def METHOD_NAME(device, dtype):
    def get_model():
        from tensorflow.keras.applications import ResNet50V2
        import tensorflow as tf
        tf.keras.backend.clear_session()
        model = ResNet50V2(include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000)
        inputs_dict = {model.input_names[0]: ((1, 3, 224, 224), "float32")}
        data = {}
        np.random.seed(0)
        for name, (shape, dtype) in inputs_dict.items():
            if dtype == "uint8":
                low, high = 0, 1
            else:
                low, high = -1, 1
            data[name] = np.random.uniform(low, high, shape).astype(dtype)
        """Convert Keras graph to relay."""
        inputs = {}
        for name, (shape, _) in inputs_dict.items():
            inputs[model.input_names[0]] = shape
        ref_outputs = model.predict(data["input_1"].transpose(0, 2, 3, 1))
        mod, params = relay.frontend.from_keras(model, inputs, layout="NCHW")
        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)