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: Fit forecaster to training data.
  
  private _fit containing the core logic, called from fit
  
  Writes to self:
      Sets fitted model attributes ending in "_".
  
  Parameters
  ----------
  y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
      Time series to which to fit the forecaster.
      if self.get_tag("scitype:y")=="univariate":
          guaranteed to have a single column/variable
      if self.get_tag("scitype:y")=="multivariate":
          guaranteed to have 2 or more columns
      if self.get_tag("scitype:y")=="both": no restrictions apply
  fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
      The forecasting horizon with the steps ahead to to predict.
      Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True
      Otherwise, if not passed in _fit, guaranteed to be passed in _predict
  X : optional (default=None)
      guaranteed to be of a type in self.get_tag("X_inner_mtype")
      Exogeneous time series to fit to.
  
  Returns
  -------
  self : reference to self
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code: def _fit(self, y, X, fh):
      """Fit forecaster to training data.
  
      private _fit containing the core logic, called from fit
  
      Writes to self:
          Sets fitted model attributes ending in "_".
  
      Parameters
      ----------
      y : guaranteed to be of a type in self.get_tag("y_inner_mtype")
          Time series to which to fit the forecaster.
          if self.get_tag("scitype:y")=="univariate":
              guaranteed to have a single column/variable
          if self.get_tag("scitype:y")=="multivariate":
              guaranteed to have 2 or more columns
          if self.get_tag("scitype:y")=="both": no restrictions apply
      fh : guaranteed to be ForecastingHorizon or None, optional (default=None)
          The forecasting horizon with the steps ahead to to predict.
          Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True
          Otherwise, if not passed in _fit, guaranteed to be passed in _predict
      X : optional (default=None)
          guaranteed to be of a type in self.get_tag("X_inner_mtype")
          Exogeneous time series to fit to.
  
      Returns
      -------
      self : reference to self
      """
      return self
  
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 2 for calling the MockUnivariateForecasterLogger obj's underlying member method code with example usage: obj._fit(y, X, fh) and returns: self