class_docstr: Base class of Aggregators that do need training.
class_docstr_tok: ["Base","class","of","Aggregators","that","do","need","training","."]
docstr: Checks if the Aggregator has been fitted before calling its `score()` function.
docstr_tok: ["Checks","if","the","Aggregator","has","been","fitted","before","calling","its","`","score","(",")","`","function","."]
code: def _assert_fit_called(self):
      """Checks if the Aggregator has been fitted before calling its `score()` function."""
  
      raise_if_not(
          self._fit_called,
          f"The Aggregator {self.__str__()} has not been fitted yet. Call `fit()` first.",
      )
  
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 2 for calling the FittableAggregator obj's underlying member method code with example usage: obj._assert_fit_called() without return types