class_docstr: Delegator mixin that delegates all methods to wrapped forecaster.
  
  Delegates inner forecaster methods to a wrapped estimator.
      Wrapped estimator is value of attribute with name self._delegate_name.
      By default, this is "estimator_", i.e., delegates to self.estimator_
      To override delegation, override _delegate_name attribute in child class.
  
  Delegates the following inner underscore methods:
      _fit, _predict, _update
      _predict_interval, _predict_quantiles, _predict_var, _predict_proba
  
  Does NOT delegate get_params, set_params.
      get_params, set_params will hence use one additional nesting level by default.
  
  Does NOT delegate or copy tags, this should be done in a child class if required.
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docstr: Update time series to incremental training data.
  
  private _update containing the core logic, called from update
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_"
      self.cutoff
  
  Writes to self:
      Sets fitted model attributes ending in "_", if update_params=True.
      Does not write to self if update_params=False.
  
  Parameters
  ----------
  y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
      Time series with which to update the forecaster.
      if self.get_tag("scitype:y")=="univariate":
          guaranteed to have a single column/variable
      if self.get_tag("scitype:y")=="multivariate":
          guaranteed to have 2 or more columns
      if self.get_tag("scitype:y")=="both": no restrictions apply
  X : pd.DataFrame, optional (default=None)
      Exogenous time series
  update_params : bool, optional (default=True)
      whether model parameters should be updated
  
  Returns
  -------
  self : reference to self
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code: def _update(self, y, X=None, update_params=True):
      """Update time series to incremental training data.
  
      private _update containing the core logic, called from update
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_"
          self.cutoff
  
      Writes to self:
          Sets fitted model attributes ending in "_", if update_params=True.
          Does not write to self if update_params=False.
  
      Parameters
      ----------
      y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
          Time series with which to update the forecaster.
          if self.get_tag("scitype:y")=="univariate":
              guaranteed to have a single column/variable
          if self.get_tag("scitype:y")=="multivariate":
              guaranteed to have 2 or more columns
          if self.get_tag("scitype:y")=="both": no restrictions apply
      X : pd.DataFrame, optional (default=None)
          Exogenous time series
      update_params : bool, optional (default=True)
          whether model parameters should be updated
  
      Returns
      -------
      self : reference to self
      """
      estimator = self._get_delegate()
      estimator.update(y=y, X=X, update_params=update_params)
      return self
  
file_path: sktime/sktime/forecasting/base/_delegate.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 4 for calling the _DelegatedForecaster obj's underlying member method code with example usage: obj._update(y, X, update_params) and returns: self