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: Forecast time series at future horizon.
  
  private _predict containing the core logic, called from predict
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_"
      self.cutoff
  
  Parameters
  ----------
  fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
      The forecasting horizon with the steps ahead to to predict.
      If not passed in _fit, guaranteed to be passed here
  X : pd.DataFrame, optional (default=None)
      Exogenous time series
  
  Returns
  -------
  y_pred : pd.Series
      Point predictions
docstr_tok: ["Forecast","time","series","at","future","horizon",".","private","_predict","containing","the","core","logic",",","called","from","predict","State","required",":","Requires","state","to","be","``","fitted","''",".","Accesses","in","self",":","Fitted","model","attributes","ending","in","``","_","''","self.cutoff","Parameters","--","--","--","--","--","fh",":","guaranteed","to","be","ForecastingHorizon","or","None",",","optional","(","default=None",")","The","forecasting","horizon","with","the","steps","ahead","to","to","predict",".","If","not","passed","in","_fit",",","guaranteed","to","be","passed","here","X",":","pd.DataFrame",",","optional","(","default=None",")","Exogenous","time","series","Returns","--","--","--","-","y_pred",":","pd.Series","Point","predictions"]
code: def _predict(self, fh, X):
      """Forecast time series at future horizon.
  
      private _predict containing the core logic, called from predict
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_"
          self.cutoff
  
      Parameters
      ----------
      fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
          The forecasting horizon with the steps ahead to to predict.
          If not passed in _fit, guaranteed to be passed here
      X : pd.DataFrame, optional (default=None)
          Exogenous time series
  
      Returns
      -------
      y_pred : pd.Series
          Point predictions
      """
      estimator = self._get_delegate()
      return estimator.predict(fh=fh, X=X)
  
file_path: sktime/sktime/forecasting/base/_delegate.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 3 for calling the _DelegatedForecaster obj's underlying member method code with example usage: obj._predict(fh, X) and returns: estimator