code: def triangulate(gm, priority_func=priority_func):
      """Triangulate the moralized Graph. (in Place)
      and return the cliques of the triangulated
      graph as well as the elimination ordering."""
  
      # First we will make a copy of gm...
      gm_ = copy.deepcopy(gm)
  
      # Now we will construct a priority q using
      # the standard library heapq module.
      # See docs for example of priority q tie
      # breaking. We will use a 3 element list
      # with entries as follows:
      #   - Number of edges added if V were selected
      #   - Weight of V (or cluster)
      #   - Pointer to node in gm_
      # Note that its unclear from Huang and Darwiche
      # what is meant by the "number of values of V"
      gmnodes = dict([(node.name, node) for node in gm.nodes])
      elimination_ordering = []
      cliques = []
      while True:
          gm_nodes = dict([(node.name, node) for node in gm_.nodes])
          if not gm_nodes:
              break
          pq = construct_priority_queue(gm_nodes, priority_func)
          # Now we select the first node in
          # the priority q and any arcs that
          # should be added in order to fully connect
          # the cluster should be added to both
          # gm and gm_
          v = gm_nodes[pq[0][2]]
          cluster = [v] + v.neighbours
          for node_a, node_b in combinations(cluster, 2):
              if node_a not in node_b.neighbours:
                  node_b.neighbours.append(node_a)
                  node_a.neighbours.append(node_b)
                  # Now also add this new arc to gm...
                  gmnodes[node_b.name].neighbours.append(
                      gmnodes[node_a.name]
                  )
                  gmnodes[node_a.name].neighbours.append(
                      gmnodes[node_b.name]
                  )
          gmcluster = set([gmnodes[c.name] for c in cluster])
          record_cliques(cliques, gmcluster)
          # Now we need to remove v from gm_...
          # This means we also have to remove it from all
          # of its neighbours that reference it...
          for neighbour in v.neighbours:
              neighbour.neighbours.remove(v)
          gm_.nodes.remove(v)
          elimination_ordering.append(v.name)
      return cliques, elimination_ordering
  
imports: import copy
  import heapq
  import logging
  from collections import defaultdict
  from io import StringIO
  from itertools import combinations, product
  from random import choice, random
  from .exceptions import VariableNotInGraphError, VariableValueNotInDomainError
  from .graph import Node, UndirectedGraph, UndirectedNode, connect
  from .utils import get_args, get_original_factors
label_desc: Use image node_id 10 for calling a global function with example usage: triangulate(gm, priority_func) and returns: cliques,  elimination_ordering