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: A general separation scheme for gmm model.
  
  It separates a GMM distribution of candidate samples into three
  parts, 0 1 and uncertain areas, and you can implement other
  separation schemes by rewriting this function.
  
  Args:
      gmm_assignment (Tensor): The prediction of GMM which is of shape
          (num_samples,). The 0/1 value indicates the distribution
          that each sample comes from.
      scores (Tensor): The probability of sample coming from the
          fit GMM distribution. The tensor is of shape (num_samples,).
      pos_inds_gmm (Tensor): All the indexes of samples which are used
          to fit GMM model. The tensor is of shape (num_samples,)
  
  Returns:
      tuple[Tensor, Tensor]: The indices of positive and ignored samples.
  
          - pos_inds_temp (Tensor): Indices of positive samples.
          - ignore_inds_temp (Tensor): Indices of ignore samples.
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code: def gmm_separation_scheme(
      self, gmm_assignment: Tensor, scores: Tensor, pos_inds_gmm: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """A general separation scheme for gmm model.
  
      It separates a GMM distribution of candidate samples into three
      parts, 0 1 and uncertain areas, and you can implement other
      separation schemes by rewriting this function.
  
      Args:
          gmm_assignment (Tensor): The prediction of GMM which is of shape
              (num_samples,). The 0/1 value indicates the distribution
              that each sample comes from.
          scores (Tensor): The probability of sample coming from the
              fit GMM distribution. The tensor is of shape (num_samples,).
          pos_inds_gmm (Tensor): All the indexes of samples which are used
              to fit GMM model. The tensor is of shape (num_samples,)
  
      Returns:
          tuple[Tensor, Tensor]: The indices of positive and ignored samples.
  
              - pos_inds_temp (Tensor): Indices of positive samples.
              - ignore_inds_temp (Tensor): Indices of ignore samples.
      """
      # The implementation is (c) in Fig.3 in origin paper instead of (b).
      # You can refer to issues such as
      # https://github.com/kkhoot/PAA/issues/8 and
      # https://github.com/kkhoot/PAA/issues/9.
      fgs = gmm_assignment == 0
      pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
      ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
      if fgs.nonzero().numel():
          _, pos_thr_ind = scores[fgs].topk(1)
          pos_inds_temp = pos_inds_gmm[fgs][: pos_thr_ind + 1]
          ignore_inds_temp = pos_inds_gmm.new_tensor([])
      return pos_inds_temp, ignore_inds_temp
  
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 5 for calling the PAAHead obj's underlying member method code with example usage: obj.gmm_separation_scheme(gmm_assignment, scores, pos_inds_gmm) and returns: pos_inds_temp,  ignore_inds_temp