code: def add_parse_args(parser: argparse.ArgumentParser):
      parser.add_argument(
          "--model_type",
          default="cnn-lstm",
          type=str,
          choices=list(MODEL_TYPE.keys()),
          help="model type to use for this tagger",
      )
      parser.add_argument(
          "--config_file",
          type=str,
          help="Embedder model configuration file",
      )
      parser.add_argument("-b", type=int, default=10, help="Batch size")
      parser.add_argument(
          "-e", type=int, default=155, help="Number of epochs"
      )
      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.",
      )
      parser.add_argument(
          "--tag_col",
          type=int,
          default=-1,
          help="Entity labels tab number in train/test files",
      )
      parser.add_argument(
          "--max_sentence_length",
          type=int,
          default=50,
          help="Max sentence length",
      )
      parser.add_argument(
          "--max_word_length",
          type=int,
          default=12,
          help="Max word length in characters",
      )
      parser.add_argument(
          "--use_crf",
          action="store_true",
          help="Use CRF classifier instead of Softmax",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.001,
          help="Learning rate for optimizer (Adam)",
      )
      parser.add_argument(
          "--embedding_file",
          help="Path to external word embedding model file",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          required=True,
          help="Output directory where the model will be saved",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="random seed for initialization",
      )
      parser.add_argument(
          "--logging_steps",
          type=int,
          default=50,
          help="Log every X updates steps.",
      )
      parser.add_argument(
          "--save_steps",
          type=int,
          default=500,
          help="Save model every X updates steps.",
      )
      parser.add_argument(
          "--overwrite_output_dir",
          action="store_true",
          help="Overwrite the content of the output directory",
      )
      parser.add_argument(
          "--no_cuda",
          action="store_true",
          help="Avoid using CUDA when available",
      )
      parser.add_argument(
          "--best_result_file",
          type=str,
          default="best_dev.txt",
          help="file path for best evaluation output",
      )
      parser.add_argument(
          "--word_dropout",
          type=float,
          default=0,
          help="word dropout rate for input tokens",
      )
      parser.add_argument(
          "--ignore_token",
          type=str,
          default="",
          help="a token to ignore when processing the data",
      )
      parser.add_argument(
          "--train_filename",
          type=str,
          default="train.txt",
          help="filename of training dataset",
      )
      parser.add_argument(
          "--dev_filename",
          type=str,
          default="dev.txt",
          help="filename of development dataset",
      )
      parser.add_argument(
          "--test_filename",
          type=str,
          default="test.txt",
          help="filename of test dataset",
      )
  
imports: import argparse
  import io
  import os
  import torch
  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.tagging import NeuralTagger
  from nlp_architect.nn.torch.modules.embedders import IDCNN, CNNLSTM
  from nlp_architect.nn.torch import setup_backend, set_seed
  from nlp_architect.nn.torch.distillation import TeacherStudentDistill
  from nlp_architect.procedures.procedure import Procedure
  from nlp_architect.procedures.registry import register_train_cmd, register_inference_cmd
  from nlp_architect.utils.embedding import get_embedding_matrix, load_embedding_file
  from nlp_architect.utils.io import prepare_output_path
  from nlp_architect.utils.text import SpacyInstance
  from nlp_architect.nn.torch.data.dataset import ParallelDataset, ConcatTensorDataset, CombinedTensorDataset
  from nlp_architect.models.transformers import TransformerTokenClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: add_parse_args(parser) without return types