def METHOD_NAME(self, draw):
    input_shape = draw(
        st.lists(
            st.integers(
                min_value=4, max_value=8), min_size=2, max_size=5))
    input_spec = [-1] * len(input_shape)
    dtype = draw(st.sampled_from(["float32", "float64"]))
    epsilon = draw(st.floats(min_value=1e-12, max_value=1e-5))
    momentum = draw(st.floats(min_value=0.1, max_value=0.9))
    if len(input_shape) == 2:
        data_format = "NC"
    elif len(input_shape) == 3:
        data_format = draw(st.sampled_from(["NCL"]))
    elif len(input_shape) == 4:
        data_format = draw(st.sampled_from(["NCHW"]))
    else:
        data_format = "NCDHW"
    use_global_stats = None
    is_use_global_stats = draw(st.sampled_from(["None", "False", "True"]))
    if is_use_global_stats == "False":
        use_global_stats = None  # has a diff
    elif is_use_global_stats == "True":
        use_global_stats = True
    config = {
        "op_names": ["batch_norm"],
        "test_data_shapes": [input_shape],
        "test_data_types": [[dtype]],
        "opset_version": [7, 9, 15],
        "input_spec_shape": [],
        "epsilon": epsilon,
        "momentum": momentum,
        "input_shape": input_shape,
        "dtype": dtype,
        "data_format": data_format,
        "use_global_stats": use_global_stats,
    }
    models = Net(config)
    return (config, models)