class_docstr: Anchor-free head (FCOS, Fovea, RepPoints, etc.).
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      feat_channels (int): Number of hidden channels. Used in child classes.
      stacked_convs (int): Number of stacking convs of the head.
      strides (tuple): Downsample factor of each feature map.
      dcn_on_last_conv (bool): If true, use dcn in the last layer of
          towers. Default: False.
      conv_bias (bool | str): If specified as `auto`, it will be decided by
          the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
          None, otherwise False. Default: "auto".
      loss_cls (dict): Config of classification loss.
      loss_bbox (dict): Config of localization loss.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Config dict for normalization layer. Default: None.
      train_cfg (dict): Training config of anchor head.
      test_cfg (dict): Testing config of anchor head.
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docstr: Test function with test time augmentation.
  
  Args:
      feats (list[Tensor]): the outer list indicates test-time
          augmentations and inner Tensor should have a shape NxCxHxW,
          which contains features for all images in the batch.
      img_metas (list[list[dict]]): the outer list indicates test-time
          augs (multiscale, flip, etc.) and the inner list indicates
          images in a batch. each dict has image information.
      rescale (bool, optional): Whether to rescale the results.
          Defaults to False.
  
  Returns:
      list[ndarray]: bbox results of each class
docstr_tok: ["Test","function","with","test","time","augmentation",".","Args",":","feats","(","list","[","Tensor","]",")",":","the","outer","list","indicates","test-time","augmentations","and","inner","Tensor","should","have","a","shape","NxCxHxW",",","which","contains","features","for","all","images","in","the","batch",".","img_metas","(","list","[","list","[","dict","]","]",")",":","the","outer","list","indicates","test-time","augs","(","multiscale",",","flip",",","etc",".",")","and","the","inner","list","indicates","images","in","a","batch",".","each","dict","has","image","information",".","rescale","(","bool",",","optional",")",":","Whether","to","rescale","the","results",".","Defaults","to","False",".","Returns",":","list","[","ndarray","]",":","bbox","results","of","each","class"]
code: def aug_test(self, feats, img_metas, rescale=False):
      """Test function with test time augmentation.
  
      Args:
          feats (list[Tensor]): the outer list indicates test-time
              augmentations and inner Tensor should have a shape NxCxHxW,
              which contains features for all images in the batch.
          img_metas (list[list[dict]]): the outer list indicates test-time
              augs (multiscale, flip, etc.) and the inner list indicates
              images in a batch. each dict has image information.
          rescale (bool, optional): Whether to rescale the results.
              Defaults to False.
  
      Returns:
          list[ndarray]: bbox results of each class
      """
      return self.aug_test_bboxes(feats, img_metas, rescale=rescale)
  
imports: from abc import abstractmethod
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import multi_apply
  from ..builder import HEADS, build_loss
  from .base_dense_head import BaseDenseHead
  from .dense_test_mixins import BBoxTestMixin
label_desc: Use image node_id 15 for calling the AnchorFreeHead obj's underlying member method code with example usage: obj.aug_test(feats, img_metas, rescale) and returns: self