code: def classify(self, dataset, missing_value_action='auto'):
          
          if (missing_value_action == 'auto'):
              missing_value_action = select_default_missing_value_policy(self, 'classify')
  
          # Low latency path
          if isinstance(dataset, list):
              return self.__proxy__.fast_classify(dataset, missing_value_action)
          if isinstance(dataset, dict):
              return self.__proxy__.fast_classify([dataset], missing_value_action)
  
          _raise_error_if_not_sframe(dataset, "dataset")
          return self.__proxy__.classify(dataset, missing_value_action)
docs: Return predictions for ``dataset``, using the trained supervised_learning
          model. Predictions are generated as class labels (0 or
          1).
  
          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 model dependent missing value action
              - '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.
              - 'error': Do not proceed with prediction and terminate with
                an error message.
          Returns
          -------
          out : SFrame
              An SFrame with model predictions.