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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):
 """ Return a classification, for each example in the ``dataset``, using the trained model. The output SFrame contains predictions as both class labels as well as probabilities that the predicted value is the associated label. 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. Returns ------- out : SFrame An SFrame with model predictions i.e class labels and probabilities. See Also create, evaluate, predict Examples -------- """

        m = self.__proxy__['classifier']
        target = self.__proxy__['target']
        f = _BOW_FEATURE_EXTRACTOR
        return m.classify(f(dataset, target))