code: def forward(
      self,
      mlvl_feats,
      img_metas,
      dn_label_query=None,
      dn_bbox_query=None,
      attn_mask=None,
  ):
      batch_size = mlvl_feats[0].size(0)
      input_img_h, input_img_w = img_metas[0]["batch_input_shape"]
      img_masks = mlvl_feats[0].new_ones(
          (batch_size, input_img_h, input_img_w)
      )
      for img_id in range(batch_size):
          img_h, img_w = img_metas[img_id]["img_shape"]
          img_masks[img_id, :img_h, :img_w] = 0
  
      mlvl_masks = []
      mlvl_positional_encodings = []
      for feat in mlvl_feats:
          mlvl_masks.append(
              F.interpolate(img_masks[None], size=feat.shape[-2:])
              .to(torch.bool)
              .squeeze(0)
          )
          mlvl_positional_encodings.append(
              self.positional_encoding(mlvl_masks[-1])
          )
  
      query_embeds = None
      (
          hs,
          inter_references,
          topk_score,
          topk_anchor,
          enc_outputs,
      ) = self.transformer(
          mlvl_feats,
          mlvl_masks,
          query_embeds,
          mlvl_positional_encodings,
          dn_label_query,
          dn_bbox_query,
          attn_mask,
          reg_branches=self.reg_branches
          if self.with_box_refine
          else None,  # noqa:E501
          cls_branches=self.cls_branches
          if self.as_two_stage
          else None,  # noqa:E501
      )
      outs = []
      num_level = len(mlvl_feats)
      start = 0
      for lvl in range(num_level):
          bs, c, h, w = mlvl_feats[lvl].shape
          end = start + h * w
          feat = enc_outputs[start:end].permute(1, 2, 0).contiguous()
          start = end
          outs.append(feat.reshape(bs, c, h, w))
      outs.append(self.downsample(outs[-1]))
  
      hs = hs.permute(0, 2, 1, 3)
  
      if dn_label_query is not None and dn_label_query.size(1) == 0:
          # NOTE: If there is no target in the image, the parameters of
          # label_embedding won't be used in producing loss, which raises
          # RuntimeError when using distributed mode.
          hs[0] += self.dn_generator.label_embedding.weight[0, 0] * 0.0
  
      outputs_classes = []
      outputs_coords = []
  
      for lvl in range(hs.shape[0]):
          reference = inter_references[lvl]
          reference = inverse_sigmoid(reference, eps=1e-3)
          outputs_class = self.cls_branches[lvl](hs[lvl])
          tmp = self.reg_branches[lvl](hs[lvl])
          if reference.shape[-1] == 4:
              tmp += reference
          else:
              assert reference.shape[-1] == 2
              tmp[..., :2] += reference
          outputs_coord = tmp.sigmoid()
          outputs_classes.append(outputs_class)
          outputs_coords.append(outputs_coord)
  
      outputs_classes = torch.stack(outputs_classes)
      outputs_coords = torch.stack(outputs_coords)
  
      return (
          outputs_classes,
          outputs_coords,
          topk_score,
          topk_anchor,
          outs,
      )
  
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 4 for calling the CoDINOHead obj's underlying member method code with example usage: obj.forward(mlvl_feats, img_metas, dn_label_query, dn_bbox_query, attn_mask) and returns: outputs_classes,  outputs_coords,  topk_score,  topk_anchor,  outs