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: Fit forecaster to training data.
  
  private _fit containing the core logic, called from fit
  
  Writes to self:
      Sets fitted model attributes ending in "_".
  
  Parameters
  ----------
  y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
      Time series to which to fit 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
  fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
      The forecasting horizon with the steps ahead to to predict.
      Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True
      Otherwise, if not passed in _fit, guaranteed to be passed in _predict
  X : optional (default=None)
      guaranteed to be of a type in self.get_tag("X_inner_mtype")
      Exogeneous time series to fit to.
  
  Returns
  -------
  self : reference to self
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code: def _fit(self, y, X, fh):
      """Fit forecaster to training data.
  
      private _fit containing the core logic, called from fit
  
      Writes to self:
          Sets fitted model attributes ending in "_".
  
      Parameters
      ----------
      y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
          Time series to which to fit 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
      fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
          The forecasting horizon with the steps ahead to to predict.
          Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True
          Otherwise, if not passed in _fit, guaranteed to be passed in _predict
      X : optional (default=None)
          guaranteed to be of a type in self.get_tag("X_inner_mtype")
          Exogeneous time series to fit to.
  
      Returns
      -------
      self : reference to self
      """
      estimator = self._get_delegate()
      estimator.fit(y=y, fh=fh, X=X)
      return self
  
file_path: sktime/sktime/forecasting/base/_delegate.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 2 for calling the _DelegatedForecaster obj's underlying member method code with example usage: obj._fit(y, X, fh) and returns: self