// is not interested in).
//
// BackwardFunction needs to be a closure which stores intermediate activations
// from the forward computation and calls a vector-jacobian product function
// (also known as adjoint function) to compute, given downstream gradients,
// upstream gradients.
//
// TODO(apassos) provide concrete template instantiations for TFE_TensorHandle

        run: |
          is_nightly=0 && tf_project_name='tensorflow_cpu_aws' && ${{ github.event_name == 'schedule' }} && is_nightly=1 && tf_project_name='tf_nightly_cpu_aws'
          echo "PyPI project name:" $tf_project_name
          CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \

}

// TF_Graph functions ---------------------------------------------------------

TF_Graph::TF_Graph()
    : graph(tensorflow::OpRegistry::Global()),
      refiner(graph.versions().producer(), graph.op_registry()),
      delete_requested(false),
      parent(nullptr),
      parent_inputs(nullptr) {
  // Tell the shape refiner to also run shape inference on functions.

    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,
        and `Model.predict_generator` are deprecated endpoints. They are

  }

 private:
  TFE_Context* context_;
  TFE_CustomDevice device_;
  void* info_;
  string name_;
};

// An adapter which wraps the shape/data produced by C custom devices and uses
// it to implement custom device methods.
class CAPICustomDeviceTensorHandle
    : public tensorflow::CustomDeviceTensorHandle {
 public:

    get_compat_files(TENSORFLOW_API_INIT_FILES_V1, 1) + get_nested_compat_files([
        1,
        2,
    ])
)
# copybara:comment_end

# Config setting used when building for products
# which requires restricted licenses to be avoided.
config_setting(
    name = "no_lgpl_deps",
    define_values = {"__TENSORFLOW_NO_LGPL_DEPS__": "1"},
    visibility = ["//visibility:public"],
)

build:rbe_linux_cpu --repo_env=TF_PYTHON_CONFIG_REPO="@sigbuild-r2.17-clang_config_python"
build:rbe_linux_cpu --python_path="/usr/bin/python3"
# These you may need to change for your own GCP project.
common:rbe_linux_cpu --remote_instance_name=projects/tensorflow-testing/instances/default_instance

# TODO(kanglan): Remove it after toolchain update is complete.
build:rbe_linux_cpu_old --config=rbe_linux