code: def read_input_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("-b", type=int, default=10, help="Batch size")
      parser.add_argument(
          "-e", type=int, default=10, help="Number of epochs"
      )
      parser.add_argument(
          "--train_file",
          type=validate_existing_filepath,
          required=True,
          help="Train file (sequential tagging dataset format)",
      )
      parser.add_argument(
          "--test_file",
          type=validate_existing_filepath,
          required=True,
          help="Test file (sequential tagging dataset format)",
      )
      parser.add_argument(
          "--tag_num",
          type=int,
          default=2,
          help="Entity labels tab number in train/test files",
      )
      parser.add_argument(
          "--sentence_length",
          type=int,
          default=50,
          help="Max sentence length",
      )
      parser.add_argument(
          "--word_length",
          type=int,
          default=12,
          help="Max word length in characters",
      )
      parser.add_argument(
          "--word_embedding_dims",
          type=int,
          default=100,
          help="Word features embedding dimension size",
      )
      parser.add_argument(
          "--character_embedding_dims",
          type=int,
          default=25,
          help="Character features embedding dimension size",
      )
      parser.add_argument(
          "--char_features_lstm_dims",
          type=int,
          default=25,
          help="Character feature extractor LSTM dimension size",
      )
      parser.add_argument(
          "--entity_tagger_lstm_dims",
          type=int,
          default=100,
          help="Entity tagger LSTM dimension size",
      )
      parser.add_argument(
          "--dropout", type=float, default=0.2, help="Dropout rate"
      )
      parser.add_argument(
          "--embedding_model",
          type=validate_existing_filepath,
          help="Path to external word embedding model file",
      )
      parser.add_argument(
          "--model_path",
          type=str,
          default="model.h5",
          help="Path for saving model weights",
      )
      parser.add_argument(
          "--model_info_path",
          type=str,
          default="model_info.dat",
          help="Path for saving model topology",
      )
      parser.add_argument(
          "--use_cudnn",
          default=False,
          action="store_true",
          help="use CUDNN based LSTM cells",
      )
      input_args = parser.parse_args()
      validate_input_args(input_args)
      return input_args
  
file_path: nlp-architect/examples/ner/train.py
imports: import argparse
  import pickle
  from os import path
  import numpy
  from tensorflow import keras
  from nlp_architect.nn.tensorflow.python.keras.callbacks import ConllCallback
  from nlp_architect.data.sequential_tagging import SequentialTaggingDataset
  from nlp_architect.models.ner_crf import NERCRF
  from nlp_architect.utils.embedding import get_embedding_matrix, load_word_embeddings
  from nlp_architect.utils.io import validate, validate_existing_filepath, validate_parent_exists
  from nlp_architect.utils.metrics import get_conll_scores
label_desc: Use image node_id 1 for calling a global function with example usage: read_input_args() and returns: input_args