code: def add_arguments(parser: argparse.ArgumentParser):
      parser.add_argument(
          "--data_dir",
          default=None,
          type=str,
          required=True,
          help="The input data dir. Should contain dataset files to be parsed "
          + "by the dataloaders.",
      )
      train_args(
          parser,
          models_family=TransformerTokenClassifier.MODEL_CLASS.keys(),
      )
      create_base_args(
          parser,
          model_types=TransformerTokenClassifier.MODEL_CLASS.keys(),
      )
      parser.add_argument(
          "--train_file_name",
          type=str,
          default="train.txt",
          help="File name of the training dataset",
      )
      parser.add_argument(
          "--ignore_token",
          type=str,
          default="",
          help="a token to ignore when processing the data",
      )
      parser.add_argument(
          "--best_result_file",
          type=str,
          default="best_result.txt",
          help="file path for best evaluation output",
      )
  
imports: import argparse
  import io
  import logging
  import os
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from nlp_architect.data.sequential_tagging import TokenClsInputExample, TokenClsProcessor
  from nlp_architect.data.utils import write_column_tagged_file
  from nlp_architect.models.transformers import TransformerTokenClassifier
  from nlp_architect.nn.torch import setup_backend, set_seed
  from nlp_architect.procedures.procedure import Procedure
  from nlp_architect.procedures.registry import register_inference_cmd, register_train_cmd
  from nlp_architect.procedures.transformers.base import create_base_args, inference_args, train_args
  from nlp_architect.utils.io import prepare_output_path
  from nlp_architect.utils.text import SpacyInstance
label_desc: Use image node_id 1 for calling the TransformerTokenClsTrain obj's underlying member method code with example usage: obj.add_arguments(parser) without return types