def METHOD_NAME(readcsv=read_csv):
    # input data file
    train_file = os.path.join("..", "data", "batch", "svm_two_class_train_dense.csv")
    predict_file = os.path.join("..", "data", "batch", "svm_two_class_test_dense.csv")
    nFeatures = 20
    train_data = readcsv(train_file, range(nFeatures), t=np.float32)
    train_labels = readcsv(train_file, range(nFeatures, nFeatures + 1), t=np.float32)
    predict_data = readcsv(predict_file, range(nFeatures), t=np.float32)
    predict_labels = readcsv(predict_file, range(nFeatures, nFeatures + 1), t=np.float32)
    predict_result_classic, decision_function_classic = compute(
        train_data, train_labels, predict_data, "boser"
    )
    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)
            predict_result_gpu, decision_function_gpu = compute(
                sycl_train_data, sycl_train_labels, sycl_predict_data, "thunder"
            )
            # assert np.allclose(predict_result_gpu, predict_result_classic)
    with sycl_context("cpu"):
        sycl_train_data = sycl_buffer(train_data)
        sycl_predict_data = sycl_buffer(predict_data)
        predict_result_cpu, decision_function_cpu = compute(
            sycl_train_data, train_labels, sycl_predict_data, "thunder"
        )
        assert np.allclose(predict_result_cpu, predict_result_classic)
    return predict_labels, predict_result_classic, decision_function_classic