code: def loss_aux_by_feat(
      self,
      all_cls_scores,
      all_bbox_preds,
      enc_cls_scores,
      enc_bbox_preds,
      aux_coords,
      aux_labels,
      aux_targets,
      aux_label_weights,
      aux_bbox_weights,
      aux_feats,
      attn_masks,
      gt_bboxes_list,
      gt_labels_list,
      img_metas,
      gt_bboxes_ignore=None,
  ):
      num_dec_layers = len(all_cls_scores)
      all_labels = [aux_labels for _ in range(num_dec_layers)]
      all_label_weights = [
          aux_label_weights for _ in range(num_dec_layers)
      ]
      all_bbox_targets = [aux_targets for _ in range(num_dec_layers)]
      all_bbox_weights = [
          aux_bbox_weights for _ in range(num_dec_layers)
      ]
      img_metas_list = [img_metas for _ in range(num_dec_layers)]
      all_gt_bboxes_ignore_list = [
          gt_bboxes_ignore for _ in range(num_dec_layers)
      ]
  
      losses_cls, losses_bbox, losses_iou = multi_apply(
          self._loss_aux_by_feat_single,
          all_cls_scores,
          all_bbox_preds,
          all_labels,
          all_label_weights,
          all_bbox_targets,
          all_bbox_weights,
          img_metas_list,
          all_gt_bboxes_ignore_list,
      )
  
      loss_dict = dict()
      # loss of proposal generated from encode feature map.
  
      # loss from the last decoder layer
      loss_dict["loss_cls_aux"] = losses_cls[-1]
      loss_dict["loss_bbox_aux"] = losses_bbox[-1]
      loss_dict["loss_iou_aux"] = losses_iou[-1]
      # loss from other decoder layers
      num_dec_layer = 0
      for loss_cls_i, loss_bbox_i, loss_iou_i in zip(
          losses_cls[:-1], losses_bbox[:-1], losses_iou[:-1]
      ):
          loss_dict[f"d{num_dec_layer}.loss_cls_aux"] = loss_cls_i
          loss_dict[f"d{num_dec_layer}.loss_bbox_aux"] = loss_bbox_i
          loss_dict[f"d{num_dec_layer}.loss_iou_aux"] = loss_iou_i
          num_dec_layer += 1
      return loss_dict
  
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 12 for calling the CoDINOHead obj's underlying member method code with example usage: obj.loss_aux_by_feat(all_cls_scores, all_bbox_preds, enc_cls_scores, enc_bbox_preds, aux_coords, aux_labels, aux_targets, aux_label_weights, aux_bbox_weights, aux_feats, attn_masks, gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore) and returns: loss_dict