code: def classify(self, dataset, verbose=True, batch_size=64):
          
          prob_vector = self.predict(dataset, output_type='probability_vector',
                                     verbose=verbose, batch_size=batch_size)
          id_to_label = self._id_to_class_label
  
          return _tc.SFrame({
              'class': prob_vector.apply(lambda v: id_to_label[_np.argmax(v)]),
              'probability': prob_vector.apply(_np.max)
          })
docs: Return the classification for each examples in the ``dataset``.
          The output SFrame contains predicted class labels and its probability.
  
          Parameters
          ----------
          dataset : SFrame | SArray | dict
              The audio data to be classified.
              If dataset is an SFrame, it must have a column with the same name as
              the feature used for model training, but does not require a target
              column. Additional columns are ignored.
  
          verbose : bool, optional
              If True, prints progress updates and model details.
  
          batch_size : int, optional
              If you are getting memory errors, try decreasing this value. If you
              have a powerful computer, increasing this value may improve performance.
  
          Returns
          -------
          out : SFrame
              An SFrame with model predictions, both class labels and probabilities.
  
          See Also
          ----------
          create, evaluate, predict
  
          Examples
          ----------
          >>> classes = model.classify(data)