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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 fit_predict(self, data, labels, unkown=None):
 """\ Fit and classify data efficiently. :param data: sparse input matrix (ideal dtype is `numpy.float32`) :type data: :class:`scipy.sparse.csr_matrix` :param labels: the labels associated with data :type labels: iterable :param unkown: the label to attribute if no label is known :returns: the labels guessed for data :rtype: `numpy.array` """

        self.fit(data, labels)
        return self._predict_from_bmus(self._bmus, unkown)