class_docstr: Base class of anomaly scorers that do not need training.
code: def __init__(self, univariate_scorer, window) -> None:
      super().__init__(
          univariate_scorer=univariate_scorer, window=window
      )
  
      # indicates if the scorer 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 _assert_same_length, _assert_timeseries, _intersect, _sanity_check_two_series, _to_list, eval_accuracy_from_scores, show_anomalies_from_scores
  from darts.logging import get_logger, raise_if_not
label_desc: Use image node_id 1 to create a new NonFittableAnomalyScorer object from inherited base classes: AnomalyScorer with example: obj = NonFittableAnomalyScorer(univariate_scorer, window)