class_docstr: Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
  Prediction for Object Detection.
  
  Code is modified from the `official github repo
  <https://github.com/kkhoot/PAA/blob/master/paa_core
  /modeling/rpn/paa/loss.py>`_.
  
  More details can be found in the `paper
  <https://arxiv.org/abs/2007.08103>`_ .
  
  Args:
      topk (int): Select topk samples with smallest loss in
          each level.
      score_voting (bool): Whether to use score voting in post-process.
      covariance_type : String describing the type of covariance parameters
          to be used in :class:`sklearn.mixture.GaussianMixture`.
          It must be one of:
  
          - 'full': each component has its own general covariance matrix
          - 'tied': all components share the same general covariance matrix
          - 'diag': each component has its own diagonal covariance matrix
          - 'spherical': each component has its own single variance
          Default: 'diag'. From 'full' to 'spherical', the gmm fitting
          process is faster yet the performance could be influenced. For most
          cases, 'diag' should be a good choice.
docstr: Compute regression and classification targets for anchors in a
  single image.
  
  This method is same as `AnchorHead._get_targets_single()`.
docstr_tok: ["Compute","regression","and","classification","targets","for","anchors","in","a","single","image",".","This","method","is","same","as","`","AnchorHead._get_targets_single","(",")","`","."]
code: def _get_targets_single(
      self,
      flat_anchors: Tensor,
      valid_flags: Tensor,
      gt_instances: InstanceData,
      img_meta: dict,
      gt_instances_ignore: Optional[InstanceData] = None,
      unmap_outputs: bool = True,
  ) -> tuple:
      """Compute regression and classification targets for anchors in a
      single image.
  
      This method is same as `AnchorHead._get_targets_single()`.
      """
      assert unmap_outputs, (
          "We must map outputs back to the original"
          "set of anchors in PAAhead"
      )
      return super(ATSSHead, self)._get_targets_single(
          flat_anchors,
          valid_flags,
          gt_instances,
          img_meta,
          gt_instances_ignore,
          unmap_outputs=True,
      )
  
imports: from typing import List, Optional, Tuple
  import numpy
  import torch
  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 ConfigType, InstanceList, OptConfigType, OptInstanceList
  from ..layers import multiclass_nms
  from ..utils import levels_to_images, multi_apply
  from .None import ATSSHead
label_desc: Use image node_id 7 for calling the PAAHead obj's underlying member method code with example usage: obj._get_targets_single(flat_anchors, valid_flags, gt_instances, img_meta, gt_instances_ignore, unmap_outputs) and returns: super