class_docstr: Base class for heterogeneous ensemble forecasters.
class_docstr_tok: ["Base","class","for","heterogeneous","ensemble","forecasters","."]
docstr: Update fitted parameters.
  
  Parameters
  ----------
  y : pd.Series
  X : pd.DataFrame
  update_params : bool, optional, default=True
  
  Returns
  -------
  self : an instance of self.
docstr_tok: ["Update","fitted","parameters",".","Parameters","--","--","--","--","--","y",":","pd.Series","X",":","pd.DataFrame","update_params",":","bool",",","optional",",","default=True","Returns","--","--","--","-","self",":","an","instance","of","self","."]
code: def _update(self, y, X=None, update_params=True):
      """Update fitted parameters.
  
      Parameters
      ----------
      y : pd.Series
      X : pd.DataFrame
      update_params : bool, optional, default=True
  
      Returns
      -------
      self : an instance of self.
      """
      for forecaster in self.forecasters_:
          forecaster.update(y, X, update_params=update_params)
      return self
  
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 5 for calling the _HeterogenousEnsembleForecaster obj's underlying member method code with example usage: obj._update(y, X, update_params) and returns: self