import tensorflow as tf

    within the assets subdirectory, we will return a None.

    Args:
      vocab_filename: The vocabulary name to lookup.
    """
    mapping_path = os.path.join(self._transformed_metadata_dir, self.ASSET_MAP)

    mapping = {}
    if tf.io.gfile.exists(mapping_path):
      with tf.io.gfile.GFile(mapping_path) as f:
        mapping = json.loads(f.read())
        if vocab_filename in mapping:
          vocab_path = os.path.join(self.transform_savedmodel_dir,
                                    tf.saved_model.ASSETS_DIRECTORY,
                                    mapping[vocab_filename])
          if tf.io.gfile.exists(vocab_path):
            return vocab_path

    prefix = os.path.join(self.transform_savedmodel_dir,
                          tf.saved_model.ASSETS_DIRECTORY,
                          sanitized_vocab_filename(filename=vocab_filename))
    files = tf.io.gfile.glob(prefix) + tf.io.gfile.glob(
        '{}.tfrecord.gz'.format(prefix))
    if not files:
      return None
    if len(files) != 1:
      raise ValueError('Found too many vocabulary files: {}'.format(files))
    return files[0]

  def _vocabulary_size_from_annotations(self,
