docstr: Transform scores to normalized values. Subtracts the mean and scales by the standard deviation.
  
  Parameters
  ----------
  scores
      `Torch.Tensor` of scores from ensemble of detectors.
  
  Returns
  -------
  `Torch.Tensor` of normalized scores.
docstr_tok: ["Transform","scores","to","normalized","values",".","Subtracts","the","mean","and","scales","by","the","standard","deviation",".","Parameters","--","--","--","--","--","scores","`","Torch.Tensor","`","of","scores","from","ensemble","of","detectors",".","Returns","--","--","--","-","`","Torch.Tensor","`","of","normalized","scores","."]
code: def transform(self, scores: torch.Tensor) -> torch.Tensor:
      """Transform scores to normalized values. Subtracts the mean and scales by the standard deviation.
  
      Parameters
      ----------
      scores
          `Torch.Tensor` of scores from ensemble of detectors.
  
      Returns
      -------
      `Torch.Tensor` of normalized scores.
      """
      self.check_fitted()
      return (scores - self.val_means) / self.val_scales
  
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 3 for calling the ShiftAndScaleNormalizer obj's underlying member method code with example usage: obj.transform(scores) and returns: unknown