class_docstr: Detection Head of RTMDet-Ins with sep-bn layers.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      share_conv (bool): Whether to share conv layers between stages.
          Defaults to True.
      norm_cfg (:obj:`ConfigDict` or dict)): Config dict for normalization
          layer. Defaults to dict(type='BN').
      act_cfg (:obj:`ConfigDict` or dict)): Config dict for activation layer.
          Defaults to dict(type='SiLU', inplace=True).
      pred_kernel_size (int): Kernel size of prediction layer. Defaults to 1.
code: def __init__(
      self,
      num_classes: int,
      in_channels: int,
      share_conv: bool = True,
      with_objectness: bool = False,
      norm_cfg: ConfigType = dict(type="BN", requires_grad=True),
      act_cfg: ConfigType = dict(type="SiLU", inplace=True),
      pred_kernel_size: int = 1,
      **kwargs,
  ) -> None:
      self.share_conv = share_conv
      super().__init__(
          num_classes,
          in_channels,
          norm_cfg=norm_cfg,
          act_cfg=act_cfg,
          pred_kernel_size=pred_kernel_size,
          with_objectness=with_objectness,
          **kwargs,
      )
  
imports: import copy
  import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, is_norm
  from mmcv.ops import batched_nms
  from mmengine.model import BaseModule, bias_init_with_prob, constant_init, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.models.utils import filter_scores_and_topk, multi_apply, select_single_mlvl, sigmoid_geometric_mean
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import cat_boxes, distance2bbox, get_box_tensor, get_box_wh, scale_boxes
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean
  from .rtmdet_head import RTMDetHead
label_desc: Use image node_id 1 to create a new RTMDetInsSepBNHead object from inherited base classes: RTMDetInsHead with example: obj = RTMDetInsSepBNHead(num_classes, in_channels, share_conv, with_objectness, norm_cfg, act_cfg, pred_kernel_size)