code: def _get_prediction_loss(
      self,
      boxes,
      logits,
      gt_bbox,
      gt_class,
      masks=None,
      gt_mask=None,
      postfix="",
      dn_match_indices=None,
      num_gts=1,
      gt_score=None,
  ):
      if dn_match_indices is None:
          match_indices = self.matcher(
              boxes,
              logits,
              gt_bbox,
              gt_class,
              masks=masks,
              gt_mask=gt_mask,
          )
      else:
          match_indices = dn_match_indices
  
      if self.use_vfl:
          if gt_score is not None:  # ssod
              _, target_score = self._get_src_target_assign(
                  logits[-1].detach(), gt_score, match_indices
              )
          elif sum(len(a) for a in gt_bbox) > 0:
              src_bbox, target_bbox = self._get_src_target_assign(
                  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
      else:
          iou_score = None
  
      loss = dict()
      loss.update(
          self._get_loss_class(
              logits,
              gt_class,
              match_indices,
              self.num_classes,
              num_gts,
              postfix,
              iou_score,
              gt_score=target_score if gt_score is not None else None,
          )
      )
      loss.update(
          self._get_loss_bbox(
              boxes, gt_bbox, match_indices, num_gts, postfix
          )
      )
      if masks is not None and gt_mask is not None:
          loss.update(
              self._get_loss_mask(
                  masks, gt_mask, match_indices, num_gts, postfix
              )
          )
      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 10 for calling the DETRLoss obj's underlying member method code with example usage: obj._get_prediction_loss(boxes, logits, gt_bbox, gt_class, masks, gt_mask, postfix, dn_match_indices, num_gts, gt_score) and returns: loss