def METHOD_NAME(self, draw):
    in_shape = draw(
        st.lists(
            st.integers(
                min_value=8, max_value=64), min_size=4, max_size=4))
    stride = draw(
        st.lists(
            st.integers(
                min_value=1, max_value=2), min_size=2, max_size=2))
    out_stride = draw(
        st.lists(
            st.integers(
                min_value=1, max_value=2), min_size=2, max_size=2))
    ker = draw(
        st.lists(
            st.integers(
                min_value=1, max_value=5), min_size=2, max_size=2))
    case_num = draw(st.sampled_from(["c1", "c2"]))
    pad_0 = draw(st.integers(min_value=0, max_value=2))
    pad_1 = draw(st.integers(min_value=0, max_value=2))
    pad = [pad_0, pad_1, pad_0, pad_1]
    def generate_input1(*args, **kwargs):
        return np.random.random(in_shape).astype(np.float32)
    def generate_input2(*args, **kwargs):
        a = np.array([in_shape[2], in_shape[3]]).astype(np.float32)
        b = a.repeat(in_shape[0], axis=0)
        return b
    if case_num == "c1":
        build_op = OpConfig(
            type="im2sequence",
            inputs={"X": ["input_data"], },
            outputs={"Out": ["output_data"]},
            attrs={"strides": stride,
                   "paddings": pad,
                   "kernels": ker})
        program_config = ProgramConfig(
            ops=[build_op],
            weights={},
            inputs={"input_data": TensorConfig(data_gen=generate_input1)},
            outputs=["output_data"])
    elif case_num == "c2":
        build_op = OpConfig(
            type="im2sequence",
            inputs={
                "X": ["input_data"],
                "Y": ["input_data2"],
            },
            outputs={"Out": ["output_data"]},
            attrs={
                "strides": stride,
                "paddings": pad,
                "kernels": ker,
                "out_stride": out_stride
            })
        program_config = ProgramConfig(
            ops=[build_op],
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=generate_input1),
                "input_data2": TensorConfig(data_gen=generate_input2)
            },
            outputs=["output_data"])
    return program_config