def METHOD_NAME(batch=1, height=448, width=448):
    from test_autochunk_unet import UNet2DModel, get_data
    model = UNet2DModel()
    latent_shape = (batch, 3, height // 7, width // 7)
    print("\nbatch: %d, height: %d, width: %d" % (batch, height, width))
    max_mem = _benchmark_autochunk_unet_origin(model, get_data(latent_shape))
    for ratio in [0.5, 0.4, 0.3, 0.2]:
        try:
            _benchmark_autochunk_unet_gm(model, get_data(latent_shape), max_mem * ratio)
        except RuntimeError as e:
            if e.args[0] == 'Search failed. Try a larger memory threshold.':
                break
        except Exception as e:
            raise e
    _benchmark_autochunk_unet_gm(model, get_data(latent_shape), None)