def METHOD_NAME(self):
    batch_size = 1
    X = np.random.randn(batch_size, 1, 3, 2).astype(np.float32)
    graph_def = make_graph(
        [make_node("Relu", ["X"], ["Y"])],
        name="test",
        inputs=[make_tensor_value_info("X", onnx.TensorProto.FLOAT,
            [batch_size, 1, 3, 2])],
        outputs=[make_tensor_value_info("Y", onnx.TensorProto.FLOAT,
            [batch_size, 1, 3, 2])])
    model_def = make_model(graph_def, producer_name='relu-test')
    op = core.CreateOperator(
        "Onnxifi",
        ["X"],
        ["Y"],
        onnx_model=model_def.SerializeToString(),
        input_names=["X"],
        output_names=["Y"],
        output_shape_hint_0=[ONNXIFI_DATATYPE_FLOAT32, batch_size, 1, 3, 2])
    workspace.FeedBlob("X", X)
    workspace.RunOperatorOnce(op)
    Y = workspace.FetchBlob("Y")
    np.testing.assert_almost_equal(Y, np.maximum(X, 0))