docstr: Computes the mean and standard deviation of the scores and stores them.
  
  Parameters
  ----------
  val_scores
      `Torch.Tensor` of scores from ensemble of detectors.
docstr_tok: ["Computes","the","mean","and","standard","deviation","of","the","scores","and","stores","them",".","Parameters","--","--","--","--","--","val_scores","`","Torch.Tensor","`","of","scores","from","ensemble","of","detectors","."]
code: def fit(self, val_scores: torch.Tensor) -> Self:
      """Computes the mean and standard deviation of the scores and stores them.
  
      Parameters
      ----------
      val_scores
          `Torch.Tensor` of scores from ensemble of detectors.
      """
      self.val_means = val_scores.mean(0)[None, :]
      self.val_scales = val_scores.std(0)[None, :]
      return self._set_fitted()
  
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 ShiftAndScaleNormalizer obj's underlying member method code with example usage: obj.fit(val_scores) and returns: self