How to use the pgmpy.models.MarkovModel.MarkovModel function in pgmpy

Parameters
        ----------
        u,v : nodes
            Nodes can be any hashable Python object.

        Examples
        --------
        >>> from pgmpy.models import MarkovModel
        >>> G = MarkovModel()
        >>> G.add_nodes_from(['Alice', 'Bob', 'Charles'])
        >>> G.add_edge('Alice', 'Bob')
        """
        # check that there is no self loop.
        if u != v:
            super(MarkovModel, self).add_edge(u, v, **kwargs)
        else:
            raise ValueError("Self loops are not allowed")

order.append(node_to_delete)

        graph_copy = nx.Graph(self.edges())
        for node in order:
            for edge in itertools.combinations(graph_copy.neighbors(node), 2):
                graph_copy.add_edge(edge[0], edge[1])
                edge_set.add(edge)
            graph_copy.remove_node(node)

        if inplace:
            for edge in edge_set:
                self.add_edge(edge[0], edge[1])
            return self

        else:
            graph_copy = MarkovModel(self.edges())
            for edge in edge_set:
                graph_copy.add_edge(edge[0], edge[1])
            return graph_copy

Examples
        --------
        >>> from pgmpy.models import FactorGraph
        >>> from pgmpy.factors.discrete import DiscreteFactor
        >>> G = FactorGraph()
        >>> G.add_nodes_from(['a', 'b', 'c'])
        >>> phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
        >>> phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
        >>> G.add_factors(phi1, phi2)
        >>> G.add_nodes_from([phi1, phi2])
        >>> G.add_edges_from([('a', phi1), ('b', phi1),
        ...                   ('b', phi2), ('c', phi2)])
        >>> mm = G.to_markov_model()
        """
        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

be the moral graph of the bayesian model.

        Examples
        --------
        >>> from pgmpy.models import BayesianModel
        >>> G = BayesianModel([('diff', 'grade'), ('intel', 'grade'),
        ...                    ('intel', 'SAT'), ('grade', 'letter')])
        >>> mm = G.to_markov_model()
        >>> mm.nodes()
        ['diff', 'grade', 'intel', 'SAT', 'letter']
        >>> mm.edges()
        [('diff', 'intel'), ('diff', 'grade'), ('intel', 'grade'),
        ('intel', 'SAT'), ('grade', 'letter')]
        """
        moral_graph = self.moralize()
        mm = MarkovModel(moral_graph.edges())
        mm.add_factors(*[cpd.to_factor() for cpd in self.cpds])

        return mm

>>> G.add_nodes_from([('a', 'b'), ('b', 'c')])
        >>> G.add_edge(('a', 'b'), ('b', 'c'))
        >>> G_copy = G.copy()
        >>> G_copy.edges()
        [(('a', 'b'), ('b', 'c'))]
        >>> G_copy.nodes()
        [('a', 'b'), ('b', 'c')]
        >>> factor = DiscreteFactor([('a', 'b')], cardinality=[3],
        ...                 values=np.random.rand(3))
        >>> G.add_factors(factor)
        >>> G.get_factors()
        []
        >>> G_copy.get_factors()
        []
        """
        clone_graph = MarkovModel(self.edges())
        clone_graph.add_nodes_from(self.nodes())

        if self.factors:
            factors_copy = [factor.copy() for factor in self.factors]
            clone_graph.add_factors(*factors_copy)

        return clone_graph