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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def classify(self, dataset, verbose=True, batch_size=64):
 """ 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 """

        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)
        })