code: def forward(
      self,
      boxes,
      logits,
      gt_bbox,
      gt_class,
      masks=None,
      gt_mask=None,
      postfix="",
      dn_out_bboxes=None,
      dn_out_logits=None,
      dn_meta=None,
      gt_score=None,
      **kwargs,
  ):
      num_gts = self._get_num_gts(gt_class)
      total_loss = super(DINOLoss, self).forward(
          boxes,
          logits,
          gt_bbox,
          gt_class,
          num_gts=num_gts,
          gt_score=gt_score,
      )
  
      if dn_meta is not None:
          dn_positive_idx, dn_num_group = (
              dn_meta["dn_positive_idx"],
              dn_meta["dn_num_group"],
          )
          assert len(gt_class) == len(dn_positive_idx)
  
          # denoising match indices
          dn_match_indices = self.get_dn_match_indices(
              gt_class, dn_positive_idx, dn_num_group
          )
  
          # compute denoising training loss
          num_gts *= dn_num_group
          dn_loss = super(DINOLoss, self).forward(
              dn_out_bboxes,
              dn_out_logits,
              gt_bbox,
              gt_class,
              postfix="_dn",
              dn_match_indices=dn_match_indices,
              num_gts=num_gts,
              gt_score=gt_score,
          )
          total_loss.update(dn_loss)
      else:
          total_loss.update(
              {
                  k + "_dn": paddle.to_tensor([0.0])
                  for k in total_loss.keys()
              }
          )
  
      return total_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 1 for calling the DINOLoss obj's underlying member method code with example usage: obj.forward(boxes, logits, gt_bbox, gt_class, masks, gt_mask, postfix, dn_out_bboxes, dn_out_logits, dn_meta, gt_score) and returns: total_loss