code: def main():
      global device
  
      argparser = argparse.ArgumentParser(
          description="implementation of C&S)"
      )
      argparser.add_argument(
          "--cpu",
          action="store_true",
          help="CPU mode. This option overrides --gpu.",
      )
      argparser.add_argument(
          "--gpu", type=int, default=0, help="GPU device ID."
      )
      argparser.add_argument(
          "--use-norm",
          action="store_true",
          help="Use symmetrically normalized adjacency matrix.",
      )
      argparser.add_argument(
          "--alpha", type=float, default=0.6, help="alpha"
      )
      argparser.add_argument(
          "--pred-files",
          type=str,
          default="./output/*.pt",
          help="address of prediction files",
      )
      args = argparser.parse_args()
  
      if args.cpu:
          device = torch.device("cpu")
      else:
          device = torch.device(f"cuda:{args.gpu}")
  
      # load data & preprocess
      (
          graph,
          labels,
          train_idx,
          val_idx,
          test_idx,
          evaluator,
      ) = load_data(dataset)
      graph = preprocess(graph)
  
      graph, labels, train_idx, val_idx, test_idx = map(
          lambda x: x.to(device),
          (graph, labels, train_idx, val_idx, test_idx),
      )
  
      # run
      val_accs, test_accs = [], []
  
      for pred_file in glob.iglob(args.pred_files):
          print("load:", pred_file)
          pred = torch.load(pred_file)
          val_acc, test_acc = run(
              args,
              graph,
              labels,
              pred,
              train_idx,
              val_idx,
              test_idx,
              evaluator,
          )
          val_accs.append(val_acc)
          test_accs.append(test_acc)
  
      print(args)
      print(f"Runned {len(val_accs)} times")
      print("Val Accs:", val_accs)
      print("Test Accs:", test_accs)
      print(
          f"Average val accuracy: {np.mean(val_accs)} ± {np.std(val_accs)}"
      )
      print(
          f"Average test accuracy: {np.mean(test_accs)} ± {np.std(test_accs)}"
      )
  
file_path: dgl/examples/pytorch/ogb/ogbn-arxiv/correct_and_smooth.py
imports: import argparse
  import glob
  import numpy
  import torch
  import torch.nn.functional
  from dgl import function
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types