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
class_docstr_tok: ["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"]
code: def __len__(self):
      return len(self.inputs)
  
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 3 for calling the TextNormalizationTestDataset obj's underlying member method code with example usage: obj.__len__() and returns: len