def METHOD_NAME(device, dtype):
    def get_model():
        from tensorflow.keras.applications import MobileNet
        import tensorflow as tf
        tf.keras.backend.clear_session()
        mobilenet = MobileNet(
            include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
        )
        inputs = {mobilenet.input_names[0]: ((1, 3, 224, 224), "float32")}
        data = {}
        np.random.seed(0)
        for name, (shape, dtype) in inputs.items():
            if dtype == "uint8":
                low, high = 0, 1
            else:
                low, high = -1, 1
            data[name] = np.random.uniform(low, high, shape).astype(dtype)
        mod, params, ref_outputs = _get_keras_model(mobilenet, inputs, data)
        return mod, params, inputs, data, ref_outputs
    mod, params, inputs, input_data, ref_outputs = get_model()
    outputs = _build_and_run_network(
        mod, params, inputs, input_data, device=device, atol=1e-5, rtol=1e-5
    )
    opencl_sort = np.argsort(outputs[1].asnumpy()).flatten()
    clml_sort = np.argsort(outputs[0].asnumpy()).flatten()
    tvm.testing.assert_allclose(opencl_sort[:10], clml_sort[:10], rtol=1e-5, atol=1e-5)