code: def validate_input_args():
      global model_path
      validate((args.b, int, 1, 100000000))
      validate((args.e, int, 1, 100000000))
      validate((args.sentence_length, int, 1, 10000))
      validate((args.token_emb_size, int, 1, 10000))
      validate((args.intent_hidden_size, int, 1, 10000))
      validate((args.lstm_hidden_size, int, 1, 10000))
      validate((args.tagger_dropout, float, 0, 1))
      model_path = path.join(
          path.dirname(path.realpath(__file__)), str(args.model_path)
      )
      validate_parent_exists(model_path)
      model_info_path = path.join(
          path.dirname(path.realpath(__file__)),
          str(args.model_info_path),
      )
      validate_parent_exists(model_info_path)
  
file_path: nlp-architect/examples/intent_extraction/train_mtl_model.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import argparse
  import pickle
  from os import path
  from tensorflow.python.keras.utils import to_categorical
  from nlp_architect.nn.tensorflow.python.keras.callbacks import ConllCallback
  from nlp_architect.data.intent_datasets import SNIPS
  from nlp_architect.models.intent_extraction import MultiTaskIntentModel
  from nlp_architect.utils.embedding import get_embedding_matrix, load_word_embeddings
  from nlp_architect.utils.generic import one_hot
  from nlp_architect.utils.io import validate, validate_existing_directory, 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: validate_input_args() without return types