class_docstr: Base class of scorers that do need training.
docstr: Fits the scorer on the given time series input.
  
  If sequence of series is given, the scorer will be fitted on the concatenation of the sequence.
  
  The assumption is that the series `series` used for training are generally anomaly-free.
  
  Parameters
  ----------
  series
      The (sequence of) series with no anomalies.
  
  Returns
  -------
  self
      Fitted Scorer.
docstr_tok: ["Fits","the","scorer","on","the","given","time","series","input",".","If","sequence","of","series","is","given",",","the","scorer","will","be","fitted","on","the","concatenation","of","the","sequence",".","The","assumption","is","that","the","series","`","series","`","used","for","training","are","generally","anomaly-free",".","Parameters","--","--","--","--","--","series","The","(","sequence","of",")","series","with","no","anomalies",".","Returns","--","--","--","-","self","Fitted","Scorer","."]
code: def fit(
      self,
      series: Union[TimeSeries, Sequence[TimeSeries]],
  ):
      """Fits the scorer on the given time series input.
  
      If sequence of series is given, the scorer will be fitted on the concatenation of the sequence.
  
      The assumption is that the series `series` used for training are generally anomaly-free.
  
      Parameters
      ----------
      series
          The (sequence of) series with no anomalies.
  
      Returns
      -------
      self
          Fitted Scorer.
      """
      list_series = _to_list(series)
  
      for idx, s in enumerate(list_series):
          _assert_timeseries(s)
  
          if idx == 0:
              self.width_trained_on = s.width
          else:
              raise_if_not(
                  s.width == self.width_trained_on,
                  "series in `series` must have the same number of components,"
                  + f" found number of components equal to {self.width_trained_on}"
                  + f" at index 0 and {s.width} at index {idx}.",
              )
          self._check_window_size(s)
  
          self._assert_deterministic(s, "series")
  
      self._fit_core(list_series)
      self._fit_called = True
  
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 7 for calling the FittableAnomalyScorer obj's underlying member method code with example usage: obj.fit(series) without return types