code: def extract_features(self, dataset, missing_value_action='auto'):
          
          _raise_error_if_not_sframe(dataset, "dataset")
          if missing_value_action == 'auto':
              missing_value_action = select_default_missing_value_policy(self,
                      'extract_features')
  
          return self.__proxy__.extract_features(dataset, missing_value_action)
docs: For each example in the dataset, extract the leaf indices of
          each tree as features.
  
          For multiclass classification, each leaf index contains #num_class
          numbers.
  
          The returned feature vectors can be used as input to train another
          supervised learning model such as a
          :py:class:`~turicreate.logistic_classifier.LogisticClassifier`,
          an :py:class:`~turicreate.svm_classifier.SVMClassifier`, or a
  
          Parameters
          ----------
          dataset : SFrame
              Dataset of new observations. Must include columns with the same
              names as the features used for model training, but does not require
              a target column. Additional columns are ignored.
  
          missing_value_action: str, optional
              Action to perform when missing values are encountered. This can be
              one of:
  
              - 'auto': Choose a model dependent missing value policy.
              - 'impute': Proceed with evaluation by filling in the missing
                          values with the mean of the training data. Missing
                          values are also imputed if an entire column of data is
                          missing during evaluation.
              - 'none': Treat missing value as is. Model must be able to handle
                        missing value.
              - 'error' : Do not proceed with prediction and terminate with
                          an error message.
  
          Returns
          -------
          out : SArray
              An SArray of dtype array.array containing extracted features.
  
          Examples
          --------
          >>> data =  turicreate.SFrame(
              'https://static.turi.com/datasets/regression/houses.csv')
  
          >>> # Regression Tree Models
          >>> data['regression_tree_features'] = model.extract_features(data)
  
          >>> # Classification Tree Models
          >>> data['classification_tree_features'] = model.extract_features(data)