def METHOD_NAME(img_transforms):
    input_shape = model_config['model']['input_shape']
    target_info = wego_torch.get_target_info()
    print("[Info] target_info: %s" % str(target_info))
    target_batch = target_info.batch
    if os.path.isdir(args.calibration_images_folder):
        dataset = torchvision.datasets.ImageFolder(args.calibration_images_folder, img_transforms)
    else:
        raise ValueError(f"calibration_images_folder doesn't exist: {args.calibration_images_folder}")
    # create a subset of dataset for calibration
    calib_dataset = torch.utils.data.Subset(dataset, list(range(args.calib_set_len)))
    # create dataloader for calibration dataset
    calib_dataloader = torch.utils.data.DataLoader(calib_dataset, batch_size=target_batch, shuffle=False)
    print(f"calibrate model with {len(calib_dataloader)} batches, batch size = {target_batch}")
    # define calibrator
    def calibrator(model, batch_data, batch_index, device):
        input_data, _ = batch_data
        input_data = input_data.to(device)
        model(input_data)
    # load original float model
    float_model = torchvision.models.inception_v3()
    if os.path.isfile(args.float_model_path):
        # https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth
        float_model.load_state_dict(torch.load(args.float_model_path))
        print(f"successfully loaded float model from {args.float_model_path}")
    else:
        raise ValueError(f"float model file doesn't exist: {args.float_model_path}")
    # quantize
    quantized_model = wego_torch.quantize(float_model, [[target_batch] + input_shape], calib_dataloader, calibrator)
    # We can remove redudant fixneurons if we want to get better performance but the accuracy may has 
    # a minor difference with the original quantized model.
    if args.mode == 'normal':
        print("[Info] running in normal mode...")
        accuracy_mode = wego_torch.AccuracyMode.ReserveReduantFixNeurons
    elif args.mode == 'perf':
        print("[Info] running in perf mode...")
        accuracy_mode = wego_torch.AccuracyMode.Default
    else:
        raise ValueError('unsupport running mode - %s, support list: [normal, perf]' % (args.mode))
    # Create wego module by compiling the original qunatized torchscript model
    wego_mod = wego_torch.compile(quantized_model, wego_torch.CompileOptions(
        inputs_meta = [
        wego_torch.InputMeta(torch.float, [1] + input_shape)
        ],
        accuracy_mode=accuracy_mode
    ))
    return wego_mod