def METHOD_NAME(model_type):
    # https://github.com/isl-org/MiDaS/blob/master/run.py
    # load transform only
    if model_type == "dpt_large":  # DPT-Large
        net_w, net_h = 384, 384
        resize_mode = "minimal"
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    elif model_type == "dpt_hybrid":  # DPT-Hybrid
        net_w, net_h = 384, 384
        resize_mode = "minimal"
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    elif model_type == "midas_v21":
        net_w, net_h = 384, 384
        resize_mode = "upper_bound"
        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    elif model_type == "midas_v21_small":
        net_w, net_h = 256, 256
        resize_mode = "upper_bound"
        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    else:
        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
    transform = Compose(
        [
            Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=True,
                ensure_multiple_of=32,
                resize_method=resize_mode,
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            normalization,
            PrepareForNet(),
        ]
    )
    return transform