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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 to_markov_model(self):
 """ Converts the factor graph into markov model. A markov model contains nodes as random variables and edge between two nodes imply interaction between them. Examples -------- """

        mm = MarkovModel()

        variable_nodes = self.get_variable_nodes()

        if len(set(self.nodes()) - set(variable_nodes)) != len(self.factors):
            raise ValueError('Factors not associated with all the factor nodes.')

        mm.add_nodes_from(variable_nodes)
        for factor in self.factors:
            scope = factor.scope()
            mm.add_edges_from(itertools.combinations(scope, 2))
            mm.add_factors(factor)

        return mm