class_docstr: Online Updating Ensemble of forecasters.
  
  Parameters
  ----------
  ensemble_algorithm : ensemble algorithm
  forecasters : list of (str, estimator) tuples
  n_jobs : int or None, optional (default=None)
      The number of jobs to run in parallel for fit. None means 1 unless
      in a joblib.parallel_backend context.
      -1 means using all processors.
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docstr: Fit to training data.
  
  Parameters
  ----------
  y : pd.Series
      Target time series to which to fit the forecaster.
  fh : int, list or np.array, optional (default=None)
      The forecasters horizon with the steps ahead to to predict.
  X : pd.DataFrame, optional (default=None)
      Exogenous variables are ignored
  
  Returns
  -------
  self : returns an instance of self.
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code: def _fit(self, y, X, fh):
      """Fit to training data.
  
      Parameters
      ----------
      y : pd.Series
          Target time series to which to fit the forecaster.
      fh : int, list or np.array, optional (default=None)
          The forecasters horizon with the steps ahead to to predict.
      X : pd.DataFrame, optional (default=None)
          Exogenous variables are ignored
  
      Returns
      -------
      self : returns an instance of self.
      """
      names, forecasters = self._check_forecasters()
      self.weights = np.ones(len(forecasters)) / len(forecasters)
      self._fit_forecasters(forecasters, y, X, fh)
      return self
  
file_path: sktime/sktime/forecasting/online_learning/_online_ensemble.py
imports: import numpy
  import pandas
  from sktime.forecasting.compose._ensemble import EnsembleForecaster
label_desc: Use image node_id 2 for calling the OnlineEnsembleForecaster obj's underlying member method code with example usage: obj._fit(y, X, fh) and returns: self