class_docstr: Head of CornerNet: Detecting Objects as Paired Keypoints.
  
  Code is modified from the `official github repo
  <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/
  kp.py#L73>`_ .
  
  More details can be found in the `paper
  <https://arxiv.org/abs/1808.01244>`_ .
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      num_feat_levels (int): Levels of feature from the previous module.
          2 for HourglassNet-104 and 1 for HourglassNet-52. Because
          HourglassNet-104 outputs the final feature and intermediate
          supervision feature and HourglassNet-52 only outputs the final
          feature. Defaults to 2.
      corner_emb_channels (int): Channel of embedding vector. Defaults to 1.
      train_cfg (:obj:`ConfigDict` or dict, optional): Training config.
          Useless in CornerHead, but we keep this variable for
          SingleStageDetector.
      test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
          CornerHead.
      loss_heatmap (:obj:`ConfigDict` or dict): Config of corner heatmap
          loss. Defaults to GaussianFocalLoss.
      loss_embedding (:obj:`ConfigDict` or dict): Config of corner embedding
          loss. Defaults to AssociativeEmbeddingLoss.
      loss_offset (:obj:`ConfigDict` or dict): Config of corner offset loss.
          Defaults to SmoothL1Loss.
      init_cfg (:obj:`ConfigDict` or dict, optional): the config to control
          the initialization.
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docstr: Forward feature of a single level.
  
  Args:
      x (Tensor): Feature of a single level.
      lvl_ind (int): Level index of current feature.
      return_pool (bool): Return corner pool feature or not.
          Defaults to False.
  
  Returns:
      tuple[Tensor]: A tuple of CornerHead's output for current feature
      level. Containing the following Tensors:
  
          - tl_heat (Tensor): Predicted top-left corner heatmap.
          - br_heat (Tensor): Predicted bottom-right corner heatmap.
          - tl_emb (Tensor | None): Predicted top-left embedding heatmap.
            None for `self.with_corner_emb == False`.
          - br_emb (Tensor | None): Predicted bottom-right embedding
            heatmap. None for `self.with_corner_emb == False`.
          - tl_off (Tensor): Predicted top-left offset heatmap.
          - br_off (Tensor): Predicted bottom-right offset heatmap.
          - tl_pool (Tensor): Top-left corner pool feature. Not must
            have.
          - br_pool (Tensor): Bottom-right corner pool feature. Not must
            have.
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code: def forward_single(
      self, x: Tensor, lvl_ind: int, return_pool: bool = False
  ) -> List[Tensor]:
      """Forward feature of a single level.
  
      Args:
          x (Tensor): Feature of a single level.
          lvl_ind (int): Level index of current feature.
          return_pool (bool): Return corner pool feature or not.
              Defaults to False.
  
      Returns:
          tuple[Tensor]: A tuple of CornerHead's output for current feature
          level. Containing the following Tensors:
  
              - tl_heat (Tensor): Predicted top-left corner heatmap.
              - br_heat (Tensor): Predicted bottom-right corner heatmap.
              - tl_emb (Tensor | None): Predicted top-left embedding heatmap.
                None for `self.with_corner_emb == False`.
              - br_emb (Tensor | None): Predicted bottom-right embedding
                heatmap. None for `self.with_corner_emb == False`.
              - tl_off (Tensor): Predicted top-left offset heatmap.
              - br_off (Tensor): Predicted bottom-right offset heatmap.
              - tl_pool (Tensor): Top-left corner pool feature. Not must
                have.
              - br_pool (Tensor): Bottom-right corner pool feature. Not must
                have.
      """
      tl_pool = self.tl_pool[lvl_ind](x)
      tl_heat = self.tl_heat[lvl_ind](tl_pool)
      br_pool = self.br_pool[lvl_ind](x)
      br_heat = self.br_heat[lvl_ind](br_pool)
  
      tl_emb, br_emb = None, None
      if self.with_corner_emb:
          tl_emb = self.tl_emb[lvl_ind](tl_pool)
          br_emb = self.br_emb[lvl_ind](br_pool)
  
      tl_off = self.tl_off[lvl_ind](tl_pool)
      br_off = self.br_off[lvl_ind](br_pool)
  
      result_list = [tl_heat, br_heat, tl_emb, br_emb, tl_off, br_off]
      if return_pool:
          result_list.append(tl_pool)
          result_list.append(br_pool)
  
      return result_list
  
imports: from logging import warning
  from math import ceil, log
  from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmcv.ops import CornerPool, batched_nms
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, bias_init_with_prob
  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 gather_feat, 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 CornerHead obj's underlying member method code with example usage: obj.forward_single(x, lvl_ind, return_pool) and returns: result_list