code: def _get_loss_aux(
      self,
      boxes,
      logits,
      gt_bbox,
      gt_class,
      bg_index,
      num_gts,
      dn_match_indices=None,
      postfix="",
      masks=None,
      gt_mask=None,
      gt_score=None,
  ):
      loss_class = []
      loss_bbox, loss_giou = [], []
      loss_mask, loss_dice = [], []
      if dn_match_indices is not None:
          match_indices = dn_match_indices
      elif self.use_uni_match:
          match_indices = self.matcher(
              boxes[self.uni_match_ind],
              logits[self.uni_match_ind],
              gt_bbox,
              gt_class,
              masks=masks[self.uni_match_ind]
              if masks is not None
              else None,
              gt_mask=gt_mask,
          )
      for i, (aux_boxes, aux_logits) in enumerate(zip(boxes, logits)):
          aux_masks = masks[i] if masks is not None else None
          if not self.use_uni_match and dn_match_indices is None:
              match_indices = self.matcher(
                  aux_boxes,
                  aux_logits,
                  gt_bbox,
                  gt_class,
                  masks=aux_masks,
                  gt_mask=gt_mask,
              )
          if self.use_vfl:
              if sum(len(a) for a in gt_bbox) > 0:
                  src_bbox, target_bbox = self._get_src_target_assign(
                      aux_boxes.detach(), gt_bbox, match_indices
                  )
                  iou_score = bbox_iou(
                      bbox_cxcywh_to_xyxy(src_bbox).split(4, -1),
                      bbox_cxcywh_to_xyxy(target_bbox).split(4, -1),
                  )
              else:
                  iou_score = None
              if gt_score is not None:
                  _, target_score = self._get_src_target_assign(
                      logits[-1].detach(), gt_score, match_indices
                  )
          else:
              iou_score = None
          loss_class.append(
              self._get_loss_class(
                  aux_logits,
                  gt_class,
                  match_indices,
                  bg_index,
                  num_gts,
                  postfix,
                  iou_score,
                  gt_score=target_score
                  if gt_score is not None
                  else None,
              )["loss_class" + postfix]
          )
          loss_ = self._get_loss_bbox(
              aux_boxes, gt_bbox, match_indices, num_gts, postfix
          )
          loss_bbox.append(loss_["loss_bbox" + postfix])
          loss_giou.append(loss_["loss_giou" + postfix])
          if masks is not None and gt_mask is not None:
              loss_ = self._get_loss_mask(
                  aux_masks, gt_mask, match_indices, num_gts, postfix
              )
              loss_mask.append(loss_["loss_mask" + postfix])
              loss_dice.append(loss_["loss_dice" + postfix])
      loss = {
          "loss_class_aux" + postfix: paddle.add_n(loss_class),
          "loss_bbox_aux" + postfix: paddle.add_n(loss_bbox),
          "loss_giou_aux" + postfix: paddle.add_n(loss_giou),
      }
      if masks is not None and gt_mask is not None:
          loss["loss_mask_aux" + postfix] = paddle.add_n(loss_mask)
          loss["loss_dice_aux" + postfix] = paddle.add_n(loss_dice)
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from .iou_loss import GIoULoss
  from ..transformers import bbox_cxcywh_to_xyxy, sigmoid_focal_loss, varifocal_loss_with_logits
  from ..bbox_utils import bbox_iou
label_desc: Use image node_id 6 for calling the DETRLoss obj's underlying member method code with example usage: obj._get_loss_aux(boxes, logits, gt_bbox, gt_class, bg_index, num_gts, dn_match_indices, postfix, masks, gt_mask, gt_score) and returns: loss