#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 \

## Resources

*   [TensorFlow.org](https://www.tensorflow.org)
*   [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
*   [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
*   [TensorFlow Examples](https://github.com/tensorflow/examples)
*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)
*   [TensorFlow Blog](https://blog.tensorflow.org)

        "//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",

# 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.

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

        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

        committer: TensorFlow Release Automation <jenkins@tensorflow.org>
        token: ${{ secrets.JENKINS_TOKEN }}
        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

# filesystem path
filesystem_path:
   - tensorflow/c/experimental/filesystem
# security path
security_path:
   - tensorflow/security
# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries: