def METHOD_NAME(self, draw):
    in_shape = draw(
        st.lists(
            st.integers(
                min_value=1, max_value=8), min_size=2, max_size=4))
    def generate_input(*args, **kwargs):
        if kwargs["type"] == "int32":
            return np.random.randint(kwargs["low"], kwargs["high"],
                                     kwargs["shape"]).astype(np.int32)
        elif kwargs["type"] == "int64":
            return np.random.randint(kwargs["low"], kwargs["high"],
                                     kwargs["shape"]).astype(np.int64)
        elif kwargs["type"] == "float32":
            return (kwargs["high"] - kwargs["low"]) * np.random.random(
                kwargs["shape"]).astype(np.float32) + kwargs["low"]
    input_type = draw(st.sampled_from(["float32", "int64", "int32"]))
    start_axis = draw(
        st.integers(
            min_value=0, max_value=len(in_shape) - 1))
    stop_axis = draw(
        st.integers(
            min_value=start_axis, max_value=len(in_shape) - 1))
    outputs_ = ["output_data", "xshape_data"]
    target_str = self.get_target()
    if target_str == "NNAdapter":
        if "nvidia_tensorrt" in self.get_nnadapter_device_name():
            assume(input_type != "int64")
    if self.get_nnadapter_device_name(
    ) in ["nvidia_tensorrt", "intel_openvino"]:
        outputs_ = ["output_data"]
    flatten_contiguous_range_op = OpConfig(
        type="flatten_contiguous_range",
        inputs={"X": ["input_data"]},
        outputs={"Out": ["output_data"],
                 "XShape": ["xshape_data"]},
        attrs={"start_axis": start_axis,
               "stop_axis": stop_axis})
    flatten_contiguous_range_op.outputs_dtype = {"output_data": input_type}
    program_config = ProgramConfig(
        ops=[flatten_contiguous_range_op],
        weights={"xshape_data": TensorConfig(shape=in_shape)},
        inputs={
            "input_data": TensorConfig(data_gen=partial(
                generate_input,
                type=input_type,
                low=-10,
                high=10,
                shape=in_shape))
        },
        outputs=outputs_)
    return program_config