class_docstr: Custom forecaster. todo: write docstring.
  
  todo: describe your custom forecaster here
  
  Parameters
  ----------
  parama : int
      descriptive explanation of parama
  paramb : string, optional (default='default')
      descriptive explanation of paramb
  paramc : boolean, optional (default= whether paramb is not the default)
      descriptive explanation of paramc
  and so on
  est : sktime.estimator, BaseEstimator descendant
      descriptive explanation of est
  est2: another estimator
      descriptive explanation of est2
  and so on
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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 :  sktime time series object, optional (default=None)
      guaranteed to be of an mtype in self.get_tag("X_inner_mtype")
      Exogeneous time series for the forecast
  alpha : list of float (guaranteed not None and floats in [0,1] interval)
      A list of probabilities at which quantile forecasts are computed.
  
  Returns
  -------
  quantiles : pd.DataFrame
      Column has multi-index: first level is variable name from y in fit,
          second level being the values of alpha passed to the function.
      Row index is fh, with additional (upper) levels equal to instance levels,
              from y seen in fit, if y_inner_mtype is Panel or Hierarchical.
      Entries are quantile forecasts, for var in col index,
          at quantile probability in second col index, for the 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 :  sktime time series object, optional (default=None)
          guaranteed to be of an mtype in self.get_tag("X_inner_mtype")
          Exogeneous time series for the forecast
      alpha : list of float (guaranteed not None and floats in [0,1] interval)
          A list of probabilities at which quantile forecasts are computed.
  
      Returns
      -------
      quantiles : pd.DataFrame
          Column has multi-index: first level is variable name from y in fit,
              second level being the values of alpha passed to the function.
          Row index is fh, with additional (upper) levels equal to instance levels,
                  from y seen in fit, if y_inner_mtype is Panel or Hierarchical.
          Entries are quantile forecasts, for var in col index,
              at quantile probability in second col index, for the row index.
      """
  
file_path: sktime/extension_templates/forecasting.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 6 for calling the MyForecaster obj's underlying member method code with example usage: obj._predict_quantiles(fh, X, alpha) without return types