def METHOD_NAME(self,
              image: Union[str, np.ndarray],
              model: torch.nn.Module,
              result: list,
              output_file: str,
              window_name: str = '',
              show_result: bool = False,
              draw_gt: bool = False,
              **kwargs):
    """visualize backend output.
    Args:
        image (Union[str, np.ndarray]): pcd file path
        model (torch.nn.Module): input pytorch model
        result (list): output bbox, score and type
        output_file (str): the directory to save output
        window_name (str, optional): display window name
        show_result (bool, optional): show result or not.
            Defaults to False.
        draw_gt (bool, optional): show gt or not. Defaults to False.
    """
    cfg = self.model_cfg
    visualizer = super().get_visualizer(window_name, output_file)
    visualizer.dataset_meta = _get_dataset_metainfo(cfg)
    # show the results
    collate_data, _ = self.create_input(pcd=image)
    visualizer.add_datasample(
        window_name,
        dict(points=collate_data['inputs']['points'][0]),
        data_sample=result,
        draw_gt=draw_gt,
        show=show_result,
        wait_time=0,
        out_file=output_file,
        pred_score_thr=0.0,
        vis_task='lidar_det')