code: def _init_layers(self):
      self.transformer = MODELS.build(self.transformer)
      self.embed_dims = self.transformer.embed_dims
      assert hasattr(self.positional_encoding, "num_feats")
      num_feats = self.positional_encoding.num_feats
      assert num_feats * 2 == self.embed_dims, (
          "embed_dims should"
          f" be exactly 2 times of num_feats. Found {self.embed_dims}"
          f" and {num_feats}."
      )
      """Initialize classification branch and regression branch of head."""
      fc_cls = Linear(self.embed_dims, self.cls_out_channels)
      reg_branch = []
      for _ in range(self.num_reg_fcs):
          reg_branch.append(Linear(self.embed_dims, self.embed_dims))
          reg_branch.append(nn.ReLU())
      reg_branch.append(Linear(self.embed_dims, 4))
      reg_branch = nn.Sequential(*reg_branch)
  
      def _get_clones(module, N):
          return nn.ModuleList(
              [copy.deepcopy(module) for i in range(N)]
          )
  
      # last reg_branch is used to generate proposal from
      # encode feature map when as_two_stage is True.
      num_pred = (
          (self.transformer.decoder.num_layers + 1)
          if self.as_two_stage
          else self.transformer.decoder.num_layers
      )
  
      self.cls_branches = _get_clones(fc_cls, num_pred)
      self.reg_branches = _get_clones(reg_branch, num_pred)
  
      self.downsample = nn.Sequential(
          nn.Conv2d(
              self.embed_dims,
              self.embed_dims,
              kernel_size=3,
              stride=2,
              padding=1,
          ),
          nn.GroupNorm(32, self.embed_dims),
      )
  
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 2 for calling the CoDINOHead obj's underlying member method code with example usage: obj._init_layers() and returns: nn