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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 _calibrate(self, data, labels):
 """\ Calibrate the network using `self._bmus`. """

        # network calibration
        classifier = defaultdict(Counter)
        for (i,j), label in zip(self._bmus, labels):
            classifier[i,j][label] += 1
        self.classifier = {}
        for ij, cnt in classifier.items():
            maxi = max(cnt.items(), key=itemgetter(1))
            nb = sum(cnt.values())
            self.classifier[ij] = maxi[0], maxi[1] / nb