class_docstr: Adapter for using `hcrystalball` forecasters in sktime.
  
  Adapter class - wraps any forecaster from `hcrystalball`
  and allows using it as an `sktime` `BaseForecaster`.
  
  Parameters
  ----------
  model : The HCrystalBall forecasting model to use.
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docstr: Fit to training data.
  
  Parameters
  ----------
  y : pd.Series
      Target time series with which to fit the forecaster.
  fh : int, list or np.array, optional (default=None)
      The forecast horizon with the steps ahead to predict.
  X : pd.DataFrame, optional (default=None)
      Exogenous variables are ignored
  
  Returns
  -------
  self : returns an instance of self.
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code: def _fit(self, y, X, fh):
      """Fit to training data.
  
      Parameters
      ----------
      y : pd.Series
          Target time series with which to fit the forecaster.
      fh : int, list or np.array, optional (default=None)
          The forecast horizon with the steps ahead to predict.
      X : pd.DataFrame, optional (default=None)
          Exogenous variables are ignored
  
      Returns
      -------
      self : returns an instance of self.
      """
      y, X = _adapt_y_X(y, X)
      self.model_ = clone(self.model)
      self.model_.fit(X, y)
  
      return self
  
file_path: sktime/sktime/forecasting/adapters/_hcrystalball.py
imports: import pandas
  from sklearn.base import clone
  from sktime.forecasting.base import BaseForecaster
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 2 for calling the HCrystalBallAdapter obj's underlying member method code with example usage: obj._fit(y, X, fh) and returns: self