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: Transform network output for a batch into bbox predictions.
  
  Args:
      center_heatmap_preds (list[Tensor]): Center predict heatmaps for
          all levels with shape (B, num_classes, H, W).
      wh_preds (list[Tensor]): WH predicts for all levels with
          shape (B, 2, H, W).
      offset_preds (list[Tensor]): Offset predicts for all levels
          with shape (B, 2, H, W).
      batch_img_metas (list[dict], optional): Batch image meta info.
          Defaults to None.
      rescale (bool): If True, return boxes in original image space.
          Defaults to True.
      with_nms (bool): If True, do nms before return boxes.
          Defaults to False.
  
  Returns:
      list[:obj:`InstanceData`]: Instance segmentation
      results of each image after the post process.
      Each item usually contains following keys.
  
          - scores (Tensor): Classification scores, has a shape
            (num_instance, )
          - labels (Tensor): Labels of bboxes, has a shape
            (num_instances, ).
          - bboxes (Tensor): Has a shape (num_instances, 4),
            the last dimension 4 arrange as (x1, y1, x2, y2).
docstr_tok: ["Transform","network","output","for","a","batch","into","bbox","predictions",".","Args",":","center_heatmap_preds","(","list","[","Tensor","]",")",":","Center","predict","heatmaps","for","all","levels","with","shape","(","B",",","num_classes",",","H",",","W",")",".","wh_preds","(","list","[","Tensor","]",")",":","WH","predicts","for","all","levels","with","shape","(","B",",","2",",","H",",","W",")",".","offset_preds","(","list","[","Tensor","]",")",":","Offset","predicts","for","all","levels","with","shape","(","B",",","2",",","H",",","W",")",".","batch_img_metas","(","list","[","dict","]",",","optional",")",":","Batch","image","meta","info",".","Defaults","to","None",".","rescale","(","bool",")",":","If","True",",","return","boxes","in","original","image","space",".","Defaults","to","True",".","with_nms","(","bool",")",":","If","True",",","do","nms","before","return","boxes",".","Defaults","to","False",".","Returns",":","list","[",":","obj",":","`","InstanceData","`","]",":","Instance","segmentation","results","of","each","image","after","the","post","process",".","Each","item","usually","contains","following","keys",".","-","scores","(","Tensor",")",":","Classification","scores",",","has","a","shape","(","num_instance",",",")","-","labels","(","Tensor",")",":","Labels","of","bboxes",",","has","a","shape","(","num_instances",",",")",".","-","bboxes","(","Tensor",")",":","Has","a","shape","(","num_instances",",","4",")",",","the","last","dimension","4","arrange","as","(","x1",",","y1",",","x2",",","y2",")","."]
code: def predict_by_feat(
      self,
      center_heatmap_preds: List[Tensor],
      wh_preds: List[Tensor],
      offset_preds: List[Tensor],
      batch_img_metas: Optional[List[dict]] = None,
      rescale: bool = True,
      with_nms: bool = False,
  ) -> InstanceList:
      """Transform network output for a batch into bbox predictions.
  
      Args:
          center_heatmap_preds (list[Tensor]): Center predict heatmaps for
              all levels with shape (B, num_classes, H, W).
          wh_preds (list[Tensor]): WH predicts for all levels with
              shape (B, 2, H, W).
          offset_preds (list[Tensor]): Offset predicts for all levels
              with shape (B, 2, H, W).
          batch_img_metas (list[dict], optional): Batch image meta info.
              Defaults to None.
          rescale (bool): If True, return boxes in original image space.
              Defaults to True.
          with_nms (bool): If True, do nms before return boxes.
              Defaults to False.
  
      Returns:
          list[:obj:`InstanceData`]: Instance segmentation
          results of each image after the post process.
          Each item usually contains following keys.
  
              - scores (Tensor): Classification scores, has a shape
                (num_instance, )
              - labels (Tensor): Labels of bboxes, has a shape
                (num_instances, ).
              - bboxes (Tensor): Has a shape (num_instances, 4),
                the last dimension 4 arrange as (x1, y1, x2, y2).
      """
      assert (
          len(center_heatmap_preds)
          == len(wh_preds)
          == len(offset_preds)
          == 1
      )
      result_list = []
      for img_id in range(len(batch_img_metas)):
          result_list.append(
              self._predict_by_feat_single(
                  center_heatmap_preds[0][img_id : img_id + 1, ...],
                  wh_preds[0][img_id : img_id + 1, ...],
                  offset_preds[0][img_id : img_id + 1, ...],
                  batch_img_metas[img_id],
                  rescale=rescale,
                  with_nms=with_nms,
              )
          )
      return result_list
  
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 8 for calling the CenterNetHead obj's underlying member method code with example usage: obj.predict_by_feat(center_heatmap_preds, wh_preds, offset_preds, batch_img_metas, rescale, with_nms) and returns: result_list