code: def read_google_data(
      data_file: str, lang: str, split: str, add_test_full=False
  ):
      """
      The function can be used to read the raw data files of the Google Text Normalization
      dataset (which can be downloaded from https://www.kaggle.com/google-nlu/text-normalization)
  
      Args:
          data_file: Path to the data file. Should be of the form output-xxxxx-of-00100
          lang: Selected language.
          split: data split
          add_test_full: do not truncate test data i.e. take the whole test file not #num of lines
      Return:
          data: list of examples
      """
      data = []
      cur_classes, cur_tokens, cur_outputs = [], [], []
      with open(data_file, "r", encoding="utf-8") as f:
          for linectx, line in tqdm(enumerate(f)):
              es = line.strip().split("\t")
              if split == "test" and not add_test_full:
                  # For the results reported in the paper "RNN Approaches to Text Normalization: A Challenge":
                  # + For English, the first 100,002 lines of output-00099-of-00100 are used for the test set
                  # + For Russian, the first 100,007 lines of output-00099-of-00100 are used for the test set
                  if (
                      lang == constants.ENGLISH
                      and linectx == TEST_SIZE_EN
                  ):
                      break
                  if (
                      lang == constants.RUSSIAN
                      and linectx == TEST_SIZE_RUS
                  ):
                      break
              if len(es) == 2 and es[0] == "<eos>":
                  data.append((cur_classes, cur_tokens, cur_outputs))
                  # Reset
                  cur_classes, cur_tokens, cur_outputs = [], [], []
                  continue
  
              # Remove _trans (for Russian)
              if lang == constants.RUSSIAN:
                  es[2] = es[2].replace("_trans", "")
              # Update the current example
              assert len(es) == 3
              cur_classes.append(es[0])
              cur_tokens.append(es[1])
              cur_outputs.append(es[2])
      return data
  
imports: from argparse import ArgumentParser
  from os import listdir, mkdir
  from os.path import isdir, isfile, join
  from tqdm import tqdm
  from nemo.collections.nlp.data.text_normalization import constants
label_desc: Use image node_id 1 for calling a global function with example usage: read_google_data(data_file, lang, split, add_test_full) and returns: data