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: Compute/return prediction quantiles for a forecast.
  
  private _predict_quantiles containing the core logic,
      called from predict_quantiles and possibly predict_interval
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_"
      self.cutoff
  
  Parameters
  ----------
  fh : guaranteed to be ForecastingHorizon
      The forecasting horizon with the steps ahead to to predict.
  X : optional (default=None)
      guaranteed to be of a type in self.get_tag("X_inner_mtype")
      Exogeneous time series to predict from.
  alpha : list of float (guaranteed not None and floats in [0,1] interval)
      A list of probabilities at which quantile forecasts are computed.
  
  Returns
  -------
  pred_quantiles : pd.DataFrame
      Column has multi-index: first level is variable name from y in fit,
          second level being the quantile forecasts for each alpha.
          Quantile forecasts are calculated for each a in alpha.
      Row index is fh. Entries are quantile forecasts, for var in col index,
          at quantile probability in second-level col index, for each row index.
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code: def _predict_quantiles(self, fh, X, alpha):
      """Compute/return prediction quantiles for a forecast.
  
      private _predict_quantiles containing the core logic,
          called from predict_quantiles and possibly predict_interval
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_"
          self.cutoff
  
      Parameters
      ----------
      fh : guaranteed to be ForecastingHorizon
          The forecasting horizon with the steps ahead to to predict.
      X : optional (default=None)
          guaranteed to be of a type in self.get_tag("X_inner_mtype")
          Exogeneous time series to predict from.
      alpha : list of float (guaranteed not None and floats in [0,1] interval)
          A list of probabilities at which quantile forecasts are computed.
  
      Returns
      -------
      pred_quantiles : pd.DataFrame
          Column has multi-index: first level is variable name from y in fit,
              second level being the quantile forecasts for each alpha.
              Quantile forecasts are calculated for each a in alpha.
          Row index is fh. Entries are quantile forecasts, for var in col index,
              at quantile probability in second-level col index, for each row index.
      """
      estimator = self._get_delegate()
      return estimator.predict_quantiles(fh=fh, X=X, alpha=alpha)
  
file_path: sktime/sktime/forecasting/base/_delegate.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 6 for calling the _DelegatedForecaster obj's underlying member method code with example usage: obj._predict_quantiles(fh, X, alpha) and returns: estimator