code: def config():
      parser = argparse.ArgumentParser(description="GCMC")
      parser.add_argument("--seed", default=123, type=int)
      parser.add_argument("--gpu", type=str, default="0")
      parser.add_argument(
          "--save_dir", type=str, help="The saving directory"
      )
      parser.add_argument(
          "--save_id", type=int, help="The saving log id"
      )
      parser.add_argument("--silent", action="store_true")
      parser.add_argument(
          "--data_name",
          default="ml-1m",
          type=str,
          help="The dataset name: ml-100k, ml-1m, ml-10m",
      )
      parser.add_argument(
          "--data_test_ratio", type=float, default=0.1
      )  ## for ml-100k the test ration is 0.2
      parser.add_argument("--data_valid_ratio", type=float, default=0.1)
      parser.add_argument(
          "--use_one_hot_fea", action="store_true", default=False
      )
      parser.add_argument(
          "--model_activation", type=str, default="leaky"
      )
      parser.add_argument("--gcn_dropout", type=float, default=0.7)
      parser.add_argument(
          "--gcn_agg_norm_symm", type=bool, default=True
      )
      parser.add_argument("--gcn_agg_units", type=int, default=500)
      parser.add_argument("--gcn_agg_accum", type=str, default="sum")
      parser.add_argument("--gcn_out_units", type=int, default=75)
      parser.add_argument("--gen_r_num_basis_func", type=int, default=2)
      parser.add_argument("--train_max_epoch", type=int, default=1000)
      parser.add_argument("--train_log_interval", type=int, default=1)
      parser.add_argument("--train_valid_interval", type=int, default=1)
      parser.add_argument("--train_optimizer", type=str, default="adam")
      parser.add_argument("--train_grad_clip", type=float, default=1.0)
      parser.add_argument("--train_lr", type=float, default=0.01)
      parser.add_argument("--train_min_lr", type=float, default=0.0001)
      parser.add_argument(
          "--train_lr_decay_factor", type=float, default=0.5
      )
      parser.add_argument(
          "--train_decay_patience", type=int, default=25
      )
      parser.add_argument(
          "--train_early_stopping_patience", type=int, default=50
      )
      parser.add_argument(
          "--share_param", default=False, action="store_true"
      )
      parser.add_argument(
          "--mix_cpu_gpu", default=False, action="store_true"
      )
      parser.add_argument("--minibatch_size", type=int, default=20000)
      parser.add_argument("--num_workers_per_gpu", type=int, default=8)
  
      args = parser.parse_args()
      ### configure save_fir to save all the info
      if args.save_dir is None:
          args.save_dir = (
              args.data_name
              + "_"
              + "".join(
                  random.choices(
                      string.ascii_uppercase + string.digits, k=2
                  )
              )
          )
      if args.save_id is None:
          args.save_id = np.random.randint(20)
      args.save_dir = os.path.join("log", args.save_dir)
      if not os.path.isdir(args.save_dir):
          os.makedirs(args.save_dir)
  
      return args
  
imports: import argparse
  import logging
  import os, time
  import random
  import string
  import traceback
  import dgl
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  import tqdm
  from data import MovieLens
  from model import BiDecoder, DenseBiDecoder, GCMCLayer
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader
  from utils import get_activation, get_optimizer, MetricLogger, to_etype_name, torch_net_info, torch_total_param_num
label_desc: Use image node_id 4 for calling a global function with example usage: config() and returns: args