code: def _init_layers(self):
      if self.with_pos_coord:
          if self.num_co_heads > 0:
              self.aux_pos_trans = nn.ModuleList()
              self.aux_pos_trans_norm = nn.ModuleList()
              self.pos_feats_trans = nn.ModuleList()
              self.pos_feats_norm = nn.ModuleList()
              for i in range(self.num_co_heads):
                  self.aux_pos_trans.append(
                      nn.Linear(self.embed_dims * 2, self.embed_dims)
                  )
                  self.aux_pos_trans_norm.append(
                      nn.LayerNorm(self.embed_dims)
                  )
                  if self.with_coord_feat:
                      self.pos_feats_trans.append(
                          nn.Linear(self.embed_dims, self.embed_dims)
                      )
                      self.pos_feats_norm.append(
                          nn.LayerNorm(self.embed_dims)
                      )
  
imports: import math
  import warnings
  import torch
  import torch.nn
  from mmcv.cnn import build_norm_layer
  from mmcv.cnn.bricks.transformer import BaseTransformerLayer, TransformerLayerSequence, build_transformer_layer_sequence
  from mmcv.ops import MultiScaleDeformableAttention
  from mmengine.model import BaseModule
  from mmengine.model.weight_init import xavier_init
  from torch.nn.init import normal_
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the CoDinoTransformer obj's underlying member method code with example usage: obj._init_layers() without return types