def METHOD_NAME(self, draw):
    input_type = draw(st.sampled_from(["int32", "int64"]))
    input_data_x_shape = draw(
        st.lists(
            st.integers(
                min_value=1, max_value=10), min_size=1, max_size=4))
    input_data_y_shape = draw(
        st.lists(
            st.integers(
                min_value=1, max_value=10), min_size=1, max_size=4))
    axis = -1  # paddle2.4.2 only support -1
    input_data_x_shape = draw(st.sampled_from([input_data_x_shape, []]))
    input_data_y_shape = draw(st.sampled_from([input_data_y_shape, []]))
    assume(
        check_broadcast(input_data_x_shape, input_data_y_shape, axis) ==
        True)
    if axis < 0:
        axis = abs(len(input_data_x_shape) - len(
            input_data_y_shape)) + axis + 1
    def generate_input(*args, **kwargs):
        if kwargs["type"] == "bool":
            return np.random.choice([True, False],
                                    kwargs["shape"]).astype(bool)
        if kwargs["type"] == "uint8":
            return np.random.normal(0, 255,
                                    kwargs["shape"]).astype(np.uint8)
        if kwargs["type"] == "int8":
            return np.random.normal(-128, 127,
                                    kwargs["shape"]).astype(np.int8)
        if kwargs["type"] == "int16":
            return np.random.normal(-32768, 32767,
                                    kwargs["shape"]).astype(np.int16)
        if kwargs["type"] == "int32":
            return np.random.normal(-2147483648, 2147483647,
                                    kwargs["shape"]).astype(np.int32)
        if kwargs["type"] == "int64":
            return np.random.normal(-9223372036854775808,
                                    9223372036854775807,
                                    kwargs["shape"]).astype(np.int64)
    bitwise_op = OpConfig(
        type="bitwise_or",
        inputs={
            "X": ["input_data"],
            "Y": ["input_data1"],
        },
        outputs={"Out": ["output_data"]},
        attrs={"axis": axis})
    if (input_type == "int32"):
        bitwise_op.outputs_dtype = {"output_data": np.int32}
    elif (input_type == "int64"):
        bitwise_op.outputs_dtype = {"output_data": np.int64}
    program_config = ProgramConfig(
        ops=[bitwise_op],
        weights={},
        inputs={
            "input_data": TensorConfig(data_gen=partial(
                generate_input, type=input_type,
                shape=input_data_x_shape)),
            "input_data1": TensorConfig(data_gen=partial(
                generate_input, type=input_type,
                shape=input_data_y_shape)),
        },
        outputs=["output_data"])
    return program_config