code: def loss(
      self, batch_inputs: Tensor, batch_data_samples: SampleList
  ) -> Union[dict, list]:
      batch_input_shape = batch_data_samples[0].batch_input_shape
      if self.use_lsj:
          for data_samples in batch_data_samples:
              img_metas = data_samples.metainfo
              input_img_h, input_img_w = batch_input_shape
              img_metas["img_shape"] = [input_img_h, input_img_w]
  
      x = self.extract_feat(batch_inputs)
  
      losses = dict()
  
      def upd_loss(losses, idx, weight=1):
          new_losses = dict()
          for k, v in losses.items():
              new_k = "{}{}".format(k, idx)
              if isinstance(v, list) or isinstance(v, tuple):
                  new_losses[new_k] = [i * weight for i in v]
              else:
                  new_losses[new_k] = v * weight
          return new_losses
  
      # DETR encoder and decoder forward
      if self.with_query_head:
          bbox_losses, x = self.query_head.loss(x, batch_data_samples)
          losses.update(bbox_losses)
  
      # RPN forward and loss
      if self.with_rpn:
          proposal_cfg = self.train_cfg[self.head_idx].get(
              "rpn_proposal", self.test_cfg[self.head_idx].rpn
          )
  
          rpn_data_samples = copy.deepcopy(batch_data_samples)
          # set cat_id of gt_labels to 0 in RPN
          for data_sample in rpn_data_samples:
              data_sample.gt_instances.labels = torch.zeros_like(
                  data_sample.gt_instances.labels
              )
  
          rpn_losses, proposal_list = self.rpn_head.loss_and_predict(
              x, rpn_data_samples, proposal_cfg=proposal_cfg
          )
  
          # avoid get same name with roi_head loss
          keys = rpn_losses.keys()
          for key in list(keys):
              if "loss" in key and "rpn" not in key:
                  rpn_losses[f"rpn_{key}"] = rpn_losses.pop(key)
  
          losses.update(rpn_losses)
      else:
          assert (
              batch_data_samples[0].get("proposals", None) is not None
          )
          # use pre-defined proposals in InstanceData for the second stage
          # to extract ROI features.
          proposal_list = [
              data_sample.proposals
              for data_sample in batch_data_samples
          ]
  
      positive_coords = []
      for i in range(len(self.roi_head)):
          roi_losses = self.roi_head[i].loss(
              x, proposal_list, batch_data_samples
          )
          if self.with_pos_coord:
              positive_coords.append(roi_losses.pop("pos_coords"))
          else:
              if "pos_coords" in roi_losses.keys():
                  roi_losses.pop("pos_coords")
          roi_losses = upd_loss(roi_losses, idx=i)
          losses.update(roi_losses)
  
      for i in range(len(self.bbox_head)):
          bbox_losses = self.bbox_head[i].loss(x, batch_data_samples)
          if self.with_pos_coord:
              pos_coords = bbox_losses.pop("pos_coords")
              positive_coords.append(pos_coords)
          else:
              if "pos_coords" in bbox_losses.keys():
                  bbox_losses.pop("pos_coords")
          bbox_losses = upd_loss(
              bbox_losses, idx=i + len(self.roi_head)
          )
          losses.update(bbox_losses)
  
      if self.with_pos_coord and len(positive_coords) > 0:
          for i in range(len(positive_coords)):
              bbox_losses = self.query_head.loss_aux(
                  x, positive_coords[i], i, batch_data_samples
              )
              bbox_losses = upd_loss(bbox_losses, idx=i)
              losses.update(bbox_losses)
  
      return losses
  
imports: import copy
  from typing import Tuple, Union
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.models.detectors.base import BaseDetector
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import InstanceList, OptConfigType, OptMultiConfig
label_desc: Use image node_id 9 for calling the CoDETR obj's underlying member method code with example usage: obj.loss(batch_inputs, batch_data_samples) and returns: losses, new_losses