docstr: Maps scores to their normalized values.
  
  Needs to be fit (see :py:obj:`~alibi_detect.od.pytorch.ensemble.BaseFittedTransformTorch`).
  Subtracts the dataset mean and scales by the standard deviation.
docstr_tok: ["Maps","scores","to","their","normalized","values",".","Needs","to","be","fit","(","see",":","py",":","obj",":","`","~alibi_detect.od.pytorch.ensemble.BaseFittedTransformTorch","`",")",".","Subtracts","the","dataset","mean","and","scales","by","the","standard","deviation","."]
code: def __init__(self):
      """Maps scores to their normalized values.
  
      Needs to be fit (see :py:obj:`~alibi_detect.od.pytorch.ensemble.BaseFittedTransformTorch`).
      Subtracts the dataset mean and scales by the standard deviation.
      """
      super().__init__()
      self.val_means = None
      self.val_scales = None
  
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 1 to create a new ShiftAndScaleNormalizer object from inherited base classes: BaseTransformTorch, FitMixinTorch with example: obj = ShiftAndScaleNormalizer()