Finds the maximal children of G when tier is added using SCC decomposition
        :param G: Networkx DiGraph of edges "locked in" so far
        :param tier: List of edges in the current tier to be added with equal weight
        :param scc: List of the strongly connected components of G U tier, each being a list of edges
        :param bridges: List of edges that are bridges between sccs of G U tier
        :param I: List of all nodes
        :param known_winners: Known PUT-winners computed by RP so far
        :param stats: Stats object containing runtime statistics
        :return: Array of Networkx DiGraphs that are the maximal children of G U T

def find_max_children_scc_decomposition(self, G, tier, scc, bridges, I, known_winners, stats):
        '''
        Finds the maximal children of G when tier is added using SCC decomposition
        :param G: Networkx DiGraph of edges "locked in" so far
        :param tier: List of edges in the current tier to be added with equal weight
        :param scc: List of the strongly connected components of G U tier, each being a list of edges
        :param bridges: List of edges that are bridges between sccs of G U tier
        :param I: List of all nodes
        :param known_winners: Known PUT-winners computed by RP so far
        :param stats: Stats object containing runtime statistics
        :return: Array of Networkx DiGraphs that are the maximal children of G U T
        '''

        if len(scc) == 1:
            children = self.explore_max_children_lp(G, tier, I, known_winners, stats)
            return children

        mc_list = []

        for x in scc:
            G_temp = nx.DiGraph(list(set(G.edges()).intersection(set(x))))
            T_temp = list(set(tier).intersection(set(x)))
            temp = self.explore_max_children_lp(G_temp, T_temp, I, known_winners, stats, f_scc = 1)
            mc_list.append(temp)

        Cartesian = itertools.product(*mc_list)
        return [nx.DiGraph(list(set(itertools.chain(*[list(y.edges()) for y in x])).union(bridges))) for x in Cartesian]