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 fully probabilistic forecasts.
  
  private _predict_proba containing the core logic, called from predict_proba
  
  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.
  marginal : bool, optional (default=True)
      whether returned distribution is marginal by time index
  
  Returns
  -------
  pred_dist : sktime BaseDistribution
      predictive distribution
      if marginal=True, will be marginal distribution by time point
      if marginal=False and implemented by method, will be joint
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code: def _predict_proba(self, fh, X, marginal=True):
      """Compute/return fully probabilistic forecasts.
  
      private _predict_proba containing the core logic, called from predict_proba
  
      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.
      marginal : bool, optional (default=True)
          whether returned distribution is marginal by time index
  
      Returns
      -------
      pred_dist : sktime BaseDistribution
          predictive distribution
          if marginal=True, will be marginal distribution by time point
          if marginal=False and implemented by method, will be joint
      """
      estimator = self._get_delegate()
      return estimator.predict_proba(fh=fh, X=X, marginal=marginal)
  
file_path: sktime/sktime/forecasting/base/_delegate.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 9 for calling the _DelegatedForecaster obj's underlying member method code with example usage: obj._predict_proba(fh, X, marginal) and returns: estimator