class_docstr: Base class for heterogeneous ensemble forecasters.
class_docstr_tok: ["Base","class","for","heterogeneous","ensemble","forecasters","."]
docstr: Fit all forecasters in parallel.
docstr_tok: ["Fit","all","forecasters","in","parallel","."]
code: def _fit_forecasters(self, forecasters, y, X, fh):
      """Fit all forecasters in parallel."""
  
      def _fit_forecaster(forecaster, y, X, fh):
          """Fit single forecaster."""
          return forecaster.fit(y, X, fh)
  
      self.forecasters_ = Parallel(n_jobs=self.n_jobs)(
          delayed(_fit_forecaster)(forecaster.clone(), y, X, fh)
          for forecaster in forecasters
      )
  
file_path: sktime/sktime/forecasting/base/_meta.py
imports: from joblib import Parallel, delayed
  from sktime.base import _HeterogenousMetaEstimator
  from sktime.forecasting.base._base import BaseForecaster
label_desc: Use image node_id 3 for calling the _HeterogenousEnsembleForecaster obj's underlying member method code with example usage: obj._fit_forecasters(forecasters, y, X, fh) and returns: forecaster