def METHOD_NAME(self, input: Input) -> Dict[str, Any]:
    """preprocess the input image, extract L-channel and convert it back to RGB
    Args:
        inputs: an input image from file or url
    Returns:
        Dict[str, Any]: the pre-processed image
    """
    img = LoadImage.convert_to_ndarray(input)
    self.height, self.width = img.shape[:2]
    img = (img / 255.0).astype(np.float32)
    self.orig_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1]
    img = cv2.resize(img, (self.input_size, self.input_size))
    img_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1]
    img_gray_lab = np.concatenate(
        (img_l, np.zeros_like(img_l), np.zeros_like(img_l)), axis=-1)
    img_gray_rgb = cv2.cvtColor(img_gray_lab, cv2.COLOR_LAB2RGB)
    tensor_gray_rgb = torch.from_numpy(img_gray_rgb.transpose(
        (2, 0, 1))).float()
    tensor_gray_rgb = tensor_gray_rgb.unsqueeze(0).to(self.device)
    result = {'img': tensor_gray_rgb}
    return result