def METHOD_NAME(args, dataset_name):
    if (
        (args.dataset != "sider")
        and (args.dataset != "clintox")
        and (args.dataset != "bbbp")
        and (args.dataset != "bace")
        and (args.dataset != "pcba")
        and (args.dataset != "tox21")
        and (args.dataset != "toxcast")
        and (args.dataset != "muv")
        and (args.dataset != "hiv")
    ):
        raise Exception("no such dataset!")
    compact = args.model == "graphsage"
    logging.info("load_data. dataset_name = %s" % dataset_name)
    _, feature_matrices, labels = get_data(args.data_cache_dir + args.dataset)
    unif = True if args.partition_method == "homo" else False
    if args.model == "gcn":
        args.normalize_features = True
        args.normalize_adjacency = True
    if args.dataset == "pcba":
        args.metric = "prc-auc"
    else:
        args.metric = "roc-auc"
    (
        train_data_num,
        val_data_num,
        test_data_num,
        train_data_global,
        val_data_global,
        test_data_global,
        data_local_num_dict,
        train_data_local_dict,
        val_data_local_dict,
        test_data_local_dict,
    ) = load_partition_data(
        args,
        args.data_cache_dir + args.dataset,
        args.client_num_in_total,
        uniform=unif,
        compact=compact,
        normalize_features=args.normalize_features,
        normalize_adj=args.normalize_adjacency,
    )
    dataset = [
        train_data_num,
        test_data_num,
        train_data_global,
        test_data_global,
        data_local_num_dict,
        train_data_local_dict,
        test_data_local_dict,
        labels[0].shape[0],
    ]
    return dataset, feature_matrices[0].shape[1], labels[0].shape[0]