class_docstr: Creates dataset to use to train a DuplexTaggerModel.
  
  Converts from raw data to an instance that can be used by Dataloader.
  
  For dataset to use to do end-to-end inference, see TextNormalizationTestDataset.
  
  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>`.
      tokenizer: tokenizer of the model that will be trained on the dataset
      tokenizer_name: name of the tokenizer,
      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.
      tagger_data_augmentation (bool): a flag indicates whether to augment the dataset with additional data instances
      lang: language of the dataset
      use_cache: Enables caching to use pickle format to store and read data from,
      max_insts: Maximum number of instances (-1 means no limit)
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code: def __len__(self):
      return len(self.labels)
  
file_path: NeMo/nemo/collections/nlp/data/text_normalization/tagger_dataset.py
imports: import os
  import pickle
  from tqdm import tqdm
  from transformers import PreTrainedTokenizerBase
  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 read_data_file
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the TextNormalizationTaggerDataset obj's underlying member method code with example usage: obj.__len__() and returns: len