text: #!user/bin/env python
  # -*- coding:utf-8 -*-
  import argparse
  
  parser = argparse.ArgumentParser()
  # parser.add_argument("--inference", action="store_true", help='complete dataset or not')
  parser.add_argument("--pretrain", action="store_true", help='use vqa2.0 or not')
  parser.add_argument('--batch_size', type=int, default=256,
                      help='minibatch size')
  parser.add_argument('--num_epochs', type=int, default=200,
                      help='number of epochs')
  parser.add_argument('--model_dir', type=str, default='contrasloss_check_v11/',
                      help='model file path')
  parser.add_argument("--load_pthpath", default="",
                      help="To continue training, path to .pth file of saved checkpoint.")
  parser.add_argument("--validate", action="store_true", help="Whether to validate on val split after every epoch.")
  parser.add_argument("--embedding", action="store_true", help="Whether to train tail embedding.")
  parser.add_argument("--accumulate", action="store_true", help="Whether to fine-tune.")
  parser.add_argument("--dataset", default="okvqa", help="dataset that model training on")
  parser.add_argument('--local_rank', default=-1, type=int,
                      help='node rank for distributed training')
  args = parser.parse_args()
  