* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
         * All you need is a good internet connection and you are all set.
      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*
   
windows_comment: >
   From the stack trace it looks like you are hitting windows path length limit.

   actually using the new version with Tensorflow.

2) In the `WORKSPACE` file, add the new version to the `python_versions`
   parameter of the `python_register_multi_toolchains` function.

3) In the `BUILD.bazel` file, add a load statement for the new version, e.g.

   ```
      load("@python//3.11:defs.bzl",
           compile_pip_requirements_3_11 = "compile_pip_requirements")
   ```

};

TEST_P(CppGradients, TestIdentityNGrad) {
  // This test is interesting because the current implementation of GradientTape
  // would return [0, 1] whereas we use build_default_zeros_grads=false here
  // so we get back [nullptr, 1].

  AbstractTensorHandlePtr x1;
  {
    AbstractTensorHandle* x1_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(

# the CACHEBUSTER to the PR number.
build --action_env=CACHEBUSTER=20220325

# Use Python 3.X as installed in container image
build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3"
build --python_path="/usr/local/bin/python3"

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Use lld as the linker
build --linkopt="-fuse-ld=lld"
build --linkopt="-lm"

      - name: Build binary and run C++ tests
        shell: bash
        run: |
          is_nightly=0 && tf_project_name='tf_ci_ext_c' && ${{ github.event_name == 'schedule' }} && is_nightly=1 && tf_project_name='tf_nightly_ci_ext_c'
          CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \

      - name: Build and test pip wheel
        shell: bash
        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

COPY builder.devtoolset/build_devtoolset.sh /build_devtoolset.sh
COPY builder.devtoolset/gcc9-fixups.patch /gcc9-fixups.patch
COPY builder.devtoolset/stringop_trunc.patch /stringop_trunc.patch

RUN /build_devtoolset.sh devtoolset-10 /dt10

# Build later version of patchelf that is not so buggy
COPY builder.patchelf/build_patchelf.sh /build_patchelf.sh
COPY apt.conf /etc/apt/
RUN /build_patchelf.sh

# if there is a change in requirements.in then all lock files will be updated
# accordingly

# All commands run relative to this directory
cd "$(dirname "${BASH_SOURCE[0]}")"

mv BUILD.bazel BUILD

SUPPORTED_VERSIONS=("3_9" "3_10" "3_11" "3_12")

for VERSION in "${SUPPORTED_VERSIONS[@]}"
do
  touch "requirements_lock_$VERSION.txt"
  bazel run \
    --experimental_convenience_symlinks=ignore \

    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,
      output_gradients, result, /*build_default_zeros_grads*/ false));
  return absl::OkStatus();
}

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.

'''
 Repository rule to set some environment variables.
 Can be set via build parameter "--repo_env=<VARIABLE_NAME>=<value>"
 e.g "--repo_env=REQUIREMENTS_FILE_NAME=requirements.in"

 List of variables:
 REQUIREMENTS_FILE_NAME
'''

def _updater_config_repository_impl(repository_ctx):
    repository_ctx.file("BUILD", "")
    requirements_file_name = repository_ctx.os.environ.get("REQUIREMENTS_FILE_NAME", "requirements.in")
    repository_ctx.file(