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 (Sequence[int] or Sequence[Tuple[int, int]]): Downsample
          factor of each feature map.
      dcn_on_last_conv (bool): If true, use dcn in the last layer of
          towers. Defaults to False.
      conv_bias (bool or 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 (:obj:`ConfigDict` or dict): Config of classification loss.
      loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
      bbox_coder (:obj:`ConfigDict` or dict): Config of bbox coder. Defaults
          'DistancePointBBoxCoder'.
      conv_cfg (:obj:`ConfigDict` or dict, Optional): Config dict for
          convolution layer. Defaults to None.
      norm_cfg (:obj:`ConfigDict` or dict, Optional): Config dict for
          normalization layer. Defaults to None.
      train_cfg (:obj:`ConfigDict` or dict, Optional): Training config of
          anchor-free head.
      test_cfg (:obj:`ConfigDict` or dict, Optional): Testing config of
          anchor-free head.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict]): Initialization config dict.
docstr: Compute regression, classification and centerness targets for points
  in multiple images.
  
  Args:
      points (list[Tensor]): Points of each fpn level, each has shape
          (num_points, 2).
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance.  It usually includes ``bboxes`` and ``labels``
          attributes.
docstr_tok: ["Compute","regression",",","classification","and","centerness","targets","for","points","in","multiple","images",".","Args",":","points","(","list","[","Tensor","]",")",":","Points","of","each","fpn","level",",","each","has","shape","(","num_points",",","2",")",".","batch_gt_instances","(","list","[",":","obj",":","`","InstanceData","`","]",")",":","Batch","of","gt_instance",".","It","usually","includes","``","bboxes","``","and","``","labels","``","attributes","."]
code: def get_targets(
      self, points: List[Tensor], batch_gt_instances: InstanceList
  ) -> Any:
      """Compute regression, classification and centerness targets for points
      in multiple images.
  
      Args:
          points (list[Tensor]): Points of each fpn level, each has shape
              (num_points, 2).
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance.  It usually includes ``bboxes`` and ``labels``
              attributes.
      """
      raise NotImplementedError
  
imports: from abc import abstractmethod
  from typing import Any, List, Sequence, Tuple, Union
  import torch.nn
  from mmcv.cnn import ConvModule
  from numpy import ndarray
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType, OptInstanceList
  from ..task_modules.prior_generators import MlvlPointGenerator
  from ..utils import multi_apply
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 10 for calling the AnchorFreeHead obj's underlying member method code with example usage: obj.get_targets(points, batch_gt_instances) without return types