code: def extreme_lags(
      self,
  ) -> Tuple[
      Optional[int],
      Optional[int],
      Optional[int],
      Optional[int],
      Optional[int],
      Optional[int],
  ]:
      extreme_lags_ = super().extreme_lags
      # shift min_target_lag in the past to account for the regression model training set
      if extreme_lags_[0] is None:
          return (-self.train_n_points,) + extreme_lags_[1:]
      else:
          return (
              extreme_lags_[0] - self.train_n_points,
          ) + extreme_lags_[1:]
  
file_path: darts/darts/models/forecasting/regression_ensemble_model.py
imports: from typing import List, Optional, Sequence, Tuple, Union
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.models.forecasting.ensemble_model import EnsembleModel
  from darts.models.forecasting.forecasting_model import ForecastingModel
  from darts.models.forecasting.linear_regression_model import LinearRegressionModel
  from darts.models.forecasting.regression_model import RegressionModel
  from darts.timeseries import TimeSeries, concatenate
  from darts.utils.utils import seq2series, series2seq
label_desc: Use image node_id 6 for calling the RegressionEnsembleModel obj's underlying member method code with example usage: obj.extreme_lags() and returns: unknown, unknown