1, 0, 0, 4, 2], mode="constant", value=2.0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3, 11, 7))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)])
+
+    def test_pad(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, None, 2)),
+             ('pads', TensorProto.INT64, (6,))],
+            [make_node('Pad', ['x', 'pads'], 'y')],
+            [],
+            initializer=[make_tensor('pads', TensorProto.INT64, (6,), (1, 3, 1, 1, 0, 1,))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, None, 4))])  # type: ignore
+
+    def test_gatherelements_basic(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (6,)),
+             ('indices', TensorProto.INT64, (2,))],
+            [make_node('GatherElements', ['x', 'indices'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2,))])
+
+    def test_gatherelements_indices_missing_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (6,)),
+             ('indices', TensorProto.INT64, None)],  # type: ignore
+            [make_node('GatherElements', ['x', 'indices'], ['y'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, None)])  # type: ignore
+
+    def test_einsum_transpose(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4))],
+            [make_node('Einsum', ['x'], ['y'], equation='ij->ji')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None))])  # type: ignore
+
+    def test_einsum_dot(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1,)),
+             ('y', TensorProto.FLOAT, (1,))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='i,i->')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_einsum_scalar(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ()),
+             ('y', TensorProto.FLOAT, ())],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation=',->')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_einsum_outer_prod(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 5)),
+             ('y', TensorProto.FLOAT, (7, 9))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='ij,ab->ijab')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None, None))])  # type: ignore
+
+    def test_einsum_sum_along_dim(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4))],
+            [make_node('Einsum', ['x'], ['y'], equation='i j->i ')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, ))])  # type: ignore
+
+    def test_einsum_ellipsis(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 4))],
+            [make_node('Einsum', ['x'], ['y'], equation='... ii ->... i')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None))])  # type: ignore
+
+    def test_einsum_ellipsis_2(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 2, 2)),
+             ('y', TensorProto.FLOAT, (2, 2, 2))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='...ij,...jk->...ik')],
+            [], )
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None))])  # type: ignore
+
+    def test_einsum_ellipsis_3(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 2, 2)),
+             ('y', TensorProto.FLOAT, (2, 2, 2))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='...ij,...jk')],
+            [], )
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None))])  # type: ignore
+
+    def test_einsum_contraction(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 6, 7, 8)),
+             ('y', TensorProto.FLOAT, (8, 9, 10))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='abcd,dfg->abcfg')],
+            [], )
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None, None, None))])  # type: ignore
+
+    def test_einsum_contraction_2(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5)),
+             ('y', TensorProto.FLOAT, (3, 5))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='ijk,ik->jk')],
+            [], )
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('z', TensorProto.FLOAT, (None, None))])  # type: ignore
+
+    def test_einsum_batch_matmul(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (5, 2, 3)),
+             ('y', TensorProto.FLOAT, (5, 3, 4))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='bij , b jk-> bik')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None))])  # type: ignore
+
+    def test_einsum_left_hand_eqn(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (2, 3)),
+             ('y', TensorProto.FLOAT, (3, 4))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='ij,kl')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None, None))])  # type: ignore
+
+    def test_einsum_incorrect_num_inputs(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 3)),
+             ("y", TensorProto.FLOAT, (2, 3)),
+             ("z", TensorProto.FLOAT, (2, 3))],
+            [make_node('Einsum', ['x', 'y'], ['z'], equation='i,...j, k, l-> i')],
+            [])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def test_negative_log_likehood_shape_is_NCdd(self) -> None:
+        N, C = 3, 4
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (N, C)),
+             ('target', TensorProto.INT64, (N,))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='none')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, ))])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NC_with_weight(self) -> None:
+        N, C = 3, 4
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (N, C)),
+             ('target', TensorProto.INT64, (N,)),
+             ('weight', TensorProto.FLOAT, (C,))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='none')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, ))])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NC_reduction_mean(self) -> None:
+        N, C = 3, 4
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (N, C)),
+             ('target', TensorProto.INT64, (N,))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='mean')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NC_with_weight_reduction_mean(self) -> None:
+        N, C = 3, 4
+        graph = self._make_graph(
+            [('input', TensorProto.FLOAT, (N, C)),
+             ('target', TensorProto.INT64, (N,)),
+             ('weight', TensorProto.FLOAT, (C,))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='mean')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NCd1d2(self) -> None:
+        N, C, d1, d2 = 3, 4, 5, 6
+        graph = self._make_graph(
+            [("input", TensorProto.FLOAT, (N, C, d1, d2)),
+             ("target", TensorProto.INT64, (N, d1, d2))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='none')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, d1, d2))])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NCd1d2_with_weight(self) -> None:
+        N, C, d1, d2 = 3, 4, 5, 6
+        graph = self._make_graph(
+            [("input", TensorProto.FLOAT, (N, C, d1, d2)),
+             ("target", TensorProto.INT64, (N, d1, d2)),
+             ("weight", TensorProto.FLOAT, (C,))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='none')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, d1, d2))])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NCd1d2_reduction_sum(self) -> None:
+        N, C, d1, d2 = 3, 4, 5, 6
+        graph = self._make_graph(
+            [("input", TensorProto.FLOAT, (N, C, d1, d2)),
+             ("target", TensorProto.INT64, (N, d1, d2))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='sum')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_negative_log_likehood_shape_is_NCd1d2_with_weight_reduction_mean(self) -> None:
+        N, C, d1, d2 = 3, 4, 5, 6
+        graph = self._make_graph(
+            [("input", TensorProto.FLOAT, (N, C, d1, d2)),
+             ("target", TensorProto.INT64, (N, d1, d2)),
+             ("weight", TensorProto.FLOAT, (C,))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='mean')],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_negative_log_likehood_input_target_shape_mismatch(self) -> None:
+        N, C, d1, d2 = 3, 4, 5, 6
+        graph = self._make_graph(
+            [("input", TensorProto.FLOAT, (N, d1, d2)),
+             ("target", TensorProto.INT64, (N, d1 + 1, d2)),
+             ("weight", TensorProto.FLOAT, (C,)),
+             ("loss", TensorProto.FLOAT, ())],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='mean')],
+            [])
+        self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+
+    def test_negative_log_likehood_input_weight_shape_mismatch(self) -> None:
+        N, C, d1, d2 = 3, 4, 5, 6
+        graph = self._make_graph(
+            [("input", TensorProto.FLOAT, (N, C, d1, d2)),
+             ("target", TensorProto.INT64, (N, d1, d2)),
+             ("weight", TensorProto.FLOAT, (C + 1,)),
+             ("loss", TensorProto.FLOAT, (N, d1, d2))],
+            [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='none')],
+            [])
+        self.assertRaises(checker.ValidationError, self._inferred, graph)
+
+    def test_softmax_cross_entropy_none(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 3)),
+             ("y", TensorProto.FLOAT, (2,))],
+            [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='none')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2,))])  # type: ignore
+
+    def test_softmax_cross_entropy_mean(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 3)),
+             ("y", TensorProto.FLOAT, (2,))],
+            [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='mean')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_softmax_cross_entropy_none_NCD1D2(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 3, 5, 8)),
+             ("y", TensorProto.FLOAT, (2, 5, 8))],
+            [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='none')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 5, 8))])  # type: ignore
+
+    def test_softmax_cross_entropy_mean_NCD1D2(self) -> None:
+        graph = self._make_graph(
+            [("x", TensorProto.FLOAT, (2, 3, 4, 5)),
+             ("y", TensorProto.FLOAT, (2, 4, 5))],
+            [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='mean')],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())])  # type: ignore
+
+    def test_celu_function_output_shape(self) -> None:
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (25, 48, 16, 16))],
+            [make_node('Celu', ['X'], ['Y'], alpha=2.0)],
+            []
+        )
+        self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 48, 16, 16))])
+
+    def prepare_input_initializer_tensors(self, initializer_shape, input_shape):  # type: ignore
+        nodes = [make_node('Add', ['x', 'y'], 'z')]
+        if initializer_shape is None:
+            initializer = []  # type: ignore
+        else:
+            size = 1
+            for d in initializer_shape:
+                size = size * d
+            vals = [0.0 for i in range(size)]
+            initializer = [make_tensor("x", TensorProto.FLOAT, initializer_shape, vals),  # type: ignore
+                make_tensor("y", TensorProto.FLOAT, initializer_shape, vals)]
+        if input_shape is None:
+            inputs = []  # type: ignore
+        else:
+            inputs = [helper.make_tensor_value_info('x', TensorProto.FLOAT, input_shape),  # type: ignore
+                helper.make_tensor_value_info('y', TensorProto.FLOAT, input_shape)]
+
+        graph = helper.make_graph(nodes, "test", inputs=inputs, outputs=[], initializer=initializer, value_info=[])
+        return helper.make_model(graph)
+
+    def test_infer_with_initializer_without_input_above_ir4(self) -> None:
+        # This is for testing IR>=4: some tensors can only exist in initializer and not in input
+        # So shape_inference should make use of initializer shapes
+        initializer_shape = (8, 7)
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, None)
+        inferred_model = onnx.shape_inference.infer_shapes(original_model, strict_mode=True)
+
+        # If shape inference fails, it will throw IndexError
+        z_tenor = inferred_model.graph.value_info.pop()
+        z_shape = (z_tenor.type.tensor_type.shape.dim[0].dim_value, z_tenor.type.tensor_type.shape.dim[1].dim_value)
+        assert z_shape == initializer_shape
+
+    def test_infer_with_initializer_without_input_below_ir4(self) -> None:
+        # This is for testing IR<4: tensors must exist both in initializer and input
+        # So shape_inference should not make use of initializer shapes
+        # Use (None, None) as empty input
+        initializer_shape = (8, 7)
+        input_shape = (None, None)
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape)
+        original_model.ir_version = 3  # test ir_version < 4
+
+        inferred_model = onnx.shape_inference.infer_shapes(original_model, strict_mode=True)
+        z_tenor = inferred_model.graph.value_info.pop()
+        z_shape = (z_tenor.type.tensor_type.shape.dim[0].dim_value, z_tenor.type.tensor_type.shape.dim[1].dim_value)
+        # If the input is not updated by the initializer, the output shape will keep empty (0, 0)
+        assert z_shape == (0, 0)
+
+    def test_infer_initializer_input_mismatch(self) -> None:
+        # Catch error if initializer and input mismatch
+        initializer_shape = (8, 7)
+        input_shape = (4, 3)
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape)
+        # Inferred shape and existing shape differ in dimension 0
+        self.assertRaises(onnx.shape_inference.InferenceError, onnx.shape_inference.infer_shapes, original_model, strict_mode=True)
+
+    def test_infer_initializer_input_consistency_all_none(self) -> None:
+        initializer_shape = (8, 7)
+        input_shape = (None, None)  # accepatble
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape)
+
+        onnx.shape_inference.infer_shapes(original_model, strict_mode=True)
+
+    def test_infer_initializer_input_consistency_single_none(self) -> None:
+        initializer_shape = (8, 7)
+        input_shape = (None, 7)  # accepatble
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape)
+
+        onnx.shape_inference.infer_shapes(original_model, strict_mode=True)
+
+    def test_infer_initializer_input_consistency_differnt_rank(self) -> None:
+        initializer_shape = (8, 7, 9)
+        input_shape = (None, 7)  # accepatble
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape)
+        # Inferred shape and existing shape differ in rank: (3) vs (2)
+        self.assertRaises(onnx.shape_inference.InferenceError, onnx.shape_inference.infer_shapes, original_model, strict_mode=True)
+
+    def test_infer_initializer_input_consistency_all_none_serialized(self) -> None:
+        # Reuse test_infer_initializer_input_consistency_all_none test case and check with
+        # Serialized model
+        initializer_shape = (8, 7)
+        input_shape = (None, None)  # accepatble
+        original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape)
+
+        onnx.shape_inference.infer_shapes(original_model.SerializeToString(), strict_mode=True)
+
+    def test_trilu_upper(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5)),
+             ('k', TensorProto.INT64, ())],
+            [make_node('Trilu', ['x', 'k'], ['y'])],
+            [],
+            initializer=[make_tensor('k', TensorProto.INT64, (), (2,))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5))])  # type: ignore
+
+    def test_trilu_lower(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5)),
+             ('k', TensorProto.INT64, ())],
+            [make_node('Trilu', ['x', 'k'], ['y'], upper=0)],
+            [],
+            initializer=[make_tensor('k', TensorProto.INT64, (), (10,))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5))])  # type: ignore
+
+    def test_trilu_upper_zero(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT64, (0, 5)),
+             ('k', TensorProto.INT64, ())],
+            [make_node('Trilu', ['x', 'k'], ['y'], upper=1)],
+            [],
+            initializer=[make_tensor('k', TensorProto.INT64, (), (5,))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (0, 5))])  # type: ignore
+
+    def test_trilu_lower_one(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.INT32, (3, 1, 5))],
+            [make_node('Trilu', ['x'], ['y'], upper=0)],
+            [],)
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT32, (3, 1, 5))])  # type: ignore
+
+    def test_batch_norm_train(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)),
+             ('scale', TensorProto.FLOAT, (4,)),
+             ('b', TensorProto.FLOAT, (4,)),
+             ('input_mean', TensorProto.FLOAT, (4,)),
+             ('input_var', TensorProto.FLOAT, (4,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'],
+                       ['out', 'output_mean', 'output_var'], training_mode=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7)),  # type: ignore
+                                      make_tensor_value_info('output_mean', TensorProto.FLOAT, (4,)),  # type: ignore
+                                      make_tensor_value_info('output_var', TensorProto.FLOAT, (4,)),  # type: ignore
+                                      ])
+
+    def test_batch_norm_train_dim_param(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 'C', 5, 6, 7)),
+             ('scale', TensorProto.FLOAT, ('C',)),
+             ('b', TensorProto.FLOAT, ('C',)),
+             ('input_mean', TensorProto.FLOAT, ('C',)),
+             ('input_var', TensorProto.FLOAT, ('C',))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'],
+                       ['out', 'output_mean', 'output_var'], training_mode=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 'C', 5, 6, 7)),  # type: ignore
+                                      make_tensor_value_info('output_mean', TensorProto.FLOAT, ('C',)),  # type: ignore
+                                      make_tensor_value_info('output_var', TensorProto.FLOAT, ('C',)),  # type: ignore
+                                      ])
+
+    def test_batch_norm_train_with_diff_type(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT16, (3, 4, 5, 6, 7)),
+             ('scale', TensorProto.FLOAT16, (4,)),
+             ('b', TensorProto.FLOAT16, (4,)),
+             ('input_mean', TensorProto.FLOAT, (4,)),
+             ('input_var', TensorProto.FLOAT, (4,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'],
+                       ['out', 'output_mean', 'output_var'], training_mode=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT16, (3, 4, 5, 6, 7)),  # type: ignore
+                                      make_tensor_value_info('output_mean', TensorProto.FLOAT, (4,)),  # type: ignore
+                                      make_tensor_value_info('output_var', TensorProto.FLOAT, (4,)),  # type: ignore
+                                      ])
+
+    def test_batch_norm_test(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)),
+             ('scale', TensorProto.FLOAT, (4,)),
+             ('b', TensorProto.FLOAT, (4,)),
+             ('input_mean', TensorProto.FLOAT, (4,)),
+             ('input_var', TensorProto.FLOAT, (4,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'],
+                       ['out'], training_mode=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))])  # type: ignore
+
+    def test_batch_norm_test_no_dim(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, None, None, None)),
+             ('scale', TensorProto.FLOAT, (4,)),
+             ('b', TensorProto.FLOAT, (4,)),
+             ('input_mean', TensorProto.FLOAT, (None,)),
+             ('input_var', TensorProto.FLOAT, (4,))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'],
+                       ['out'], training_mode=0)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, None, None, None))])  # type: ignore
+
+    def test_batch_norm_train_no_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, None),
+             ('scale', TensorProto.FLOAT, None),
+             ('b', TensorProto.FLOAT, None),
+             ('input_mean', TensorProto.FLOAT, ('C',)),
+             ('input_var', TensorProto.FLOAT, ('C',))],
+            [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'],
+                       ['out', 'running_mean', 'running_var'], training_mode=1)],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, None),  # type: ignore
+                                      make_tensor_value_info('running_mean', TensorProto.FLOAT, ('C',)),  # type: ignore
+                                      make_tensor_value_info('running_var', TensorProto.FLOAT, ('C',)),  # type: ignore
+                                      ])
+
+    def test_nonzero(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (None,))],
+            [make_node('NonZero', ['x'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.INT64, (1, None))])  # type: ignore
+
+    def test_nonzero_no_shape(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, None)],
+            [make_node('NonZero', ['x'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.INT64, (None, None))])  # type: ignore
+
+    def test_nonzero_existing_dim_param(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3,))],
+            [make_node('NonZero', ['x'], ['y'])],
+            [make_tensor_value_info('y', TensorProto.INT64, (None, 'NZ'))])
+        self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1, 'NZ'))])  # type: ignore
+
+    def test_optional_construct_empty_tensor(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.FLOAT, shape=[1, 2, 3])
+        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='output',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [],
+            [make_node('Optional', [], ['output'], type=tensor_type_proto)],
+            [])
+        self._assert_inferred(graph, [optional_val_info])  # type: ignore
+
+    def test_optional_construct_empty_sequence(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.INT32, shape=[1, 2, 3])
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='output_sequence',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [],
+            [make_node('Optional', [], ['output_sequence'], type=sequence_type_proto)],
+            [])
+        self._assert_inferred(graph, [optional_val_info])  # type: ignore
+
+    def test_optional_construct_tensor(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.FLOAT, shape=[2, 3, 4])
+        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='output',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('Optional', ['input1'], ['output'])],
+            [])
+        self._assert_inferred(graph, [optional_val_info])  # type: ignore
+
+    def test_optional_construct_sequence(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.INT64, shape=[2, 3, 0])
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        sequence_val_info = helper.make_value_info(
+            name='input_sequence',
+            type_proto=sequence_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='output_sequence',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [('input1', TensorProto.INT64, (2, 3, 0))],
+            [make_node('SequenceConstruct', ['input1'], ['input_sequence']),
+             make_node('Optional', ['input_sequence'], ['output_sequence'])],
+            [])
+        self._assert_inferred(graph, [sequence_val_info, optional_val_info])  # type: ignore
+
+    def test_optional_tensor_has_element(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.FLOAT, shape=[2, 3, 4])
+        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='sequence',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (2, 3, 4))],
+            [make_node('Optional', ['input1'], ['sequence']),
+             make_node('OptionalHasElement', ['sequence'], ['output'])],
+            [])
+        self._assert_inferred(graph, [optional_val_info,
+                                      make_tensor_value_info('output', TensorProto.BOOL, ())])  # type: ignore
+
+    def test_optional_sequence_has_element(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.FLOAT, shape=[0, 3, 4])
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        sequence_val_info = helper.make_value_info(
+            name='sequence',
+            type_proto=sequence_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='optional',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (0, 3, 4))],
+            [make_node('SequenceConstruct', ['input1'], ['sequence']),
+             make_node('Optional', ['sequence'], ['optional']),
+             make_node('OptionalHasElement', ['optional'], ['output'])],
+            [])
+        self._assert_inferred(graph, [sequence_val_info, optional_val_info,
+                                      make_tensor_value_info('output', TensorProto.BOOL, ())])  # type: ignore
+
+    def test_optional_tensor_get_element(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.DOUBLE, shape=[2, 1, 4])
+        tensor_val_into = helper.make_value_info(
+            name='output',
+            type_proto=tensor_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='optional',
+            type_proto=optional_type_proto)
+        graph = self._make_graph(
+            [('input1', TensorProto.DOUBLE, (2, 1, 4))],
+            [make_node('Optional', ['input1'], ['optional']),
+             make_node('OptionalGetElement', ['optional'], ['output'])],
+            [])
+        self._assert_inferred(graph, [optional_val_info, tensor_val_into])  # type: ignore
+
+    def test_optional_sequence_get_element(self) -> None:
+        tensor_type_proto = helper.make_tensor_type_proto(elem_type=TensorProto.INT32, shape=[2, 0, 4])
+        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
+        sequence_val_into = helper.make_value_info(
+            name='sequence',
+            type_proto=sequence_type_proto)
+        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
+        optional_val_info = helper.make_value_info(
+            name='optional',
+            type_proto=optional_type_proto)
+        output_val_into = helper.make_value_info(
+            name='output',
+            type_proto=sequence_type_proto)
+        graph = self._make_graph(
+            [('input1', TensorProto.INT32, (2, 0, 4))],
+            [make_node('SequenceConstruct', ['input1'], ['sequence']),
+             make_node('Optional', ['sequence'], ['optional']),
+             make_node('OptionalGetElement', ['optional'], ['output'])],
+            [])
+        self._assert_inferred(graph, [optional_val_info, sequence_val_into, output_val_into])  # type: ignore
+
+    def test_where_bfloat(self) -> None:
+        graph = self._make_graph(
+            [('cond', TensorProto.BOOL, (10,)), ('x', TensorProto.BFLOAT16, (10,)), ('y', TensorProto.BFLOAT16, (10,))],
+            [make_node('Where', ['cond', 'x', 'y'], ['out'])],
+            [])
+        self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.BFLOAT16, (10,))])  # type: ignore
+
+    def test_parse_data_with_unsupported_tensor_type(self) -> None:
+        model = helper.make_model(
+            graph=helper.make_graph(
+                name='graph_with_unsupported_type',
+                inputs=[],
+                outputs=[helper.make_tensor_value_info('y', TensorProto.FLOAT, shape=None)],
+                nodes=[make_node('ConstantOfShape', ['x'], ['y'])],
+                # ConstantOfShape only accepts np.int64 instead of np.int32
+                initializer=[numpy_helper.from_array(np.array([4, 3], dtype=np.int32), name='x')]))
+        # Strict shape inference should catch this invalid type error (int32 is not supported)
+        self.assertRaises(onnx.shape_inference.InferenceError,
+            onnx.shape_inference.infer_shapes, model, strict_mode=True)
+        # Even nornmal shape inference should not produce any invalid shape due to wrong type for ParseData
+        inferred_model = onnx.shape_inference.infer_shapes(model)
+        self.assertFalse(inferred_model.graph.output[0].type.tensor_type.HasField('shape'))
+
+    def test_parse_data_with_undefined_tensor_type(self) -> None:
+        model = helper.make_model(
+            graph=helper.make_graph(
+                name='graph_with_undefined_type',
+                inputs=[],
+                outputs=[helper.make_tensor_value_info('y', TensorProto.FLOAT, shape=None)],
+                nodes=[make_node('ConstantOfShape', ['x'], ['y'])],
+                initializer=[numpy_helper.from_array(np.array([4, 3], dtype=np.int64), name='x')]))
+        # Hardcode the tensor type as UNDEFINED to test catching undefined type error
+        model.graph.initializer[0].data_type = TensorProto.UNDEFINED
+        # Strict shape inference should catch this undefined type error
+        self.assertRaises(onnx.shape_inference.InferenceError,
+            onnx.shape_inference.infer_shapes, model, strict_mode=True)
+        # Even nornmal shape inference should not produce any invalid shape due to undefined type for ParseData
+        inferred_model = onnx.shape_inference.infer_shapes(model)
+        self.assertFalse(inferred_model.graph.output[0].type.tensor_type.HasField('shape'))
+
+    def test_gridsample(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (1, 1, 3, 3)),
+             ('grid', TensorProto.INT64, (1, 3, 3, 2))],
+            [make_node("GridSample", ['x', 'grid'], ['y'], mode='nearest', padding_mode='border', align_corners=1)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.FLOAT, (1, 1, 3, 3))])  # type: ignore
+
+    def test_gridsample_defaults(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')),
+             ('grid', TensorProto.FLOAT, ('N', 'H_out', 'W_out', 2))],
+            [make_node("GridSample", ['x', 'grid'], ['y'])],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.FLOAT, ('N', 'C', 'H_out', 'W_out'))])  # type: ignore
+
+    def test_gridsample_no_dim(self) -> None:
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, ('N', 'C', None, None)),
+             ('grid', TensorProto.FLOAT, ('N', None, None, 2))],
+            [make_node("GridSample", ['x', 'grid'], ['y'], mode='bilinear', padding_mode='border')],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_value_info('y', TensorProto.FLOAT, ('N', 'C', None, None))])  # type: ignore
+
+    def test_sequence_map_identity_known_dims(self):  # type: () -> None
+        input_value_infos = [make_tensor_value_info(
+            'input', TensorProto.FLOAT, (220, 220, 3))]
+        output_value_infos = [make_tensor_value_info(
+            'output', TensorProto.FLOAT, (220, 220, 3))]
+        body_graph = helper.make_graph(
+            [make_node('Identity', ['input'], ['output'])],
+            "body_graph",
+            input_value_infos,
+            output_value_infos
+        )
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (220, 220, 3)),
+             ('input2', TensorProto.FLOAT, (220, 220, 3)),
+             ('input3', TensorProto.FLOAT, (220, 220, 3)),
+             ],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceMap', ['in_sequence'], ['out_sequence'], body=body_graph)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (220, 220, 3)),
+             make_tensor_sequence_value_info('out_sequence', TensorProto.FLOAT, (220, 220, 3))]
+        )  # type: ignore
+
+    def test_sequence_map_identity_unknown_dims(self):  # type: () -> None
+        input_value_infos = [make_tensor_value_info(
+            'input', TensorProto.FLOAT, ('H', 'W', 3))]
+        output_value_infos = [make_tensor_value_info(
+            'output', TensorProto.FLOAT, ('H', 'W', 3))]
+        body_graph = helper.make_graph(
+            [make_node('Identity', ['input'], ['output'])],
+            "body_graph",
+            input_value_infos,
+            output_value_infos
+        )
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (200, 300, 3)),
+             ('input2', TensorProto.FLOAT, (100, 200, 3)),
+             ('input3', TensorProto.FLOAT, (5, 1, 3)),
+             ],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceMap', ['in_sequence'], ['out_sequence'], body=body_graph)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (None, None, 3)),
+             make_tensor_sequence_value_info('out_sequence', TensorProto.FLOAT, (None, None, 3))])  # type: ignore
+
+    def test_sequence_map_slice_outs_known_dims(self):  # type: () -> None
+        body_graph = helper.make_graph(
+            nodes=[make_node('Slice', ['x', 'starts1', 'ends1', 'axes', ''], ['y1']),
+                   make_node('Slice', ['x', 'starts2', 'ends2', 'axes', ''], ['y2'])],
+            name='body_graph',
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    'x', onnx.TensorProto.FLOAT, ('H', 'W', 3))
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    'y1', onnx.TensorProto.FLOAT, (10, 20, 3)),
+                onnx.helper.make_tensor_value_info(
+                    'y2', onnx.TensorProto.FLOAT, (30, 40, 3)),
+            ],
+            initializer=[make_tensor('axes', TensorProto.INT64, (2,), (0, 1)),
+                         make_tensor('starts1', TensorProto.INT64,
+                                     (2,), (0, 0)),
+                         make_tensor('ends1', TensorProto.INT64,
+                                     (2,), (10, 20)),
+                         make_tensor('starts2', TensorProto.INT64,
+                                     (2,), (0, 0)),
+                         make_tensor('ends2', TensorProto.INT64,
+                                     (2,), (30, 40)),
+                         ]
+        )  # type: ignore
+
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (220, 310, 3)),
+             ('input2', TensorProto.FLOAT, (110, 210, 3)),
+             ('input3', TensorProto.FLOAT, (90, 110, 3)),
+             ],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceMap', ['in_sequence'], ['out_sequence1', 'out_sequence2'], body=body_graph)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (None, None, 3)),
+             make_tensor_sequence_value_info(
+                 'out_sequence1', TensorProto.FLOAT, (10, 20, 3)),
+             make_tensor_sequence_value_info(
+                 'out_sequence2', TensorProto.FLOAT, (30, 40, 3)),
+             ])  # type: ignore
+
+    def test_sequence_map_slice_outs_unknown_dims(self):  # type: () -> None
+        body_graph = helper.make_graph(
+            nodes=[make_node('Slice', ['x', 'starts1', 'ends1', 'axes', ''], ['y1']),
+                   make_node('Slice', ['x', 'starts2', 'ends2', 'axes', ''], ['y2'])],
+            name='body_graph',
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    'x', onnx.TensorProto.FLOAT, ('H', 'W', 3))
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    'y1', onnx.TensorProto.FLOAT, ('H1', 'W1', 3)),
+                onnx.helper.make_tensor_value_info(
+                    'y2', onnx.TensorProto.FLOAT, ('H2', 'W2', 3)),
+            ],
+            initializer=[make_tensor('axes', TensorProto.INT64, (2,), (0, 1)),
+                         make_tensor('starts1', TensorProto.INT64,
+                                     (2,), (0, 0)),
+                         make_tensor('ends1', TensorProto.INT64,
+                                     (2,), (10, 20)),
+                         make_tensor('starts2', TensorProto.INT64,
+                                     (2,), (0, 0)),
+                         make_tensor('ends2', TensorProto.INT64,
+                                     (2,), (30, 40)),
+                         ]
+        )  # type: ignore
+
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (220, 310, 3)),
+             ('input2', TensorProto.FLOAT, (110, 210, 3)),
+             ('input3', TensorProto.FLOAT, (90, 110, 3)),
+             ],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceMap', ['in_sequence'], ['out_sequence1', 'out_sequence2'], body=body_graph)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (None, None, 3)),
+             make_tensor_sequence_value_info(
+                 'out_sequence1', TensorProto.FLOAT, (None, None, 3)),
+             make_tensor_sequence_value_info(
+                 'out_sequence2', TensorProto.FLOAT, (None, None, 3)),
+             ])  # type: ignore
+
+    def test_sequence_map_different_tensor_type(self):  # type: () -> None
+        body_graph = helper.make_graph(
+            nodes=[make_node('Shape', ['x'], ['shape'])],
+            name='body_graph',
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    'x', onnx.TensorProto.FLOAT, ('H', 'W', 'C'))
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    'shape', onnx.TensorProto.INT64, (3,))
+            ],
+        )  # type: ignore
+
+        graph = self._make_graph(
+            [('input1', TensorProto.FLOAT, (220, 310, 3)),
+             ('input2', TensorProto.FLOAT, (110, 210, 3)),
+             ('input3', TensorProto.FLOAT, (90, 110, 3)),
+             ],
+            [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']),
+             make_node('SequenceMap', ['in_sequence'], ['shapes'], body=body_graph)],
+            [])
+        self._assert_inferred(
+            graph,
+            [make_tensor_sequence_value_info('in_sequence', TensorProto.FLOAT, (None, None, 3)),
+             make_tensor_sequence_value_info('shapes', TensorProto.INT64, (3,)),
+             ])  # type: ignore
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/symbolic_shape_test.py b/third_party/onnx/onnx/test/symbolic_shape_test.py
new file mode 100644
index 0000000000..d1b3731907
--- /dev/null
+++ b/third_party/onnx/onnx/test/symbolic_shape_test.py
@@ -0,0 +1,153 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx import checker, helper, TensorProto, NodeProto, GraphProto, ValueInfoProto, ModelProto, TensorShapeProto, ONNX_ML
+from onnx.helper import make_model, make_node, make_tensor, make_tensor_value_info
+from typing import Sequence, Union, Text, Tuple, List, Any, Optional
+import onnx.shape_inference
+import unittest
+
+
+class TestSymbolicShape(unittest.TestCase):
+
+    def _assert_valueinfo_shape(self, onnx_model: ModelProto, value_infos: List[ValueInfoProto]) -> None:
+        """
+            Assert onnx_model.value_info should be the same as expected value_infos
+            Instead of exact symbol, use -1 to represent symbolic shape in expected value_infos
+        """
+        for expected_vi in value_infos:
+            shape = self._get_shape_from_name(onnx_model, expected_vi.name)
+            assert shape is not None, '{}'.format(onnx_model)
+            if expected_vi.type.HasField('tensor_type'):
+                expected_shape = expected_vi.type.tensor_type.shape
+            elif expected_vi.type.HasField('sparse_tensor_type'):
+                expected_shape = expected_vi.type.sparse_tensor_type.shape
+            assert len(shape.dim) == len(expected_shape.dim), '{}'.format(onnx_model)
+            for dim_i in range(len(shape.dim)):
+                dim = shape.dim[dim_i]
+                expected_dim = expected_shape.dim[dim_i]
+                # -1 means it's a symbolic shape
+                if expected_dim.dim_value == -1:
+                    # symbolic dimension must exist
+                    assert dim.dim_param, '%s' % (onnx_model)
+                else:
+                    assert dim.dim_value == expected_dim.dim_value, '{}'.format(onnx_model)
+
+    def _count_unique_dim_param_number(self, onnx_model: ModelProto) -> int:
+        """
+           return the total number of unique symbolic shape
+        """
+        symbol_shape_set = set()
+        inputs = list(onnx_model.graph.input)
+        outputs = list(onnx_model.graph.output)
+        valueinfos = list(onnx_model.graph.value_info)
+        for v in inputs + outputs + valueinfos:
+            for dim in v.type.tensor_type.shape.dim:
+                if dim.dim_param:
+                    symbol_shape_set.add(dim.dim_param)
+        return len(symbol_shape_set)
+
+    def _get_shape_from_name(self, onnx_model: ModelProto, name: Text) -> Optional[TensorShapeProto]:
+        """
+            Get shape from tensor_type or sparse_tensor_type according to given name
+        """
+        inputs = list(onnx_model.graph.input)
+        outputs = list(onnx_model.graph.output)
+        valueinfos = list(onnx_model.graph.value_info)
+        for v in inputs + outputs + valueinfos:
+            if v.name == name:
+                if v.type.HasField('tensor_type'):
+                    return v.type.tensor_type.shape
+                elif v.type.HasField('sparse_tensor_type'):
+                    return v.type.sparse_tensor_type.shape
+        return None
+
+    def test_concat_enable_symbolic(self) -> None:
+        concat = helper.make_node('Concat', inputs=['A', 'B'], outputs=['C'], name='Concat', axis=1)
+        cast = onnx.helper.make_node('Cast',
+            inputs=['C'],
+            outputs=['output'],
+            to=getattr(TensorProto, 'FLOAT'))
+        graph_def = helper.make_graph(name='test_graph',
+            nodes=[concat, cast],
+            inputs=[helper.make_tensor_value_info('A', TensorProto.FLOAT, [2, 'A']),
+                helper.make_tensor_value_info('B', TensorProto.FLOAT, [2, 3])],
+            outputs=[helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, None])]
+        )
+
+        onnx_model = make_model(graph_def)
+        inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
+        self._assert_valueinfo_shape(inferred_model, [make_tensor_value_info("C", TensorProto.FLOAT, (2, -1))])
+        # the symbolic shape of C and output should be the same
+        assert self._get_shape_from_name(inferred_model, 'C') == self._get_shape_from_name(inferred_model, 'output')
+
+    def test_two_symbolic_concat(self) -> None:
+        concat1 = helper.make_node('Concat', inputs=['A', 'B'], outputs=['C'], name='Concat', axis=1)
+        concat2 = helper.make_node('Concat', inputs=['C', 'D'], outputs=['E'], name='Concat', axis=1)
+        cast = onnx.helper.make_node('Cast',
+            inputs=['E'],
+            outputs=['output'],
+            to=getattr(TensorProto, 'FLOAT'))
+        graph_def = helper.make_graph(name='test_graph',
+            nodes=[concat1, concat2, cast],
+            inputs=[helper.make_tensor_value_info('A', TensorProto.FLOAT, [2, 'A']),
+                helper.make_tensor_value_info('B', TensorProto.FLOAT, [2, 3]),
+                helper.make_tensor_value_info('D', TensorProto.FLOAT, [2, 'D'])],
+            outputs=[helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, None])]
+        )
+
+        onnx_model = make_model(graph_def)
+        inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
+        self._assert_valueinfo_shape(inferred_model, [
+            make_tensor_value_info("C", TensorProto.FLOAT, (2, -1)),
+            make_tensor_value_info("E", TensorProto.FLOAT, (2, -1))])
+        # the symbolic shape of E and output should be the same
+        assert self._get_shape_from_name(inferred_model, 'E') == self._get_shape_from_name(inferred_model, 'output')
+
+    def test_duplicate_symbolic_shape(self) -> None:
+        concat1 = helper.make_node('Concat', inputs=['A', 'B'], outputs=['C'], name='Concat', axis=1)
+        concat2 = helper.make_node('Concat', inputs=['C', 'D'], outputs=['E'], name='Concat', axis=1)
+        cast = onnx.helper.make_node('Cast',
+            inputs=['E'],
+            outputs=['output'],
+            to=getattr(TensorProto, 'FLOAT'))
+        graph_def = helper.make_graph(name='test_graph',
+            nodes=[concat1, concat2, cast],
+            inputs=[helper.make_tensor_value_info('A', TensorProto.FLOAT, [2, 'unk__0']),
+                helper.make_tensor_value_info('B', TensorProto.FLOAT, [2, 3]),
+                helper.make_tensor_value_info('D', TensorProto.FLOAT, [2, 'unk__1'])],
+            outputs=[helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, 'unk__0'])]
+        )
+
+        onnx_model = make_model(graph_def)
+        original_count = self._count_unique_dim_param_number(onnx_model)
+        inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
+        inferred_count = self._count_unique_dim_param_number(inferred_model)
+        # to prevent duplicate so the inferred count will be count + 2
+        # new symbol 'unk__2' and 'unk__3' should be generated
+        # original: {'unk_0', 'unk__1'}
+        # inferred: {'unk_0', 'unk__1', 'unk__2', 'unk__3'}
+        assert inferred_count == original_count + 2, '%s%s' % (inferred_model, onnx_model)
+
+    def test_unknown_shape(self) -> None:
+        concat = helper.make_node('Concat', inputs=['A', 'B'], outputs=['C'], name='Concat', axis=1)
+        cast = onnx.helper.make_node('Cast',
+            inputs=['C'],
+            outputs=['output'],
+            to=getattr(TensorProto, 'FLOAT'))
+        graph_def = helper.make_graph(name='test_graph',
+            nodes=[concat, cast],
+            inputs=[helper.make_tensor_value_info('A', TensorProto.FLOAT, [3, None]),  # unknown shape
+                helper.make_tensor_value_info('B', TensorProto.FLOAT, [3, None])],
+            outputs=[helper.make_tensor_value_info('output', TensorProto.FLOAT, [3, None])]
+        )
+
+        onnx_model = make_model(graph_def)
+        inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
+        self._assert_valueinfo_shape(inferred_model, [make_tensor_value_info("C", TensorProto.FLOAT, (3, -1))])
+        # the symbolic shape of C and output should be the same
+        # ('unk__0', 'unk__1')
+        assert self._get_shape_from_name(inferred_model, 'C') == self._get_shape_from_name(inferred_model, 'output')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/test_backend_test.py b/third_party/onnx/onnx/test/test_backend_test.py
new file mode 100644
index 0000000000..9f77660bf2
--- /dev/null
+++ b/third_party/onnx/onnx/test/test_backend_test.py
@@ -0,0 +1,102 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import itertools
+import os
+import platform
+import unittest
+import onnx.backend.base
+import onnx.backend.test
+
+from onnx.backend.base import Device, DeviceType
+from onnx.backend.test.runner import BackendIsNotSupposedToImplementIt
+import onnx.shape_inference
+import onnx.version_converter
+from typing import Optional, Text, Any, Tuple, Sequence
+from onnx import NodeProto, ModelProto, TensorProto
+import numpy  # type: ignore
+
+# The following just executes the fake backend through the backend test
+# infrastructure. Since we don't have full reference implementation of all ops
+# in ONNX repo, it's impossible to produce the proper results. However, we can
+# run 'checker' (that's what base Backend class does) to verify that all tests
+# fed are actually well-formed ONNX models.
+#
+# If everything is fine, all the tests would be marked as "skipped".
+#
+# We don't enable report in this test because the report collection logic itself
+# fails when models are mal-formed.
+
+
+class DummyBackend(onnx.backend.base.Backend):
+    @classmethod
+    def prepare(cls,
+                model: ModelProto,
+                device: Text = 'CPU',
+                **kwargs: Any
+                ) -> Optional[onnx.backend.base.BackendRep]:
+        super(DummyBackend, cls).prepare(model, device, **kwargs)
+
+        # test shape inference
+        model = onnx.shape_inference.infer_shapes(model)
+        value_infos = {vi.name: vi for vi in itertools.chain(model.graph.value_info, model.graph.output)}
+
+        if do_enforce_test_coverage_safelist(model):
+            for node in model.graph.node:
+                for i, output in enumerate(node.output):
+                    if node.op_type == 'Dropout' and i != 0:
+                        continue
+                    assert output in value_infos
+                    tt = value_infos[output].type.tensor_type
+                    assert tt.elem_type != TensorProto.UNDEFINED
+                    for dim in tt.shape.dim:
+                        assert dim.WhichOneof('value') == 'dim_value'
+
+        raise BackendIsNotSupposedToImplementIt(
+            "This is the dummy backend test that doesn't verify the results but does run the checker")
+
+    @classmethod
+    def run_node(cls,
+                 node: NodeProto,
+                 inputs: Any,
+                 device: Text = 'CPU',
+                 outputs_info: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] = None,
+                 **kwargs: Any
+                 ) -> Optional[Tuple[Any, ...]]:
+        super(DummyBackend, cls).run_node(node, inputs, device=device, outputs_info=outputs_info)
+        raise BackendIsNotSupposedToImplementIt(
+            "This is the dummy backend test that doesn't verify the results but does run the checker")
+
+    @classmethod
+    def supports_device(cls, device: Text) -> bool:
+        d = Device(device)
+        if d.type == DeviceType.CPU:
+            return True
+        return False
+
+
+test_coverage_safelist = set(
+    ['bvlc_alexnet', 'densenet121', 'inception_v1', 'inception_v2',
+     'resnet50', 'shufflenet', 'SingleRelu', 'squeezenet_old', 'vgg19', 'zfnet'])
+
+
+def do_enforce_test_coverage_safelist(model: ModelProto) -> bool:
+    if model.graph.name not in test_coverage_safelist:
+        return False
+    for node in model.graph.node:
+        if node.op_type in set(['RNN', 'LSTM', 'GRU']):
+            return False
+    return True
+
+
+backend_test = onnx.backend.test.BackendTest(DummyBackend, __name__)
+if os.getenv('APPVEYOR'):
+    backend_test.exclude(r'(test_vgg19|test_zfnet)')
+if platform.architecture()[0] == '32bit':
+    backend_test.exclude(r'(test_vgg19|test_zfnet|test_bvlc_alexnet)')
+
+# import all test cases at global scope to make them visible to python.unittest
+globals().update(backend_test
+                 .test_cases)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/test_external_data.py b/third_party/onnx/onnx/test/test_external_data.py
new file mode 100644
index 0000000000..ea95981af1
--- /dev/null
+++ b/third_party/onnx/onnx/test/test_external_data.py
@@ -0,0 +1,496 @@
+# SPDX-License-Identifier: Apache-2.0
+import tempfile
+import unittest
+import uuid
+
+import numpy as np  # type: ignore
+import shutil
+
+import os
+import os.path as Path
+
+import onnx
+from onnx import checker, helper, shape_inference
+from onnx import ModelProto, TensorProto
+from onnx.external_data_helper import set_external_data
+from onnx.external_data_helper import convert_model_to_external_data
+from onnx.external_data_helper import convert_model_from_external_data
+from onnx.external_data_helper import load_external_data_for_model, load_external_data_for_tensor
+from onnx.numpy_helper import to_array, from_array
+from typing import Any, Tuple, Text, List
+import pytest  # type: ignore
+import sys
+
+
+class TestLoadExternalDataBase(unittest.TestCase):
+
+    def setUp(self) -> None:
+        self.temp_dir: Text = tempfile.mkdtemp()
+        self.initializer_value = np.arange(6).reshape(3, 2).astype(np.float32) + 512
+        self.attribute_value = np.arange(6).reshape(2, 3).astype(np.float32) + 256
+        self.model_filename = self.create_test_model()
+
+    def tearDown(self) -> None:
+        shutil.rmtree(self.temp_dir)
+
+    def get_temp_model_filename(self) -> Text:
+        return os.path.join(self.temp_dir, str(uuid.uuid4()) + '.onnx')
+
+    def create_external_data_tensor(self, value: List[Any], tensor_name: Text) -> TensorProto:
+        tensor = from_array(np.array(value))
+        tensor.name = tensor_name
+        tensor_filename = "{}.bin".format(tensor_name)
+        set_external_data(tensor, location=tensor_filename)
+
+        with open(os.path.join(self.temp_dir, tensor_filename), 'wb') as data_file:
+            data_file.write(tensor.raw_data)
+        tensor.ClearField('raw_data')
+        tensor.data_location = onnx.TensorProto.EXTERNAL
+        return tensor
+
+    def create_test_model(self) -> Text:
+
+        constant_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['values'],
+            value=self.create_external_data_tensor(self.attribute_value, "attribute_value")
+        )
+
+        initializers = [self.create_external_data_tensor(self.initializer_value, "input_value")]
+        inputs = [helper.make_tensor_value_info("input_value",
+                                                onnx.TensorProto.FLOAT,
+                                                self.initializer_value.shape)]
+
+        graph = helper.make_graph([constant_node], "test_graph",
+                                  inputs=inputs, outputs=[],
+                                  initializer=initializers)
+        model = helper.make_model(graph)
+
+        model_filename = os.path.join(self.temp_dir, "model.onnx")
+        with open(model_filename, "wb") as model_file:
+            model_file.write(model.SerializeToString())
+
+        return model_filename
+
+    def test_check_model(self) -> None:
+        checker.check_model(self.model_filename)
+
+
+class TestLoadExternalData(TestLoadExternalDataBase):
+
+    def test_load_external_data(self) -> None:
+        model = onnx.load_model(self.model_filename)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+    def test_load_external_data_for_model(self) -> None:
+        model = onnx.load_model(self.model_filename, load_external_data=False)
+        load_external_data_for_model(model, self.temp_dir)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+    def test_save_external_data(self) -> None:
+        model = onnx.load_model(self.model_filename)
+
+        temp_dir = os.path.join(self.temp_dir, "save_copy")
+        os.mkdir(temp_dir)
+        new_model_filename = os.path.join(temp_dir, 'model.onnx')
+        onnx.save_model(model, new_model_filename)
+
+        new_model = onnx.load_model(new_model_filename)
+        initializer_tensor = new_model.graph.initializer[0]
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = new_model.graph.node[0].attribute[0].t
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+
+class TestLoadExternalDataSingleFile(TestLoadExternalDataBase):
+
+    def create_external_data_tensors(self, tensors_data: List[Tuple[List[Any], Any]]) -> List[TensorProto]:
+        tensor_filename = "tensors.bin"
+        tensors = []
+
+        with open(os.path.join(self.temp_dir, tensor_filename), 'ab') as data_file:
+            for (value, tensor_name) in tensors_data:
+                tensor = from_array(np.array(value))
+                offset = data_file.tell()
+                if offset % 4096 != 0:
+                    data_file.write(b"\0" * (4096 - offset % 4096))
+                    offset = offset + 4096 - offset % 4096
+
+                data_file.write(tensor.raw_data)
+                set_external_data(tensor, location=tensor_filename, offset=offset, length=data_file.tell() - offset)
+                tensor.name = tensor_name
+                tensor.ClearField("raw_data")
+                tensor.data_location = onnx.TensorProto.EXTERNAL
+                tensors.append(tensor)
+
+        return tensors
+
+    def test_load_external_single_file_data(self) -> None:
+        model = onnx.load_model(self.model_filename)
+
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+    def test_save_external_single_file_data(self) -> None:
+        model = onnx.load_model(self.model_filename)
+
+        temp_dir = os.path.join(self.temp_dir, "save_copy")
+        os.mkdir(temp_dir)
+        new_model_filename = os.path.join(temp_dir, 'model.onnx')
+        onnx.save_model(model, new_model_filename)
+
+        new_model = onnx.load_model(new_model_filename)
+        initializer_tensor = new_model.graph.initializer[0]
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = new_model.graph.node[0].attribute[0].t
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+
+class TestSaveAllTensorsAsExternalData(TestLoadExternalDataBase):
+
+    def setUp(self) -> None:
+        self.temp_dir: Text = tempfile.mkdtemp()
+        self.initializer_value = np.arange(6).reshape(3, 2).astype(np.float32) + 512
+        self.attribute_value = np.arange(6).reshape(2, 3).astype(np.float32) + 256
+        self.model = self.create_test_model_proto()
+
+    def create_data_tensors(self, tensors_data: List[Tuple[List[Any], Any]]) -> List[TensorProto]:
+        tensors = []
+        for (value, tensor_name) in tensors_data:
+            tensor = from_array(np.array(value))
+            tensor.name = tensor_name
+            tensors.append(tensor)
+
+        return tensors
+
+    def create_test_model_proto(self) -> ModelProto:
+        tensors = self.create_data_tensors([
+            (self.attribute_value, "attribute_value"),
+            (self.initializer_value, "input_value"),
+        ])
+
+        constant_node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['values'],
+            value=tensors[0]
+        )
+
+        inputs = [helper.make_tensor_value_info("input_value",
+                                                onnx.TensorProto.FLOAT,
+                                                self.initializer_value.shape)]
+
+        graph = helper.make_graph([constant_node], "test_graph",
+                                  inputs=inputs, outputs=[],
+                                  initializer=[tensors[1]])
+        return helper.make_model(graph)
+
+    def test_check_model(self) -> None:
+        checker.check_model(self.model)
+
+    def test_convert_model_to_external_data_with_size_threshold(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+
+        convert_model_to_external_data(self.model, size_threshold=1024)
+        onnx.save_model(self.model, model_file_path)
+
+        model = onnx.load_model(model_file_path)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertFalse(initializer_tensor.HasField("data_location"))
+
+    def test_convert_model_to_external_data_without_size_threshold(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+        convert_model_to_external_data(self.model, size_threshold=0)
+        onnx.save_model(self.model, model_file_path)
+
+        model = onnx.load_model(model_file_path)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(initializer_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+    def test_convert_model_to_external_data_from_one_file_with_location(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+        external_data_file = str(uuid.uuid4())
+
+        convert_model_to_external_data(self.model, size_threshold=0, all_tensors_to_one_file=True, location=external_data_file)
+        onnx.save_model(self.model, model_file_path)
+
+        self.assertTrue(Path.isfile(os.path.join(self.temp_dir, external_data_file)))
+
+        model = onnx.load_model(model_file_path)
+
+        # test convert model from external data
+        convert_model_from_external_data(model)
+        model_file_path = self.get_temp_model_filename()
+        onnx.save_model(model, model_file_path)
+        model = onnx.load_model(model_file_path)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertFalse(len(initializer_tensor.external_data))
+        self.assertEqual(initializer_tensor.data_location, TensorProto.DEFAULT)
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertFalse(len(attribute_tensor.external_data))
+        self.assertEqual(attribute_tensor.data_location, TensorProto.DEFAULT)
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+    def test_convert_model_to_external_data_from_one_file_without_location_uses_model_name(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+
+        convert_model_to_external_data(self.model, size_threshold=0, all_tensors_to_one_file=True)
+        onnx.save_model(self.model, model_file_path)
+
+        self.assertTrue(Path.isfile(model_file_path))
+        self.assertTrue(Path.isfile(os.path.join(self.temp_dir, model_file_path)))
+
+    def test_convert_model_to_external_data_one_file_per_tensor_without_attribute(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+
+        convert_model_to_external_data(self.model, size_threshold=0, all_tensors_to_one_file=False, convert_attribute=False)
+        onnx.save_model(self.model, model_file_path)
+
+        self.assertTrue(Path.isfile(model_file_path))
+        self.assertTrue(Path.isfile(os.path.join(self.temp_dir, "input_value")))
+        self.assertFalse(Path.isfile(os.path.join(self.temp_dir, "attribute_value")))
+
+    def test_convert_model_to_external_data_one_file_per_tensor_with_attribute(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+
+        convert_model_to_external_data(self.model, size_threshold=0, all_tensors_to_one_file=False, convert_attribute=True)
+        onnx.save_model(self.model, model_file_path)
+
+        self.assertTrue(Path.isfile(model_file_path))
+        self.assertTrue(Path.isfile(os.path.join(self.temp_dir, "input_value")))
+        self.assertTrue(Path.isfile(os.path.join(self.temp_dir, "attribute_value")))
+
+    def test_convert_model_to_external_data_does_not_convert_attribute_values(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+
+        convert_model_to_external_data(self.model, size_threshold=0, convert_attribute=False, all_tensors_to_one_file=False)
+        onnx.save_model(self.model, model_file_path)
+
+        self.assertTrue(Path.isfile(os.path.join(self.temp_dir, "input_value")))
+        self.assertFalse(Path.isfile(os.path.join(self.temp_dir, "attribute_value")))
+
+        model = onnx.load_model(model_file_path)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(initializer_tensor.HasField("data_location"))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertFalse(attribute_tensor.HasField("data_location"))
+
+    def test_convert_model_to_external_data_converts_attribute_values(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+
+        convert_model_to_external_data(self.model, size_threshold=0, convert_attribute=True)
+        onnx.save_model(self.model, model_file_path)
+
+        model = onnx.load_model(model_file_path)
+
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+        self.assertTrue(initializer_tensor.HasField("data_location"))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+        self.assertTrue(attribute_tensor.HasField("data_location"))
+
+    def test_save_model_does_not_convert_to_external_data_and_saves_the_model(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+        onnx.save_model(self.model, model_file_path, save_as_external_data=False)
+        self.assertTrue(Path.isfile(model_file_path))
+
+        model = onnx.load_model(model_file_path)
+        initializer_tensor = model.graph.initializer[0]
+        self.assertFalse(initializer_tensor.HasField("data_location"))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertFalse(attribute_tensor.HasField("data_location"))
+
+    def test_save_model_does_convert_and_saves_the_model(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+        onnx.save_model(self.model,
+                        model_file_path,
+                        save_as_external_data=True,
+                        all_tensors_to_one_file=True,
+                        location=None,
+                        size_threshold=0,
+                        convert_attribute=False)
+
+        model = onnx.load_model(model_file_path)
+
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(initializer_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertFalse(attribute_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+    def test_save_model_without_loading_external_data(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+        onnx.save_model(self.model,
+                        model_file_path,
+                        save_as_external_data=True,
+                        location=None,
+                        size_threshold=0,
+                        convert_attribute=False)
+        # Save without load_external_data
+        model = onnx.load_model(model_file_path, load_external_data=False)
+        onnx.save_model(model,
+                        model_file_path,
+                        save_as_external_data=True,
+                        location=None,
+                        size_threshold=0,
+                        convert_attribute=False)
+        # Load the saved model again; Only works if the saved path is under the same directory
+        model = onnx.load_model(model_file_path)
+
+        initializer_tensor = model.graph.initializer[0]
+        self.assertTrue(initializer_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(initializer_tensor), self.initializer_value))
+
+        attribute_tensor = model.graph.node[0].attribute[0].t
+        self.assertFalse(attribute_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(attribute_tensor), self.attribute_value))
+
+    def test_save_model_with_existing_raw_data_should_override(self) -> None:
+        model_file_path = self.get_temp_model_filename()
+        original_raw_data = self.model.graph.initializer[0].raw_data
+        onnx.save_model(self.model, model_file_path, save_as_external_data=True, size_threshold=0)
+        self.assertTrue(Path.isfile(model_file_path))
+
+        model = onnx.load_model(model_file_path, load_external_data=False)
+        initializer_tensor = model.graph.initializer[0]
+        initializer_tensor.raw_data = b'dummpy_raw_data'
+        # If raw_data and external tensor exist at the same time, override existing raw_data
+        load_external_data_for_tensor(initializer_tensor, self.temp_dir)
+        self.assertEqual(initializer_tensor.raw_data, original_raw_data)
+
+
+class TestExternalDataToArray(unittest.TestCase):
+    def setUp(self) -> None:
+        self.temp_dir: Text = tempfile.mkdtemp()
+        self.model_file_path: Text = os.path.join(self.temp_dir, 'model.onnx')
+        self.large_data = np.random.rand(10, 60, 100).astype(np.float32)
+        self.small_data = (200, 300)
+        self.model = self.create_test_model()
+
+    def tearDown(self) -> None:
+        shutil.rmtree(self.temp_dir)
+
+    def get_temp_model_filename(self) -> Text:
+        return os.path.join(self.temp_dir, str(uuid.uuid4()) + '.onnx')
+
+    def create_test_model(self) -> ModelProto:
+        X = helper.make_tensor_value_info('X', TensorProto.FLOAT, self.large_data.shape)
+        input_init = helper.make_tensor(name='X', data_type=TensorProto.FLOAT,
+            dims=self.large_data.shape, vals=self.large_data.tobytes(), raw=True)
+
+        shape_data = np.array(self.small_data, np.int64)
+        shape_init = helper.make_tensor(name='Shape', data_type=TensorProto.INT64,
+            dims=shape_data.shape, vals=shape_data.tobytes(), raw=True)
+        C = helper.make_tensor_value_info('C', TensorProto.INT64, self.small_data)
+
+        reshape = onnx.helper.make_node(
+            'Reshape',
+            inputs=['X', 'Shape'],
+            outputs=['Y'],
+        )
+        cast = onnx.helper.make_node(
+            'Cast',
+            inputs=['Y'],
+            outputs=['C'],
+            to=getattr(TensorProto, 'INT64')
+        )
+
+        graph_def = helper.make_graph(
+            [reshape, cast],
+            'test-model',
+            [X],
+            [C],
+            initializer=[input_init, shape_init],
+        )
+        model = helper.make_model(graph_def, producer_name='onnx-example')
+        return model
+
+    def test_check_model(self) -> None:
+        checker.check_model(self.model)
+
+    def test_reshape_inference_with_external_data_fail(self) -> None:
+        onnx.save_model(self.model, self.model_file_path, save_as_external_data=True, all_tensors_to_one_file=False, size_threshold=0)
+        model_without_external_data = onnx.load(self.model_file_path, load_external_data=False)
+        # Shape inference of Reshape uses ParseData
+        # ParseData cannot handle external data and should throw the error as follows:
+        # Cannot parse data from external tensors. Please load external data into raw data for tensor: Shape
+        self.assertRaises(shape_inference.InferenceError, shape_inference.infer_shapes,
+            model_without_external_data, strict_mode=True)
+
+    def test_to_array_with_external_data(self) -> None:
+        onnx.save_model(self.model,
+                        self.model_file_path,
+                        save_as_external_data=True,
+                        all_tensors_to_one_file=False,
+                        size_threshold=0)
+        # raw_data of external tensor is not loaded
+        model = onnx.load(self.model_file_path, load_external_data=False)
+        # Specify self.temp_dir to load external tensor
+        loaded_large_data = to_array(model.graph.initializer[0], self.temp_dir)
+        self.assertTrue(np.allclose(loaded_large_data, self.large_data))
+
+    def test_save_model_with_external_data_multiple_times(self) -> None:
+        # Test onnx.save should respectively handle typical tensor and external tensor properly
+        # 1st save: save two tensors which have raw_data
+        # Only w_large will be stored as external tensors since it's larger than 1024
+        onnx.save_model(self.model,
+                        self.model_file_path,
+                        save_as_external_data=True,
+                        all_tensors_to_one_file=False,
+                        location=None,
+                        size_threshold=1024,
+                        convert_attribute=True)
+        model_without_loading_external = onnx.load(self.model_file_path, load_external_data=False)
+        large_input_tensor = model_without_loading_external.graph.initializer[0]
+        self.assertTrue(large_input_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(large_input_tensor, self.temp_dir), self.large_data))
+
+        small_shape_tensor = model_without_loading_external.graph.initializer[1]
+        self.assertTrue(not small_shape_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(small_shape_tensor), self.small_data))
+
+        # 2nd save: one tensor has raw_data (small); one external tensor (large)
+        # Save them both as external tensors this time
+        onnx.save_model(model_without_loading_external,
+                        self.model_file_path,
+                        save_as_external_data=True,
+                        all_tensors_to_one_file=False,
+                        location=None,
+                        size_threshold=0,
+                        convert_attribute=True)
+
+        model_without_loading_external = onnx.load(self.model_file_path, load_external_data=False)
+        large_input_tensor = model_without_loading_external.graph.initializer[0]
+        self.assertTrue(large_input_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(large_input_tensor, self.temp_dir), self.large_data))
+
+        small_shape_tensor = model_without_loading_external.graph.initializer[1]
+        self.assertTrue(small_shape_tensor.HasField("data_location"))
+        self.assertTrue(np.allclose(to_array(small_shape_tensor, self.temp_dir), self.small_data))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/test_with_ort.py b/third_party/onnx/onnx/test/test_with_ort.py
new file mode 100644
index 0000000000..f1cff4e520
--- /dev/null
+++ b/third_party/onnx/onnx/test/test_with_ort.py
@@ -0,0 +1,34 @@
+# SPDX-License-Identifier: Apache-2.0
+# This file is for testing ONNX with ONNXRuntime during ONNX Release
+# Create a general scenario to use ONNXRuntime with ONNX
+
+def example_test_with_ort() -> None:
+    import onnx
+    import numpy  # type: ignore
+    import onnxruntime as rt  # type: ignore
+    from onnxruntime.datasets import get_example  # type: ignore
+    import numpy.random  # type: ignore
+
+    # get certain example model from ORT
+    example1 = get_example("sigmoid.onnx")
+
+    # test ONNX functions
+    model = onnx.load(example1)
+    onnx.checker.check_model(model)
+    onnx.checker.check_model(model, True)
+    inferred_model = onnx.shape_inference.infer_shapes(model, True)
+    temp_filename = "temp.onnx"
+    onnx.save(inferred_model, temp_filename)
+
+    # test ONNXRuntime functions
+    sess = rt.InferenceSession(temp_filename)
+    input_name = sess.get_inputs()[0].name
+    output_name = sess.get_outputs()[0].name
+    x = numpy.random.random((3, 4, 5))
+    x = x.astype(numpy.float32)
+
+    sess.run([output_name], {input_name: x})
+
+
+if __name__ == "__main__":
+    example_test_with_ort()
diff --git a/third_party/onnx/onnx/test/tools_test.py b/third_party/onnx/onnx/test/tools_test.py
new file mode 100644
index 0000000000..259f66a88e
--- /dev/null
+++ b/third_party/onnx/onnx/test/tools_test.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+import onnx
+from onnx.tools import update_model_dims
+from onnx import helper, TensorProto
+
+
+class TestToolsFunctions(unittest.TestCase):
+    def test_update_inputs_outputs_dim(self) -> None:
+        node_def = helper.make_node(
+            "Conv",
+            inputs=['x', 'W'],
+            outputs=['y'],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+        )
+        graph_def = helper.make_graph(
+            [node_def],
+            'test',
+            [helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5]),
+             helper.make_tensor_value_info('W', TensorProto.FLOAT, [1, 1, 3, 3])],
+            [helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 2, 2])]
+        )
+        model_def = helper.make_model(graph_def, producer_name='test')
+        updated_def = update_model_dims.update_inputs_outputs_dims(
+            model_def,
+            {
+                "x": [1, 1, 'x1', -1],
+                "W": [1, 1, 3, 3],
+            },
+            {
+                "y": [1, 1, -1, -1],
+            })
+        onnx.checker.check_model(updated_def)
+        self.assertEqual(updated_def.graph.input[0].type.tensor_type.shape.dim[2].dim_param, 'x1')
+        self.assertEqual(updated_def.graph.input[0].type.tensor_type.shape.dim[3].dim_param, 'x_3')
+        self.assertEqual(updated_def.graph.output[0].type.tensor_type.shape.dim[2].dim_param, 'y_2')
+        self.assertEqual(updated_def.graph.output[0].type.tensor_type.shape.dim[3].dim_param, 'y_3')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/training_tool_test.py b/third_party/onnx/onnx/test/training_tool_test.py
new file mode 100644
index 0000000000..bcff8feae0
--- /dev/null
+++ b/third_party/onnx/onnx/test/training_tool_test.py
@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+import onnx
+import numpy as np  # type: ignore
+from onnx.tools import update_model_dims
+from onnx import helper, numpy_helper, shape_inference, TensorProto
+
+
+class TestTrainingTool(unittest.TestCase):
+    def test_training_info_proto(self) -> None:
+        # Inference graph.
+        A_shape = [2, 2]
+        A_name = 'A'
+        A = np.random.rand(*A_shape).astype(np.float32)
+        A_initializer = numpy_helper.from_array(A, name=A_name)
+        A_value_info = helper.make_tensor_value_info(
+            A_name, TensorProto.FLOAT, A_shape)
+
+        B_shape = [2, 2]
+        B_name = 'B'
+        B = np.random.rand(*B_shape).astype(np.float32)
+        B_initializer = numpy_helper.from_array(B, name=B_name)
+        B_value_info = helper.make_tensor_value_info(
+            B_name, TensorProto.FLOAT, B_shape)
+
+        C_shape = [2, 2]
+        C_name = 'C'
+        C_value_info = helper.make_tensor_value_info(
+            C_name, TensorProto.FLOAT, C_shape)
+
+        inference_node = helper.make_node(
+            'MatMul',
+            inputs=[A_name, B_name],
+            outputs=[C_name]
+        )
+
+        inference_graph = helper.make_graph(
+            [inference_node],
+            'simple_inference',
+            [A_value_info, B_value_info],
+            [C_value_info],
+            [A_initializer, B_initializer]
+        )
+
+        # Training graph
+        X_shape = [2, 2]
+        X_name = 'X'
+        X = np.random.rand(*X_shape).astype(np.float32)
+        X_initializer = numpy_helper.from_array(X, name=X_name)
+        X_value_info = helper.make_tensor_value_info(
+            X_name, TensorProto.FLOAT, X_shape)
+
+        Y_shape = [2, 2]
+        Y_name = 'Y'
+        Y_value_info = helper.make_tensor_value_info(
+            Y_name, TensorProto.FLOAT, Y_shape)
+
+        node = helper.make_node(
+            'MatMul',
+            inputs=[X_name, C_name],  # tensor "C" is from inference graph.
+            outputs=[Y_name]
+        )
+
+        training_graph = helper.make_graph(
+            [node],
+            'simple_training',
+            [X_value_info],
+            [Y_value_info],
+            [X_initializer]
+        )
+
+        # Capture assignment of B <--- Y.
+        training_info = helper.make_training_info(
+            training_graph, [(B_name, Y_name)], None, None)
+
+        # Create a model with both inference and training information.
+        model = helper.make_model(inference_graph)
+        # Check if the inference-only part is correct.
+        onnx.checker.check_model(model)
+        # Insert training information.
+        new_training_info = model.training_info.add()
+        new_training_info.CopyFrom(training_info)
+
+        # Generate the actual training graph from training information so that
+        # we can run onnx checker to check if the full training graph is a valid
+        # graph. As defined in spec, full training graph forms by concatenating
+        # corresponding fields.
+        full_training_graph = helper.make_graph(
+            list(model.graph.node) + list(model.training_info[0].algorithm.node),
+            'full_training_graph',
+            list(model.graph.input) + list(model.training_info[0].algorithm.input),
+            list(model.graph.output) + list(model.training_info[0].algorithm.output),
+            list(model.graph.initializer) + list(model.training_info[0].algorithm.initializer))
+
+        # Wrap full training graph as a ModelProto so that we can run checker.
+        full_training_model = helper.make_model(full_training_graph)
+        full_training_model_with_shapes = shape_inference.infer_shapes(full_training_model)
+        onnx.checker.check_model(full_training_model_with_shapes)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/utils_test.py b/third_party/onnx/onnx/test/utils_test.py
new file mode 100644
index 0000000000..b7e10fc572
--- /dev/null
+++ b/third_party/onnx/onnx/test/utils_test.py
@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import shutil
+import tempfile
+import unittest
+
+import onnx
+from onnx import helper, TensorProto
+
+
+class TestUtilityFunctions(unittest.TestCase):
+    def test_extract_model(self) -> None:
+        def create_tensor(name):  # type: ignore
+            return helper.make_tensor_value_info(name, TensorProto.FLOAT, [1, 2])
+        A0 = create_tensor("A0")
+        A1 = create_tensor("A1")
+        B0 = create_tensor("B0")
+        B1 = create_tensor("B1")
+        B2 = create_tensor("B2")
+        C0 = create_tensor("C0")
+        C1 = create_tensor("C1")
+        D0 = create_tensor("D0")
+        L0_0 = helper.make_node("Add", ["A0", "A1"], ["B0"])
+        L0_1 = helper.make_node("Sub", ["A0", "A1"], ["B1"])
+        L0_2 = helper.make_node("Mul", ["A0", "A1"], ["B2"])
+        L1_0 = helper.make_node("Add", ["B0", "B1"], ["C0"])
+        L1_1 = helper.make_node("Sub", ["B1", "B2"], ["C1"])
+        L2_0 = helper.make_node("Mul", ["C0", "C1"], ["D0"])
+
+        g0 = helper.make_graph(
+            [L0_0, L0_1, L0_2, L1_0, L1_1, L2_0],
+            "test",
+            [A0, A1],
+            [D0])
+        m0 = helper.make_model(g0, producer_name='test')
+        tdir = tempfile.mkdtemp()
+        p0 = os.path.join(tdir, "original.onnx")
+        onnx.save(m0, p0)
+
+        p1 = os.path.join(tdir, "extracted.onnx")
+        input_names = ["B0", "B1", "B2"]
+        output_names = ["C0", "C1"]
+        onnx.utils.extract_model(p0, p1, input_names, output_names)
+
+        m1 = onnx.load(p1)
+        self.assertEqual(m1.producer_name, 'onnx.utils.extract_model')
+        self.assertEqual(m1.ir_version, m0.ir_version)
+        self.assertEqual(m1.opset_import, m0.opset_import)
+        self.assertEqual(len(m1.graph.node), 2)
+        self.assertEqual(len(m1.graph.input), 3)
+        self.assertEqual(len(m1.graph.output), 2)
+        self.assertEqual(m1.graph.input[0], B0)
+        self.assertEqual(m1.graph.input[1], B1)
+        self.assertEqual(m1.graph.input[2], B2)
+        self.assertEqual(m1.graph.output[0], C0)
+        self.assertEqual(m1.graph.output[1], C1)
+        shutil.rmtree(tdir, ignore_errors=True)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/test/version_converter_test.py b/third_party/onnx/onnx/test/version_converter_test.py
new file mode 100644
index 0000000000..d082ca3241
--- /dev/null
+++ b/third_party/onnx/onnx/test/version_converter_test.py
@@ -0,0 +1,1555 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx import checker, helper, ModelProto, TensorProto, GraphProto, NodeProto, OperatorSetIdProto
+from typing import Sequence, Text, Tuple, List, Callable
+from onnx import numpy_helper
+
+import numpy as np  # type: ignore
+import struct
+
+import onnx.version_converter
+import unittest
+
+
+class TestVersionConverter(unittest.TestCase):
+
+    def _converted(
+            self,
+            graph: GraphProto,
+            initial_version: OperatorSetIdProto,
+            target_version: int
+    ) -> ModelProto:
+        orig_model = helper.make_model(graph, producer_name='onnx-test', opset_imports=[initial_version])
+        # print(type(orig_model))
+        converted_model = onnx.version_converter.convert_version(orig_model,
+                target_version)
+        checker.check_model(converted_model)
+        return converted_model
+
+    # Test 1: Backwards Incompatible Conversion: Reshape: 8 -> 2
+    def test_backwards_incompatible(self) -> None:
+        def test() -> None:
+            nodes = [helper.make_node('Add', ["W", "Z"], ["shape"]),
+                        helper.make_node('Reshape', ["X", "shape"], ["A"]),
+                        helper.make_node('Add', ["A", "W"], ["Y"])]
+            graph = helper.make_graph(
+                nodes,
+                "test",
+                [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+                    helper.make_tensor_value_info("W", TensorProto.FLOAT, (1,)),
+                    helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1,))],
+                [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+            self._converted(graph, helper.make_operatorsetid("", 8), 2)
+        self.assertRaises(RuntimeError, test)
+
+    # Test 2: Backwards Compatible Conversion (No Adaptations): Add: 3 -> 2
+    def test_backwards_compatible(self) -> None:
+        nodes = [helper.make_node('Add', ["X1", "X2"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 3), 2)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Add"
+        assert converted_model.opset_import[0].version == 2
+
+    # Test 3: Non-Existent Op Conversion: Cos: 8 -> 6
+    def test_non_existent_op(self) -> None:
+        def test() -> None:
+            nodes = [helper.make_node('Cos', ["X"], ["Y"])]
+            graph = helper.make_graph(
+                nodes,
+                "test",
+                [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+                [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+            self._converted(graph, helper.make_operatorsetid("", 8), 6)
+        self.assertRaises(RuntimeError, test)
+
+    # Test Add Adapter: 8 -> 5
+    def test_add_8_5(self) -> None:
+        nodes = [helper.make_node('Add', ["X1", "X2"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 5)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Add"
+        assert converted_model.opset_import[0].version == 5
+
+    # Test Add Adapter: 5 -> 8
+    def test_add_5_8(self) -> None:
+        nodes = [helper.make_node('Add', ["X1", "X2"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Add"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Add Adapter: 5 -> 8, requiring insertion of an Unsqueeze node
+    def test_add_5_8_with_unsqueeze(self) -> None:
+        nodes = [helper.make_node('Add', ["X1", "X2"], ["Y"], axis=0, broadcast=1)]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5, 2)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Unsqueeze"
+        assert converted_model.graph.node[1].op_type == "Add"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Mul Adapter: 8 -> 5
+    def test_mul_8_5(self) -> None:
+        nodes = [helper.make_node('Mul', ["X1", "X2"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 5)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Mul"
+        assert converted_model.opset_import[0].version == 5
+
+    # Test Mul Adapter: 5 -> 8
+    def test_mul_5_8(self) -> None:
+        nodes = [helper.make_node('Mul', ["X1", "X2"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Mul"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Gemm Adapter: 1 -> 8
+    def test_gemm_up(self) -> None:
+        nodes = [helper.make_node('Gemm', ["A", "B", "C"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5, 5,)),
+                helper.make_tensor_value_info("B", TensorProto.FLOAT, (5, 5,)),
+                helper.make_tensor_value_info("C", TensorProto.FLOAT, (5, 5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 1), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Gemm"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Gemm Adapter: 8 -> 1
+    def test_gemm_down(self) -> None:
+        nodes = [helper.make_node('Gemm', ["A", "B", "C"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5, 5,)),
+                helper.make_tensor_value_info("B", TensorProto.FLOAT, (5, 5,)),
+                helper.make_tensor_value_info("C", TensorProto.FLOAT, (5, 5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 1)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Gemm"
+        assert converted_model.opset_import[0].version == 1
+
+    # Test Relu Adapter: 5 -> 7
+    def test_relu_5_7(self) -> None:
+        nodes = [helper.make_node('Relu', ["X"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 7)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Relu"
+        assert converted_model.opset_import[0].version == 7
+
+    # Test Relu Adapter: 7 -> 5
+    def test_relu_7_5(self) -> None:
+        nodes = [helper.make_node('Relu', ["X"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 7), 5)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Relu"
+        assert converted_model.opset_import[0].version == 5
+
+    # Test BatchNormalization Adapter: 8 -> 5
+    def test_batch_normalization_8_5(self) -> None:
+        nodes = [helper.make_node('BatchNormalization', ["X", "scale", "B",
+            "mean", "var"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("scale", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("B", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("mean", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("var", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 5)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "BatchNormalization"
+        assert converted_model.opset_import[0].version == 5
+
+    # Test BatchNormalization Adapter: 5 -> 8
+    def test_batch_normalization_5_8(self) -> None:
+        nodes = [helper.make_node('BatchNormalization', ["X", "scale", "B",
+            "mean", "var"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("scale", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("B", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("mean", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("var", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "BatchNormalization"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Concat Adapter: 3 -> 5
+    def test_concat_3_5(self) -> None:
+        nodes = [helper.make_node('Concat', ["X1", "X2", "X3",
+            "X4", "X5"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X3", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X4", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X5", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 3), 5)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Concat"
+        assert converted_model.opset_import[0].version == 5
+
+    # Test Concat Adapter: 5 -> 3
+    def test_concat_5_3(self) -> None:
+        nodes = [helper.make_node('Concat', ["X1", "X2", "X3",
+            "X4", "X5"], ["Y"], axis=0)]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X1", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X3", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X4", TensorProto.FLOAT, (1,)),
+                helper.make_tensor_value_info("X5", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 3)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Concat"
+        assert converted_model.opset_import[0].version == 3
+
+    # Test Reshape Adapter: 6 -> 4
+    def test_reshape_6_4(self) -> None:
+        nodes = [helper.make_node('Constant', [], ["shape"],
+                    value=helper.make_tensor("", TensorProto.INT64, [1],
+                        [5])),
+                    helper.make_node('Reshape', ["X", "shape"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 6), 4)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Reshape"
+        assert converted_model.opset_import[0].version == 4
+
+    # Test Reshape Adapter: 4 -> 6
+    def test_reshape_4_6(self) -> None:
+        nodes = [helper.make_node('Reshape', ["X"], ["Y"], shape=[5])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 4), 6)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.graph.node[1].op_type == "Reshape"
+        assert converted_model.opset_import[0].version == 6
+
+    # Test Sum Adapter: 7 -> 8
+    def test_sum_7_8(self) -> None:
+        nodes = [helper.make_node('Sum', ["data_0", "data_1", "data_2",
+            "data_3", "data_4"], ["sum"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("data_0", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_2", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_3", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_4", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("sum", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 7), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Sum"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Sum Adapter: 5 -> 8
+    def test_sum_5_8(self) -> None:
+        nodes = [helper.make_node('Sum', ["data_0", "data_1", "data_2",
+            "data_3", "data_4"], ["sum"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("data_0", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_2", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_3", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_4", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("sum", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 5), 7)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Sum"
+        assert converted_model.opset_import[0].version == 7
+
+    # Test Sum Adapter: 8 -> 5
+    def test_sum_8_5(self) -> None:
+        nodes = [helper.make_node('Sum', ["data_0", "data_1", "data_2",
+            "data_3", "data_4"], ["sum"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("data_0", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_1", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_2", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_3", TensorProto.FLOAT, (5,)),
+                helper.make_tensor_value_info("data_4", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("sum", TensorProto.FLOAT, (5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 5)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Sum"
+        assert converted_model.opset_import[0].version == 5
+
+    # Test AveragePool Adapter: 1 -> 8
+    def test_averagepool_up(self) -> None:
+        nodes = [helper.make_node('AveragePool', ["X"], ["Y"], kernel_shape=[1, 1])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 1), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "AveragePool"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test AveragePool Adapter: 8 -> 1
+    def test_averagepool_down(self) -> None:
+        nodes = [helper.make_node('AveragePool', ["X"], ["Y"], kernel_shape=[1, 1])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 1)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "AveragePool"
+        assert converted_model.opset_import[0].version == 1
+
+    # Test Dropout Adapter: 1 -> 8
+    def test_dropout_up(self) -> None:
+        nodes = [helper.make_node('Dropout', ["data"], ["output"], is_test=1)]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("data", TensorProto.FLOAT, (5, 5,))],
+            [helper.make_tensor_value_info("output", TensorProto.FLOAT, (5, 5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 1), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Dropout"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Dropout Adapter: 8 -> 1
+    def test_dropout_down(self) -> None:
+        nodes = [helper.make_node('Dropout', ["data"], ["output"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("data", TensorProto.FLOAT, (5, 5,))],
+            [helper.make_tensor_value_info("output", TensorProto.FLOAT, (5, 5,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 1)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Dropout"
+        assert converted_model.opset_import[0].version == 1
+
+    # Test Max Adapter: 7 -> 8
+    def test_max_7_8(self) -> None:
+        from_opset = 7
+        to_opset = 8
+        data_type = TensorProto.FLOAT
+        data_shape = (2, 3, 4)
+
+        nodes = [onnx.helper.make_node(
+            "Max",
+            inputs=["X"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_max",
+            [onnx.helper.make_tensor_value_info("X", data_type, data_shape)],
+            [onnx.helper.make_tensor_value_info("Y", data_type, data_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Max"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Min Adapter: 7 -> 8
+    def test_min_7_8(self) -> None:
+        from_opset = 7
+        to_opset = 8
+        data_type = TensorProto.FLOAT
+        data_shape = (2, 3, 4)
+
+        nodes = [onnx.helper.make_node(
+            "Min",
+            inputs=["X"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_min",
+            [onnx.helper.make_tensor_value_info("X", data_type, data_shape)],
+            [onnx.helper.make_tensor_value_info("Y", data_type, data_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Min"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Mean Adapter: 7 -> 8
+    def test_mean_7_8(self) -> None:
+        from_opset = 7
+        to_opset = 8
+        data_type = TensorProto.FLOAT
+        data_shape = (3,)
+
+        nodes = [onnx.helper.make_node(
+            "Mean",
+            inputs=["X"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_mean",
+            [onnx.helper.make_tensor_value_info("X", data_type, data_shape)],
+            [onnx.helper.make_tensor_value_info("Y", data_type, data_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Mean"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test MaxPool Adapter: 1 -> 8
+    def test_maxpool_up(self) -> None:
+        nodes = [helper.make_node('MaxPool', ["X"], ["Y"], kernel_shape=[1, 1])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 1), 8)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "MaxPool"
+        assert converted_model.opset_import[0].version == 8
+
+    # Test Upsample Adapter: 6 -> 7
+    def test_upsample_6_7(self) -> None:
+        from_opset = 6
+        to_opset = 7
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Upsample",
+            inputs=["X"],
+            outputs=["Y"],
+            mode="nearest",
+            width_scale=3.0,
+            height_scale=2.0
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_upsample_6_7",
+            [onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])]
+        )
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert len(converted_model.graph.node) == 1
+        assert converted_model.graph.node[0].op_type == "Upsample"
+        attribute_names = [attr.name for attr in converted_model.graph.node[0].attribute]
+        assert 'scales' in attribute_names
+        assert 'width_scale' not in attribute_names
+        assert 'height_scale' not in attribute_names
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test MaxPool Adapter: 8 -> 1
+    def test_maxpool_down(self) -> None:
+        nodes = [helper.make_node('MaxPool', ["X"], ["Y"], kernel_shape=[1, 1])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 8), 1)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "MaxPool"
+        assert converted_model.opset_import[0].version == 1
+
+    # Test BatchNormalization Adapter: 8 -> 9
+    def test_batch_normalization_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [helper.make_node(
+            'BatchNormalization',
+            inputs=["x", "s", "bias", "mean", "var"],
+            outputs=["y"]
+        )]
+
+        input_shape = (1, 2, 1, 3)
+        x = helper.make_tensor_value_info("x", data_type, input_shape)
+        scale = helper.make_tensor_value_info("s", data_type, [input_shape[1]])
+        B = helper.make_tensor_value_info("bias", data_type, [input_shape[1]])
+        mean = helper.make_tensor_value_info("mean", data_type, [input_shape[1]])
+        var = helper.make_tensor_value_info("var", data_type, [input_shape[1]])
+        y = helper.make_tensor_value_info("y", data_type, input_shape)
+
+        graph = helper.make_graph(
+            nodes,
+            "test_batchnormalization_8_9",
+            [x, scale, B, mean, var],
+            [y]
+        )
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "BatchNormalization"
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test BatchNormalization Adapter: 9 -> 8
+    def test_batchnormalization_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            'BatchNormalization',
+            inputs=['X', 'scale', 'B', 'mean', 'var'],
+            outputs=['Y'],
+        )]
+
+        input_shape = (2, 3, 4, 5)
+        x = onnx.helper.make_tensor_value_info("X", data_type, input_shape)
+        scale = onnx.helper.make_tensor_value_info("scale", data_type, [input_shape[1]])
+        B = onnx.helper.make_tensor_value_info("B", data_type, [input_shape[1]])
+        mean = onnx.helper.make_tensor_value_info("mean", data_type, [input_shape[1]])
+        var = onnx.helper.make_tensor_value_info("var", data_type, [input_shape[1]])
+        y = onnx.helper.make_tensor_value_info("Y", data_type, input_shape)
+
+        graph = onnx.helper.make_graph(
+            nodes, "test_batchnormalization", [x, scale, B, mean, var], [y]
+        )
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "BatchNormalization"
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Constant Adapter: 8 -> 9
+    def test_constant_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        output_shape = [2, 3, 4]
+        output_value = np.arange(24)
+
+        nodes = [helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["Y"],
+            value=helper.make_tensor("", data_type, output_shape, output_value))]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_constant",
+            [],
+            [onnx.helper.make_tensor_value_info("Y", data_type, output_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Constant Adapter: 9 -> 8
+    def test_constant_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        output_shape = [2, 3, 4]
+        output_value = np.arange(24)
+
+        nodes = [helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["Y"],
+            value=helper.make_tensor("", data_type, output_shape, output_value))]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_constant",
+            [],
+            [onnx.helper.make_tensor_value_info("Y", data_type, output_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Flatten Adapter: 8 -> 9
+    def test_flatten_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Flatten",
+            inputs=["X"],
+            outputs=["Y"],
+            axis=1
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_flatten",
+            [onnx.helper.make_tensor_value_info("X", data_type, [2, 3, 4])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [2, 12])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Flatten"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Flatten Adapter: 9 -> 8
+    def test_flatten_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        nodes = [onnx.helper.make_node(
+            "Flatten",
+            inputs=["X"],
+            outputs=["Y"],
+            axis=1
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_flatten",
+            [onnx.helper.make_tensor_value_info("X", data_type, [2, 3, 4])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [2, 12])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[1].op_type == "Flatten"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test PRelu Adapter: 8 -> 9
+    def test_prelu_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "PRelu",
+            inputs=["X", "Slope"],
+            outputs=["Y"]
+        )]
+
+        input_shape = [2, 3, 4]
+        graph = helper.make_graph(
+            nodes,
+            "test_prelu",
+            [onnx.helper.make_tensor_value_info("X", data_type, input_shape),
+             onnx.helper.make_tensor_value_info("Slope", data_type, input_shape)],
+            [onnx.helper.make_tensor_value_info("Y", data_type, input_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "PRelu"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test PRelu Adapter: 9 -> 8
+    def test_prelu_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        nodes = [onnx.helper.make_node(
+            "PRelu",
+            inputs=["X", "Slope"],
+            outputs=["Y"]
+        )]
+
+        input_shape = [2, 3, 4]
+        graph = helper.make_graph(
+            nodes,
+            "test_prelu",
+            [onnx.helper.make_tensor_value_info("X", data_type, input_shape),
+             onnx.helper.make_tensor_value_info("Slope", data_type, input_shape)],
+            [onnx.helper.make_tensor_value_info("Y", data_type, input_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[2].op_type == "PRelu"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Greater Adapter: 8 -> 9
+    def test_greater_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Greater",
+            inputs=["X1", "X2"],
+            outputs=["Y"]
+        )]
+
+        input_shape = [2, 3, 4]
+        graph = helper.make_graph(
+            nodes,
+            "test_greater",
+            [onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
+             onnx.helper.make_tensor_value_info("X2", data_type, input_shape)],
+            [onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Greater"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == TensorProto.BOOL
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Greater Adapter: 9 -> 8
+    def test_greater_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        nodes = [onnx.helper.make_node(
+            "Greater",
+            inputs=["X1", "X2"],
+            outputs=["Y"]
+        )]
+
+        input_shape = [2, 3, 4]
+        graph = helper.make_graph(
+            nodes,
+            "test_greater",
+            [onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
+             onnx.helper.make_tensor_value_info("X2", data_type, input_shape)],
+            [onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[2].op_type == "Greater"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == TensorProto.BOOL
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Less Adapter: 8 -> 9
+    def test_less_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Less",
+            inputs=["X1", "X2"],
+            outputs=["Y"]
+        )]
+
+        input_shape = [2, 3, 4]
+        graph = helper.make_graph(
+            nodes,
+            "test_less",
+            [onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
+             onnx.helper.make_tensor_value_info("X2", data_type, input_shape)],
+            [onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Less"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == TensorProto.BOOL
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Less Adapter: 9 -> 8
+    def test_less_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        nodes = [onnx.helper.make_node(
+            "Less",
+            inputs=["X1", "X2"],
+            outputs=["Y"]
+        )]
+
+        input_shape = [2, 3, 4]
+        graph = helper.make_graph(
+            nodes,
+            "test_less",
+            [onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
+             onnx.helper.make_tensor_value_info("X2", data_type, input_shape)],
+            [onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[2].op_type == "Less"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == TensorProto.BOOL
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test MatMul Adapter: 8 -> 9
+    def test_matmul_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "MatMul",
+            inputs=["X1", "X2"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_matmul",
+            [onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
+             onnx.helper.make_tensor_value_info("X2", data_type, [4, 3])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "MatMul"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test MatMul Adapter: 9 -> 8
+    def test_matmul_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        nodes = [onnx.helper.make_node(
+            "MatMul",
+            inputs=["X1", "X2"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_matmul",
+            [onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
+             onnx.helper.make_tensor_value_info("X2", data_type, [4, 3])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[2].op_type == "MatMul"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Gemm Adapter: 8 -> 9
+    def test_gemm_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Gemm",
+            inputs=["X1", "X2", "X3"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_gemm",
+            [onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
+             onnx.helper.make_tensor_value_info("X2", data_type, [4, 3]),
+             onnx.helper.make_tensor_value_info("X3", data_type, [3, 3])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Gemm"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Gemm Adapter: 9 -> 8
+    def test_gemm_9_8(self) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.UINT64
+
+        nodes = [onnx.helper.make_node(
+            "Gemm",
+            inputs=["X1", "X2", "X3"],
+            outputs=["Y"]
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_gemm",
+            [onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
+             onnx.helper.make_tensor_value_info("X2", data_type, [4, 3]),
+             onnx.helper.make_tensor_value_info("X3", data_type, [3, 3])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[3].op_type == "Gemm"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Upsample Adapter: 8 -> 9
+    def test_upsample_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Upsample",
+            inputs=["X"],
+            outputs=["Y"],
+            mode="nearest",
+            scales=[1.0, 1.0, 2.0, 3.0],
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_upsample_8_9",
+            [onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])]
+        )
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert len(converted_model.graph.node) == 2
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.graph.node[1].op_type == "Upsample"
+        assert len(converted_model.graph.node[1].attribute) == 1
+        assert converted_model.graph.node[1].attribute[0].name == "mode"
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Helper for Upsample Adapter: 9 -> 8
+    def helper_upsample_with_initializer(self, raw_scale: bool = False) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.FLOAT
+
+        nodes = [onnx.helper.make_node(
+            "Upsample",
+            inputs=["X", "Scales"],
+            outputs=["Y"],
+            mode="nearest"
+        )]
+
+        scale_value = [1.0, 1.0, 2.0, 3.0]
+        scale_tensor = onnx.helper.make_tensor("Scales", onnx.TensorProto.FLOAT, [4], bytes(struct.pack("4f", *scale_value)) if raw_scale else scale_value, raw_scale)
+
+        graph = helper.make_graph(
+            nodes,
+            "test_upsample",
+            [onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2]),
+             onnx.helper.make_tensor_value_info("Scales", data_type, [4])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
+            [scale_tensor])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Upsample"
+        assert len(converted_model.graph.initializer) == 0
+        assert len(converted_model.graph.node[0].attribute) == 2
+        assert converted_model.graph.node[0].attribute[1].name == "scales"
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Helper for Upsample Adapter: 9 -> 8
+    def helper_upsample_with_constant(self, raw_scale: bool = False) -> None:
+        from_opset = 9
+        to_opset = 8
+        data_type = TensorProto.FLOAT
+
+        scale_value = [1.0, 1.0, 2.0, 3.0]
+        scale_tensor = onnx.helper.make_tensor("const_value", onnx.TensorProto.FLOAT, [4], bytes(struct.pack("4f", *scale_value)) if raw_scale else scale_value, raw_scale)
+        nodes = [
+            onnx.helper.make_node(
+                'Constant',
+                inputs=[],
+                outputs=['Constant_Output'],
+                value=scale_tensor),
+            onnx.helper.make_node(
+                "Upsample",
+                inputs=["X", "Constant_Output"],
+                outputs=["Y"],
+                mode="nearest")]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_upsample",
+            [onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
+            [onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
+            value_info=[onnx.helper.make_tensor_value_info("Constant_Output", data_type, [4])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert len(converted_model.graph.node) == 1
+        assert converted_model.graph.node[0].op_type == "Upsample"
+        assert len(converted_model.graph.node[0].attribute) == 2
+        assert converted_model.graph.node[0].attribute[1].name == "scales"
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Upsample Adapter: 9 -> 8
+    def test_upsample_with_constant_node_9_8(self) -> None:
+        self.helper_upsample_with_constant(raw_scale=False)
+
+    # Test Upsample Adapter: 9 -> 8
+    def test_upsample_with_initializer_9_8(self) -> None:
+        self.helper_upsample_with_initializer(raw_scale=False)
+
+    # Test Upsample Adapter: 9 -> 8
+    def test_upsample_with_raw_initializer_9_8(self) -> None:
+        self.helper_upsample_with_constant(raw_scale=True)
+
+    # Test Upsample Adapter: 9 -> 8
+    def test_upsample_with_raw_constant_node_9_8(self) -> None:
+        self.helper_upsample_with_constant(raw_scale=True)
+
+    # Test Scan Adapter: 8 -> 9
+    def test_scan_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type = TensorProto.FLOAT
+
+        node1 = onnx.helper.make_node("Add", inputs=["sum_in", "next"], outputs=["sum_out"],)
+        node2 = onnx.helper.make_node("Identity", inputs=["sum_out"], outputs=["scan_out"],)
+        g = onnx.helper.make_graph(
+            [node1, node2],
+            "scan_body",
+            [onnx.helper.make_tensor_value_info("sum_in", data_type, [2]),
+             onnx.helper.make_tensor_value_info("next", data_type, [2])],
+            [onnx.helper.make_tensor_value_info("sum_out", data_type, [2]),
+             onnx.helper.make_tensor_value_info("scan_out", data_type, [2])]
+        )
+
+        no_sequence_lens = ''   # optional input, not supplied
+        nodes = [onnx.helper.make_node(
+            "Scan",
+            inputs=[no_sequence_lens, "initial", "x"],
+            outputs=["y", "z"],
+            body=g,
+            num_scan_inputs=1,
+        )]
+
+        initial = onnx.helper.make_tensor_value_info("initial", data_type, [1, 2])
+        x = onnx.helper.make_tensor_value_info("x", data_type, [1, 3, 2])
+        y = onnx.helper.make_tensor_value_info("y", data_type, [1, 2])
+        z = onnx.helper.make_tensor_value_info("z", data_type, [1, 3, 2])
+
+        graph = onnx.helper.make_graph(
+            nodes, "test_scan_8_9", [initial, x], [y, z]
+        )
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Scan"
+        assert converted_model.opset_import[0].version == to_opset
+
+    # Test Cast Adapter: 8 -> 9
+    def test_cast_8_9(self) -> None:
+        from_opset = 8
+        to_opset = 9
+        data_type_from = TensorProto.FLOAT
+        data_type_to = TensorProto.UINT32
+
+        nodes = [onnx.helper.make_node(
+            "Cast",
+            inputs=["X"],
+            outputs=["Y"],
+            to=TensorProto.UINT32
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_cast",
+            [onnx.helper.make_tensor_value_info("X", data_type_from, [2, 3])],
+            [onnx.helper.make_tensor_value_info("Y", data_type_to, [2, 3])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "Cast"
+        assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type_to
+        assert converted_model.opset_import[0].version == to_opset
+
+    #Test Split Adapter: 13 -> 12
+    def test_split_13_12(self) -> None:
+        nodes = [helper.make_node('Constant', [], ["split"],
+                                  value=helper.make_tensor("", TensorProto.INT64, [2],
+                                                           [2, 3])),
+                 helper.make_node('Split', ["X", "split"], ["Y1", "Y2"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y1", TensorProto.FLOAT, (2,)),
+             helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (3,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 13), 12)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Split"
+        assert converted_model.opset_import[0].version == 12
+
+    # Test Split Adapter: 12 -> 13
+    def test_split_12_13(self) -> None:
+        nodes = [helper.make_node('Split', ["X"], ["Y1", "Y2"], split=[2, 3])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y1", TensorProto.FLOAT, (2,)),
+             helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (3,))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 12), 13)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.graph.node[1].op_type == "Split"
+        assert converted_model.opset_import[0].version == 13
+
+    # Test AxesInputToAttribute Adapter: 13 -> 12
+    def test_axes_input_to_attr_13_12(self) -> None:
+        nodes = [helper.make_node('Constant', [], ["axes"],
+                                  value=helper.make_tensor("", TensorProto.INT64, [1],
+                                                           [0])),
+                 helper.make_node('ReduceSum', ["X", "axes"], ["Y"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 13), 12)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "ReduceSum"
+        assert converted_model.opset_import[0].version == 12
+
+    # Test AxesAttributeToInput Adapter: 12 -> 13
+    def test_axes_attr_to_input_12_13(self) -> None:
+        nodes = [helper.make_node('ReduceSum', ["X"], ["Y"], axes=[0])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 12), 13)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.opset_import[0].version == 13
+
+    # Test Slice Adapter: 9 -> 10
+    def test_slice_9_10(self) -> None:
+        nodes = [helper.make_node('Slice', ["X"], ["Y"],
+                                  axes=[0, 1],
+                                  starts=[0, 0],
+                                  ends=[3, 10])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (20, 10, 5))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 10, 5))])
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 9), 10)
+
+        assert converted_model.graph.node[0].op_type == "Constant"
+        assert converted_model.graph.node[1].op_type == "Constant"
+        assert converted_model.graph.node[2].op_type == "Constant"
+        assert converted_model.graph.node[3].op_type == "Slice"
+        assert converted_model.opset_import[0].version == 10
+        assert len(converted_model.graph.node[3].input) == 4
+        assert len(converted_model.graph.node[3].attribute) == 0
+
+    # Test RNN Adapter: 13 -> 14
+    def test_rnn_13_14(self) -> None:
+        from_opset = 13
+        to_opset = 14
+        data_type = TensorProto.FLOAT
+
+        seq_length = 1
+        batch_size = 2
+        input_size = 3
+        num_directions = 1
+        hidden_size = 5
+
+        nodes = [onnx.helper.make_node(
+            'RNN',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_rnn",
+            [onnx.helper.make_tensor_value_info("X", data_type, [seq_length, batch_size, input_size]),
+             onnx.helper.make_tensor_value_info("W", data_type, [num_directions, hidden_size, input_size]),
+             onnx.helper.make_tensor_value_info("R", data_type, [num_directions, hidden_size, hidden_size]),
+             onnx.helper.make_tensor_value_info("B", data_type, [num_directions, 2 * hidden_size])],
+            [onnx.helper.make_tensor_value_info("Y_h", data_type, [num_directions, batch_size, hidden_size])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "RNN"
+        assert converted_model.opset_import[0].version == to_opset
+        assert len(converted_model.graph.node[0].attribute) == 2
+        assert converted_model.graph.node[0].attribute[1].name == "layout"
+
+    # Test GRU Adapter: 13 -> 14
+    def test_gru_13_14(self) -> None:
+        from_opset = 13
+        to_opset = 14
+        data_type = TensorProto.FLOAT
+
+        seq_length = 1
+        batch_size = 2
+        input_size = 3
+        num_directions = 1
+        hidden_size = 5
+
+        nodes = [onnx.helper.make_node(
+            'GRU',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_gru",
+            [onnx.helper.make_tensor_value_info("X", data_type, [seq_length, batch_size, input_size]),
+             onnx.helper.make_tensor_value_info("W", data_type, [num_directions, 3 * hidden_size, input_size]),
+             onnx.helper.make_tensor_value_info("R", data_type, [num_directions, 3 * hidden_size, hidden_size]),
+             onnx.helper.make_tensor_value_info("B", data_type, [num_directions, 6 * hidden_size])],
+            [onnx.helper.make_tensor_value_info("Y_h", data_type, [num_directions, batch_size, hidden_size])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "GRU"
+        assert converted_model.opset_import[0].version == to_opset
+        assert len(converted_model.graph.node[0].attribute) == 2
+        assert converted_model.graph.node[0].attribute[1].name == "layout"
+
+    # Test LSTM Adapter: 13 -> 14
+    def test_lstm_13_14(self) -> None:
+        from_opset = 13
+        to_opset = 14
+        data_type = TensorProto.FLOAT
+
+        seq_length = 1
+        batch_size = 2
+        input_size = 3
+        num_directions = 1
+        hidden_size = 5
+
+        nodes = [onnx.helper.make_node(
+            'LSTM',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_lstm",
+            [onnx.helper.make_tensor_value_info("X", data_type, [seq_length, batch_size, input_size]),
+             onnx.helper.make_tensor_value_info("W", data_type, [num_directions, 4 * hidden_size, input_size]),
+             onnx.helper.make_tensor_value_info("R", data_type, [num_directions, 4 * hidden_size, hidden_size]),
+             onnx.helper.make_tensor_value_info("B", data_type, [num_directions, 8 * hidden_size])],
+            [onnx.helper.make_tensor_value_info("Y_h", data_type, [num_directions, batch_size, hidden_size])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "LSTM"
+        assert converted_model.opset_import[0].version == to_opset
+        assert len(converted_model.graph.node[0].attribute) == 2
+        assert converted_model.graph.node[0].attribute[1].name == "layout"
+
+    # Test RNN Adapter: 14 -> 13
+    def test_rnn_14_13(self) -> None:
+        from_opset = 14
+        to_opset = 13
+        data_type = TensorProto.FLOAT
+
+        seq_length = 1
+        batch_size = 2
+        input_size = 3
+        num_directions = 1
+        hidden_size = 5
+
+        nodes = [onnx.helper.make_node(
+            'RNN',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size,
+            layout=0,
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_rnn",
+            [onnx.helper.make_tensor_value_info("X", data_type, [seq_length, batch_size, input_size]),
+             onnx.helper.make_tensor_value_info("W", data_type, [num_directions, hidden_size, input_size]),
+             onnx.helper.make_tensor_value_info("R", data_type, [num_directions, hidden_size, hidden_size]),
+             onnx.helper.make_tensor_value_info("B", data_type, [num_directions, 2 * hidden_size])],
+            [onnx.helper.make_tensor_value_info("Y_h", data_type, [num_directions, batch_size, hidden_size])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "RNN"
+        assert converted_model.opset_import[0].version == to_opset
+        assert len(converted_model.graph.node[0].attribute) == 1
+
+    # Test GRU Adapter: 14 -> 13
+    def test_gru_14_13(self) -> None:
+        from_opset = 14
+        to_opset = 13
+        data_type = TensorProto.FLOAT
+
+        seq_length = 1
+        batch_size = 2
+        input_size = 3
+        num_directions = 1
+        hidden_size = 5
+
+        nodes = [onnx.helper.make_node(
+            'GRU',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size,
+            layout=0,
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_gru",
+            [onnx.helper.make_tensor_value_info("X", data_type, [seq_length, batch_size, input_size]),
+             onnx.helper.make_tensor_value_info("W", data_type, [num_directions, 3 * hidden_size, input_size]),
+             onnx.helper.make_tensor_value_info("R", data_type, [num_directions, 3 * hidden_size, hidden_size]),
+             onnx.helper.make_tensor_value_info("B", data_type, [num_directions, 6 * hidden_size])],
+            [onnx.helper.make_tensor_value_info("Y_h", data_type, [num_directions, batch_size, hidden_size])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "GRU"
+        assert converted_model.opset_import[0].version == to_opset
+        assert len(converted_model.graph.node[0].attribute) == 1
+
+    # Test LSTM Adapter: 14 -> 13
+    def test_lstm_14_13(self) -> None:
+        from_opset = 14
+        to_opset = 13
+        data_type = TensorProto.FLOAT
+
+        seq_length = 1
+        batch_size = 2
+        input_size = 3
+        num_directions = 1
+        hidden_size = 5
+
+        nodes = [onnx.helper.make_node(
+            'LSTM',
+            inputs=['X', 'W', 'R'],
+            outputs=['', 'Y_h'],
+            hidden_size=hidden_size,
+            layout=0,
+        )]
+
+        graph = helper.make_graph(
+            nodes,
+            "test_lstm",
+            [onnx.helper.make_tensor_value_info("X", data_type, [seq_length, batch_size, input_size]),
+             onnx.helper.make_tensor_value_info("W", data_type, [num_directions, 4 * hidden_size, input_size]),
+             onnx.helper.make_tensor_value_info("R", data_type, [num_directions, 4 * hidden_size, hidden_size]),
+             onnx.helper.make_tensor_value_info("B", data_type, [num_directions, 8 * hidden_size])],
+            [onnx.helper.make_tensor_value_info("Y_h", data_type, [num_directions, batch_size, hidden_size])])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid("", from_opset), to_opset)
+
+        assert converted_model.graph.node[0].op_type == "LSTM"
+        assert converted_model.opset_import[0].version == to_opset
+        assert len(converted_model.graph.node[0].attribute) == 1
+
+    # Test that subgraphs are converted
+    def test_if_subgraph_10_11(self) -> None:
+        from_opset = 10
+        to_opset = 11
+        data_type = TensorProto.FLOAT
+        data_shape = [2]
+
+        subg1_node = [onnx.helper.make_node(
+            'Clip',
+            inputs=['sub_in'],
+            outputs=['sub_out'],
+            min=2.0,
+            max=3.0
+        )]
+        subg1_input = [
+            onnx.helper.make_tensor_value_info('sub_in', data_type, data_shape)
+        ]
+        subg1_output = [
+            onnx.helper.make_tensor_value_info('sub_out', data_type, data_shape)
+        ]
+        subg1 = helper.make_graph(subg1_node, "then_g", subg1_input, subg1_output)
+
+        subg2_node = [onnx.helper.make_node(
+            'Clip',
+            inputs=['sub_in'],
+            outputs=['sub_out'],
+            min=2.0,
+            max=3.0
+        )]
+        subg2_input = [
+            onnx.helper.make_tensor_value_info('sub_in', data_type, data_shape)
+        ]
+        subg2_output = [
+            onnx.helper.make_tensor_value_info('sub_out', data_type, data_shape)
+        ]
+        subg2 = helper.make_graph(subg2_node, "then_g", subg2_input, subg2_output)
+
+        node = [onnx.helper.make_node(
+            'If',
+            inputs=['cond'],
+            outputs=['out'],
+            then_branch=subg1,
+            else_branch=subg2
+        )]
+        input = [
+            onnx.helper.make_tensor_value_info('cond', TensorProto.BOOL, [])
+        ]
+        output = [
+            onnx.helper.make_tensor_value_info('out', data_type, data_shape)
+        ]
+        init = [helper.make_tensor('sub_in', data_type, data_shape, [4.0, 5.0])]
+        graph = helper.make_graph(node, 'test_subgraphs', input, output, init)
+
+        converted = self._converted(graph, helper.make_operatorsetid('', from_opset), to_opset)
+
+        assert converted.graph.node[0].op_type == 'If'
+        assert converted.opset_import[0].version == to_opset
+        assert converted.graph.node[0].attribute[0].g.node[2].op_type == 'Clip'
+        assert len(converted.graph.node[0].attribute[0].g.node[2].attribute) == 0
+        assert converted.graph.node[0].attribute[1].g.node[2].op_type == 'Clip'
+        assert len(converted.graph.node[0].attribute[1].g.node[2].attribute) == 0
+
+    # Use initializer as node inputs (instead of graph input)
+    # to test whether IR (version_converter) can handle it
+    def test_initializer_not_in_input_above_ir4(self):  # type: () -> None
+        nodes = [helper.make_node('BatchNormalization', ["X", "scale", "B",
+            "mean", "var"], ["Y"])]
+
+        scale_value = [0.55, 0.72]
+        scale_tensor = onnx.helper.make_tensor("scale", onnx.TensorProto.FLOAT, [2], scale_value)
+        b_value = [0.60, 0.54]
+        b_tensor = onnx.helper.make_tensor("B", onnx.TensorProto.FLOAT, [2], b_value)
+        mean_value = [0.42, 0.65]
+        mean_tensor = onnx.helper.make_tensor("mean", onnx.TensorProto.FLOAT, [2], mean_value)
+        var_value = [0.44, 0.89]
+        var_tensor = onnx.helper.make_tensor("var", onnx.TensorProto.FLOAT, [2], var_value)
+
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2, 2, 3))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2, 2, 3))],
+            [scale_tensor, b_tensor, mean_tensor, var_tensor])
+
+        converted_model = self._converted(graph, helper.make_operatorsetid(
+            "", 11), 12)
+        # Assert equality of graph and converted_model
+        assert converted_model.graph.node[0].op_type == "BatchNormalization"
+        assert converted_model.opset_import[0].version == 12
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/onnx/onnx/tools/__init__.py b/third_party/onnx/onnx/tools/__init__.py
new file mode 100644
index 0000000000..9881313609
--- /dev/null
+++ b/third_party/onnx/onnx/tools/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/third_party/onnx/onnx/tools/net_drawer.py b/third_party/onnx/onnx/tools/net_drawer.py
new file mode 100644
index 0000000000..86a8767ba3
--- /dev/null
+++ b/third_party/onnx/onnx/tools/net_drawer.py
@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+# A library and utility for drawing ONNX nets. Most of this implementation has
+# been borrowed from the caffe2 implementation
+# https://github.com/caffe2/caffe2/blob/master/caffe2/python/net_drawer.py
+#
+# The script takes two required arguments:
+#   -input: a path to a serialized ModelProto .pb file.
+#   -output: a path to write a dot file representation of the graph
+#
+# Given this dot file representation, you can-for example-export this to svg
+# with the graphviz `dot` utility, like so:
+#
+#   $ dot -Tsvg my_output.dot -o my_output.svg
+
+import argparse
+from collections import defaultdict
+import json
+from onnx import ModelProto, GraphProto, NodeProto
+import pydot  # type: ignore
+from typing import Text, Any, Callable, Optional, Dict
+
+
+OP_STYLE = {
+    'shape': 'box',
+    'color': '#0F9D58',
+    'style': 'filled',
+    'fontcolor': '#FFFFFF'
+}
+
+BLOB_STYLE = {'shape': 'octagon'}
+
+_NodeProducer = Callable[[NodeProto, int], pydot.Node]
+
+
+def _escape_label(name: Text) -> Text:
+    # json.dumps is poor man's escaping
+    return json.dumps(name)
+
+
+def _form_and_sanitize_docstring(s: Text) -> Text:
+    url = 'javascript:alert('
+    url += _escape_label(s).replace('"', '\'').replace('<', '').replace('>', '')
+    url += ')'
+    return url
+
+
+def GetOpNodeProducer(embed_docstring: bool = False, **kwargs: Any) -> _NodeProducer:
+    def ReallyGetOpNode(op: NodeProto, op_id: int) -> pydot.Node:
+        if op.name:
+            node_name = '%s/%s (op#%d)' % (op.name, op.op_type, op_id)
+        else:
+            node_name = '%s (op#%d)' % (op.op_type, op_id)
+        for i, input in enumerate(op.input):
+            node_name += '\n input' + str(i) + ' ' + input
+        for i, output in enumerate(op.output):
+            node_name += '\n output' + str(i) + ' ' + output
+        node = pydot.Node(node_name, **kwargs)
+        if embed_docstring:
+            url = _form_and_sanitize_docstring(op.doc_string)
+            node.set_URL(url)
+        return node
+    return ReallyGetOpNode
+
+
+def GetPydotGraph(
+    graph: GraphProto,
+    name: Optional[Text] = None,
+    rankdir: Text = 'LR',
+    node_producer: Optional[_NodeProducer] = None,
+    embed_docstring: bool = False,
+) -> pydot.Dot:
+    if node_producer is None:
+        node_producer = GetOpNodeProducer(embed_docstring=embed_docstring, **OP_STYLE)
+    pydot_graph = pydot.Dot(name, rankdir=rankdir)
+    pydot_nodes: Dict[Text, pydot.Node] = {}
+    pydot_node_counts: Dict[Text, int] = defaultdict(int)
+    for op_id, op in enumerate(graph.node):
+        op_node = node_producer(op, op_id)
+        pydot_graph.add_node(op_node)
+        for input_name in op.input:
+            if input_name not in pydot_nodes:
+                input_node = pydot.Node(
+                    _escape_label(
+                        input_name + str(pydot_node_counts[input_name])),
+                    label=_escape_label(input_name),
+                    **BLOB_STYLE
+                )
+                pydot_nodes[input_name] = input_node
+            else:
+                input_node = pydot_nodes[input_name]
+            pydot_graph.add_node(input_node)
+            pydot_graph.add_edge(pydot.Edge(input_node, op_node))
+        for output_name in op.output:
+            if output_name in pydot_nodes:
+                pydot_node_counts[output_name] += 1
+            output_node = pydot.Node(
+                _escape_label(
+                    output_name + str(pydot_node_counts[output_name])),
+                label=_escape_label(output_name),
+                **BLOB_STYLE
+            )
+            pydot_nodes[output_name] = output_node
+            pydot_graph.add_node(output_node)
+            pydot_graph.add_edge(pydot.Edge(op_node, output_node))
+    return pydot_graph
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="ONNX net drawer")
+    parser.add_argument(
+        "--input",
+        type=Text, required=True,
+        help="The input protobuf file.",
+    )
+    parser.add_argument(
+        "--output",
+        type=Text, required=True,
+        help="The output protobuf file.",
+    )
+    parser.add_argument(
+        "--rankdir", type=Text, default='LR',
+        help="The rank direction of the pydot graph.",
+    )
+    parser.add_argument(
+        "--embed_docstring", action="store_true",
+        help="Embed docstring as javascript alert. Useful for SVG format.",
+    )
+    args = parser.parse_args()
+    model = ModelProto()
+    with open(args.input, 'rb') as fid:
+        content = fid.read()
+        model.ParseFromString(content)
+    pydot_graph = GetPydotGraph(
+        model.graph,
+        name=model.graph.name,
+        rankdir=args.rankdir,
+        node_producer=GetOpNodeProducer(
+            embed_docstring=args.embed_docstring,
+            **OP_STYLE
+        ),
+    )
+    pydot_graph.write_dot(args.output)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/onnx/tools/update_model_dims.py b/third_party/onnx/onnx/tools/update_model_dims.py
new file mode 100644
index 0000000000..f9d312d689
--- /dev/null
+++ b/third_party/onnx/onnx/tools/update_model_dims.py
@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any, List, Text, Dict, Set
+from onnx import ModelProto, ValueInfoProto
+
+import onnx.checker
+
+
+def update_inputs_outputs_dims(model: ModelProto, input_dims: Dict[Text, List[Any]], output_dims: Dict[Text, List[Any]]) -> ModelProto:
+    """
+        This function updates the dimension sizes of the model's inputs and outputs to the values
+        provided in input_dims and output_dims. if the dim value provided is negative, a unique dim_param
+        will be set for that dimension.
+
+        Example. if we have the following shape for inputs and outputs:
+                shape(input_1) = ('b', 3, 'w', 'h')
+                shape(input_2) = ('b', 4)
+                and shape(output)  = ('b', 'd', 5)
+
+            The parameters can be provided as:
+                input_dims = {
+                    "input_1": ['b', 3, 'w', 'h'],
+                    "input_2": ['b', 4],
+                }
+                output_dims = {
+                    "output": ['b', -1, 5]
+                }
+
+            Putting it together:
+                model = onnx.load('model.onnx')
+                updated_model = update_inputs_outputs_dims(model, input_dims, output_dims)
+                onnx.save(updated_model, 'model.onnx')
+    """
+    dim_param_set: Set[Text] = set()
+
+    def init_dim_param_set(dim_param_set: Set[Text], value_infos: List[ValueInfoProto]) -> None:
+        for info in value_infos:
+            shape = info.type.tensor_type.shape
+            for dim in shape.dim:
+                if dim.HasField('dim_param'):
+                    dim_param_set.add(dim.dim_param)  # type: ignore
+
+    init_dim_param_set(dim_param_set, model.graph.input)  # type: ignore
+    init_dim_param_set(dim_param_set, model.graph.output)  # type: ignore
+    init_dim_param_set(dim_param_set, model.graph.value_info)  # type: ignore
+
+    def update_dim(tensor: ValueInfoProto, dim: Any, j: int, name: Text) -> None:
+        dim_proto = tensor.type.tensor_type.shape.dim[j]
+        if isinstance(dim, int):
+            if dim >= 0:
+                if dim_proto.HasField('dim_value') and dim_proto.dim_value != dim:
+                    raise ValueError('Unable to set dimension value to {} for axis {} of {}. Contradicts existing dimension value {}.'
+                        .format(dim, j, name, dim_proto.dim_value))
+                dim_proto.dim_value = dim
+            else:
+                generated_dim_param = name + '_' + str(j)
+                if generated_dim_param in dim_param_set:
+                    raise ValueError('Unable to generate unique dim_param for axis {} of {}. Please manually provide a dim_param value.'
+                        .format(j, name))
+                dim_proto.dim_param = generated_dim_param
+        elif isinstance(dim, str):
+            dim_proto.dim_param = dim
+        else:
+            raise ValueError('Only int or str is accepted as dimension value, incorrect type: {}'.format(type(dim)))
+
+    for input in model.graph.input:
+        input_name = input.name
+        input_dim_arr = input_dims[input_name]
+        for j, dim in enumerate(input_dim_arr):
+            update_dim(input, dim, j, input_name)
+
+    for output in model.graph.output:
+        output_name = output.name
+        output_dim_arr = output_dims[output_name]
+        for j, dim in enumerate(output_dim_arr):
+            update_dim(output, dim, j, output_name)
+
+    onnx.checker.check_model(model)
+    return model
diff --git a/third_party/onnx/onnx/utils.py b/third_party/onnx/onnx/utils.py
new file mode 100644
index 0000000000..825256ab15
--- /dev/null
+++ b/third_party/onnx/onnx/utils.py
@@ -0,0 +1,196 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from typing import List, Tuple, Text
+
+import onnx.checker
+import onnx.helper
+import onnx.shape_inference
+
+from onnx import ModelProto, NodeProto, TensorProto, ValueInfoProto, FunctionProto
+
+
+class Extractor:
+    def __init__(self, model: ModelProto) -> None:
+        self.model = onnx.shape_inference.infer_shapes(model)
+        self.graph = self.model.graph
+        self.wmap = self._build_name2obj_dict(self.graph.initializer)
+        self.vimap = self._build_name2obj_dict(self.graph.value_info)
+
+    @staticmethod
+    def _build_name2obj_dict(objs):  # type: ignore
+        return {obj.name: obj for obj in objs}
+
+    def _collect_new_io_core(self, original_io, io_names_to_extract):  # type: ignore
+        original_io_map = self._build_name2obj_dict(original_io)
+        original_io_names = set(original_io_map.keys())
+        s_io_names_to_extract = set(io_names_to_extract)
+        io_names_to_keep = s_io_names_to_extract & original_io_names
+        new_io_names_to_add = s_io_names_to_extract - original_io_names
+
+        new_io_tensors = []
+        for name in io_names_to_keep:
+            new_io_tensors.append(original_io_map[name])
+        for name in new_io_names_to_add:
+            # activation become input or output
+            new_io_tensors.append(self.vimap[name])
+
+        # adjust sequence
+        new_io_tensors_map = self._build_name2obj_dict(new_io_tensors)
+        return [new_io_tensors_map[name] for name in io_names_to_extract]
+
+    def _collect_new_inputs(self, names: List[Text]) -> List[ValueInfoProto]:
+        return self._collect_new_io_core(self.graph.input, names)  # type: ignore
+
+    def _collect_new_outputs(self, names: List[Text]) -> List[ValueInfoProto]:
+        return self._collect_new_io_core(self.graph.output, names)  # type: ignore
+
+    def _dfs_search_reachable_nodes(
+            self,
+            node_output_name: Text,
+            graph_input_names: List[Text],
+            reachable_nodes: List[NodeProto],
+    ) -> None:
+        if node_output_name in graph_input_names:
+            return
+        for node in self.graph.node:
+            if node in reachable_nodes:
+                continue
+            if node_output_name not in node.output:
+                continue
+            reachable_nodes.append(node)
+            for name in node.input:
+                self._dfs_search_reachable_nodes(name, graph_input_names, reachable_nodes)
+
+    def _collect_reachable_nodes(
+            self,
+            input_names: List[Text],
+            output_names: List[Text],
+    ) -> List[NodeProto]:
+        reachable_nodes = list()  # type: ignore
+        for name in output_names:
+            self._dfs_search_reachable_nodes(name, input_names, reachable_nodes)
+        # needs to be topology sorted.
+        nodes = [n for n in self.graph.node if n in reachable_nodes]
+        return nodes
+
+    def _collect_referred_local_functions(
+            self,
+            nodes,  # type: List[NodeProto]
+    ):  # type: (...) -> List[FunctionProto]
+        # a node in a model graph may refer a function.
+        # a function contains nodes, some of which may in turn refer a function.
+        # we need to find functions referred by graph nodes and
+        # by nodes used to define functions.
+        def find_referred_funcs(nodes, referred_local_functions):  # type: ignore
+            new_nodes = []  # type: List[NodeProto]
+            for node in nodes:
+                # check if the node is a function op
+                match_function = next((
+                    f for f in self.model.functions
+                    if f.name == node.op_type and f.domain == node.domain),
+                    None)
+                if match_function and match_function not in referred_local_functions:
+                    referred_local_functions.append(match_function)
+                    new_nodes.extend(match_function.node)
+
+            return new_nodes
+
+        referred_local_functions = []  # type: List[FunctionProto]
+        new_nodes = find_referred_funcs(nodes, referred_local_functions)
+        while new_nodes:
+            new_nodes = find_referred_funcs(new_nodes, referred_local_functions)
+
+        return referred_local_functions
+
+    def _collect_reachable_tensors(
+            self,
+            nodes: List[NodeProto],
+    ) -> Tuple[List[TensorProto], List[ValueInfoProto]]:
+        all_tensors_name = set()
+        for node in nodes:
+            for name in node.input:
+                all_tensors_name.add(name)
+            for name in node.output:
+                all_tensors_name.add(name)
+
+        initializer = [self.wmap[t] for t in self.wmap.keys() if t in all_tensors_name]
+        value_info = [self.vimap[t] for t in self.vimap.keys() if t in all_tensors_name]
+        assert(len(self.graph.sparse_initializer) == 0)
+        assert(len(self.graph.quantization_annotation) == 0)
+        return (initializer, value_info)
+
+    def _make_model(
+            self,
+            nodes: List[NodeProto],
+            inputs: List[ValueInfoProto],
+            outputs: List[ValueInfoProto],
+            initializer: List[TensorProto],
+            value_info: List[ValueInfoProto],
+            local_functions: List[FunctionProto],
+    ) -> ModelProto:
+        name = 'Extracted from {' + self.graph.name + '}'
+        graph = onnx.helper.make_graph(nodes, name, inputs, outputs, initializer=initializer,
+                                      value_info=value_info)
+
+        meta = {
+            'ir_version': self.model.ir_version,
+            'opset_imports': self.model.opset_import,
+            'producer_name': 'onnx.utils.extract_model',
+            'functions': local_functions,
+        }
+        return onnx.helper.make_model(graph, **meta)
+
+    def extract_model(
+            self,
+            input_names: List[Text],
+            output_names: List[Text],
+    ) -> ModelProto:
+        inputs = self._collect_new_inputs(input_names)
+        outputs = self._collect_new_outputs(output_names)
+        nodes = self._collect_reachable_nodes(input_names, output_names)
+        initializer, value_info = self._collect_reachable_tensors(nodes)
+        local_functions = self._collect_referred_local_functions(nodes)
+        model = self._make_model(nodes, inputs, outputs, initializer, value_info, local_functions)
+
+        return model
+
+
+def extract_model(
+        input_path: Text,
+        output_path: Text,
+        input_names: List[Text],
+        output_names: List[Text],
+        check_model: bool = True,
+) -> None:
+    """Extracts sub-model from an ONNX model.
+
+    The sub-model is defined by the names of the input and output tensors *exactly*.
+
+    Note: For control-flow operators, e.g. If and Loop, the _boundary of sub-model_,
+    which is defined by the input and output tensors, should not _cut through_ the
+    subgraph that is connected to the _main graph_ as attributes of these operators.
+
+    Arguments:
+        input_path (string): The path to original ONNX model.
+        output_path (string): The path to save the extracted ONNX model.
+        input_names (list of string): The names of the input tensors that to be extracted.
+        output_names (list of string): The names of the output tensors that to be extracted.
+        check_model (bool): Whether to run model checker on the extracted model.
+    """
+    if not os.path.exists(input_path):
+        raise ValueError("Invalid input model path: %s" % input_path)
+    if not output_path:
+        raise ValueError("Output model path shall not be empty!")
+    if not output_names:
+        raise ValueError("Output tensor names shall not be empty!")
+
+    onnx.checker.check_model(input_path)
+    model = onnx.load(input_path)
+
+    e = Extractor(model)
+    extracted = e.extract_model(input_names, output_names)
+
+    onnx.save(extracted, output_path)
+    if check_model:
+        onnx.checker.check_model(output_path)
diff --git a/third_party/onnx/onnx/version_converter.py b/third_party/onnx/onnx/version_converter.py
new file mode 100644
index 0000000000..6deca00ea8
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter.py
@@ -0,0 +1,170 @@
+# SPDX-License-Identifier: Apache-2.0
+"""onnx version converter
+
+This enables users to convert their models between different opsets within the
+default domain ("" or "ai.onnx").
+"""
+
+import onnx.onnx_cpp2py_export.version_converter as C
+from onnx import load_from_string, ModelProto
+
+
+def convert_version(model: ModelProto, target_version: int) -> ModelProto:
+    """Apply the version conversion on the serialized ModelProto.
+
+    Arguments:
+        input (ModelProto): model
+        target_version (int): target opset version
+
+    Returns:
+        return (ModelProto) converted model
+
+    Raises Exceptions:
+        RuntimeError when some necessary conversion is not supported
+
+    Supported adapters:
+        - Add from Opset 7 to Opset 6
+        - Add from Opset 6 to Opset 5
+        - Add from Opset 6 to Opset 7
+        - Add from Opset 5 to Opset 6
+        - Mul from Opset 6 to Opset 7
+        - Mul from Opset 7 to Opset 6
+        - Mul from Opset 6 to Opset 5
+        - Mul from Opset 5 to Opset 6
+        - Gemm from Opset 7 to Opset 6
+        - Gemm from Opset 6 to Opset 5
+        - Gemm from Opset 6 to Opset 7
+        - Gemm from Opset 5 to Opset 6
+        - Relu from Opset 6 to Opset 5
+        - Relu from Opset 5 to Opset 6
+        - BatchNorm from Opset 7 to Opset 6
+        - BatchNorm from Opset 6 to Opset 7
+        - BatchNorm from Opset 6 to Opset 5
+        - BatchNorm from Opset 5 to Opset 6
+        - Concat from Opset 4 to Opset 3
+        - Concat from Opset 3 to Opset 4
+        - Reshape from Opset 5 to Opset 4
+        - Reshape from Opset 4 to Opset 5
+        - Sum from Opset 7 to Opset 8
+        - Sum from Opset 8 to Opset 7
+        - Sum from Opset 6 to Opset 5
+        - Sum from Opset 5 to Opset 6
+        - MaxPool from Opset 8 to Opset 7
+        - MaxPool from Opset 7 to Opset 8
+        - AveragePool from Opset 7 to Opset 6
+        - AveragePool from Opset 6 to Opset 7
+        - Dropout from Opset 7 to Opset 6
+        - Dropout from Opset 6 to Opset 5
+        - Dropout from Opset 6 to Opset 7
+        - Dropout from Opset 5 to Opset 6
+        - RNN from Opset 13 to Opset 14
+        - RNN from Opset 14 to Opset 13
+        - GRU from Opset 13 to Opset 14
+        - GRU from Opset 14 to Opset 13
+        - LSTM from Opset 13 to Opset 14
+        - LSTM from Opset 14 to Opset 13
+
+    Unsupported adapters:
+        - Min from Opset 8 to Opset 7
+        - Min from Opset 7 to Opset 8
+        - Min from Opset 6 to Opset 5
+        - Min from Opset 5 to Opset 6
+        - Mean from Opset 8 to Opset 7
+        - Mean from Opset 7 to Opset 8
+        - Mean from Opset 6 to Opset 5
+        - Mean from Opset 5 to Opset 6
+        - Max from Opset 8 to Opset 7
+        - Max from Opset 7 to Opset 8
+        - Max from Opset 6 to Opset 5
+        - Max from Opset 5 to Opset 6
+        - Xor from Opset 6 to Opset 7
+        - Xor from Opset 7 to Opset 6
+        - Upsample from Opset 6 to Opset 7
+        - Upsample from Opset 7 to Opset 6
+        - Sub from Opset 6 to Opset 7
+        - Sub from Opset 7 to Opset 6
+        - Sub from Opset 6 to Opset 5
+        - Sub from Opset 5 to Opset 6
+        - RNN from Opset 6 to Opset 7
+        - RNN from Opset 7 to Opset 6
+        - Pow from Opset 6 to Opset 7
+        - Pow from Opset 7 to Opset 6
+        - PRelu from Opset 6 to Opset 7
+        - PRelu from Opset 7 to Opset 6
+        - PRelu from Opset 6 to Opset 5
+        - PRelu from Opset 5 to Opset 6
+        - Or from Opset 6 to Opset 7
+        - Or from Opset 7 to Opset 6
+        - Less from Opset 6 to Opset 7
+        - Less from Opset 7 to Opset 6
+        - LSTM from Opset 6 to Opset 7
+        - LSTM from Opset 7 to Opset 6
+        - Greater from Opset 6 to Opset 7
+        - Greater from Opset 7 to Opset 6
+        - GRU from Opset 6 to Opset 7
+        - GRU from Opset 7 to Opset 6
+        - GRU from Opset 3 to Opset 2
+        - GRU from Opset 2 to Opset 3
+        - Equal from Opset 6 to Opset 7
+        - Equal from Opset 7 to Opset 6
+        - Div from Opset 6 to Opset 7
+        - Div from Opset 7 to Opset 6
+        - Div from Opset 6 to Opset 5
+        - Div from Opset 5 to Opset 6
+        - And from Opset 6 to Opset 7
+        - And from Opset 7 to Opset 6
+        - And from Opset 6 to Opset 5
+        - And from Opset 5 to Opset 6
+        - Tile from Opset 6 to Opset 5
+        - Tile from Opset 5 to Opset 6
+        - Sqrt from Opset 6 to Opset 5
+        - Sqrt from Opset 5 to Opset 6
+        - Sigmoid from opset 6 to opset 5
+        - Sigmoid from opset 5 to opset 6
+        - Selu from opset 6 to opset 5
+        - Selu from opset 5 to opset 6
+        - Reciprocal from opset 6 to opset 5
+        - Reciprocal from opset 5 to opset 6
+        - Neg from opset 6 to opset 5
+        - Neg from opset 5 to opset 6
+        - Log from opset 6 to opset 5
+        - Log from opset 5 to opset 6
+        - LeakyRelu from opset 6 to opset 5
+        - LeakyRelu from opset 5 to opset 6
+        - InstanceNormalization from opset 6 to opset 5
+        - InstanceNormalization from opset 5 to opset 6
+        - HardSigmoid from opset 6 to opset 5
+        - HardSigmoid from opset 5 to opset 6
+        - Floor from opset 6 to opset 5
+        - Floor from opset 5 to opset 6
+        - Exp from opset 6 to opset 5
+        - Exp from opset 5 to opset 6
+        - Elu from opset 6 to opset 5
+        - Elu from opset 5 to opset 6
+        - Clip from opset 6 to opset 5
+        - Clip from opset 5 to opset 6
+        - Ceil from opset 6 to opset 5
+        - Ceil from opset 5 to opset 6
+        - Cast from opset 6 to opset 5
+        - Cast from opset 5 to opset 6
+        - Abs from opset 6 to opset 5
+        - Abs from opset 5 to opset 6
+        - Split from opset 2 to opset 1
+        - Split from opset 1 to opset 2
+        - Pad from opset 2 to opset 1
+        - Pad from opset 1 to opset 2
+        - LpPool from opset 2 to opset 1
+        - LpPool from opset 1 to opset 2
+        - GlobalLpPool from opset 2 to opset 1
+        - GlobalLpPool from opset 1 to opset 2
+    """
+    if not isinstance(model, ModelProto):
+        raise ValueError('VersionConverter only accepts ModelProto as model, incorrect type: {}'.format(type(model)))
+    if not isinstance(target_version, int):
+        raise ValueError('VersionConverter only accepts int as target_version, incorrect type: {}'.format(type(target_version)))
+    model_str = model.SerializeToString()
+    converted_model_str = C.convert_version(model_str, target_version)
+    return load_from_string(converted_model_str)
+
+
+ConvertError = C.ConvertError
diff --git a/third_party/onnx/onnx/version_converter/BaseConverter.h b/third_party/onnx/onnx/version_converter/BaseConverter.h
new file mode 100644
index 0000000000..dcaa6e52f2
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/BaseConverter.h
@@ -0,0 +1,88 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Version converter interface for ONNX models between different opset versions.
+
+#pragma once
+
+#include "onnx/common/ir.h"
+#include "onnx/common/ir_pb_converter.h"
+#include "onnx/common/stl_backports.h"
+#include "onnx/proto_utils.h"
+#include "onnx/defs/schema.h"
+#include <utility>
+#include <iostream>
+#include <stdlib.h>
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+// TODO: Consider creating interface for this class.
+class BaseVersionConverter {
+  // Schema for adapters: {<op_name>:{<from_domain>$<from_version>:{<to_domain>
+  // <to_version>: adapter}}}
+  protected:
+    std::unordered_map<std::string, std::unordered_map<std::string, std::unordered_map<std::string, std::unique_ptr<Adapter>>>> adapters;
+
+    // Map of All Versions of format {op_name: {domain: {version: schema}}}
+    std::unordered_map<std::string, std::unordered_map<std::string, std::map<int64_t, const OpSchema*>>>  all_schemas;
+
+  public:
+    BaseVersionConverter() = default;
+
+    virtual ~BaseVersionConverter() = default;
+
+    // adapter_lookup should be called in convert_version when the user would
+    // like to identify the proper registered adapter in the adapters map for
+    // a given Node from a certain version to another. It should only be called
+    // when the user knows that an adapter should exist for the given context.
+    const Adapter& adapter_lookup(const Node* op,
+        const OpSetID& initial_version,
+        const OpSetID& target_version) const {
+      const std::string op_name = op->kind().toString();
+      const std::string initial = initial_version.toString();
+      const std::string target = target_version.toString();
+      // Find appropriate adapter in adapters map for provided initial and target versions
+      // TODO: Consider abstracting elements of this that are specific to
+      // DefaultConverter to separate methods here and maintain the procedure in Base Converter
+      const auto op_adapters = adapters.find(op_name);
+      if (op_adapters != adapters.end()) {
+        // If we're adapting downwards, we just want to find the one downwards
+        // adapter implemented for initial_version. If we're adapting upwards, we
+        // want to actually use the SinceVersion value for the given op.
+        const auto target_map = op_adapters->second.find(initial);
+        if (target_map != op_adapters->second.end()) {
+          // Either adapt from SinceVersion or Incompatible Breaking Change
+          const auto adapter_ptr = target_map->second.find(target);
+          if (adapter_ptr != target_map->second.end()) {
+            return *(adapter_ptr->second);
+          } else {
+            ONNX_ASSERTM(false, "No Adapter To Version %s for %s", target.c_str(), op_name.c_str());
+          }
+        } else {
+          ONNX_ASSERTM(false, "No Adapter From Version %s for %s", initial.c_str(), op_name.c_str());
+        }
+      } else {
+        // No adapters exist for the given op
+        ONNX_ASSERTM(false, "No Adapter For %s", op_name.c_str());
+      }
+  }
+
+  virtual ModelProto convert_version(
+      const ModelProto& mp_in,
+      const OpSetID& initial_version,
+      const OpSetID& target_version) const = 0;
+
+  void registerAdapter(std::unique_ptr<Adapter> a_ptr) {
+    const OpSetID& iv = a_ptr->initial_version();
+    const OpSetID& tv = a_ptr->target_version();
+    adapters[a_ptr->name()][iv.toString()][tv.toString()] = std::move(a_ptr);
+  }
+
+  void registerAdapter(const char* op, int64_t from, int64_t to, NodeTransformerFunction transformer) {
+    registerAdapter(make_unique<GenericAdapter>(op, from, to, transformer));
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/adapter.h b/third_party/onnx/onnx/version_converter/adapters/adapter.h
new file mode 100644
index 0000000000..a406526e40
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/adapter.h
@@ -0,0 +1,65 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Interface for Op Version Adapters
+
+#pragma once
+
+#include <functional>
+
+#include "onnx/onnx_pb.h"
+#include "onnx/version_converter/helper.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Adapter {
+  private:
+    std::string name_;
+    OpSetID initial_version_;
+    OpSetID target_version_;
+
+  public:
+    virtual ~Adapter() noexcept = default;
+
+    explicit Adapter(const std::string& name, const OpSetID& initial_version,
+        const OpSetID& target_version)
+      : name_(name), initial_version_(initial_version),
+        target_version_(target_version) {
+    }
+
+    // This will almost always return its own node argument after modifying it in place.
+    // The only exception are adapters for deprecated operators: in this case the input
+    // node must be destroyed and a new one must be created and returned. See e.g.
+    // upsample_9_10.h
+    virtual Node* adapt(std::shared_ptr<Graph> /*graph*/, Node* node) const = 0;
+
+    const std::string& name() const {
+      return name_;
+    }
+
+    const OpSetID& initial_version() const {
+      return initial_version_;
+    }
+
+    const OpSetID& target_version() const {
+      return target_version_;
+    }
+};
+
+using NodeTransformerFunction = std::function<Node*(std::shared_ptr<Graph>, Node* node)>;
+
+class GenericAdapter final : public Adapter {
+ public:
+  GenericAdapter(const char* op, int64_t from, int64_t to, NodeTransformerFunction transformer)
+      : Adapter(op, OpSetID(from), OpSetID(to)), transformer_(transformer) {}
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    return transformer_(graph, node);
+  }
+
+ private:
+  NodeTransformerFunction transformer_;
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/axes_attribute_to_input.h b/third_party/onnx/onnx/version_converter/adapters/axes_attribute_to_input.h
new file mode 100644
index 0000000000..1c9ab176c9
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/axes_attribute_to_input.h
@@ -0,0 +1,50 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for all ops that remove consumed_inputs
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE {
+namespace version_conversion {
+
+class AxesAttributeToInput : public Adapter {
+ public:
+  explicit AxesAttributeToInput(
+      const std::string& op_name,
+      const OpSetID& initial,
+      const OpSetID& target)
+      : Adapter(op_name, initial, target) {}
+
+  void attrToInput(
+      std::shared_ptr<Graph> graph,
+      Node* node,
+      std::vector<int64_t> axes) const {
+    Tensor t;
+    t.elem_type() = TensorProto_DataType_INT64;
+    t.sizes() = std::vector<int64_t>{static_cast<int64_t>(axes.size())};
+    auto& data = t.int64s();
+    for (auto a : axes) {
+      data.emplace_back(a);
+    }
+    
+    Node* constant = graph->create(kConstant);
+    constant->insertBefore(node);
+    constant->t_(kvalue, t);        
+    node->addInput(constant->output());
+  }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    if (node->hasAttribute(kaxes)){
+      attrToInput(graph, node, node->is(kaxes));
+      node->removeAttribute(kaxes);
+    }
+    return node;
+  }
+};
+
+} // namespace version_conversion
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/version_converter/adapters/axes_input_to_attribute.h b/third_party/onnx/onnx/version_converter/adapters/axes_input_to_attribute.h
new file mode 100644
index 0000000000..061e4b36c7
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/axes_input_to_attribute.h
@@ -0,0 +1,74 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for all ops that remove consumed_inputs
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE {
+namespace version_conversion {
+
+class AxesInputToAttribute : public Adapter {
+ public:
+  explicit AxesInputToAttribute(
+      const std::string& op_name,
+      const OpSetID& initial,
+      const OpSetID& target)
+      : Adapter(op_name, initial, target) {}
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    // Identify if axes is statically determined; if so, feed as attribute
+    const ArrayRef<Value*>& inputs = node->inputs();
+    // Get axes from initializer or constant operator
+    // Identify whether we have a Constant Op or an Initializer
+    Value* const_val = inputs[1];
+    Node* node_ptr = const_val->node();
+    if (node_ptr->kind() == kConstant) {
+      // Get value attribute of kConstant
+      const std::vector<int64_t>& int64s = node_ptr->t(kvalue).int64s();
+      if (int64s.empty()) {
+        // Also handle raw data
+        std::string raw_data = node_ptr->t(kvalue).raw();
+        ONNX_ASSERTM(
+            raw_data.size() != 0 && raw_data.size() % 8 == 0,
+            "Raw Data must be non-empty and size must be a multiple of 8");
+        int64_t* raw = (int64_t*)const_cast<char*>(raw_data.c_str());
+        node->is_(
+            kaxes,
+            std::vector<int64_t>(
+                raw, raw + node_ptr->t(kvalue).size_from_dim(0)));
+      } else {
+        node->is_(kaxes, std::forward<const std::vector<int64_t>>(int64s));
+      }
+      // If Constant node isn't used anywhere else, remove it
+      node->removeInput(1);
+      if (const_val->uses().size() < 1) {
+        node_ptr->destroy();
+      }
+    } else {
+      // Get Value name, find Initializer with same name
+      for (const auto& initializer : graph->initializers()) {
+        if (initializer.name() == inputs[1]->uniqueName()) {
+          node->is_(
+              kaxes,
+              std::forward<const std::vector<int64_t>>(initializer.int64s()));
+          node->removeInput(1);
+          // Remove initializer
+          if (const_val->uses().size() < 1)
+            graph->eraseInitializerAndInput(const_val);
+          break;
+        }
+      }
+    }
+    ONNX_ASSERTM(
+        node->hasAttribute(kaxes),
+        "No initializer or constant input to node found");
+    return node;
+  }
+};
+
+} // namespace version_conversion
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/version_converter/adapters/batch_normalization_13_14.h b/third_party/onnx/onnx/version_converter/adapters/batch_normalization_13_14.h
new file mode 100644
index 0000000000..9c85a8093d
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/batch_normalization_13_14.h
@@ -0,0 +1,29 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for BatchNormalization in default domain from version 13 to 14
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class BatchNormalization_13_14 final : public Adapter {
+  public:
+    explicit BatchNormalization_13_14()
+      : Adapter("BatchNormalization", OpSetID(13), OpSetID(14)) {}
+
+    void adapt_batch_normalization_13_14(Node* node) const {
+      ONNX_ASSERTM(node->outputs().size() < 4, "BatchNormalization outputs 4 and 5 are not "
+          "supported in Opset 14.");
+    }
+
+    Node* adapt(std::shared_ptr<Graph> , Node* node) const override {
+      adapt_batch_normalization_13_14(node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/broadcast_backward_compatibility.h b/third_party/onnx/onnx/version_converter/adapters/broadcast_backward_compatibility.h
new file mode 100644
index 0000000000..45c61da748
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/broadcast_backward_compatibility.h
@@ -0,0 +1,48 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for broadcasting ops in default domain from version 7 to 6
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class BroadcastBackwardCompatibility final : public Adapter {
+  public:
+    explicit BroadcastBackwardCompatibility(const std::string& op_name, const OpSetID&
+      initial, const OpSetID& target): Adapter(op_name, initial, target) {}
+
+    void adapt_broadcast_backward_compatibility(std::shared_ptr<Graph>, Node* node) const {
+      // Verify that broadcasts are allowed in limited spec of opset version 6
+      // Multidirectional broadcasting, as defined in Broadcasting.md
+      // MathDocGenerator provides differences
+      // Main change: encode broadcasting commands as explicit attribute
+      const ArrayRef<Value*>& inputs = node->inputs();
+      assertInputsAvailable(inputs, name().c_str(), 2);
+      const std::vector<Dimension>& A_sizes = inputs[0]->sizes();
+      const std::vector<Dimension>& B_sizes = inputs[1]->sizes();
+      // Ensure that first input is larger than or equal to the second
+      // numpy_unibroadcastable here is considered to be equivalent to opset1_broadcastable
+      // This is because backwards conversion does not allow for an axis that is not
+      // suffix matching
+      int req_broadcast = check_numpy_unibroadcastable_and_require_broadcast(A_sizes, B_sizes);
+      ONNX_ASSERTM(req_broadcast != -1, "%s being converted from %d to %d does "
+          "not have broadcastable inputs.", name().c_str(), initial_version()
+          .version(), target_version().version());
+      if(req_broadcast == 1) {
+        // If conditional is not fulfilled, we have a default broadcast
+        // Add broadcast attribute
+        node->i_(kbroadcast, 1);
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_broadcast_backward_compatibility(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/broadcast_forward_compatibility.h b/third_party/onnx/onnx/version_converter/adapters/broadcast_forward_compatibility.h
new file mode 100644
index 0000000000..df7fb68aaa
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/broadcast_forward_compatibility.h
@@ -0,0 +1,78 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for broadcasting ops in default domain from version 6 to 7
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class BroadcastForwardCompatibility final : public Adapter {
+  public:
+    explicit BroadcastForwardCompatibility(const std::string& op_name, const OpSetID&
+      initial, const OpSetID& target): Adapter(op_name, initial, target) {}
+
+    void adapt_broadcast_forward_compatibility(std::shared_ptr<Graph> graph, Node* node)
+      const {
+      // Remove axis and broadcast attributes
+      // Assess whether axis requires reshaping
+      if (node->hasAttribute(kbroadcast)) {
+        const ArrayRef<Value*>& inputs = node->inputs();
+        assertInputsAvailable(inputs, name().c_str(), 2);
+        const std::vector<Dimension>& A_sizes = inputs[0]->sizes();
+        const std::vector<Dimension>& B_sizes = inputs[1]->sizes();
+        // Also assert that broadcasting syntax are correct if axis is not present
+        if (node->hasAttribute(kaxis)) {
+          if (node->i(kaxis) != (int) (A_sizes.size() - B_sizes.size())) {
+            // Add a Reshape node before input B
+            Node * n = graph->create(kUnsqueeze);
+            n->addInput(inputs[1]);
+            std::vector<int64_t> axes;
+            std::vector<Dimension> new_sizes = B_sizes;
+            auto size = A_sizes.size() > B_sizes.size() ? A_sizes.size() - B_sizes.size() : 0;
+            axes.reserve(size);
+            new_sizes.reserve(new_sizes.size() + size);
+            for (size_t i = 0; i < size; i++) {
+              axes.emplace_back(B_sizes.size() + i);
+              new_sizes.emplace_back(Dimension(1));
+            }
+            if (target_version().version() >= 13){ //Unsqueeze takes 'axes' input
+              Tensor t;
+              t.elem_type() = TensorProto_DataType_INT64;
+              t.sizes() = std::vector<int64_t>{static_cast<int64_t>(axes.size())};
+              auto& data = t.int64s();
+              for (auto a : axes) {
+                data.emplace_back(a);
+              }
+              Node* constant = graph->create(kConstant);
+              constant->insertBefore(node);
+              constant->t_(kvalue, t);        
+              node->addInput(constant->output());
+            } else { // Unsqueeze takes 'axes' attribute
+              n->is_(kaxes, std::forward<const std::vector<int64_t>>(axes));
+            }
+            // Move n before node
+            n->insertBefore(node);
+            // Set 2nd input to node to 1st of n and output of n to 2nd input to node
+            n->output()->setSizes(new_sizes);
+            node->replaceInput(1, n->output());
+          }
+        }
+        node->removeAttribute(kbroadcast);
+      }
+      if (node->hasAttribute(kaxis)) node->removeAttribute(kaxis);
+      // Assert multi_broadcastable on inputs
+      const ArrayRef<Value*>& inputs = node->inputs();
+      assert_numpy_multibroadcastable(inputs[0]->sizes(), inputs[1]->sizes());
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_broadcast_forward_compatibility(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/cast_9_8.h b/third_party/onnx/onnx/version_converter/adapters/cast_9_8.h
new file mode 100644
index 0000000000..8faf1cf061
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/cast_9_8.h
@@ -0,0 +1,29 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Cast in default domain from version 9 to 8
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Cast_9_8 final : public Adapter {
+  public:
+    explicit Cast_9_8()
+      : Adapter("Cast", OpSetID(9), OpSetID(8)) {}
+
+    void adapt_cast_9_8(std::shared_ptr<Graph>, Node* node) const {
+        if (node->inputs()[0]->elemType() == TensorProto_DataType_STRING || node->i(kto) == TensorProto_DataType_STRING)
+            ONNX_ASSERT("Casting From/To STRING data type is not supported")
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_cast_9_8(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/clip_10_11.h b/third_party/onnx/onnx/version_converter/adapters/clip_10_11.h
new file mode 100644
index 0000000000..4ff1c02728
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/clip_10_11.h
@@ -0,0 +1,50 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Clip in default domain from version 10 to 11
+
+#pragma once
+#include <limits>
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Clip_10_11 final : public Adapter {
+  public:
+    explicit Clip_10_11()
+      : Adapter("Clip", OpSetID(10), OpSetID(11)) {}
+
+    void adapt_clip_10_11(std::shared_ptr<Graph> graph, Node* node) const {
+      bool has_min = node->hasAttribute(kmin);
+      bool has_max = node->hasAttribute(kmax);
+
+      // Turn min/max attributes into tensor (if present) and add value as input
+      if(has_min) {
+        attrToInput(graph, node, node->f(kmin));
+        node->removeAttribute(kmin);
+      }
+      if(has_max) {
+        if(!has_min) {attrToInput(graph, node, std::numeric_limits<float>::lowest());}
+        attrToInput(graph, node, node->f(kmax));
+        node->removeAttribute(kmax);
+      }
+    }
+
+    void attrToInput(std::shared_ptr<Graph> graph, Node *node, float val) const {
+      Tensor t;
+      t.elem_type() = TensorProto_DataType_FLOAT;
+      auto& data = t.floats();
+      data.emplace_back(val);
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t);        
+      node->addInput(constant->output());     
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_clip_10_11(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/compatible.h b/third_party/onnx/onnx/version_converter/adapters/compatible.h
new file mode 100644
index 0000000000..55a42d6214
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/compatible.h
@@ -0,0 +1,23 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter indicating compatibility of op between opsets with separate
+// definitions
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+struct CompatibleAdapter final : public Adapter {
+  explicit CompatibleAdapter(const std::string& op_name, const OpSetID&
+    initial, const OpSetID& target): Adapter(op_name, initial, target) {}
+
+  Node* adapt(std::shared_ptr<Graph>, Node* node) const override {
+    return node;
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/dropout_11_12.h b/third_party/onnx/onnx/version_converter/adapters/dropout_11_12.h
new file mode 100644
index 0000000000..dedad4b19c
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/dropout_11_12.h
@@ -0,0 +1,42 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Dropout in default domain from version 11 to 12
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Dropout_11_12 final : public Adapter {
+  public:
+    explicit Dropout_11_12()
+      : Adapter("Dropout", OpSetID(11), OpSetID(12)) {}
+
+    void adapt_dropout_11_12(std::shared_ptr<Graph> graph, Node* node) const {
+      float ratio;
+      if(node->hasAttribute(kratio)) {
+        ratio = node->f(kratio);
+        node->removeAttribute(kratio);
+      }
+      else {
+        ratio = 0.5;
+      }
+
+      Tensor t_ratio;
+      t_ratio.elem_type() = TensorProto_DataType_FLOAT;
+      auto& data_ratio = t_ratio.floats();
+      data_ratio.emplace_back(ratio);
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t_ratio);        
+      node->addInput(constant->output());
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+	    adapt_dropout_11_12(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/extend_supported_types.h b/third_party/onnx/onnx/version_converter/adapters/extend_supported_types.h
new file mode 100644
index 0000000000..9e032f8486
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/extend_supported_types.h
@@ -0,0 +1,107 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter indicating compatibility of op between opsets with separate
+// definitions
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+struct ExtendSupportedTypes final : public Adapter {
+    explicit ExtendSupportedTypes(const std::string& op_name, const OpSetID&
+        initial, const OpSetID& target): Adapter(op_name, initial, target) {}
+
+    Node *create_cast_op(
+        std::shared_ptr<Graph> graph,
+        ArrayRef<Value *> inputs,
+        const int to_type,
+        const std::vector<Dimension>& output_shape,
+        const std::string& name) const{
+        Node *node = graph->create(kCast, inputs);
+        node->i_(kto, to_type);
+        node->output()->setUniqueName(name);
+        node->output()->setSizes(output_shape);
+        node->output()->setElemType(to_type);
+        return node;
+    }
+
+    void adapt_type_extension(std::shared_ptr<Graph> graph, Node* node) const{
+        const ArrayRef<Value*>& inputs = node->inputs();
+        const ArrayRef<Value*>& outputs = node->outputs();
+        const std::string original_output_name = node->output()->uniqueName();
+
+        const int input_type = inputs.size() > 0 ? inputs[0]->elemType() : -1;
+        const int output_type = outputs[0]->elemType();
+
+        const std::unordered_set<int> &supported_version8_types = {
+            TensorProto_DataType::TensorProto_DataType_FLOAT,
+            TensorProto_DataType::TensorProto_DataType_FLOAT16,
+            TensorProto_DataType::TensorProto_DataType_DOUBLE,
+        };
+
+        const std::unordered_set<int> &unsupported_version9_types = {
+            TensorProto_DataType::TensorProto_DataType_COMPLEX128,
+            TensorProto_DataType::TensorProto_DataType_COMPLEX64,
+            TensorProto_DataType::TensorProto_DataType_STRING,
+        };
+
+        ONNX_ASSERTM(unsupported_version9_types.find(input_type) == unsupported_version9_types.end(), "Unsupported Input Type");
+        ONNX_ASSERTM(unsupported_version9_types.find(output_type) == unsupported_version9_types.end(), "Unsupported Output Type");
+
+        bool castInput = (node->kind() != kConstant);
+        bool castOutput = (node->kind() != kGreater && node->kind() != kLess);
+        if (castInput &&
+            supported_version8_types.find(input_type) ==
+                supported_version8_types.end()) {
+          for (size_t i = 0; i < inputs.size(); i++) {
+            Node* pre_cast = create_cast_op(
+                graph,
+                inputs[i],
+                TensorProto_DataType::TensorProto_DataType_FLOAT,
+                inputs[i]->sizes(),
+                "pre_cast_" + ONNX_NAMESPACE::to_string(i));
+            pre_cast->insertBefore(node);
+            node->replaceInput(i, pre_cast->output());
+          }
+        }
+        if (castOutput &&
+            supported_version8_types.find(output_type) ==
+                supported_version8_types.end()) {
+          const use_list original_uses(node->output()->uses());
+          node->output()->setElemType(
+              TensorProto_DataType::TensorProto_DataType_FLOAT);
+          node->output()->setUniqueName(
+              original_output_name + "_intermediate_output");
+          Node* post_cast = create_cast_op(
+              graph,
+              outputs[0],
+              output_type,
+              outputs[0]->sizes(),
+              original_output_name);
+
+          post_cast->insertAfter(node);
+
+          for (Use u : original_uses) {
+            u.user->replaceInputWith(node->output(), post_cast->output());
+          }
+
+          for (size_t i = 0; i < graph->outputs().size(); i++) {
+            if (graph->outputs()[i]->uniqueName() ==
+                node->output()->uniqueName()) {
+              graph->return_node()->replaceInput(i, post_cast->output());
+            }
+          }
+        }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+        adapt_type_extension(graph, node);
+        return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/gemm_6_7.h b/third_party/onnx/onnx/version_converter/adapters/gemm_6_7.h
new file mode 100644
index 0000000000..cdfd6e60cc
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/gemm_6_7.h
@@ -0,0 +1,48 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Gemm in default domain from version 6 to 7
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Gemm_6_7 final : public Adapter {
+  public:
+    explicit Gemm_6_7(): Adapter("Gemm", OpSetID(6), OpSetID(7)) {}
+
+    void adapt_gemm_6_7(std::shared_ptr<Graph>, Node* node) const {
+      const ArrayRef<Value*>& inputs = node->inputs();
+      assertInputsAvailable(inputs, name().c_str(), 3);
+      const auto& A_shape = inputs[0]->sizes();
+      const auto& B_shape = inputs[1]->sizes();
+      // Determine if C is broadcastable
+      const auto& C_shape = inputs[2]->sizes();
+      // Create (M, N) to input to numpy_unibroadcastable
+      std::vector<Dimension> MN;
+      if (node->hasAttribute(ktransA) && node->i(ktransA) == 1) {
+        MN.emplace_back(A_shape[1]);
+      } else {
+        MN.emplace_back(A_shape[0]);
+      }
+      if (node->hasAttribute(ktransB) && node->i(ktransB) == 1) {
+        MN.emplace_back(B_shape[0]);
+      } else {
+        MN.emplace_back(B_shape[1]);
+      }
+      ONNX_ASSERTM(check_numpy_unibroadcastable_and_require_broadcast(MN,
+            C_shape) != -1, "Gemm being converted from 6 to 7 does not have "
+          "broadcastable inputs.");
+      if (node->hasAttribute(kbroadcast)) node->removeAttribute(kbroadcast);
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_gemm_6_7(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/gemm_7_6.h b/third_party/onnx/onnx/version_converter/adapters/gemm_7_6.h
new file mode 100644
index 0000000000..d0071aa31b
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/gemm_7_6.h
@@ -0,0 +1,53 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Gemm in default domain from version 7 to 6
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Gemm_7_6 final : public Adapter {
+  public:
+    explicit Gemm_7_6(): Adapter("Gemm", OpSetID(7), OpSetID(6)) {}
+
+    void adapt_gemm_7_6(std::shared_ptr<Graph>, Node* node) const {
+      const ArrayRef<Value*>& inputs = node->inputs();
+      assertInputsAvailable(inputs, name().c_str(), 3);
+      const auto& A_shape = inputs[0]->sizes();
+      const auto& B_shape = inputs[1]->sizes();
+      // Determine if C is broadcastable
+      const auto& C_shape = inputs[2]->sizes();
+      // Create (M, N) to input to numpy_unibroadcastable
+      // TODO: Reconcile fact that shapes aren't determined for 1st 2 inputs
+      std::vector<Dimension> MN;
+      if (node->hasAttribute(ktransA) && node->i(ktransA) == 1) {
+        MN.emplace_back(A_shape[1]);
+      } else {
+        MN.emplace_back(A_shape[0]);
+      }
+      if (node->hasAttribute(ktransB) && node->i(ktransB) == 1) {
+        MN.emplace_back(B_shape[0]);
+      } else {
+        MN.emplace_back(B_shape[1]);
+      }
+      int req_broadcast = check_numpy_unibroadcastable_and_require_broadcast(MN,
+          C_shape);
+      ONNX_ASSERTM(req_broadcast != -1, "%s being converted from %d to %d does "
+          "not have broadcastable inputs.", name().c_str(), initial_version()
+          .version(), target_version().version());
+      if (req_broadcast == 1) {
+        node->i_(kbroadcast, 1);
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_gemm_7_6(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/maxpool_8_7.h b/third_party/onnx/onnx/version_converter/adapters/maxpool_8_7.h
new file mode 100644
index 0000000000..28a582dcac
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/maxpool_8_7.h
@@ -0,0 +1,31 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for MaxPool in default domain from version 8 to 7
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class MaxPool_8_7 final : public Adapter {
+  public:
+    explicit MaxPool_8_7()
+      : Adapter("MaxPool", OpSetID(8), OpSetID(7)) {}
+
+    void adapt_maxpool_8_7(std::shared_ptr<Graph>, Node* node) const {
+      const ArrayRef<Value*>& outputs = node->outputs();
+      ONNX_ASSERTM(outputs.size() != 2,
+          "Opset version 7 of MaxPool cannot include Indices output");
+      if (node->hasAttribute(kstorage_order)) node->removeAttribute(kstorage_order);
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_maxpool_8_7(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/no_previous_version.h b/third_party/onnx/onnx/version_converter/adapters/no_previous_version.h
new file mode 100644
index 0000000000..8f83f04f32
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/no_previous_version.h
@@ -0,0 +1,26 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter indicating lack of a previous version of some op before a given
+// opset version.
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class NoPreviousVersionAdapter final : public Adapter {
+  public:
+    explicit NoPreviousVersionAdapter(const std::string& op_name, const OpSetID&
+      initial, const OpSetID& target): Adapter(op_name,
+      initial, target) {}
+
+  Node* adapt(std::shared_ptr<Graph>, Node* node) const override {
+    ONNX_ASSERTM(false, "No Previous Version of %s exists", name().c_str());
+    return node;
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/pad_10_11.h b/third_party/onnx/onnx/version_converter/adapters/pad_10_11.h
new file mode 100644
index 0000000000..04426a1b8b
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/pad_10_11.h
@@ -0,0 +1,48 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Pad in default domain from version 10 to 11
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Pad_10_11 final : public Adapter {
+  public:
+    explicit Pad_10_11()
+      : Adapter("Pad", OpSetID(10), OpSetID(11)) {}
+
+    void adapt_pad_10_11(std::shared_ptr<Graph> graph, Node* node) const {
+      // Turn pads attribute into input
+      Tensor t_pads;
+      t_pads.elem_type() = TensorProto_DataType_INT64;
+      auto& data_pads = t_pads.int64s();
+      for (int64_t shape : node->is(kpads)) {
+        data_pads.emplace_back(shape);
+      }
+      t_pads.sizes() = std::vector<int64_t> {(int64_t)data_pads.size()};
+      Value* v_pads = graph->addInitializerAndCreateValue(t_pads);
+      node->addInput(v_pads);
+      node->removeAttribute(kpads);
+      // Turn value attribute into input
+      if (!node->hasAttribute(kmode) || node->s(kmode) == "constant") {
+        Tensor t_value;
+        t_value.elem_type() = TensorProto_DataType_FLOAT;
+        auto& data_value = t_value.floats();
+        data_value.emplace_back(node->f(kvalue));
+        Node* constant = graph->create(kConstant);
+        constant->insertBefore(node);
+        constant->t_(kvalue, t_value);
+        node->addInput(constant->output());
+        node->removeAttribute(kvalue);
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+	    adapt_pad_10_11(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/remove_consumed_inputs.h b/third_party/onnx/onnx/version_converter/adapters/remove_consumed_inputs.h
new file mode 100644
index 0000000000..16f804bf3e
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/remove_consumed_inputs.h
@@ -0,0 +1,24 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for all ops that remove consumed_inputs
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class RemoveConsumedInputs : public Adapter {
+  public:
+    explicit RemoveConsumedInputs(const std::string& op_name, const OpSetID&
+      initial, const OpSetID& target): Adapter(op_name, initial, target) {}
+
+    Node* adapt(std::shared_ptr<Graph>, Node* node) const override {
+      if (node->hasAttribute(kconsumed_inputs)) node->removeAttribute(kconsumed_inputs);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/reshape_4_5.h b/third_party/onnx/onnx/version_converter/adapters/reshape_4_5.h
new file mode 100644
index 0000000000..f3b439314e
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/reshape_4_5.h
@@ -0,0 +1,44 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Reshape in default domain from version 4 to 5
+
+#pragma once
+
+#include "onnx/version_converter/adapters/remove_consumed_inputs.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Reshape_4_5 final : public RemoveConsumedInputs {
+  public:
+    explicit Reshape_4_5()
+      : RemoveConsumedInputs("Reshape", OpSetID(4), OpSetID(5)) {}
+
+    void adapt_reshape_4_5(std::shared_ptr<Graph> graph, Node* node) const {
+      // Create Input from Attribute - add as Initializer
+      // Create tensor for value attribute
+      Tensor t;
+      t.elem_type() = TensorProto_DataType_INT64;
+      auto& data = t.int64s();
+      // Turn shapes attribute into tensor
+      for (int64_t shape : node->is(kshape)) {
+        data.emplace_back(shape);
+      }
+      // Add value as input to node
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t);        
+      node->addInput(constant->output());
+      // Remove kshape attribute
+      node->removeAttribute(kshape);
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      RemoveConsumedInputs::adapt(graph, node);
+	    adapt_reshape_4_5(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/reshape_5_4.h b/third_party/onnx/onnx/version_converter/adapters/reshape_5_4.h
new file mode 100644
index 0000000000..f6ee196f6a
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/reshape_5_4.h
@@ -0,0 +1,69 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Reshape in default domain from version 5 to 4
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Reshape_5_4 final : public Adapter {
+  public:
+    explicit Reshape_5_4()
+      : Adapter("Reshape", OpSetID(5), OpSetID(4)) {}
+
+    void adapt_reshape_5_4(std::shared_ptr<Graph> graph, Node* node) const {
+      // Identify if shape is statically determined; if so, feed as attribute
+      const ArrayRef<Value*>& inputs = node->inputs();
+      // Get shape from initializer or constant operator, not actual shape
+      // Identify whether we have a Constant Op or an Initializer
+      Value* const_val = inputs[1];
+      Node* node_ptr = const_val->node();
+      if (node_ptr->kind() == kConstant) {
+        // Get value attribute of kConstant
+        const std::vector<int64_t>& int64s = node_ptr->t(kvalue).int64s();
+        if (int64s.empty()) {
+          // Also handle raw data
+          std::string raw_data = node_ptr->t(kvalue).raw();
+          ONNX_ASSERTM(raw_data.size() != 0 && raw_data.size() % 8 == 0,
+              "Raw Data must be non-empty and size must be a multiple of 8");
+          int64_t* raw = (int64_t*)const_cast<char*>(raw_data.c_str());
+          node->is_(
+              kshape,
+              std::vector<int64_t>(
+                  raw, raw + node_ptr->t(kvalue).size_from_dim(0)));
+        } else {
+          node->is_(kshape, std::forward<const std::vector<int64_t>>(int64s));
+        }
+        // If Constant node isn't used anywhere else, remove it
+        node->removeInput(1);
+        if (const_val->uses().size() < 1) {
+          node_ptr->destroy();
+        }
+      } else {
+        // Get Value name, find Initializer with same name
+        for (const auto& initializer: graph->initializers()) {
+          if (initializer.name() == inputs[1]->uniqueName()) {
+            node->is_(kshape, std::forward<const std::vector<int64_t>>(
+                  initializer.int64s()));
+	          node->removeInput(1);
+            // Remove initializer
+            if (const_val->uses().size() < 1) graph->eraseInitializerAndInput(const_val);
+            break;
+          }
+        }
+      }
+      ONNX_ASSERTM(node->hasAttribute(kshape),
+          "No initializer or constant input to Reshape node found");
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_reshape_5_4(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/resize_10_11.h b/third_party/onnx/onnx/version_converter/adapters/resize_10_11.h
new file mode 100644
index 0000000000..3feebcb2d5
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/resize_10_11.h
@@ -0,0 +1,44 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Resize in default domain from version 10 to 11
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Resize_10_11 final : public Adapter {
+  public:
+    explicit Resize_10_11()
+      : Adapter("Resize", OpSetID(10), OpSetID(11)) {}
+
+    void adapt_resize_10_11(std::shared_ptr<Graph> graph, Node* node) const {
+      int input_rank = node->inputs()[0]->sizes().size();
+
+      Value* scales_input = node->inputs()[1];
+      node->addInput(scales_input);
+
+      Tensor t;
+      t.sizes() = std::vector<int64_t>{2 * input_rank};
+      t.elem_type() = TensorProto_DataType_FLOAT;
+      auto& data = t.floats();
+
+      for(int i = 0; i < input_rank; i++)
+        data.emplace_back(0);
+      for(int i = 0; i < input_rank; i++)
+        data.emplace_back(1);
+
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t);        
+      node->replaceInput(1, constant->output());
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_resize_10_11(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/scan_8_9.h b/third_party/onnx/onnx/version_converter/adapters/scan_8_9.h
new file mode 100644
index 0000000000..e7da2def77
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/scan_8_9.h
@@ -0,0 +1,62 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Scan in default domain from version 8 to 9
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+struct Scan_8_9 final : public Adapter {
+  explicit Scan_8_9()
+    : Adapter("Scan", OpSetID(8), OpSetID(9)) {
+    }
+
+  void adapt_scan_8_9(std::shared_ptr<Graph>, Node* node) const {
+
+    const std::vector<Value*> inputs(node->inputs().vec());
+    const std::vector<Value*> outputs(node->outputs().vec());
+
+    // Handling Attribute Changes
+
+    Symbol dirs = Symbol("directions");
+    if (node->hasAttribute(dirs)) {
+      const std::vector<int64_t> directions(node->is(dirs));
+      node->removeAttribute(dirs);
+      node->is_(Symbol("scan_input_directions"), std::move(directions));
+    }
+
+    // Handling Input and Output Changes
+
+    node->removeAllInputs();
+
+    if (inputs[0]->uniqueName() != "") {
+          ONNX_ASSERT("Unsupported conversion to opset 9");
+    }
+
+    for (Value* input : inputs) {
+      if (!input->sizes().empty()) {
+        std::vector<Dimension> new_sizes(input->sizes().begin()+1, input->sizes().end());
+        input->setSizes(new_sizes);
+        node->addInput(input);
+      }
+    }
+
+    for (Value* output : outputs) {
+      if (!output->sizes().empty()) {
+        std::vector<Dimension> new_sizes(output->sizes().begin()+1, output->sizes().end());
+        output->setSizes(new_sizes);
+      }
+    }
+  }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    adapt_scan_8_9(graph, node);
+    return node;
+  }
+
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/scan_9_8.h b/third_party/onnx/onnx/version_converter/adapters/scan_9_8.h
new file mode 100644
index 0000000000..936fb61497
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/scan_9_8.h
@@ -0,0 +1,88 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Scan in default domain from version 9 to 8
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+struct Scan_9_8 final : public Adapter {
+  explicit Scan_9_8()
+    : Adapter("Scan", OpSetID(9), OpSetID(8)) {
+    }
+
+  void adapt_scan_9_8(std::shared_ptr<Graph>, Node* node) const {
+
+    const std::vector<Value*> inputs(node->inputs().vec());
+    const std::vector<Value*> outputs(node->outputs().vec());
+
+    // Handling Attribute Changes
+
+    Symbol input_dirs = Symbol("scan_input_directions");
+    if (node->hasAttribute(input_dirs)){
+      const std::vector<int64_t> scan_input_directions(node->is(input_dirs));
+      node->removeAttribute(input_dirs);
+      node->is_(Symbol("directions"), std::move(scan_input_directions));
+    }
+
+    Symbol output_dirs = Symbol("scan_output_directions");
+    if (node->hasAttribute(output_dirs)){
+      const std::vector<int64_t> scan_output_directions(node->is(output_dirs));
+      for (int64_t x: scan_output_directions){
+        ONNX_ASSERTM(x == 0, "Unsupported output direction for Version 8");
+      }
+      node->removeAttribute(output_dirs);
+    }
+
+    Symbol input_axes = Symbol("scan_input_axes");
+    if (node->hasAttribute(input_axes)){
+      const std::vector<int64_t> scan_input_axes(node->is(input_axes));
+      for (int64_t x: scan_input_axes){
+        ONNX_ASSERTM(x == 0, "Unsupported input axes for Version 8");
+      }
+      node->removeAttribute(input_axes);
+    }
+
+    Symbol output_axes = Symbol("scan_output_axes");
+    if (node->hasAttribute(output_axes)){
+      const std::vector<int64_t> scan_output_axes(node->is(output_axes));
+      for (int64_t x: scan_output_axes){
+        ONNX_ASSERTM(x == 0, "Unsupported output axes for Version 8");
+      }
+      node->removeAttribute(output_axes);
+    }
+
+    // Handling Input and Ouput Changes
+
+    node->removeAllInputs();
+
+    Value* v = new Value(node, 0);
+    v->setUniqueName("");
+    v->setElemType(TensorProto_DataType::TensorProto_DataType_INT32);
+    node->addInput(v);
+
+    for (Value* input: inputs){
+      std::vector<Dimension> new_sizes {Dimension(1)};
+      new_sizes.insert(new_sizes.end(), input->sizes().begin(), input->sizes().end());
+      input->setSizes(new_sizes);
+      node->addInput(input);
+    }
+
+    for (Value* output: outputs){
+      std::vector<Dimension> new_sizes {Dimension(1)};
+      new_sizes.insert(new_sizes.end(), output->sizes().begin(), output->sizes().end());
+      output->setSizes(new_sizes);
+    }
+  }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    adapt_scan_9_8(graph, node);
+    return node;
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/scatter_10_11.h b/third_party/onnx/onnx/version_converter/adapters/scatter_10_11.h
new file mode 100644
index 0000000000..042c4ec1f3
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/scatter_10_11.h
@@ -0,0 +1,38 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Scatter in default domain from version 10 to 11
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Scatter_10_11 final : public Adapter {
+  public:
+    explicit Scatter_10_11()
+      : Adapter("Scatter", OpSetID(10), OpSetID(11)) {}
+
+    Node* adapt_scatter_10_11(std::shared_ptr<Graph> graph, Node* node) const {
+      int axis = node->hasAttribute(kaxis) ? node->i(kaxis) : 0;
+
+      // Replace the node with an equivalent ScatterElements node
+      Node* scatter_elements = graph->create(kScatterElements);
+      scatter_elements->i_(kaxis, axis); 
+      scatter_elements->addInput(node->inputs()[0]);
+      scatter_elements->addInput(node->inputs()[1]);
+      scatter_elements->addInput(node->inputs()[2]);
+      node->replaceAllUsesWith(scatter_elements);
+
+      scatter_elements->insertBefore(node);
+      node->destroy();
+
+      return scatter_elements;
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      return adapt_scatter_10_11(graph, node);
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/slice_9_10.h b/third_party/onnx/onnx/version_converter/adapters/slice_9_10.h
new file mode 100644
index 0000000000..8ddea0d635
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/slice_9_10.h
@@ -0,0 +1,51 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Slice in default domain from version 9 to 10
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Slice_9_10 final : public Adapter {
+  public:
+    explicit Slice_9_10()
+      : Adapter("Slice", OpSetID(9), OpSetID(10)) {}
+
+    void attrToInput(
+        std::shared_ptr<Graph> graph,
+        Node* node,
+        const std::vector<int64_t>& attr) const {
+      Tensor t;
+      t.elem_type() = TensorProto_DataType_INT64;
+      t.sizes() = std::vector<int64_t>{static_cast<int64_t>(attr.size())};
+      auto& data = t.int64s();
+      for (auto a : attr) {
+        data.emplace_back(a);
+      }
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t);        
+      node->addInput(constant->output());
+    }
+
+    void adapt_slice_9_10(std::shared_ptr<Graph> graph, Node* node) const {
+      attrToInput(graph, node, node->is(kstarts));
+      node->removeAttribute(kstarts);
+      attrToInput(graph, node, node->is(kends));
+      node->removeAttribute(kends);
+
+      if (node->hasAttribute(kaxes)){
+        attrToInput(graph, node, node->is(kaxes));
+        node->removeAttribute(kaxes);
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_slice_9_10(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/softmax_12_13.h b/third_party/onnx/onnx/version_converter/adapters/softmax_12_13.h
new file mode 100644
index 0000000000..2ffc592b27
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/softmax_12_13.h
@@ -0,0 +1,89 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Softmax amd LogSoftmax in default domain from version 12 to 13
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Softmax_12_13 final : public Adapter {
+  public:
+    explicit Softmax_12_13(const std::string& op_name)
+      : Adapter(op_name, OpSetID(12), OpSetID(13)) {}
+
+    void adapt_softmax_12_13(std::shared_ptr<Graph> graph, Node* node) const {
+      int old_axis = node->hasAttribute(kaxis) ? node->i(kaxis) : 1;
+      int input_rank = node->inputs()[0]->sizes().size();
+
+      if(old_axis < 0) old_axis = input_rank + old_axis;
+
+      if(old_axis == input_rank - 1)
+        node->i_(kaxis, -1);
+      else {
+        // Insert Flatten node before softmax
+        Node* flatten = graph->create(kFlatten);
+        flatten->addInput(node->inputs()[0]);
+        flatten->insertBefore(node);
+        flatten->i_(kaxis, old_axis);        
+        node->replaceInput(0, flatten->output());
+
+        if(old_axis == 0) {
+          node->i_(kaxis, 1);
+        } else {
+          node->i_(kaxis, -1);
+        }
+
+        // Insert Reshape node after softmax
+        const std::string original_output_name = node->output()->uniqueName();
+        const use_list original_uses(node->output()->uses());
+        node->output()->setUniqueName(original_output_name + "_intermediate");
+        Node* reshape = graph->create(kReshape);
+        reshape->addInput(node->outputs()[0]);
+        reshape->output()->setUniqueName(original_output_name);
+        reshape->insertAfter(node);
+
+        // Set shape input of Reshape
+        const std::vector<Dimension>& target_shape = flatten->inputs()[0]->sizes();
+
+        ONNX_ASSERTM(target_shape.size() != 0, "Version conversion for Softmax failed because "
+          "input shape is unknown.");
+
+        Tensor t;
+        t.elem_type() = TensorProto_DataType_INT64;
+        t.sizes() = std::vector<int64_t> {static_cast<int64_t>(target_shape.size())};
+        auto& data = t.int64s();
+        for (Dimension dim : target_shape) {
+          data.emplace_back(dim.dim);
+        }
+        Node* constant = graph->create(kConstant);
+        constant->insertBefore(node);
+        constant->t_(kvalue, t);        
+        reshape->addInput(constant->output());
+
+        // Fix outputs & wiring
+        node->output()->wipeSizes();
+        reshape->output()->setSizes(target_shape);
+        reshape->output()->setElemType(node->output()->elemType());
+        for (Use u : original_uses) {
+          u.user->replaceInputWith(node->output(), reshape->output());
+        }
+        for (size_t i = 0; i < graph->outputs().size(); i++) {
+          if (graph->outputs()[i]->uniqueName() ==
+              node->output()->uniqueName()) {
+            graph->return_node()->replaceInput(i, reshape->output());
+          }
+        }
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_softmax_12_13(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/split_12_13.h b/third_party/onnx/onnx/version_converter/adapters/split_12_13.h
new file mode 100644
index 0000000000..c26b150ab1
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/split_12_13.h
@@ -0,0 +1,45 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for all ops that remove consumed_inputs
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE {
+namespace version_conversion {
+
+class Split_12_13 : public Adapter {
+ public:
+  explicit Split_12_13() : Adapter("Split", OpSetID(12), OpSetID(13)) {}
+
+  void attrToInput(
+      std::shared_ptr<Graph> graph,
+      Node* node,
+      std::vector<int64_t> axes) const {
+    Tensor t;
+    t.elem_type() = TensorProto_DataType_INT64;
+    t.sizes() = std::vector<int64_t>{static_cast<int64_t>(axes.size())};
+    auto& data = t.int64s();
+    for (auto a : axes) {
+      data.emplace_back(a);
+    }
+    Node* constant = graph->create(kConstant);
+    constant->insertBefore(node);
+    constant->t_(kvalue, t);        
+    node->addInput(constant->output());
+  }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    if (node->hasAttribute(ksplit)){
+      attrToInput(graph, node, node->is(ksplit));
+      node->removeAttribute(ksplit);
+    }
+    return node;
+  }
+};
+
+} // namespace version_conversion
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/version_converter/adapters/split_13_12.h b/third_party/onnx/onnx/version_converter/adapters/split_13_12.h
new file mode 100644
index 0000000000..d54acc0410
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/split_13_12.h
@@ -0,0 +1,70 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for all ops that remove consumed_inputs
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE {
+namespace version_conversion {
+
+class Split_13_12 : public Adapter {
+ public:
+  explicit Split_13_12() : Adapter("Split", OpSetID(13), OpSetID(12)) {}
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    // Identify if 'split' is statically determined; if so, feed as attribute
+    const ArrayRef<Value*>& inputs = node->inputs();
+    // Get 'split' from initializer or constant operator
+    // Identify whether we have a Constant Op or an Initializer
+    Value* const_val = inputs[1];
+    Node* node_ptr = const_val->node();
+    if (node_ptr->kind() == kConstant) {
+      // Get value attribute of kConstant
+      const std::vector<int64_t>& int64s = node_ptr->t(kvalue).int64s();
+      if (int64s.empty()) {
+        // Also handle raw data
+        std::string raw_data = node_ptr->t(kvalue).raw();
+        ONNX_ASSERTM(
+            raw_data.size() != 0 && raw_data.size() % 8 == 0,
+            "Raw Data must be non-empty and size must be a multiple of 8");
+        int64_t* raw = (int64_t*)const_cast<char*>(raw_data.c_str());
+        node->is_(
+            ksplit,
+            std::vector<int64_t>(
+                raw, raw + node_ptr->t(kvalue).size_from_dim(0)));
+      } else {
+        node->is_(ksplit, std::forward<const std::vector<int64_t>>(int64s));
+      }
+      // If Constant node isn't used anywhere else, remove it
+      node->removeInput(1);
+      if (const_val->uses().size() < 1) {
+        node_ptr->destroy();
+      }
+    } else {
+      // Get Value name, find Initializer with same name
+      for (const auto& initializer : graph->initializers()) {
+        if (initializer.name() == inputs[1]->uniqueName()) {
+          node->is_(
+              ksplit,
+              std::forward<const std::vector<int64_t>>(initializer.int64s()));
+          node->removeInput(1);
+          // Remove initializer
+          if (const_val->uses().size() < 1)
+            graph->eraseInitializerAndInput(const_val);
+          break;
+        }
+      }
+    }
+    ONNX_ASSERTM(
+        node->hasAttribute(ksplit),
+        "No initializer or constant input to node found");
+    return node;
+  }
+};
+
+} // namespace version_conversion
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/onnx/onnx/version_converter/adapters/sum_8_7.h b/third_party/onnx/onnx/version_converter/adapters/sum_8_7.h
new file mode 100644
index 0000000000..123e09ff52
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/sum_8_7.h
@@ -0,0 +1,34 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Sum in default domain from version 8 to 7
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Sum_8_7 final : public Adapter {
+  public:
+    explicit Sum_8_7(): Adapter("Sum", OpSetID(8), OpSetID(7)) {}
+
+    void adapt_sum_8_7(std::shared_ptr<Graph>, Node* node) const {
+      // Throw an exception if any broadcasting occurs
+      const ArrayRef<Value*>& inputs = node->inputs();
+      // Determine if inputs are of different sizes
+      for (int i = 1; i < (int) inputs.size(); i++) {
+        std::vector<Dimension> A_sizes = inputs[i - 1]->sizes();
+        std::vector<Dimension> B_sizes = inputs[i]->sizes();
+        assert_numpy_multibroadcastable(A_sizes, B_sizes);
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_sum_8_7(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/topk_9_10.h b/third_party/onnx/onnx/version_converter/adapters/topk_9_10.h
new file mode 100644
index 0000000000..9672d1b13f
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/topk_9_10.h
@@ -0,0 +1,37 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for TopK in default domain from version 9 to 10
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class TopK_9_10 final : public Adapter {
+  public:
+    explicit TopK_9_10()
+      : Adapter("TopK", OpSetID(9), OpSetID(10)) {}
+
+    void adapt_topk_9_10(std::shared_ptr<Graph> graph, Node* node) const {
+      Tensor t;
+      t.elem_type() = TensorProto_DataType_INT64;
+      t.sizes() = std::vector<int64_t> {1};
+      auto& data = t.int64s();
+      data.emplace_back(node->i(kk));
+      
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t);        
+      node->addInput(constant->output());
+
+      node->removeAttribute(kk);
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_topk_9_10(graph, node);
+      return node;
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/transformers.h b/third_party/onnx/onnx/version_converter/adapters/transformers.h
new file mode 100644
index 0000000000..d7f249e42c
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/transformers.h
@@ -0,0 +1,79 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <cinttypes>
+
+// Node transformers commonly used in version-adapters:
+
+// Capture context by copying values; the graph is unused by these transformers.
+
+#define NODE_TRANSFORMER(node) [=](std::shared_ptr<Graph>, Node* node)
+
+namespace ONNX_NAMESPACE {
+namespace version_conversion {
+
+inline NodeTransformerFunction RemoveAttribute(Symbol attr) {
+  return NODE_TRANSFORMER(node) {
+    if (node->hasAttribute(attr)) {
+      node->removeAttribute(attr);
+    }
+    return node;
+  };
+}
+
+inline NodeTransformerFunction RemoveAttribute(Symbol attr, int64_t value) {
+  return NODE_TRANSFORMER(node) {
+    if (node->hasAttribute(attr)) {
+      ONNX_ASSERTM(node->i(attr) == value, "Attribute %s must have value %" PRId64, attr.toString(), value);
+      node->removeAttribute(attr);
+    }
+    return node;
+  };
+}
+
+inline NodeTransformerFunction RemoveAttributeNotEq(Symbol attr, int64_t value) {
+  return NODE_TRANSFORMER(node) {
+    if (node->hasAttribute(attr)) {
+      ONNX_ASSERTM(node->i(attr) != value, "Attribute %s must not have value %" PRId64, attr.toString(), value);
+      node->removeAttribute(attr);
+    }
+    return node;
+  };
+}
+
+inline NodeTransformerFunction SetAttribute(Symbol attr, int64_t value) {
+  return NODE_TRANSFORMER(node) {
+    node->i_(attr, value);
+    return node;
+  };
+}
+
+inline NodeTransformerFunction SetAttribute(Symbol attr, const std::string& value) {
+  return NODE_TRANSFORMER(node) {
+    node->s_(attr, value);
+    return node;
+  };
+}
+
+inline NodeTransformerFunction SetAttribute(Symbol attr, std::vector<int64_t> value) {
+  return NODE_TRANSFORMER(node) {
+    std::vector<int64_t> local(value);
+    node->is_(attr, std::move(local));
+    return node;
+  };
+}
+
+inline NodeTransformerFunction SetAttributeIfAbsent(Symbol attr, int64_t value) {
+  return NODE_TRANSFORMER(node) {
+    if (!node->hasAttribute(attr)) {
+      node->i_(attr, value);
+    }
+    return node;
+  };
+}
+
+} // namespace version_conversion
+} // namespace ONNX_NAMESPACE
\ No newline at end of file
diff --git a/third_party/onnx/onnx/version_converter/adapters/type_restriction.h b/third_party/onnx/onnx/version_converter/adapters/type_restriction.h
new file mode 100644
index 0000000000..81042d11ab
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/type_restriction.h
@@ -0,0 +1,47 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Add in default domain from version 6 to 5
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class TypeRestriction : public Adapter {
+  public:
+    explicit TypeRestriction(const std::string& op_name, const OpSetID&
+      initial, const OpSetID& target, const std::vector<TensorProto_DataType>&
+      unallowed_types): Adapter(op_name, initial, target), unallowed_types_(
+        unallowed_types) {}
+
+    void adapt_type_restriction(std::shared_ptr<Graph>, Node* node) const {
+      // Since consumed_inputs is optional, no need to add it (as in batchnorm)
+      // Iterate over all inputs and outputs
+      for (Value* input : node->inputs()) {
+        isUnallowed(input);
+      }
+      for (Value* output : node->outputs()) {
+        isUnallowed(output);
+      }
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      adapt_type_restriction(graph, node);
+      return node;
+    }
+
+  private:
+    std::vector<TensorProto_DataType> unallowed_types_;
+
+    void isUnallowed(Value* val) const {
+      ONNX_ASSERTM(std::find(std::begin(unallowed_types_), std::end(unallowed_types_),
+        val->elemType()) == std::end(unallowed_types_), "DataType of Input or Output"
+        " of Add is of an unallowed type for Opset Version %d.",
+        target_version().version());
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/upsample_6_7.h b/third_party/onnx/onnx/version_converter/adapters/upsample_6_7.h
new file mode 100644
index 0000000000..931a64c15d
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/upsample_6_7.h
@@ -0,0 +1,40 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Upsample in default domain from version 6 to 7
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+struct Upsample_6_7 final: public Adapter {
+  explicit Upsample_6_7() : Adapter("Upsample", OpSetID(6), OpSetID(7)) {}
+
+  void adapt_upsample_6_7(std::shared_ptr<Graph>, Node* node) const {
+
+    Symbol width_scale_symbol = Symbol("width_scale");
+    Symbol height_scale_symbol = Symbol("height_scale");
+    ONNX_ASSERTM(node->hasAttribute(width_scale_symbol) && node->hasAttribute(height_scale_symbol), "Upsample in opset 1 needs to have width_scale and height_scale attributes");
+
+    auto width_scale = node->f(width_scale_symbol);
+    auto height_scale = node->f(height_scale_symbol);
+
+    auto input_shape = node->inputs()[0]->sizes();
+    ONNX_ASSERTM(input_shape.size() == 4, "Upsample in opset 1 supports only 4D input tensor");
+    std::vector<double> scales = {1.0, 1.0, height_scale, width_scale};
+
+    node->fs_(kscales, std::move(scales));
+    node->removeAttribute(width_scale_symbol);
+    node->removeAttribute(height_scale_symbol);
+  }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    adapt_upsample_6_7(graph, node);
+    return node;
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/upsample_8_9.h b/third_party/onnx/onnx/version_converter/adapters/upsample_8_9.h
new file mode 100644
index 0000000000..012d7477d5
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/upsample_8_9.h
@@ -0,0 +1,44 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Upsample in default domain from version 8 to 9
+
+#pragma once
+
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+struct Upsample_8_9 final: public Adapter {
+  explicit Upsample_8_9() : Adapter("Upsample", OpSetID(8), OpSetID(9)) {}
+
+  void adapt_upsample_8_9(std::shared_ptr<Graph> graph, Node* node) const {
+
+    Symbol input_dirs = Symbol("scales");
+    int dim = (int)(node->fs(kscales).size());
+    Tensor t;
+    t.elem_type() = TensorProto_DataType_FLOAT;
+    t.sizes() = std::vector<int64_t>{dim};
+    auto& data = t.floats();
+
+    if (node->hasAttribute(input_dirs)) {
+      for(double scale : node->fs(kscales)) {
+        data.emplace_back((float)scale);
+      }
+
+      Node* constant = graph->create(kConstant);
+      constant->insertBefore(node);
+      constant->t_(kvalue, t);        
+      node->addInput(constant->output());
+      node->removeAttribute(kscales);
+      }
+    }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    adapt_upsample_8_9(graph, node);
+    return node;
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/upsample_9_10.h b/third_party/onnx/onnx/version_converter/adapters/upsample_9_10.h
new file mode 100644
index 0000000000..b7acfa5cec
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/upsample_9_10.h
@@ -0,0 +1,37 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Upsample in default domain from version 9 to 10
+
+#pragma once
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class Upsample_9_10 final : public Adapter {
+  public:
+    explicit Upsample_9_10()
+      : Adapter("Upsample", OpSetID(9), OpSetID(10)) {}
+
+    Node* adapt_upsample_9_10(std::shared_ptr<Graph> graph, Node* node) const {
+      std::string mode = node->hasAttribute(kmode) ? node->s(kmode) : "nearest";
+
+      // Replace the node with an equivalent Resize node
+      Node* resize = graph->create(kResize);
+      resize->s_(kmode, mode); 
+      resize->addInput(node->inputs()[0]);
+      resize->addInput(node->inputs()[1]);
+      node->replaceAllUsesWith(resize);
+
+      resize->insertBefore(node);
+      node->destroy();
+
+      return resize;
+    }
+
+    Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+      return adapt_upsample_9_10(graph, node);
+    }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/adapters/upsample_9_8.h b/third_party/onnx/onnx/version_converter/adapters/upsample_9_8.h
new file mode 100644
index 0000000000..f006cd4d97
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/adapters/upsample_9_8.h
@@ -0,0 +1,81 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Adapter for Upsample in default domain from version 9 to 8
+
+#pragma once
+
+#include "onnx/defs/tensor_proto_util.h"
+#include "onnx/defs/tensor_util.h"
+#include "onnx/version_converter/adapters/adapter.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+struct Upsample_9_8 final : public Adapter {
+  explicit Upsample_9_8()
+    : Adapter("Upsample", OpSetID(9), OpSetID(8)) {
+    }
+
+  void adapt_upsample_9_8(std::shared_ptr<Graph> graph, Node* node) const {
+      const ArrayRef<Value*>& inputs = node->inputs();
+      const std::vector<Tensor>& initializers = graph->initializers();
+
+      ONNX_ASSERTM(inputs.size() == 2, "Upsample in opset 9 needs to have 2 inputs.");
+      std::string scale_input_name = node->inputs()[1]->uniqueName();
+
+      for(size_t i = 0; i < initializers.size(); i++)
+      {
+          if(initializers[i].name() == inputs[1]->uniqueName())
+          {
+            std::vector<float> value = ParseData<float>(&initializers[i]);
+            std::vector<double> d_values;
+            d_values.reserve(value.size());
+            for (size_t j = 0; j < value.size(); j++)
+            {
+              d_values.push_back(static_cast<double>(value[j]));
+            }
+            node->fs_(kscales, const_cast<std::vector<double>&&>(d_values));
+
+            node->removeInput(1);
+            graph->eraseInitializer(initializers[i].name());
+            for(size_t j = 0; j < graph->inputs().size(); j++)
+            {
+              if(graph->inputs()[j]->uniqueName() == scale_input_name)
+              {
+                graph->eraseInput(j);
+                break;
+              }
+            }
+            return;
+          }
+      }
+
+      for(Node *op : graph->nodes())
+      {
+        if (op->kind() == kConstant && op->outputs()[0]->uniqueName() == scale_input_name)
+        {
+          std::vector<float> value = ParseData<float>(&op->t(kvalue));
+          std::vector<double> d_values;
+          d_values.reserve(value.size());
+          for (size_t j = 0; j < value.size(); j++)
+          {
+            d_values.push_back(static_cast<double>(value[j]));
+          }
+          node->fs_(kscales, const_cast<std::vector<double>&&>(d_values));
+          node->removeInput(1);
+          op->destroy();
+          return;
+        }
+      }
+
+      ONNX_ASSERTM(false, "Unsuppported conversion due to unavailable input: scale");
+  }
+
+  Node* adapt(std::shared_ptr<Graph> graph, Node* node) const override {
+    adapt_upsample_9_8(graph, node);
+    return node;
+  }
+};
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/convert.cc b/third_party/onnx/onnx/version_converter/convert.cc
new file mode 100644
index 0000000000..b62676989a
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/convert.cc
@@ -0,0 +1,137 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/version_converter/convert.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+ModelProto ConvertVersion(
+    const ModelProto& mp_in,
+    int target_version) {
+  // Get initial_opsetid from mp_in
+  OpSetID initial_struct(0);
+  for (auto it = mp_in.opset_import().begin(); it != mp_in.opset_import().end(); ++it) {
+    if (it->domain() == "" || it->domain() == "ai.onnx") {
+      initial_struct.setVersion(it->version());
+      break;
+    }
+  }
+  OpSetID target_struct = OpSetID(target_version);
+  DefaultVersionConverter v;
+  return v.convert_version(mp_in, initial_struct, target_struct);
+}
+
+void DefaultVersionConverter::convert_graph(
+  std::shared_ptr<Graph> g,
+  const OpSetID& initial_version,
+  const OpSetID& target_version
+  ) const {
+  assertNonNull(g);
+
+  // TODO: Move to Inter-Domain Converter
+  // Get initial model versions
+  // std::vector<OpSetID> initial_versions = g->opset_versions_mutable();
+
+  // No conversion necessary if Model has single, equivalent opset version
+  // if (initial_versions.size() == 1 && initial_versions[0].version ==
+  //    target_version.version && initial_versions[0].domain ==
+  //    target_version.domain) {
+  //  return mp_in;
+  // }
+
+  // Check if versions are valid
+  assertInVersionRange(initial_version.version());
+  assertInVersionRange(target_version.version());
+
+  // Iterate over all versions to target_version for specified
+  int64_t curr_version = initial_version.version();
+  int64_t step;
+  if (target_version.version() > initial_version.version()) {
+    step = 1;
+  } else {
+    step = -1;
+  }
+  // Identify index of this domain in g.opset_versions
+  unsigned int domain_index = 0;
+  for (unsigned int i = 0; i < g->opset_versions_mutable().size(); i++) {
+    if (g->opset_versions_mutable()[i].domain() == "") {
+      domain_index = i;
+    }
+  }
+  while (curr_version != target_version.version()) {
+    debug("curr_version: " + ONNX_NAMESPACE::to_string(curr_version) + ", next_version: " +
+        ONNX_NAMESPACE::to_string(curr_version + step));
+    Node *cur_op;
+    graph_node_list_iterator it = g->begin();
+    // Iterate through and call adapter returned by adapter_lookup for ops from 
+    // current_version opset. We have to manipulate the iterator explicitly because cur_op 
+    // might change when applying the adapter (e.g. for deprecated ops)
+    while ( it != g->end() ) {
+      cur_op = *it;
+      debug(std::string("Finding schema for ") + std::string(cur_op->kind().toString()));
+      const std::string op_name = cur_op->kind().toString();
+      if (op_name == "ConstantFill")
+      {
+        std::cerr << "Warning: skipping schema search for experimental op 'ConstantFill' and keeping the op as is. "
+        "Please be advised the converted model may not be working properly if target runtime does not support this "
+        "experimental op." << std::endl;
+        continue;
+      }
+      if (op_name != "Undefined" && op_name != "Captured") {
+        auto& op_domain_map = all_schemas.at(op_name);
+        OpSetID curr_id(curr_version);
+        OpSetID next_id(curr_version + step);
+        if (searchOpDomainMap(op_domain_map, curr_version, step)) {
+          // Op is specifically defined for this domain and version
+          auto& op_adapter = adapter_lookup(cur_op, curr_id, next_id);
+          // If adapter_lookup returns null, no adapter is present.
+          // Error thrown by adapter_lookup
+          if (DEBUG) std::cerr << "Applying adapter" << std::endl;
+          // adapt should handle replacing node in graph
+          cur_op = op_adapter.adapt(g, cur_op);
+          it = graph_node_list_iterator(cur_op, kNextDirection);
+        }
+        // Recursively convert any subgraph attributes
+        for (const auto& attr : cur_op->attributeNames()) {
+          if (cur_op->kindOf(attr) == AttributeKind::g) {
+            convert_graph(cur_op->g(attr), curr_id, next_id);
+          }
+        }
+      }
+      it++;
+    }
+    // Update model version
+    curr_version += step;
+    g->opset_versions_mutable()[domain_index].incrementVersion(step);
+  }
+}
+
+ModelProto DefaultVersionConverter::convert_version(
+    const ModelProto& mp_in,
+    const OpSetID& initial_version,
+    const OpSetID& target_version) const {
+  const std::string& initial_domain = initial_version.domain();
+  const std::string& target_domain = target_version.domain();
+  assertDefaultDomain(initial_domain, target_domain);
+
+  for (auto it = mp_in.opset_import().begin(); it != mp_in.opset_import()
+      .end(); ++it) {
+    if (it->domain() == initial_version.domain()) {
+      ONNX_ASSERTM(initial_version.version() == it->version(),
+          "initial_version does not reflect current state of model");
+    }
+  }
+
+  std::shared_ptr<Graph> g(ImportModelProto(mp_in));
+
+  convert_graph(g, initial_version, target_version);
+
+  // Export g as ModelProto
+  debug("Finished conversion; returning model");
+  ModelProto mp_out = PrepareOutput(mp_in);
+  ExportModelProto(&mp_out, g);
+  return mp_out;
+}
+
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/convert.h b/third_party/onnx/onnx/version_converter/convert.h
new file mode 100644
index 0000000000..75bbfe6819
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/convert.h
@@ -0,0 +1,754 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Default converter for ONNX models between different opset versions
+// in the default domain ("" or "ai.onnx").
+
+#pragma once
+
+#include "onnx/version_converter/BaseConverter.h"
+#include "onnx/version_converter/adapters/axes_attribute_to_input.h"
+#include "onnx/version_converter/adapters/axes_input_to_attribute.h"
+#include "onnx/version_converter/adapters/broadcast_backward_compatibility.h"
+#include "onnx/version_converter/adapters/broadcast_forward_compatibility.h"
+#include "onnx/version_converter/adapters/cast_9_8.h"
+#include "onnx/version_converter/adapters/clip_10_11.h"
+#include "onnx/version_converter/adapters/compatible.h"
+#include "onnx/version_converter/adapters/dropout_11_12.h"
+#include "onnx/version_converter/adapters/extend_supported_types.h"
+#include "onnx/version_converter/adapters/gemm_6_7.h"
+#include "onnx/version_converter/adapters/gemm_7_6.h"
+#include "onnx/version_converter/adapters/maxpool_8_7.h"
+#include "onnx/version_converter/adapters/no_previous_version.h"
+#include "onnx/version_converter/adapters/reshape_4_5.h"
+#include "onnx/version_converter/adapters/reshape_5_4.h"
+#include "onnx/version_converter/adapters/scan_8_9.h"
+#include "onnx/version_converter/adapters/scan_9_8.h"
+#include "onnx/version_converter/adapters/split_12_13.h"
+#include "onnx/version_converter/adapters/split_13_12.h"
+#include "onnx/version_converter/adapters/sum_8_7.h"
+#include "onnx/version_converter/adapters/slice_9_10.h"
+#include "onnx/version_converter/adapters/type_restriction.h"
+#include "onnx/version_converter/adapters/upsample_6_7.h"
+#include "onnx/version_converter/adapters/upsample_8_9.h"
+#include "onnx/version_converter/adapters/upsample_9_8.h"
+#include "onnx/version_converter/adapters/resize_10_11.h"
+#include "onnx/version_converter/adapters/topk_9_10.h"
+#include "onnx/version_converter/adapters/pad_10_11.h"
+#include "onnx/version_converter/adapters/scatter_10_11.h"
+#include "onnx/version_converter/adapters/softmax_12_13.h"
+#include "onnx/version_converter/adapters/batch_normalization_13_14.h"
+#include "onnx/version_converter/adapters/upsample_9_10.h"
+
+#include "onnx/version_converter/adapters/transformers.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+
+class DefaultVersionConverter : public BaseVersionConverter {
+  private:
+    bool DEBUG = false;
+
+    std::pair<int, int> version_range;
+
+    bool searchOpDomainMap(const std::unordered_map<std::string, std::map<
+      int64_t, const OpSchema*>>& op_domain_map, int64_t curr_version,
+      int64_t step) const {
+      bool up = step == 1;
+      const auto version_it = op_domain_map.find("");
+      return version_it != op_domain_map.end() &&
+          ((version_it->second.find(curr_version) !=
+          version_it->second.end() && !up) ||
+          (version_it->second.find(curr_version + step) !=
+          version_it->second.end() && up));
+    }
+
+    void debug(const std::string& str) const {
+      if (DEBUG) std::cerr << str << std::endl;
+    }
+
+    void assertInVersionRange(int64_t version) const {
+      ONNX_ASSERTM(version >= version_range.first && version <=
+          version_range.second,
+          "Warning: invalid version (must be between %s and %s)",
+          version_range.first, version_range.second);
+    }
+
+    void assertDefaultDomain(const std::string& initial_domain,
+        const std::string& target_domain) const {
+      ONNX_ASSERTM((initial_domain == "" || initial_domain == "ai.onnx") &&
+          (target_domain == "" || target_domain == "ai.onnx"),
+          "Warning: default onnx version converter can only convert "
+          " between default domain opset versions ('' or 'ai.onnx')\n");
+      ONNX_ASSERTM(initial_domain == target_domain,
+          "initial_version and target_version must have the same domains");
+    }
+
+  void convert_graph(std::shared_ptr<Graph> g, const OpSetID& initial_version,
+    const OpSetID& target_version) const;
+
+  public:
+    DefaultVersionConverter() {
+      const std::unordered_map<std::string, std::pair<int, int>>& versions_map =
+        OpSchemaRegistry::DomainToVersionRange::Instance().Map();
+      version_range = versions_map.at("");
+      // Register adapters to the version converter
+      const std::vector<OpSchema> all_opschemas =
+        OpSchemaRegistry::get_all_schemas_with_history();
+
+      for (const OpSchema& schema : all_opschemas) {
+        all_schemas[schema.Name()][schema.domain()][(int64_t)
+          schema.since_version()] = &schema;
+      }
+
+      // Iterate through all_schemas to determine NoPreviousVersionAdapters
+      for (auto& op_pair : all_schemas) {
+        const auto default_versions = op_pair.second.find("");
+        if (default_versions != op_pair.second.end()) {
+          int64_t min_version = version_range.second;
+          for (auto& version_pair : default_versions->second) {
+            if (version_pair.first < min_version) {
+              min_version = version_pair.first;
+            }
+          }
+          if (min_version > 1) {
+            registerAdapter(make_unique<NoPreviousVersionAdapter>(op_pair.first,
+              OpSetID(min_version), OpSetID(min_version - 1)));
+          }
+        }
+      }
+
+      /******** 1 -> 2 ********/
+      // Missing in this group: GlobalLpPool, LpPool, Pad, Split
+
+      /******** 2 -> 3 ********/
+      // Missing in this group: GRU
+
+      /******** 3 -> 4 ********/
+      registerAdapter("Concat", 3, 4, SetAttributeIfAbsent(kaxis, 1));
+
+      /******** 4 -> 3 ********/
+      std::vector<TensorProto_DataType> concat_unallowed_types = {
+        TensorProto_DataType_INT32, TensorProto_DataType_INT64,
+        TensorProto_DataType_UINT32, TensorProto_DataType_UINT64,
+        TensorProto_DataType_UINT8, TensorProto_DataType_UINT16,
+        TensorProto_DataType_INT8, TensorProto_DataType_INT16,
+        TensorProto_DataType_STRING, TensorProto_DataType_BOOL};
+      registerAdapter(make_unique<TypeRestriction>("Concat", OpSetID(4),
+            OpSetID(3), concat_unallowed_types));
+
+      /******** 4 -> 5 ********/
+      registerAdapter(make_unique<Reshape_4_5>());
+
+      /******** 5 -> 4 ********/
+      registerAdapter(make_unique<Reshape_5_4>());
+
+      /******** 5 -> 6 ********/
+      // Missing in this group: Cast, Tile
+      auto removeConsumedInputs = RemoveAttribute(kconsumed_inputs);
+      registerAdapter("Add", 5, 6, removeConsumedInputs);
+      registerAdapter("Mul", 5, 6, removeConsumedInputs);
+      registerAdapter(make_unique<CompatibleAdapter>("Gemm", OpSetID(5), OpSetID(6)));
+      registerAdapter("Relu", 5, 6, removeConsumedInputs);
+      registerAdapter("BatchNormalization", 5, 6, removeConsumedInputs);
+      registerAdapter("Sum", 5, 6, removeConsumedInputs);
+      registerAdapter("Dropout", 5, 6, removeConsumedInputs);
+      registerAdapter("Abs", 5, 6, removeConsumedInputs);
+      registerAdapter("Ceil", 5, 6, removeConsumedInputs);
+      registerAdapter("Clip", 5, 6, removeConsumedInputs);
+      registerAdapter("Div", 5, 6, removeConsumedInputs);
+      registerAdapter("Elu", 5, 6, removeConsumedInputs);
+      registerAdapter("Exp", 5, 6, removeConsumedInputs);
+      registerAdapter("Floor", 5, 6, removeConsumedInputs);
+      registerAdapter("HardSigmoid", 5, 6, removeConsumedInputs);
+      registerAdapter("InstanceNormalization", 5, 6, removeConsumedInputs);
+      registerAdapter("LeakyRelu", 5, 6, removeConsumedInputs);
+      registerAdapter("Log", 5, 6, removeConsumedInputs);
+      registerAdapter("Max", 5, 6, removeConsumedInputs);
+      registerAdapter("Mean", 5, 6, removeConsumedInputs);
+      registerAdapter("Min", 5, 6, removeConsumedInputs);
+      registerAdapter("Neg", 5, 6, removeConsumedInputs);
+      registerAdapter("PRelu", 5, 6, removeConsumedInputs);
+      registerAdapter("Reciprocal", 5, 6, removeConsumedInputs);
+      registerAdapter("Selu", 5, 6, removeConsumedInputs);
+      registerAdapter("Sigmoid", 5, 6, removeConsumedInputs);
+      registerAdapter("Sqrt", 5, 6, removeConsumedInputs);
+      registerAdapter("Sub", 5, 6, removeConsumedInputs);
+      registerAdapter("Tanh", 5, 6, removeConsumedInputs);
+
+      /******** 6 -> 5 ********/
+      std::vector<TensorProto_DataType> broadcast_unallowed_types = {
+        TensorProto_DataType_INT32, TensorProto_DataType_INT64,
+        TensorProto_DataType_UINT32, TensorProto_DataType_UINT64};
+      std::vector<TensorProto_DataType> int_unallowed_types = {
+        TensorProto_DataType_UINT8, TensorProto_DataType_UINT16,
+        TensorProto_DataType_UINT32, TensorProto_DataType_UINT64,
+        TensorProto_DataType_INT8, TensorProto_DataType_INT16,
+        TensorProto_DataType_INT32, TensorProto_DataType_INT64};
+      std::vector<TensorProto_DataType> neg_unallowed_types = {
+        TensorProto_DataType_INT32, TensorProto_DataType_INT8,
+        TensorProto_DataType_UINT16, TensorProto_DataType_INT64};
+      registerAdapter(make_unique<TypeRestriction>("Add",
+        OpSetID(6), OpSetID(5), broadcast_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Mul",
+        OpSetID(6), OpSetID(5), broadcast_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Sub",
+        OpSetID(6), OpSetID(5), broadcast_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Div",
+        OpSetID(6), OpSetID(5), broadcast_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Abs",
+        OpSetID(6), OpSetID(5), int_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Neg",
+        OpSetID(6), OpSetID(5), neg_unallowed_types));
+      registerAdapter("BatchNormalization", 6, 5, SetAttribute(kconsumed_inputs, std::vector<int64_t>({0, 0})));
+      registerAdapter(make_unique<CompatibleAdapter>("Gemm",
+        OpSetID(6), OpSetID(5)));
+      registerAdapter(make_unique<CompatibleAdapter>("Relu",
+        OpSetID(6), OpSetID(5)));
+      registerAdapter(make_unique<CompatibleAdapter>("Sum",
+        OpSetID(6), OpSetID(5)));
+      registerAdapter(make_unique<CompatibleAdapter>("Dropout",
+        OpSetID(6), OpSetID(5)));
+
+      /******** 6 -> 7 ********/
+      // Missing in this group: And, Equal, Greater, GRU, Less, LSTM, Or, RNN, Upsample, Xor
+      registerAdapter(make_unique<BroadcastForwardCompatibility>("Add",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<CompatibleAdapter>("AveragePool",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<BroadcastForwardCompatibility>("Div",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<BroadcastForwardCompatibility>("Mul",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<BroadcastForwardCompatibility>("Pow",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<CompatibleAdapter>("PRelu",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<BroadcastForwardCompatibility>("Sub",
+        OpSetID(6), OpSetID(7)));
+      registerAdapter(make_unique<Gemm_6_7>());
+      registerAdapter("BatchNormalization", 6, 7, RemoveAttributeNotEq(kis_test, 0));
+      registerAdapter("Dropout", 6, 7, RemoveAttributeNotEq(kis_test, 0));
+      registerAdapter(make_unique<Upsample_6_7>());
+
+      /******** 7 -> 6 ********/
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Add",
+        OpSetID(7), OpSetID(6)));
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Div",
+        OpSetID(7), OpSetID(6)));
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Mul",
+        OpSetID(7), OpSetID(6)));
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Pow",
+        OpSetID(7), OpSetID(6)));
+      registerAdapter(make_unique<CompatibleAdapter>("PRelu",
+        OpSetID(7), OpSetID(6)));
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Sub",
+        OpSetID(7), OpSetID(6)));
+      registerAdapter("BatchNormalization", 7, 6, SetAttribute(kis_test, 1));
+      registerAdapter("Dropout", 7, 6, SetAttribute(kis_test, 1));
+      registerAdapter(make_unique<Gemm_7_6>());
+      registerAdapter("AveragePool", 7, 6, RemoveAttribute(kcount_include_pad, 0));
+
+      /******** 7 -> 8 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("Max",
+        OpSetID(7), OpSetID(8)));
+      registerAdapter(make_unique<CompatibleAdapter>("Min",
+        OpSetID(7), OpSetID(8)));
+      registerAdapter(make_unique<CompatibleAdapter>("Mean",
+        OpSetID(7), OpSetID(8)));
+      registerAdapter(make_unique<CompatibleAdapter>("Sum",
+        OpSetID(7), OpSetID(8)));
+      registerAdapter(make_unique<CompatibleAdapter>("MaxPool",
+        OpSetID(7), OpSetID(8)));
+
+      /******** 8 -> 7 ********/
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Max",
+        OpSetID(8), OpSetID(7)));
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Min",
+        OpSetID(8), OpSetID(7)));
+      registerAdapter(make_unique<BroadcastBackwardCompatibility>("Mean",
+        OpSetID(8), OpSetID(7)));
+      registerAdapter(make_unique<Sum_8_7>());
+      registerAdapter(make_unique<MaxPool_8_7>());
+
+      /******** 8 -> 9 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("Flatten",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("Constant",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("MatMul",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("Gemm",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("PRelu",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("Greater",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("Less",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter(make_unique<CompatibleAdapter>("Cast",
+        OpSetID(8), OpSetID(9)));
+      registerAdapter("BatchNormalization", 8, 9, RemoveAttribute(kspatial, 1));
+      registerAdapter(make_unique<Scan_8_9>());
+      registerAdapter(make_unique<Upsample_8_9>());
+
+      /******** 9 -> 8 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("BatchNormalization",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("Flatten",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("Constant",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("MatMul",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("Gemm",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("PRelu",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("Greater",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<ExtendSupportedTypes>("Less",
+        OpSetID(9), OpSetID(8)));
+      registerAdapter(make_unique<Cast_9_8>());
+      registerAdapter(make_unique<Scan_9_8>());
+      registerAdapter(make_unique<Upsample_9_8>());
+
+      /******** 9 -> 10 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("AveragePool",
+        OpSetID(9), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("MaxPool",
+        OpSetID(9), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("Dropout",
+        OpSetID(9), OpSetID(10)));
+      registerAdapter(make_unique<Slice_9_10>());
+      registerAdapter(make_unique<TopK_9_10>());
+      registerAdapter(make_unique<Upsample_9_10>());
+
+      /******** 10 -> 9 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("Dropout",
+        OpSetID(10), OpSetID(9)));
+
+      /******** 10 -> 11 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("ArgMax",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ArgMin",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("AveragePool",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Concat",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Constant",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Compress",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Conv",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ConvTranspose",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("DepthToSpace",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Equal",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Flatten",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Gather",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Gemm",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Hardmax",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("If",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("LogSoftmax",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Loop",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("LpPool",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("MaxPool",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("MaxUnpool",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("NonMaxSuppression",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("OneHot",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceL1",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceL2",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceLogSum",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceLogSumExp",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMax",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMean",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMin",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceProd",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceSum",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceSumSquare",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Scan",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Softmax",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Slice",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Split",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Squeeze",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("TopK",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<CompatibleAdapter>("Unsqueeze",
+        OpSetID(10), OpSetID(11)));
+      registerAdapter(make_unique<Clip_10_11>());
+      registerAdapter(make_unique<Pad_10_11>());
+      registerAdapter(make_unique<Resize_10_11>());
+      registerAdapter(make_unique<Scatter_10_11>());
+
+      /******** 11 -> 10 ********/
+      std::vector<TensorProto_DataType> equal_unallowed_types = {
+        TensorProto_DataType_UINT8, TensorProto_DataType_UINT16,
+        TensorProto_DataType_UINT32, TensorProto_DataType_UINT64,
+        TensorProto_DataType_INT8, TensorProto_DataType_INT16,
+        TensorProto_DataType_FLOAT16, TensorProto_DataType_FLOAT,
+        TensorProto_DataType_DOUBLE};
+      registerAdapter(make_unique<CompatibleAdapter>("ArgMax",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ArgMin",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("AveragePool",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("Concat",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("Constant",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("Conv",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ConvTranspose",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<TypeRestriction>("Equal",
+        OpSetID(11), OpSetID(10), equal_unallowed_types));
+      registerAdapter(make_unique<CompatibleAdapter>("Flatten",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("LogSoftmax",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("MaxPool",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceL1",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceL2",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceLogSum",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceLogSumExp",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMax",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMean",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMin",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceProd",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceSum",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceSumSquare",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("Softmax",
+        OpSetID(11), OpSetID(10)));
+      registerAdapter(make_unique<CompatibleAdapter>("Unsqueeze",
+        OpSetID(11), OpSetID(10)));
+
+      /******** 11 -> 12 ********/
+      registerAdapter(make_unique<CompatibleAdapter>("ArgMax",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("ArgMin",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("BatchNormalization",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("Constant",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("Clip",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("GatherND",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("Min",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("Max",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("MaxPool",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("Pow",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMax",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<CompatibleAdapter>("ReduceMin",
+        OpSetID(11), OpSetID(12)));
+      registerAdapter(make_unique<Dropout_11_12>());
+
+      /******** 12 -> 11 ********/
+      std::vector<TensorProto_DataType> maxpool_unallowed_types = {
+        TensorProto_DataType_UINT8, TensorProto_DataType_INT8};
+      registerAdapter("ArgMax", 12, 11, RemoveAttribute(kselect_last_index, 0));
+      registerAdapter("ArgMin", 12, 11, RemoveAttribute(kselect_last_index, 0));
+      registerAdapter(make_unique<CompatibleAdapter>("BatchNormalization",
+        OpSetID(12), OpSetID(11)));
+      registerAdapter(make_unique<TypeRestriction>("Clip",
+        OpSetID(12), OpSetID(11), int_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Min",
+        OpSetID(12), OpSetID(11), int_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("Max",
+        OpSetID(12), OpSetID(11), int_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("MaxPool",
+        OpSetID(12), OpSetID(11), maxpool_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("ReduceMax",
+        OpSetID(12), OpSetID(11), maxpool_unallowed_types));
+      registerAdapter(make_unique<TypeRestriction>("ReduceMin",
+        OpSetID(12), OpSetID(11), maxpool_unallowed_types));
+
+      /******** 12 -> 13 ********/
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Abs", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Add", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ArgMin", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ArgMax", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Cast", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Ceil", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Clip", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Concat", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Constant", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "DepthToSpace", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "DequantizeLinear", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Div", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Dropout", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Equal", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Erf", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Exp", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Expand", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Flatten", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Floor", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Gather", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "GatherElements", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "GatherND", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Gemm", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Greater", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Hardmax", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Identity", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "If", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "IsNaN", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Less", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Log", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Loop", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "LRN", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "NegativeLogLikelihoodLoss", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "MatMul", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Max", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Mean", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "MeanVarianceNormalization", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Min", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Mod", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Mul", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Neg", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "NonZero", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Pow", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Pad", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "QuantizeLinear", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Reciprocal", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceL1", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceL2", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceLogSum", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceLogSumExp", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceMean", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceMax", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceMin", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceProd", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ReduceSumSquare", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Relu", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Reshape", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Resize", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ScatterElements", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ScatterND", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Shape", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Sigmoid", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Sign", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Size", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Slice", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "SoftmaxCrossEntropyLoss", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "SpaceToDepth", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Sqrt", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Sub", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Sum", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Tanh", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Tile", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Transpose", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<AxesAttributeToInput>(
+          "ReduceSum", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<AxesAttributeToInput>(
+          "Squeeze", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<AxesAttributeToInput>(
+          "Unsqueeze", OpSetID(12), OpSetID(13)));
+      registerAdapter(make_unique<Split_12_13>());
+      registerAdapter(make_unique<Softmax_12_13>("Softmax"));
+      registerAdapter(make_unique<Softmax_12_13>("LogSoftmax"));
+
+      /******** 13 -> 12 ********/
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Constant", OpSetID(13), OpSetID(12)));
+      registerAdapter(make_unique<AxesInputToAttribute>(
+          "ReduceSum", OpSetID(13), OpSetID(12)));
+      registerAdapter(make_unique<AxesInputToAttribute>(
+          "Squeeze", OpSetID(13), OpSetID(12)));
+      registerAdapter(make_unique<AxesInputToAttribute>(
+          "Unsqueeze", OpSetID(13), OpSetID(12)));
+      registerAdapter(make_unique<Split_13_12>());
+
+      /******** 13 -> 14 ********/
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Add", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "CumSum", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Div", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Identity", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Mul", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Relu", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Reshape", OpSetID(13), OpSetID(14)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Sub", OpSetID(13), OpSetID(14)));
+      registerAdapter("GRU", 13, 14, SetAttribute(klayout, 0));
+      registerAdapter("LSTM", 13, 14, SetAttribute(klayout, 0));
+      registerAdapter("RNN", 13, 14, SetAttribute(klayout, 0));
+      registerAdapter(make_unique<BatchNormalization_13_14>());
+
+      /******** 14 -> 13 ********/
+    registerAdapter("GRU", 14, 13, RemoveAttribute(klayout, 0));
+    registerAdapter("LSTM", 14, 13, RemoveAttribute(klayout, 0));
+    registerAdapter("RNN", 14, 13, RemoveAttribute(klayout, 0));
+
+      /******** 14 -> 15 ********/
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "BatchNormalization", OpSetID(14), OpSetID(15)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Pow", OpSetID(14), OpSetID(15)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "Shape", OpSetID(14), OpSetID(15)));
+
+      /******** 15 -> 16 ********/
+      registerAdapter("RoiAlign", 15, 16, SetAttribute(kcoordinate_transformation_mode, "output_half_pixel"));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ScatterElements", OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>(
+          "ScatterND", OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("Identity",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("Loop",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("If",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("Where",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("Scan",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("LessOrEqual",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("GreaterOrEqual",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("LeakyRelu",
+        OpSetID(15), OpSetID(16)));
+      registerAdapter(make_unique<CompatibleAdapter>("PRelu",
+        OpSetID(15), OpSetID(16)));
+    }
+
+    ModelProto convert_version(
+        const ModelProto& mp_in,
+        const OpSetID& initial_version,
+        const OpSetID& target_version) const override;
+};
+
+ModelProto ConvertVersion(
+    const ModelProto& mp_in,
+    int target_version);
+}} // namespace ONNX_NAMESPACE::version_conversion
diff --git a/third_party/onnx/onnx/version_converter/helper.cc b/third_party/onnx/onnx/version_converter/helper.cc
new file mode 100644
index 0000000000..1b68f81286
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/helper.cc
@@ -0,0 +1,70 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Helper Methods for Adapters
+
+#include "onnx/version_converter/helper.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+    int check_numpy_unibroadcastable_and_require_broadcast(
+        const std::vector<Dimension>& input1_sizes,
+        const std::vector<Dimension>& input2_sizes) {
+      // Check that input1 is larger
+      if(input1_sizes.size() < input2_sizes.size()) return -1;
+      // Check that axis is input1_sizes.size()-input2_sizes.size()
+      bool broadcast = false;
+      int axis = (int) (input1_sizes.size() - input2_sizes.size());
+      for (int i = 0; i < (int) input2_sizes.size(); i++) {
+        if(input2_sizes[i].dim != input1_sizes[axis + i].dim &&
+            input2_sizes[i].dim != 1) return -1;
+        if (input2_sizes[i].dim != input1_sizes[axis + i].dim) broadcast = true;
+      }
+      // Return true if broadcasting is required
+      if (input1_sizes.size() > input2_sizes.size() || broadcast) return 1;
+      else return 0;
+    }
+
+    void assert_numpy_multibroadcastable(const std::vector<Dimension>& input1_sizes,
+        const std::vector<Dimension>& input2_sizes) {
+      // Generalize above for multibroadcastable case
+      const std::vector<Dimension>* A_ptr;
+      const std::vector<Dimension>* B_ptr;
+      int A;
+      int B;
+      if (input1_sizes.size() < input2_sizes.size()) {
+        A_ptr = &input2_sizes;
+        B_ptr = &input1_sizes;
+        A = 2;
+        B = 1;
+      } else {
+        A_ptr = &input1_sizes;
+        B_ptr = &input2_sizes;
+        A = 1;
+        B = 2;
+      }
+      const std::vector<Dimension>& A_sizes = *A_ptr;
+      const std::vector<Dimension>& B_sizes = *B_ptr;
+      int axis = (int) (A_sizes.size() - B_sizes.size());
+      for (int i = 0; i < (int) B_sizes.size(); i++) {
+        ONNX_ASSERTM(B_sizes[i].dim == A_sizes[axis + i].dim ||
+            B_sizes[i].dim == 1 || A_sizes[axis + i].dim == 1, "Dimension %d of input %d does not match "
+            "dimension %d of input %d, and neither's value is 1", i, B, axis + i, A);
+      }
+    }
+
+    void assertNotParams(const std::vector<Dimension>& sizes) {
+      for (const Dimension& dim : sizes) {
+        ONNX_ASSERTM(dim.is_int, "%s Dimension is a param instead of an int.", dim.param.c_str());
+      }
+    }
+
+    void assertInputsAvailable(const ArrayRef<Value*>& inputs, const char* name, uint64_t num_inputs) {
+      ONNX_ASSERTM(inputs.size() == num_inputs, "%s in opset version 6 can only broadcast"
+        " between %d inputs", name, num_inputs);
+      for (int i = 0; i < (int) num_inputs; i++) {
+        ONNX_ASSERTM(inputs[i]->has_sizes(), "Shape of input %d is not available.", num_inputs);
+        assertNotParams(inputs[i]->sizes());
+      }
+    }
+}}
diff --git a/third_party/onnx/onnx/version_converter/helper.h b/third_party/onnx/onnx/version_converter/helper.h
new file mode 100644
index 0000000000..d0e5255903
--- /dev/null
+++ b/third_party/onnx/onnx/version_converter/helper.h
@@ -0,0 +1,22 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// Helper Methods for Adapters
+
+#pragma once
+
+#include "onnx/common/ir.h"
+
+namespace ONNX_NAMESPACE { namespace version_conversion {
+    int check_numpy_unibroadcastable_and_require_broadcast(
+        const std::vector<Dimension>& input1_sizes,
+        const std::vector<Dimension>& input2_sizes);
+
+    void assert_numpy_multibroadcastable(const std::vector<Dimension>& input1_sizes,
+        const std::vector<Dimension>& input2_sizes);
+
+    void assertNotParams(const std::vector<Dimension>& sizes);
+
+    void assertInputsAvailable(const ArrayRef<Value*>& inputs, const char* name, uint64_t num_inputs);
+}}
diff --git a/third_party/onnx/requirements-dev.txt b/third_party/onnx/requirements-dev.txt
new file mode 100644
index 0000000000..af479dcb8d
--- /dev/null
+++ b/third_party/onnx/requirements-dev.txt
@@ -0,0 +1,11 @@
+numpy
+protobuf
+typing-extensions
+pytest
+pytest-cov
+nbval
+wheel
+jupyter
+pyzmq
+setuptools
+twine
\ No newline at end of file
diff --git a/third_party/onnx/requirements-release.txt b/third_party/onnx/requirements-release.txt
new file mode 100644
index 0000000000..52b79c3311
--- /dev/null
+++ b/third_party/onnx/requirements-release.txt
@@ -0,0 +1,8 @@
+numpy == 1.21.5
+protobuf == 3.16.0
+pytest
+nbval
+ipython
+wheel
+setuptools
+twine
\ No newline at end of file
diff --git a/third_party/onnx/requirements.txt b/third_party/onnx/requirements.txt
new file mode 100644
index 0000000000..6a6b7a4fa0
--- /dev/null
+++ b/third_party/onnx/requirements.txt
@@ -0,0 +1,3 @@
+numpy >= 1.16.6 # TODO: update once TensorFlow 2.6 is end of life
+protobuf >= 3.12.2
+typing-extensions >= 3.6.2.1
\ No newline at end of file
diff --git a/third_party/onnx/setup.cfg b/third_party/onnx/setup.cfg
new file mode 100644
index 0000000000..219f65b8db
--- /dev/null
+++ b/third_party/onnx/setup.cfg
@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+
+[aliases]
+test=pytest
+
+[tool:pytest]
+addopts = --nbval --current-env
+testpaths = onnx/test onnx/examples
+
+[metadata]
+license_files = LICENSE
+classifiers =
+    Programming Language :: Python :: 3
+
+[flake8]
+select = B,C,E,F,P,T4,W,B9
+max-line-length = 80
+### DEFAULT IGNORES FOR 4-space INDENTED PROJECTS ###
+# E127, E128 are hard to silence in certain nested formatting situations.
+# E265, E266 talk about comment formatting which is too opinionated.
+# E402 warns on imports coming after statements. There are important use cases
+# like demandimport (https://fburl.com/demandimport) that require statements
+# before imports.
+# E501 is not flexible enough, we're using B950 instead.
+# E722 is a duplicate of B001.
+# F405 is hard to silence since we indeed do star import
+# P207 is a duplicate of B003.
+# P208 is a duplicate of C403.
+# W503 talks about operator formatting which is too opinionated.
+# F401 clashes with PEP484 requiring us to import types that are only used in
+# type comments.
+ignore = E127, E128, E265, E266, E402, E501, E722, F405, P207, P208, W503, F401
+exclude =
+  .git,
+  __pycache__,
+  build/*,
+  third_party/*
+  *_pb2.py,
+  .cache/*
+  .eggs
+  .setuptools-cmake-build/*
+
+[mypy]
+# follow-imports = silent  # TODO remove this
+mypy_path = stubs:third_party/pybind11
+strict_optional = True
+warn_return_any = True
+warn_no_return = True
+# TODO warn_unused_ignores = True
+warn_redundant_casts = True
+warn_incomplete_stub = True
+# TODO disallow_untyped_calls = True
+check_untyped_defs = True
+disallow_any_generics = True
+no_implicit_optional = True
+# TODO disallow_incomplete_defs = True
+# TODO disallow_subclassing_any = True
+disallow_untyped_decorators = True
+warn_unused_configs = True
+
+[mypy-onnx.*]
+disallow_untyped_defs = True
+
+[mypy-tools.*]
+disallow_untyped_defs = True
+
+# Ignore errors in setup.py
+[mypy-setup]
+ignore_errors = True
diff --git a/third_party/onnx/setup.py b/third_party/onnx/setup.py
new file mode 100644
index 0000000000..e7a9354283
--- /dev/null
+++ b/third_party/onnx/setup.py
@@ -0,0 +1,357 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from distutils.spawn import find_executable
+from distutils import sysconfig, log
+import setuptools
+import setuptools.command.build_py
+import setuptools.command.develop
+import setuptools.command.build_ext
+
+from collections import namedtuple
+from contextlib import contextmanager
+from datetime import date
+import glob
+import os
+import shlex
+import subprocess
+import sys
+import platform
+from textwrap import dedent
+import multiprocessing
+
+
+TOP_DIR = os.path.realpath(os.path.dirname(__file__))
+SRC_DIR = os.path.join(TOP_DIR, "onnx")
+TP_DIR = os.path.join(TOP_DIR, "third_party")
+CMAKE_BUILD_DIR = os.path.join(TOP_DIR, ".setuptools-cmake-build")
+PACKAGE_NAME = "onnx"
+
+WINDOWS = (os.name == "nt")
+
+CMAKE = find_executable("cmake3") or find_executable("cmake")
+MAKE = find_executable("make")
+
+install_requires = []
+setup_requires = []
+tests_require = []
+extras_require = {}
+
+################################################################################
+# Global variables for controlling the build variant
+################################################################################
+
+# Default value is set to TRUE\1 to keep the settings same as the current ones.
+# However going forward the recommended way to is to set this to False\0
+ONNX_ML = not bool(os.getenv("ONNX_ML") == "0")
+ONNX_VERIFY_PROTO3 = bool(os.getenv("ONNX_VERIFY_PROTO3") == "1")
+ONNX_NAMESPACE = os.getenv("ONNX_NAMESPACE", "onnx")
+ONNX_BUILD_TESTS = bool(os.getenv("ONNX_BUILD_TESTS") == "1")
+ONNX_DISABLE_EXCEPTIONS = bool(os.getenv("ONNX_DISABLE_EXCEPTIONS") == "1")
+
+USE_MSVC_STATIC_RUNTIME = bool(os.getenv("USE_MSVC_STATIC_RUNTIME", "0") == "1")
+DEBUG = bool(os.getenv("DEBUG", "0") == "1")
+COVERAGE = bool(os.getenv("COVERAGE", "0") == "1")
+
+################################################################################
+# Version
+################################################################################
+
+try:
+    git_version = subprocess.check_output(["git", "rev-parse", "HEAD"],
+                                          cwd=TOP_DIR).decode("ascii").strip()
+except (OSError, subprocess.CalledProcessError):
+    git_version = None
+
+with open(os.path.join(TOP_DIR, "VERSION_NUMBER")) as version_file:
+    VERSION_NUMBER = version_file.read().strip()
+    if "--weekly_build" in sys.argv:
+        today_number = date.today().strftime("%Y%m%d")
+        VERSION_NUMBER += ".dev" + today_number
+        PACKAGE_NAME = "onnx-weekly"
+        sys.argv.remove("--weekly_build")
+    VersionInfo = namedtuple("VersionInfo", ["version", "git_version"])(
+        version=VERSION_NUMBER,
+        git_version=git_version
+    )
+
+################################################################################
+# Pre Check
+################################################################################
+
+assert CMAKE, "Could not find cmake executable!"
+
+################################################################################
+# Utilities
+################################################################################
+
+
+@contextmanager
+def cd(path):
+    if not os.path.isabs(path):
+        raise RuntimeError("Can only cd to absolute path, got: {}".format(path))
+    orig_path = os.getcwd()
+    os.chdir(path)
+    try:
+        yield
+    finally:
+        os.chdir(orig_path)
+
+
+################################################################################
+# Customized commands
+################################################################################
+
+
+class ONNXCommand(setuptools.Command):
+    user_options = []
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+
+class create_version(ONNXCommand):
+    def run(self):
+        with open(os.path.join(SRC_DIR, "version.py"), "w") as f:
+            f.write(dedent("""\
+            # This file is generated by setup.py. DO NOT EDIT!
+
+
+            version = "{version}"
+            git_version = "{git_version}"
+            """.format(**dict(VersionInfo._asdict()))))
+
+
+class cmake_build(setuptools.Command):
+    """
+    Compiles everything when `python setup.py build` is run using cmake.
+
+    Custom args can be passed to cmake by specifying the `CMAKE_ARGS`
+    environment variable.
+
+    The number of CPUs used by `make` can be specified by passing `-j<ncpus>`
+    to `setup.py build`.  By default all CPUs are used.
+    """
+    user_options = [
+        (str("jobs="), str("j"), str("Specifies the number of jobs to use with make"))
+    ]
+
+    built = False
+
+    def initialize_options(self):
+        self.jobs = None
+
+    def finalize_options(self):
+        self.set_undefined_options("build", ("parallel", "jobs"))
+        if self.jobs is None and os.getenv("MAX_JOBS") is not None:
+            self.jobs = os.getenv("MAX_JOBS")
+        self.jobs = multiprocessing.cpu_count() if self.jobs is None else int(self.jobs)
+
+    def run(self):
+        if cmake_build.built:
+            return
+        cmake_build.built = True
+        if not os.path.exists(CMAKE_BUILD_DIR):
+            os.makedirs(CMAKE_BUILD_DIR)
+
+        with cd(CMAKE_BUILD_DIR):
+            build_type = "Release"
+            # configure
+            cmake_args = [
+                CMAKE,
+                "-DPYTHON_INCLUDE_DIR={}".format(sysconfig.get_python_inc()),
+                "-DPYTHON_EXECUTABLE={}".format(sys.executable),
+                "-DBUILD_ONNX_PYTHON=ON",
+                "-DCMAKE_EXPORT_COMPILE_COMMANDS=ON",
+                "-DONNX_NAMESPACE={}".format(ONNX_NAMESPACE),
+                "-DPY_EXT_SUFFIX={}".format(sysconfig.get_config_var("EXT_SUFFIX") or ''),
+            ]
+            if COVERAGE:
+                cmake_args.append("-DONNX_COVERAGE=ON")
+            if COVERAGE or DEBUG:
+                # in order to get accurate coverage information, the
+                # build needs to turn off optimizations
+                build_type = "Debug"
+            cmake_args.append("-DCMAKE_BUILD_TYPE=%s" % build_type)
+            if WINDOWS:
+                cmake_args.extend([
+                    # we need to link with libpython on windows, so
+                    # passing python version to window in order to
+                    # find python in cmake
+                    "-DPY_VERSION={}".format("{0}.{1}".format(*sys.version_info[:2])),
+                ])
+                if USE_MSVC_STATIC_RUNTIME:
+                    cmake_args.append("-DONNX_USE_MSVC_STATIC_RUNTIME=ON")
+                if platform.architecture()[0] == "64bit":
+                    cmake_args.extend(["-A", "x64", "-T", "host=x64"])
+                else:
+                    cmake_args.extend(["-A", "Win32", "-T", "host=x86"])
+            if ONNX_ML:
+                cmake_args.append("-DONNX_ML=1")
+            if ONNX_VERIFY_PROTO3:
+                cmake_args.append("-DONNX_VERIFY_PROTO3=1")
+            if ONNX_BUILD_TESTS:
+                cmake_args.append("-DONNX_BUILD_TESTS=ON")
+            if ONNX_DISABLE_EXCEPTIONS:
+                cmake_args.append("-DONNX_DISABLE_EXCEPTIONS=ON")
+            if "CMAKE_ARGS" in os.environ:
+                extra_cmake_args = shlex.split(os.environ["CMAKE_ARGS"])
+                # prevent crossfire with downstream scripts
+                del os.environ["CMAKE_ARGS"]
+                log.info("Extra cmake args: {}".format(extra_cmake_args))
+                cmake_args.extend(extra_cmake_args)
+            cmake_args.append(TOP_DIR)
+            log.info("Using cmake args: {}".format(cmake_args))
+            if "-DONNX_DISABLE_EXCEPTIONS=ON" in cmake_args:
+                raise RuntimeError("-DONNX_DISABLE_EXCEPTIONS=ON option is only available for c++ builds. Python binding require exceptions to be enabled.")
+            subprocess.check_call(cmake_args)
+
+            build_args = [CMAKE, "--build", os.curdir]
+            if WINDOWS:
+                build_args.extend(["--config", build_type])
+                build_args.extend(["--", "/maxcpucount:{}".format(self.jobs)])
+            else:
+                build_args.extend(["--", "-j", str(self.jobs)])
+            subprocess.check_call(build_args)
+
+
+class build_py(setuptools.command.build_py.build_py):
+    def run(self):
+        self.run_command("create_version")
+        self.run_command("cmake_build")
+
+        generated_python_files = \
+            glob.glob(os.path.join(CMAKE_BUILD_DIR, "onnx", "*.py")) + \
+            glob.glob(os.path.join(CMAKE_BUILD_DIR, "onnx", "*.pyi"))
+
+        for src in generated_python_files:
+            dst = os.path.join(
+                TOP_DIR, os.path.relpath(src, CMAKE_BUILD_DIR))
+            self.copy_file(src, dst)
+
+        return setuptools.command.build_py.build_py.run(self)
+
+
+class develop(setuptools.command.develop.develop):
+    def run(self):
+        self.run_command("build_py")
+        setuptools.command.develop.develop.run(self)
+
+
+class build_ext(setuptools.command.build_ext.build_ext):
+    def run(self):
+        self.run_command("cmake_build")
+        setuptools.command.build_ext.build_ext.run(self)
+
+    def build_extensions(self):
+        for ext in self.extensions:
+            fullname = self.get_ext_fullname(ext.name)
+            filename = os.path.basename(self.get_ext_filename(fullname))
+
+            lib_path = CMAKE_BUILD_DIR
+            if os.name == "nt":
+                debug_lib_dir = os.path.join(lib_path, "Debug")
+                release_lib_dir = os.path.join(lib_path, "Release")
+                if os.path.exists(debug_lib_dir):
+                    lib_path = debug_lib_dir
+                elif os.path.exists(release_lib_dir):
+                    lib_path = release_lib_dir
+            src = os.path.join(lib_path, filename)
+            dst = os.path.join(os.path.realpath(self.build_lib), "onnx", filename)
+            self.copy_file(src, dst)
+
+
+class mypy_type_check(ONNXCommand):
+    description = "Run MyPy type checker"
+
+    def run(self):
+        """Run command."""
+        onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py"))
+        returncode = subprocess.call([sys.executable, onnx_script])
+        sys.exit(returncode)
+
+
+cmdclass = {
+    "create_version": create_version,
+    "cmake_build": cmake_build,
+    "build_py": build_py,
+    "develop": develop,
+    "build_ext": build_ext,
+    "typecheck": mypy_type_check,
+}
+
+################################################################################
+# Extensions
+################################################################################
+
+ext_modules = [
+    setuptools.Extension(
+        name=str("onnx.onnx_cpp2py_export"),
+        sources=[])
+]
+
+################################################################################
+# Packages
+################################################################################
+
+# no need to do fancy stuff so far
+packages = setuptools.find_packages()
+
+requirements_file = "requirements.txt"
+requirements_path = os.path.join(os.getcwd(), requirements_file)
+if not os.path.exists(requirements_path):
+    this = os.path.dirname(__file__)
+    requirements_path = os.path.join(this, requirements_file)
+if not os.path.exists(requirements_path):
+    raise FileNotFoundError("Unable to find " + requirements_file)
+with open(requirements_path) as f:
+    install_requires = f.read().splitlines()
+
+################################################################################
+# Test
+################################################################################
+
+setup_requires.append("pytest-runner")
+tests_require.append("pytest")
+tests_require.append("nbval")
+tests_require.append("tabulate")
+
+extras_require["lint"] = [
+    "clang-format==13.0.0",
+    "flake8",
+    "mypy==0.782",
+    "types-protobuf==3.18.4",
+]
+
+################################################################################
+# Final
+################################################################################
+
+setuptools.setup(
+    name=PACKAGE_NAME,
+    version=VersionInfo.version,
+    description="Open Neural Network Exchange",
+    long_description=open("README.md").read(),
+    long_description_content_type="text/markdown",
+    ext_modules=ext_modules,
+    cmdclass=cmdclass,
+    packages=packages,
+    license="Apache License v2.0",
+    include_package_data=True,
+    install_requires=install_requires,
+    setup_requires=setup_requires,
+    tests_require=tests_require,
+    extras_require=extras_require,
+    author="ONNX",
+    author_email="onnx-technical-discuss@lists.lfaidata.foundation",
+    url="https://github.com/onnx/onnx",
+    entry_points={
+        "console_scripts": [
+            "check-model = onnx.bin.checker:check_model",
+            "check-node = onnx.bin.checker:check_node",
+            "backend-test-tools = onnx.backend.test.cmd_tools:main",
+        ]
+    },
+)
diff --git a/third_party/onnx/tools/__init__.pyi b/third_party/onnx/tools/__init__.pyi
new file mode 100644
index 0000000000..28dd7361d8
--- /dev/null
+++ b/third_party/onnx/tools/__init__.pyi
@@ -0,0 +1,2 @@
+# This file needs to be here to enable mypy type checking for this folder.
+# It doesn't make this a python module for installation purposes as a __init__.py would.
diff --git a/third_party/onnx/tools/gen_coverage_report.py b/third_party/onnx/tools/gen_coverage_report.py
new file mode 100755
index 0000000000..ae5c7deb14
--- /dev/null
+++ b/third_party/onnx/tools/gen_coverage_report.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import os
+import subprocess
+from typing import Text
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(os.path.basename(__file__))
+    parser.add_argument('-r', '--root',
+                        default=os.path.dirname(
+                            os.path.dirname(os.path.abspath(__file__))),
+                        help='onnx root directory (default: %(default)s)')
+    parser.add_argument('-o', '--out', required=True,
+                        help='output directory')
+    return parser.parse_args()
+
+
+def gen_trace_file(root_dir: Text, out_path: Text) -> None:
+    subprocess.check_output([
+        'lcov',
+        '-c',
+        '-d',
+        root_dir,
+        '--no-external',
+        '--path',
+        root_dir,
+        '-o',
+        out_path])
+
+    subprocess.check_output([
+        'lcov',
+        '-r',
+        out_path,
+        os.path.join(root_dir, 'third_party', '*'),
+        '-o',
+        out_path])
+
+    subprocess.check_output([
+        'lcov',
+        '-r',
+        out_path,
+        os.path.join(root_dir, '.setuptools-cmake-build', '*'),
+        '-o',
+        out_path
+    ])
+
+
+def gen_html_files(root_dir: Text, trace_path: Text, out_dir: Text) -> None:
+    subprocess.check_output([
+        'genhtml',
+        trace_path,
+        '-p',
+        root_dir,
+        '-o',
+        out_dir,
+    ])
+
+
+def main() -> None:
+    args = parse_args()
+
+    root = os.path.abspath(args.root)
+    out = os.path.abspath(args.out)
+    if not os.path.exists(out):
+        os.makedirs(out)
+
+    trace_path = os.path.join(out, 'onnx-coverage.info')
+    gen_trace_file(root, trace_path)
+
+    html_dir = os.path.join(out, 'html')
+    gen_html_files(root, trace_path, html_dir)
+
+    print('Static HTML files have been generated at:\n\t{}'.format(html_dir))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/tools/mypy-onnx.py b/third_party/onnx/tools/mypy-onnx.py
new file mode 100644
index 0000000000..383b3fa5df
--- /dev/null
+++ b/third_party/onnx/tools/mypy-onnx.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+import subprocess
+import os
+
+
+def main() -> None:
+    try:
+        root_folder = os.path.realpath(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        os.chdir(root_folder)
+        # Use --no-site-packages to prevent mypy catching other typecheck errors which are not related to ONNX itself
+        subprocess.check_call(["mypy", ".", "--no-site-packages"])
+
+        exit(0)
+    except subprocess.CalledProcessError:
+        # Catch this exception because we don't want it to output a backtrace that would clutter the mypy output
+        exit(1)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/tools/protobuf-bench.cc b/third_party/onnx/tools/protobuf-bench.cc
new file mode 100644
index 0000000000..1f0525ea5a
--- /dev/null
+++ b/third_party/onnx/tools/protobuf-bench.cc
@@ -0,0 +1,150 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <benchmark/benchmark.h>
+
+#include <onnx/onnx_pb.h>
+
+using namespace ONNX_NAMESPACE;
+
+
+inline void createValueInfo4D(
+    ValueInfoProto& value_info,
+    const std::string& name,
+    int64_t n,
+    int64_t c,
+    int64_t h,
+    int64_t w) {
+  value_info.set_name(name);
+
+  TypeProto_Tensor* tensor_type =
+      value_info.mutable_type()->mutable_tensor_type();
+  tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
+
+  TensorShapeProto* shape = tensor_type->mutable_shape();
+  shape->add_dim()->set_dim_value(n);
+  shape->add_dim()->set_dim_value(c);
+  shape->add_dim()->set_dim_value(h);
+  shape->add_dim()->set_dim_value(w);
+}
+
+inline void createValueInfo2D(
+    ValueInfoProto& value_info,
+    const std::string& name,
+    int64_t h,
+    int64_t w) {
+  value_info.set_name(name);
+
+  TypeProto* type = value_info.mutable_type();
+
+  TypeProto_Tensor* tensor_type = type->mutable_tensor_type();
+  tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
+  TensorShapeProto* shape = tensor_type->mutable_shape();
+  shape->add_dim()->set_dim_value(h);
+  shape->add_dim()->set_dim_value(w);
+}
+
+inline void createConv2D(
+    NodeProto& node,
+    const std::string& input,
+    const std::string& weights,
+    const std::string& bias,
+    const std::string& output,
+    uint32_t kernel_size) {
+  node.set_op_type("Conv");
+  node.add_input(input);
+  node.add_input(weights);
+  node.add_input(bias);
+  node.add_output(output);
+
+  {
+    AttributeProto* kernel = node.add_attribute();
+    kernel->set_name("kernel_shape");
+    kernel->set_type(AttributeProto::INTS);
+    kernel->add_ints(kernel_size);
+    kernel->add_ints(kernel_size);
+  }
+  {
+    AttributeProto* dilation = node.add_attribute();
+    dilation->set_name("dilations");
+    dilation->set_type(AttributeProto::INTS);
+    dilation->add_ints(1);
+    dilation->add_ints(1);
+  }
+  {
+    AttributeProto* stride = node.add_attribute();
+    stride->set_name("strides");
+    stride->set_type(AttributeProto::INTS);
+    stride->add_ints(1);
+    stride->add_ints(1);
+  }
+  {
+    AttributeProto* group = node.add_attribute();
+    group->set_name("group");
+    group->set_type(AttributeProto::INTS);
+    group->set_i(1);
+  }
+  {
+    AttributeProto* padding = node.add_attribute();
+    padding->set_name("pads");
+    padding->set_type(AttributeProto::INTS);
+    /* Use "same" padding */
+    padding->add_ints(kernel_size / 2);
+    padding->add_ints(kernel_size / 2);
+    padding->add_ints(kernel_size - 1 - kernel_size / 2);
+    padding->add_ints(kernel_size - 1 - kernel_size / 2);
+  }
+}
+
+static void ConvGraph(benchmark::State& state) {
+  while (state.KeepRunning()) {
+    std::string data;
+    GraphProto graph;
+
+    createConv2D(*graph.add_node(), "input", "weights", "bias", "output", 3);
+
+    createValueInfo4D(*graph.add_input(), "input", 1, 3, 224, 224);
+    createValueInfo4D(*graph.add_input(), "weights", 16, 16, 3, 3);
+    createValueInfo2D(*graph.add_input(), "bias", 1, 16);
+    createValueInfo4D(*graph.add_output(), "output", 16, 3, 224, 224);
+
+    graph.SerializeToString(&data);
+
+    GraphProto decodedGraph;
+    decodedGraph.ParseFromString(data);
+  }
+
+  state.SetItemsProcessed(int64_t(state.iterations()));
+}
+BENCHMARK(ConvGraph)->Unit(benchmark::kMicrosecond);
+
+static void ConvModel(benchmark::State& state) {
+  while (state.KeepRunning()) {
+    std::string data;
+    ModelProto model;
+    model.set_ir_version(IR_VERSION);
+    OperatorSetIdProto* op_set_id = model.add_opset_import();
+    op_set_id->set_domain("");
+    op_set_id->set_version(4);
+
+    GraphProto* graph = model.mutable_graph();
+
+    createConv2D(*graph->add_node(), "input", "weights", "bias", "output", 3);
+
+    createValueInfo4D(*graph->add_input(), "input", 1, 3, 224, 224);
+    createValueInfo4D(*graph->add_input(), "weights", 16, 16, 3, 3);
+    createValueInfo2D(*graph->add_input(), "bias", 1, 16);
+    createValueInfo4D(*graph->add_output(), "output", 16, 3, 224, 224);
+
+    model.SerializeToString(&data);
+
+    ModelProto decodedModel;
+    decodedModel.ParseFromString(data);
+  }
+
+  state.SetItemsProcessed(int64_t(state.iterations()));
+}
+BENCHMARK(ConvModel)->Unit(benchmark::kMicrosecond);
+
+BENCHMARK_MAIN();
diff --git a/third_party/onnx/tools/protoc-gen-mypy.bat b/third_party/onnx/tools/protoc-gen-mypy.bat
new file mode 100644
index 0000000000..932bc80fe8
--- /dev/null
+++ b/third_party/onnx/tools/protoc-gen-mypy.bat
@@ -0,0 +1,4 @@
+:: SPDX-License-Identifier: Apache-2.0
+
+@echo off
+python -u %~dp0\protoc-gen-mypy.py
diff --git a/third_party/onnx/tools/protoc-gen-mypy.py b/third_party/onnx/tools/protoc-gen-mypy.py
new file mode 100755
index 0000000000..2711860f32
--- /dev/null
+++ b/third_party/onnx/tools/protoc-gen-mypy.py
@@ -0,0 +1,342 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+# Taken from https://github.com/dropbox/mypy-protobuf/blob/d984389124eae6dbbb517f766b9266bb32171510/python/protoc-gen-mypy
+# (Apache 2.0 License)
+# with own fixes to
+# - appease flake8
+# - exit without error when protobuf isn't installed
+# - fix recognition of whether an identifier is defined locally
+#   (unfortunately, we use a python package name ONNX_NAMESPACE_FOO_BAR_FOR_CI
+#    on CI, which by the original protoc-gen-mypy script was recognized to be
+#    camel case and therefore handled as an entry in the local package)
+
+
+"""Protoc Plugin to generate mypy stubs. Loosely based on @zbarsky's go implementation"""
+from __future__ import (
+    absolute_import,
+    division,
+    print_function,
+)
+
+import sys
+from collections import defaultdict
+from contextlib import contextmanager
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    List,
+    Set,
+    Text,
+    cast,
+    Optional
+)
+
+try:
+    import google.protobuf.descriptor_pb2 as d_typed
+    from google.protobuf.compiler import plugin_pb2 as plugin
+except ImportError as e:
+    sys.stderr.write('Failed to generate mypy stubs: {}\n'.format(e))
+    sys.exit(0)
+
+
+# Hax to get around fact that google protobuf libraries aren't in typeshed yet
+d: Any = d_typed
+
+GENERATED = "@ge" + "nerated"  # So phabricator doesn't think this file is generated
+HEADER = "# {} by generate_proto_mypy_stubs.py.  Do not edit!\n".format(GENERATED)
+
+
+class Descriptors(object):
+
+    def __init__(self, request: plugin.CodeGeneratorRequest) -> None:
+        files = {f.name: f for f in request.proto_file}
+        to_generate = {n: files[n] for n in request.file_to_generate}
+        self.files: Dict[Text, d.FileDescriptorProto] = files
+        self.to_generate: Dict[Text, d.FileDescriptorProto] = to_generate
+        self.messages: Dict[Text, d.DescriptorProto] = {}
+        self.message_to_fd: Dict[Text, d.FileDescriptorProto] = {}
+
+        def _add_enums(enums: d.EnumDescriptorProto, prefix: Text, fd: d.FileDescriptorProto) -> None:
+            for enum in enums:
+                self.message_to_fd[prefix + enum.name] = fd
+
+        def _add_messages(messages: d.DescriptorProto, prefix: Text, fd: d.FileDescriptorProto) -> None:
+            for message in messages:
+                self.messages[prefix + message.name] = message
+                self.message_to_fd[prefix + message.name] = fd
+                sub_prefix = prefix + message.name + "."
+                _add_messages(message.nested_type, sub_prefix, fd)
+                _add_enums(message.enum_type, sub_prefix, fd)
+
+        for fd in request.proto_file:
+            start_prefix = "." + fd.package + "."
+            _add_messages(fd.message_type, start_prefix, fd)
+            _add_enums(fd.enum_type, start_prefix, fd)
+
+
+class PkgWriter(object):
+    """Writes a single pyi file"""
+
+    def __init__(self, fd: d.FileDescriptorProto, descriptors: Descriptors) -> None:
+        self.fd = fd
+        self.descriptors = descriptors
+        self.lines: List[Text] = []
+        self.indent = ""
+
+        # dictionary of x->y for `from {x} import {y}`
+        self.imports: Dict[Text, Set[Text]] = defaultdict(set)
+        self.locals: Set[Text] = set()
+
+    def _import(self, path: Text, name: Text, import_as: Optional[Text] = None) -> Text:
+        """Imports a stdlib path and returns a handle to it
+        eg. self._import("typing", "Optional") -> "Optional"
+        """
+        imp = path.replace('/', '.')
+        if import_as is not None:
+            self.imports[imp].add("{} as {}".format(name, import_as))
+            return import_as
+        else:
+            self.imports[imp].add(name)
+            return name
+
+    def _import_message(self, type_name: d.FieldDescriptorProto) -> Text:
+        """Import a referenced message and return a handle"""
+        name = cast(Text, type_name)
+
+        if name[0] == '.' and name[1].isupper() and name[2].islower():
+            # Message defined in this file
+            return name[1:]
+
+        message_fd = self.descriptors.message_to_fd[name]
+        if message_fd.name == self.fd.name:
+            # message defined in this package
+            split = name.split('.')
+            for i, segment in enumerate(split):
+                if segment and segment[0].isupper() and segment[1].islower():
+                    return ".".join(split[i:])
+
+        # Not in package. Must import
+        split = name.split(".")
+        for i, segment in enumerate(split):
+            if segment and segment[0].isupper() and segment[1].islower():
+                assert message_fd.name.endswith('.proto')
+                import_name = self._import(message_fd.name[:-6].replace('-', '_') + "_pb2", segment)
+                remains = ".".join(split[i + 1:])
+                if not remains:
+                    return import_name
+                raise AssertionError("Don't support nested imports yet")
+                # return new_nested_import(import_name, remains)
+
+        raise AssertionError("Could not parse local name " + name)
+
+    @contextmanager  # type: ignore
+    def _indent(self) -> Generator[None, None, None]:
+        self.indent = self.indent + "    "
+        yield
+        self.indent = self.indent[:-4]
+
+    def _write_line(self, line: Text, *args: Text) -> None:
+        self.lines.append(self.indent + line.format(*args))
+
+    def write_enums(self, enums: List[d.EnumDescriptorProto]) -> None:
+        line = self._write_line
+        for enum in enums:
+            line("class {}(int):", enum.name)
+            with self._indent():
+                line("@classmethod")
+                line("def Name(cls, number: int) -> str: ...")
+                line("@classmethod")
+                line("def Value(cls, name: str) -> int: ...")
+                line("@classmethod")
+                line("def keys(cls) -> {}[str]: ...",
+                    self._import("typing", "List"))
+                line("@classmethod")
+                line("def values(cls) -> {}[int]: ...",
+                    self._import("typing", "List"))
+                line("@classmethod")
+                line("def items(cls) -> {}[{}[str, int]]: ...",
+                    self._import("typing", "List"),
+                    self._import("typing", "Tuple"))
+
+            for val in enum.value:
+                line("{} = {}({}, {})", val.name, self._import("typing", "cast"), enum.name, val.number)
+            line("")
+
+    def write_messages(self, messages: List[d.DescriptorProto], prefix: Text) -> None:
+        line = self._write_line
+        message_class = self._import("google.protobuf.message", "Message")
+
+        for desc in messages:
+            self.locals.add(desc.name)
+            qualified_name = prefix + desc.name
+            line("class {}({}):", desc.name, message_class)
+            with self._indent():
+                # Nested enums/messages
+                self.write_enums(desc.enum_type)
+                self.write_messages(desc.nested_type, qualified_name + ".")
+
+                # Scalar fields
+                for field in [f for f in desc.field if is_scalar(f)]:
+                    if field.label == d.FieldDescriptorProto.LABEL_REPEATED:
+                        container = self._import("google.protobuf.internal.containers", "RepeatedScalarFieldContainer")
+                        line("{} = ... # type: {}[{}]", field.name, container, self.python_type(field))
+                    else:
+                        line("{} = ... # type: {}", field.name, self.python_type(field))
+                line("")
+
+                # Getters for non-scalar fields
+                for field in [f for f in desc.field if not is_scalar(f)]:
+                    line("@property")
+                    if field.label == d.FieldDescriptorProto.LABEL_REPEATED:
+                        msg = self.descriptors.messages[field.type_name]
+                        if msg.options.map_entry:
+                            # map generates a special Entry wrapper message
+                            container = self._import("typing", "MutableMapping")
+                            line("def {}(self) -> {}[{}, {}]: ...", field.name, container, self.python_type(msg.field[0]), self.python_type(msg.field[1]))
+                        else:
+                            container = self._import("google.protobuf.internal.containers", "RepeatedCompositeFieldContainer")
+                            line("def {}(self) -> {}[{}]: ...", field.name, container, self.python_type(field))
+                    else:
+                        line("def {}(self) -> {}: ...", field.name, self.python_type(field))
+                    line("")
+
+                # Constructor
+                line("def __init__(self,")
+                with self._indent():
+                    # Required args
+                    for field in [f for f in desc.field if f.label == d.FieldDescriptorProto.LABEL_REQUIRED]:
+                        line("{} : {},", field.name, self.python_type(field))
+                    for field in [f for f in desc.field if f.label != d.FieldDescriptorProto.LABEL_REQUIRED]:
+                        if field.label == d.FieldDescriptorProto.LABEL_REPEATED:
+                            if field.type_name != '' and self.descriptors.messages[field.type_name].options.map_entry:
+                                msg = self.descriptors.messages[field.type_name]
+                                line("{} : {}[{}[{}, {}]] = None,", field.name, self._import("typing", "Optional", "OptionalType"),
+                                    self._import("typing", "Mapping"), self.python_type(msg.field[0]), self.python_type(msg.field[1]))
+                            else:
+                                line("{} : {}[{}[{}]] = None,", field.name, self._import("typing", "Optional", "OptionalType"),
+                                  self._import("typing", "Iterable"), self.python_type(field))
+                        else:
+                            line("{} : {}[{}] = None,", field.name, self._import("typing", "Optional", "OptionalType"),
+                              self.python_type(field))
+                    line(") -> None: ...")
+
+                # Standard message methods
+                line("@classmethod")
+                line("def FromString(cls, s: bytes) -> {}: ...", qualified_name)
+                line("def MergeFrom(self, other_msg: {}) -> None: ...", message_class)
+                line("def CopyFrom(self, other_msg: {}) -> None: ...", message_class)
+            line("")
+
+    def write_services(self, services: d.ServiceDescriptorProto) -> None:
+        line = self._write_line
+
+        for service in services:
+            # The service definition interface
+            line("class {}({}, metaclass={}):", service.name, self._import("google.protobuf.service", "Service"), self._import("abc", "ABCMeta"))
+            with self._indent():
+                for method in service.method:
+                    line("@{}", self._import("abc", "abstractmethod"))
+                    line("def {}(self,", method.name)
+                    with self._indent():
+                        line("rpc_controller: {},", self._import("google.protobuf.service", "RpcController"))
+                        line("request: {},", self._import_message(method.input_type))
+                        line("done: {}[{}[[{}], None]],",
+                          self._import("typing", "Optional"),
+                          self._import("typing", "Callable"),
+                          self._import_message(method.output_type))
+                    line(") -> {}[{}]: ...", self._import("concurrent.futures", "Future"), self._import_message(method.output_type))
+
+            # The stub client
+            line("class {}({}):", service.name + "_Stub", service.name)
+            with self._indent():
+                line("def __init__(self, rpc_channel: {}) -> None: ...",
+                  self._import("google.protobuf.service", "RpcChannel"))
+
+    def python_type(self, field: d.FieldDescriptorProto) -> Text:
+        mapping: Dict[int, Callable[[], Text]] = {
+            d.FieldDescriptorProto.TYPE_DOUBLE: lambda: "float",
+            d.FieldDescriptorProto.TYPE_FLOAT: lambda: "float",
+
+            d.FieldDescriptorProto.TYPE_INT64: lambda: "int",
+            d.FieldDescriptorProto.TYPE_UINT64: lambda: "int",
+            d.FieldDescriptorProto.TYPE_FIXED64: lambda: "int",
+            d.FieldDescriptorProto.TYPE_SFIXED64: lambda: "int",
+            d.FieldDescriptorProto.TYPE_SINT64: lambda: "int",
+            d.FieldDescriptorProto.TYPE_INT32: lambda: "int",
+            d.FieldDescriptorProto.TYPE_UINT32: lambda: "int",
+            d.FieldDescriptorProto.TYPE_FIXED32: lambda: "int",
+            d.FieldDescriptorProto.TYPE_SFIXED32: lambda: "int",
+            d.FieldDescriptorProto.TYPE_SINT32: lambda: "int",
+
+            d.FieldDescriptorProto.TYPE_BOOL: lambda: "bool",
+            d.FieldDescriptorProto.TYPE_STRING: lambda: self._import("typing", "Text"),
+            d.FieldDescriptorProto.TYPE_BYTES: lambda: "bytes",
+
+            d.FieldDescriptorProto.TYPE_ENUM: lambda: self._import_message(field.type_name),
+            d.FieldDescriptorProto.TYPE_MESSAGE: lambda: self._import_message(field.type_name),
+            d.FieldDescriptorProto.TYPE_GROUP: lambda: self._import_message(field.type_name),
+        }
+
+        assert field.type in mapping, "Unrecognized type: " + field.type
+        return mapping[field.type]()
+
+    def write(self) -> Text:
+        imports = []
+        for pkg, items in self.imports.items():
+            imports.append(u"from {} import (".format(pkg))
+            for item in sorted(items):
+                imports.append(u"    {},".format(item))
+            imports.append(u")\n")
+
+        return "\n".join(imports + self.lines)
+
+
+def is_scalar(fd: d.FileDescriptorProto) -> bool:
+    return not (
+        fd.type == d.FieldDescriptorProto.TYPE_MESSAGE
+        or fd.type == d.FieldDescriptorProto.TYPE_GROUP
+    )
+
+
+def generate_mypy_stubs(descriptors: Descriptors, response: plugin.CodeGeneratorResponse) -> None:
+    for name, fd in descriptors.to_generate.items():
+        pkg_writer = PkgWriter(fd, descriptors)
+        pkg_writer.write_enums(fd.enum_type)
+        pkg_writer.write_messages(fd.message_type, "")
+        pkg_writer.write_services(fd.service)
+
+        assert name == fd.name
+        assert fd.name.endswith('.proto')
+        output = response.file.add()
+        output.name = fd.name[:-6].replace('-', '_') + '_pb2.pyi'
+        output.content = HEADER + pkg_writer.write()
+        print("Writing mypy to", output.name, file=sys.stderr)
+
+
+def main() -> None:
+    # Read request message from stdin
+    data = sys.stdin.buffer.read()
+
+    # Parse request
+    request = plugin.CodeGeneratorRequest()
+    request.ParseFromString(data)
+
+    # Create response
+    response = plugin.CodeGeneratorResponse()
+
+    # Generate mypy
+    generate_mypy_stubs(Descriptors(request), response)
+
+    # Serialise response message
+    output = response.SerializeToString()
+
+    # Write to stdout
+    sys.stdout.buffer.write(output)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/tools/protoc-gen-mypy.sh.in b/third_party/onnx/tools/protoc-gen-mypy.sh.in
new file mode 100644
index 0000000000..f7ce1f92ab
--- /dev/null
+++ b/third_party/onnx/tools/protoc-gen-mypy.sh.in
@@ -0,0 +1,4 @@
+#!/bin/bash
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+# use PYTHON_EXECUTABLE from CMake to get the right python to execute
+@PYTHON_EXECUTABLE@ -u "$DIR"/protoc-gen-mypy.py
diff --git a/third_party/onnx/tools/style.sh b/third_party/onnx/tools/style.sh
new file mode 100755
index 0000000000..fe4850fb3e
--- /dev/null
+++ b/third_party/onnx/tools/style.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+set -o errexit
+set -o nounset
+
+
+cd "$(git rev-parse --show-toplevel)"
+
+echo -e "===> check flake8"
+flake8 onnx tools workflow_scripts
+
+echo -e "\n===> check mypy"
+mypy . --no-site-packages
+
+# Currently, clang-format is not checked on CIs.
+if [ "${ENABLE_CLANG_FORMAT:-0}" == "1" ]; then
+    echo -e "\n===> run clang-format"
+    git ls-files --exclude-standard -- '*/*.cc' '*/*.h' | \
+        xargs ${CLANG_FORMAT_BIN:-clang-format} -i
+fi
+
+git diff --exit-code
diff --git a/third_party/onnx/tools/update_doc.bat b/third_party/onnx/tools/update_doc.bat
new file mode 100644
index 0000000000..23780c9764
--- /dev/null
+++ b/third_party/onnx/tools/update_doc.bat
@@ -0,0 +1,20 @@
+:: SPDX-License-Identifier: Apache-2.0
+
+:: Run this script from ONNX root directory under Anaconda.
+set CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON"
+set ONNX_ML=1
+
+python onnx\gen_proto.py -l
+
+python onnx\gen_proto.py -l --ml
+
+python setup.py develop
+
+python onnx\backend\test\cmd_tools.py generate-data
+
+python onnx\backend\test\stat_coverage.py
+
+python onnx\defs\gen_doc.py
+set ONNX_ML=0
+python onnx\defs\gen_doc.py
+set ONNX_ML=1
diff --git a/third_party/onnx/tools/update_doc.sh b/third_party/onnx/tools/update_doc.sh
new file mode 100755
index 0000000000..ff177420c5
--- /dev/null
+++ b/third_party/onnx/tools/update_doc.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+# export ONNX_ML=0, # if you need to disable ONNX_ML
+
+python_exist=`command -v python`
+if [ -z $python_exist ]; then
+  echo "No python is found, please set it in your environment."
+  exit 1
+fi
+
+if [ ! -f "onnx/defs/gen_doc.py" ]; then
+  echo "Please run this script in the ONNX root folder."
+  exit 1
+fi
+
+set -e
+
+echo -e "===> recompile onnx"
+python setup.py develop --user
+
+echo -e "\n===> regenerate test data from node test"
+python onnx/backend/test/cmd_tools.py generate-data
+
+echo -e "\n===> regenerate stats of test data"
+python onnx/backend/test/stat_coverage.py
+
+echo -e "\n===> regenerate the docs"
+python onnx/defs/gen_doc.py
+ONNX_ML=0 python onnx/defs/gen_doc.py
+
+echo -e "\n===> the update is done!"
diff --git a/third_party/onnx/workflow_scripts/config.py b/third_party/onnx/workflow_scripts/config.py
new file mode 100644
index 0000000000..12fc2ade6e
--- /dev/null
+++ b/third_party/onnx/workflow_scripts/config.py
@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# Originally there are some checker failures in weekly-CI
+# Skip them in test_model_zoo.py for now
+# TODO: fix these checker failures
+SKIP_CHECKER_MODELS = {'vision/classification/alexnet/model/bvlcalexnet-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/caffenet/model/caffenet-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/densenet-121/model/densenet-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/inception_and_googlenet/inception_v1/model/inception-v1-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/rcnn_ilsvrc13/model/rcnn-ilsvrc13-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/resnet/model/resnet50-caffe2-v1-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/shufflenet/model/shufflenet-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/squeezenet/model/squeezenet1.0-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/vgg/model/vgg19-caffe2-3.onnx',  # opset1 typeinference function missing
+                       'vision/classification/zfnet-512/model/zfnet512-3.onnx'}  # opset1 typeinference function missing
diff --git a/third_party/onnx/workflow_scripts/test_generated_backend.py b/third_party/onnx/workflow_scripts/test_generated_backend.py
new file mode 100644
index 0000000000..f7335ebb44
--- /dev/null
+++ b/third_party/onnx/workflow_scripts/test_generated_backend.py
@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+import config
+import onnx
+import os
+import sys
+
+
+def main():
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    directory = os.path.join(script_dir, '../onnx/backend/test/data/node')
+    count = failed_count = 0
+    for root, _, files in os.walk(directory):
+        for file in files:
+            if file.endswith('.onnx'):
+                test_dir_name = os.path.basename(os.path.normpath(root))
+                onnx_model_path = os.path.join(root, file)
+                try:
+                    model = onnx.load(onnx_model_path)
+                    # check model by ONNX checker
+                    inferred_model = onnx.shape_inference.infer_shapes(model, check_type=True, strict_mode=True)
+                    onnx.checker.check_model(inferred_model)
+
+                except Exception as e:
+                    failed_count += 1
+                    print("{} failed: {}".format(test_dir_name, e))
+                count += 1
+    print('-----------------------------')
+    if failed_count == 0:
+        print("{} backend models passed.".format(count))
+    else:
+        print("{} failed in {} backend models.".format(failed_count, count))
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/onnx/workflow_scripts/test_model_zoo.py b/third_party/onnx/workflow_scripts/test_model_zoo.py
new file mode 100644
index 0000000000..ddc1071ce8
--- /dev/null
+++ b/third_party/onnx/workflow_scripts/test_model_zoo.py
@@ -0,0 +1,102 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import config
+import gc
+import onnx
+import os
+from pathlib import Path
+import subprocess
+import sys
+import time
+
+cwd_path = Path.cwd()
+
+
+def run_lfs_install():
+    result = subprocess.run(['git', 'lfs', 'install'], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    print('Git LFS install completed with return code= {}'.format(result.returncode))
+
+
+def pull_lfs_file(file_name):
+    result = subprocess.run(['git', 'lfs', 'pull', '--include', file_name, '--exclude', '\'\''], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    print('LFS pull completed with return code= {}'.format(result.returncode))
+    print(result)
+
+
+def run_lfs_prune():
+    result = subprocess.run(['git', 'lfs', 'prune'], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    print('LFS prune completed with return code= {}'.format(result.returncode))
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Test settings')
+    # default: test all models in the repo
+    # if test_dir is specified, only test files under that specified path
+    parser.add_argument('--test_dir', required=False, default='', type=str,
+                        help='Directory path for testing. e.g., text, vision')
+    args = parser.parse_args()
+    parent_dir = []
+    # if not set, go throught each directory
+    if not args.test_dir:
+        for file in os.listdir():
+            if os.path.isdir(file):
+                parent_dir.append(file)
+    else:
+        parent_dir.append(args.test_dir)
+    model_list = []
+    for directory in parent_dir:
+        for root, _, files in os.walk(directory):
+            for file in files:
+                if file.endswith('.onnx'):
+                    onnx_model_path = os.path.join(root, file)
+                    model_list.append(onnx_model_path)
+                    print(onnx_model_path)
+    # run lfs install before starting the tests
+    run_lfs_install()
+
+    print('=== Running ONNX Checker on {} models ==='.format(len(model_list)))
+    # run checker on each model
+    failed_models = []
+    failed_messages = []
+    skip_models = []
+    for model_path in model_list:
+        start = time.time()
+        model_name = model_path.split('/')[-1]
+        # if the model_path exists in the skip list, simply skip it
+        if model_path.replace('\\', '/') in config.SKIP_CHECKER_MODELS:
+            print('Skip model: {}'.format(model_path))
+            skip_models.append(model_path)
+            continue
+        print('-----------------Testing: {}-----------------'.format(model_name))
+        try:
+            pull_lfs_file(model_path)
+            model = onnx.load(model_path)
+            # stricter onnx.checker with onnx.shape_inference
+            onnx.checker.check_model(model, True)
+            # remove the model to save space in CIs
+            if os.path.exists(model_path):
+                os.remove(model_path)
+            # clean git lfs cache
+            run_lfs_prune()
+            print('[PASS]: {} is checked by onnx. '.format(model_name))
+
+        except Exception as e:
+            print('[FAIL]: {}'.format(e))
+            failed_models.append(model_path)
+            failed_messages.append((model_name, e))
+        end = time.time()
+        print('--------------Time used: {} secs-------------'.format(end - start))
+        # enable gc collection to prevent MemoryError by loading too many large models
+        gc.collect()
+
+    if len(failed_models) == 0:
+        print('{} models have been checked. {} models were skipped.'.format(len(model_list), len(skip_models)))
+    else:
+        print('In all {} models, {} models failed, {} models were skipped'.format(len(model_list), len(failed_models), len(skip_models)))
+        for model, error in failed_messages:
+            print('{} failed because: {}'.format(model, error))
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/third_party/optimizer/.clang-format b/third_party/optimizer/.clang-format
new file mode 100644
index 0000000000..1a6ab93385
--- /dev/null
+++ b/third_party/optimizer/.clang-format
@@ -0,0 +1,7 @@
+BasedOnStyle: Google
+AllowShortBlocksOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine: false
+AllowShortFunctionsOnASingleLine: Empty
+AllowShortLoopsOnASingleLine: false
+AllowShortIfStatementsOnASingleLine: false
+
diff --git a/third_party/optimizer/.gitignore b/third_party/optimizer/.gitignore
new file mode 100644
index 0000000000..9b976ffdee
--- /dev/null
+++ b/third_party/optimizer/.gitignore
@@ -0,0 +1,84 @@
+## General
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.cuo
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.pyd
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+
+# Compiled python
+*.pyc
+
+# Compiled MATLAB
+*.mex*
+
+# IPython notebook checkpoints
+.ipynb_checkpoints
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# Sublime Text settings
+*.sublime-workspace
+*.sublime-project
+
+# Eclipse Project settings
+*.*project
+.settings
+
+# QtCreator files
+*.user
+
+# PyCharm files
+.idea
+
+# Visual Studio Code files
+.vscode
+
+# OSX dir files
+.DS_Store
+
+## ONNX
+
+# build, distribute, and bins (+ python proto bindings)
+build/
+build_*/
+.build_debug/*
+.build_release/*
+.setuptools-cmake-build/*
+
+# setup.py intermediates
+.eggs
+dist
+*.egg-info
+*.ninja
+.ninja_deps
+.ninja_log
+compile_commands.json
+
+# generated files
+onnxoptimizer/version.py
+compile_commands.json
+
+# autocomplete
+.ycm_extra_conf.py
+
+# test coverage data files
+*.gcov
+
+.mypy_cache
+virtualenv
+venv
diff --git a/third_party/optimizer/CMakeLists.txt b/third_party/optimizer/CMakeLists.txt
new file mode 100644
index 0000000000..b393609f27
--- /dev/null
+++ b/third_party/optimizer/CMakeLists.txt
@@ -0,0 +1,152 @@
+cmake_minimum_required(VERSION 3.1)
+
+project(onnx_optimizer C CXX)
+
+# Set C++11 as standard for the whole project
+if(NOT MSVC)
+  set(CMAKE_CXX_STANDARD 11)
+endif(NOT MSVC)
+
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+
+option(protobuf_BUILD_TESTS "" OFF)
+add_subdirectory(${PROJECT_SOURCE_DIR}/third_party/protobuf/cmake)
+
+set(ONNX_ROOT ${PROJECT_SOURCE_DIR}/third_party/onnx)
+add_subdirectory(${ONNX_ROOT})
+
+file(READ "${PROJECT_SOURCE_DIR}/VERSION_NUMBER" ONNX_OPTIMIZER_VERSION)
+string(STRIP "${ONNX_OPTIMIZER_VERSION}" ONNX_OPTIMIZER_VERSION)
+
+file(GLOB_RECURSE onnx_opt_srcs "onnxoptimizer/*.cc"
+    "onnxoptimizer/*.h"
+    )
+list(REMOVE_ITEM onnx_opt_srcs "${PROJECT_SOURCE_DIR}/onnxoptimizer/cpp2py_export.cc")
+
+add_library(onnx_optimizer ${onnx_opt_srcs})
+target_link_libraries(onnx_optimizer PUBLIC onnx)
+target_include_directories(onnx_optimizer PUBLIC
+    $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}>
+    $<INSTALL_INTERFACE:include>
+    )
+
+add_executable(onnx_optimizer_exec examples/onnx_optimizer_exec.cpp)
+target_link_libraries(onnx_optimizer_exec onnx_optimizer)
+
+if(BUILD_ONNX_PYTHON)
+  if("${PY_EXT_SUFFIX}" STREQUAL "")
+    if(MSVC)
+      set(PY_EXT_SUFFIX ".pyd")
+    else()
+      set(PY_EXT_SUFFIX ".so")
+    endif()
+  endif()
+
+  add_library(onnx_opt_cpp2py_export MODULE "onnxoptimizer/cpp2py_export.cc")
+  set_target_properties(onnx_opt_cpp2py_export PROPERTIES PREFIX "")
+  set_target_properties(onnx_opt_cpp2py_export
+                        PROPERTIES COMPILE_FLAGS "-fvisibility=hidden")
+  set_target_properties(onnx_opt_cpp2py_export PROPERTIES SUFFIX ${PY_EXT_SUFFIX})
+  set_target_properties(onnx_opt_cpp2py_export
+                        PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})
+  target_include_directories(onnx_opt_cpp2py_export PRIVATE
+                             $<BUILD_INTERFACE:${ONNX_ROOT}>
+                             $<INSTALL_INTERFACE:include>
+                             ${PYTHON_INCLUDE_DIR})
+  # pybind11 is a header only lib
+  find_package(pybind11 2.2)
+  if(pybind11_FOUND)
+    target_include_directories(onnx_opt_cpp2py_export PUBLIC
+      ${pybind11_INCLUDE_DIRS})
+  else()
+    if(EXISTS ${ONNX_ROOT}/third_party/pybind11/include/pybind11/pybind11.h)
+      target_include_directories(onnx_opt_cpp2py_export PUBLIC
+        ${ONNX_ROOT}/third_party/pybind11/include)
+    else()
+      message(FATAL_ERROR "cannot find pybind")
+    endif()
+  endif()
+
+  if(APPLE)
+    set_target_properties(onnx_opt_cpp2py_export
+                          PROPERTIES LINK_FLAGS "-undefined dynamic_lookup")
+    target_link_libraries(onnx_opt_cpp2py_export
+                          PRIVATE -Wl,-force_load,$<TARGET_FILE:onnx_optimizer>)
+  elseif(MSVC)
+    # In MSVC, we will add whole archive in default
+    target_link_libraries(onnx_opt_cpp2py_export
+                          PRIVATE -WHOLEARCHIVE:$<TARGET_FILE:onnx_optimizer>)
+  elseif(CMAKE_SYSTEM_NAME STREQUAL "AIX")
+    # whole-archive linker option not available on AIX
+    target_sources(onnx_opt_cpp2py_export
+                          PRIVATE $<TARGET_OBJECTS:onnx_optimizer>)
+  else()
+    # Assume everything else is like gcc
+    target_link_libraries(onnx_opt_cpp2py_export
+                          PRIVATE "-Wl,--whole-archive" $<TARGET_FILE:onnx_optimizer>
+                                  "-Wl,--no-whole-archive")
+    set_target_properties(onnx_opt_cpp2py_export
+                          PROPERTIES LINK_FLAGS "-Wl,--exclude-libs,ALL")
+  endif()
+
+  target_link_libraries(onnx_opt_cpp2py_export PRIVATE onnx_optimizer)
+
+  if(MSVC)
+    find_package(PythonInterp ${PY_VERSION} REQUIRED)
+    find_package(PythonLibs ${PY_VERSION} REQUIRED)
+    target_link_libraries(onnx_opt_cpp2py_export PRIVATE ${PYTHON_LIBRARIES})
+    target_compile_options(onnx_opt_cpp2py_export
+                           PRIVATE /MP
+                                   /WX
+                                   /wd4800 # disable warning type' : forcing
+                                           # value to bool 'true' or 'false'
+                                           # (performance warning)
+                                   /wd4503 # identifier' : decorated name length
+                                           # exceeded, name was truncated
+                                   /wd4146 # unary minus operator applied to
+                                           # unsigned type, result still
+                                           # unsigned from include\google\protob
+                                           # uf\wire_format_lite.h
+                                 /wd4244 # 'argument': conversion from 'google::
+                                         # protobuf::uint64' to 'int', possible
+                                         # loss of data
+                                 /wd4267 # Conversion from 'size_t' to 'int',
+                                         # possible loss of data
+                                 /wd4996 # The second parameter is ignored.
+                                   ${EXTRA_FLAGS})
+    if(ONNX_USE_PROTOBUF_SHARED_LIBS)
+      target_compile_options(onnx_opt_cpp2py_export
+                             PRIVATE /wd4251 # 'identifier' : class 'type1' needs to
+                                             # have dll-interface to be used by
+                                             # clients of class 'type2'
+                            )
+    endif()
+    add_msvc_runtime_flag(onnx_opt_cpp2py_export)
+  endif()
+endif()
+
+include(GNUInstallDirs)
+
+install(DIRECTORY ${PROJECT_SOURCE_DIR}/onnxoptimizer
+        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+        FILES_MATCHING
+        PATTERN "*.h")
+
+configure_file(
+  ${PROJECT_SOURCE_DIR}/cmake/ONNXOptimizerConfigVersion.cmake.in
+  ${PROJECT_BINARY_DIR}/ONNXOptimizerConfigVersion.cmake
+  @ONLY)
+configure_file(
+  ${PROJECT_SOURCE_DIR}/cmake/ONNXOptimizerConfig.cmake.in
+  ${PROJECT_BINARY_DIR}/ONNXOptimizerConfig.cmake
+  @ONLY)
+install(FILES
+  ${PROJECT_BINARY_DIR}/ONNXOptimizerConfigVersion.cmake
+  ${PROJECT_BINARY_DIR}/ONNXOptimizerConfig.cmake
+  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ONNXOptimizer
+  COMPONENT dev)
+install(EXPORT ONNXOptimizerTargets DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/ONNXOptimizer")
+install(TARGETS
+  onnx_optimizer
+  EXPORT ONNXOptimizerTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
+
diff --git a/third_party/optimizer/LICENSE b/third_party/optimizer/LICENSE
new file mode 100644
index 0000000000..d645695673
--- /dev/null
+++ b/third_party/optimizer/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
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+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
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+      institute patent litigation against any entity (including a
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+
+   4. Redistribution. You may reproduce and distribute copies of the
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+      meet the following conditions:
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+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
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+      (d) If the Work includes a "NOTICE" text file as part of its
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+          do not modify the License. You may add Your own attribution
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+      You may add Your own copyright statement to Your modifications and
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
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+      this License, without any additional terms or conditions.
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+      the terms of any separate license agreement you may have executed
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+
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+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
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diff --git a/third_party/optimizer/MANIFEST.in b/third_party/optimizer/MANIFEST.in
new file mode 100644
index 0000000000..ef37609822
--- /dev/null
+++ b/third_party/optimizer/MANIFEST.in
@@ -0,0 +1,7 @@
+recursive-include onnxoptimizer *.h *.c *.cc *.proto
+recursive-include examples *
+recursive-include cmake *
+recursive-include tools *
+recursive-include third_party *
+include VERSION_NUMBER
+include CMakeLists.txt
diff --git a/third_party/optimizer/README.md b/third_party/optimizer/README.md
new file mode 100644
index 0000000000..730e0f004f
--- /dev/null
+++ b/third_party/optimizer/README.md
@@ -0,0 +1,55 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Optimizer
+
+[![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/onnx/optimizer/pulls)
+
+## Introduction
+
+ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.
+
+The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.
+
+You may be interested in invoking the provided passes, or in implementing new ones (or both).
+
+## Installation
+
+You can install onnxoptimizer from PyPI:
+
+```bash
+pip3 install onnxoptimizer
+```
+
+Note that you may need to upgrade your pip first if you have trouble:
+
+```bash
+pip3 install -U pip
+```
+
+If you want to build from source:
+
+```bash
+git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
+cd onnxoptimizer
+pip3 install -e .
+```
+
+Note that you need to install protobuf before building from source.
+
+## Roadmap
+
+* Command-line API (e.g. `python3 -m onnxoptimizer model.onnx output.onnx`)
+* More built-in pass
+* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details)
+
+## Relevant tools
+
+* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer
+
+* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box
+
+## Code of Conduct
+
+[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)
diff --git a/third_party/optimizer/VERSION_NUMBER b/third_party/optimizer/VERSION_NUMBER
new file mode 100644
index 0000000000..b0032849c8
--- /dev/null
+++ b/third_party/optimizer/VERSION_NUMBER
@@ -0,0 +1 @@
+0.2.7
diff --git a/third_party/optimizer/cmake/ONNXOptimizerConfig.cmake.in b/third_party/optimizer/cmake/ONNXOptimizerConfig.cmake.in
new file mode 100644
index 0000000000..72dcc88d6e
--- /dev/null
+++ b/third_party/optimizer/cmake/ONNXOptimizerConfig.cmake.in
@@ -0,0 +1,24 @@
+# - Config file for the ONNX Optimizer package
+# It defines the following variable(s)
+#   ONNX_OPTIMIZER_INCLUDE_DIRS     - include directories for onnx optimizer
+# as well as ONNX Optimizer targets for other cmake libraries to use.
+
+# library version information
+set(ONNX_OPTIMIZER_VERSION "@ONNX_OPTIMIZER_VERSION@")
+
+# import targets
+include ("${CMAKE_CURRENT_LIST_DIR}/ONNXOptimizerTargets.cmake")
+
+# include directory.
+#
+# Newer versions of CMake set the INTERFACE_INCLUDE_DIRECTORIES property
+# of the imported targets. It is hence not necessary to add this path
+# manually to the include search path for targets which link to gflags.
+# The following lines are here for backward compatibility, in case one
+# would like to use the old-style include path.
+get_filename_component(
+    CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(
+    _INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+set(ONNX_OPTIMIZER_INCLUDE_DIRS "${_INSTALL_PREFIX}/include")
+
diff --git a/third_party/optimizer/cmake/ONNXOptimizerConfigVersion.cmake.in b/third_party/optimizer/cmake/ONNXOptimizerConfigVersion.cmake.in
new file mode 100644
index 0000000000..76724ae13d
--- /dev/null
+++ b/third_party/optimizer/cmake/ONNXOptimizerConfigVersion.cmake.in
@@ -0,0 +1,12 @@
+set(PACKAGE_VERSION "@ONNX_OPTIMIZER_VERSION@")
+
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
+
diff --git a/third_party/optimizer/examples/onnx_optimizer_exec.cpp b/third_party/optimizer/examples/onnx_optimizer_exec.cpp
new file mode 100644
index 0000000000..cd9d35cf55
--- /dev/null
+++ b/third_party/optimizer/examples/onnx_optimizer_exec.cpp
@@ -0,0 +1,31 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <onnxoptimizer/optimize.h>
+
+#include <onnx/checker.h>
+#include <onnx/onnx_pb.h>
+
+#include <fstream>
+
+int main(int argc, char **argv) {
+  ONNX_NAMESPACE::ModelProto model;
+  std::ifstream ifs(argv[1]);
+  bool success = model.ParseFromIstream(&ifs);
+  if (!success) {
+    std::cout << "load failed" << std::endl;
+    return -1;
+  }
+  onnx::checker::check_model(model);
+  const auto new_model = onnx::optimization::Optimize(
+      model, onnx::optimization::GetFuseAndEliminationPass());
+  onnx::checker::check_model(new_model);
+  std::ofstream ofs(argv[2]);
+  success = new_model.SerializePartialToOstream(&ofs);
+  if (!success) {
+    std::cout << "save failed" << std::endl;
+    return -1;
+  }
+  return 0;
+}
diff --git a/third_party/optimizer/onnxoptimizer/__init__.py b/third_party/optimizer/onnxoptimizer/__init__.py
new file mode 100644
index 0000000000..657a7b8a5a
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/__init__.py
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# ATTENTION: The code in this file is highly EXPERIMENTAL.
+# Adventurous users should note that the APIs will probably change.
+
+"""onnx optimizer
+
+This enables users to optimize their models.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import onnx
+import onnxoptimizer.onnx_opt_cpp2py_export as C
+from onnx import ModelProto
+from typing import Text, Sequence, Optional
+
+get_available_passes = C.get_available_passes
+
+get_fuse_and_elimination_passes = C.get_fuse_and_elimination_passes
+
+
+def optimize(model, passes=None, fixed_point=False):  # type: (ModelProto, Optional[Sequence[Text]], bool) -> ModelProto
+    """Apply the optimization on the serialized ModelProto.
+
+    Arguments:
+        input (ModelProto): model
+        names (list of string): list of optimization names
+
+    Return:
+        return (ModelProto) optimized model
+    """
+
+    if passes is None:
+        print('WARNING: defualt optimization passes will be enlarged to all fuse and elimination passes in the next version')
+        passes = ['eliminate_nop_transpose',
+                  'eliminate_nop_pad',
+                  'fuse_consecutive_transposes',
+                  'fuse_transpose_into_gemm']
+    if not isinstance(model, ModelProto):
+        raise ValueError(
+            'Optimizer only accepts ModelProto, incorrect type: {}'.format(type(model)))
+
+    model_str = model.SerializeToString()
+    if fixed_point:
+        optimized_model_str = C.optimize_fixedpoint(model_str, passes)
+    else:
+        optimized_model_str = C.optimize(model_str, passes)
+
+    return onnx.load_from_string(optimized_model_str)
+
+
+__all__ = ['optimize', 'get_available_passes', 'get_fuse_and_elimination_passes']
diff --git a/third_party/optimizer/onnxoptimizer/cpp2py_export.cc b/third_party/optimizer/onnxoptimizer/cpp2py_export.cc
new file mode 100644
index 0000000000..adb11bfeab
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/cpp2py_export.cc
@@ -0,0 +1,42 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include "onnx/py_utils.h"
+
+#include "onnxoptimizer/optimize.h"
+
+namespace ONNX_NAMESPACE {
+namespace py = pybind11;
+using namespace pybind11::literals;
+PYBIND11_MODULE(onnx_opt_cpp2py_export, onnx_opt_cpp2py_export) {
+  onnx_opt_cpp2py_export.doc() = "ONNX Optimizer";
+
+  onnx_opt_cpp2py_export.def(
+      "optimize",
+      [](const py::bytes& bytes, const std::vector<std::string>& names) {
+        ModelProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        auto const result = optimization::Optimize(proto, names);
+        std::string out;
+        result.SerializeToString(&out);
+        return py::bytes(out);
+      });
+
+  onnx_opt_cpp2py_export.def(
+      "optimize_fixedpoint",
+      [](const py::bytes& bytes, const std::vector<std::string>& names) {
+        ModelProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        auto const result =
+            optimization::OptimizeFixed(proto, names);
+        std::string out;
+        result.SerializeToString(&out);
+        return py::bytes(out);
+      });
+  onnx_opt_cpp2py_export.def("get_available_passes", &optimization::GetAvailablePasses);
+  onnx_opt_cpp2py_export.def("get_fuse_and_elimination_passes", &optimization::GetFuseAndEliminationPass);
+}
+}
diff --git a/third_party/optimizer/onnxoptimizer/optimize.cc b/third_party/optimizer/onnxoptimizer/optimize.cc
new file mode 100644
index 0000000000..2115327353
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/optimize.cc
@@ -0,0 +1,52 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include "onnxoptimizer/optimize.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+GlobalPassRegistry Optimizer::passes;
+
+Optimizer::Optimizer(
+    const std::vector<std::string>& names,
+    const bool fixed_point) {
+  if (fixed_point) {
+    this->pass_manager =
+        std::shared_ptr<FixedPointPassManager>(new FixedPointPassManager());
+  } else {
+    this->pass_manager =
+        std::shared_ptr<GeneralPassManager>(new GeneralPassManager());
+  }
+  for (const auto& name : names) {
+    auto pass = passes.find(name);
+    this->pass_manager->add(pass);
+  }
+}
+Optimizer::~Optimizer() {}
+
+ModelProto Optimize(
+    const ModelProto& mp_in,
+    const std::vector<std::string>& names) {
+  Optimizer current_opt(names, false);
+  return current_opt.optimize(mp_in);
+}
+ModelProto OptimizeFixed(
+    const ModelProto& mp_in,
+    const std::vector<std::string>& names) {
+  Optimizer current_opt(names, true);
+  return current_opt.optimize(mp_in);
+}
+const std::vector<std::string> GetAvailablePasses() {
+  return Optimizer::passes.GetAvailablePasses();
+}
+const std::vector<std::string> GetFuseAndEliminationPass() {
+  return Optimizer::passes.GetFuseAndEliminationPass();
+}
+
+} // namespace optimization
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/optimize.h b/third_party/optimizer/onnxoptimizer/optimize.h
new file mode 100644
index 0000000000..66e04b117a
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/optimize.h
@@ -0,0 +1,97 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnx/common/ir.h"
+#include "onnx/common/ir_pb_converter.h"
+#include "onnx/common/stl_backports.h"
+#include "onnx/proto_utils.h"
+
+#include "onnxoptimizer/pass_manager.h"
+#include "onnxoptimizer/pass_registry.h"
+
+#include "vector"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct Optimizer {
+  static GlobalPassRegistry passes;
+
+ public:
+  Optimizer(const std::vector<std::string> &names, const bool fixed_point);
+  ~Optimizer();
+
+  ModelProto optimize(const ModelProto &mp_in) {
+    ModelProto mp_compatible = AddInitializerToInput(mp_in);
+    bool has_initializer_not_in_input =
+        (mp_in.graph().input_size() != mp_compatible.graph().input_size());
+    std::shared_ptr<Graph> g(ImportModelProto(mp_compatible));
+
+    if (g.get() == nullptr) {
+      std::cerr << "Warning: onnx optimizer is unable to parse input model. "
+                << "(The IR version of the ONNX model may be too old.)"
+                << std::endl;
+      // If we can't parse the file, just return the input.
+      return mp_in;
+    }
+
+    ModelProto mp_out = PrepareOutput(mp_in);
+    this->pass_manager->run(*g);
+    ExportModelProto(&mp_out, g);
+    // `has_initializer_not_in_input` means the original model prefer
+    // initializer to be not in input, so the new initializer introduced by
+    // both `AddInitializerToInput` and optimization passes will be remove from
+    // input
+    if (has_initializer_not_in_input) {
+      mp_out.mutable_graph()->mutable_input()->DeleteSubrange(
+          mp_in.graph().input_size(),
+          mp_out.graph().input_size() - mp_in.graph().input_size());
+    }
+    return mp_out;
+  }
+
+ private:
+  std::shared_ptr<PassManager> pass_manager;
+
+  ModelProto AddInitializerToInput(const ModelProto &original_model) {
+    ModelProto model = original_model;
+    std::vector<std::string> input_names;
+    for (const auto &x : model.graph().input()) {
+      input_names.push_back(x.name());
+    }
+    for (const auto &x : model.graph().initializer()) {
+      if (std::find(input_names.begin(), input_names.end(), x.name()) ==
+          input_names.end()) {
+        auto *value_info = model.mutable_graph()->add_input();
+        value_info->set_name(x.name());
+        TypeProto *type = value_info->mutable_type();
+        auto *tensor = type->mutable_tensor_type();
+        tensor->set_elem_type(x.data_type());
+        auto *shape = tensor->mutable_shape();
+        for (const auto &dim : x.dims()) {
+          TensorShapeProto::Dimension *new_dim = shape->add_dim();
+          new_dim->set_dim_value(dim);
+        }
+      }
+    }
+    return model;
+  }
+};
+
+const std::vector<std::string> GetAvailablePasses();
+
+const std::vector<std::string> GetFuseAndEliminationPass();
+
+ModelProto Optimize(const ModelProto &mp_in,
+                    const std::vector<std::string> &names);
+
+ModelProto OptimizeFixed(const ModelProto &mp_in,
+                         const std::vector<std::string> &names);
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/pass.cc b/third_party/optimizer/onnxoptimizer/pass.cc
new file mode 100644
index 0000000000..c68eb6c6e4
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/pass.cc
@@ -0,0 +1,108 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnx/common/assertions.h"
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+Pass::Pass(
+    PassType pass_type,
+    PassEfficiency pass_efficiency,
+    PassOptimizationType pass_optimization_type) {
+  this->pass_type = pass_type;
+  this->pass_efficiency = pass_efficiency;
+  this->pass_optimization_type = pass_optimization_type;
+}
+
+Pass::~Pass() {}
+
+unsigned int Pass::DescendOnGraphAttributesAndCount(
+    Node* n,
+    std::function<unsigned int(Graph&)> fn) {
+  unsigned int num_changes = 0;
+  for (auto name : n->attributeNames()) {
+    auto kind = n->kindOf(name);
+    if (kind == AttributeKind::g) {
+      num_changes += fn(*n->g(name));
+    }
+    if (kind == AttributeKind::gs) {
+      for (auto& g : n->gs(name)) {
+        num_changes += fn(*g);
+      }
+    }
+  }
+  return num_changes;
+}
+
+void Pass::DescendOnGraphAttributesUnconstrained(
+    Node* n,
+    std::function<void(Graph&)> fn) {
+  for (auto name : n->attributeNames()) {
+    auto kind = n->kindOf(name);
+    if (kind == AttributeKind::g) {
+      fn(*n->g(name));
+    }
+    if (kind == AttributeKind::gs) {
+      for (auto& g : n->gs(name)) {
+        fn(*g);
+      }
+    }
+  }
+}
+
+PredicateBasedPass::~PredicateBasedPass() {}
+
+unsigned int PredicateBasedPass::_runPassInternal(Graph& graph) {
+  unsigned int num_changes = false;
+  for (auto it = graph.begin(); it != graph.end(); ++it) {
+    auto* n = *it;
+    num_changes += this->DescendOnGraphAttributesAndCount(
+        n, [this](Graph& g) { return _runPassInternal(g); });
+    if (this->patternMatchPredicate(n)) {
+      NodeDestroyType destroy_type = NodeDestroyType::DestroyZero;
+      num_changes += this->runTransform(n, graph, destroy_type);
+
+      if (destroy_type == NodeDestroyType::DestroyOne) {
+        it.destroyCurrent();
+      }
+      if (destroy_type == NodeDestroyType::DestroyTwo) {
+        it.destroyCurrent();
+        it.destroyCurrent();
+      }
+    }
+  }
+  return num_changes;
+}
+
+PassAnalysisType PredicateBasedPass::getPassAnalysisType() const {
+  return PassAnalysisType::CountBased;
+}
+
+std::shared_ptr<PostPassAnalysis> PredicateBasedPass::runPass(Graph& graph) {
+  bool initialized_pass = this->initializePass(graph);
+  unsigned int touched_optimizations = this->_runPassInternal(graph);
+  bool finalized_pass = this->finalizePass(graph);
+
+  return std::shared_ptr<PostPassAnalysis>(new CountBasedPassAnalysis(
+      this, touched_optimizations, initialized_pass, finalized_pass));
+}
+
+CountBasedPassAnalysis::CountBasedPassAnalysis(
+    Pass* pass,
+    unsigned int num_positive_transforms,
+    bool initialization_done,
+    bool finalization_done) {
+  this->pass = pass;
+  this->num_positive_transforms = num_positive_transforms;
+  this->initialization_done = initialization_done;
+  this->finalization_done = finalization_done;
+}
+
+FullGraphBasedPass::~FullGraphBasedPass() {}
+
+} // namespace optimization
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/pass.h b/third_party/optimizer/onnxoptimizer/pass.h
new file mode 100644
index 0000000000..a917560ca8
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/pass.h
@@ -0,0 +1,259 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include <string>
+#include "onnx/common/ir.h"
+#include "onnx/onnx_pb.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+// Base struct representing result of a pass.
+struct PostPassAnalysis {
+  virtual ~PostPassAnalysis() = default;
+};
+
+// Enum that represents the type of optimization it is.
+enum PassType {
+  // Class of optimizations that fuses operations.
+  Fuse = 0,
+  // Class of optimizations that removes useless operations.
+  Nop = 1,
+  // Class of optimizations that includes some form of seperation.
+  Separate = 2,
+  // Immutable pass, also sometimes referred to as an analysis pass.
+  Immutable = 3,
+  // Other type of pass.
+  Other = 4
+};
+
+// Enum that represents the return type of the analysis.
+enum PassAnalysisType {
+  // An empty analysis is returned. Most likely will return PostPassAnalysis.
+  Empty = 0,
+  // A count based analysis is returned. Most likely of type
+  // CountBasedPassAnalysis
+  CountBased = 1
+};
+
+enum PassEfficiency {
+  // A partially efficient optimization pass cannot guarantee that running two
+  // consecutive passes
+  // will return the same result as running a single pass.
+  Partial = 0,
+  // A completely efficient optimization guarantees that running two consecutive
+  // passes is equivalent
+  // to running a single pass.
+  Complete = 1
+};
+
+// Describes what the optimization pass is attempting to optimize.
+enum PassOptimizationType {
+  // Is not optimizing anything. Most likely will be used in an immutable pass.
+  None = 0,
+  // Optimizes for compute.
+  Compute = 1,
+  // Optimizes for memory.
+  Memory = 2,
+  // Optimizes for both compute and memory.
+  ComputeMemory = 3,
+  // Optimizes for stability (e.g. log-sum-exp trick).
+  Stability = 4
+};
+
+enum NodeDestroyType {
+  // Does not destroy node
+  DestroyZero = 0,
+  // Equivalent to calling it.destroyCurrent() once.
+  DestroyOne = 1,
+  // Equivalent to calling it.destroyCurrent() twice.
+  DestroyTwo = 2
+};
+
+// Base class for all optimizations within ONNX. A pass must contain the
+// annotations described above. Furthermore each pass is given the ability to
+// initialize and finalize it's pass. Each pass must have a unique name that
+// pass managers/registry will use as identification. Finally the pass
+// implements runPass which completes the pass inplace.
+class Pass {
+  PassType pass_type;
+  PassEfficiency pass_efficiency;
+  PassOptimizationType pass_optimization_type;
+
+ public:
+  Pass(PassType pass_type, PassEfficiency pass_efficiency,
+       PassOptimizationType pass_optimization_type);
+  virtual ~Pass();
+
+  PassType getPassType() const {
+    return this->pass_type;
+  }
+  PassEfficiency getPassEfficiency() const {
+    return this->pass_efficiency;
+  }
+  PassOptimizationType getPassOptimizationType() const {
+    return this->pass_optimization_type;
+  }
+  virtual PassAnalysisType getPassAnalysisType() const = 0;
+  virtual std::string getPassName() const = 0;
+
+  virtual bool initializePass(Graph &) {
+    return false;
+  }
+  virtual bool finalizePass(Graph &) {
+    return false;
+  }
+  virtual std::shared_ptr<PostPassAnalysis> runPass(Graph &graph) = 0;
+
+ protected:
+  // Iterates through the elements in the graph and counts the number of times
+  // the transform is successfully run.
+  unsigned int DescendOnGraphAttributesAndCount(
+      Node *n, std::function<unsigned int(Graph &)> fn);
+  // A more general version of the function above that doesn't constrain the
+  // return type of fn.
+  void DescendOnGraphAttributesUnconstrained(Node *n,
+                                             std::function<void(Graph &)> fn);
+};
+
+class ImmutablePass : Pass {
+ public:
+  explicit ImmutablePass()
+      : Pass(PassType::Immutable, PassEfficiency::Complete,
+             PassOptimizationType::None) {}
+  ~ImmutablePass() override;
+};
+
+// Pass Analysis done after a predicate based pass.
+struct CountBasedPassAnalysis : PostPassAnalysis {
+  // Have to use raw pointer here. The idea is that the pass will pass <this> as
+  // a parameter to the constructor. We could use std::enable_shared_from_this
+  // but this complicates the memory model. Also since all passes come from
+  // GlobalPassRegistry which already utilizes smart pointers we don't have to
+  // worry about memory leaks from passes.
+  Pass *pass;
+  unsigned int num_positive_transforms;
+  bool initialization_done;
+  bool finalization_done;
+
+ public:
+  explicit CountBasedPassAnalysis(Pass *pass,
+                                  unsigned int num_positive_transforms,
+                                  bool initialization_done,
+                                  bool finalization_done);
+
+  bool graphChanged() {
+    return this->num_positive_transforms > 0;
+  }
+  bool numSucceededTransforms() {
+    return this->num_positive_transforms;
+  }
+
+  // Whether or not a repeated application of the pass might be useful.
+  bool fixedPointOptimizationNeeded() {
+    return this->graphChanged() &&
+           pass->getPassEfficiency() == PassEfficiency::Partial;
+  }
+};
+
+// A pass that is based on pattern matching. The majority of passes will
+// implement this pass. In order for the pass to work the patternMatchPredicate
+// function must be implemented witch matches a subgraph to the respective
+// optimization pass. Lastly the runTransform method must also be implemented
+// which simply implements the pass on any node which passes
+// patternMatchPredicate.
+class PredicateBasedPass : public Pass {
+ public:
+  explicit PredicateBasedPass(PassType pass_type,
+                              PassEfficiency pass_efficiency,
+                              PassOptimizationType pass_optimization_type)
+      : Pass(pass_type, pass_efficiency, pass_optimization_type) {}
+  ~PredicateBasedPass() override;
+
+  virtual bool patternMatchPredicate(Node *node) = 0;
+  // Run transform is given the current node in the iterator, a reference to the
+  // current graph as well as a reference describing how to treat the current
+  // node in the iterator post transform. Run transform is then responsible for
+  // running the actual transform as well as describing how to treat the
+  // iterator node. By default the current node will not call destroy. Do not
+  // internally delete node instead set the correct destroy_current type.
+  virtual bool runTransform(Node *node, Graph &graph,
+                            NodeDestroyType &destroy_current) = 0;
+
+  std::shared_ptr<PostPassAnalysis> runPass(Graph &graph) override;
+  PassAnalysisType getPassAnalysisType() const override;
+
+  static int getOpsetVersion(const Graph &g) {
+    // this hack is due to `opset_versions_mutable` doesn't have a const version
+    Graph &mut_g = const_cast<Graph &>(g);
+    for (const OpSetID &opset : mut_g.opset_versions_mutable()) {
+      if (opset.domain() == "") {
+        return opset.version();
+      }
+    }
+    return 0;
+  }
+
+ private:
+  unsigned int _runPassInternal(Graph &graph);
+};
+
+// The most general pass which allows the user to run a pass given only a graph.
+class FullGraphBasedPass : public Pass {
+ public:
+  explicit FullGraphBasedPass(PassType pass_type,
+                              PassEfficiency pass_efficiency,
+                              PassOptimizationType pass_optimization_type)
+      : Pass(pass_type, pass_efficiency, pass_optimization_type) {}
+  ~FullGraphBasedPass() override;
+};
+
+// If both value1 and value2 are input/output,
+// we cannot replace one with another and also keeping the
+// input/output names unchanged.
+inline bool areTwoValuesBothInputOrOutput(const Value *value1,
+                                          const Value *value2) {
+  const auto IsInputOrOutput = [](const Value *value) {
+    const auto graph = value->owningGraph();
+    const bool is_output =
+        std::find(graph->outputs().rbegin(), graph->outputs().rend(), value) !=
+        graph->outputs().rend();
+    const bool is_input =
+        value->node()->kind() == kCaptured ||
+        std::find(graph->inputs().rbegin(), graph->inputs().rend(), value) !=
+            graph->inputs().rend();
+    return is_output || is_input;
+  };
+  return IsInputOrOutput(value1) && IsInputOrOutput(value2);
+}
+
+inline bool tryReplacingAllUsesWith(Value *oldValue, Value *newValue) {
+  if (areTwoValuesBothInputOrOutput(oldValue, newValue)) {
+    return false;
+  }
+  oldValue->replaceAllUsesWith(newValue);
+  return true;
+}
+
+inline bool tryReplacingAllUsesWith(Node *oldNode, Node *newNode) {
+  ONNX_ASSERT(oldNode->outputs().size() == newNode->outputs().size());
+  size_t nOutputs = oldNode->outputs().size();
+  for (size_t i = 0; i < nOutputs; i++) {
+    const auto *oldValue = oldNode->outputs()[i];
+    const auto *newValue = newNode->outputs()[i];
+    if (areTwoValuesBothInputOrOutput(oldValue, newValue)) {
+      return false;
+    }
+  }
+  oldNode->replaceAllUsesWith(newNode);
+  return true;
+}
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/pass_manager.cc b/third_party/optimizer/onnxoptimizer/pass_manager.cc
new file mode 100644
index 0000000000..7389121b03
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/pass_manager.cc
@@ -0,0 +1,51 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "onnxoptimizer/pass_manager.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+PassManager::PassManager() {}
+PassManager::~PassManager() {}
+
+GeneralPassManager::~GeneralPassManager() {
+  this->passes.clear();
+}
+void GeneralPassManager::add(std::shared_ptr<Pass> pass) {
+  this->passes.push_back(std::move(pass));
+}
+
+std::shared_ptr<PassManagerAnalysis> GeneralPassManager::run(Graph& graph) {
+  for (const std::shared_ptr<Pass>& pass : this->passes) {
+    auto pass_analysis = pass->runPass(graph);
+  }
+  return std::shared_ptr<PassManagerAnalysis>(new EmptyPassManagerAnalysis());
+}
+
+std::shared_ptr<PassManagerAnalysis> FixedPointPassManager::run(Graph& graph) {
+  bool fixed_point_optimization_done;
+
+  do {
+    fixed_point_optimization_done = false;
+    for (const std::shared_ptr<Pass>& pass : this->passes) {
+      std::shared_ptr<PostPassAnalysis> analysis = pass->runPass(graph);
+      if (pass->getPassAnalysisType() == PassAnalysisType::Empty) {
+        continue;
+      }
+      std::shared_ptr<CountBasedPassAnalysis> count_analysis =
+          std::static_pointer_cast<CountBasedPassAnalysis>(analysis);
+
+      while (count_analysis->fixedPointOptimizationNeeded()) {
+        count_analysis = std::static_pointer_cast<CountBasedPassAnalysis>(
+            pass->runPass(graph));
+        fixed_point_optimization_done = true;
+      }
+    }
+  } while (fixed_point_optimization_done);
+
+  return std::shared_ptr<PassManagerAnalysis>(new EmptyPassManagerAnalysis());
+}
+} // namespace optimization
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/pass_manager.h b/third_party/optimizer/onnxoptimizer/pass_manager.h
new file mode 100644
index 0000000000..9e795b9e52
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/pass_manager.h
@@ -0,0 +1,54 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include <vector>
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+// An analysis returned from the run done by a manager
+struct PassManagerAnalysis {};
+struct EmptyPassManagerAnalysis : PassManagerAnalysis {};
+
+// Base class of all PassManager's. The class should be able to add new passes
+// as well as run the passes given a graph.
+class PassManager {
+ public:
+  PassManager();
+  virtual ~PassManager();
+
+  virtual void add(std::shared_ptr<Pass> P) = 0;
+  virtual std::shared_ptr<PassManagerAnalysis> run(Graph& graph) = 0;
+};
+
+// The GeneralPassManager has no restriction on type of Pass and runs the passes
+// once in a linear fashion.
+class GeneralPassManager : public PassManager {
+ public:
+  GeneralPassManager() {}
+  ~GeneralPassManager() override;
+
+  void add(std::shared_ptr<Pass> pass) override;
+  std::shared_ptr<PassManagerAnalysis> run(Graph& graph) override;
+
+ protected:
+  // use vector here to ensure the order of the passes
+  // for some pass, order is critical, for example,
+  // split_init and split_predict should be the last in the list
+  std::vector<std::shared_ptr<Pass>> passes;
+};
+
+// Exhibits the same behavior as GeneralPassManager but will instead check
+// whether or not fixed point optimization is needed.
+class FixedPointPassManager : public GeneralPassManager {
+  std::shared_ptr<PassManagerAnalysis> run(Graph& graph) override;
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/pass_registry.cc b/third_party/optimizer/onnxoptimizer/pass_registry.cc
new file mode 100644
index 0000000000..4690354614
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/pass_registry.cc
@@ -0,0 +1,33 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#include "onnxoptimizer/pass_registry.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+const std::vector<std::string> GlobalPassRegistry::GetAvailablePasses() {
+  std::vector<std::string> names;
+  for (const auto& pass : this->passes) {
+    names.push_back(pass.first);
+  }
+  return names;
+}
+
+const std::vector<std::string> GlobalPassRegistry::GetFuseAndEliminationPass() {
+  std::vector<std::string> names;
+  for (const auto& pass : this->passes) {
+    const auto pass_type = pass.second->getPassType();
+    if (pass_type == PassType::Fuse || pass_type == PassType::Nop) {
+      names.push_back(pass.first);
+    }
+  }
+  return names;
+}
+
+} // namespace optimization
+} // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/pass_registry.h b/third_party/optimizer/onnxoptimizer/pass_registry.h
new file mode 100644
index 0000000000..7fa910cdf5
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/pass_registry.h
@@ -0,0 +1,103 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnx/common/ir.h"
+#include "onnx/common/ir_pb_converter.h"
+#include "onnx/common/stl_backports.h"
+#include "onnx/proto_utils.h"
+
+#include "onnxoptimizer/passes/eliminate_deadend.h"
+#include "onnxoptimizer/passes/eliminate_duplicate_initializer.h"
+#include "onnxoptimizer/passes/eliminate_identity.h"
+#include "onnxoptimizer/passes/eliminate_if_with_const_cond.h"
+#include "onnxoptimizer/passes/eliminate_nop_cast.h"
+#include "onnxoptimizer/passes/eliminate_nop_dropout.h"
+#include "onnxoptimizer/passes/eliminate_nop_flatten.h"
+#include "onnxoptimizer/passes/eliminate_nop_monotone_argmax.h"
+#include "onnxoptimizer/passes/eliminate_nop_pad.h"
+#include "onnxoptimizer/passes/eliminate_nop_transpose.h"
+#include "onnxoptimizer/passes/eliminate_unused_initializer.h"
+#include "onnxoptimizer/passes/extract_constant_to_initializer.h"
+#include "onnxoptimizer/passes/fuse_add_bias_into_conv.h"
+#include "onnxoptimizer/passes/fuse_bn_into_conv.h"
+#include "onnxoptimizer/passes/fuse_consecutive_concats.h"
+#include "onnxoptimizer/passes/fuse_consecutive_log_softmax.h"
+#include "onnxoptimizer/passes/fuse_consecutive_reduce_unsqueeze.h"
+#include "onnxoptimizer/passes/fuse_consecutive_squeezes.h"
+#include "onnxoptimizer/passes/fuse_consecutive_transposes.h"
+#include "onnxoptimizer/passes/fuse_matmul_add_bias_into_gemm.h"
+#include "onnxoptimizer/passes/fuse_pad_into_conv.h"
+#include "onnxoptimizer/passes/fuse_transpose_into_gemm.h"
+#include "onnxoptimizer/passes/lift_lexical_references.h"
+#include "onnxoptimizer/passes/nop.h"
+#include "onnxoptimizer/passes/split.h"
+
+#include <unordered_set>
+#include <vector>
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+// Registry containing all passes available in ONNX.
+struct GlobalPassRegistry {
+  std::map<std::string, std::shared_ptr<Pass>> passes;
+
+  GlobalPassRegistry() {
+    // Register the optimization passes to the optimizer.
+    registerPass<NopEmptyPass>();
+    registerPass<EliminateDeadEnd>();
+    registerPass<EliminateDuplicateInitializer>();
+    registerPass<EliminateNopCast>();
+    registerPass<EliminateNopDropout>();
+    registerPass<EliminateNopFlatten>();
+    registerPass<EliminateIdentity>();
+    registerPass<EliminateIfWithConstCond>();
+    registerPass<EliminateNopMonotoneArgmax>();
+    registerPass<EliminateNopPad>();
+    registerPass<EliminateNopTranspose>();
+    registerPass<EliminateUnusedInitializer>();
+    registerPass<ExtractConstantToInitializer>();
+    registerPass<FuseAddBiasIntoConv>();
+    registerPass<FuseBNIntoConv>();
+    registerPass<FuseConsecutiveConcats>();
+    registerPass<FuseConsecutiveLogSoftmax>();
+    registerPass<FuseConsecutiveReduceUnsqueeze>();
+    registerPass<FuseConsecutiveSqueezes>();
+    registerPass<FuseConsecutiveTransposes>();
+    registerPass<FuseMatMulAddBiasIntoGemm>();
+    registerPass<FusePadIntoConv>();
+    registerPass<FuseTransposeIntoGemm>();
+    registerPass<LiftLexicalReferences>();
+    registerPass<SplitInit>();
+    registerPass<SplitPredict>();
+  }
+
+  ~GlobalPassRegistry() {
+    this->passes.clear();
+  }
+
+  std::shared_ptr<Pass> find(std::string pass_name) {
+    auto it = this->passes.find(pass_name);
+    ONNX_ASSERTM(it != this->passes.end(), "pass %s is unknown.",
+                 pass_name.c_str());
+    return it->second;
+  }
+  const std::vector<std::string> GetAvailablePasses();
+
+  const std::vector<std::string> GetFuseAndEliminationPass();
+
+  template <typename T>
+  void registerPass() {
+    static_assert(std::is_base_of<Pass, T>::value, "T must inherit from Pass");
+    std::shared_ptr<Pass> pass(new T());
+    passes[pass->getPassName()] = pass;
+  }
+};
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_deadend.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_deadend.h
new file mode 100644
index 0000000000..242f1e916d
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_deadend.h
@@ -0,0 +1,40 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+#pragma once
+#include "onnxoptimizer/pass.h"
+namespace ONNX_NAMESPACE {
+namespace optimization {
+struct EliminateDeadEnd final : public FullGraphBasedPass {
+  explicit EliminateDeadEnd()
+      : FullGraphBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override {
+    return "eliminate_deadend";
+  }
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::CountBased;
+  }
+  unsigned int EliminateDead(Graph& graph) {
+    unsigned int nodes_removed = 0;
+    auto nodes = graph.nodes().reverse();
+    for (auto it = nodes.begin(); it != nodes.end(); it++) {
+      auto node = *it;
+      if (!node->hasUses()) {
+        nodes_removed++;
+        it.destroyCurrent();
+      }
+    }
+    return nodes_removed;
+  }
+  std::shared_ptr<PostPassAnalysis> runPass(Graph& graph) override {
+    auto nodes_removed = this->EliminateDead(graph);
+    return std::shared_ptr<PostPassAnalysis>(
+        new CountBasedPassAnalysis(this, nodes_removed, false, false));
+  }
+};
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_duplicate_initializer.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_duplicate_initializer.h
new file mode 100644
index 0000000000..7c2b8f0ab4
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_duplicate_initializer.h
@@ -0,0 +1,154 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+#pragma once
+
+// Before:
+//   A, B are in the initializer list, and A is equal to B
+//   E = Add(D, A)
+//   F = Add(F, B)
+//   G = Add(E, F)
+// After:
+//   A is in the initializer list
+//   E = Add(D, A)
+//   F = Add(F, A)
+//   G = Add(E, F)
+//
+// NOTE: ONNX IR has an bug that an initializer must also
+// be an graph input. Currently we are using a workaround
+// that adds initializers to inputs before optimization
+// and removes the added initializers from inputs after
+// optimization. That makes us cannot distinguish the
+// initializers really in the inputs and the initializers
+// not in the inputs. While only the latter can be eliminated,
+// we eliminate all duplicated initializers instead. That
+// may cause unexpected behavior in some rare cases.
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateDuplicateInitializer final : public FullGraphBasedPass {
+  explicit EliminateDuplicateInitializer()
+      : FullGraphBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Memory) {}
+  std::string getPassName() const override {
+    return "eliminate_duplicate_initializer";
+  }
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::CountBased;
+  }
+  unsigned int EliminateInitializer(Graph &graph) {
+    unsigned int initializers_removed = 0;
+    const std::vector<Tensor> &initializers = graph.initializers();
+    std::map<std::vector<int64_t>, std::vector<std::string>> init_dict_by_shape;
+
+    // Make {name : Value} map
+    std::map<std::string, Value *> input_map;
+    for (auto inp : graph.inputs()) {
+      if (inp->has_unique_name()) {
+        input_map[inp->uniqueName()] = inp;
+      }
+    }
+
+    std::map<std::string, Value *> output_map;
+    for (auto out : graph.outputs()) {
+      if (out->has_unique_name()) {
+        output_map[out->uniqueName()] = out;
+      }
+    }
+
+    // Cluster initializers by shape
+    for (auto initializer : initializers) {
+      if (!initializer.hasName()) {
+        continue;
+      }
+      // Ignore initializer which is not an input
+      if (input_map.find(initializer.name()) == input_map.end()) {
+        continue;
+      }
+      // Ignore initializer which is output
+      if (output_map.find(initializer.name()) != output_map.end()) {
+        continue;
+      }
+      auto initializers_iter = init_dict_by_shape.find(initializer.sizes());
+      if (initializers_iter != init_dict_by_shape.end()) {
+        initializers_iter->second.emplace_back(initializer.name());
+      } else {
+        std::vector<std::string> vec{initializer.name()};
+        init_dict_by_shape.insert(
+            std::make_pair(std::move(initializer.sizes()), vec));
+      }
+    }
+
+    for (auto pair : init_dict_by_shape) {
+      std::set<std::string> visited;
+
+      // pair.second --> vector initializers with same shape
+      // Use iter_i, iter_j to loop it
+      for (auto iter_i = pair.second.begin(); iter_i != pair.second.end();
+           ++iter_i) {
+        if (visited.find(*iter_i) != visited.end()) {
+          continue;
+        }
+        const auto iter_i_initializer = graph.getInitializer(*iter_i);
+        if (iter_i_initializer == graph.initializers().end()) {
+          continue;
+        }
+        Tensor i_tensor = *iter_i_initializer;
+        Value *i_value = input_map.find(i_tensor.name())->second;
+
+#define DO_COMPARISON(data_type)                                             \
+  const std::vector<data_type> i_data = ParseData<data_type>(&i_tensor);     \
+  for (auto iter_j = iter_i + 1; iter_j != pair.second.end(); ++iter_j) {    \
+    const auto iter_j_initializer = graph.getInitializer(*iter_j);           \
+    if (iter_j_initializer == graph.initializers().end()) {                  \
+      visited.insert(*iter_j);                                               \
+      continue;                                                              \
+    }                                                                        \
+    Tensor j_tensor = *iter_j_initializer;                                   \
+    if (i_tensor.elem_type() != j_tensor.elem_type()) {                      \
+      continue;                                                              \
+    } else {                                                                 \
+      const std::vector<data_type> j_data = ParseData<data_type>(&j_tensor); \
+      if (std::equal(i_data.begin(), i_data.end(), j_data.begin())) {        \
+        visited.insert(*iter_j);                                             \
+        Value *j_value = input_map.find(j_tensor.name())->second;            \
+        j_value->replaceAllUsesWith(i_value);                                \
+        graph.eraseInitializerAndInput(j_value);                             \
+        initializers_removed++;                                              \
+      }                                                                      \
+    }                                                                        \
+  }
+#define CASE_DO_COMPARISON(ONNX_DTYPE_SUFFIX, CPP_DTYPE)           \
+  case ONNX_NAMESPACE::TensorProto_DataType_##ONNX_DTYPE_SUFFIX: { \
+    DO_COMPARISON(CPP_DTYPE)                                       \
+    break;                                                         \
+  }
+        switch (i_tensor.elem_type()) {
+          CASE_DO_COMPARISON(FLOAT, float)
+          CASE_DO_COMPARISON(DOUBLE, double)
+          CASE_DO_COMPARISON(INT32, int32_t)
+          CASE_DO_COMPARISON(INT64, int64_t)
+          default:
+            break;
+        }
+#undef CASE_DO_COMPARISON
+#undef DO_COMPARISON
+      }
+    }
+    return initializers_removed;
+  }
+  std::shared_ptr<PostPassAnalysis> runPass(Graph &graph) override {
+    auto initializers_removed = this->EliminateInitializer(graph);
+    return std::shared_ptr<PostPassAnalysis>(
+        new CountBasedPassAnalysis(this, initializers_removed, false, false));
+  }
+};
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_identity.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_identity.h
new file mode 100644
index 0000000000..bf4d6b2fef
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_identity.h
@@ -0,0 +1,40 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateIdentity final : public PredicateBasedPass {
+  explicit EliminateIdentity()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_identity";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kIdentity;
+  }
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->input());
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_if_with_const_cond.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_if_with_const_cond.h
new file mode 100644
index 0000000000..55eef57998
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_if_with_const_cond.h
@@ -0,0 +1,111 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+// This optimization works well especially when used together with
+// constant folding (onnx-simplifier), for example, the if node
+// introduced by PyTorch squeeze op will be eliminated when the input
+// shape is known.
+// Ideally eliminate_if_with_const_cond + eliminate_deadend + constant
+// folding can be replaced by the more powerful sparse conditional
+// constant propagation, which obviously cannot be implemented in
+// the current optimizer framework.
+
+struct EliminateIfWithConstCond final : public PredicateBasedPass {
+  explicit EliminateIfWithConstCond()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_if_with_const_cond";
+  }
+
+  // step 1: find "if" node with constant cond (i.e. const true or false)
+  bool patternMatchPredicate(Node *node) override {
+    if (node->kind() == kIf) {
+      const auto cond_value = node->input();
+      if ((cond_value->node()->kind() == kConstant ||
+           cond_value->node()->kind() == kParam)) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  // step 2: inline the subgraph (for example, inline then_branch when cond ===
+  // true)
+  //         by re-creating all subgraph nodes in parent graph
+  //         note: handle captured value
+  // step 3: Delete "if" node itself
+  bool runTransform(Node *if_node, Graph &graph,
+                    NodeDestroyType &destroy_current) override {
+    const auto cond_value = if_node->input();
+    Tensor cond_tensor;
+    if (cond_value->node()->kind() == kConstant) {
+      cond_tensor = cond_value->node()->t(kvalue);
+    } else {
+      cond_tensor = *graph.getInitializer(cond_value->uniqueName());
+    }
+    const bool cond = static_cast<bool>(cond_tensor.data<int32_t>()[0]);
+    auto &parent_graph = graph;
+    const auto subgraph = if_node->g(cond ? kthen_branch : kelse_branch);
+
+    std::unordered_map<std::string, Value *> unique_name_to_value_in_parent;
+
+    for (auto *x : parent_graph.nodes()) {
+      for (auto *x_output : x->outputs()) {
+        unique_name_to_value_in_parent[x_output->uniqueName()] = x_output;
+      }
+    }
+    std::unordered_map<std::string, Value *> value_dict;
+    for (auto *node : subgraph->nodes()) {
+      auto *new_node =
+          parent_graph.create(node->kind(), node->outputs().size());
+      new_node->insertBefore(if_node);
+      new_node->copyAttributes(*node);
+      for (const auto *input : node->inputs()) {
+        const auto &unique_name = input->uniqueName();
+        if (value_dict.find(unique_name) == value_dict.end()) {
+          ONNX_ASSERT(input->node()->kind() == kCaptured);
+          auto it = unique_name_to_value_in_parent.find(unique_name);
+          if (it == unique_name_to_value_in_parent.end()) {
+            // a value from the parent graph of parent_graph
+            auto *captured_node = parent_graph.create(kCaptured, 1);
+            captured_node->output()->setUniqueName(unique_name);
+            new_node->addInput(captured_node->output());
+          } else {
+            new_node->addInput(it->second);
+          }
+        } else {
+          new_node->addInput(value_dict[unique_name]);
+        }
+      }
+      for (int i = 0; i < node->outputs().size(); i++) {
+        const auto *output_in_subgraph = node->outputs()[i];
+        auto *output_in_parent_graph = new_node->outputs()[i];
+        value_dict[output_in_subgraph->uniqueName()] = output_in_parent_graph;
+      }
+    }
+    const auto &subgraph_outputs = subgraph->outputs();
+    for (int i = 0; i < subgraph_outputs.size(); i++) {
+      auto *new_output = value_dict[subgraph_outputs[i]->uniqueName()];
+      auto *if_output = if_node->outputs()[i];
+      if_output->replaceAllUsesWith(new_output);
+    }
+    destroy_current = DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_cast.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_cast.h
new file mode 100644
index 0000000000..a70c6f48bf
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_cast.h
@@ -0,0 +1,42 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateNopCast final : public PredicateBasedPass {
+  explicit EliminateNopCast()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_nop_cast";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return (node->kind() == kCast && node->hasAttribute(kto) &&
+            node->input()->elemType() == node->i(kto));
+  }
+
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->input());
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_dropout.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_dropout.h
new file mode 100644
index 0000000000..9985e3e708
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_dropout.h
@@ -0,0 +1,48 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateNopDropout final : public PredicateBasedPass {
+  explicit EliminateNopDropout()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_nop_dropout";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    // in opset 12, ratio is an input of Dropout rather than an attribute,
+    // however we don't want to to remove Dropout fro opset 12+, since it
+    // supports training-friendly models, for which the Dropout ops are required
+    return (node->kind() == kDropout && node->hasAttribute(kratio)) &&
+           node->f(kratio) == 0.0;
+  }
+
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    // Don't assume that theres only one output.
+    for (size_t i = 0; i < node->outputs().size(); ++i) {
+      const bool replacing_success =
+          tryReplacingAllUsesWith(node->outputs()[i], node->input());
+      if (!replacing_success) {
+        return false;
+      }
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_flatten.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_flatten.h
new file mode 100644
index 0000000000..911fa33962
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_flatten.h
@@ -0,0 +1,59 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateNopFlatten final : public PredicateBasedPass {
+  explicit EliminateNopFlatten()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_nop_flatten";
+  }
+
+  bool patternMatchPredicate(Node *node) override {
+    if (node->kind() != Symbol("Flatten")) {
+      return false;
+    }
+    const Value *input = node->input();
+    if (!input->has_sizes()) {
+      return false;
+    }
+    const auto input_shape = input->sizes();
+    const int axis = node->hasAttribute(kaxis) ? node->i(kaxis) : 1;
+    if (input_shape.size() == 2) {
+      if (axis == 1 || axis == -1) {
+        return true;
+      }
+      if (input_shape[0].is_int && input_shape[0].dim == 1 && axis == 0) {
+        return true;
+      }
+    }
+
+    return false;
+  }
+
+  bool runTransform(Node *node, Graph &graph,
+                    NodeDestroyType &destroy_current) override {
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->input());
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_monotone_argmax.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_monotone_argmax.h
new file mode 100644
index 0000000000..e77299e094
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_monotone_argmax.h
@@ -0,0 +1,72 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+// Note for log and sqrt this optimization is not always right,
+// because it is a undefined behavior when the input is negative
+const std::unordered_set<NodeKind> monotone_node_no_axis_kind{kLog, kExp,
+                                                              kSqrt};
+
+const std::unordered_set<NodeKind> monotone_node_axis_kind{kSoftmax,
+                                                           kLogSoftmax};
+
+struct EliminateNopMonotoneArgmax final : public PredicateBasedPass {
+  explicit EliminateNopMonotoneArgmax()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Partial,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_nop_monotone_argmax";
+  }
+
+  static inline bool satisfies_monotone_condition(int64_t axis, Node* node) {
+    if (monotone_node_no_axis_kind.find(node->kind()) !=
+        monotone_node_no_axis_kind.end()) {
+      return true;
+    }
+    if (monotone_node_axis_kind.find(node->kind()) !=
+        monotone_node_axis_kind.end()) {
+      if (node->hasAttribute(kaxis)) {
+        return axis == node->i(kaxis);
+      }
+    }
+    return false;
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    if (node->kind() == kArgMax) {
+      if (node->hasAttribute(kaxis)) {
+        auto node_axis = node->i(kaxis);
+        return node->inputs().size() == 1 &&
+               satisfies_monotone_condition(node_axis, node->input()->node());
+      }
+    }
+    return false;
+  }
+
+  bool runTransform(Node* node, Graph&, NodeDestroyType&) override {
+    Node* monotone_node = node->input()->node();
+    if (monotone_node->output()->uses().size() == 1) {
+      const bool replacing_success = tryReplacingAllUsesWith(
+          monotone_node->output(), monotone_node->input());
+      if (!replacing_success) {
+        return false;
+      }
+      monotone_node->destroy();
+      return true;
+    }
+    return false;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_pad.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_pad.h
new file mode 100644
index 0000000000..8e1cfb1aa6
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_pad.h
@@ -0,0 +1,80 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateNopPad final : public PredicateBasedPass {
+  explicit EliminateNopPad()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_nop_pad";
+  }
+
+  static bool is_nop_pad(Node* node, Graph& graph) {
+    if (node->hasAttribute(kpads)) {
+      // opset 10 and below
+      const auto& pads = node->is(kpads);
+      for (size_t i = 0; i < pads.size(); i++) {
+        // if pad constant_value is non-zero, this is not a nop pad
+        if (pads[i] != 0) {
+          return false;
+        }
+      }
+      return true;
+    } else {
+      // opset 11 and above
+      const auto& pads_name = node->inputs()[1]->uniqueName();
+      const auto pads_initializer = graph.getInitializer(pads_name);
+      // 'pad' node has the 'pads' input which has not been initialized -
+      // can't proceed with elimination
+      if (pads_initializer == graph.initializers().end())
+        return false;
+
+      // validate values within 'pads'
+      if (pads_initializer->elem_type() == TensorProto::INT64) {
+        const auto& pads = ParseData<int64_t>(&*pads_initializer);
+        for (const auto& val : pads) {
+          // if pad constant_value is non-zero, this is not a nop pad
+          if (val != 0) {
+            return false;
+          }
+        }
+        return true;
+      }
+    }
+
+    return false;
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kPad;
+  }
+
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    if (!is_nop_pad(node, graph))
+      return false;
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->inputs()[0]);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_transpose.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_transpose.h
new file mode 100644
index 0000000000..0a1b1605d5
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_nop_transpose.h
@@ -0,0 +1,49 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateNopTranspose final : public PredicateBasedPass {
+  explicit EliminateNopTranspose()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "eliminate_nop_transpose";
+  }
+
+  static bool is_nop_transpose(const std::vector<int64_t>& perm) {
+    for (size_t i = 0; i < perm.size(); i++)
+      if (perm[i] != (int)i)
+        return false;
+    return true;
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return (node->kind() == kTranspose && node->hasAttribute(kperm)) &&
+           is_nop_transpose(node->is(kperm));
+  }
+
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->input());
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/eliminate_unused_initializer.h b/third_party/optimizer/onnxoptimizer/passes/eliminate_unused_initializer.h
new file mode 100644
index 0000000000..922432ecff
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/eliminate_unused_initializer.h
@@ -0,0 +1,80 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//   A, B, C are in the initializer list
+//   D = Add(B, C)
+// After:
+//   B, C are in the initializer list and A is removed
+//   D = Add(B, C)
+//
+// this pass can handle the case satisfy all following conditions:
+//   condition 1: A is not used as any node's input
+//   condition 2: A is not an output
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct EliminateUnusedInitializer final : public FullGraphBasedPass {
+  explicit EliminateUnusedInitializer()
+      : FullGraphBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Memory) {}
+
+  std::string getPassName() const override {
+    return "eliminate_unused_initializer";
+  }
+
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::Empty;
+  }
+
+  void erase_used_initializers(
+      Graph& g, std::unordered_set<std::string>* initializer_names) {
+    for (auto output : g.outputs()) {
+      initializer_names->erase(output->uniqueName());
+    }
+    for (auto it = g.begin(); it != g.end(); ++it) {
+      auto* n = *it;
+      DescendOnGraphAttributesUnconstrained(
+          n, [this, initializer_names](Graph& graph) {
+            erase_used_initializers(graph, initializer_names);
+          });
+      for (auto* input : n->inputs()) {
+        initializer_names->erase(input->uniqueName());
+      }
+    }
+  }
+
+  void eliminate_unused_initializer(Graph& graph) {
+    std::unordered_set<std::string> initializer_names(
+        graph.initializer_names().begin(), graph.initializer_names().end());
+    erase_used_initializers(graph, &initializer_names);
+
+    // remove initializer and input if need
+    for (std::string name : initializer_names) {
+      graph.eraseInitializer(name);
+      auto iter = std::find_if(
+          graph.inputs().begin(), graph.inputs().end(),
+          [&name](Value* input) { return input->uniqueName() == name; });
+      if (iter != graph.inputs().end()) {
+        graph.eraseInput(std::distance(graph.inputs().begin(), iter));
+      }
+    }
+  }
+
+  std::shared_ptr<PostPassAnalysis> runPass(Graph& graph) override {
+    eliminate_unused_initializer(graph);
+    return std::shared_ptr<PostPassAnalysis>(new PostPassAnalysis());
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/extract_constant_to_initializer.h b/third_party/optimizer/onnxoptimizer/passes/extract_constant_to_initializer.h
new file mode 100644
index 0000000000..3d7fca8492
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/extract_constant_to_initializer.h
@@ -0,0 +1,62 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//	 A = Constant()
+// After:
+//	 A is in the initializer list
+//
+//	 this pass can handle the case satisfy all following conditions:
+//	   condition 1: A is the output of a Constant node
+#include "onnx/common/assertions.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct ExtractConstantToInitializer final : public PredicateBasedPass {
+  explicit ExtractConstantToInitializer()
+      : PredicateBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::Memory) {}
+
+  std::string getPassName() const override {
+    return "extract_constant_to_initializer";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kConstant;
+  }
+
+  bool runTransform(Node* node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    Tensor t = node->t(kvalue);
+    Value* new_init;
+    if (node->output()->has_unique_name() &&
+        std::find(graph.outputs().rbegin(), graph.outputs().rend(),
+                  node->output()) == graph.outputs().rend()) {
+      new_init = graph.addInitializerAndInput(t, node->output()->uniqueName());
+      node->output()->setUniqueName(
+          ONNX_NAMESPACE::to_string(graph.getNextUnique()), false);
+    } else {
+      // the unique_name will be set in `replaceAllUsesWith` if
+      // node->output() is in graph output
+      new_init = graph.addInitializerAndInput(t);
+    }
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), new_init);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_add_bias_into_conv.h b/third_party/optimizer/onnxoptimizer/passes/fuse_add_bias_into_conv.h
new file mode 100644
index 0000000000..c68e5e17c1
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_add_bias_into_conv.h
@@ -0,0 +1,184 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//   Z = Conv(X, Y)
+//   B = Z + A
+// After:
+//   B = Conv(X, Y, A)
+//
+// the pass can handle the following cases:
+//   case 1: A is 1D tensor and A.dim[0] == Z.dim[1]
+//   case 2: A is 1-element 1D tensor
+
+#include <numeric>
+
+#include "onnx/common/assertions.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseAddBiasIntoConv final : public PredicateBasedPass {
+  explicit FuseAddBiasIntoConv()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override {
+    return "fuse_add_bias_into_conv";
+  }
+  bool patternMatchPredicate(Node *node) override {
+    return node->kind() == kAdd && node->inputs()[0]->node()->kind() == kConv &&
+           node->inputs()[0]->node()->inputs().size() == 2;
+  }
+  static Node *makeSqueezeOrUnsqueeze(Graph &graph, std::vector<int64_t> &axes,
+                                      Value *input, Node *target_node,
+                                      BuiltinSymbol k) {
+    assert(k == kSqueeze || k == kUnsqueeze);
+    Node *squeeze = graph.create(k, 1);
+    int opset_version = getOpsetVersion(graph);
+    squeeze->addInput(input);
+    int version_threshold = 13;
+    if (opset_version < version_threshold && opset_version != 0) {
+      squeeze->is_(kaxes, std::move(axes));
+    } else {
+      Tensor t;
+      t.sizes().push_back(axes.size());
+      t.int64s() = axes;
+      t.elem_type() = TensorProto_DataType_INT64;
+      Value *tv = graph.addInitializerAndInput(t);
+      squeeze->addInput(tv);
+    }
+    squeeze->insertBefore(target_node);
+    return squeeze;
+  }
+  bool runTransform(Node *n, Graph &graph,
+                    NodeDestroyType &destroy_current) override {
+    // due to current broadcasting's constraint, Conv has to be the first
+    // operand
+    destroy_current = NodeDestroyType::DestroyZero;
+    auto orig_conv = n->inputs()[0];
+    auto orig_bias = n->inputs()[1];
+    // check if bias is Const or in graph's initializers
+    if (orig_bias->node()->kind() != kConstant &&
+        orig_bias->node()->kind() != kParam) {
+      return false;
+    }
+    // check if conv is only used by Add
+    if (orig_conv->uses().size() > 1) {
+      return false;
+    }
+    auto conv_shape = orig_conv->sizes();
+    auto bias_shape = orig_bias->sizes();
+    auto weight_shape = orig_conv->node()->inputs()[1]->sizes();
+    int64_t M = -1;
+    int64_t rank = -1;
+    // try to get feature M and rank from conv_shape
+    if (conv_shape.size() > 1 && conv_shape[1].is_int) {
+      M = conv_shape[1].dim;
+      rank = conv_shape.size();
+    }
+    // try to get feature M and rank from weight_shape
+    if (weight_shape.size() > 0 && weight_shape[0].is_int) {
+      ONNX_ASSERT(M == -1 || M == weight_shape[0].dim);
+      M = weight_shape[0].dim;
+      ONNX_ASSERT(rank == -1 ||
+                  rank == static_cast<int64_t>(weight_shape.size()));
+      rank = weight_shape.size();
+    }
+    int64_t num_el = 1;
+    for (int i = 0; i < static_cast<int64_t>(bias_shape.size()); ++i) {
+      if (bias_shape[i].is_int) {
+        num_el *= bias_shape[i].dim;
+      } else {
+        num_el = -1;
+        return false;
+      }
+    }
+    if (M == -1 || num_el == -1) {
+      // No enough information, bail out
+      return false;
+    }
+    if (rank < static_cast<int64_t>(bias_shape.size())) {
+      return false;
+    }
+    if (num_el == 1) {
+      if (orig_bias->node()->kind() != kParam &&
+          orig_conv->node()->isBefore(orig_bias->node())) {
+        orig_bias->node()->moveBefore(orig_conv->node());
+      }
+      Value *conv_3rd_input = orig_bias;
+      if (bias_shape.size() > 1) {
+        std::vector<int64_t> axes(bias_shape.size() - 1);
+        std::iota(axes.begin(), axes.end(), 0);
+        Node *squeeze = makeSqueezeOrUnsqueeze(graph, axes, conv_3rd_input,
+                                               orig_conv->node(), kSqueeze);
+        conv_3rd_input = squeeze->output();
+      } else if (bias_shape.size() == 0) {
+        std::vector<int64_t> axes = {0};
+        Node *unsqueeze = makeSqueezeOrUnsqueeze(graph, axes, conv_3rd_input,
+                                                 orig_conv->node(), kUnsqueeze);
+        conv_3rd_input = unsqueeze->output();
+      }
+      if (M > 1) {
+        Node *constant = graph.create(kConstant, 1);
+        Tensor t;
+        t.sizes().push_back(static_cast<int64_t>(1));
+        t.int64s().push_back(M);
+        t.elem_type() = TensorProto_DataType_INT64;
+        Symbol sym = Symbol("value");
+        constant->t_(sym, t);
+        std::vector<Dimension> s = {1};
+        constant->output()->setSizes(s);
+        constant->output()->setElemType(TensorProto_DataType_INT64);
+        constant->insertBefore(orig_conv->node());
+        Node *tile = graph.create(kTile, 1);
+        tile->addInput(conv_3rd_input);
+        tile->addInput(constant->output());
+        conv_3rd_input = tile->output();
+        tile->insertBefore(orig_conv->node());
+      }
+      orig_conv->node()->addInput(conv_3rd_input);
+    } else if (rank > static_cast<int64_t>(bias_shape.size()) + 1) {
+      return false;
+    } else if (num_el == M &&
+               bias_shape[1 + bias_shape.size() - static_cast<unsigned>(rank)]
+                       .dim == M) {
+      ONNX_ASSERT(bias_shape.size() > 1);
+      if (orig_bias->node()->kind() != kParam &&
+          orig_conv->node()->isBefore(orig_bias->node())) {
+        orig_bias->node()->moveBefore(orig_conv->node());
+      }
+      std::vector<int64_t> axes(bias_shape.size());
+      std::iota(axes.begin(), axes.end(), static_cast<int64_t>(0));
+      axes.erase(axes.begin() +
+                 (1 + bias_shape.size() - static_cast<unsigned>(rank)));
+      Node *squeeze = makeSqueezeOrUnsqueeze(graph, axes, orig_bias,
+                                             orig_conv->node(), kSqueeze);
+      orig_conv->node()->addInput(squeeze->output());
+    } else {
+      return false;
+    }
+    if (orig_conv->sizes().size() == 0 && n->output()->sizes().size() > 0) {
+      orig_conv->setSizes(n->output()->sizes());
+    }
+    if (n->output()->elemType() != TensorProto_DataType_UNDEFINED) {
+      orig_conv->setElemType(n->output()->elemType());
+    }
+    const bool replacing_success =
+        tryReplacingAllUsesWith(n, orig_conv->node());
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_bn_into_conv.h b/third_party/optimizer/onnxoptimizer/passes/fuse_bn_into_conv.h
new file mode 100644
index 0000000000..c00fe46e51
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_bn_into_conv.h
@@ -0,0 +1,191 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//	 conv = Conv()
+//   bn = BatchNormalization()
+//
+// After:
+//	 bn is deleted
+//   new inputs/initializers to conv are added to graph
+//   any no longer used inputs/initializers are erased from graph
+//
+//	 this pass can handle the case satisfy all following conditions:
+//	   condition 1: Run in testing mode
+//     condition 2: Inputs 1 - 4 of bn are all initializer_size
+//     condition 3: Output of initial conv has no other uses
+//     condition 3: Currently works for only DOUBLE, FLOAT32 tensor types
+//
+// Formula for transformation
+// $$ X_{bn} = \frac{s(X - m)}{\sqrt{\sigma + \epsilon}} + b_{bn}$$
+// $$ X_{conv} = X * W + b_{conv} $$
+// thus, substituting $X$ with $X_{conv}$ in the BN equation we get:
+// $$X_{bn} = X * \frac{sW}{\sqrt{\sigma + \epsilon}} + \frac{s(b_{conv} -
+// m)}{\sqrt{\sigma + \epsilon}} + b_{bn}$$ or
+// $$ W' = W\frac{s}{\sqrt{\sigma + \epsilon}}$$
+// $$ b' = (b_{conv} - m)\frac{s}{\sqrt{\sigma + \epsilon}} + b_{bn}$$
+
+#include "onnx/common/assertions.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+// TODO: Currently broken for complex values and float16
+struct FuseBNIntoConv final : public PredicateBasedPass {
+  explicit FuseBNIntoConv()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_bn_into_conv";
+  }
+
+  void replace_inputs(Tensor& W, Tensor& b, Node* conv, Graph& graph) {
+    Value* new_W_value = graph.addInitializerAndInput(W);
+    Value* old_W_value = conv->inputs()[1];
+    conv->replaceInput(1, new_W_value);
+    if (old_W_value->uses().size() == 0) {
+      graph.eraseInitializerAndInput(old_W_value);
+    }
+
+    if (conv->inputs().size() == 3) {
+      Value* new_b_value = graph.addInitializerAndInput(b);
+      Value* old_b_value = conv->inputs()[2];
+      conv->replaceInput(2, new_b_value);
+      if (old_b_value->uses().size() == 0) {
+        graph.eraseInitializerAndInput(old_b_value);
+      }
+    } else {
+      Value* new_b_value = graph.addInitializerAndInput(b);
+      conv->addInput(new_b_value);
+    }
+  }
+
+  bool modify_conv(Node* conv, Node* bn, Graph& graph) {
+    const auto& bn_inputs = bn->inputs();
+    const auto& conv_inputs = conv->inputs();
+    auto end_iter = graph.initializers().end();
+    auto s_iter = graph.getInitializer(bn_inputs[1]->uniqueName());
+    auto bbn_iter = graph.getInitializer(bn_inputs[2]->uniqueName());
+    auto m_iter = graph.getInitializer(bn_inputs[3]->uniqueName());
+    auto var_iter = graph.getInitializer(bn_inputs[4]->uniqueName());
+    auto W_iter = graph.getInitializer(conv_inputs[1]->uniqueName());
+    if (s_iter == end_iter || bbn_iter == end_iter || m_iter == end_iter ||
+        var_iter == end_iter || W_iter == end_iter) {
+      return false;
+    }
+
+    ONNX_ASSERT(s_iter->sizes().size() == 1);
+    ONNX_ASSERT(bbn_iter->sizes().size() == 1 &&
+                bbn_iter->sizes()[0] == s_iter->sizes()[0]);
+    ONNX_ASSERT(m_iter->sizes().size() == 1 &&
+                m_iter->sizes()[0] == s_iter->sizes()[0]);
+    ONNX_ASSERT(var_iter->sizes().size() == 1 &&
+                var_iter->sizes()[0] == s_iter->sizes()[0]);
+    ONNX_ASSERT(W_iter->sizes().size() > 2 &&
+                W_iter->sizes()[0] == s_iter->sizes()[0]);
+    ONNX_ASSERT(s_iter->elem_type() == bbn_iter->elem_type() &&
+                s_iter->elem_type() == m_iter->elem_type() &&
+                s_iter->elem_type() == var_iter->elem_type() &&
+                s_iter->elem_type() == W_iter->elem_type());
+    if (s_iter->elem_type() != ONNX_NAMESPACE::TensorProto_DataType_FLOAT &&
+        s_iter->elem_type() != ONNX_NAMESPACE::TensorProto_DataType_DOUBLE) {
+      return false;
+    }
+
+    Tensor bc;
+    if (conv_inputs.size() == 3) {
+      auto bc_iter = graph.getInitializer(conv_inputs[2]->uniqueName());
+      if (bc_iter == end_iter) {
+        return false;
+      }
+      bc = *bc_iter;
+      ONNX_ASSERT(bc.sizes().size() == 1 &&
+                  bc.sizes()[0] == s_iter->sizes()[0]);
+    }
+
+    Tensor s = *s_iter;
+    const Tensor& bbn = *bbn_iter;
+    const Tensor& m = *m_iter;
+    Tensor var = *var_iter;
+    Tensor W = *W_iter;
+    float epsilon = bn->hasAttribute(kepsilon) ? (float)bn->f(kepsilon) : 1e-5f;
+    Tensor eps;
+
+#define DO_COMPUTATION(TENSOR_TYPE, vec)                                 \
+  eps.sizes().push_back(s.sizes()[0]);                                   \
+  eps.elem_type() = ONNX_NAMESPACE::TensorProto_DataType_##TENSOR_TYPE;  \
+  for (int64_t i = 0; i < eps.sizes()[0]; ++i) {                         \
+    eps.vec().push_back(epsilon);                                        \
+  }                                                                      \
+  if (conv_inputs.size() != 3) {                                         \
+    bc.sizes().push_back(s.sizes()[0]);                                  \
+    bc.elem_type() = ONNX_NAMESPACE::TensorProto_DataType_##TENSOR_TYPE; \
+    for (int64_t i = 0; i < eps.sizes()[0]; ++i) {                       \
+      bc.vec().push_back(0.f);                                           \
+    }                                                                    \
+  }                                                                      \
+  var.add(eps);                                                          \
+  var.sqrt();                                                            \
+  s.divide(var);                                                         \
+  W.scale_by_first_dim(s);                                               \
+  bc.subtract(m);                                                        \
+  bc.multiply(s);                                                        \
+  bc.add(bbn);
+
+    switch (s.elem_type()) {
+      case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
+        DO_COMPUTATION(FLOAT, floats)
+        break;
+      }
+      case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
+        DO_COMPUTATION(DOUBLE, doubles)
+        break;
+      }
+      default:
+        return false;
+    }
+#undef DO_COMPUTATION
+    replace_inputs(W, bc, conv, graph);
+    return true;
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kBatchNormalization &&
+           node->inputs()[0]->node()->kind() == kConv;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    Node* bn = n;
+    Node* conv = n->inputs()[0]->node();
+    auto origInput = bn->inputs()[0];
+    if (origInput->uses().size() > 1 || bn->outputs().size() > 1 ||
+        !modify_conv(conv, bn, graph)) {
+      destroy_current = NodeDestroyType::DestroyZero;
+      return false;
+    }
+    for (int i = 4; i >= 1; --i) {
+      if (bn->inputs()[i]->uses().size() == 1) {
+        auto input = bn->inputs()[i];
+        bn->removeInput(i);
+        graph.eraseInitializerAndInput(input);
+      }
+    }
+    const bool replacing_success =
+        tryReplacingAllUsesWith(bn->output(), origInput);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_concats.h b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_concats.h
new file mode 100644
index 0000000000..55eab23655
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_concats.h
@@ -0,0 +1,78 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConsecutiveConcats final : public PredicateBasedPass {
+  explicit FuseConsecutiveConcats()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Partial,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_consecutive_concats";
+  }
+
+  void insertInput(Node* node, size_t i, Value* value) {
+    const auto input_size = node->inputs().size();
+    if (i == input_size) {
+      node->addInput(value);
+    } else {
+      for (size_t j = input_size - 1; j >= i; j--) {
+        Value* cur_input = node->input(j);
+        if (j == input_size - 1) {
+          node->addInput(cur_input);
+        } else {
+          node->replaceInput(j + 1, cur_input);
+        }
+      }
+      node->replaceInput(i, value);
+    }
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    // we don't check if our concat node has inputs which are also concat nodes
+    // because this requires a for loop through the inputs. If it turns out
+    // there is then we still have to do a for loop in the runTransform portion
+    // of the code. In order not to waste a loop we don't check the real pattern
+    // match condition.
+    return node->kind() == kConcat && node->hasAttribute(kaxis);
+  }
+  bool runTransform(Node* concat_node, Graph&,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+    bool transform_ran = false;
+    for (size_t i = 0; i < concat_node->inputs().size(); i++) {
+      Value* cur_input_value = concat_node->inputs()[i];
+      Node* cur_input_node = cur_input_value->node();
+      if (cur_input_node->kind() == kConcat &&
+          cur_input_value->uses().size() == 1 &&
+          cur_input_node->hasAttribute(kaxis) &&
+          cur_input_node->i(kaxis) == concat_node->i(kaxis)) {
+        transform_ran = true;
+        // Inserts n inputs of cur_input_node at index i+1~i+1+(n-1),
+        // and remove cur_input_node at index i.
+        // As a result, cur_input_node is replaced by its inputs inplace,
+        // instead of always appending its inputs at the end.
+        for (size_t j = 0; j < cur_input_node->inputs().size(); j++) {
+          Value* value = cur_input_node->input(j);
+          insertInput(concat_node, i + 1 + j, value);
+        }
+        concat_node->removeInput(i);
+        cur_input_node->destroy();
+      }
+    }
+    return transform_ran;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_log_softmax.h b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_log_softmax.h
new file mode 100644
index 0000000000..837241546e
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_log_softmax.h
@@ -0,0 +1,53 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConsecutiveLogSoftmax final : public PredicateBasedPass {
+  explicit FuseConsecutiveLogSoftmax()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_consecutive_log_softmax";
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kLog && node->input()->node()->kind() == kSoftmax &&
+           node->input()->uses().size() == 1;
+  }
+  bool runTransform(Node* log_node, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    Value* log_node_output = log_node->output();
+    Node* softmax_node = log_node->inputs()[0]->node();
+    Node* log_softmax_node = graph.create(kLogSoftmax, 1);
+
+    // log_softmax_node construction
+    log_softmax_node->i_(kaxis, softmax_node->i(kaxis));
+    log_softmax_node->addInput(softmax_node->input());
+    log_softmax_node->insertBefore(softmax_node);
+    log_softmax_node->output()->setSizes(log_node_output->sizes());
+    log_softmax_node->output()->setElemType(log_node_output->elemType());
+
+    const bool replacing_success =
+        tryReplacingAllUsesWith(log_node, log_softmax_node);
+    if (!replacing_success) {
+      return false;
+    }
+    log_node->removeAllInputs();
+    destroy_current = NodeDestroyType::DestroyTwo;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_reduce_unsqueeze.h b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_reduce_unsqueeze.h
new file mode 100644
index 0000000000..1c07cd56e0
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_reduce_unsqueeze.h
@@ -0,0 +1,113 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnx/defs/tensor_proto_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+const std::unordered_set<NodeKind> reduction_operators{
+    kReduceL1,   kReduceL2,  kReduceLogSum, kReduceLogSumExp, kReduceMax,
+    kReduceMean, kReduceMin, kReduceProd,   kReduceSum,       kReduceSumSquare};
+
+struct FuseConsecutiveReduceUnsqueeze final : public PredicateBasedPass {
+  explicit FuseConsecutiveReduceUnsqueeze()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_consecutive_reduce_unsqueeze";
+  }
+  static bool IsAxesAnAttr(const Graph &graph, const Node *n) {
+    int opset_version = getOpsetVersion(graph);
+    int opset_threshold;
+    if (n->kind() == kUnsqueeze || n->kind() == kReduceSum) {
+      opset_threshold = 12;
+      return opset_version <= opset_threshold && opset_version != 0;
+    }
+    return true;
+  }
+  static bool getAxes(const Node *n, Graph &graph, std::vector<int64_t> &axes) {
+    if (IsAxesAnAttr(graph, n)) {
+      if (!n->hasAttribute(kaxes)) {
+        return false;
+      }
+      axes = n->is(kaxes);
+    } else {
+      if (n->inputs().size() < 2) {
+        return false;
+      }
+      auto axes_value = n->inputs()[1];
+      if ((axes_value->node()->kind() != kConstant &&
+           axes_value->node()->kind() != kParam)) {
+        return false;
+      }
+      Tensor axes_t;
+      if (axes_value->node()->kind() == kConstant) {
+        axes_t = axes_value->node()->t(kvalue);
+      } else {
+        const auto axes_i = graph.getInitializer(axes_value->uniqueName());
+        axes_t = *axes_i;
+      }
+      axes = ParseData<int64_t>(&axes_t);
+    }
+    return true;
+  }
+  bool patternMatchPredicate(Node *node) override {
+    // check that the current node is of type Unsqueeze and has defined axes
+    bool cur_node_check = node->kind() == kUnsqueeze;
+    if (cur_node_check) {
+      Node *prev_node = node->inputs()[0]->node();
+      // check that the previous node a reduction operator and has defined
+      // axes/keepdims
+      bool reduction_node_check = reduction_operators.find(prev_node->kind()) !=
+                                      reduction_operators.end() &&
+                                  prev_node->hasAttribute(kkeepdims);
+      if (reduction_node_check) {
+        // insure that keepdims is set to false currently
+        return prev_node->i(kkeepdims) == 0;
+      }
+    }
+    return false;
+  }
+  bool runTransform(Node *node, Graph &graph,
+                    NodeDestroyType &destroy_current) override {
+    Node *prev_node = node->inputs()[0]->node();
+    std::vector<int64_t> axes;
+    bool success = getAxes(node, graph, axes);
+    if (!success) {
+      return false;
+    }
+    std::vector<int64_t> prev_axes;
+    success = getAxes(prev_node, graph, prev_axes);
+    if (!success) {
+      return false;
+    }
+    if (axes != prev_axes) {
+      return false;
+    }
+    Node *reduction_op = node->inputs()[0]->node();
+    // set keepdims flag to be true
+    reduction_op->i_(kkeepdims, 1);
+    // remove unnecessary unsqueeze
+    reduction_op->output()->setSizes(node->output()->sizes());
+    reduction_op->output()->setElemType(node->output()->elemType());
+    const bool replacing_success =
+        tryReplacingAllUsesWith(node->output(), node->inputs()[0]);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyOne;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_squeezes.h b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_squeezes.h
new file mode 100644
index 0000000000..dec39817a5
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_squeezes.h
@@ -0,0 +1,141 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//   X is a tensor with shape=[1, 1, 2, 3, 1, 5, 1]
+//   Y = Squeeze(X, axes=[1, 4]) -> shape=[1, 2, 3, 5, 1]
+//   Z = Squeeze(Y, axes=[0, 4]) -> shape=[2, 3, 5]
+// After:
+//   Z = Squeeze(X, axes=[0, 1, 4, 6])
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConsecutiveSqueezes final : public PredicateBasedPass {
+  explicit FuseConsecutiveSqueezes()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_consecutive_squeezes";
+  }
+  static bool IsAxesAnAttr(const Graph &graph) {
+    const int opset_version = getOpsetVersion(graph);
+    return opset_version <= 12 && opset_version != 0;
+  }
+  static bool getAxes(const Node *n, const Graph &graph,
+                      std::vector<int64_t> &axes) {
+    if (IsAxesAnAttr(graph)) {
+      axes = n->is(kaxes);
+    } else {
+      assert(n->inputs().size() == 2);
+      auto axes_value = n->inputs()[1];
+      if ((axes_value->node()->kind() != kConstant &&
+           axes_value->node()->kind() != kParam)) {
+        return false;
+      }
+      Tensor axes_t;
+      if (axes_value->node()->kind() == kConstant) {
+        axes_t = axes_value->node()->t(kvalue);
+      } else {
+        // this hack is due to `getInitializer` lacks a const version
+        Graph &mut_graph = const_cast<Graph &>(graph);
+        const auto axes_i = mut_graph.getInitializer(axes_value->uniqueName());
+        axes_t = *axes_i;
+      }
+      axes = ParseData<int64_t>(&axes_t);
+    }
+    return true;
+  }
+  // modify the vector `composed_axes` such that squeeze by it is equivalent
+  // to squeeze by `axes_1` and then by `axes_2`
+  static bool compose_squeezes(const Node *input_n, const Node *n,
+                               const Graph &graph,
+                               std::vector<int64_t> &composed_axes) {
+    std::vector<int64_t> axes_1;
+    std::vector<int64_t> axes_2;
+    bool success = getAxes(input_n, graph, axes_1);
+    if (!success) {
+      return false;
+    }
+    success = getAxes(n, graph, axes_2);
+    if (!success) {
+      return false;
+    }
+    std::vector<int64_t> &ret = composed_axes;
+    ret.clear();
+    ret.reserve(axes_1.size() + axes_2.size());
+    std::vector<int64_t> sorted_axes_1(axes_1.begin(), axes_1.end());
+    std::sort(sorted_axes_1.begin(), sorted_axes_1.end());
+    std::copy(sorted_axes_1.begin(), sorted_axes_1.end(),
+              std::back_inserter(ret));
+
+    for (int64_t i : axes_2) {
+      for (auto iter = sorted_axes_1.begin(); iter != sorted_axes_1.end();
+           ++iter) {
+        // if current axis 1 - prev_num is bigger than axis 2
+        // put axis 2 + prev_num as new axis
+        int64_t prev_num = std::distance(sorted_axes_1.begin(), iter);
+        if (*iter - prev_num > i) {
+          ret.push_back(i + prev_num);
+          break;
+        }
+        // if no current axis 1 - prev_num is bigger than axis 2
+        // put axis 2 + prev_num + 1 as new axis
+        if (std::next(iter) == sorted_axes_1.end()) {
+          ret.push_back(i + prev_num + 1);
+        }
+      }
+    }
+    std::sort(ret.begin(), ret.end());
+    return true;
+  }
+
+  bool patternMatchPredicate(Node *node) override {
+    return node->kind() == kSqueeze &&
+           node->inputs()[0]->node()->kind() == kSqueeze;
+  }
+  bool runTransform(Node *n, Graph &graph,
+                    NodeDestroyType &destroy_current) override {
+    auto orig_input = n->inputs()[0];
+    std::vector<int64_t> rs;
+    bool success = compose_squeezes(orig_input->node(), n, graph, rs);
+    if (!success) {
+      return false;
+    }
+    n->replaceInput(0, orig_input->node()->inputs()[0]);
+    if (orig_input->uses().size() == 0) {
+      orig_input->node()->destroy();
+    }
+    if (IsAxesAnAttr(graph)) {
+      n->is_(kaxes, std::move(rs));
+    } else {
+      Tensor t;
+      t.sizes().push_back(rs.size());
+      t.int64s() = rs;
+      t.elem_type() = TensorProto_DataType_INT64;
+      auto axes_v = n->inputs()[1];
+      Value *tv = graph.addInitializerAndInput(t);
+      n->replaceInput(1, tv);
+      if (axes_v->uses().size() == 0) {
+        if (axes_v->node()->kind() == kConstant) {
+          axes_v->node()->destroy();
+        } else {
+          graph.eraseInitializerAndInput(axes_v);
+        }
+      }
+    }
+    destroy_current = NodeDestroyType::DestroyZero;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_transposes.h b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_transposes.h
new file mode 100644
index 0000000000..559da2778a
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_consecutive_transposes.h
@@ -0,0 +1,79 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseConsecutiveTransposes final : public PredicateBasedPass {
+  explicit FuseConsecutiveTransposes()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+
+  std::string getPassName() const override {
+    return "fuse_consecutive_transposes";
+  }
+
+  // returns a vector `ret` such that transposing by `ret` is equivalent
+  // to transposing by `t1` and then by `t2`
+  std::vector<int64_t> compose_transposes(const std::vector<int64_t>& t1,
+                                          const std::vector<int64_t>& t2) {
+    ONNX_ASSERT(t1.size() == t2.size());
+    std::vector<int64_t> ret;
+    ret.reserve(t1.size());
+    for (size_t i = 0; i < t1.size(); i++) {
+      ONNX_ASSERT(t2[i] < static_cast<int64_t>(t1.size()));
+      ONNX_ASSERT(t1[static_cast<size_t>(t2[i])] <
+                  static_cast<int64_t>(t1.size()));
+      ret.push_back(t1[static_cast<size_t>(t2[i])]);
+    }
+    return ret;
+  }
+
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kTranspose &&
+           node->input()->node()->kind() == kTranspose;
+  }
+
+  bool runTransform(Node* n, Graph&,
+                    NodeDestroyType& destroy_current) override {
+    auto origInput = n->input();
+    if (!n->hasAttribute(kperm) && !origInput->node()->hasAttribute(kperm)) {
+      // One special case (two consecutive transposes with no perm,
+      // since we do not have the shape information here, we have
+      // to eliminate two transpose together.
+      if (n->output()->has_sizes()) {
+        origInput->node()->input()->setSizes(n->output()->sizes());
+      }
+      const bool replacing_success =
+          tryReplacingAllUsesWith(n, origInput->node()->input()->node());
+      if (!replacing_success) {
+        return false;
+      }
+      destroy_current = NodeDestroyType::DestroyTwo;
+      return true;
+    }
+    if (!n->hasAttribute(kperm) || !origInput->node()->hasAttribute(kperm)) {
+      destroy_current = NodeDestroyType::DestroyZero;
+      return false;
+    }
+    n->is_(kperm,
+           compose_transposes(origInput->node()->is(kperm), n->is(kperm)));
+    n->replaceInput(0, origInput->node()->input());
+    if (origInput->uses().size() == 0) {
+      origInput->node()->destroy();
+    }
+    destroy_current = NodeDestroyType::DestroyZero;
+    return false;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_matmul_add_bias_into_gemm.h b/third_party/optimizer/onnxoptimizer/passes/fuse_matmul_add_bias_into_gemm.h
new file mode 100644
index 0000000000..a5b59be024
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_matmul_add_bias_into_gemm.h
@@ -0,0 +1,110 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//   Z = MatMul(X, Y)
+//   A = Z + Bias
+// After:
+//   A = Gemm(X, Y, Bias)
+//
+// the pass can handle the case when:
+//   case 1: Bias is 1D tensor and Bias.dim[0] == Z.dim[1]
+//   case 2: Bias is 2D tensor and Bias.dim[0] == Z.dim[0] or 1
+//           and Bias.dim[1] = Z.dim[1]
+
+#include <numeric>
+
+#include "onnx/common/assertions.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseMatMulAddBiasIntoGemm final : public PredicateBasedPass {
+  explicit FuseMatMulAddBiasIntoGemm()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override {
+    return "fuse_matmul_add_bias_into_gemm";
+  }
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kAdd && node->inputs()[0]->node()->kind() == kMatMul;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    // due to current broadcasting's constraint, MatMul has to be the first
+    // operand
+    destroy_current = NodeDestroyType::DestroyZero;
+    auto orig_matmul = n->inputs()[0];
+    auto orig_bias = n->inputs()[1];
+    // check if bias is Const or in graph's initializers
+    if (orig_bias->node()->kind() != kConstant &&
+        orig_bias->node()->kind() != kParam) {
+      return false;
+    }
+    // check if MatMul is only used by Add
+    if (orig_matmul->uses().size() > 1) {
+      return false;
+    }
+    auto x_shape = orig_matmul->node()->inputs()[0]->sizes();
+    auto y_shape = orig_matmul->node()->inputs()[1]->sizes();
+    int64_t z_N = -1;
+    int64_t z_M = -1;
+    // try to get feature N from x_shape
+    if (static_cast<int64_t>(x_shape.size()) == 2 && x_shape[0].is_int) {
+      z_N = x_shape[0].dim;
+    } else {
+      return false;
+    }
+    // try to get feature M from y_shape
+    if (static_cast<int64_t>(y_shape.size()) == 2 && y_shape[1].is_int) {
+      z_M = y_shape[1].dim;
+    } else {
+      return false;
+    }
+    // check if bias_shape is compatible
+    auto bias_shape = orig_bias->sizes();
+    auto bias_dim = static_cast<int64_t>(bias_shape.size());
+    int64_t bias_N = -1;
+    int64_t bias_M = -1;
+    if (bias_dim == 1 && bias_shape[0].is_int) {
+      bias_N = 1;
+      bias_M = bias_shape[0].dim;
+    } else if (bias_dim == 2 && bias_shape[0].is_int && bias_shape[1].is_int) {
+      bias_N = bias_shape[0].dim;
+      bias_M = bias_shape[1].dim;
+    } else {
+      return false;
+    }
+    if ((bias_N != z_N && bias_N != 1) || bias_M != z_M) {
+      return false;
+    }
+    // proceed to fuse MatMul and Add into Gemm
+    Node* gemm =
+        graph.create(kGemm, orig_matmul->node()->inputs(), n->outputs().size());
+    gemm->addInput(n->inputs()[1]);
+    for (int i = 0; i < static_cast<int64_t>(gemm->outputs().size()); ++i) {
+      gemm->outputs()[i]->copyMetadata(n->outputs()[i]);
+    }
+    gemm->f_(kalpha, 1.0);
+    gemm->f_(kbeta, 1.0);
+    gemm->i_(ktransA, 0);
+    gemm->i_(ktransB, 0);
+    gemm->insertBefore(orig_matmul->node());
+    const bool replacing_success = tryReplacingAllUsesWith(n, gemm);
+    if (!replacing_success) {
+      return false;
+    }
+    destroy_current = NodeDestroyType::DestroyTwo;
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_pad_into_conv.h b/third_party/optimizer/onnxoptimizer/passes/fuse_pad_into_conv.h
new file mode 100644
index 0000000000..eedd4a6fe3
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_pad_into_conv.h
@@ -0,0 +1,179 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+// Before:
+//   P = Pad(X) - opset 10 and below (or) Pad(X, Pads, [Constant_value]) - opset
+//   11 and above Z = Conv(P, Y)
+// After:
+//   Z = Conv(X, Y) with "pads" attribute set
+//
+// the pass handles the case when Pad is zero-padding the input
+// (i.e. mode=constant and Constant_value=0)
+
+#include <numeric>
+
+#include "onnx/defs/tensor_util.h"
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FusePadIntoConv final : public PredicateBasedPass {
+  explicit FusePadIntoConv()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override {
+    return "fuse_pad_into_conv";
+  }
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kConv && node->inputs()[0]->node()->kind() == kPad;
+  }
+  bool runTransform(Node* n, Graph& graph,
+                    NodeDestroyType& destroy_current) override {
+    destroy_current = NodeDestroyType::DestroyZero;
+
+    // check if Pad is only used by Conv
+    if (n->inputs()[0]->uses().size() > 1) {
+      return false;
+    }
+
+    Node* conv = n;
+    Node* pad = n->inputs()[0]->node();
+
+    // Process 'pads' data
+    std::vector<int64_t> pads;
+    if (pad->hasAttribute(kpads)) {
+      // opset 10 and below
+      pads = pad->is(kpads);
+    } else {
+      // opset 11 and above - first check if 'pad' node has 'pads' input
+      // initialized
+      const auto& pads_name = pad->inputs()[1]->uniqueName();
+      const auto pads_initializer = graph.getInitializer(pads_name);
+      // 'pad' node has the 'pads' input which has not been initialized -
+      // can't proceed with fusing
+      if (pads_initializer == graph.initializers().end()) {
+        return false;
+      }
+
+      // make sure the type of 'pads' is INT64
+      if (pads_initializer->elem_type() != TensorProto::INT64) {
+        return false;
+      }
+
+      // parse 'pads' data from the initialized input
+      pads = ParseData<int64_t>(&*pads_initializer);
+    }
+
+    // Process 'mode'
+    std::string pad_mode;
+    if (pad->hasAttribute(kmode)) {
+      pad_mode = pad->s(kmode);
+    } else {
+      pad_mode = "constant";
+    }
+
+    // cannot fuse if the pad mode is not "Constant"
+    if (pad_mode != "constant") {
+      return false;
+    }
+
+    // Process 'Constant_value'
+    // opset 10 and below
+    if (pad->hasAttribute(kvalue) &&
+        static_cast<double>(pad->f(kvalue)) != 0.0) {
+      return false;
+    } else if (pad->inputs().size() == 3) {
+      // opset 11 and above - check if the 'pad' node has the optional
+      // 'Constant_value' input check if it has data initialized
+      const auto& value_name = pad->inputs()[2]->uniqueName();
+      const auto value_initializer = graph.getInitializer(value_name);
+
+      // 'pad' node has the 'Constant_value' input which has not been
+      // initialized - can't proceed with fusing
+      if (value_initializer == graph.initializers().end()) {
+        return false;
+      }
+
+      // parse 'Constant_value' data from the initialized input and stop
+      // optimizer if the Constant_value is non-zero
+      switch (value_initializer->elem_type()) {
+        case TensorProto::FLOAT:
+          if (ParseData<float>(&*value_initializer)[0] != 0)
+            return false;  // cannot fuse Pad into Conv
+          else
+            break;
+
+        case TensorProto::DOUBLE:
+          if (ParseData<double>(&*value_initializer)[0] != 0)
+            return false;  // cannot fuse Pad into Conv
+          else
+            break;
+
+        case TensorProto::INT32:
+          if (ParseData<int32_t>(&*value_initializer)[0] != 0)
+            return false;  // cannot fuse Pad into Conv
+          else
+            break;
+
+        case TensorProto::INT64:
+          if (ParseData<int64_t>(&*value_initializer)[0] != 0)
+            return false;  // cannot fuse Pad into Conv
+          else
+            break;
+
+        // TODO: Support more uncommon but valid types for Pad op (int8, uint8,
+        // int16, uint16, etc.)
+
+        default:
+          return false;  // Either type of Constant_value is invalid or not yet
+                         // supported by data parsing logic. Since we canot
+                         // validate the data present in 'Constant_value', we
+                         // exit the optimizer
+      }
+    }
+
+    // check if some values in 'pads' prevents us from fusing it into 'Conv'
+    // node
+    int pads_size = static_cast<int>(pads.size());
+
+    // check if padding is applied only on feature dims
+    if (pads[0] != 0 || pads[1] != 0 || pads[pads_size / 2] != 0 ||
+        pads[pads_size / 2 + 1] != 0) {
+      return false;
+    }
+
+    // check if padding is only positive
+    if (std::any_of(pads.begin(), pads.end(),
+                    [](int64_t local_value) { return local_value < 0; })) {
+      return false;
+    }
+
+    int conv_pads_size = pads_size - 4;
+    std::vector<int64_t> conv_pads(conv_pads_size, 0);
+    // Fuse into existing padding, if available
+    if (conv->hasAttribute(kpads)) {
+      conv_pads = conv->is(kpads);
+    }
+
+    for (int i = 2, j = 0; i < pads_size / 2; ++i, ++j) {
+      conv_pads[j] += pads[i];
+      conv_pads[conv_pads_size / 2 + j] += pads[pads_size / 2 + i];
+    }
+
+    conv->is_(kpads, std::move(conv_pads));
+    conv->replaceInput(0, pad->inputs()[0]);
+    pad->destroy();
+
+    return true;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/fuse_transpose_into_gemm.h b/third_party/optimizer/onnxoptimizer/passes/fuse_transpose_into_gemm.h
new file mode 100644
index 0000000000..6eebaf8567
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/fuse_transpose_into_gemm.h
@@ -0,0 +1,48 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct FuseTransposeIntoGemm final : public PredicateBasedPass {
+  explicit FuseTransposeIntoGemm()
+      : PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
+                           PassOptimizationType::Compute) {}
+  std::string getPassName() const override {
+    return "fuse_transpose_into_gemm";
+  }
+  bool patternMatchPredicate(Node* node) override {
+    return node->kind() == kGemm;
+  }
+  bool runTransform(Node* n, Graph&,
+                    NodeDestroyType& destroy_current) override {
+    const std::vector<int64_t> simple_trans_perm({1, 0});
+    destroy_current = NodeDestroyType::DestroyZero;
+    bool ret_val = false;
+    for (size_t i : {0, 1}) {
+      auto inp = n->inputs()[i];
+      auto trans = i == 0 ? ktransA : ktransB;
+      if (inp->node()->kind() == kTranspose &&
+          inp->node()->is(kperm) == simple_trans_perm) {
+        n->replaceInput(i, inp->node()->input());
+        n->i_(trans, n->hasAttribute(trans) ? !n->i(trans) : 1);
+        if (inp->uses().size() == 0) {
+          inp->node()->destroy();
+          ret_val = true;
+        }
+      }
+    }
+    return ret_val;
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/lift_lexical_references.h b/third_party/optimizer/onnxoptimizer/passes/lift_lexical_references.h
new file mode 100644
index 0000000000..471d4ca517
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/lift_lexical_references.h
@@ -0,0 +1,233 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include <set>
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+// Lift lexically-scoped references within control operators to be inputs of the
+// ops themselves. This transformation yields a graph that does not conform to
+// the ONNX spec.
+//
+// The purpose of this pass is to expose the data dependencies within control
+// blocks for frameworks that use those dependencies to schedule parallel
+// execution. e.g. caffe2 graph execution.
+//
+// Example:
+// ******************************** Before *************************************
+// graph test (%X[FLOAT, 5]) {
+//   %Y = Identity(%X)
+//   %trip_count = Constant[value = <Scalar Tensor [10]>]()
+//   %condition = Constant[value = <Scalar Tensor [1]>]()
+//   %Y2, %Y3 = Loop[body = <graph body_graph>](%trip_count, %condition, %)
+//   return %Y, %Y2
+// }
+//
+// graph body_graph (%i[INT32, scalar], %cond[BOOL, scalar]) {
+//   %_Y2 = Identity(%X)
+//   %_Y3 = Identity(%Y)
+//   return %cond, %_Y2, %_Y3
+// }
+//
+// ******************************** After **************************************
+// graph test (%X[FLOAT, 5]) {
+//   %Y = Identity(%X)
+//   %trip_count = Constant[value = <Scalar Tensor [10]>]()
+//   %condition = Constant[value = <Scalar Tensor [1]>]()
+//   %Y2, %Y3 = Loop[__control_inputs = ['X', 'Y'], body = <graph
+//   body_graph>](%trip_count, %condition, %)
+//                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+//   return %Y, %Y2
+// }
+//
+// graph body_graph (%i[INT32, scalar], %cond[BOOL, scalar]) {
+//   %_Y2 = Identity(%X)
+//   %_Y3 = Identity(%Y)
+//   return %cond, %_Y2, %_Y3
+// }
+//
+// ******************************** Continue Docs*******************************
+//
+// The algorithm is roughly:
+//  symbol_table_stack = empty stack of symbol tables
+//
+//  liftreferences(graph)
+//      -> a set of unresolved reference strings:
+//    unresolved_references = {}
+//
+//    symbol_table_stack.push(new symbol table containing inputs for this
+//    sub-graph) for each node in the graph:
+//      for input in node.inputs:
+//        if input is not in this frame:
+//          unresolved_references.insert(input)
+//      if node is a control flow operator:
+//        for each sub-graph g:
+//          for each output in g's body:
+//            if output is defined in current scope:
+//              control_inputs.insert(output)
+//          refs = liftreferences(g)
+//          for each ref in refs:
+//            if ref is in this frame or any parent frame (control_inputs):
+//              control_inputs.insert(ref)
+//            else:
+//              unresolved_references.insert(ref)
+//          set the control inputs attribute to the node
+//        for output in node.outputs:
+//          symbol_table_stack.top()[output] = Value*
+//    return unresolved_references
+struct LiftLexicalReferences : public FullGraphBasedPass {
+  explicit LiftLexicalReferences()
+      : FullGraphBasedPass(PassType::Separate, PassEfficiency::Complete,
+                           PassOptimizationType::Memory) {}
+
+  std::string getPassName() const override {
+    return "lift_lexical_references";
+  }
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::Empty;
+  }
+
+  using ValueTable = std::unordered_map<std::string, Value*>;
+
+  // Environment stack, please to store value table and
+  // controlled inputs
+  struct Environment {
+    Environment(std::shared_ptr<Environment> next = nullptr) : next(next) {}
+
+    std::shared_ptr<Environment> next;
+
+    Value* findInThisFrame(const std::string& name) {
+      auto it = value_table.find(name);
+      if (it != value_table.end()) {
+        return it->second;
+      }
+      return nullptr;
+    }
+
+    Value* findInParentFrame(const std::string& name) {
+      return next ? next->findInAnyFrame(name) : nullptr;
+    }
+
+    Value* findInAnyFrame(const std::string& name) {
+      for (auto runner = this; runner; runner = runner->next.get()) {
+        if (auto r = runner->findInThisFrame(name)) {
+          return r;
+        }
+      }
+      return nullptr;
+    }
+
+    void setVar(const std::string& name, Value* value) {
+      value_table[name] = value;
+    }
+
+   private:
+    ValueTable value_table;
+  };
+
+  std::shared_ptr<Environment> environment_stack;
+
+  // environment stack helper
+  void pushFrame() {
+    environment_stack = std::make_shared<Environment>(environment_stack);
+  }
+
+  std::shared_ptr<Environment> popFrame() {
+    auto old_frame = environment_stack;
+    environment_stack = environment_stack->next;
+    return old_frame;
+  }
+
+  std::set<std::string> liftReferences(Graph* g) {
+    std::set<std::string> unresolved_references;
+    pushFrame();
+    for (auto& inp : g->inputs()) {
+      environment_stack->setVar(inp->uniqueName(), inp);
+    }
+
+    for (auto* n : g->nodes()) {
+      // Skip optional input/captured value node.
+      if (n->kind() == ONNX_NAMESPACE::kUndefined ||
+          n->kind() == ONNX_NAMESPACE::kCaptured) {
+        continue;
+      }
+      for (auto* inp : n->inputs()) {
+        // Empty string is 0-input variadic argument. Skip that one.
+        if (!inp->uniqueName().empty() &&
+            !environment_stack->findInThisFrame(inp->uniqueName())) {
+          unresolved_references.insert(inp->uniqueName());
+        }
+      }
+
+      std::set<std::string> local_unresolved;
+
+      // if a graph body output has already already been emitted outside of the
+      // subgraph scope, then it must be added as an input to the subgraph
+      auto add_subgraph_outputs = [&](Graph* body_graph) {
+        for (auto* out : body_graph->outputs()) {
+          if (environment_stack->findInAnyFrame(out->uniqueName())) {
+            local_unresolved.insert(out->uniqueName());
+          }
+        }
+      };
+
+      if (n->kind() == ONNX_NAMESPACE::kLoop) {
+        auto* body_graph = n->g(ONNX_NAMESPACE::kbody).get();
+        local_unresolved = liftReferences(body_graph);
+        add_subgraph_outputs(body_graph);
+      } else if (n->kind() == ONNX_NAMESPACE::kIf) {
+        auto* then_graph = n->g(ONNX_NAMESPACE::kthen_branch).get();
+        add_subgraph_outputs(then_graph);
+        auto then_unresolved = liftReferences(then_graph);
+        local_unresolved.insert(then_unresolved.begin(), then_unresolved.end());
+        auto* else_graph = n->g(ONNX_NAMESPACE::kelse_branch).get();
+        add_subgraph_outputs(else_graph);
+        auto else_unresolved = liftReferences(else_graph);
+        local_unresolved.insert(else_unresolved.begin(), else_unresolved.end());
+      }
+
+      std::vector<std::string> control_inputs;
+      for (auto& unresolved : local_unresolved) {
+        if (environment_stack->findInAnyFrame(unresolved)) {
+          control_inputs.push_back(unresolved);
+        } else {
+          unresolved_references.insert(unresolved);
+        }
+      }
+
+      // Create this attribute so the backend knows how many of these inputs
+      // are simply there for control dependencies
+      if (!control_inputs.empty()) {
+        n->ss_(ONNX_NAMESPACE::k__control_inputs, std::move(control_inputs));
+      }
+
+      for (auto* out : n->outputs()) {
+        environment_stack->setVar(out->uniqueName(), out);
+      }
+    }
+
+    popFrame();
+    return unresolved_references;
+  }
+
+  std::shared_ptr<PostPassAnalysis> runPass(Graph& graph) override {
+    auto unresolved = liftReferences(&graph);
+
+    if (unresolved.size()) {
+      std::string errmsg = "Unresolved value references: ";
+      for (auto& ref : unresolved) {
+        errmsg += ref + ",";
+      }
+      throw std::runtime_error(errmsg);
+    }
+    return std::shared_ptr<PostPassAnalysis>(new PostPassAnalysis());
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/nop.h b/third_party/optimizer/onnxoptimizer/passes/nop.h
new file mode 100644
index 0000000000..26182e5982
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/nop.h
@@ -0,0 +1,28 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+struct NopEmptyPass final : public FullGraphBasedPass {
+  explicit NopEmptyPass()
+      : FullGraphBasedPass(PassType::Nop, PassEfficiency::Complete,
+                           PassOptimizationType::None) {}
+
+  std::string getPassName() const override {
+    return "nop";
+  }
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::Empty;
+  }
+  std::shared_ptr<PostPassAnalysis> runPass(Graph&) override {
+    return std::make_shared<PostPassAnalysis>();
+  }
+};
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/passes/split.h b/third_party/optimizer/onnxoptimizer/passes/split.h
new file mode 100644
index 0000000000..3d6fcb806d
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/passes/split.h
@@ -0,0 +1,235 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+// ATTENTION: The code in this file is highly EXPERIMENTAL.
+// Adventurous users should note that the APIs will probably change.
+
+#pragma once
+
+#include "onnxoptimizer/pass.h"
+
+namespace ONNX_NAMESPACE {
+namespace optimization {
+
+static constexpr const char* impure_operators[] = {
+    "RandomNormal",  "RandomNormalLike",
+    "RandomUniform", "RandomUniformLike",
+    "Loop",          "If",
+    "Scan",
+};
+
+static bool is_pure_operator(Node* n) {
+  for (auto x : impure_operators) {
+    if (n->kind() == Symbol(x)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+static bool has_uses(Node* n){
+
+  for(auto output: n->outputs()){
+    if (!output->uses().empty())
+      return true;
+  }
+
+  return false;
+
+}
+// Split the graph into 'init' and 'predict' nets. This is kind of
+// like constant folding, except that rather than actually execute the
+// constant computations, we simply split them out into a separate
+// graph. Nodes that have any transitive dependency on the
+// initializers, or on impure operators, must remain in the predict
+// net. All others may be moved to the init net.
+//
+// This function destructively mutates the graph into either the init
+// or the predict net. If you want both, which you probably do,
+// arrange to call it twice.
+//
+// NOTE POTENTIAL BREAKAGE:
+//
+// The ONNX spec provides no guarantees about "staging", i.e. which
+// inputs change on every invocation vs which generally stay the same.
+// Here we make the assumption that inputs which have an initializer
+// value provided for them vary only between invocations of the init
+// net, and are constant across runs of the predict net.
+//
+static void split_init_and_predict(Graph& graph, bool init, bool predict) {
+  // The first step is to identify which Values are reachable from
+  // either of
+  //   - inputs without corresponding initializers
+  //   - impure operators
+  // Any such Values belong to the predict net. Nodes belong to the
+  // predict net if they are impure or if any of their inputs do.
+
+  std::unordered_set<Value*> predict_net_values;
+
+  auto value_belongs_to_predict_net = [&](Value* v) {
+    return predict_net_values.count(v) > 0;
+  };
+  auto node_belongs_to_predict_net = [&](Node* n) {
+    return !is_pure_operator(n) ||
+           std::any_of(n->inputs().begin(), n->inputs().end(),
+                       value_belongs_to_predict_net);
+  };
+
+  {
+    std::unordered_set<std::string> initializer_names(
+        graph.initializer_names().begin(), graph.initializer_names().end());
+
+    for (Value* v : graph.inputs()) {
+      if (initializer_names.count(v->uniqueName()) == 0) {
+        predict_net_values.insert(v);
+      }
+    }
+  }
+
+  for (Node* n : graph.nodes()) {
+    if (node_belongs_to_predict_net(n)) {
+      for (Value* v : n->outputs()) {
+        predict_net_values.insert(v);
+      }
+    }
+  }
+
+  // Any Value which is not itself in the predict net, but which
+  // is used by a Node which is, becomes an output of the init
+  // graph and an input of the predict net
+  std::unordered_set<Value*> new_interface;
+  for (Node* n : graph.nodes()) {
+    if (node_belongs_to_predict_net(n)) {
+      for (Value* v : n->inputs()) {
+        if (!value_belongs_to_predict_net(v)) {
+          new_interface.insert(v);
+        }
+      }
+    }
+  }
+
+  for (Value* v : graph.outputs()) {
+    if (!value_belongs_to_predict_net(v)) {
+      new_interface.insert(v);
+    }
+  }
+
+  if (init) {
+    // Add new outputs corresponding to the boundary between init and
+    // predict nets, ensuring that we don't duplicate outputs.
+    for (Value* v : graph.outputs()) {
+      new_interface.erase(v);
+    }
+    for (Value* v : new_interface) {
+      if (v->node()->kind() == kUndefined) {
+        continue;
+      }
+      graph.registerOutput(v);
+    }
+
+    // Remove outputs that belong to the predict net.
+    for (auto i = graph.outputs().size(); i--;) {
+      if (value_belongs_to_predict_net(graph.outputs()[i])) {
+        graph.return_node()->removeInput(i);
+      }
+    }
+
+    // Delete nodes that belong to the predict net, in reverse
+    // topological order.
+    for (auto it = graph.nodes().rbegin(); it != graph.nodes().rend(); it++) {
+      if (node_belongs_to_predict_net(*it)) {
+        it.destroyCurrent();
+      }
+    }
+
+    // Remove inputs that belong to the predict net.
+    for (auto i = graph.inputs().size(); i--;) {
+      if (value_belongs_to_predict_net(graph.inputs()[i])) {
+        graph.eraseInput(i);
+      }
+    }
+  } else if (predict) {
+    // When creating the predict net, 'undefined' nodes will
+    // naturally go into the init net. We need to have a place to
+    // copy the ones we want to keep in the predict net.
+    auto* optionalInputDummyNode = graph.create(kUndefined, 1);
+    graph.appendNode(optionalInputDummyNode);
+    optionalInputDummyNode->outputs()[0]->setUniqueName("");
+
+    // Add new inputs, ensuring that we don't introduce duplicates.
+    // Also cut the boundary between init and predict net by replacing
+    // the Values along the boundary with replaceAllUsesWith.
+    for (Value* v : graph.inputs()) {
+      new_interface.erase(v);
+    }
+    for (Value* v : new_interface) {
+      if (v->node()->kind() == kUndefined) {
+        v->replaceAllUsesWith(optionalInputDummyNode->outputs()[0]);
+      } else {
+        Value* newv = graph.addInput()->copyMetadata(v);
+        v->replaceAllUsesWith(newv);
+      }
+    }
+
+    // Delete nodes that aren't in the predict net, in reverse
+    // topological order.
+    for (auto it = graph.nodes().rbegin(); it != graph.nodes().rend(); it++) {
+      if (*it == optionalInputDummyNode) {
+        continue;
+      }
+      if (node_belongs_to_predict_net(*it)) {
+        continue;
+      }
+
+      if (!has_uses(*it))
+        it.destroyCurrent();
+    }
+
+    // Remove inputs that aren't used by the predict net.
+    for (auto i = graph.inputs().size(); i--;) {
+      if (graph.inputs()[i]->uses().empty()) {
+        graph.eraseInput(i);
+      }
+    }
+
+    // Remove all initializers, they are already in the init net.
+    graph.clearInitializers();
+  }
+}
+
+struct SplitInit final : public FullGraphBasedPass {
+  explicit SplitInit()
+      : FullGraphBasedPass(PassType::Separate, PassEfficiency::Complete,
+                           PassOptimizationType::Memory) {}
+
+  std::string getPassName() const override {
+    return "split_init";
+  }
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::Empty;
+  }
+  std::shared_ptr<PostPassAnalysis> runPass(Graph& graph) override {
+    split_init_and_predict(graph, true, false);
+    return std::shared_ptr<PostPassAnalysis>(new PostPassAnalysis());
+  }
+};
+
+struct SplitPredict final : public FullGraphBasedPass {
+  explicit SplitPredict()
+      : FullGraphBasedPass(PassType::Separate, PassEfficiency::Complete,
+                           PassOptimizationType::Memory) {}
+  std::string getPassName() const override {
+    return "split_predict";
+  }
+  PassAnalysisType getPassAnalysisType() const override {
+    return PassAnalysisType::Empty;
+  }
+  std::shared_ptr<PostPassAnalysis> runPass(Graph& graph) override {
+    split_init_and_predict(graph, false, true);
+    return std::shared_ptr<PostPassAnalysis>(new PostPassAnalysis());
+  }
+};
+
+}  // namespace optimization
+}  // namespace ONNX_NAMESPACE
diff --git a/third_party/optimizer/onnxoptimizer/test/optimizer_test.py b/third_party/optimizer/onnxoptimizer/test/optimizer_test.py
new file mode 100644
index 0000000000..e6d8e32429
--- /dev/null
+++ b/third_party/optimizer/onnxoptimizer/test/optimizer_test.py
@@ -0,0 +1,2397 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from collections import OrderedDict
+from typing import Sequence, Text, Any, Tuple, List, Callable, Optional, Dict, Union
+import io
+import unittest
+import os
+
+import numpy as np  # type: ignore
+
+try:
+    import torch
+    import torchvision as tv
+    has_tv = True
+except:
+    has_tv = False
+
+import onnx
+from onnx import checker, helper, ModelProto, TensorProto, GraphProto, NodeProto, shape_inference
+from onnx import numpy_helper
+from onnx.numpy_helper import to_array
+try:
+    import onnxruntime as rt
+    has_ort = True
+except:
+    has_ort = False
+
+import onnxoptimizer
+
+
+TensorShape = List[int]
+TensorShapes = Dict[Optional[str], TensorShape]
+
+LATEST_STABLE_OPSET_VERSION = 13
+
+
+class TestOptimizer(unittest.TestCase):
+    def _compare(self, model_opt: onnx.ModelProto, model_ori: onnx.ModelProto, n_times: int = 5,
+                 input_shapes: Optional[TensorShapes] = None, verbose=True) -> bool:
+        """
+        :param input_shapes: Shapes of generated random inputs
+        :param model_opt: The simplified ONNX model
+        :param model_ori: The original ONNX model
+        :param n_times: Generate n random inputs
+        """
+
+        def get_shape_from_value_info_proto(v: onnx.ValueInfoProto) -> List[int]:
+            return [dim.dim_value for dim in v.type.tensor_type.shape.dim]
+
+        def get_value_info_all(m: onnx.ModelProto, name: str) -> Optional[onnx.ValueInfoProto]:
+            for v in m.graph.value_info:
+                if v.name == name:
+                    return v
+
+            for v in m.graph.input:
+                if v.name == name:
+                    return v
+
+            for v in m.graph.output:
+                if v.name == name:
+                    return v
+
+            return None
+
+        def get_shape(m: onnx.ModelProto, name: str) -> TensorShape:
+            """
+            Note: This method relies on onnx shape inference, which is not reliable. So only use it on input or output tensors
+            """
+            v = get_value_info_all(m, name)
+            if v is not None:
+                return get_shape_from_value_info_proto(v)
+            raise RuntimeError('Cannot get shape of "{}"'.format(name))
+
+        def get_elem_type(m: onnx.ModelProto, name: str) -> Optional[int]:
+            v = get_value_info_all(m, name)
+            if v is not None:
+                return v.type.tensor_type.elem_type
+            return None
+
+        def get_np_type_from_elem_type(elem_type: int) -> int:
+            sizes = (None, np.float32, np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, str, np.bool,
+                     np.float16, np.double, np.uint32, np.uint64, np.complex64, np.complex128, np.float16)
+            assert len(sizes) == 17
+            size = sizes[elem_type]
+            assert size is not None
+            return size
+
+        def get_input_names(model: onnx.ModelProto) -> List[str]:
+            input_names = list(set([ipt.name for ipt in model.graph.input])
+                               - set([x.name for x in model.graph.initializer]))
+            return input_names
+
+        def generate_rand_input(model, input_shapes: Optional[TensorShapes] = None):
+            if input_shapes is None:
+                input_shapes = {}
+            input_names = get_input_names(model)
+            full_input_shapes = {ipt: get_shape(
+                model, ipt) for ipt in input_names}
+            assert None not in input_shapes
+            full_input_shapes.update(input_shapes)  # type: ignore
+            for key in full_input_shapes:
+                if np.prod(full_input_shapes[key]) <= 0:
+                    raise RuntimeError(
+                        'The shape of input "{}" has dynamic size, '
+                        'please set an input shape manually'.format(key))
+
+            inputs = {ipt: np.array(np.random.rand(*full_input_shapes[ipt]),
+                                    dtype=get_np_type_from_elem_type(get_elem_type(model, ipt))) for ipt in
+                      input_names}
+            return inputs
+
+        def forward(model, inputs=None, input_shapes: Optional[TensorShapes] = None) -> Dict[str, np.ndarray]:
+            if input_shapes is None:
+                input_shapes = {}
+            sess_options = rt.SessionOptions()
+            sess_options.graph_optimization_level = rt.GraphOptimizationLevel(0)
+            sess_options.log_severity_level = 3
+            sess = rt.InferenceSession(model.SerializeToString(
+            ), sess_options=sess_options, providers=['CPUExecutionProvider'])
+            if inputs is None:
+                inputs = generate_rand_input(model, input_shapes=input_shapes)
+            outputs = [x.name for x in sess.get_outputs()]
+            run_options = rt.RunOptions()
+            run_options.log_severity_level = 3
+            res = OrderedDict(zip(outputs, sess.run(
+                outputs, inputs, run_options=run_options)))
+            return res
+
+        if input_shapes is None:
+            input_shapes = {}
+        onnx.checker.check_model(model_opt)
+        for i in range(n_times):
+            rand_input = generate_rand_input(
+                model_opt, input_shapes=input_shapes)
+            res_ori = forward(model_ori, inputs=rand_input)
+            res_opt = forward(model_opt, inputs=rand_input)
+
+            for name in res_opt.keys():
+                if not np.allclose(res_opt[name], res_ori[name], rtol=1e-4, atol=1e-5):
+                    if verbose:
+                        print("Tensor {} changes after optimization. The max diff is {}.".format(
+                            name, np.max(np.abs(res_opt[name] - res_ori[name]))))
+                        print("After optimization:")
+                        print(res_opt[name])
+                        print("Before optimization:")
+                        print(res_ori[name])
+                        print("----------------")
+                    return False
+        return True
+
+    # type: (Union[GraphProto, ModelProto], Sequence[Text], bool, **Any) -> ModelProto
+    def _optimized(self, graph_or_model, opts, fixed_point=False, compare_result=True, check=True, **kwargs):
+        if compare_result and not check:
+            self.fail("compare_result cannot be True if check is False")
+
+        if isinstance(graph_or_model, ModelProto):
+            orig_model = graph_or_model
+        else:
+            opset_imports = kwargs.pop('opset_imports', None)
+            if opset_imports is None:
+                opset_imports = [helper.make_opsetid(
+                    "", LATEST_STABLE_OPSET_VERSION)]
+
+            orig_model = helper.make_model(
+                graph_or_model, producer_name='onnx-test', opset_imports=opset_imports, **kwargs)
+        checker.check_model(orig_model)
+        optimized_model = onnxoptimizer.optimize(orig_model, opts, fixed_point)
+        # NOTE(daquexian): Some passes (like lift_lexical_references) generate illegal model intentionally
+        if check:
+            checker.check_model(optimized_model)
+        if compare_result and len(optimized_model.graph.node) > 0:
+            if has_ort:
+                assert self._compare(optimized_model, orig_model)
+            else:
+                print("Skip onnxruntime test because it is not installed.")
+        return optimized_model
+
+    # input_types and output_types are lists of triples of (name, type, shape)
+    # NOTE(daquexian): only values that change across loop iterations should be in `input_types` and `output_types`. The pseudocode showing how loop op works is:
+    # loop_value_inputs = graph_value_inputs
+    # while cond:
+    #   loop_value_outputs = body(loop_value_inputs)
+    #   loop_value_inputs = loop_value_outputs
+    # graph_value_outputs = loop_value_outputs
+    def _make_fake_loop_op(self,
+                           body_nodes,   # type: Sequence[NodeProto]
+                           # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]]
+                           input_types,
+                           # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]]
+                           output_types,
+                           check_legality=True,
+                           ):  # type: (...) -> List[NodeProto]
+        if check_legality:
+            assert len(input_types) == len(output_types)
+        zero = helper.make_tensor(
+            "trip_count_value", TensorProto.INT64, (), [1])
+        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])
+        # lcd is a dummy loop-carried dependency that only exists because
+        # right now the schema checker is broken and assumes a variadic
+        # input needs at least one value.
+        graph_inputs = [helper.make_tensor_value_info("i", TensorProto.INT64, ()),
+                        helper.make_tensor_value_info("cond", TensorProto.BOOL, ())]
+        for type, shape, name in input_types:
+            graph_inputs.append(
+                helper.make_tensor_value_info("_" + name, type, shape))
+        graph_outputs = [helper.make_tensor_value_info(
+            "cond", TensorProto.BOOL, ())]
+        for type, shape, name in output_types:
+            graph_outputs.append(
+                helper.make_tensor_value_info("_" + name, type, shape))
+        body_graph = helper.make_graph(body_nodes, "body_graph", graph_inputs,
+                                       graph_outputs)
+        loop_inputs = ["trip_count", "condition"]
+        loop_inputs.extend([name for _, _, name in input_types])
+        # TODO: fix checker to accept 0-input variadic inputs
+        if len(loop_inputs) == 2:
+            loop_inputs.append("")
+        loop_outputs = [name for _, _, name in output_types]
+        retval_nodes = [
+            helper.make_node("Constant", [], ["trip_count"], value=zero),
+            helper.make_node("Constant", [], ["condition"], value=true),
+            helper.make_node("Loop", loop_inputs, loop_outputs, body=body_graph)
+        ]
+        return retval_nodes
+
+    def _make_fake_if_op(self,
+                         true_nodes,   # type: Sequence[NodeProto]
+                         false_nodes,  # type: Sequence[NodeProto]
+                         # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]]
+                         output_types
+                         ):  # type: (...) -> List[NodeProto]
+        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])
+        true_graph = helper.make_graph(true_nodes, "true_graph", [], [])
+        false_graph = helper.make_graph(false_nodes, "false_graph", [], [])
+        if_inputs = ["condition"]
+        if_outputs = [name for _, _, name in output_types]
+        retval_nodes = [
+            helper.make_node("Constant", [], ["condition"], value=true),
+            helper.make_node("If", if_inputs, if_outputs, then_branch=true_graph,
+                             else_branch=false_graph)
+        ]
+        return retval_nodes
+
+    # fn is a function that takes a single node as argument
+    # type: (GraphProto, Callable[[NodeProto], None]) -> None
+    def _visit_all_nodes_recursive(self, graph, fn):
+        for node in graph.node:
+            fn(node)
+            for attr in node.attribute:
+                if attr.g is not None:
+                    self._visit_all_nodes_recursive(attr.g, fn)
+                if len(attr.graphs):
+                    for gr in attr.graphs:
+                        self._visit_all_nodes_recursive(gr, fn)
+
+    def test_get_available_passes(self):  # type: () -> None
+        # FIXME does not guarantees to be listing all
+        graph = helper.make_graph([], "dummy_graph", [], [])
+        list_of_passes = onnxoptimizer.get_available_passes()
+        assert isinstance(list_of_passes, (list)) and len(list_of_passes) > 0
+        for pass_name in list_of_passes:
+            # If pass_name is invalid it throws a RuntimeError
+            self._optimized(graph, [pass_name])
+
+    def test_eliminate_identity_single_use(self):  # type: () -> None
+        nodes = [helper.make_node("Add", ["X", "Y"], ["A"]),
+                 helper.make_node("Identity", ["A"], ["B"])]
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Relu", ["_B"], ["_B1"]),
+             helper.make_node("Identity", ["_B1"], ["_B2"])],
+            [(TensorProto.FLOAT, (5,), "B")],
+            [(TensorProto.FLOAT, (5,), "B2")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("B2", TensorProto.FLOAT, (5,))])
+        optimized_model = self._optimized(graph, ["eliminate_identity"])
+
+        # All identity nodes should have been eliminated
+        def check_identity(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Identity"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_identity)
+        # Use of the output from the Identity node in the main graph should
+        # have been replaced with the input to the identity node
+        assert len(optimized_model.graph.output) == 2
+        assert optimized_model.graph.output[0].name == "B"
+        # Use of the output from the Identity node in the loop graph should
+        # have been replaced with the input to that identity node
+        assert len(optimized_model.graph.node[3].attribute[0].g.output) == 2
+        assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_B2"
+
+    # type: () -> None
+    def test_eliminate_identity_both_graph_input_and_output(self):
+        # We should not eliminate an op when its input is also graph input,
+        # and its output is also graph output, because we want to always keep
+        # the name of graph input and output unchanged.
+        identity = helper.make_node("Identity", ["A"], ["B"])
+        graph = helper.make_graph(
+            [identity],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))])
+        optimized_model = self._optimized(graph, ["eliminate_identity"])
+
+        assert optimized_model.graph == graph
+
+    def test_eliminate_if_with_const_cond(self):  # type: () -> None
+        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])
+
+        subgraph_output_info = helper.make_tensor_value_info(
+            "C", TensorProto.FLOAT, (5,))
+
+        sin = helper.make_node("Sin", ["A"], ["B"])
+        hard_sigmoid = helper.make_node(
+            "HardSigmoid", ["B"], ["C"], alpha=0.4, beta=0.6)
+        true_graph = helper.make_graph(
+            [sin, hard_sigmoid], "true_graph", [], [subgraph_output_info])
+
+        identity = helper.make_node("Identity", ["A"], ["C"])
+        false_graph = helper.make_graph(
+            [identity], "false_graph", [], [subgraph_output_info])
+
+        graph = helper.make_graph([
+            helper.make_node("Constant", [], ["condition"], value=true),
+            helper.make_node("If", ["condition"], ["result"], then_branch=true_graph,
+                             else_branch=false_graph)],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("result", TensorProto.FLOAT, (5,))])
+        optimized_model = self._optimized(
+            graph, ["eliminate_if_with_const_cond"])
+        assert len(optimized_model.graph.node) == 3
+        assert optimized_model.graph.node[0].op_type == "Constant"
+        assert optimized_model.graph.node[1].op_type == "Sin"
+        assert optimized_model.graph.node[2].op_type == "HardSigmoid"
+
+    def test_eliminate_identity_graph_output(self):  # type: () -> None
+        add = helper.make_node("Add", ["X", "Y"], ["A"])
+        identity = helper.make_node("Identity", ["A"], ["B"])
+        graph = helper.make_graph(
+            [add, identity],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))])
+        optimized_model = self._optimized(graph, ["eliminate_identity"])
+
+        for node in optimized_model.graph.node:
+            assert node.op_type != "Identity"
+        assert len(
+            optimized_model.graph.output) == 1 and optimized_model.graph.output[0].name == 'B'
+        assert len(optimized_model.graph.node) == 1
+
+    def test_eliminate_identity_multiple_uses(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["X"], ["Y"])
+        add = helper.make_node("Add", ["Z", "Y"], ["A"])
+        mul = helper.make_node("Mul", ["A", "Y"], ["B"])
+        graph = helper.make_graph(
+            [identity, add, mul],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))])
+        optimized_model = self._optimized(graph, ["eliminate_identity"])
+
+        for node in optimized_model.graph.node:
+            assert node.op_type != "Identity"
+        assert len(optimized_model.graph.node) == 2
+
+    def test_not_fuse_non_nop_flatten(self):
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        flatten = helper.make_node("Flatten", ["X"], ["B"], axis=2)
+        graph = helper.make_graph(
+            [identity, flatten],
+            "test",
+            [helper.make_tensor_value_info(
+                "A", TensorProto.FLOAT, (1, 10, 3, 1, 1))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (10, 3))])
+
+        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])
+
+        assert len(optimized_model.graph.node) == 2
+        assert optimized_model.graph.node[0].op_type == 'Identity'
+        assert optimized_model.graph.node[1].op_type == 'Flatten'
+
+    def test_nop_flatten_axis0_graph_output(self):
+        add = helper.make_node("Add", ["X", "Y"], ["A"])
+        flatten = helper.make_node("Flatten", ["A"], ["B"], axis=0)
+        graph = helper.make_graph(
+            [add, flatten],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 10)),
+             ],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 10))],
+            # the tensor_value_info of "A" is necessary to this optimizer
+            value_info=[helper.make_tensor_value_info(
+                "A", TensorProto.FLOAT, (1, 10))]
+        )
+        # The existence of shape infos of graoh outputs is checked in _optimized
+        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])
+
+        assert len(optimized_model.graph.node) == 1
+        assert optimized_model.graph.node[0].op_type == 'Add'
+
+    def test_nop_flatten_axis0(self):
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        flatten = helper.make_node("Flatten", ["X"], ["B"], axis=0)
+        graph = helper.make_graph(
+            [identity, flatten],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 10))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 10))],
+            value_info=[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10))])
+
+        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])
+
+        assert len(optimized_model.graph.node) == 1
+        assert optimized_model.graph.node[0].op_type == "Identity"
+
+    def test_nop_flatten_axis1(self):
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        flatten = helper.make_node("Flatten", ["X"], ["B"], axis=1)
+        graph = helper.make_graph(
+            [identity, flatten],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))],
+            value_info=[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))])
+
+        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])
+
+        assert len(optimized_model.graph.node) == 1
+        assert optimized_model.graph.node[0].op_type == "Identity"
+
+    def test_eliminate_duplicate_initializer(self):  # type: () -> None
+        add_1 = helper.make_node("Add", ["A", "I_0"], ["B"])
+        add_2 = helper.make_node("Add", ["B", "I_1"], ["C"])
+        i = np.random.rand(5).astype(np.float32)
+        graph = helper.make_graph(
+            [add_1, add_2],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("C", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor("I_0", TensorProto.FLOAT,
+                                dims=(5,),
+                                vals=i.tobytes(),
+                                raw=True),
+             helper.make_tensor("I_1", TensorProto.FLOAT,
+                                dims=(5,),
+                                vals=i.tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(
+            graph, ["eliminate_duplicate_initializer"])
+        assert len(optimized_model.graph.node) == 2
+        assert len(optimized_model.graph.initializer) == 1
+        assert len(optimized_model.graph.input) == 1
+        assert optimized_model.graph.node[0].input[1] == "I_0"
+
+    def test_nop_cast(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["X"], ["A"])
+        cast = helper.make_node("Cast", ["A"], ["B"], to=TensorProto.FLOAT)
+        graph = helper.make_graph(
+            [identity, cast],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))],
+            value_info=[helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))])
+
+        optimized_model = self._optimized(graph, ["eliminate_nop_cast"])
+
+        assert len(optimized_model.graph.node) == 1
+        assert optimized_model.graph.node[0].op_type == "Identity"
+
+    def test_nop_transpose_graph_output(self):  # type: () -> None
+        add = helper.make_node("Add", ["X", "Y"], ["A"])
+        trans = helper.make_node("Transpose", ["A"], ["B"], perm=[0, 1])
+        graph = helper.make_graph(
+            [add, trans],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))])
+        # The existence of shape infos of graoh outputs is checked in _optimized
+        optimized_model = self._optimized(graph, ["eliminate_nop_transpose"])
+
+        def check_transpose(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Transpose"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_transpose)
+        assert len(optimized_model.graph.node) == 1
+
+    def test_nop_transpose(self):  # type: () -> None
+        nodes = [helper.make_node("Identity", ["A"], ["X"]),
+                 helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 1])]
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Identity", ["_Y"], ["Y1"]),
+             helper.make_node("Transpose", ["Y1"], ["_Y2"], perm=[0, 1])],
+            [(TensorProto.FLOAT, (2, 3), "Y")],
+            [(TensorProto.FLOAT, (2, 3), "Y2")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3)),
+             helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (2, 3))],
+            value_info=[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))])
+        optimized_model = self._optimized(graph, ["eliminate_nop_transpose"])
+
+        def check_transpose(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Transpose"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_transpose)
+        # Use of the output from the Transpose node in the main graph should
+        # have been replaced with the input to the identity node
+        assert len(optimized_model.graph.output) == 2
+        assert optimized_model.graph.output[0].name == "Y"
+        # Use of the output from the Transpose node in the loop graph should
+        # have been replaced with the input to that identity node
+        assert len(optimized_model.graph.node[3].attribute[0].g.output) == 2
+        assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_Y2"
+
+    def test_nop_transpose_default(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        trans = helper.make_node("Transpose", ["X"], ["Y"])
+        graph = helper.make_graph(
+            [identity, trans],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2))])
+        optimized_model = self._optimized(graph, ["eliminate_nop_transpose"])
+
+        assert optimized_model.graph == graph
+
+    def test_nop_pad_opset10(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        pad = helper.make_node("Pad", ["X"], ["Y"], pads=[0, 0, 0, 0])
+        graph = helper.make_graph(
+            [identity, pad],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))])
+        optimized_model = self._optimized(
+            graph, ["eliminate_nop_pad"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        def check_pad(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Pad"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_pad)
+        assert len(optimized_model.graph.output) == 1
+        assert optimized_model.graph.output[0].name == "Y"
+        assert len(optimized_model.graph.node) == 1
+
+    def test_nop_pad_graph_output(self):  # type: () -> None
+        add = helper.make_node("Add", ["X", "Y"], ["A"])
+        pad = helper.make_node("Pad", ["A", "Pads"], ["B"])
+        graph = helper.make_graph(
+            [add, pad],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (2,))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(2,),
+                                vals=np.array([0, 0]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        # The existence of shape infos of graoh outputs is checked in _optimized
+        optimized_model = self._optimized(graph, ["eliminate_nop_pad"])
+
+        def check_pad(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Pad"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_pad)
+        assert len(optimized_model.graph.node) == 1
+
+    def test_nop_pad(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        pad = helper.make_node("Pad", ["X", "Pads"], ["Y"])
+        graph = helper.make_graph(
+            [identity, pad],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (4,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(4,),
+                                vals=np.array([0, 0, 0, 0]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["eliminate_nop_pad"])
+
+        def check_pad(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Pad"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_pad)
+        assert len(optimized_model.graph.output) == 1
+        assert optimized_model.graph.output[0].name == "Y"
+        assert len(optimized_model.graph.node) == 1
+
+    def test_nop_pad_default_opset10(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        pad = helper.make_node("Pad", ["X"], ["Y"], pads=[0, 0, 1, 1])
+        graph = helper.make_graph(
+            [identity, pad],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4))])
+        optimized_model = self._optimized(
+            graph, ["eliminate_nop_pad"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph == graph
+
+    def test_nop_pad_default(self):  # type: () -> None
+        identity = helper.make_node("Identity", ["A"], ["X"])
+        pad = helper.make_node("Pad", ["X", "Pads"], ["Y"])
+        graph = helper.make_graph(
+            [identity, pad],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (4,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(4,),
+                                vals=np.array([0, 1, 0, 0]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["eliminate_nop_pad"])
+
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph == graph
+
+    def test_eliminate_unused_initializer(self):  # type: () -> None
+        add = helper.make_node("Add", ["X", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor("A", TensorProto.FLOAT,
+                                dims=(2, 3),
+                                vals=np.random.randn(2, 3).astype(
+                                    np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(
+            graph, ["eliminate_unused_initializer"])
+
+        assert len(list(optimized_model.graph.initializer)) == 0
+
+    def test_eliminate_unused_initializer_input(self):  # type: () -> None
+        add = helper.make_node("Add", ["X", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor("A", TensorProto.FLOAT,
+                                dims=(2, 3),
+                                vals=np.random.randn(2, 3).astype(
+                                    np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(
+            graph, ["eliminate_unused_initializer"])
+
+        assert len(list(optimized_model.graph.initializer)) == 0
+        assert len(optimized_model.graph.input) == 2
+
+    # type: () -> None
+    def test_eliminate_unused_initializer_no_eliminate_used_default(self):
+        add = helper.make_node("Add", ["X", "A"], ["Z"])
+        graph = helper.make_graph(
+            [add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor("A", TensorProto.FLOAT,
+                                dims=(1, 2),
+                                vals=np.random.randn(1, 2).astype(
+                                    np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(
+            graph, ["eliminate_unused_initializer"])
+
+        assert len(list(optimized_model.graph.initializer)) == 1
+
+    # type: () -> None
+    def test_eliminate_unused_initializer_no_eliminate_used(self):
+        nodes = [helper.make_node("Add", ["X", "A"], ["Z"])]
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Add", ["_X", "A"], ["_Z2"])],
+            [(TensorProto.FLOAT, (1, 2), "X")],
+            [(TensorProto.FLOAT, (1, 2), "Z2")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
+            [helper.make_tensor("A", TensorProto.FLOAT,
+                                dims=(1, 2),
+                                vals=np.random.randn(1, 2).astype(
+                                    np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(
+            graph, ["eliminate_unused_initializer"])
+
+        # Add, Constant (trip count), Constant (cond), Loop
+        assert len(list(optimized_model.graph.node)) == 4
+        assert optimized_model.graph.node[0].op_type == "Add"
+        assert optimized_model.graph.output[0].name == "Z"
+        # Add
+        assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1
+        assert optimized_model.graph.node[3].attribute[0].g.node[0].op_type == 'Add'
+        assert optimized_model.graph.node[3].attribute[0].g.output[1].name == '_Z2'
+
+        assert len(list(optimized_model.graph.initializer)) == 1
+
+    # type: () -> None
+    def test_eliminate_unused_initializer_no_eliminate_output(self):
+        add = helper.make_node("Add", ["X", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor("A", TensorProto.FLOAT,
+                                dims=(2, 3),
+                                vals=np.random.randn(2, 3).astype(
+                                    np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(
+            graph, ["eliminate_unused_initializer"])
+
+        assert len(list(optimized_model.graph.initializer)) == 1
+        assert "Z" in [o.name for o in optimized_model.graph.output]
+
+    def test_extract_constant_to_initializer(self):  # type: () -> None
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        constant = helper.make_node("Constant", [], ["A"],
+                                    value=helper.make_tensor(
+                                        name="bias",
+                                        data_type=TensorProto.FLOAT,
+                                        dims=(16, 1, 1),
+                                        vals=np.random.randn(16).astype(np.float32).tolist()))
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [conv, constant, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 1))],
+        )
+        optimized_model = self._optimized(
+            graph, ["extract_constant_to_initializer"])
+
+        self.assertEqual(len(optimized_model.graph.initializer), 1)
+        init = optimized_model.graph.initializer[0]
+        self.assertEqual(init.name, 'A')
+        self.assertEqual(init.dims, [16, 1, 1])
+        self.assertEqual(init.data_type, TensorProto.FLOAT)
+
+        self.assertEqual(
+            [n.op_type for n in optimized_model.graph.node], ['Conv', 'Add'])
+
+    def test_fuse_concats(self):  # type: () -> None
+        nodes = [helper.make_node("Concat", ["A", "B", "C"], ["X"], axis=0),
+                 helper.make_node("Concat", ["D", "E", "F"], ["Y"], axis=0),
+                 helper.make_node("Concat", ["X", "G", "Y"], ["Z"], axis=0)]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 3, 4)),
+             helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 3, 4)),
+             helper.make_tensor_value_info("D", TensorProto.FLOAT, (4, 3, 4)),
+             helper.make_tensor_value_info("E", TensorProto.FLOAT, (2, 3, 4)),
+             helper.make_tensor_value_info("F", TensorProto.FLOAT, (4, 3, 4)),
+             helper.make_tensor_value_info("G", TensorProto.FLOAT, (4, 3, 4))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (22, 3, 4))])
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_concats"], True)  # two passes are needed to simplify the graph to its simplest state.
+
+        assert len(optimized_model.graph.node) == 1
+        assert len(optimized_model.graph.node[0].input) == 7
+        assert optimized_model.graph.node[0].input == [
+            "A", "B", "C", "G", "D", "E", "F"]
+        assert optimized_model.graph.node[0].op_type == "Concat"
+
+    def test_fuse_concats_different_axis(self):  # type: () -> None
+        nodes = [helper.make_node("Concat", ["A", "B", "C"], ["X"], axis=0),
+                 helper.make_node("Concat", ["D", "E", "F"], ["Y"], axis=1),
+                 helper.make_node("Concat", ["X", "Y"], ["Z"], axis=2)]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 9, 4)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 9, 4)),
+             helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 9, 4)),
+             helper.make_tensor_value_info("D", TensorProto.FLOAT, (8, 3, 4)),
+             helper.make_tensor_value_info("E", TensorProto.FLOAT, (8, 3, 4)),
+             helper.make_tensor_value_info("F", TensorProto.FLOAT, (8, 3, 4))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (8, 9, 8))])
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_concats"])
+
+        assert optimized_model.graph == graph
+
+    def test_fuse_transpose(self):  # type: () -> None
+        nodes = [helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2]),
+                 helper.make_node("Transpose", ["Y"], ["Z"], perm=[2, 0, 1]),
+                 helper.make_node("Transpose", ["Z"], ["A"], perm=[2, 0, 1])]
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Transpose", ["_X"], ["_Y2"], perm=[1, 0, 2]),
+             helper.make_node("Transpose", ["_Y2"], ["_Y3"], perm=[2, 0, 1]),
+             helper.make_node("Transpose", ["_Y3"], ["_Y4"], perm=[2, 0, 1])],
+            [(TensorProto.FLOAT, (2, 3, 4), "X")],
+            [(TensorProto.FLOAT, (2, 4, 3), "Y4")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 4, 3)),
+             helper.make_tensor_value_info("Y4", TensorProto.FLOAT, (4, 3, 2))])
+        original_model = helper.make_model(graph)
+        shape_inference.infer_shapes(original_model)
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_transposes"])
+        shape_inference.infer_shapes(optimized_model)
+
+        # Transpose, Constant (trip count), Constant (cond), Loop
+        assert len(list(optimized_model.graph.node)) == 4
+        # Transpose
+        assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1
+
+    def test_fuse_transpose_default_graph_output(self):  # type: () -> None
+        add = helper.make_node("Add", ["X", "Y"], ["A"])
+        trans1 = helper.make_node("Transpose", ["A"], ["B"])
+        trans2 = helper.make_node("Transpose", ["B"], ["C"])
+        graph = helper.make_graph(
+            [add, trans1, trans2],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))],
+            [helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 3))])
+        # The existence of shape infos of graoh outputs is checked in _optimized
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_transposes"])
+
+        def check_transpose(node):  # type: (NodeProto) -> None
+            assert node.op_type != "Transpose"
+        self._visit_all_nodes_recursive(optimized_model.graph, check_transpose)
+        assert len(optimized_model.graph.node) == 1
+
+    def test_fuse_transpose_default(self):  # type: () -> None
+        identity1 = helper.make_node("Identity", ["A"], ["X"])
+        trans1 = helper.make_node("Transpose", ["X"], ["Y"])
+        trans2 = helper.make_node("Transpose", ["Y"], ["Z"])
+        identity2 = helper.make_node("Identity", ["Z"], ["B"])
+        graph = helper.make_graph(
+            [identity1, trans1, trans2, identity2],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3, 4))])
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_transposes"])
+
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph.node[0].op_type == "Identity"
+        assert optimized_model.graph.node[1].op_type == "Identity"
+
+    def test_fuse_transpose_default_no_fuse(self):  # type: () -> None
+        identity1 = helper.make_node("Identity", ["A"], ["X"])
+        trans1 = helper.make_node("Transpose", ["X"], ["Y"])
+        trans2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[0, 1, 2])
+        identity2 = helper.make_node("Identity", ["Z"], ["B"])
+        graph = helper.make_graph(
+            [identity1, trans1, trans2, identity2],
+            "test",
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4))],
+            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 3, 2))])
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_transposes"])
+
+        assert len(list(optimized_model.graph.node)) == 4
+        assert optimized_model.graph == graph
+
+    def test_fuse_transpose_into_gemm(self):  # type: () -> None
+        nodes = [helper.make_node("Transpose", ["X"], ["A"], perm=[1, 0]),
+                 helper.make_node("Transpose", ["Y"], ["B"], perm=[1, 0]),
+                 helper.make_node("Gemm", ["A", "B", "C"], ["Z"])]
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Transpose", ["_X"], ["_A"], perm=[1, 0]),
+             helper.make_node("Transpose", ["Y"], ["_B"], perm=[1, 0]),
+             helper.make_node("Gemm", ["_A", "_B", "C"], ["_Z2"])],
+            [(TensorProto.FLOAT, (2, 3), "X")],
+            [(TensorProto.FLOAT, (3, 5), "Z2")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 2)),
+             helper.make_tensor_value_info("C", TensorProto.FLOAT, (3, 5))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (3, 5))])
+        optimized_model = self._optimized(graph, ["fuse_transpose_into_gemm"])
+
+        # Gemm, Constant (trip count), Constant (cond), Loop
+        assert len(list(optimized_model.graph.node)) == 4
+        assert optimized_model.graph.node[0].op_type == "Gemm"
+        # Gemm
+        assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1
+        assert optimized_model.graph.node[3].attribute[0].g.node[0].op_type == "Gemm"
+
+    def test_fuse_add_bias_into_conv_with_scalar_bias(self):  # type: () -> None
+        nodes = [helper.make_node("Conv", ["X", "Y"], ["Z"]),
+                 helper.make_node("Add", ["Z", "A"], ["B"])]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "Y", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, ())],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 1))],
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        # Unsqueeze, Conv
+        assert len(optimized_model.graph.node) == 4
+        assert optimized_model.graph.node[0].op_type == 'Unsqueeze'
+        assert optimized_model.graph.node[1].op_type == 'Constant'
+        assert optimized_model.graph.node[2].op_type == 'Tile'
+        assert optimized_model.graph.node[3].op_type == 'Conv'
+
+    def test_fuse_add_bias_into_conv_use_weight_shape(self):  # type: () -> None
+        nodes = [helper.make_node("Conv", ["X", "Y"], ["Z"]),
+                 helper.make_node("Add", ["Z", "A"], ["B"])]
+        # FIXME(daquexian): It looks like subgraph cannot get value info from parent subgraph
+        # nodes.extend(self._make_fake_loop_op(
+        #     [helper.make_node("Conv", ["_X", "Y"], ["_Z"]),
+        #      helper.make_node("Add", ["_Z", "A"], ["_B2"])],
+        #     [(TensorProto.FLOAT, (1, 5, 3, 3), "X")],
+        #     [(TensorProto.FLOAT, (1, 16, 1, 1), "B2")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "Y", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 1, 1))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 1))],
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        # # Squeeze, Conv, Constant (trip count), Constant (condition), Loop
+        # assert len(list(optimized_model.graph.node)) == 5
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph.node[0].op_type == 'Squeeze'
+        assert optimized_model.graph.node[1].op_type == 'Conv'
+        assert optimized_model.graph.output[0].name == 'B'
+        # # Squeeze, Conv
+        # assert len(optimized_model.graph.node[4].attribute[0].g.node) == 2
+        # assert optimized_model.graph.node[4].attribute[0].g.node[0].op_type == 'Squeeze'
+        # assert optimized_model.graph.node[4].attribute[0].g.node[1].op_type == 'Conv'
+        # # Output 1 since 0 is 'cond'
+        # assert optimized_model.graph.node[4].attribute[0].g.output[1].name == 'B2'
+
+    # type: () -> None
+    def test_fuse_add_bias_into_conv_use_weight_shape_with_tile(self):
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [conv, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "Y", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (1,))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 1))],
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 3
+        assert len(optimized_model.graph.value_info) == 1
+        assert optimized_model.graph.value_info[0].type.tensor_type.elem_type == TensorProto.INT64
+        assert len(
+            optimized_model.graph.value_info[0].type.tensor_type.shape.dim) == 1
+        assert optimized_model.graph.node[0].op_type == 'Constant'
+        assert optimized_model.graph.node[1].op_type == 'Tile'
+        assert optimized_model.graph.node[2].op_type == 'Conv'
+        assert optimized_model.graph.output[0].name == 'B'
+
+    def test_fuse_add_bias_into_conv_use_conv_shape(self):  # type: () -> None
+        sub = helper.make_node("Sub", ["M", "N"], ["Y"])
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [sub, conv, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "M", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "N", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 1))],
+            value_info=[
+                helper.make_tensor_value_info(
+                    "Z", TensorProto.FLOAT, (1, 16, 1, 1))
+            ],
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        assert len(optimized_model.graph.node) == 3
+        assert optimized_model.graph.node[0].op_type == 'Sub'
+        assert optimized_model.graph.node[1].op_type == 'Squeeze'
+        assert optimized_model.graph.node[2].op_type == 'Conv'
+        assert optimized_model.graph.output[0].name == 'B'
+        assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+        assert len(
+            optimized_model.graph.output[0].type.tensor_type.shape.dim) == 4
+
+    # type: () -> None
+    def test_fuse_add_bias_into_conv_use_move_constant(self):
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        constant = helper.make_node("Constant", [], ["A"],
+                                    value=helper.make_tensor(
+                                        name="bias",
+                                        data_type=TensorProto.FLOAT,
+                                        dims=(16, 1, 1),
+                                        vals=np.random.randn(16).astype(np.float32).tolist()))
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [conv, constant, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 1))],
+            value_info=[
+                helper.make_tensor_value_info(
+                    "A", TensorProto.FLOAT, (16, 1, 1)),
+            ]
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        assert len(optimized_model.graph.node) == 3
+        assert optimized_model.graph.node[0].op_type == 'Constant'
+        assert optimized_model.graph.node[1].op_type == 'Squeeze'
+        assert optimized_model.graph.node[2].op_type == 'Conv'
+        assert optimized_model.graph.output[0].name == 'B'
+        assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+        assert len(
+            optimized_model.graph.output[0].type.tensor_type.shape.dim) == 4
+
+    # type: () -> None
+    def test_fuse_add_bias_into_conv_squeeze_1d_bias_no_fuse(self):
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [conv, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "Y", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (3,))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 1, 3))],
+            value_info=[
+                helper.make_tensor_value_info(
+                    "Z", TensorProto.FLOAT, (1, 16, 1, 1)),
+            ]
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph.node[0].op_type == 'Conv'
+        assert optimized_model.graph.node[1].op_type == 'Add'
+
+    # type: () -> None
+    def test_fuse_add_bias_into_conv_squeeze_3d_bias_no_fuse(self):
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [conv, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "Y", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 3, 3))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 3, 3))],
+            value_info=[
+                helper.make_tensor_value_info(
+                    "Z", TensorProto.FLOAT, (1, 16, 1, 1)),
+            ]
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph.node[0].op_type == 'Conv'
+        assert optimized_model.graph.node[1].op_type == 'Add'
+
+    # type: () -> None
+    def test_fuse_add_bias_into_conv_squeeze_4d_bias_no_fuse(self):
+        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "A"], ["B"])
+        graph = helper.make_graph(
+            [conv, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
+             helper.make_tensor_value_info(
+                 "Y", TensorProto.FLOAT, (16, 5, 3, 3)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 16, 3, 3))],
+            [helper.make_tensor_value_info(
+                "B", TensorProto.FLOAT, (1, 16, 3, 3))]
+        )
+        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 2
+        assert optimized_model.graph.node[0].op_type == 'Conv'
+        assert optimized_model.graph.node[1].op_type == 'Add'
+
+    def test_fuse_matmul_add_bias_into_gemm(self):  # type: () -> None
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "B"], ["A"])
+        graph = helper.make_graph(
+            [matmul, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (16,))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Gemm"
+
+    def test_fuse_matmul_add_bias_into_gemm_2d_bias(self):  # type: () -> None
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "B"], ["A"])
+        graph = helper.make_graph(
+            [matmul, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Gemm"
+
+    # type: () -> None
+    def test_fuse_matmul_add_bias_into_gemm_2d_bias_same_shape(self):
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "B"], ["A"])
+        graph = helper.make_graph(
+            [matmul, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (32, 16))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Gemm"
+
+    # type: () -> None
+    def test_fuse_matmul_add_bias_into_gemm_2d_bias_bcast_no_fuse(self):
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "B"], ["A"])
+        graph = helper.make_graph(
+            [matmul, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (16, 16))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 16))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"])
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_matmul_add_bias_into_gemm_3d_matmul_no_fuse(self):
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "B"], ["A"])
+        graph = helper.make_graph(
+            [matmul, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4, 3)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (3, 3))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 3))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"])
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_matmul_add_bias_into_gemm_3d_bias_no_fuse(self):
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        add = helper.make_node("Add", ["Z", "B"], ["A"])
+        graph = helper.make_graph(
+            [matmul, add],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 1, 16))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
+        )
+        # 3d bias for 2d matmul is not legal. So disable onnxruntime checking
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"], compare_result=False)
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_matmul_add_bias_into_gemm_multiple_use_no_fuse(self):
+        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
+        identity = helper.make_node("Identity", ["Z"], ["A1"])
+        add = helper.make_node("Add", ["Z", "B"], ["A2"])
+        graph = helper.make_graph(
+            [matmul, add, identity],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
+             helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16))],
+            [helper.make_tensor_value_info("A1", TensorProto.FLOAT, (32, 16)),
+             helper.make_tensor_value_info("A2", TensorProto.FLOAT, (32, 16))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_matmul_add_bias_into_gemm"])
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_no_optional_value_opset10(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X"],
+            ["P"],
+            mode="constant",
+            pads=[0, 0, 0, 0, 0, 0, 1, 1]
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [
+            0, 0, 1, 1]
+
+    def test_fuse_pad_into_conv_no_optional_value(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [
+            0, 0, 1, 1]
+
+    def test_fuse_pad_into_conv_with_optional_value(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads", "Constant_value"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info(
+                 "Constant_value", TensorProto.FLOAT, ()),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(
+                                    np.int64).tobytes(),
+                                raw=True),
+             helper.make_tensor("Constant_value", TensorProto.FLOAT,
+                                dims=(),
+                                vals=np.array([0]).astype(np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [
+            0, 0, 1, 1]
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_with_nonzero_optional_value(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads", "Constant_value"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info(
+                 "Constant_value", TensorProto.FLOAT, ()),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(
+                                    np.int64).tobytes(),
+                                raw=True),
+             helper.make_tensor("Constant_value", TensorProto.FLOAT,
+                                dims=(),
+                                # non-zero Constant_value -> so no pad
+                                vals=np.array([25]).astype(
+                                    np.float32).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert optimized_model.graph == graph
+
+    def test_fuse_pad_into_conv_1d_opset10(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X"],
+            ["P"],
+            mode="constant",
+            pads=[0, 0, 1, 0, 0, 1]
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 30)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 32))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1]
+
+    def test_fuse_pad_into_conv_1d(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 30)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (6,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 32))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(6,),
+                                vals=np.array([0, 0, 1, 0, 0, 1]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1]
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_existing_conv_pad_opset10(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X"],
+            ["P"],
+            mode="constant",
+            pads=[0, 0, 0, 0, 0, 0, 1, 1]
+        )
+        conv = helper.make_node(
+            "Conv",
+            ["P", "Y"],
+            ["Z"],
+            pads=[1, 1, 0, 0]
+        )
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 4, 4))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [
+            1, 1, 1, 1]
+
+    def test_fuse_pad_into_conv_existing_conv_pad(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node(
+            "Conv",
+            ["P", "Y"],
+            ["Z"],
+            pads=[1, 1, 0, 0]
+        )
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 4, 4))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert len(list(optimized_model.graph.node)) == 1
+        assert optimized_model.graph.node[0].op_type == "Conv"
+        assert optimized_model.graph.node[0].attribute[0].name == "pads"
+        assert list(optimized_model.graph.node[0].attribute[0].ints) == [
+            1, 1, 1, 1]
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_pad_feature_no_fuse_opset10(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X"],
+            ["P"],
+            mode="constant",
+            pads=[0, 1, 0, 0, 0, 0, 0, 0]
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert optimized_model.graph == graph
+
+    def test_fuse_pad_into_conv_pad_feature_no_fuse(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array([0, 1, 0, 0, 0, 0, 0, 0]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_negative_pad_no_fuse_opset10(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X"],
+            ["P"],
+            mode="constant",
+            pads=[0, 0, 0, 0, 0, 0, -1, -1]
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 4, 4)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert optimized_model.graph == graph
+
+    def test_fuse_pad_into_conv_negative_pad_no_fuse(self):  # type: () -> None
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads"],
+            ["P"],
+            mode="constant"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 4, 4)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array(
+                                    [0, 0, 0, 0, 0, 0, -1, -1]).astype(np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_reflection_pad_no_fuse_opset10(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X"],
+            ["P"],
+            mode="reflect",
+            pads=[0, 0, 0, 0, 0, 0, 1, 1]
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))]
+        )
+        optimized_model = self._optimized(
+            graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)])
+
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_fuse_pad_into_conv_reflection_pad_no_fuse(self):
+        pad = helper.make_node(
+            "Pad",
+            ["X", "Pads"],
+            ["P"],
+            mode="reflect"
+        )
+        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
+        graph = helper.make_graph(
+            [pad, conv],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
+             helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (1, 16, 1, 1))],
+            [helper.make_tensor("Pads", TensorProto.INT64,
+                                dims=(8,),
+                                vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(
+                                    np.int64).tobytes(),
+                                raw=True)])
+        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])
+
+        assert optimized_model.graph == graph
+
+    def test_fuse_consecutive_squeezes(self):  # type: () -> None
+        nodes = [helper.make_node("Squeeze", ["X", "X_axes"], ["Y"]),
+                 helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])]
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Squeeze", ["_X", "X_axes"], ["_Y"]),
+             helper.make_node("Squeeze", ["_Y", "Y_axes"], ["_Z2"])],
+            [(TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9), "X")],
+            [(TensorProto.FLOAT, (2, 3, 1, 8, 9), "Z2")]))
+        initializers = [
+            helper.make_tensor(name, TensorProto.INT64,
+                               npa.shape, npa.tobytes(), raw=True)
+            for name, npa in [('X_axes', np.array([0, 4, 5], dtype=np.int64)),
+                              ('Y_axes', np.array([0, 3], dtype=np.int64))]
+        ]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)),
+                helper.make_tensor_value_info("X_axes", TensorProto.INT64, [3]),
+                helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2])],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (2, 3, 1, 8, 9))],
+            initializer=initializers)
+        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])
+
+        # Squeeze, Constant (trip count), Constant (cond), Loop
+        assert optimized_model.graph.node[0].op_type == "Squeeze"
+        for init in optimized_model.graph.initializer:
+            if init.name == optimized_model.graph.node[0].input[1]:
+                assert list(to_array(init)) == [0, 1, 4, 5, 6]
+        assert len(list(optimized_model.graph.node)) == 4
+
+    def test_fuse_consecutive_squeezes_default(self):  # type: () -> None
+        squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"])
+        squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])
+        squeeze3 = helper.make_node("Squeeze", ["Z", "Z_axes"], ["A"])
+        nodes = [squeeze1, squeeze2, squeeze3]
+        initializers = [
+            helper.make_tensor(name, TensorProto.INT64,
+                               npa.shape, npa.tobytes(), raw=True)
+            for name, npa in [('X_axes', np.array([0, 4, 5], dtype=np.int64)),
+                              ('Y_axes', np.array([0, 3], dtype=np.int64)),
+                              ('Z_axes', np.array([2], dtype=np.int64))]
+        ]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)),
+                helper.make_tensor_value_info("X_axes", TensorProto.INT64, [3]),
+                helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2]),
+                helper.make_tensor_value_info("Z_axes", TensorProto.INT64, [1])],
+            [helper.make_tensor_value_info(
+                "A", TensorProto.FLOAT, (2, 3, 8, 9))],
+            initializer=initializers)
+        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])
+
+        assert optimized_model.graph.node[0].op_type == "Squeeze"
+        for init in optimized_model.graph.initializer:
+            if init.name == optimized_model.graph.node[0].input[1]:
+                assert list(to_array(init)) == [0, 1, 4, 5, 6, 7]
+        assert len(list(optimized_model.graph.node)) == 1
+
+    def test_fuse_consecutive_squeezes_random(self):  # type: () -> None
+        x_shape = [1, 1, 1, 3, 4, 1, 6, 1, 1, 9]
+        s1_one_indices = [i for i, a in enumerate(x_shape) if a == 1]
+        s1_axes = np.random.choice(s1_one_indices,
+                                   size=np.random.randint(
+                                       low=1, high=len(s1_one_indices) - 1),
+                                   replace=False).astype(np.int64)
+        s2_x_shape = [a for i, a in enumerate(x_shape) if i not in s1_axes]
+        s2_one_indices = [i for i, a in enumerate(s2_x_shape) if a == 1]
+        s2_axes = np.array(s2_one_indices).astype(np.int64)
+
+        squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"])
+        squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])
+        initializers = [
+            helper.make_tensor(name, TensorProto.INT64,
+                               npa.shape, npa.tobytes(), raw=True)
+            for name, npa in [('X_axes', s1_axes),
+                              ('Y_axes', s2_axes)]
+        ]
+        nodes = [squeeze1, squeeze2]
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, x_shape),
+             helper.make_tensor_value_info(
+                 "X_axes", TensorProto.INT64, s1_axes.shape),
+             helper.make_tensor_value_info("Y_axes", TensorProto.INT64, s2_axes.shape)],
+            [helper.make_tensor_value_info(
+                "Z", TensorProto.FLOAT, (3, 4, 6, 9))],
+            initializer=initializers
+        )
+        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])
+
+        assert optimized_model.graph.node[0].op_type == "Squeeze"
+        for init in optimized_model.graph.initializer:
+            if init.name == optimized_model.graph.node[0].input[1]:
+                assert list(to_array(init)) == [0, 1, 2, 5, 7, 8]
+        assert len(list(optimized_model.graph.node)) == 1
+
+    def test_fuse_consecutive_squeezes_multi_uses(self):  # type: () -> None
+        squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"])
+        add = helper.make_node("Add", ["Y", "A"], ["Z2"])
+        squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])
+        initializers = [
+            helper.make_tensor(name, TensorProto.INT64,
+                               npa.shape, npa.tobytes(), raw=True)
+            for name, npa in [('X_axes', np.array([0, 4, 5], dtype=np.int64)),
+                              ('Y_axes', np.array([0, 3], dtype=np.int64)), ]
+        ]
+        graph = helper.make_graph(
+            [squeeze1, add, squeeze2],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)),
+             helper.make_tensor_value_info("A", TensorProto.FLOAT, (1,)),
+             helper.make_tensor_value_info("X_axes", TensorProto.INT64, [3]),
+             helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2]),
+             ],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (2, 3, 1, 8, 9)),
+             helper.make_tensor_value_info("Z2", TensorProto.FLOAT, (1, 2, 3, 1, 1, 8, 9))],
+            initializer=initializers
+        )
+        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])
+
+        assert optimized_model.graph.node[0].op_type == "Squeeze"
+        assert optimized_model.graph.node[2].op_type == "Squeeze"
+        assert optimized_model.graph.node[2].input[0] == "X"
+        assert len(list(optimized_model.graph.node)) == 3
+        for init in optimized_model.graph.initializer:
+            if init.name == optimized_model.graph.node[0].input[1]:
+                assert list(to_array(init)) == [
+                    0, 4, 5]
+            if init.name == optimized_model.graph.node[2].input[1]:
+                assert list(to_array(init)) == [
+                    0, 1, 4, 5, 6]
+
+    def test_fuse_consecutive_softmax_log_axis(self):  # type: () -> None
+        for axis in range(3):
+            softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=axis)
+            log = helper.make_node("Log", ["Y"], ["Z"])
+            graph = helper.make_graph(
+                [softmax, log],
+                "test",
+                [helper.make_tensor_value_info(
+                    "X", TensorProto.FLOAT, (5, 7, 11))],
+                [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11))])
+            optimized_model = self._optimized(
+                graph, ["fuse_consecutive_log_softmax"])
+
+            assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+            assert len(optimized_model.graph.output) == 1
+            assert len(optimized_model.graph.node) == 1
+            assert optimized_model.graph.node[0].op_type == "LogSoftmax"
+            assert optimized_model.graph.node[0].attribute[0].name == "axis"
+            assert optimized_model.graph.node[0].attribute[0].i == axis
+
+    def test_fuse_consecutive_softmax_log_side_effect(self):  # type: () -> None
+        softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
+        log = helper.make_node("Log", ["Y"], ["Z"])
+        graph = helper.make_graph(
+            [softmax, log],
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (5, 7, 11))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11)),
+             helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 7, 11))])
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_log_softmax"])
+
+        assert graph == optimized_model.graph
+
+    # type: () -> None
+    def test_fuse_consecutive_softmax_log_multiple_out(self):
+        softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
+        log = helper.make_node("Log", ["Y"], ["Z"])
+        exp = helper.make_node("Exp", ["Z"], ["Z1"])
+        graph = helper.make_graph(
+            [softmax, log, exp],
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (5, 7, 11))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11)),
+             helper.make_tensor_value_info("Z1", TensorProto.FLOAT, (5, 7, 11))])
+        optimized_model = self._optimized(
+            graph, ["fuse_consecutive_log_softmax"])
+
+        assert len(optimized_model.graph.output) == 2
+        assert len(optimized_model.graph.node) == 2
+        assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+        assert optimized_model.graph.output[1].type.tensor_type.elem_type == TensorProto.FLOAT
+        assert optimized_model.graph.node[0].op_type == "LogSoftmax"
+        assert optimized_model.graph.node[0].attribute[0].name == "axis"
+        assert optimized_model.graph.node[0].attribute[0].i == 2
+        assert optimized_model.graph.node[1].op_type == "Exp"
+
+    def test_preserve_value_info(self):  # type: () -> None
+        trans1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])
+        trans2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[2, 0, 1])
+        trans3 = helper.make_node("Transpose", ["Z"], ["A"], perm=[2, 0, 1])
+        graph = helper.make_graph(
+            [trans1, trans2, trans3],
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))],
+            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 4, 3))])
+        vi = helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))
+        graph.value_info.extend([vi])
+        optimized_model = self._optimized(graph, ["nop"])
+        assert list(optimized_model.graph.value_info) == [vi]
+        assert len(list(optimized_model.graph.node)) == 3
+
+    def test_split(self):  # type: () -> None
+        node = onnx.helper.make_node(
+            'Constant',
+            inputs=[],
+            outputs=['X'],
+            value=onnx.helper.make_tensor(
+                name='X',
+                data_type=TensorProto.FLOAT,
+                dims=[1],
+                vals=[5],
+            ),
+        )
+        graph = helper.make_graph(
+            [node],
+            'test-optimize-split',
+            [],
+            [helper.make_tensor_value_info('X', TensorProto.FLOAT, (1,))])
+
+        init_model = self._optimized(graph, ['split_init'])
+        self.assertEqual(len(init_model.graph.node), 1)
+        self.assertEqual(len(init_model.graph.output), 1)
+        self.assertEqual(init_model.graph.node[0].op_type, 'Constant')
+
+        predict_model = self._optimized(graph, ['split_predict'])
+        self.assertEqual(len(predict_model.graph.node), 0)
+        self.assertEqual(len(predict_model.graph.input), 1)
+        self.assertEqual(predict_model.graph.input[0].name, 'X')
+
+    def test_lift_lex_loop(self):  # type: () -> None
+        nodes = [helper.make_node("Identity", ["X"], ["Y"])]
+        # 'lift_lexical_references' is legacy code and I don't know how it works.
+        # More error occurs if I make this loop op legal.
+        # So don't check legality here
+        nodes.extend(self._make_fake_loop_op(
+            [helper.make_node("Identity", ["X"], ["_Y2"]),
+             helper.make_node("Identity", ["Y"], ["_Y3"])],
+            [],
+            [(TensorProto.FLOAT, (5,), "Y2"),
+             (TensorProto.FLOAT, (5,), "Y3")],
+            check_legality=False))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (5,))])
+        # "lift_lexical_references" pass produces a graph that does not conform to
+        # the ONNX spec. Disable checking.
+        optimized_model = self._optimized(
+            graph, ["lift_lexical_references"], compare_result=False)
+        assert len(optimized_model.graph.node) == 4
+        # body_graph, __control_inputs
+        assert len(optimized_model.graph.node[3].attribute) == 2
+        assert optimized_model.graph.node[3].attribute[1].name == "__control_inputs"
+        assert optimized_model.graph.node[3].attribute[1].strings[0] == b"X"
+        assert optimized_model.graph.node[3].attribute[1].strings[1] == b"Y"
+
+    def test_lift_lex_if(self):  # type: () -> None
+        nodes = [helper.make_node("Identity", ["X"], ["Y"])]
+        nodes.extend(self._make_fake_if_op(
+            [helper.make_node("Identity", ["X"], ["_Y2"]),
+             helper.make_node("Identity", ["Y"], ["_Y3"])],
+            [helper.make_node("Identity", ["X"], ["_Y2"]),
+             helper.make_node("Identity", ["X"], ["_Y3"])],
+            [(TensorProto.FLOAT, (5,), "Y2"),
+             (TensorProto.FLOAT, (5,), "Y3")]))
+        graph = helper.make_graph(
+            nodes,
+            "test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
+            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
+             helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (5,))])
+        # "If" node now diverges from ONNX schema. Disable checking.
+        optimized_model = self._optimized(
+            graph, ["lift_lexical_references"], compare_result=False)
+
+        # Identity, Constant (condition), If
+        assert len(optimized_model.graph.node) == 3
+        # else_branch, then_branch, __control_inputs
+        assert len(optimized_model.graph.node[2].attribute) == 3
+        assert optimized_model.graph.node[2].attribute[2].name == "__control_inputs"
+        assert optimized_model.graph.node[2].attribute[2].strings[0] == b"X"
+        assert optimized_model.graph.node[2].attribute[2].strings[1] == b"Y"
+
+    def test_fuse_bn_into_conv_simple(self):  # type: () -> None
+        for (tensor_type, np_type) in [(TensorProto.FLOAT, np.float32)]:
+            conv = helper.make_node("Conv", ["X", "W", "B"], ["Y"])
+            bn = helper.make_node("BatchNormalization", [
+                                  "Y", "scale", "b", "mean", "var"], ["Z"])
+
+            W = np.random.randn(3, 2, 5, 5).astype(np_type) + 2
+            B = np.random.randn(3,).astype(np_type) + 2
+            scale = np.random.randn(3,).astype(np_type) + 2
+            b = np.random.randn(3,).astype(np_type) + 2
+            mean = np.random.randn(3,).astype(np_type) + 2
+            var = np.abs(np.random.randn(3,).astype(np_type)) + 2
+
+            initializers = [
+                helper.make_tensor(name, tensor_type,
+                                   npa.shape, npa.tobytes(), raw=True)
+                for name, npa in [('W', W), ('B', B), ('scale', scale), ('b', b), ('mean', mean), ('var', var)]
+            ]
+            graph = helper.make_graph(
+                [conv, bn],
+                "test",
+                [helper.make_tensor_value_info(
+                    "X", tensor_type, (5, 2, 28, 28))],
+                [helper.make_tensor_value_info(
+                    "Z", tensor_type, (5, 3, 24, 24))],
+                initializer=initializers,
+                value_info=[
+                    helper.make_tensor_value_info(
+                        "Y", tensor_type, (5, 3, 24, 24))
+                ]
+            )
+            optimized_model = self._optimized(graph, ["fuse_bn_into_conv"])
+
+            self.assertEqual(len(optimized_model.graph.node), 1)
+            self.assertEqual(optimized_model.graph.node[0].op_type, 'Conv')
+            self.assertEqual(len(optimized_model.graph.initializer), 2)
+            new_W = numpy_helper.to_array(optimized_model.graph.initializer[0])
+            new_b = numpy_helper.to_array(optimized_model.graph.initializer[1])
+
+            f = scale / np.sqrt(var + 1e-5)
+            np.testing.assert_almost_equal((B - mean) * f + b, new_b)
+            np.testing.assert_almost_equal(
+                W * f[:, np.newaxis, np.newaxis, np.newaxis], new_W)
+
+    def _internal_test_deadend_elimination(self, fixed):  # type: (bool) -> None
+        softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
+        log = helper.make_node("Log", ["Y"], ["Z"])
+        exp = helper.make_node("Exp", ["Z"], ["Z1"])
+        exp1 = helper.make_node("Log", ["Z"], ["Z2"])
+        exp2 = helper.make_node("Sqrt", ["Z1"], ["Z3"])
+        graph = helper.make_graph(
+            [softmax, log, exp, exp1, exp2],
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (5, 7, 11))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11))])
+        optimized_model = self._optimized(
+            graph, ["eliminate_deadend"], fixed)
+        assert len(optimized_model.graph.output) == 1
+        assert len(optimized_model.graph.node) == 2
+        assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+        assert optimized_model.graph.node[0].op_type == "Softmax"
+        assert optimized_model.graph.node[0].attribute[0].name == "axis"
+        assert optimized_model.graph.node[0].attribute[0].i == 2
+        assert optimized_model.graph.node[1].op_type == "Log"
+
+    def test_deadend_elimination_simple(self):  # type: () -> None
+        self._internal_test_deadend_elimination(False)
+
+    def test_deadend_elimination_simple_fixed(self):  # type: () -> None
+        self._internal_test_deadend_elimination(True)
+
+    def _get_argmax_output_shape(self, input_shape, axis, keepdims):
+        assert keepdims
+        output_shape = list(input_shape[:])
+        output_shape[axis] = 1
+        output_shape = tuple(output_shape)
+        return output_shape
+
+    # type: () -> None
+
+    def test_eliminate_nop_monotone_argmax_basic_no_node_axis(self):
+        input_shape = (5, 7, 11)
+        for node_name in ["Exp"]:
+            for axis in range(3):
+                node = helper.make_node(node_name, ["X"], ["Y"])
+                argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis)
+                output_shape = self._get_argmax_output_shape(
+                    input_shape, axis, True)
+                graph = helper.make_graph(
+                    [node, argmax],
+                    "test",
+                    [helper.make_tensor_value_info(
+                        "X", TensorProto.FLOAT, input_shape)],
+                    [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)])
+                optimized_model = self._optimized(
+                    graph, ["eliminate_nop_monotone_argmax"])
+                assert len(optimized_model.graph.output) == 1
+                assert len(optimized_model.graph.node) == 1
+                assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64
+                assert optimized_model.graph.node[0].op_type == "ArgMax"
+                assert optimized_model.graph.node[0].attribute[0].name == "axis"
+                assert optimized_model.graph.node[0].attribute[0].i == axis
+
+    # type: () -> None
+    def test_eliminate_nop_monotone_argmax_basic_with_node_axis(self):
+        input_shape = (5, 7, 11)
+        for node_name in ["Softmax", "LogSoftmax"]:
+            for axis_n in range(3):
+                for axis_max in range(3):
+                    node = helper.make_node(
+                        node_name, ["X"], ["Y"], axis=axis_n)
+                    argmax = helper.make_node(
+                        "ArgMax", ["Y"], ["Z"], axis=axis_max)
+                    output_shape = self._get_argmax_output_shape(
+                        input_shape, axis_max, True)
+                    graph = helper.make_graph(
+                        [node, argmax],
+                        "test",
+                        [helper.make_tensor_value_info(
+                            "X", TensorProto.FLOAT, input_shape)],
+                        [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)])
+                    optimized_model = self._optimized(
+                        graph, ["eliminate_nop_monotone_argmax"])
+                    if axis_max == axis_n:
+                        assert len(optimized_model.graph.output) == 1
+                        assert len(optimized_model.graph.node) == 1
+                        assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64
+                        assert optimized_model.graph.node[0].op_type == "ArgMax"
+                        assert optimized_model.graph.node[0].attribute[0].name == "axis"
+                        assert optimized_model.graph.node[0].attribute[0].i == axis_max
+                    else:
+                        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_eliminate_nop_monotone_argmax_multiple_out(self):
+        input_shape = (5, 7, 11)
+        for node_name in ["Exp"]:
+            for axis in range(3):
+                node = helper.make_node(node_name, ["X"], ["Y"])
+                node2 = helper.make_node(node_name, ["Y"], ["Z1"])
+                argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis)
+                argmax_output_shape = self._get_argmax_output_shape(
+                    input_shape, axis, True)
+                graph = helper.make_graph(
+                    [node, node2, argmax],
+                    "test",
+                    [helper.make_tensor_value_info(
+                        "X", TensorProto.FLOAT, input_shape)],
+                    [helper.make_tensor_value_info("Z", TensorProto.INT64, argmax_output_shape),
+                     helper.make_tensor_value_info("Z1", TensorProto.FLOAT, input_shape)])
+                optimized_model = self._optimized(
+                    graph, ["eliminate_nop_monotone_argmax"])
+                assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_eliminate_nop_monotone_argmax_consecutive(self):
+        # type: (GraphProto, ModelProto, bool, int) -> None
+        input_shape = (5, 7, 11)
+
+        def _assertion(graph, optimized_model, axis_aligned, true_axis):
+            if axis_aligned:
+                assert len(optimized_model.graph.output) == 1
+                assert len(optimized_model.graph.node) == 1
+                assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64
+                assert optimized_model.graph.node[0].op_type == "ArgMax"
+                assert optimized_model.graph.node[0].attribute[0].name == "axis"
+                assert optimized_model.graph.node[0].attribute[0].i == true_axis
+            else:
+                assert optimized_model.graph == graph
+        # no axis X no axis test
+        for node_name_0 in ["Exp"]:
+            for node_name_1 in ["Exp"]:
+                for axis in range(3):
+                    node = helper.make_node(node_name_0, ["X"], ["Y"])
+                    node2 = helper.make_node(node_name_1, ["Y"], ["Y1"])
+                    argmax = helper.make_node(
+                        "ArgMax", ["Y1"], ["Z"], axis=axis)
+                    output_shape = self._get_argmax_output_shape(
+                        input_shape, axis, True)
+                    graph = helper.make_graph(
+                        [node, node2, argmax],
+                        "test",
+                        [helper.make_tensor_value_info(
+                            "X", TensorProto.FLOAT, input_shape)],
+                        [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)])
+                    optimized_model = self._optimized(
+                        graph, ["eliminate_nop_monotone_argmax"], True)
+                    _assertion(graph, optimized_model, True, axis)
+        # no axis X axis test
+        for node_name_0 in ["Exp"]:
+            for node_name_1 in ["Softmax", "LogSoftmax"]:
+                for axis_0 in range(3):
+                    for axis_1 in range(3):
+                        node = helper.make_node(node_name_0, ["X"], ["Y"])
+                        node2 = helper.make_node(
+                            node_name_1, ["Y"], ["Y1"], axis=axis_0)
+                        argmax = helper.make_node(
+                            "ArgMax", ["Y1"], ["Z"], axis=axis_1)
+                        output_shape = self._get_argmax_output_shape(
+                            input_shape, axis_1, True)
+                        graph = helper.make_graph(
+                            [node, node2, argmax],
+                            "test",
+                            [helper.make_tensor_value_info(
+                                "X", TensorProto.FLOAT, (5, 7, 11))],
+                            [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)])
+                        optimized_model = self._optimized(
+                            graph, ["eliminate_nop_monotone_argmax"], True)
+                        _assertion(graph, optimized_model,
+                                   axis_0 == axis_1, axis_1)
+        # axis X axis test
+        for node_name_0 in ["Softmax", "LogSoftmax"]:
+            for node_name_1 in ["Softmax", "LogSoftmax"]:
+                for axis_0 in range(3):
+                    for axis_1 in range(3):
+                        for axis_2 in range(3):
+                            node = helper.make_node(
+                                node_name_0, ["X"], ["Y"], axis=axis_0)
+                            node2 = helper.make_node(
+                                node_name_1, ["Y"], ["Y1"], axis=axis_1)
+                            argmax = helper.make_node(
+                                "ArgMax", ["Y1"], ["Z"], axis=axis_2)
+                            output_shape = self._get_argmax_output_shape(
+                                input_shape, axis_2, True)
+                            graph = helper.make_graph(
+                                [node, node2, argmax],
+                                "test",
+                                [helper.make_tensor_value_info(
+                                    "X", TensorProto.FLOAT, input_shape)],
+                                [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)])
+                            optimized_model = self._optimized(
+                                graph, ["eliminate_nop_monotone_argmax"], True)
+                            if axis_0 == axis_1:  # we can reduce both of the monotonic ops
+                                _assertion(graph, optimized_model,
+                                           axis_1 == axis_2, axis_2)
+                            elif axis_1 == axis_2:  # we can reduce one of the monotonic ops
+                                assert len(optimized_model.graph.output) == 1
+                                assert len(optimized_model.graph.node) == 2
+                                assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64
+                                assert optimized_model.graph.node[-1].op_type == "ArgMax"
+                                assert optimized_model.graph.node[-1].attribute[0].name == "axis"
+                                assert optimized_model.graph.node[-1].attribute[0].i == axis_2
+                            else:  # we can't reduce anything
+                                assert optimized_model.graph == graph
+
+    def test_eliminate_nop_dropout(self):  # type: () -> None
+        node = helper.make_node("Dropout", ["X"], ["Y"])
+        node1 = helper.make_node("Log", ["Y"], ["Z"])
+        graph = helper.make_graph(
+            [node, node1],
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (5, 7))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))])
+        optimized_model = self._optimized(
+            graph, ["eliminate_nop_dropout"], False)
+
+        # we don't want to eliminate the dropoutin opset 12,
+        # even when it';s an optional parameter (defaults to 0)
+        assert optimized_model.graph == graph
+
+    # type: () -> None
+    def test_eliminate_nop_dropout_opset11_graph_output(self):
+        node = helper.make_node("Log", ["X"], ["Y"])
+        node1 = helper.make_node("Dropout", ["Y"], ["Z"], ratio=0.0)
+        graph = helper.make_graph(
+            [node, node1],
+            "test",
+            [helper.make_tensor_value_info(
+                "X", TensorProto.FLOAT, (5, 7))],
+            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))])
+        optimized_model = self._optimized(
+            graph, ["eliminate_nop_dropout"], False, opset_imports=[helper.make_opsetid("", 11)])
+
+        assert len(optimized_model.graph.output) == 1
+        assert len(optimized_model.graph.node) == 1
+        assert optimized_model.graph.node[0].op_type == "Log"
+        assert optimized_model.graph.output[0].name == 'Z'
+
+    def test_eliminate_nop_dropout_opset11(self):  # type: () -> None
+        for ratio in [0.0, 0.5]:
+            node = helper.make_node("Dropout", ["X"], ["Y"], ratio=ratio)
+            node1 = helper.make_node("Log", ["Y"], ["Z"])
+            graph = helper.make_graph(
+                [node, node1],
+                "test",
+                [helper.make_tensor_value_info(
+                    "X", TensorProto.FLOAT, (5, 7))],
+                [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))])
+            optimized_model = self._optimized(
+                graph, ["eliminate_nop_dropout"], False, opset_imports=[helper.make_opsetid("", 11)])
+
+            if ratio > 0.0:
+                assert optimized_model.graph == graph
+            else:
+                assert len(optimized_model.graph.output) == 1
+                assert len(optimized_model.graph.node) == 1
+                assert optimized_model.graph.node[0].op_type == "Log"
+
+    def test_fuse_reduction_unsqueeze(self):  # type: () -> None
+        # type: (Tuple[int, ...], List[int], List[int], bool) -> Tuple[int, ...]
+        def _calculate_post_transform_shape(input_shape, reduction_axes, unsqueeze_axes, keepdim):
+            post_reduce_shape = None
+            if keepdim:
+                post_reduce_shape = tuple(
+                    [(x if i not in reduction_axes else 1) for i, x in enumerate(input_shape)])
+            else:
+                post_reduce_shape = tuple(
+                    [x for i, x in enumerate(input_shape) if i not in reduction_axes])
+            post_unsqueeze_shape = list(post_reduce_shape)
+            for ax in unsqueeze_axes:
+                post_unsqueeze_shape.insert(ax, 1)
+            return tuple(post_unsqueeze_shape)
+
+        for reduction in ["ReduceL1", "ReduceL2", "ReduceLogSum",
+                          "ReduceLogSumExp", "ReduceMax", "ReduceMean",
+                          "ReduceMin", "ReduceProd", "ReduceSum", "ReduceSumSquare"]:
+            for axes1 in [[1], [1, 2], [2]]:
+                for axes2 in [[0], [0, 1], [1]]:
+                    for keepdim in [False, True]:
+                        input_shape = (5, 7, 9)
+                        output_shape = _calculate_post_transform_shape(
+                            input_shape, axes1, axes2, keepdim)  # type: Tuple[int, ...]
+                        axes2_arr = np.array(axes2, dtype=np.int64)
+                        graph_input = [helper.make_tensor_value_info(
+                            "X", TensorProto.FLOAT, input_shape),
+                            helper.make_tensor_value_info("Y_axes", TensorProto.INT64, axes2_arr.shape)]
+                        graph_initializer = [
+                            helper.make_tensor("Y_axes", TensorProto.INT64,
+                                               axes2_arr.shape, axes2_arr.tobytes(), raw=True)
+                        ]
+                        if reduction in ("ReduceSum"):
+                            axes1_arr = np.array(axes1, dtype=np.int64)
+                            node = helper.make_node(
+                                reduction, ["X", "X_axes"], ["Y"], keepdims=keepdim)
+                            graph_input.append(
+                                helper.make_tensor_value_info("X_axes", TensorProto.INT64, axes1_arr.shape))
+                            graph_initializer.append(helper.make_tensor("X_axes", TensorProto.INT64,
+                                                                        axes1_arr.shape, axes1_arr.tobytes(), raw=True))
+                        else:
+                            node = helper.make_node(
+                                reduction, ["X"], ["Y"], axes=axes1, keepdims=keepdim)
+
+                        node1 = helper.make_node(
+                            "Unsqueeze", ["Y", "Y_axes"], ["Z"])
+                        graph = helper.make_graph(
+                            [node, node1],
+                            "test",
+                            graph_input,
+                            [helper.make_tensor_value_info(
+                                "Z", TensorProto.FLOAT, output_shape)],
+                            initializer=graph_initializer
+                        )
+                        optimized_model = self._optimized(
+                            graph, ["fuse_consecutive_reduce_unsqueeze"], False)
+
+                        if keepdim or axes1 != axes2:
+                            assert optimized_model.graph == graph
+                        else:
+                            assert len(optimized_model.graph.output) == 1
+                            assert len(optimized_model.graph.node) == 1
+                            assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+                            assert optimized_model.graph.node[-1].op_type == reduction
+
+                            if reduction in ("ReduceSum"):
+                                for init in optimized_model.graph.initializer:
+                                    if init.name == optimized_model.graph.node[-1].input[1]:
+                                        assert list(to_array(init)) == axes1
+                            else:
+                                assert optimized_model.graph.node[-1].attribute[0].name == "axes"
+                                assert optimized_model.graph.node[-1].attribute[0].ints == axes1
+                            optimized_output_shape = tuple(
+                                x.dim_value for x in optimized_model.graph.output[0].type.tensor_type.shape.dim)
+                            assert optimized_output_shape == output_shape
+
+    def test_split_predict_and_lift_lexical_references_for_caffe2_backend(self):
+        model_str = b'\x08\x06\x12\x07pytorch\x1a\x031.9:\xe5\x02\n\'\x12\x011"\x08Constant*\x18\n\x05value*\x0c\x10\x07J\x08\x05\x00\x00\x00\x00\x00\x00\x00\xa0\x01\x04\n \x12\x012"\x08Constant*\x11\n\x05value*\x05\x10\tJ\x01\x01\xa0\x01\x04\n\xd1\x01\n\x011\n\x012\n\x03x.1\x12\x013"\x04Loop*\xba\x01\n\x04body2\xae\x01\n\x1a\n\x04x.11\n\x03i.1\x12\x017\x1a\x05Add_0"\x03Add\n\x1c\n\x012\x12\x018\x1a\nIdentity_1"\x08Identity\x12\x11torch-jit-export1Z\r\n\x03i.1\x12\x06\n\x04\x08\x07\x12\x00Z\x0e\n\x04cond\x12\x06\n\x04\x08\t\x12\x00Z\x1a\n\x04x.11\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03b\x0b\n\x018\x12\x06\n\x04\x08\t\x12\x00b\x17\n\x017\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03\xa0\x01\x05\x12\x10torch-jit-exportZ\x19\n\x03x.1\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03b\x17\n\x013\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03B\x02\x10\t'
+        model = onnx.load_from_string(model_str)
+        passes = ['fuse_consecutive_transposes',
+                  'eliminate_nop_transpose',
+                  'fuse_transpose_into_gemm',
+                  'lift_lexical_references',
+                  'split_predict']
+        self._optimized(model, passes, fixed_point=True, compare_result=False, check=False)
+
+    @unittest.skipUnless(has_tv, "This test needs torchvision")
+    def test_torchvision_fasterrcnn_fpn(self):    # type: () -> None
+        model = tv.models.detection.fasterrcnn_resnet50_fpn(pretrained=False)
+        x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
+        with io.BytesIO() as f:
+            torch.onnx.export(model, x, f, opset_version=11)
+            model = onnx.load_model_from_string(f.getvalue())
+            self._optimized(
+                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True)
+
+    # maskrcnn is only supported in opset 11 and higher
+    @unittest.skipUnless(has_tv, "This test needs torchvision")
+    def test_torchvision_maskrcnn_fpn_opset11(self):    # type: () -> None
+        model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False)
+        x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
+        with io.BytesIO() as f:
+            torch.onnx.export(model, x, f, opset_version=11)
+            model = onnx.load_model_from_string(f.getvalue())
+            self._optimized(
+                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True)
+
+    # keypointrcnn is only supported in opset 11 and higher
+    @unittest.skipUnless(has_tv, "This test needs torchvision")
+    def test_torchvision_keypointrcnn_fpn(self):    # type: () -> None
+        model = tv.models.detection.keypointrcnn_resnet50_fpn(pretrained=False)
+        x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
+        with io.BytesIO() as f:
+            torch.onnx.export(model, x, f, opset_version=11)
+            model = onnx.load_model_from_string(f.getvalue())
+            self._optimized(
+                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True)
+
+    @unittest.skipUnless(has_tv, "This test needs torchvision")
+    def test_torchvision_shufflenet_v2(self):    # type: () -> None
+        model = tv.models.shufflenet_v2_x1_0(pretrained=False)
+        x = torch.rand(1, 3, 224, 224)
+        with io.BytesIO() as f:
+            torch.onnx.export(model, x, f)
+            model = onnx.load_model_from_string(f.getvalue())
+            self._optimized(
+                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True)
+
+    @unittest.skipUnless(has_tv, "This test needs torchvision")
+    def test_torchvision_mnasnet(self):    # type: () -> None
+        model = tv.models.mnasnet1_0(pretrained=False)
+        x = torch.rand(1, 3, 224, 224)
+        with io.BytesIO() as f:
+            torch.onnx.export(model, x, f)
+            model = onnx.load_model_from_string(f.getvalue())
+            self._optimized(
+                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True)
+
+    @unittest.skipUnless(has_tv, "This test needs torchvision")
+    def test_torchvision_deeplabv3(self):    # type: () -> None
+        model = tv.models.segmentation.deeplabv3_resnet50(pretrained=False)
+        x = torch.rand(1, 3, 224, 224)
+        with io.BytesIO() as f:
+            torch.onnx.export(model, x, f)
+            model = onnx.load_model_from_string(f.getvalue())
+            self._optimized(
+                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/third_party/optimizer/setup.cfg b/third_party/optimizer/setup.cfg
new file mode 100644
index 0000000000..288d5e7503
--- /dev/null
+++ b/third_party/optimizer/setup.cfg
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+
+[aliases]
+test=pytest
+
+[tool:pytest]
+addopts = --nbval --current-env
+testpaths = onnxoptimizer/test/
+
+[metadata]
+license-file = LICENSE
+
+[flake8]
+select = B,C,E,F,P,T4,W,B9
+max-line-length = 80
+### DEFAULT IGNORES FOR 4-space INDENTED PROJECTS ###
+# E127, E128 are hard to silence in certain nested formatting situations.
+# E265, E266 talk about comment formatting which is too opinionated.
+# E402 warns on imports coming after statements. There are important use cases
+# like demandimport (https://fburl.com/demandimport) that require statements
+# before imports.
+# E501 is not flexible enough, we're using B950 instead.
+# E722 is a duplicate of B001.
+# F405 is hard to silence since we indeed do star import
+# P207 is a duplicate of B003.
+# P208 is a duplicate of C403.
+# W503 talks about operator formatting which is too opinionated.
+# F401 clashes with PEP484 requiring us to import types that are only used in
+# type comments.
+ignore = E127, E128, E265, E266, E402, E501, E722, F405, P207, P208, W503, F401
+exclude =
+  .git,
+  __pycache__,
+  build/*,
+  third_party/*
+  *_pb2.py,
+  .cache/*
+  .eggs
+  .setuptools-cmake-build/*
+
+[mypy]
+# follow-imports = silent  # TODO remove this
+mypy_path = stubs:third_party/onnx/third_party/pybind11
+strict_optional = True
+warn_return_any = True
+warn_no_return = True
+# TODO warn_unused_ignores = True
+warn_redundant_casts = True
+warn_incomplete_stub = True
+# TODO disallow_untyped_calls = True
+check_untyped_defs = True
+disallow_any_generics = True
+no_implicit_optional = True
+# TODO disallow_incomplete_defs = True
+# TODO disallow_subclassing_any = True
+disallow_untyped_decorators = True
+warn_unused_configs = True
+
+[mypy-onnxoptimizer.*]
+disallow_untyped_defs = True
+
+[mypy-onnxoptimizer.onnx_opt_cpp2py_export]
+ignore_missing_imports = True
+
+[mypy-onnx.*]
+disallow_untyped_defs = True
+ignore_missing_imports = True
+
+[mypy-tools.*]
+disallow_untyped_defs = True
+
+# Ignore errors in setup.py
+[mypy-setup]
+ignore_errors = True
+
diff --git a/third_party/optimizer/setup.py b/third_party/optimizer/setup.py
new file mode 100644
index 0000000000..c693bc877e
--- /dev/null
+++ b/third_party/optimizer/setup.py
@@ -0,0 +1,334 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from distutils.spawn import find_executable
+from distutils import sysconfig, log
+import setuptools
+import setuptools.command.build_py
+import setuptools.command.develop
+import setuptools.command.build_ext
+
+from collections import namedtuple
+from contextlib import contextmanager
+import glob
+import os
+import shlex
+import subprocess
+import sys
+import platform
+from textwrap import dedent
+import multiprocessing
+
+
+TOP_DIR = os.path.realpath(os.path.dirname(__file__))
+SRC_DIR = os.path.join(TOP_DIR, 'onnxoptimizer')
+CMAKE_BUILD_DIR = os.path.join(TOP_DIR, '.setuptools-cmake-build')
+
+WINDOWS = (os.name == 'nt')
+
+CMAKE = find_executable('cmake3') or find_executable('cmake')
+MAKE = find_executable('make')
+
+install_requires = []
+setup_requires = []
+extras_require = {}
+
+################################################################################
+# Global variables for controlling the build variant
+################################################################################
+
+# Default value is set to TRUE\1 to keep the settings same as the current ones.
+# However going forward the recomemded way to is to set this to False\0
+USE_MSVC_STATIC_RUNTIME = bool(os.getenv('USE_MSVC_STATIC_RUNTIME', '1') == '1')
+ONNX_ML = not bool(os.getenv('ONNX_ML') == '0')
+ONNX_VERIFY_PROTO3 = bool(os.getenv('ONNX_VERIFY_PROTO3') == '1')
+ONNX_NAMESPACE = os.getenv('ONNX_NAMESPACE', 'onnx')
+ONNX_BUILD_TESTS = bool(os.getenv('ONNX_BUILD_TESTS') == '1')
+
+DEBUG = bool(os.getenv('DEBUG'))
+COVERAGE = bool(os.getenv('COVERAGE'))
+
+################################################################################
+# Version
+################################################################################
+
+try:
+    git_version = subprocess.check_output(['git', 'rev-parse', 'HEAD'],
+                                          cwd=TOP_DIR).decode('ascii').strip()
+except (OSError, subprocess.CalledProcessError):
+    git_version = None
+
+with open(os.path.join(TOP_DIR, 'VERSION_NUMBER')) as version_file:
+    VersionInfo = namedtuple('VersionInfo', ['version', 'git_version'])(
+        version=version_file.read().strip(),
+        git_version=git_version
+    )
+
+################################################################################
+# Pre Check
+################################################################################
+
+assert CMAKE, 'Could not find "cmake" executable!'
+
+################################################################################
+# Utilities
+################################################################################
+
+
+@contextmanager
+def cd(path):
+    if not os.path.isabs(path):
+        raise RuntimeError('Can only cd to absolute path, got: {}'.format(path))
+    orig_path = os.getcwd()
+    os.chdir(path)
+    try:
+        yield
+    finally:
+        os.chdir(orig_path)
+
+
+################################################################################
+# Customized commands
+################################################################################
+
+
+class ONNXCommand(setuptools.Command):
+    user_options = []
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+
+class create_version(ONNXCommand):
+    def run(self):
+        with open(os.path.join(SRC_DIR, 'version.py'), 'w') as f:
+            f.write(dedent('''\
+            # This file is generated by setup.py. DO NOT EDIT!
+
+            from __future__ import absolute_import
+            from __future__ import division
+            from __future__ import print_function
+            from __future__ import unicode_literals
+
+            version = '{version}'
+            git_version = '{git_version}'
+            '''.format(**dict(VersionInfo._asdict()))))
+
+
+class cmake_build(setuptools.Command):
+    """
+    Compiles everything when `python setupmnm.py build` is run using cmake.
+
+    Custom args can be passed to cmake by specifying the `CMAKE_ARGS`
+    environment variable.
+
+    The number of CPUs used by `make` can be specified by passing `-j<ncpus>`
+    to `setup.py build`.  By default all CPUs are used.
+    """
+    user_options = [
+        (str('jobs='), str('j'), str('Specifies the number of jobs to use with make'))
+    ]
+
+    built = False
+
+    def initialize_options(self):
+        self.jobs = None
+
+    def finalize_options(self):
+        if sys.version_info[0] >= 3:
+            self.set_undefined_options('build', ('parallel', 'jobs'))
+        if self.jobs is None and os.getenv("MAX_JOBS") is not None:
+            self.jobs = os.getenv("MAX_JOBS")
+        self.jobs = multiprocessing.cpu_count() if self.jobs is None else int(self.jobs)
+
+    def run(self):
+        if cmake_build.built:
+            return
+        cmake_build.built = True
+        if not os.path.exists(CMAKE_BUILD_DIR):
+            os.makedirs(CMAKE_BUILD_DIR)
+
+        with cd(CMAKE_BUILD_DIR):
+            build_type = 'Release'
+            # configure
+            cmake_args = [
+                CMAKE,
+                '-DPYTHON_INCLUDE_DIR={}'.format(sysconfig.get_python_inc()),
+                '-DPYTHON_EXECUTABLE={}'.format(sys.executable),
+                '-DBUILD_ONNX_PYTHON=ON',
+                '-DONNX_USE_LITE_PROTO=ON',
+                '-DCMAKE_EXPORT_COMPILE_COMMANDS=ON',
+                '-DONNX_NAMESPACE={}'.format(ONNX_NAMESPACE),
+                '-DPY_EXT_SUFFIX={}'.format(
+                    sysconfig.get_config_var('EXT_SUFFIX') or ''),
+            ]
+            if COVERAGE:
+                cmake_args.append('-DONNX_COVERAGE=ON')
+            if COVERAGE or DEBUG:
+                # in order to get accurate coverage information, the
+                # build needs to turn off optimizations
+                build_type = 'Debug'
+            cmake_args.append('-DCMAKE_BUILD_TYPE=%s' % build_type)
+            if WINDOWS:
+                cmake_args.extend([
+                    # we need to link with libpython on windows, so
+                    # passing python version to window in order to
+                    # find python in cmake
+                    '-DPY_VERSION={}'.format('{0}.{1}'.format(* \
+                                                              sys.version_info[:2])),
+                ])
+                if USE_MSVC_STATIC_RUNTIME:
+                    cmake_args.append('-DONNX_USE_MSVC_STATIC_RUNTIME=ON')
+                if platform.architecture()[0] == '64bit':
+                    cmake_args.extend(['-A', 'x64', '-T', 'host=x64'])
+                else:
+                    cmake_args.extend(['-A', 'Win32', '-T', 'host=x86'])
+            if ONNX_ML:
+                cmake_args.append('-DONNX_ML=1')
+            if ONNX_VERIFY_PROTO3:
+                cmake_args.append('-DONNX_VERIFY_PROTO3=1')
+            if ONNX_BUILD_TESTS:
+                cmake_args.append('-DONNX_BUILD_TESTS=ON')
+            if 'CMAKE_ARGS' in os.environ:
+                extra_cmake_args = shlex.split(os.environ['CMAKE_ARGS'])
+                # prevent crossfire with downstream scripts
+                del os.environ['CMAKE_ARGS']
+                log.info('Extra cmake args: {}'.format(extra_cmake_args))
+                cmake_args.extend(extra_cmake_args)
+            cmake_args.append(TOP_DIR)
+            subprocess.check_call(cmake_args)
+
+            build_args = [CMAKE, '--build', os.curdir]
+            if WINDOWS:
+                build_args.extend(['--config', build_type])
+                build_args.extend(['--', '/maxcpucount:{}'.format(self.jobs)])
+            else:
+                build_args.extend(['--', '-j', str(self.jobs)])
+            subprocess.check_call(build_args)
+
+
+class build_py(setuptools.command.build_py.build_py):
+    def run(self):
+        self.run_command('create_version')
+        self.run_command('cmake_build')
+
+        generated_python_files = \
+            glob.glob(os.path.join(CMAKE_BUILD_DIR, 'onnxoptimizer', '*.py')) + \
+            glob.glob(os.path.join(CMAKE_BUILD_DIR, 'onnxoptimizer', '*.pyi'))
+
+        for src in generated_python_files:
+            dst = os.path.join(
+                TOP_DIR, os.path.relpath(src, CMAKE_BUILD_DIR))
+            self.copy_file(src, dst)
+
+        return setuptools.command.build_py.build_py.run(self)
+
+
+class develop(setuptools.command.develop.develop):
+    def run(self):
+        self.run_command('build_py')
+        setuptools.command.develop.develop.run(self)
+
+
+class build_ext(setuptools.command.build_ext.build_ext):
+    def run(self):
+        self.run_command('cmake_build')
+        setuptools.command.build_ext.build_ext.run(self)
+
+    def build_extensions(self):
+        for ext in self.extensions:
+            fullname = self.get_ext_fullname(ext.name)
+            filename = os.path.basename(self.get_ext_filename(fullname))
+
+            lib_path = CMAKE_BUILD_DIR
+            if os.name == 'nt':
+                debug_lib_dir = os.path.join(lib_path, "Debug")
+                release_lib_dir = os.path.join(lib_path, "Release")
+                if os.path.exists(debug_lib_dir):
+                    lib_path = debug_lib_dir
+                elif os.path.exists(release_lib_dir):
+                    lib_path = release_lib_dir
+            src = os.path.join(lib_path, filename)
+            dst = os.path.join(os.path.realpath(
+                self.build_lib), "onnxoptimizer", filename)
+            self.copy_file(src, dst)
+
+
+class mypy_type_check(ONNXCommand):
+    description = 'Run MyPy type checker'
+
+    def run(self):
+        """Run command."""
+        onnx_script = os.path.realpath(os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py"))
+        returncode = subprocess.call([sys.executable, onnx_script])
+        sys.exit(returncode)
+
+
+cmdclass = {
+    'create_version': create_version,
+    'cmake_build': cmake_build,
+    'build_py': build_py,
+    'develop': develop,
+    'build_ext': build_ext,
+    'typecheck': mypy_type_check,
+}
+
+################################################################################
+# Extensions
+################################################################################
+
+ext_modules = [
+    setuptools.Extension(
+        name=str('onnxoptimizer.onnx_opt_cpp2py_export'),
+        sources=[])
+]
+
+################################################################################
+# Packages
+################################################################################
+
+# no need to do fancy stuff so far
+packages = setuptools.find_packages()
+
+install_requires.extend([
+    'onnx'
+])
+
+################################################################################
+# Test
+################################################################################
+
+setup_requires.append('pytest-runner')
+
+if sys.version_info[0] == 3:
+    # Mypy doesn't work with Python 2
+    extras_require['mypy'] = ['mypy==0.600']
+
+################################################################################
+# Final
+################################################################################
+
+setuptools.setup(
+    name="onnxoptimizer",
+    version=VersionInfo.version,
+    description="Open Neural Network Exchange",
+    ext_modules=ext_modules,
+    cmdclass=cmdclass,
+    packages=packages,
+    license='Apache License v2.0',
+    include_package_data=True,
+    install_requires=install_requires,
+    setup_requires=setup_requires,
+    extras_require=extras_require,
+    author='ONNX Optimizer Authors',
+    author_email='onnx-technical-discuss@lists.lfai.foundation',
+    url='https://github.com/onnx/optimizer',
+)
diff --git a/third_party/optimizer/tools/mypy-onnx.py b/third_party/optimizer/tools/mypy-onnx.py
new file mode 100644
index 0000000000..c0fec7d024
--- /dev/null
+++ b/third_party/optimizer/tools/mypy-onnx.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+import subprocess
+import os
+
+
+def main():  # type: () -> None
+    try:
+        root_folder = os.path.realpath(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        os.chdir(root_folder)
+
+        subprocess.check_call(["mypy", "."])
+        subprocess.check_call(["mypy", "--py2", "."])
+
+        exit(0)
+    except subprocess.CalledProcessError:
+        # Catch this exception because we don't want it to output a backtrace that would clutter the mypy output
+        exit(1)
+
+
+if __name__ == '__main__':
+    main()