class_docstr: Creates dataset to use to do end-to-end inference
  
  Args:
      input_file: path to the raw data file (e.g., train.tsv). 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>`.
      mode: should be one of the values ['tn', 'itn', 'joint'].  `tn` mode is for TN only. `itn` mode is for ITN only. `joint` is for training a system that can do both TN and ITN at the same time.
      lang: Language of the dataset
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docstr: Function for checking whether the predicted string can be considered
  the same as the target string
  
  Args:
      pred: Predicted string
      target: Target string
      inst_dir: Direction of the instance (i.e., INST_BACKWARD or INST_FORWARD).
  Return: an int value (0/1) indicating whether pred and target are the same.
docstr_tok: ["Function","for","checking","whether","the","predicted","string","can","be","considered","the","same","as","the","target","string","Args",":","pred",":","Predicted","string","target",":","Target","string","inst_dir",":","Direction","of","the","instance","(","i.e.",",","INST_BACKWARD","or","INST_FORWARD",")",".","Return",":","an","int","value","(","0\/1",")","indicating","whether","pred","and","target","are","the","same","."]
code: def is_same(pred: str, target: str, inst_dir: str):
      """
      Function for checking whether the predicted string can be considered
      the same as the target string
  
      Args:
          pred: Predicted string
          target: Target string
          inst_dir: Direction of the instance (i.e., INST_BACKWARD or INST_FORWARD).
      Return: an int value (0/1) indicating whether pred and target are the same.
      """
      if inst_dir == constants.INST_BACKWARD:
          pred = remove_puncts(pred)
          target = remove_puncts(target)
      pred = normalize_str(pred)
      target = normalize_str(target)
      return int(pred == target)
  
file_path: NeMo/nemo/collections/nlp/data/text_normalization/test_dataset.py
imports: from collections import defaultdict
  from typing import List
  from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor
  from nemo.collections.nlp.data.text_normalization import constants
  from nemo.collections.nlp.data.text_normalization.utils import normalize_str, read_data_file, remove_puncts
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the TextNormalizationTestDataset obj's underlying member method code with example usage: obj.is_same(pred, target, inst_dir) and returns: int