`tensorflow/python/saved_model/load_test.py`:

    (Requires `python>=3.7`)

    ```bash
    bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables*
    ```

    **Note:** You can add `--test_sharding_strategy=disabled` to the `flags` to
    disable the sharding so that all the test outputs are in one file. However,

          absl::StrCat("The specified bucket gs://", bucket, " was not found.")
              .c_str());
    return result;
  }

  bool is_folder = FolderExists(gcs_file, path, status);
  if (TF_GetCode(status) != TF_OK) return false;
  if (is_folder) return true;

  bool is_object = ObjectExists(gcs_file, path, bucket, object, status);
  if (TF_GetCode(status) != TF_OK) return false;
  if (is_object) {

static void Cleanup(TF_RandomAccessFile* file) {
  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  close(posix_file->fd);
  // This would be safe to free using `free` directly as it is only opaque.
  // However, it is better to be consistent everywhere.
  plugin_memory_free(const_cast<char*>(posix_file->filename));
  delete posix_file;
}

static int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,

//  - target: dy in dx/dy
//  - source: dx in dx/dy
//  - tensor: one of the many inputs or outputs of an operation
//
// Below here we do the gradient algorithm. It works as follows:
//
// First we filter the tape to just the subset of operations we want to
// differentiate. In the process of doing so we count how many times each Tensor
// is used as an input to an op (so we know when we're done computing gradients

      k -= 1
      continue
    previous_end_line = lines.get('end') or start_line

  return result_store_links


def indent_xml(elem, level=0) -> None:
  """Indents and newlines the XML for better output."""
  indent_str = '\n' + level * '  '
  if len(elem):  # pylint: disable=g-explicit-length-test  # `if elem` not valid
    if not elem.text or not elem.text.strip():
      elem.text = indent_str + '  '

  TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status);
  TFE_OpSetAttrString(op.get(), "container", "", 0);
  // Use the special GUID for no buffer sharing
  //
  // TODO(allenl): Should we provide a better API for this? AFAIK this is the
  // only reasonable way to make variables with no aliasing using the eager C
  // API.
  std::string no_sharing = "cd2c89b7-88b7-44c8-ad83-06c2a9158347";