code: def argument():
      parser = argparse.ArgumentParser(description="GRAND")
  
      # data source params
      parser.add_argument(
          "--dataname",
          type=str,
          default="cora",
          help="Name of dataset.",
      )
      # cuda params
      parser.add_argument(
          "--gpu",
          type=int,
          default=-1,
          help="GPU index. Default: -1, using CPU.",
      )
      # training params
      parser.add_argument(
          "--epochs", type=int, default=200, help="Training epochs."
      )
      parser.add_argument(
          "--early_stopping",
          type=int,
          default=200,
          help="Patient epochs to wait before early stopping.",
      )
      parser.add_argument(
          "--lr", type=float, default=0.01, help="Learning rate."
      )
      parser.add_argument(
          "--weight_decay", type=float, default=5e-4, help="L2 reg."
      )
      # model params
      parser.add_argument(
          "--hid_dim",
          type=int,
          default=32,
          help="Hidden layer dimensionalities.",
      )
      parser.add_argument(
          "--dropnode_rate",
          type=float,
          default=0.5,
          help="Dropnode rate (1 - keep probability).",
      )
      parser.add_argument(
          "--input_droprate",
          type=float,
          default=0.0,
          help="dropout rate of input layer",
      )
      parser.add_argument(
          "--hidden_droprate",
          type=float,
          default=0.0,
          help="dropout rate of hidden layer",
      )
      parser.add_argument(
          "--order", type=int, default=8, help="Propagation step"
      )
      parser.add_argument(
          "--sample",
          type=int,
          default=4,
          help="Sampling times of dropnode",
      )
      parser.add_argument(
          "--tem",
          type=float,
          default=0.5,
          help="Sharpening temperature",
      )
      parser.add_argument(
          "--lam",
          type=float,
          default=1.0,
          help="Coefficient of consistency regularization",
      )
      parser.add_argument(
          "--use_bn",
          action="store_true",
          default=False,
          help="Using Batch Normalization",
      )
  
      args = parser.parse_args()
  
      # check cuda
      if args.gpu != -1 and th.cuda.is_available():
          args.device = "cuda:{}".format(args.gpu)
      else:
          args.device = "cpu"
  
      return args
  
file_path: dgl/examples/pytorch/grand/main.py
imports: import argparse
  import warnings
  import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
  from model import GRAND
label_desc: Use image node_id 1 for calling a global function with example usage: argument() and returns: args