code: def predict(self, dataset, missing_value_action='auto',
                  output_type='', options={}, **kwargs):
          
          if missing_value_action == 'auto':
              missing_value_action = select_default_missing_value_policy(self, 'predict')
  
          # Low latency path
          if isinstance(dataset, list):
              return self.__proxy__.fast_predict(
                  dataset, missing_value_action, output_type)
          if isinstance(dataset, dict):
              return self.__proxy__.fast_predict(
                  [dataset], missing_value_action, output_type)
  
          # Batch predictions path
          else:
              _raise_error_if_not_sframe(dataset, "dataset")
  
              return self.__proxy__.predict(
                  dataset, missing_value_action, output_type)
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 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.
  
          output_type : str, optional
              output type that maybe needed by some of the toolkits
  
          options : dict
              additional options to be passed in to prediction
  
          kwargs : dict
              additional options to be passed into prediction
  
          Returns
          -------
          out : SArray
              An SArray with model predictions.