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: 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
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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
      """
      index = fh.to_absolute_index(self.cutoff)
      return pd.Series(self.prediction_constant, index=index)
  
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 3 for calling the MockUnivariateForecasterLogger obj's underlying member method code with example usage: obj._predict(fh, X) and returns: pd