code: def predict(self, *args, **kwargs):
          
          return super(PmmlModel, self).predict(*args, **kwargs)
docs: Predict given DataFrame using the given model. Actual prediction steps will not
          be executed till an operational step is called.
  
          After execution, three columns will be appended to the table:
  
          +-------------------+--------+----------------------------------------------------+
          | Field name        | Type   | Comments                                           |
          +===================+========+====================================================+
          | prediction_result | string | field indicating the predicted label, absent if    |
          |                   |        | the model is a regression model                    |
          +-------------------+--------+----------------------------------------------------+
          | prediction_score  | double | field indicating the score value if the model is   |
          |                   |        | a classification model, or the predicted value if  |
          |                   |        | the model is a regression model.                   |
          +-------------------+--------+----------------------------------------------------+
          | prediction_detail | string | field in JSON format indicating the score for      |
          |                   |        | every class.                                       |
          +-------------------+--------+----------------------------------------------------+
  
          :type df: DataFrame
          :rtype: DataFrame
  
          :Example:
  
          >>> model = PmmlModel(odps.get_offline_model('model_name'))
          >>> data = DataFrame(odps.get_table('table_name'))
          >>> # prediction below will not be executed till predicted.persist is called
          >>> predicted = model.predict(data)
          >>> predicted.persist('predicted')