class_docstr: Base class for all anomaly models.
docstr: Checks if `actual_anomalies` contains only univariate series, which
  is required if any of the scorers returns a univariate score.
docstr_tok: ["Checks","if","`","actual_anomalies","`","contains","only","univariate","series",",","which","is","required","if","any","of","the","scorers","returns","a","univariate","score","."]
code: def _check_univariate(self, actual_anomalies):
      """Checks if `actual_anomalies` contains only univariate series, which
      is required if any of the scorers returns a univariate score.
      """
  
      if self.univariate_scoring:
          raise_if_not(
              all([s.width == 1 for s in actual_anomalies]),
              "Anomaly model contains scorer {} that will return".format(
                  [
                      s.__str__()
                      for s in self.scorers
                      if s.univariate_scorer
                  ]
              )
              + " a univariate anomaly score series (width=1). Found a"
              + " multivariate `actual_anomalies`. The evaluation of the"
              + " accuracy cannot be computed. If applicable, think about"
              + " setting the scorer parameter `componenet_wise` to True.",
          )
  
file_path: darts/darts/ad/anomaly_model/anomaly_model.py
imports: from abc import ABC, abstractmethod
  from typing import Dict, Sequence, Union
  from darts.ad.scorers.scorers import AnomalyScorer
  from darts.ad.utils import _to_list, eval_accuracy_from_scores, show_anomalies_from_scores
  from darts.logging import raise_if_not
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 2 for calling the AnomalyModel obj's underlying member method code with example usage: obj._check_univariate(actual_anomalies) without return types