code: def validate_input_args(input_args):
      validate((input_args.b, int, 1, 100000))
      validate((input_args.e, int, 1, 100000))
      validate((input_args.tag_num, int, 1, 1000))
      validate((input_args.sentence_length, int, 1, 10000))
      validate((input_args.word_length, int, 1, 100))
      validate((input_args.word_embedding_dims, int, 1, 10000))
      validate((input_args.character_embedding_dims, int, 1, 1000))
      validate((input_args.char_features_lstm_dims, int, 1, 10000))
      validate((input_args.entity_tagger_lstm_dims, int, 1, 10000))
      validate((input_args.dropout, float, 0, 1))
      model_path = path.join(
          path.dirname(path.realpath(__file__)),
          str(input_args.model_path),
      )
      validate_parent_exists(model_path)
      model_info_path = path.join(
          path.dirname(path.realpath(__file__)),
          str(input_args.model_info_path),
      )
      validate_parent_exists(model_info_path)
  
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 2 for calling a global function with example usage: validate_input_args(input_args) without return types