def METHOD_NAME(
        self,
        cls_scores: List[Tensor],
        bbox_preds: List[Tensor],
        angle_preds: List[Tensor],
        batch_img_metas: Optional[List[dict]] = None,
        cfg: Optional[ConfigDict] = None,
        rescale: bool = False,
        with_nms: bool = True) -> Tuple[Tensor]:
    """Rewrite `predict_by_feat` of `Rotated RTMDet` for default backend.
    Rewrite this function to deploy model, transform network output for a
    batch into bbox predictions.
    Args:
        cls_scores (list[Tensor]): Classification scores for all
            scale levels, each is a 4D-tensor, has shape
            (batch_size, num_priors * num_classes, H, W).
        bbox_preds (list[Tensor]): Box energies / deltas for all
            scale levels, each is a 4D-tensor, has shape
            (batch_size, num_priors * 4, H, W).
        angle_preds (list[Tensor]): Box angle for each scale level
            with shape (batch_size, num_priors * angle_dim, H, W)
        batch_img_metas (list[dict], Optional): Batch image meta info.
            Defaults to None.
        cfg (ConfigDict, optional): Test / postprocessing
            configuration, if None, test_cfg would be used.
            Defaults to None.
        rescale (bool): If True, return boxes in original image space.
            Defaults to False.
        with_nms (bool): If True, do nms before return boxes.
            Defaults to True.
    Returns:
        tuple[Tensor, Tensor]: The first item is an (N, num_box, 6) tensor,
            where 5 represent (x, y, w, h, angle, score), N is batch
            size and the score between 0 and 1. The shape of the second
            tensor in the tuple is (N, num_box), and each element
            represents the class label of the corresponding box.
    """
    ctx = FUNCTION_REWRITER.get_context()
    assert len(cls_scores) == len(bbox_preds)
    device = cls_scores[0].device
    cfg = self.test_cfg if cfg is None else cfg
    batch_size = bbox_preds[0].shape[0]
    featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
    mlvl_priors = self.prior_generator.grid_priors(
        featmap_sizes, device=device)
    flatten_cls_scores = [
        cls_score.permute(0, 2, 3, 1).reshape(batch_size, -1,
                                              self.cls_out_channels)
        for cls_score in cls_scores
    ]
    flatten_bbox_preds = [
        bbox_pred.permute(0, 2, 3, 1).reshape(batch_size, -1, 4)
        for bbox_pred in bbox_preds
    ]
    flatten_angle_preds = [
        angle_pred.permute(0, 2, 3, 1).reshape(batch_size, -1,
                                               self.angle_coder.encode_size)
        for angle_pred in angle_preds
    ]
    flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
    flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
    flatten_angle_preds = torch.cat(flatten_angle_preds, dim=1)
    priors = torch.cat(mlvl_priors)
    angle = self.angle_coder.decode(flatten_angle_preds, keepdim=True)
    distance = flatten_bbox_preds
    cos_angle, sin_angle = torch.cos(angle), torch.sin(angle)
    rot_matrix = torch.cat([cos_angle, -sin_angle, sin_angle, cos_angle],
                           dim=-1)
    rot_matrix = rot_matrix.reshape(*rot_matrix.shape[:-1], 2, 2)
    wh = distance[..., :2] + distance[..., 2:]
    offset_t = (distance[..., 2:] - distance[..., :2]) / 2
    offset_t = offset_t.unsqueeze(-1)
    offset = torch.matmul(rot_matrix, offset_t).squeeze(-1)
    ctr = priors[..., :2] + offset
    angle_regular = norm_angle(angle, self.angle_version)
    bboxes = torch.cat([ctr, wh, angle_regular], dim=-1)
    # directly multiply score factor and feed to nms
    max_scores, _ = torch.max(flatten_cls_scores, 1)
    mask = max_scores >= cfg.score_thr
    scores = flatten_cls_scores.where(mask, flatten_cls_scores.new_zeros(1))
    if not with_nms:
        return bboxes, scores
    deploy_cfg = ctx.cfg
    post_params = get_post_processing_params(deploy_cfg)
    max_output_boxes_per_class = post_params.max_output_boxes_per_class
    iou_threshold = cfg.nms.get('iou_threshold', post_params.iou_threshold)
    score_threshold = cfg.get('score_thr', post_params.score_threshold)
    pre_top_k = post_params.pre_top_k
    keep_top_k = cfg.get('max_per_img', post_params.keep_top_k)
    return multiclass_nms_rotated(bboxes, scores, max_output_boxes_per_class,
                                  iou_threshold, score_threshold, pre_top_k,
                                  keep_top_k)