code: def _preprocess_file(input_file: str) -> List[Tuple[List[str]]]:
      """
      Performs initial preprocessing, i.e., urls formatting, removal of "_trans" from Ru set
  
      Args:
          input_file: path to a file in google TN format
  
      Returns:
          Processed data. Each element is a Tuple(List[semiotic classes], List[written words], List[spoken words])
      """
      print(
          f"Reading and running initial pre-processing of {input_file}..."
      )
      cur_split = []
      with open(input_file, "r", encoding="utf-8") as f:
          # Loop through each line of the file
          cur_classes, cur_tokens, cur_outputs = [], [], []
          for linectx, line in tqdm(enumerate(f)):
              es = line.strip().split("\t")
              if len(es) == 2 and es[0] == "<eos>":
                  cur_split.append(
                      (cur_classes, cur_tokens, cur_outputs)
                  )
                  # Reset
                  cur_classes, cur_tokens, cur_outputs = [], [], []
                  continue
              assert len(es) == 3
              cur_classes.append(es[0])
              cur_tokens.append(es[1])
              cur_outputs.append(es[2])
      return cur_split
  
file_path: NeMo/examples/nlp/duplex_text_normalization/data/create_tarred_dataset.py
imports: import argparse
  import json
  import os
  import pickle
  import random
  import tarfile
  from glob import glob
  from typing import List, Tuple
  from joblib import Parallel, delayed
  from tqdm import tqdm
  from transformers import AutoTokenizer
  import nemo.collections.nlp.data.text_normalization.constants
  from nemo.collections.nlp.data.text_normalization.decoder_dataset import TextNormalizationDecoderDataset
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: _preprocess_file(input_file) and returns: cur_split