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: Transform a single image's features extracted from the head into
  bbox results.
  
  Args:
      cls_score_list (list[Tensor]): Box scores from all scale
          levels of a single image, each item has shape
          (num_priors * num_classes, H, W).
      bbox_pred_list (list[Tensor]): Box energies / deltas from
          all scale levels of a single image, each item has shape
          (num_priors * 4, H, W).
      score_factor_list (list[Tensor]): Score factors from all scale
          levels of a single image, each item has shape
          (num_priors * 1, H, W).
      mlvl_priors (list[Tensor]): Each element in the list is
          the priors of a single level in feature pyramid, has shape
          (num_priors, 4).
      img_meta (dict): Image meta info.
      cfg (:obj:`ConfigDict` or dict, optional): Test / postprocessing
          configuration, if None, test_cfg would be used.
      rescale (bool): If True, return boxes in original image space.
          Default: False.
      with_nms (bool): If True, do nms before return boxes.
          Default: True.
  
  Returns:
      :obj:`InstanceData`: Detection results of each image
      after the post process.
      Each item usually contains following keys.
  
          - scores (Tensor): Classification scores, has a shape
            (num_instance, )
          - labels (Tensor): Labels of bboxes, has a shape
            (num_instances, ).
          - bboxes (Tensor): Has a shape (num_instances, 4),
            the last dimension 4 arrange as (x1, y1, x2, y2).
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code: def _predict_by_feat_single(
      self,
      cls_score_list: List[Tensor],
      bbox_pred_list: List[Tensor],
      score_factor_list: List[Tensor],
      mlvl_priors: List[Tensor],
      img_meta: dict,
      cfg: OptConfigType = None,
      rescale: bool = False,
      with_nms: bool = True,
  ) -> InstanceData:
      """Transform a single image's features extracted from the head into
      bbox results.
  
      Args:
          cls_score_list (list[Tensor]): Box scores from all scale
              levels of a single image, each item has shape
              (num_priors * num_classes, H, W).
          bbox_pred_list (list[Tensor]): Box energies / deltas from
              all scale levels of a single image, each item has shape
              (num_priors * 4, H, W).
          score_factor_list (list[Tensor]): Score factors from all scale
              levels of a single image, each item has shape
              (num_priors * 1, H, W).
          mlvl_priors (list[Tensor]): Each element in the list is
              the priors of a single level in feature pyramid, has shape
              (num_priors, 4).
          img_meta (dict): Image meta info.
          cfg (:obj:`ConfigDict` or dict, optional): Test / postprocessing
              configuration, if None, test_cfg would be used.
          rescale (bool): If True, return boxes in original image space.
              Default: False.
          with_nms (bool): If True, do nms before return boxes.
              Default: True.
  
      Returns:
          :obj:`InstanceData`: Detection results of each image
          after the post process.
          Each item usually contains following keys.
  
              - scores (Tensor): Classification scores, has a shape
                (num_instance, )
              - labels (Tensor): Labels of bboxes, has a shape
                (num_instances, ).
              - bboxes (Tensor): Has a shape (num_instances, 4),
                the last dimension 4 arrange as (x1, y1, x2, y2).
      """
      cfg = self.test_cfg if cfg is None else cfg
      img_shape = img_meta["img_shape"]
      nms_pre = cfg.get("nms_pre", -1)
  
      mlvl_bboxes = []
      mlvl_scores = []
      mlvl_score_factors = []
      for level_idx, (
          cls_score,
          bbox_pred,
          score_factor,
          priors,
      ) in enumerate(
          zip(
              cls_score_list,
              bbox_pred_list,
              score_factor_list,
              mlvl_priors,
          )
      ):
          assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
  
          scores = (
              cls_score.permute(1, 2, 0)
              .reshape(-1, self.cls_out_channels)
              .sigmoid()
          )
          bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
          score_factor = (
              score_factor.permute(1, 2, 0).reshape(-1).sigmoid()
          )
  
          if 0 < nms_pre < scores.shape[0]:
              max_scores, _ = (
                  (scores * score_factor[:, None]).sqrt().max(dim=1)
              )
              _, topk_inds = max_scores.topk(nms_pre)
              priors = priors[topk_inds, :]
              bbox_pred = bbox_pred[topk_inds, :]
              scores = scores[topk_inds, :]
              score_factor = score_factor[topk_inds]
  
          bboxes = self.bbox_coder.decode(
              priors, bbox_pred, max_shape=img_shape
          )
          mlvl_bboxes.append(bboxes)
          mlvl_scores.append(scores)
          mlvl_score_factors.append(score_factor)
  
      results = InstanceData()
      results.bboxes = torch.cat(mlvl_bboxes)
      results.scores = torch.cat(mlvl_scores)
      results.score_factors = torch.cat(mlvl_score_factors)
  
      return self._bbox_post_process(
          results, cfg, rescale, with_nms, img_meta
      )
  
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 9 for calling the PAAHead obj's underlying member method code with example usage: obj._predict_by_feat_single(cls_score_list, bbox_pred_list, score_factor_list, mlvl_priors, img_meta, cfg, rescale, with_nms) and returns: self