code: def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
      """Returns the set of induced nodes in the path from s to t.
  
      Parameters
      ----------
      G : graph
        A chordal NetworkX graph
      s : node
          Source node to look for induced nodes
      t : node
          Destination node to look for induced nodes
      treewidth_bound: float
          Maximum treewidth acceptable for the graph H. The search
          for induced nodes will end as soon as the treewidth_bound is exceeded.
  
      Returns
      -------
      induced_nodes : Set of nodes
          The set of induced nodes in the path from s to t in G
  
      Raises
      ------
      NetworkXError
          The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
          If the input graph is an instance of one of these classes, a
          :exc:`NetworkXError` is raised.
          The algorithm can only be applied to chordal graphs. If the input
          graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
  
      Examples
      --------
      >>> G = nx.Graph()
      >>> G = nx.generators.classic.path_graph(10)
      >>> induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
      >>> sorted(induced_nodes)
      [1, 2, 3, 4, 5, 6, 7, 8, 9]
  
      Notes
      -----
      G must be a chordal graph and (s,t) an edge that is not in G.
  
      If a treewidth_bound is provided, the search for induced nodes will end
      as soon as the treewidth_bound is exceeded.
  
      The algorithm is inspired by Algorithm 4 in [1]_.
      A formal definition of induced node can also be found on that reference.
  
      Self Loops are ignored
  
      References
      ----------
      .. [1] Learning Bounded Treewidth Bayesian Networks.
         Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.
         http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf
      """
      if not is_chordal(G):
          raise nx.NetworkXError("Input graph is not chordal.")
  
      H = nx.Graph(G)
      H.add_edge(s, t)
      induced_nodes = set()
      triplet = _find_chordality_breaker(H, s, treewidth_bound)
      while triplet:
          (u, v, w) = triplet
          induced_nodes.update(triplet)
          for n in triplet:
              if n != s:
                  H.add_edge(s, n)
          triplet = _find_chordality_breaker(H, s, treewidth_bound)
      if induced_nodes:
          # Add t and the second node in the induced path from s to t.
          induced_nodes.add(t)
          for u in G[s]:
              if len(induced_nodes & set(G[u])) == 2:
                  induced_nodes.add(u)
                  break
      return induced_nodes
  
file_path: networkx/networkx/algorithms/chordal.py
imports: import sys
  import networkx
  from networkx.algorithms.components import connected_components
  from networkx.utils import arbitrary_element, not_implemented_for
label_desc: Use image node_id 2 for calling a global function with example usage: find_induced_nodes(G, s, t, treewidth_bound) and returns: induced_nodes