code: def argument():
      parser = argparse.ArgumentParser(description="InfoGraph")
      # data source params
      parser.add_argument(
          "--dataname",
          type=str,
          default="MUTAG",
          help="Name of dataset.",
      )
  
      # training params
      parser.add_argument(
          "--gpu",
          type=int,
          default=-1,
          help="GPU index, default:-1, using CPU.",
      )
      parser.add_argument(
          "--epochs", type=int, default=20, help="Training epochs."
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=128,
          help="Training batch size.",
      )
      parser.add_argument(
          "--lr", type=float, default=0.01, help="Learning rate."
      )
      parser.add_argument(
          "--log_interval",
          type=int,
          default=1,
          help="Interval between two evaluations.",
      )
  
      # model params
      parser.add_argument(
          "--n_layers",
          type=int,
          default=3,
          help="Number of graph convolution layers before each pooling.",
      )
      parser.add_argument(
          "--hid_dim",
          type=int,
          default=32,
          help="Hidden layer dimensionalities.",
      )
  
      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/infograph/unsupervised.py
imports: import argparse
  import dgl
  import torch
  from dgl.data import GINDataset
  from dgl.dataloading import GraphDataLoader
  from evaluate_embedding import evaluate_embedding
  from model import InfoGraph
label_desc: Use image node_id 1 for calling a global function with example usage: argument() and returns: args