code: def _original_vocab(tmp_dir):
      """Returns a set containing the original vocabulary.
  
      This is important for comparing with published results.
  
      Args:
        tmp_dir: directory containing dataset.
  
      Returns:
        a set of strings
      """
      vocab_url = (
          "http://download.tensorflow.org/models/LM_LSTM_CNN/"
          "vocab-2016-09-10.txt"
      )
      vocab_filename = os.path.basename(vocab_url + ".en")
      vocab_filepath = os.path.join(tmp_dir, vocab_filename)
      if not os.path.exists(vocab_filepath):
          generator_utils.maybe_download(
              tmp_dir, vocab_filename, vocab_url
          )
      return set(
          [
              text_encoder.native_to_unicode(l.strip())
              for l in tf.gfile.Open(vocab_filepath)
          ]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tarfile
  from six.moves import range
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: _original_vocab(tmp_dir) and returns: set