def METHOD_NAME(
    self,
    sky_image: torch.Tensor,
    sky_image_refine: torch.Tensor,
    scene_image: torch.Tensor,
    scene_image_refine: torch.Tensor,
    img_metas: Dict[str, Any],
):
    """
    Args:
        sky_image (`torch.Tensor`): batched image tensor, shape is [1, 3, h', w'].
        sky_image_refine (`torch.Tensor`): batched image tensor, shape is [1, 3, refine_net_h, refine_net_w].
        scene_image (`torch.Tensor`): batched image tensor, shape is [1, 3, h, w].
        scene_image_refine (`torch.Tensor`): batched image tensor, shape is [1, 3, refine_net_h, refine_net_w].
        img_metas (`Dict[str, Any]`): image meta info.
    Return:
        `IMAGE: shape is [h, w, 3] (0~255)`
    """
    start = time.time()
    sky_img_metas, scene_img_metas, input_size = img_metas[
        'sky_img_metas'], img_metas['scene_img_metas'], img_metas[
            'input_size']
    sky_mask = self.inference_mask(sky_image_refine, sky_img_metas,
                                   input_size)
    scene_mask = self.inference_mask(scene_image_refine, scene_img_metas,
                                     input_size)
    end = time.time()
    logger.info(
        'Time of inferencing mask of sky and scene images:{}'.format(
            end - start))
    start = time.time()
    scene_mask = scene_mask * 255
    sky_mask = sky_mask * 255
    res = blend(scene_image, scene_mask, sky_image, sky_mask)
    end = time.time()
    logger.info('Time of blending: {}'.format(end - start))
    return res