def METHOD_NAME(self, draw):
    in_shape = draw(
        st.lists(
            st.integers(
                min_value=10, max_value=20),
            min_size=4,
            max_size=4))
    lod_data = draw(
        st.sampled_from([[0, 3, 5, in_shape[0]], [0, 4, 7, in_shape[0]],
                         [0, 4, in_shape[0]], [0, 7, in_shape[0]]]))
    lod_data1 = draw(
        st.sampled_from([[0, 3, 5, in_shape[0]], [0, 4, 7, in_shape[0]],
                         [0, 4, in_shape[0]], [0, 7, in_shape[0]]]))
    lod_data2 = draw(
        st.sampled_from([[0, 3, 5, in_shape[0]], [0, 4, 7, in_shape[0]],
                         [0, 4, in_shape[0]], [0, 7, in_shape[0]]]))
    case_num = draw(st.sampled_from([0, 1]))
    def generate_input_x(*args, **kwargs):
        return np.random.random(in_shape).astype(np.float32)
    def generate_input_y(*args, **kwargs):
        return np.array(lod_data1).astype(np.int32)
    if case_num == 0:
        build_ops = OpConfig(
            type="lod_reset",
            inputs={"X": ["input_data_x"],
                    "Y": []},
            outputs={"Out": ["output_data"], },
            attrs={"target_lod": lod_data,
                   'append': True})
        program_config = ProgramConfig(
            ops=[build_ops],
            weights={},
            inputs={
                "input_data_x": TensorConfig(data_gen=partial(
                    generate_input_x, lod=list(lod_data2))),
            },
            outputs=["output_data"])
    elif case_num == 1:
        build_ops = OpConfig(
            type="lod_reset",
            inputs={"X": ["input_data_x"],
                    "Y": ["input_data_y"]},
            outputs={"Out": ["output_data"], },
            attrs={"target_lod": [],
                   'append': True})
        program_config = ProgramConfig(
            ops=[build_ops],
            weights={},
            inputs={
                "input_data_x": TensorConfig(data_gen=partial(
                    generate_input_x, lod=list(lod_data2))),
                "input_data_y":
                TensorConfig(data_gen=partial(generate_input_y)),
            },
            outputs=["output_data"])
    return program_config