code: def ensemble(
      self,
      predictions: Union[TimeSeries, Sequence[TimeSeries]],
      series: Union[TimeSeries, Sequence[TimeSeries]],
      num_samples: int = 1,
      predict_likelihood_parameters: bool = False,
  ) -> Union[TimeSeries, Sequence[TimeSeries]]:
      is_single_series = (
          isinstance(series, TimeSeries) or series is None
      )
      predictions = series2seq(predictions)
      series = series2seq(series) if series is not None else [None]
  
      ensembled = [
          self.regression_model.predict(
              n=len(prediction),
              series=serie,
              future_covariates=prediction,
              num_samples=num_samples,
              predict_likelihood_parameters=predict_likelihood_parameters,
          )
          for serie, prediction in zip(series, predictions)
      ]
      return seq2series(ensembled) if is_single_series else ensembled
  
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 5 for calling the RegressionEnsembleModel obj's underlying member method code with example usage: obj.ensemble(predictions, series, num_samples, predict_likelihood_parameters) and returns: unknown