def METHOD_NAME(readcsv=read_csv):
    nFeatures = 3
    # input data file
    train_file = os.path.join("..", "data", "batch", "df_classification_train.csv")
    predict_file = os.path.join("..", "data", "batch", "df_classification_test.csv")
    # Read train data. Let's use 3 features per observation
    train_data = readcsv(train_file, range(nFeatures), t=np.float32)
    train_labels = readcsv(train_file, range(nFeatures, nFeatures + 1), t=np.float32)
    # Read test data (with same #features)
    predict_data = readcsv(predict_file, range(nFeatures), t=np.float32)
    predict_labels = readcsv(predict_file, range(nFeatures, nFeatures + 1), t=np.float32)
    # Using of the classic way (computations on CPU)
    train_result, predict_result = compute(train_data, train_labels, predict_data)
    assert predict_result.prediction.shape == (predict_labels.shape[0], 1)
    assert (np.mean(predict_result.prediction != predict_labels) < 0.04).any()
    train_data = to_numpy(train_data)
    train_labels = to_numpy(train_labels)
    predict_data = to_numpy(predict_data)
    # It is possible to specify to make the computations on GPU
    if gpu_available:
        with sycl_context("gpu"):
            sycl_train_data = sycl_buffer(train_data)
            sycl_train_labels = sycl_buffer(train_labels)
            sycl_predict_data = sycl_buffer(predict_data)
            train_result, predict_result = compute(
                sycl_train_data, sycl_train_labels, sycl_predict_data
            )
            assert predict_result.prediction.shape == (predict_labels.shape[0], 1)
            assert (np.mean(predict_result.prediction != predict_labels) < 0.03).any()
    return (train_result, predict_result, predict_labels)