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: Forecast variance at future horizon.
  
  private _predict_var containing the core logic, called from predict_var
  
  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 :  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
  cov : bool, optional (default=False)
      if True, computes covariance matrix forecast.
      if False, computes marginal variance forecasts.
  
  Returns
  -------
  pred_var : pd.DataFrame, format dependent on `cov` variable
      If cov=False:
          Column names are exactly those of `y` passed in `fit`/`update`.
              For nameless formats, column index will be a RangeIndex.
          Row index is fh, with additional levels equal to instance levels,
              from y seen in fit, if y_inner_mtype is Panel or Hierarchical.
          Entries are variance forecasts, for var in col index.
          A variance forecast for given variable and fh index is a predicted
              variance for that variable and index, given observed data.
      If cov=True:
          Column index is a multiindex: 1st level is variable names (as above)
              2nd level is fh.
          Row index is fh, with additional levels equal to instance levels,
              from y seen in fit, if y_inner_mtype is Panel or Hierarchical.
          Entries are (co-)variance forecasts, for var in col index, and
              covariance between time index in row and col.
          Note: no covariance forecasts are returned between different variables.
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code: def _predict_var(self, fh, X=None, cov=False):
      """Forecast variance at future horizon.
  
      private _predict_var containing the core logic, called from predict_var
  
      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 :  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
      cov : bool, optional (default=False)
          if True, computes covariance matrix forecast.
          if False, computes marginal variance forecasts.
  
      Returns
      -------
      pred_var : pd.DataFrame, format dependent on `cov` variable
          If cov=False:
              Column names are exactly those of `y` passed in `fit`/`update`.
                  For nameless formats, column index will be a RangeIndex.
              Row index is fh, with additional levels equal to instance levels,
                  from y seen in fit, if y_inner_mtype is Panel or Hierarchical.
              Entries are variance forecasts, for var in col index.
              A variance forecast for given variable and fh index is a predicted
                  variance for that variable and index, given observed data.
          If cov=True:
              Column index is a multiindex: 1st level is variable names (as above)
                  2nd level is fh.
              Row index is fh, with additional levels equal to instance levels,
                  from y seen in fit, if y_inner_mtype is Panel or Hierarchical.
              Entries are (co-)variance forecasts, for var in col index, and
                  covariance between time index in row and col.
              Note: no covariance forecasts are returned between different variables.
      """
  
file_path: sktime/extension_templates/forecasting.py
imports: from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 8 for calling the MyForecaster obj's underlying member method code with example usage: obj._predict_var(fh, X, cov) without return types