for name in cuda_env_names:
          if name in environ_cp:
            write_action_env_to_bazelrc(name, environ_cp[name])
        break

      # Restore settings changed below if CUDA config could not be validated.
      environ_cp = dict(environ_save)

      set_tf_cuda_version(environ_cp)
      set_tf_cudnn_version(environ_cp)
      if is_windows():

    # Note that you could generate a list of all the affected targets with e.g.:
    # bazel query $(paste -sd "+" $BATS_FILE_TMPDIR/changed_files) --keep_going
    # Only shows Added, Changed, Modified, Renamed, and Type-changed files
    if [[ "$(git rev-parse --abbrev-ref HEAD)" = "pull_branch" ]]; then
        # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"'

TFCI_ARTIFACT_LATEST_GCS_URI="gs://tensorflow/nightly/latest/"
TFCI_ARTIFACT_STAGING_GCS_ENABLE=1
TFCI_ARTIFACT_STAGING_GCS_URI="gs://tensorflow-ci-artifact-staging/staging/nightly/$(git rev-parse HEAD)/"

# 2. The internal version numbers get changed
TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1

# 3. Push results to public build cache (unique cache for MacOS)
if [[ $(uname -s) == "Darwin" ]]; then

namespace tensorflow {
namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.

  ASSERT_TRUE(CompareSubString(0, 4, result, read));

  read = tf_random_access_file::Read(file, content_view.length() - 2, 4, result,
                                     status_);
  ASSERT_EQ(TF_GetCode(status_), TF_OUT_OF_RANGE) << TF_Message(status_);
  ASSERT_TRUE(CompareSubString(content_view.length() - 2, 2, result, read));

  delete[] result;
  tf_random_access_file::Cleanup(file);
  delete file;
}

*   Clone the TensorFlow repo and switch to the corresponding branch for your
    desired TensorFlow version, for example, branch `r2.8` for version 2.8.
*   Apply (that is, cherry-pick) the desired changes and resolve any code
    conflicts.
*   Run TensorFlow tests and ensure they pass.
*   [Build](https://www.tensorflow.org/install/source) the TensorFlow pip
    package from source.

## Continuous build status

  with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
    log_lines = f.read().splitlines()

  result_store_links: ResultDictType = {}
  current_url = None
  for i in range(len(log_lines)):
    line = log_lines[i]
    result_store_line_match = re.search(RESULT_STORE_LINK_RE, line)
    if not result_store_line_match:
      continue

    url = result_store_line_match.group(1)

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Use large matrices so that RPCs don't return before we get a chance
  // to call TFE_DeleteContext.
  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle100x100(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle100x100(ctx);
  const char remote_device_name[] =