class_docstr: AutoAssignHead head used in AutoAssign.
  
  More details can be found in the `paper
  <https://arxiv.org/abs/2007.03496>`_ .
  
  Args:
      force_topk (bool): Used in center prior initialization to
          handle extremely small gt. Default is False.
      topk (int): The number of points used to calculate the
          center prior when no point falls in gt_bbox. Only work when
          force_topk if True. Defaults to 9.
      pos_loss_weight (float): The loss weight of positive loss
          and with default value 0.25.
      neg_loss_weight (float): The loss weight of negative loss
          and with default value 0.75.
      center_loss_weight (float): The loss weight of center prior
          loss and with default value 0.75.
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docstr: Compute regression targets and each point inside or outside gt_bbox
  in multiple images.
  
  Args:
      points (list[Tensor]): Points of all 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.
  
  Returns:
      tuple(list[Tensor], list[Tensor]):
  
      - inside_gt_bbox_mask_list (list[Tensor]): Each Tensor is with             bool type and shape of (num_points, num_gt), each value is used             to mark whether this point falls within a certain gt.
      - concat_lvl_bbox_targets (list[Tensor]): BBox targets of each             level. Each tensor has shape (num_points, num_gt, 4).
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code: def get_targets(
      self, points: List[Tensor], batch_gt_instances: InstanceList
  ) -> Tuple[List[Tensor], List[Tensor]]:
      """Compute regression targets and each point inside or outside gt_bbox
          in multiple images.
  
          Args:
              points (list[Tensor]): Points of all 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.
  
          Returns:
              tuple(list[Tensor], list[Tensor]):
  
              - inside_gt_bbox_mask_list (list[Tensor]): Each Tensor is with \
              bool type and shape of (num_points, num_gt), each value is used \
              to mark whether this point falls within a certain gt.
              - concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
              level. Each tensor has shape (num_points, num_gt, 4).
          """
  
      concat_points = torch.cat(points, dim=0)
      # the number of points per img, per lvl
      inside_gt_bbox_mask_list, bbox_targets_list = multi_apply(
          self._get_targets_single,
          batch_gt_instances,
          points=concat_points,
      )
      return inside_gt_bbox_mask_list, bbox_targets_list
  
imports: from typing import Dict, List, Sequence, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Scale
  from mmengine.model import bias_init_with_prob, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox_overlaps
  from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import MlvlPointGenerator
  from ..utils import levels_to_images, multi_apply
  from .fcos_head import FCOSHead
label_desc: Use image node_id 7 for calling the AutoAssignHead obj's underlying member method code with example usage: obj.get_targets(points, batch_gt_instances) and returns: inside_gt_bbox_mask_list,  bbox_targets_list