class_docstr: Base class of Aggregators that do not need training.
class_docstr_tok: ["Base","class","of","Aggregators","that","do","not","need","training","."]
code: def __init__(self) -> None:
      super().__init__()
  
      # indicates if the Aggregator is trainable or not
      self.trainable = False
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Sequence, Union
  import numpy
  from darts import TimeSeries
  from darts.ad.utils import _to_list, eval_accuracy_from_binary_prediction
  from darts.logging import raise_if_not
label_desc: Use image node_id 1 to create a new NonFittableAggregator object from inherited base classes: Aggregator with example: obj = NonFittableAggregator()