class_docstr: Objects as Points Head. CenterHead use center_point to indicate object's
  position. Paper link <https://arxiv.org/abs/1904.07850>
  
  Args:
      in_channels (int): Number of channel in the input feature map.
      feat_channels (int): Number of channel in the intermediate feature map.
      num_classes (int): Number of categories excluding the background
          category.
      loss_center_heatmap (:obj:`ConfigDict` or dict): Config of center
          heatmap loss. Defaults to
          dict(type='GaussianFocalLoss', loss_weight=1.0)
      loss_wh (:obj:`ConfigDict` or dict): Config of wh loss. Defaults to
           dict(type='L1Loss', loss_weight=0.1).
      loss_offset (:obj:`ConfigDict` or dict): Config of offset loss.
          Defaults to dict(type='L1Loss', loss_weight=1.0).
      train_cfg (:obj:`ConfigDict` or dict, optional): Training config.
          Useless in CenterNet, but we keep this variable for
          SingleStageDetector.
      test_cfg (:obj:`ConfigDict` or dict, optional): Testing config
          of CenterNet.
      init_cfg (:obj:`ConfigDict` or dict or list[dict] or
          list[:obj:`ConfigDict`], optional): Initialization
          config dict.
docstr: Build head for each branch.
docstr_tok: ["Build","head","for","each","branch","."]
code: def _build_head(
      self, in_channels: int, feat_channels: int, out_channels: int
  ) -> nn.Sequential:
      """Build head for each branch."""
      layer = nn.Sequential(
          nn.Conv2d(
              in_channels, feat_channels, kernel_size=3, padding=1
          ),
          nn.ReLU(inplace=True),
          nn.Conv2d(feat_channels, out_channels, kernel_size=1),
      )
      return layer
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from mmcv.ops import batched_nms
  from mmengine.config import ConfigDict
  from mmengine.model import bias_init_with_prob, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, OptMultiConfig
  from ..utils import gaussian_radius, gen_gaussian_target, get_local_maximum, get_topk_from_heatmap, multi_apply, transpose_and_gather_feat
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 2 for calling the CenterNetHead obj's underlying member method code with example usage: obj._build_head(in_channels, feat_channels, out_channels) and returns: layer