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