error_msg='Invalid gcc path. %s cannot be found.',
  )

  write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)


def choose_compiler(environ_cp):
  question = 'Do you want to use Clang to build TensorFlow?'
  yes_reply = 'Clang will be used to compile TensorFlow.'
  no_reply = 'GCC will be used to compile TensorFlow.'
  var = int(
      get_var(

        with:
          #Comma separated list of labels that can be assigned to issues to exclude them from being marked as stale
          exempt-issue-labels: 'override-stale'
          #Comma separated list of labels that can be assigned to PRs to exclude them from being marked as stale
          exempt-pr-labels: "override-stale"

// Tensor or IndexedSlices or None, which here we map to nullptr. Gradients need
// to allow their size to be computed and they need to be passable to a backward
// function and deleted (as the backprop code creates lots of gradients the user
// is not interested in).
//
// BackwardFunction needs to be a closure which stores intermediate activations

//
// The API here is subject to changes in the future.
// --------------------------------------------------------------------------

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//

    ]

  def test_import_runtime(self):
    """Try to import all packages from api packages file one by one."""
    version = "v2"
    failed_packages = []

    logging.info("Try to import packages at runtime...")
    for package_name in self.api_packages_v2:
      # Convert package name to the short version:
      # tensorflow._api.v2.distribute.experimental to
      # tensorflow.distribute.experimental

# --incompatible_remove_legacy_whole_archive flag does.
# This flag is set to true in Bazel 1.0 and newer versions. We tried to migrate
# Tensorflow to the default, however test coverage wasn't enough to catch the
# errors.
# There is ongoing work on Bazel team's side to provide support for transitive
# shared libraries. As part of migrating to transitive shared libraries, we
# hope to provide a better mechanism for control over symbol exporting, and

# bazel-out directory. Next, it archives the generated source files
# into a srcjar directly under the root. There doesn't appear to be a
# simple way to parameterize this from bazel, hence this helper to
# "normalize" the srcjar layout.
#
# Arguments:
#   src_jar - path to the original srcjar
#   dest_jar - path to the destination
# Returns:
#   None
function cp_normalized_srcjar() {
  src_jar="$1"
  dest_jar="$2"

  console.log(domain);
  return domain;
};

/** For trusted parters like Intel, we want to auto-run tests
    This allows us to reduce the delay to external partners
    Add Labels - kokoro:force-run
    The PR is also assigned to specific teams to fast track review
    Additional reviewers can be added manually based on PR contents
  @param {!object}

  # Column 1 is left only, Column 2 is right only, Column 3 is shared lines
  # Select lines unique to actual_licenses, i.e. extra licenses.
  comm -1 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_extra > $BATS_TEST_TMPDIR/actual_extra_licenses || true
  # Select lines unique to expected_licenses, i.e. missing licenses

      with:
        title: Update the RBE images to the latest container versions
        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        reviewers: mihaimaruseac,learning-to-play,nitins17
        body: |
          This PR was created by a GitHub Actions workflow to update all the SIG Build-based RBE containers to the most recent containers. See: