class_docstr: Base class of scorers that do need training.
docstr: Computes the anomaly score on the given series.
  
  If a sequence of series is given, the scorer will score each series independently
  and return an anomaly score for each series in the sequence.
  
  Parameters
  ----------
  series
      The (sequence of) series to detect anomalies from.
  
  Returns
  -------
  Union[TimeSeries, Sequence[TimeSeries]]
      (Sequence of) anomaly score time series
docstr_tok: ["Computes","the","anomaly","score","on","the","given","series",".","If","a","sequence","of","series","is","given",",","the","scorer","will","score","each","series","independently","and","return","an","anomaly","score","for","each","series","in","the","sequence",".","Parameters","--","--","--","--","--","series","The","(","sequence","of",")","series","to","detect","anomalies","from",".","Returns","--","--","--","-","Union","[","TimeSeries",",","Sequence","[","TimeSeries","]","]","(","Sequence","of",")","anomaly","score","time","series"]
code: def score(
      self,
      series: Union[TimeSeries, Sequence[TimeSeries]],
  ) -> Union[TimeSeries, Sequence[TimeSeries]]:
      """Computes the anomaly score on the given series.
  
      If a sequence of series is given, the scorer will score each series independently
      and return an anomaly score for each series in the sequence.
  
      Parameters
      ----------
      series
          The (sequence of) series to detect anomalies from.
  
      Returns
      -------
      Union[TimeSeries, Sequence[TimeSeries]]
          (Sequence of) anomaly score time series
      """
  
      self.check_if_fit_called()
  
      list_series = _to_list(series)
  
      anomaly_scores = []
      for s in list_series:
          _assert_timeseries(s)
          self._check_window_size(s)
          anomaly_scores.append(
              self._score_core(self._assert_deterministic(s, "series"))
          )
  
      if len(anomaly_scores) == 1 and not isinstance(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 4 for calling the FittableAnomalyScorer obj's underlying member method code with example usage: obj.score(series) and returns: anomaly_scores, anomaly_scores