class_docstr: Base class of anomaly scorers that do not need training.
docstr: Computes the anomaly score on the two (sequence of) series.
  
  If a pair of sequences is given, they must contain the same number
  of series. The scorer will score each pair of series independently
  and return an anomaly score for each pair.
  
  Parameters
  ----------
  actual_series:
      The (sequence of) actual series.
  pred_series
      The (sequence of) predicted series.
  
  Returns
  -------
  Union[TimeSeries, Sequence[TimeSeries]]
      (Sequence of) anomaly score time series
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code: def score_from_prediction(
      self,
      actual_series: Union[TimeSeries, Sequence[TimeSeries]],
      pred_series: Union[TimeSeries, Sequence[TimeSeries]],
  ) -> Union[TimeSeries, Sequence[TimeSeries]]:
      """Computes the anomaly score on the two (sequence of) series.
  
      If a pair of sequences is given, they must contain the same number
      of series. The scorer will score each pair of series independently
      and return an anomaly score for each pair.
  
      Parameters
      ----------
      actual_series:
          The (sequence of) actual series.
      pred_series
          The (sequence of) predicted series.
  
      Returns
      -------
      Union[TimeSeries, Sequence[TimeSeries]]
          (Sequence of) anomaly score time series
      """
      list_actual_series, list_pred_series = _to_list(
          actual_series
      ), _to_list(pred_series)
      _assert_same_length(list_actual_series, list_pred_series)
  
      anomaly_scores = []
  
      for s1, s2 in zip(list_actual_series, list_pred_series):
          _sanity_check_two_series(s1, s2)
          s1, s2 = _intersect(s1, s2)
          self._check_window_size(s1)
          self._check_window_size(s2)
          anomaly_scores.append(
              self._score_core_from_prediction(s1, s2)
          )
  
      if (
          len(anomaly_scores) == 1
          and not isinstance(pred_series, Sequence)
          and not isinstance(actual_series, Sequence)
      ):
          return anomaly_scores[0]
      else:
          return anomaly_scores
  
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 3 for calling the NonFittableAnomalyScorer obj's underlying member method code with example usage: obj.score_from_prediction(actual_series, pred_series) and returns: anomaly_scores, anomaly_scores