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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 reassign_labels(self, labels, new_label, relabel=False):
 """ Reassign one or more label numbers. Multiple input ``labels`` will all be reassigned to the same ``new_label`` number. If ``new_label`` is already present in the segmentation image, then it will be combined with the input ``labels``. Parameters labels : int, array-like (1D, int) The label numbers(s) to reassign. new_label : int The reassigned label number. relabel : bool, optional If `True`, then the segmentation image will be relabeled such that the labels are in consecutive order starting from 1. Examples -------- array([[2, 2, 0, 0, 4, 4], [0, 0, 0, 0, 0, 4], [0, 0, 3, 3, 0, 0], [2, 0, 0, 0, 0, 5], [2, 2, 0, 5, 5, 5], [2, 2, 0, 0, 5, 5]]) array([[4, 4, 0, 0, 4, 4], [0, 0, 0, 0, 0, 4], [0, 0, 3, 3, 0, 0], [4, 0, 0, 0, 0, 5], [4, 4, 0, 5, 5, 5], [4, 4, 0, 0, 5, 5]]) array([[1, 1, 0, 0, 3, 3], [0, 0, 0, 0, 0, 3], [0, 0, 2, 2, 0, 0], [1, 0, 0, 0, 0, 4], [1, 1, 0, 4, 4, 4], [1, 1, 0, 0, 4, 4]]) """


        self.check_labels(labels)

        labels = np.atleast_1d(labels)
        if len(labels) == 0:
            return

        idx = np.zeros(self.max_label + 1, dtype=int)
        idx[self.labels] = self.labels
        idx[labels] = new_label

        # calling the data setter resets all cached properties
        self.data = idx[self.data]

        if relabel:
            self.relabel_consecutive()