def METHOD_NAME(config, train_data):  #, valid_data, test_data):
    data_mean = paddle.to_tensor(train_data.data_mean, dtype="float32")
    data_scale = paddle.to_tensor(train_data.data_scale, dtype="float32")
    graph = train_data.graph
    graph = graph.tensor()
    model = WPFModel(config=config)
    global_step = load_model(config.output_path, model)
    model.eval()
    test_x = sorted(glob.glob(os.path.join("predict_data", "test_x", "*")))
    test_y = sorted(glob.glob(os.path.join("predict_data", "test_y", "*")))
    maes, rmses = [], []
    for i, (test_x_f, test_y_f) in enumerate(zip(test_x, test_y)):
        test_x_ds = TestPGL4WPFDataset(filename=test_x_f)
        test_y_ds = TestPGL4WPFDataset(filename=test_y_f)
        test_x = paddle.to_tensor(
            test_x_ds.get_data()[:, :, -config.input_len:, :], dtype="float32")
        test_y = paddle.to_tensor(
            test_y_ds.get_data()[:, :, :config.output_len, :], dtype="float32")
        pred_y = model(test_x, test_y, data_mean, data_scale, graph)
        pred_y = F.relu(pred_y * data_scale[:, :, :, -1] + data_mean[:, :, :,
                                                                     -1])
        pred_y = np.expand_dims(pred_y.numpy(), -1)
        test_y = test_y[:, :, :, -1:].numpy()
        pred_y = np.transpose(pred_y, [
            1,
            0,
            2,
            3,
        ])
        test_y = np.transpose(test_y, [
            1,
            0,
            2,
            3,
        ])
        test_y_df = test_y_ds.get_raw_df()
        _mae, _rmse = regressor_detailed_scores(
            pred_y, test_y, test_y_df, config.capacity, config.output_len)
        print('\n\tThe {}-th prediction for File {} -- '
              'RMSE: {}, MAE: {}, Score: {}'.format(i, test_y_f, _rmse, _mae, (
                  _rmse + _mae) / 2))
        maes.append(_mae)
        rmses.append(_rmse)
    avg_mae = np.array(maes).mean()
    avg_rmse = np.array(rmses).mean()
    total_score = (avg_mae + avg_rmse) / 2
    print('\n --- Final MAE: {}, RMSE: {} ---'.format(avg_mae, avg_rmse))
    print('--- Final Score --- \n\t{}'.format(total_score))