code: def create_argument_parser():
      _parser = argparse.ArgumentParser()
      _parser.add_argument(
          "--data_dir",
          type=validate_existing_directory,
          help="Path to directory containing CONLL2000 files",
      )
      _parser.add_argument(
          "--embedding_model",
          type=validate_existing_filepath,
          help="Word embedding model path (GloVe/Fasttext/textual)",
      )
      _parser.add_argument(
          "--sentence_length",
          default=50,
          type=int,
          help="Maximum sentence length",
      )
      _parser.add_argument(
          "--char_features",
          default=False,
          action="store_true",
          help="use word character features in addition to words",
      )
      _parser.add_argument(
          "--max_word_length",
          type=int,
          default=12,
          help="maximum number of character in one word "
          "(if --char_features is enabled)",
      )
      _parser.add_argument(
          "--feature_size",
          default=100,
          type=int,
          help="Feature vector size (in embedding and LSTM layers)",
      )
      _parser.add_argument(
          "--use_cudnn",
          default=False,
          action="store_true",
          help="use CUDNN based LSTM cells",
      )
      _parser.add_argument(
          "--classifier",
          default="crf",
          choices=["crf", "softmax"],
          type=str,
          help="classifier to use in last layer",
      )
      _parser.add_argument(
          "-b", default=10, type=int, help="batch size"
      )
      _parser.add_argument(
          "-e",
          default=10,
          type=int,
          help="number of epochs run fit model",
      )
      _parser.add_argument(
          "--model_name",
          default="chunker_model",
          type=str,
          help="Model name (used for saving the model)",
      )
      return _parser
  
file_path: nlp-architect/examples/chunker/train.py
imports: from __future__ import division, print_function, unicode_literals, absolute_import
  import argparse
  import pickle
  from os import path
  from tensorflow import keras
  from nlp_architect.nn.tensorflow.python.keras.callbacks import ConllCallback
  from nlp_architect.data.sequential_tagging import CONLL2000
  from nlp_architect.models.chunker import SequenceChunker
  from nlp_architect.utils.embedding import load_word_embeddings, get_embedding_matrix
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, validate, validate_existing_directory
  from nlp_architect.utils.metrics import get_conll_scores
label_desc: Use image node_id 1 for calling a global function with example usage: create_argument_parser() and returns: _parser