code: def _loss_aux_by_feat_single(
      self,
      cls_scores,
      bbox_preds,
      labels,
      label_weights,
      bbox_targets,
      bbox_weights,
      img_metas,
      gt_bboxes_ignore_list=None,
  ):
      num_imgs = cls_scores.size(0)
      num_q = cls_scores.size(1)
  
      try:
          labels = labels.reshape(num_imgs * num_q)
          label_weights = label_weights.reshape(num_imgs * num_q)
          bbox_targets = bbox_targets.reshape(num_imgs * num_q, 4)
          bbox_weights = bbox_weights.reshape(num_imgs * num_q, 4)
      except Exception:
          return (
              cls_scores.mean() * 0,
              cls_scores.mean() * 0,
              cls_scores.mean() * 0,
          )
  
      bg_class_ind = self.num_classes
      num_total_pos = len(
          ((labels >= 0) & (labels < bg_class_ind)).nonzero().squeeze(1)
      )
      num_total_neg = num_imgs * num_q - num_total_pos
  
      # classification loss
      cls_scores = cls_scores.reshape(-1, self.cls_out_channels)
      # construct weighted avg_factor to match with the official DETR repo
      cls_avg_factor = (
          num_total_pos * 1.0 + num_total_neg * self.bg_cls_weight
      )
      if self.sync_cls_avg_factor:
          cls_avg_factor = reduce_mean(
              cls_scores.new_tensor([cls_avg_factor])
          )
      cls_avg_factor = max(cls_avg_factor, 1)
  
      bg_class_ind = self.num_classes
      pos_inds = (
          ((labels >= 0) & (labels < bg_class_ind)).nonzero().squeeze(1)
      )
      scores = label_weights.new_zeros(labels.shape)
      pos_bbox_targets = bbox_targets[pos_inds]
      pos_decode_bbox_targets = bbox_cxcywh_to_xyxy(pos_bbox_targets)
      pos_bbox_pred = bbox_preds.reshape(-1, 4)[pos_inds]
      pos_decode_bbox_pred = bbox_cxcywh_to_xyxy(pos_bbox_pred)
      scores[pos_inds] = bbox_overlaps(
          pos_decode_bbox_pred.detach(),
          pos_decode_bbox_targets,
          is_aligned=True,
      )
      loss_cls = self.loss_cls(
          cls_scores,
          (labels, scores),
          weight=label_weights,
          avg_factor=cls_avg_factor,
      )
  
      # Compute the average number of gt boxes across all gpus, for
      # normalization purposes
      num_total_pos = loss_cls.new_tensor([num_total_pos])
      num_total_pos = torch.clamp(
          reduce_mean(num_total_pos), min=1
      ).item()
  
      # construct factors used for rescale bboxes
      factors = []
      for img_meta, bbox_pred in zip(img_metas, bbox_preds):
          img_h, img_w = img_meta["img_shape"]
          factor = (
              bbox_pred.new_tensor([img_w, img_h, img_w, img_h])
              .unsqueeze(0)
              .repeat(bbox_pred.size(0), 1)
          )
          factors.append(factor)
      factors = torch.cat(factors, 0)
  
      # DETR regress the relative position of boxes (cxcywh) in the image,
      # thus the learning target is normalized by the image size. So here
      # we need to re-scale them for calculating IoU loss
      bbox_preds = bbox_preds.reshape(-1, 4)
      bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors
      bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors
  
      # regression IoU loss, defaultly GIoU loss
      loss_iou = self.loss_iou(
          bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos
      )
  
      # regression L1 loss
      loss_bbox = self.loss_bbox(
          bbox_preds,
          bbox_targets,
          bbox_weights,
          avg_factor=num_total_pos,
      )
      return loss_cls, loss_bbox, loss_iou
  
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 13 for calling the CoDINOHead obj's underlying member method code with example usage: obj._loss_aux_by_feat_single(cls_scores, bbox_preds, labels, label_weights, bbox_targets, bbox_weights, img_metas, gt_bboxes_ignore_list) and returns: loss_cls,  loss_bbox,  loss_iou, unknown,  unknown,  unknown