docstr: Takes the mean of the top `k` scores.
  
  Parameters
  ----------
  scores
      `Torch.Tensor` of scores from ensemble of detectors.
  
  Returns
  -------
  `Torch.Tensor` of mean of top `k` scores.
docstr_tok: ["Takes","the","mean","of","the","top","`","k","`","scores",".","Parameters","--","--","--","--","--","scores","`","Torch.Tensor","`","of","scores","from","ensemble","of","detectors",".","Returns","--","--","--","-","`","Torch.Tensor","`","of","mean","of","top","`","k","`","scores","."]
code: def transform(self, scores: torch.Tensor) -> torch.Tensor:
      """Takes the mean of the top `k` scores.
  
      Parameters
      ----------
      scores
          `Torch.Tensor` of scores from ensemble of detectors.
  
      Returns
      -------
      `Torch.Tensor` of mean of top `k` scores.
      """
      if self.k is None:
          self.k = int(np.ceil(scores.shape[1] / 2))
      sorted_scores, _ = torch.sort(scores, 1)
      return sorted_scores[:, -self.k :].mean(-1)
  
imports: from abc import ABC, abstractmethod
  from typing import Optional
  from typing_extensions import Self
  import torch
  import numpy
  from torch.nn import Module
  from alibi_detect.exceptions import NotFittedError
label_desc: Use image node_id 2 for calling the TopKAggregator obj's underlying member method code with example usage: obj.transform(scores) and returns: sorted_scores