code: def _fit(
      self,
      series: TimeSeries,
      future_covariates: Optional[TimeSeries] = None,
  ) -> None:
      super()._fit(series, future_covariates)
  
      self._assert_multivariate(series)
  
      # storing to restore the statsmodels model results object
      self.training_historic_future_covariates = future_covariates
  
      m = staVARMA(
          endog=series.values(copy=False),
          exog=future_covariates.values(copy=False)
          if future_covariates
          else None,
          order=(self.p, self.q),
          trend=self.trend,
      )
  
      self.model = m.fit(disp=0)
  
file_path: darts/darts/models/forecasting/varima.py
imports: from typing import Optional
  import numpy
  import pandas
  from statsmodels.tsa.api import VARMAX
  from darts.logging import get_logger, raise_if
  from darts.models.forecasting.forecasting_model import TransferableFutureCovariatesLocalForecastingModel
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 4 for calling the VARIMA obj's underlying member method code with example usage: obj._fit(series, future_covariates) without return types