def METHOD_NAME():
    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)}")