code: def main(args):
      """
      Main function
      Args:
          args: output of argparse with all input arguments
  
      Returns:
          None
      """
      hidden_sizes = [args.nhid] * args.levels
      kernel_size = args.ksize
      dropout = args.dropout
      em_dropout = args.em_dropout
      seq_len = args.seq_len
      batch_size = args.batch_size
      n_epochs = args.epochs
      embedding_size = args.em_len
      datadir = os.path.abspath(args.datadir)
      results_dir = os.path.abspath(args.results_dir)
  
      ptb_dict = PTBDictionary(data_dir=datadir, dataset=args.dataset)
      ptb_dataset_train = PTBDataLoader(
          ptb_dict,
          data_dir=datadir,
          seq_len=seq_len,
          split_type="train",
          skip=seq_len / 2,
          dataset=args.dataset,
      )
      ptb_dataset_valid = PTBDataLoader(
          ptb_dict,
          data_dir=datadir,
          seq_len=seq_len,
          split_type="valid",
          loop=False,
          skip=seq_len / 2,
          dataset=args.dataset,
      )
      ptb_dataset_test = PTBDataLoader(
          ptb_dict,
          data_dir=datadir,
          seq_len=seq_len,
          split_type="test",
          loop=False,
          skip=seq_len / 2,
          dataset=args.dataset,
      )
  
      n_train = ptb_dataset_train.n_train
      num_words = len(ptb_dataset_train.idx2word)
      n_per_epoch = int(n_train * 1.0 / batch_size)
      if not args.inference:
          num_iterations = int(n_train * n_epochs * 1.0 / batch_size)
          print(
              "Training examples per epoch: {}, num iterations per epoch: {}".format(
                  n_train, num_iterations // n_epochs
              )
          )
      else:
          num_iterations = 0
  
      model = TCNForLM(
          seq_len,
          embedding_size,
          hidden_sizes,
          kernel_size=kernel_size,
          dropout=dropout,
      )
  
      model.build_train_graph(
          num_words=num_words,
          max_gradient_norm=args.grad_clip_value,
          em_dropout=em_dropout,
      )
  
      if not args.inference:
          model.run(
              {
                  "train": ptb_dataset_train,
                  "valid": ptb_dataset_valid,
                  "test": ptb_dataset_test,
              },
              args.lr,
              num_iterations=num_iterations,
              log_interval=n_per_epoch,
              result_dir=results_dir,
              ckpt=None,
          )
      else:
          sequences = model.run_inference(
              args.ckpt,
              num_samples=args.num_samples,
              sos=ptb_dict.sos_symbol,
              eos=ptb_dict.eos_symbol,
          )
          for seq in sequences:
              sentence = []
              for idx in seq:
                  while idx == ptb_dict.sos_symbol:
                      continue
                  sentence.append(ptb_dict.idx2word[idx])
              print(" ".join(sentence) + "\n")
  
imports: import argparse
  import os
  from examples.word_language_model_with_tcn.mle_language_model.lm_model import TCNForLM
  from nlp_architect.data.ptb import PTBDataLoader, PTBDictionary
  from nlp_architect.utils.io import validate_existing_directory, validate_existing_filepath, validate_parent_exists, check_size
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types