code: def _fit(
      self,
      series: TimeSeries,
      future_covariates: Optional[TimeSeries] = None,
  ):
      super()._fit(series, future_covariates)
      self._assert_univariate(series)
      series = self.training_series
      self.model.fit(
          series.values(copy=False).flatten(),
          X=future_covariates.values(copy=False)
          if future_covariates
          else None,
      )
      return self
  
file_path: darts/darts/models/forecasting/sf_auto_arima.py
imports: from typing import Optional
  from statsforecast.models import AutoARIMA
  from darts import TimeSeries
  from darts.models.components.statsforecast_utils import create_normal_samples, one_sigma_rule, unpack_sf_dict
  from darts.models.forecasting.forecasting_model import FutureCovariatesLocalForecastingModel
label_desc: Use image node_id 2 for calling the StatsForecastAutoARIMA obj's underlying member method code with example usage: obj._fit(series, future_covariates) and returns: self