code: def get_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--dataset_yaml",
          type=str,
          default="examples/pytorch/math_word_problem/mawps/config/new_dynamic_graphsage_undirected.yaml",  # noqa
      )
  
      parser.add_argument("--learning-rate", type=float, default=1e-3)
      parser.add_argument(
          "--gpuid",
          type=int,
          default=1,
          help="which gpu to use. -1 = use CPU",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=123,
          help="torch manual random number generator seed",
      )
      parser.add_argument(
          "--init-weight",
          type=float,
          default=0.08,
          help="initailization weight",
      )
      parser.add_argument("--weight-decay", type=float, default=0)
      parser.add_argument(
          "--max-epochs",
          type=int,
          default=200,
          help="number of full passes through the training data",
      )
      parser.add_argument(
          "--min-freq",
          type=int,
          default=1,
          help="minimum frequency for vocabulary",
      )
      parser.add_argument(
          "--grad-clip",
          type=int,
          default=5,
          help="clip gradients at this value",
      )
  
      # dataset config
      parser.add_argument(
          "--batch-size",
          type=int,
          default=20,
          help="the size of one mini-batch",
      )
      parser.add_argument(
          "--share-vocab",
          type=bool,
          default=True,
          help="whether to share vocab",
      )
  
      parser.add_argument(
          "--pretrained_word_emb_name", type=str, default="6B", help=""
      )
      parser.add_argument(
          "--pretrained_word_emb_url", type=str, default=None, help=""
      )
      parser.add_argument(
          "--pretrained_word_emb_cache_dir",
          type=str,
          default=".vector_cache",
          help="",
      )
  
      parser.add_argument(
          "--checkpoint-save-path",
          type=str,
          default="examples/pytorch/math_word_problem/mawps/save",
      )
  
      parser.add_argument(
          "--beam-size",
          type=int,
          default=4,
          help="the beam size of beam search",
      )
  
      cfg = parser.parse_args()
  
      our_args = get_yaml_config(cfg.dataset_yaml)
      template = get_basic_args(
          graph_construction_name=our_args["graph_construction_name"],
          graph_embedding_name=our_args["graph_embedding_name"],
          decoder_name=our_args["decoder_name"],
      )
      update_values(
          to_args=template, from_args_list=[our_args, vars(cfg)]
      )
      return template
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_args() and returns: template