def METHOD_NAME(self, draw):
    in_shape = draw(
        st.lists(
            st.integers(
                min_value=2, max_value=100),
            min_size=2,
            max_size=2))
    id_shape = draw(
        st.lists(
            st.integers(
                min_value=2, max_value=100),
            min_size=2,
            max_size=3))
    pidx = draw(st.sampled_from([-1, 0, 1, 2]))
    op_type_str = draw(
        st.sampled_from(["lookup_table", "lookup_table_v2"]))
    if "intel_openvino" in self.get_nnadapter_device_name():
        op_type_str = "lookup_table_v2"
    def generate_input(*args, **kwargs):
        return np.random.random(in_shape).astype(np.float32)
    def generate_ids(*args, **kwargs):
        extend_id = copy.deepcopy(id_shape)
        extend_id.append(1)
        return np.random.randint(
            low=0, high=in_shape[0], size=extend_id).astype(np.int64)
    build_ops = OpConfig(
        type=op_type_str,
        inputs={
            "W": ["w_data"],
            "Ids": ["ids_data"],
        },
        outputs={"Out": ["output_data"], },
        attrs={"padding_idx": int(pidx), })
    program_config = ProgramConfig(
        ops=[build_ops],
        weights={},
        inputs={
            "w_data": TensorConfig(data_gen=partial(generate_input)),
            "ids_data": TensorConfig(data_gen=partial(generate_ids)),
        },
        outputs=["output_data"])
    return program_config