code: def _fit_core(
      self,
      list_series: Sequence[TimeSeries],
  ):
      self.training_data = np.concatenate(
          [s.all_values(copy=False) for s in list_series]
      ).squeeze(-1)
  
      if not self.component_wise:
          self.training_data = self.training_data.flatten()
  
file_path: darts/darts/ad/scorers/wasserstein_scorer.py
imports: from typing import Sequence
  import numpy
  from numpy.lib.stride_tricks import sliding_window_view
  from scipy.stats import wasserstein_distance
  from darts.ad.scorers.scorers import FittableAnomalyScorer
  from darts.logging import get_logger, raise_if_not
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 3 for calling the WassersteinScorer obj's underlying member method code with example usage: obj._fit_core(list_series) without return types