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.
code: def __init__(
      self,
      num_classes: int,
      num_ins: int,
      in_channels: int,
      feat_channels: int,
      stacked_convs: int = 3,
      norm_cfg: OptConfigType = dict(
          type="BN", momentum=1e-2, eps=1e-3
      ),
      init_cfg: OptMultiConfig = None,
      **kwargs,
  ) -> None:
      self.num_ins = num_ins
      self.stacked_convs = stacked_convs
      self.norm_cfg = norm_cfg
      super().__init__(
          num_classes=num_classes,
          in_channels=in_channels,
          feat_channels=feat_channels,
          init_cfg=init_cfg,
          **kwargs,
      )
  
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 1 to create a new EfficientDetSepBNHead object from inherited base classes: AnchorHead with example: obj = EfficientDetSepBNHead(num_classes, num_ins, in_channels, feat_channels, stacked_convs, norm_cfg, init_cfg)