code: def read_data_file(fp: str, lang: str, max_insts: int = -1):
      """Reading the raw data from a file of NeMo format
      For more info about the data format, refer to the
      `text_normalization doc <https://github.com/NVIDIA/NeMo/blob/main/docs/source/nlp/text_normalization.rst>`.
  
      Args:
          fp: file paths
          lang: language
          max_insts: Maximum number of instances (-1 means no limit)
      Returns:
          insts: List of sentences parsed as list of words
      """
      insts, w_words, s_words, classes = [], [], [], []
      # Read input file
      with open(fp, "r", encoding="utf-8") as f:
          for line in tqdm(f):
              es = [
                  e.strip()
                  for e in input_preprocessing(
                      line.strip(), lang=lang
                  ).split("\t")
              ]
              if es[0] == "<eos>":
                  inst = (
                      deepcopy(classes),
                      deepcopy(w_words),
                      deepcopy(s_words),
                  )
                  insts.append(inst)
                  # Reset
                  w_words, s_words, classes = [], [], []
  
                  if max_insts > 0 and len(insts) >= max_insts:
                      break
              else:
                  classes.append(es[0])
                  w_words.append(es[1])
                  s_words.append(es[2])
      return insts
  
file_path: NeMo/nemo/collections/nlp/data/text_normalization/utils.py
imports: import string
  from copy import deepcopy
  import regex
  from tqdm import tqdm
  from nemo.collections.nlp.data.text_normalization import constants
label_desc: Use image node_id 6 for calling a global function with example usage: read_data_file(fp, lang, max_insts) and returns: insts