code: def get_aux_targets(
      self, pos_coords, img_metas, mlvl_feats, head_idx
  ):
      coords, labels, targets = pos_coords[:3]
      head_name = pos_coords[-1]
      bs, c = len(coords), mlvl_feats[0].shape[1]
      max_num_coords = 0
      all_feats = []
      for i in range(bs):
          label = labels[i]
          feats = [
              feat[i].reshape(c, -1).transpose(1, 0)
              for feat in mlvl_feats
          ]
          feats = torch.cat(feats, dim=0)
          bg_class_ind = self.num_classes
          pos_inds = (
              ((label >= 0) & (label < bg_class_ind))
              .nonzero()
              .squeeze(1)
          )
          max_num_coords = max(max_num_coords, len(pos_inds))
          all_feats.append(feats)
      max_num_coords = min(self.max_pos_coords, max_num_coords)
      max_num_coords = max(9, max_num_coords)
  
      if self.use_zero_padding:
          attn_masks = []
          label_weights = coords[0].new_zeros([bs, max_num_coords])
      else:
          attn_masks = None
          label_weights = coords[0].new_ones([bs, max_num_coords])
      bbox_weights = coords[0].new_zeros([bs, max_num_coords, 4])
  
      aux_coords, aux_labels, aux_targets, aux_feats = [], [], [], []
  
      for i in range(bs):
          coord, label, target = coords[i], labels[i], targets[i]
          feats = all_feats[i]
          if "rcnn" in head_name:
              feats = pos_coords[-2][i]
              num_coords_per_point = 1
          else:
              num_coords_per_point = coord.shape[0] // feats.shape[0]
          feats = feats.unsqueeze(1).repeat(1, num_coords_per_point, 1)
          feats = feats.reshape(
              feats.shape[0] * num_coords_per_point, feats.shape[-1]
          )
          img_meta = img_metas[i]
          img_h, img_w = img_meta["img_shape"]
          factor = coord.new_tensor(
              [img_w, img_h, img_w, img_h]
          ).unsqueeze(0)
          bg_class_ind = self.num_classes
          pos_inds = (
              ((label >= 0) & (label < bg_class_ind))
              .nonzero()
              .squeeze(1)
          )
          neg_inds = (label == bg_class_ind).nonzero().squeeze(1)
          if pos_inds.shape[0] > max_num_coords:
              indices = torch.randperm(pos_inds.shape[0])[
                  :max_num_coords
              ].cuda()
              pos_inds = pos_inds[indices]
  
          coord = bbox_xyxy_to_cxcywh(coord[pos_inds] / factor)
          label = label[pos_inds]
          target = bbox_xyxy_to_cxcywh(target[pos_inds] / factor)
          feat = feats[pos_inds]
  
          if self.use_zero_padding:
              label_weights[i][: len(label)] = 1
              bbox_weights[i][: len(label)] = 1
              attn_mask = (
                  torch.zeros(
                      [
                          max_num_coords,
                          max_num_coords,
                      ]
                  )
                  .bool()
                  .to(coord.device)
              )
          else:
              bbox_weights[i][: len(label)] = 1
  
          if coord.shape[0] < max_num_coords:
              padding_shape = max_num_coords - coord.shape[0]
              if self.use_zero_padding:
                  padding_coord = coord.new_zeros([padding_shape, 4])
                  padding_label = (
                      label.new_ones([padding_shape]) * self.num_classes
                  )
                  padding_target = target.new_zeros([padding_shape, 4])
                  padding_feat = feat.new_zeros([padding_shape, c])
                  attn_mask[
                      coord.shape[0] :,
                      0 : coord.shape[0],
                  ] = True
                  attn_mask[
                      :,
                      coord.shape[0] :,
                  ] = True
              else:
                  indices = torch.randperm(neg_inds.shape[0])[
                      :padding_shape
                  ].cuda()
                  neg_inds = neg_inds[indices]
                  padding_coord = bbox_xyxy_to_cxcywh(
                      coords[i][neg_inds] / factor
                  )
                  padding_label = labels[i][neg_inds]
                  padding_target = bbox_xyxy_to_cxcywh(
                      targets[i][neg_inds] / factor
                  )
                  padding_feat = feats[neg_inds]
              coord = torch.cat((coord, padding_coord), dim=0)
              label = torch.cat((label, padding_label), dim=0)
              target = torch.cat((target, padding_target), dim=0)
              feat = torch.cat((feat, padding_feat), dim=0)
          if self.use_zero_padding:
              attn_masks.append(attn_mask.unsqueeze(0))
          aux_coords.append(coord.unsqueeze(0))
          aux_labels.append(label.unsqueeze(0))
          aux_targets.append(target.unsqueeze(0))
          aux_feats.append(feat.unsqueeze(0))
  
      if self.use_zero_padding:
          attn_masks = (
              torch.cat(attn_masks, dim=0)
              .unsqueeze(1)
              .repeat(1, 8, 1, 1)
          )
          attn_masks = attn_masks.reshape(
              bs * 8, max_num_coords, max_num_coords
          )
      else:
          attn_masks = None
  
      aux_coords = torch.cat(aux_coords, dim=0)
      aux_labels = torch.cat(aux_labels, dim=0)
      aux_targets = torch.cat(aux_targets, dim=0)
      aux_feats = torch.cat(aux_feats, dim=0)
      aux_label_weights = label_weights
      aux_bbox_weights = bbox_weights
      return (
          aux_coords,
          aux_labels,
          aux_targets,
          aux_label_weights,
          aux_bbox_weights,
          aux_feats,
          attn_masks,
      )
  
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 11 for calling the CoDINOHead obj's underlying member method code with example usage: obj.get_aux_targets(pos_coords, img_metas, mlvl_feats, head_idx) and returns: aux_coords,  aux_labels,  aux_targets,  aux_label_weights,  aux_bbox_weights,  aux_feats,  attn_masks