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

"""
        from pgmpy.models import JunctionTree

        # Check whether the model is valid or not
        self.check_model()

        # Triangulate the graph to make it chordal
        triangulated_graph = self.triangulate()

        # Find maximal cliques in the chordal graph
        cliques = list(map(tuple, nx.find_cliques(triangulated_graph)))

        # If there is only 1 clique, then the junction tree formed is just a
        # clique tree with that single clique as the node
        if len(cliques) == 1:
            clique_trees = JunctionTree()
            clique_trees.add_node(cliques[0])

        # Else if the number of cliques is more than 1 then create a complete
        # graph with all the cliques as nodes and weight of the edges being
        # the length of sepset between two cliques
        elif len(cliques) >= 2:
            complete_graph = UndirectedGraph()
            edges = list(itertools.combinations(cliques, 2))
            weights = list(map(lambda x: len(set(x[0]).intersection(set(x[1]))), edges))
            for edge, weight in zip(edges, weights):
                complete_graph.add_edge(*edge, weight=-weight)

            # Create clique trees by minimum (or maximum) spanning tree method
            clique_trees = JunctionTree(
                nx.minimum_spanning_tree(complete_graph).edges()
            )

# Conversion of set to tuple just for indexing
        nodes_with_query_variables = tuple(nodes_with_query_variables)
        # As junction tree is a tree, that means that there would be only path between any two nodes in the tree
        # thus we can just take the path between any two nodes; no matter there order is
        for i in range(len(nodes_with_query_variables) - 1):
            subtree_nodes.update(
                nx.shortest_path(
                    self.junction_tree,
                    nodes_with_query_variables[i],
                    nodes_with_query_variables[i + 1],
                )
            )
        subtree_undirected_graph = self.junction_tree.subgraph(subtree_nodes)
        # Converting subtree into a junction tree
        if len(subtree_nodes) == 1:
            subtree = JunctionTree()
            subtree.add_node(subtree_nodes.pop())
        else:
            subtree = JunctionTree(subtree_undirected_graph.edges())

        # Selecting a node is root node. Root node would be having only one neighbor
        if len(subtree.nodes()) == 1:
            root_node = list(subtree.nodes())[0]
        else:
            root_node = tuple(
                filter(lambda x: len(list(subtree.neighbors(x))) == 1, subtree.nodes())
            )[0]
        clique_potential_list = [self.clique_beliefs[root_node]]

        # For other nodes in the subtree compute the clique potentials as follows
        # As all the nodes are nothing but tuples so simple set(root_node) won't work at it would update the set with'
        # all the elements of the tuple; instead use set([root_node]) as it would include only the tuple not the

# thus we can just take the path between any two nodes; no matter there order is
        for i in range(len(nodes_with_query_variables) - 1):
            subtree_nodes.update(
                nx.shortest_path(
                    self.junction_tree,
                    nodes_with_query_variables[i],
                    nodes_with_query_variables[i + 1],
                )
            )
        subtree_undirected_graph = self.junction_tree.subgraph(subtree_nodes)
        # Converting subtree into a junction tree
        if len(subtree_nodes) == 1:
            subtree = JunctionTree()
            subtree.add_node(subtree_nodes.pop())
        else:
            subtree = JunctionTree(subtree_undirected_graph.edges())

        # Selecting a node is root node. Root node would be having only one neighbor
        if len(subtree.nodes()) == 1:
            root_node = list(subtree.nodes())[0]
        else:
            root_node = tuple(
                filter(lambda x: len(list(subtree.neighbors(x))) == 1, subtree.nodes())
            )[0]
        clique_potential_list = [self.clique_beliefs[root_node]]

        # For other nodes in the subtree compute the clique potentials as follows
        # As all the nodes are nothing but tuples so simple set(root_node) won't work at it would update the set with'
        # all the elements of the tuple; instead use set([root_node]) as it would include only the tuple not the
        # internal elements within it.
        parent_nodes = set([root_node])
        nodes_traversed = set()

else:
            self.variables = model.nodes()

        self.cardinality = {}
        self.factors = defaultdict(list)

        if isinstance(model, BayesianModel):
            for node in model.nodes():
                cpd = model.get_cpds(node)
                if isinstance(cpd, TabularCPD):
                    self.cardinality[node] = cpd.variable_card
                    cpd = cpd.to_factor()
                for var in cpd.scope():
                    self.factors[var].append(cpd)

        elif isinstance(model, (MarkovModel, FactorGraph, JunctionTree)):
            self.cardinality = model.get_cardinality()

            for factor in model.get_factors():
                for var in factor.variables:
                    self.factors[var].append(factor)

        elif isinstance(model, DynamicBayesianNetwork):
            self.start_bayesian_model = BayesianModel(model.get_intra_edges(0))
            self.start_bayesian_model.add_cpds(*model.get_cpds(time_slice=0))
            cpd_inter = [model.get_cpds(node) for node in model.get_interface_nodes(1)]
            self.interface_nodes = model.get_interface_nodes(0)
            self.one_and_half_model = BayesianModel(
                model.get_inter_edges() + model.get_intra_edges(1)
            )
            self.one_and_half_model.add_cpds(
                *(model.get_cpds(time_slice=1) + cpd_inter)

def __init__(self, model):
        self.model = model
        model.check_model()

        if isinstance(model, JunctionTree):
            self.variables = set(chain(*model.nodes()))
        else:
            self.variables = model.nodes()

        self.cardinality = {}
        self.factors = defaultdict(list)

        if isinstance(model, BayesianModel):
            for node in model.nodes():
                cpd = model.get_cpds(node)
                if isinstance(cpd, TabularCPD):
                    self.cardinality[node] = cpd.variable_card
                    cpd = cpd.to_factor()
                for var in cpd.scope():
                    self.factors[var].append(cpd)

if len(cliques) == 1:
            clique_trees = JunctionTree()
            clique_trees.add_node(cliques[0])

        # Else if the number of cliques is more than 1 then create a complete
        # graph with all the cliques as nodes and weight of the edges being
        # the length of sepset between two cliques
        elif len(cliques) >= 2:
            complete_graph = UndirectedGraph()
            edges = list(itertools.combinations(cliques, 2))
            weights = list(map(lambda x: len(set(x[0]).intersection(set(x[1]))), edges))
            for edge, weight in zip(edges, weights):
                complete_graph.add_edge(*edge, weight=-weight)

            # Create clique trees by minimum (or maximum) spanning tree method
            clique_trees = JunctionTree(
                nx.minimum_spanning_tree(complete_graph).edges()
            )

        # Check whether the factors are defined for all the random variables or not
        all_vars = itertools.chain(*[factor.scope() for factor in self.factors])
        if set(all_vars) != set(self.nodes()):
            ValueError("DiscreteFactor for all the random variables not specified")

        # Dictionary stating whether the factor is used to create clique
        # potential or not
        # If false, then it is not used to create any clique potential
        is_used = {factor: False for factor in self.factors}

        for node in clique_trees.nodes():
            clique_factors = []
            for factor in self.factors: