code: def feature_importances_(self):
          
          if self._n_features is None:
              raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.')
          return self.booster_.feature_importance(importance_type=self.importance_type)
docs: Get feature importances.
  
          Note
          ----
          Feature importance in sklearn interface used to normalize to 1,
          it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now.
          ``importance_type`` attribute is passed to the function
          to configure the type of importance values to be extracted.