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: Transpose and gather feature according to index.
  
  Args:
      feat (Tensor): Target feature map.
      ind (Tensor): Target coord index.
  
  Returns:
      feat (Tensor): Transposed and gathered feature.
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code: def _transpose_and_gather_feat(self, feat, ind):
      """Transpose and gather feature according to index.
  
      Args:
          feat (Tensor): Target feature map.
          ind (Tensor): Target coord index.
  
      Returns:
          feat (Tensor): Transposed and gathered feature.
      """
      feat = feat.permute(0, 2, 3, 1).contiguous()
      feat = feat.view(feat.size(0), -1, feat.size(3))
      feat = self._gather_feat(feat, ind)
      return feat
  
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 17 for calling the CornerHead obj's underlying member method code with example usage: obj._transpose_and_gather_feat(feat, ind) and returns: feat