code: def forward(self, outputs, batch_gt_instances, batch_img_metas):
      batch_indices = self.assigner(
          outputs, batch_gt_instances, batch_img_metas
      )
      # Compute all the requested losses
      loss_cls = self.loss_classification(
          outputs, batch_gt_instances, batch_indices
      )
      loss_bbox, loss_giou = self.loss_boxes(
          outputs, batch_gt_instances, batch_indices
      )
  
      losses = dict(
          loss_cls=loss_cls, loss_bbox=loss_bbox, loss_giou=loss_giou
      )
  
      if self.deep_supervision:
          assert "aux_outputs" in outputs
          for i, aux_outputs in enumerate(outputs["aux_outputs"]):
              batch_indices = self.assigner(
                  aux_outputs, batch_gt_instances, batch_img_metas
              )
              loss_cls = self.loss_classification(
                  aux_outputs, batch_gt_instances, batch_indices
              )
              loss_bbox, loss_giou = self.loss_boxes(
                  aux_outputs, batch_gt_instances, batch_indices
              )
              tmp_losses = dict(
                  loss_cls=loss_cls,
                  loss_bbox=loss_bbox,
                  loss_giou=loss_giou,
              )
              for name, value in tmp_losses.items():
                  losses[f"s.{i}.{name}"] = value
      return losses
  
imports: from typing import List, Tuple, Union
  import torch
  import torch.nn
  from mmengine.config import ConfigDict
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh
  from mmdet.utils import ConfigType
label_desc: Use image node_id 2 for calling the DiffusionDetCriterion obj's underlying member method code with example usage: obj.forward(outputs, batch_gt_instances, batch_img_metas) and returns: losses