code: def loss(self, x, batch_data_samples):
      assert self.dn_generator is not None, '"dn_cfg" must be set'
  
      batch_gt_instances = []
      batch_img_metas = []
      for data_sample in batch_data_samples:
          batch_img_metas.append(data_sample.metainfo)
          batch_gt_instances.append(data_sample.gt_instances)
  
      (
          dn_label_query,
          dn_bbox_query,
          attn_mask,
          dn_meta,
      ) = self.dn_generator(batch_data_samples)
  
      outs = self(
          x, batch_img_metas, dn_label_query, dn_bbox_query, attn_mask
      )
  
      loss_inputs = outs[:-1] + (
          batch_gt_instances,
          batch_img_metas,
          dn_meta,
      )
      losses = self.loss_by_feat(*loss_inputs)
      enc_outputs = outs[-1]
      return losses, enc_outputs
  
imports: import copy
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Linear
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models import DINOHead
  from mmdet.models.layers import CdnQueryGenerator
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.models.utils import multi_apply
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_overlaps, bbox_xyxy_to_cxcywh
  from mmdet.utils import InstanceList, reduce_mean
label_desc: Use image node_id 8 for calling the CoDINOHead obj's underlying member method code with example usage: obj.loss(x, batch_data_samples) and returns: losses,  enc_outputs