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 forward_single_cls(
      self, feat: Tensor, level_id: int, i: int
  ) -> Tensor:
      conv_op = self.cls_conv_list[i]
      bn = self.cls_bn_list[i][level_id]
  
      feat = conv_op(feat)
      feat = bn(feat)
      feat = self.swish(feat)
  
      return feat
  
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 5 for calling the EfficientDetSepBNHead obj's underlying member method code with example usage: obj.forward_single_cls(feat, level_id, i) and returns: feat