code: def _parse_dataset(file_path, tmp_dir, train):
      """Convert the dataset in to a simpler format.
  
      This function creates two files. One for being processed to produce a vocab
      and another to generate the data.
  
      Args:
        file_path: string, path to the file to parse.
        tmp_dir: string, path to the directory to output the files.
        train: bool, indicating if we are parsing the training set.
      """
      input_path = file_path
      file_name = "train" if train else "dev"
      gen_output_path = os.path.join(tmp_dir, file_name + ".txt")
      example_output_path = os.path.join(tmp_dir, _EXAMPLES_FILE)
  
      print("input path: " + input_path)
      print("gen_output_path: " + gen_output_path)
      print("example_output_path: " + example_output_path)
  
      input_file = tf.gfile.Open(input_path, mode="r")
      examples = []
      for counter, line in enumerate(input_file):
          if counter == 0:  # Ignore first line since its a header.
              continue
          # Get the token and embedding vector.
          line_split = line.split("\t")
  
          parse1 = line_split[_PARSE1_INDEX]
          parse2 = line_split[_PARSE2_INDEX]
          consensus_label = line_split[_LABEL_INDEX]
  
          tokens1 = _get_tokens_and_tags(parse1)
          tokens2 = _get_tokens_and_tags(parse2)
  
          tokens1_str = " ".join(tokens1)
          tokens2_str = " ".join(tokens2)
  
          if consensus_label != "-":
              examples.append(
                  [tokens1_str, tokens2_str, consensus_label]
              )
  
      input_file.close()
  
      # Output tab delimited file of lines of examples (sentence1, sentence2, label)
      with tf.gfile.GFile(gen_output_path, "w") as f:
          for tokens1_str, tokens2_str, consensus_label in examples:
              f.write(
                  "%s\t%s\t%s\n"
                  % (tokens1_str, tokens2_str, consensus_label)
              )
  
      if train:
          # Output file containing all the sentences for generating the vocab from.
          with tf.gfile.GFile(example_output_path, "w") as f:
              for tokens1_str, tokens2_str, consensus_label in examples:
                  f.write("%s %s\n" % (tokens1_str, tokens2_str))
  
file_path: tensor2tensor/tensor2tensor/data_generators/snli.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import zipfile
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import tokenizer
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling a global function with example usage: _parse_dataset(file_path, tmp_dir, train) without return types