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 weights of the head.
docstr_tok: ["Initialize","weights","of","the","head","."]
code: def init_weights(self) -> None:
      """Initialize weights of the head."""
      for m in self.reg_conv_list:
          nn.init.constant_(m.pointwise_conv.bias, 0.0)
      for m in self.cls_conv_list:
          nn.init.constant_(m.pointwise_conv.bias, 0.0)
      bias_cls = bias_init_with_prob(0.01)
      nn.init.constant_(self.cls_header.pointwise_conv.bias, bias_cls)
      nn.init.constant_(self.reg_header.pointwise_conv.bias, 0.0)
  
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 3 for calling the EfficientDetSepBNHead obj's underlying member method code with example usage: obj.init_weights() without return types