/tensorflow/c/eager @qqfish
/tensorflow/core/common_runtime/eager @qqfish
/tenosrflow/core/debug @caisq
/tensorflow/core/kernels/mkl/ @penpornk
/tensorflow/core/kernels/sparse/ @penpornk
/tensorflow/core/nccl/ @azaks2 @chsigg
/tensorflow/python/autograph/ @mdanatg
/tensorflow/python/debug @caisq
/tensorflow/python/eager @rohan100jain
/tensorflow/tools/docs/ @markdaoust
/tensorflow/compiler/mlir/ @aminim

            "//tensorflow/cc:while_loop",
            "//tensorflow/cc/saved_model:loader_lite",
            "//tensorflow/core:core_cpu",
            "//tensorflow/core:core_cpu_internal",
            "//tensorflow/core:framework",
            "//tensorflow/core:lib",
            "//tensorflow/core:lib_internal",
            "//tensorflow/core:op_gen_lib",
            "//tensorflow/core:protos_all_cc",

        "//tensorflow/compiler/mlir/quantization/tensorflow/python:quantize_model_cc_impl",
        "//tensorflow/compiler/mlir/quantization/tensorflow:passes",
        "//tensorflow/compiler/mlir/tensorflow",
        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",
        "//tensorflow/compiler/mlir/tensorflow:error_util",
        "//tensorflow/compiler/mlir/tensorflow:mlir_import_options",

#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/net.h"
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

using tensorflow::FunctionDef;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::Status;

    tensorflow/c/tf_buffer.h  \
    tensorflow/c/tf_datatype.h \
    tensorflow/c/tf_status.h \
    tensorflow/c/tf_tensor.h \
    tensorflow/c/tf_tensor_helper.h \
    tensorflow/c/tf_tstring.h \
    tensorflow/c/tf_file_statistics.h \
    tensorflow/c/tensor_interface.h \
    tensorflow/c/c_api_macros.h \
    tensorflow/c/c_api_experimental.h \
    ${LIB_PKG}/include/tensorflow/c
  cp tensorflow/c/eager/c_api.h \

# requiring conditional path adjustments throughout the CI scripts.
# Note: This does not work for `docker cp` commands.
# For a little more detail, see:
# https://github.com/tensorflow/tensorflow/blob/cc3006af6fea8d2a1360b3b5e65f9d14bbdb7094/ci/devinfra/docker/windows2022/Dockerfile#L196
TFCI_OUTPUT_WIN_DOCKER_DIR='C:/drive_t'

# Docker on Windows doesn't support the `host` networking mode, and so

This document discusses the TensorFlow security model. It describes the security
risks to consider when using models, checkpoints or input data for training or
serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.

This document applies to other repositories in the TensorFlow organization,
covering security practices for the entirety of the TensorFlow ecosystem.

_current_module.__path__ = [_module_dir] + _current_module.__path__

_CONTRIB_WARNING = """
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.
"""

        You can collect some of this information using our [environment capture script](https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh). You can also obtain the TensorFlow version with the following: <br> 1. TensorFlow 1.0: `python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"` <br>2. TensorFlow 2.0: `python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"`

  - type: dropdown
    id: source

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML