class_docstr: Mock univariate forecaster that logs the methods called and their parameters.
  
  Parameters
  ----------
  prediction_constant : float, optional
      The forecasted value for all steps in the horizon, by default 10
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.utils.estimators import MockUnivariateForecasterLogger
  >>> y = load_airline()
  >>> forecaster = MockUnivariateForecasterLogger()
  >>> forecaster.fit(y)
  MockUnivariateForecasterLogger(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])
  >>> print(forecaster.log)
  [('_fit', ...), ('_predict', ...)]
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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 : int, list, np.array or ForecastingHorizon
      Forecasting horizon
  X : pd.DataFrame, optional (default=None)
      Exogenous time series
  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 : int, list, np.array or ForecastingHorizon
          Forecasting horizon
      X : pd.DataFrame, optional (default=None)
          Exogenous time series
      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.
      """
      var_names = self._get_varnames()
      var_name = var_names[0]
  
      fh_index = fh.to_absolute_index(self.cutoff)
      col_index = pd.MultiIndex.from_product([var_names, alpha])
      pred_quantiles = pd.DataFrame(columns=col_index, index=fh_index)
  
      for a in alpha:
          pred_quantiles[(var_name, a)] = pd.Series(
              self.prediction_constant * 2 * a, index=fh_index
          )
  
      return pred_quantiles
  
file_path: sktime/sktime/utils/estimators/_forecasters.py
imports: import pandas
  from sktime.forecasting.base import BaseForecaster
  from sktime.utils.estimators._base import _method_logger, _MockEstimatorMixin
label_desc: Use image node_id 5 for calling the MockUnivariateForecasterLogger obj's underlying member method code with example usage: obj._predict_quantiles(fh, X, alpha) and returns: pred_quantiles