code: def loss_aux(
      self, x, pos_coords=None, head_idx=0, batch_data_samples=None
  ):
      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)
  
      gt_bboxes = [b.bboxes for b in batch_gt_instances]
      gt_labels = [b.labels for b in batch_gt_instances]
  
      aux_targets = self.get_aux_targets(
          pos_coords, batch_img_metas, x, head_idx
      )
      outs = self.forward_aux(
          x[:-1], batch_img_metas, aux_targets, head_idx
      )
      outs = outs + aux_targets
      if gt_labels is None:
          loss_inputs = outs + (gt_bboxes, batch_img_metas)
      else:
          loss_inputs = outs + (gt_bboxes, gt_labels, batch_img_metas)
      losses = self.loss_aux_by_feat(*loss_inputs)
      return losses
  
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 10 for calling the CoDINOHead obj's underlying member method code with example usage: obj.loss_aux(x, pos_coords, head_idx, batch_data_samples) and returns: losses