docstr: An Ensembler applies normalization and aggregation operations to the scores of an ensemble of detectors.
  
  Parameters
  ----------
  normalizer
      `BaseFittedTransformTorch` object to normalize the scores. If ``None`` then no normalization
      is applied.
  aggregator
      `BaseTransformTorch` object to aggregate the scores. If ``None`` defaults to `AverageAggregator`.
docstr_tok: ["An","Ensembler","applies","normalization","and","aggregation","operations","to","the","scores","of","an","ensemble","of","detectors",".","Parameters","--","--","--","--","--","normalizer","`","BaseFittedTransformTorch","`","object","to","normalize","the","scores",".","If","``","None","``","then","no","normalization","is","applied",".","aggregator","`","BaseTransformTorch","`","object","to","aggregate","the","scores",".","If","``","None","``","defaults","to","`","AverageAggregator","`","."]
code: def __init__(
      self,
      normalizer: Optional[BaseTransformTorch] = None,
      aggregator: BaseTransformTorch = None,
  ):
      """An Ensembler applies normalization and aggregation operations to the scores of an ensemble of detectors.
  
      Parameters
      ----------
      normalizer
          `BaseFittedTransformTorch` object to normalize the scores. If ``None`` then no normalization
          is applied.
      aggregator
          `BaseTransformTorch` object to aggregate the scores. If ``None`` defaults to `AverageAggregator`.
      """
      super().__init__()
      self.normalizer = normalizer
      if self.normalizer is None:
          self.fitted = True
      if aggregator is None:
          aggregator = AverageAggregator()
      self.aggregator = aggregator
  
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 Ensembler object from inherited base classes: BaseTransformTorch, FitMixinTorch with example: obj = Ensembler(normalizer, aggregator)