code: def test_optimal_edit_paths(self):
      G1 = path_graph(3)
      G2 = cycle_graph(3)
      paths, cost = optimal_edit_paths(G1, G2)
      assert cost == 1
      assert len(paths) == 6
  
      def canonical(vertex_path, edge_path):
          return (
              tuple(sorted(vertex_path)),
              tuple(sorted(edge_path, key=lambda x: (None in x, x))),
          )
  
      expected_paths = [
          (
              [(0, 0), (1, 1), (2, 2)],
              [((0, 1), (0, 1)), ((1, 2), (1, 2)), (None, (0, 2))],
          ),
          (
              [(0, 0), (1, 2), (2, 1)],
              [((0, 1), (0, 2)), ((1, 2), (1, 2)), (None, (0, 1))],
          ),
          (
              [(0, 1), (1, 0), (2, 2)],
              [((0, 1), (0, 1)), ((1, 2), (0, 2)), (None, (1, 2))],
          ),
          (
              [(0, 1), (1, 2), (2, 0)],
              [((0, 1), (1, 2)), ((1, 2), (0, 2)), (None, (0, 1))],
          ),
          (
              [(0, 2), (1, 0), (2, 1)],
              [((0, 1), (0, 2)), ((1, 2), (0, 1)), (None, (1, 2))],
          ),
          (
              [(0, 2), (1, 1), (2, 0)],
              [((0, 1), (1, 2)), ((1, 2), (0, 1)), (None, (0, 2))],
          ),
      ]
      assert {canonical(*p) for p in paths} == {
          canonical(*p) for p in expected_paths
      }
  
imports: import pytest
  import networkx
  from networkx.algorithms.similarity import graph_edit_distance, optimal_edit_paths, optimize_graph_edit_distance
  from networkx.generators.classic import circular_ladder_graph, cycle_graph, path_graph, wheel_graph
label_desc: Use image node_id 9 for calling the TestSimilarity obj's underlying member method code with example usage: obj.test_optimal_edit_paths() and returns: tuple,  tuple