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 batch of output features extracted from the head into
  bbox results.
  
  This method is same as `BaseDenseHead.get_results()`.
docstr_tok: ["Transform","a","batch","of","output","features","extracted","from","the","head","into","bbox","results",".","This","method","is","same","as","`","BaseDenseHead.get_results","(",")","`","."]
code: def predict_by_feat(
      self,
      cls_scores: List[Tensor],
      bbox_preds: List[Tensor],
      score_factors: Optional[List[Tensor]] = None,
      batch_img_metas: Optional[List[dict]] = None,
      cfg: OptConfigType = None,
      rescale: bool = False,
      with_nms: bool = True,
  ) -> InstanceList:
      """Transform a batch of output features extracted from the head into
      bbox results.
  
      This method is same as `BaseDenseHead.get_results()`.
      """
      assert with_nms, (
          'PAA only supports "with_nms=True" now and it '
          "means PAAHead does not support "
          "test-time augmentation"
      )
      return super().predict_by_feat(
          cls_scores=cls_scores,
          bbox_preds=bbox_preds,
          score_factors=score_factors,
          batch_img_metas=batch_img_metas,
          cfg=cfg,
          rescale=rescale,
          with_nms=with_nms,
      )
  
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 8 for calling the PAAHead obj's underlying member method code with example usage: obj.predict_by_feat(cls_scores, bbox_preds, score_factors, batch_img_metas, cfg, rescale, with_nms) and returns: super