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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 clust_coef(self, node):
 """ Computes and returns the local clustering coefficient of node. The local cluster coefficient is proportion of the actual number of edges between neighbours of node and the maximum number of edges between those neighbours. See <http://en.wikipedia.org/wiki/Clustering_coefficient#Local_clustering_coefficient> for a formal definition. """

        num = 0
        nbr_set = set(self.out_nbrs(node))

        if node in nbr_set:
            nbr_set.remove(node) # loop defense

        for nbr in nbr_set:
            sec_set = set(self.out_nbrs(nbr))
            if nbr in sec_set:
                sec_set.remove(nbr) # loop defense
            num += len(nbr_set & sec_set)

        nbr_num = len(nbr_set)
        if nbr_num:
            clust_coef = float(num) / (nbr_num * (nbr_num - 1))
        else:
            clust_coef = 0.0
        return clust_coef