docstr: Maps scores to there p-values.
  
  Needs to be fit (see :py:obj:`~alibi_detect.od.pytorch.ensemble.BaseFittedTransformTorch`).
  Returns the proportion of scores in the reference dataset that are greater than the score of
  interest. Output is between ``1`` and ``0``. Small values are likely to be outliers.
docstr_tok: ["Maps","scores","to","there","p-values",".","Needs","to","be","fit","(","see",":","py",":","obj",":","`","~alibi_detect.od.pytorch.ensemble.BaseFittedTransformTorch","`",")",".","Returns","the","proportion","of","scores","in","the","reference","dataset","that","are","greater","than","the","score","of","interest",".","Output","is","between","``","1","``","and","``","0","``",".","Small","values","are","likely","to","be","outliers","."]
code: def __init__(self):
      """Maps scores to there p-values.
  
      Needs to be fit (see :py:obj:`~alibi_detect.od.pytorch.ensemble.BaseFittedTransformTorch`).
      Returns the proportion of scores in the reference dataset that are greater than the score of
      interest. Output is between ``1`` and ``0``. Small values are likely to be outliers.
      """
      super().__init__()
      self.val_scores = 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 PValNormalizer object from inherited base classes: BaseTransformTorch, FitMixinTorch with example: obj = PValNormalizer()