code: def test_triangulate(self, huang_darwiche_moralized):
      # Because of ties in the priority q we will
      # override the priority function here to
      # insert tie breakers to ensure the same
      # elimination ordering as Darwich Huang.
      def priority_func_override(node):
          introduced_arcs = 0
          cluster = [node] + node.neighbours
          for node_a, node_b in combinations(cluster, 2):
              if node_a not in node_b.neighbours:
                  assert node_b not in node_a.neighbours
                  introduced_arcs += 1
          introduced_arcs_dict = {
              "f_h": [introduced_arcs, 0],
              "f_g": [introduced_arcs, 1],
              "f_c": [introduced_arcs, 2],
              "f_b": [introduced_arcs, 3],
              "f_d": [introduced_arcs, 4],
              "f_e": [introduced_arcs, 5],
              "others": [introduced_arcs, 10],
          }
          if node.name in introduced_arcs_dict:
              return introduced_arcs_dict[node.name]
  
          return introduced_arcs_dict["others"]
  
      cliques, elimination_ordering = triangulate(
          huang_darwiche_moralized, priority_func_override
      )
      nodes = {
          node.name: node for node in huang_darwiche_moralized.nodes
      }
      assert len(cliques) == 6
      assert cliques[0].nodes == set(
          [nodes["f_e"], nodes["f_g"], nodes["f_h"]]
      )
      assert cliques[1].nodes == set(
          [nodes["f_c"], nodes["f_e"], nodes["f_g"]]
      )
      assert cliques[2].nodes == set(
          [nodes["f_d"], nodes["f_e"], nodes["f_f"]]
      )
      assert cliques[3].nodes == set(
          [nodes["f_a"], nodes["f_c"], nodes["f_e"]]
      )
      assert cliques[4].nodes == set(
          [nodes["f_a"], nodes["f_b"], nodes["f_d"]]
      )
      assert cliques[5].nodes == set(
          [nodes["f_a"], nodes["f_d"], nodes["f_e"]]
      )
  
      assert elimination_ordering == [
          "f_h",
          "f_g",
          "f_f",
          "f_c",
          "f_b",
          "f_d",
          "f_e",
          "f_a",
      ]
      # Now lets ensure the triangulated graph is
      # the same as Darwiche Huang fig. 2 pg. 13
      nodes = {
          node.name: node for node in huang_darwiche_moralized.nodes
      }
      assert set(nodes["f_a"].neighbours) == set(
          [nodes["f_b"], nodes["f_c"], nodes["f_d"], nodes["f_e"]]
      )
      assert set(nodes["f_b"].neighbours) == set(
          [nodes["f_a"], nodes["f_d"]]
      )
      assert set(nodes["f_c"].neighbours) == set(
          [nodes["f_a"], nodes["f_e"], nodes["f_g"]]
      )
      assert set(nodes["f_d"].neighbours) == set(
          [nodes["f_b"], nodes["f_f"], nodes["f_e"], nodes["f_a"]]
      )
      assert set(nodes["f_e"].neighbours) == set(
          [
              nodes["f_c"],
              nodes["f_f"],
              nodes["f_h"],
              nodes["f_d"],
              nodes["f_g"],
              nodes["f_a"],
          ]
      )
      assert set(nodes["f_f"].neighbours) == set(
          [nodes["f_d"], nodes["f_e"]]
      )
      assert set(nodes["f_g"].neighbours) == set(
          [nodes["f_c"], nodes["f_h"], nodes["f_e"]]
      )
      assert set(nodes["f_h"].neighbours) == set(
          [nodes["f_e"], nodes["f_g"]]
      )
  
imports: from __future__ import division
  import copy
  from collections import Counter
  import pytest
  from causalnex.ebaybbn import BBNNode, JoinTree, JoinTreeCliqueNode, SepSet, build_bbn, build_bbn_from_conditionals, build_join_tree, combinations, make_moralized_copy, make_node_func, make_undirected_copy, priority_func, triangulate
  from causalnex.ebaybbn.exceptions import VariableNotInGraphError, VariableValueNotInDomainError
  from causalnex.ebaybbn.graph import Node, UndirectedNode
  from causalnex.ebaybbn.utils import get_args, get_original_factors, make_key
label_desc: Use image node_id 6 for calling the TestBBN obj's underlying member method code with example usage: obj.test_triangulate(huang_darwiche_moralized) and returns: introduced_arcs_dict, introduced_arcs_dict