def METHOD_NAME(index):
    model_funcs = [
        torchvision.models.detection.fasterrcnn_resnet50_fpn,
        torchvision.models.detection.maskrcnn_resnet50_fpn,
    ]
    model_func = model_funcs[index]
    model = TraceWrapper(model_func(pretrained=True, rpn_pre_nms_top_n_test=1000))
    model.eval()
    inp = torch.Tensor(np.random.uniform(0.0, 250.0, size=(1, 3, in_size, in_size)))
    with torch.no_grad():
        out = model(inp)
        script_module = do_trace(model, inp)
        script_out = script_module(inp)
        assert len(out[0]) > 0 and len(script_out[0]) > 0
        return script_module