def METHOD_NAME(backend, keras_model_1d, data_1d, io_type):
    model, model_type, padding = keras_model_1d
    config = hls4ml.utils.config_from_keras_model(model, default_precision='ap_fixed<32,9>', granularity='name')
    hls_model = hls4ml.converters.convert_from_keras_model(
        model,
        hls_config=config,
        io_type=io_type,
        output_dir=str(test_root_path / f'hls4mlprj_globalplool1d_{backend}_{io_type}_{model_type}_padding_{padding}'),
        backend=backend,
    )
    hls_model.compile()
    y_keras = model.predict(data_1d)
    y_hls = hls_model.predict(data_1d).reshape(y_keras.shape)
    np.testing.assert_allclose(y_keras, y_hls, rtol=0, atol=atol, verbose=True)