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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)
  ])