def METHOD_NAME(config):
    model = getattr(models, config.model.name).GNNModel(
        **dict(config.model.items()))
    _create_if_not_exist(config.output_path)
    load_model(config.output_path, model)
    model.eval()
    pred_temp = []
    dataset = MAG240M(config.data_dir, seed=123)
    evaluator = MAG240MEvaluator()
    dataset.prepare_data()
    test_iter = DataGenerator(
        dataset=dataset,
        samples=[160] * len(config.samples),
        batch_size=16,
        num_workers=config.num_workers,
        data_type="test")
    for batch in test_iter.generator():
        graph_list, x, y = batch
        x = paddle.to_tensor(x, dtype='float32')
        y = paddle.to_tensor(y, dtype='int64')
        graph_list = [(item[0].tensor(), paddle.to_tensor(item[2]))
                      for item in graph_list]
        out = model(graph_list, x)
        pred_temp.append(out.numpy())
    pred_temp = np.concatenate(pred_temp, axis=0)
    y_pred = pred_temp.argmax(axis=-1)
    res = {'y_pred': y_pred}
    evaluator.save_test_submission(res, 'results')