code: def feature_importances(data, top_n=None, feature_names=None):
      
      if data is None:
          raise ValueError('data is needed to tabulate feature importances. '
                           'When plotting using the evaluator you need to pass '
                           'an estimator ')
  
      res = compute.feature_importances(data, top_n, feature_names)
      return Table(res, res.dtype.names)
docs: Get and order feature importances from a scikit-learn model
      or from an array-like structure.
  
      If data is a scikit-learn model with sub-estimators (e.g. RandomForest,
      AdaBoost) the function will compute the standard deviation of each
      feature.
  
      Parameters
      ----------
      data : sklearn model or array-like structure
          Object to get the data from.
      top_n : int
          Only get results for the top_n features.
      feature_names : array-like
          Feature_names
  
      Returns
      -------
      table
          Table object with the data. Columns are
          feature_name, importance (`std_` only included for models with
          sub-estimators)