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. Default: 2.
      corner_emb_channels (int): Channel of embedding vector. Default: 1.
      train_cfg (dict | None): Training config. Useless in CornerHead,
          but we keep this variable for SingleStageDetector. Default: None.
      test_cfg (dict | None): Testing config of CornerHead. Default: None.
      loss_heatmap (dict | None): Config of corner heatmap loss. Default:
          GaussianFocalLoss.
      loss_embedding (dict | None): Config of corner embedding loss. Default:
          AssociativeEmbeddingLoss.
      loss_offset (dict | None): Config of corner offset loss. Default:
          SmoothL1Loss.
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docstr: Forward features from the upstream network.
  
  Args:
      feats (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple: Usually a tuple of corner heatmaps, offset heatmaps and
      embedding heatmaps.
          - tl_heats (list[Tensor]): Top-left corner heatmaps for all
            levels, each is a 4D-tensor, the channels number is
            num_classes.
          - br_heats (list[Tensor]): Bottom-right corner heatmaps for all
            levels, each is a 4D-tensor, the channels number is
            num_classes.
          - tl_embs (list[Tensor] | list[None]): Top-left embedding
            heatmaps for all levels, each is a 4D-tensor or None.
            If not None, the channels number is corner_emb_channels.
          - br_embs (list[Tensor] | list[None]): Bottom-right embedding
            heatmaps for all levels, each is a 4D-tensor or None.
            If not None, the channels number is corner_emb_channels.
          - tl_offs (list[Tensor]): Top-left offset heatmaps for all
            levels, each is a 4D-tensor. The channels number is
            corner_offset_channels.
          - br_offs (list[Tensor]): Bottom-right offset heatmaps for all
            levels, each is a 4D-tensor. The channels number is
            corner_offset_channels.
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code: def forward(self, feats):
      """Forward features from the upstream network.
  
      Args:
          feats (tuple[Tensor]): Features from the upstream network, each is
              a 4D-tensor.
  
      Returns:
          tuple: Usually a tuple of corner heatmaps, offset heatmaps and
          embedding heatmaps.
              - tl_heats (list[Tensor]): Top-left corner heatmaps for all
                levels, each is a 4D-tensor, the channels number is
                num_classes.
              - br_heats (list[Tensor]): Bottom-right corner heatmaps for all
                levels, each is a 4D-tensor, the channels number is
                num_classes.
              - tl_embs (list[Tensor] | list[None]): Top-left embedding
                heatmaps for all levels, each is a 4D-tensor or None.
                If not None, the channels number is corner_emb_channels.
              - br_embs (list[Tensor] | list[None]): Bottom-right embedding
                heatmaps for all levels, each is a 4D-tensor or None.
                If not None, the channels number is corner_emb_channels.
              - tl_offs (list[Tensor]): Top-left offset heatmaps for all
                levels, each is a 4D-tensor. The channels number is
                corner_offset_channels.
              - br_offs (list[Tensor]): Bottom-right offset heatmaps for all
                levels, each is a 4D-tensor. The channels number is
                corner_offset_channels.
      """
      lvl_ind = list(range(self.num_feat_levels))
      return multi_apply(self.forward_single, feats, lvl_ind)
  
imports: from math import ceil, log
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, bias_init_with_prob
  from mmcv.ops import CornerPool, batched_nms
  from mmdet.core import multi_apply
  from ..builder import HEADS, build_loss
  from ..utils import gaussian_radius, gen_gaussian_target
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 7 for calling the CornerHead obj's underlying member method code with example usage: obj.forward(feats) and returns: multi_apply