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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 predict(self, features, verbose=False):
 """ Probability estimates of each feature See sklearn's SGDClassifier predict and predict_proba methods. Args: features (:obj:`list` of :obj:`list` of :obj:`float`) verbose: Boolean, optional. If true returns an array where each element is a dictionary, where keys are labels and values are the respective probabilities. Defaults to False. Returns: Array of array of numbers, or array of dictionaries if verbose i True """

        probs = self.clf.predict_proba(features)
        if verbose:
            labels = self.labels.classes_
            res = []
            for prob in probs:
                vals = {}
                for i, val in enumerate(prob):
                    label = labels[i]
                    vals[label] = val
                res.append(vals)
            return res
        else:
            return probs