class_docstr: EfficientDetHead with separate BN.
  
  num_classes (int): Number of categories num_ins (int):  Number of the input
  feature map. in_channels (int): Number of channels in the input feature
  map. feat_channels (int): Number of hidden channels. stacked_convs (int):
  Number of repetitions of conv norm_cfg (dict): Config dict for
  normalization layer. anchor_generator (dict): Config dict for anchor
  generator bbox_coder (dict): Config of bounding box coder. loss_cls (dict):
  Config of classification loss. loss_bbox (dict): Config of localization
  loss. train_cfg (dict): Training config of anchor head. test_cfg (dict):
  Testing config of anchor head. init_cfg (dict or list[dict], optional):
  Initialization config dict.
docstr: Initialize layers of the head.
docstr_tok: ["Initialize","layers","of","the","head","."]
code: def _init_layers(self) -> None:
      """Initialize layers of the head."""
      self.reg_conv_list = nn.ModuleList()
      self.cls_conv_list = nn.ModuleList()
      for i in range(self.stacked_convs):
          channels = self.in_channels if i == 0 else self.feat_channels
          self.reg_conv_list.append(
              DepthWiseConvBlock(
                  channels, self.feat_channels, apply_norm=False
              )
          )
          self.cls_conv_list.append(
              DepthWiseConvBlock(
                  channels, self.feat_channels, apply_norm=False
              )
          )
  
      self.reg_bn_list = nn.ModuleList(
          [
              nn.ModuleList(
                  [
                      build_norm_layer(
                          self.norm_cfg, num_features=self.feat_channels
                      )[1]
                      for j in range(self.num_ins)
                  ]
              )
              for i in range(self.stacked_convs)
          ]
      )
  
      self.cls_bn_list = nn.ModuleList(
          [
              nn.ModuleList(
                  [
                      build_norm_layer(
                          self.norm_cfg, num_features=self.feat_channels
                      )[1]
                      for j in range(self.num_ins)
                  ]
              )
              for i in range(self.stacked_convs)
          ]
      )
  
      self.cls_header = DepthWiseConvBlock(
          self.in_channels,
          self.num_base_priors * self.cls_out_channels,
          apply_norm=False,
      )
      self.reg_header = DepthWiseConvBlock(
          self.in_channels, self.num_base_priors * 4, apply_norm=False
      )
      self.swish = Swish()
  
imports: from typing import List, Tuple
  import torch
  import torch.nn
  from mmcv.cnn.bricks import Swish, build_norm_layer
  from mmengine.model import bias_init_with_prob
  from torch import Tensor
  from mmdet.models.dense_heads.anchor_head import AnchorHead
  from mmdet.models.utils import images_to_levels, multi_apply
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import cat_boxes, get_box_tensor
  from mmdet.utils import InstanceList, OptConfigType, OptInstanceList, OptMultiConfig, reduce_mean
  from .utils import DepthWiseConvBlock
label_desc: Use image node_id 2 for calling the EfficientDetSepBNHead obj's underlying member method code with example usage: obj._init_layers() without return types