code: def test_simrank_no_source_no_target(self, simrank_similarity):
      G = nx.cycle_graph(5)
      expected = {
          0: {
              0: 1,
              1: 0.3951219505902448,
              2: 0.5707317069281646,
              3: 0.5707317069281646,
              4: 0.3951219505902449,
          },
          1: {
              0: 0.3951219505902448,
              1: 1,
              2: 0.3951219505902449,
              3: 0.5707317069281646,
              4: 0.5707317069281646,
          },
          2: {
              0: 0.5707317069281646,
              1: 0.3951219505902449,
              2: 1,
              3: 0.3951219505902449,
              4: 0.5707317069281646,
          },
          3: {
              0: 0.5707317069281646,
              1: 0.5707317069281646,
              2: 0.3951219505902449,
              3: 1,
              4: 0.3951219505902449,
          },
          4: {
              0: 0.3951219505902449,
              1: 0.5707317069281646,
              2: 0.5707317069281646,
              3: 0.3951219505902449,
              4: 1,
          },
      }
      actual = simrank_similarity(G)
      for k, v in expected.items():
          assert v == pytest.approx(actual[k], abs=1e-2)
  
      # For a DiGraph test, use the first graph from the paper cited in
      # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
      G = nx.DiGraph()
      G.add_node(0, label="Univ")
      G.add_node(1, label="ProfA")
      G.add_node(2, label="ProfB")
      G.add_node(3, label="StudentA")
      G.add_node(4, label="StudentB")
      G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
  
      expected = {
          0: {
              0: 1,
              1: 0.0,
              2: 0.1323363991265798,
              3: 0.0,
              4: 0.03387811817640443,
          },
          1: {
              0: 0.0,
              1: 1,
              2: 0.4135512472705618,
              3: 0.0,
              4: 0.10586911930126384,
          },
          2: {
              0: 0.1323363991265798,
              1: 0.4135512472705618,
              2: 1,
              3: 0.04234764772050554,
              4: 0.08822426608438655,
          },
          3: {
              0: 0.0,
              1: 0.0,
              2: 0.04234764772050554,
              3: 1,
              4: 0.3308409978164495,
          },
          4: {
              0: 0.03387811817640443,
              1: 0.10586911930126384,
              2: 0.08822426608438655,
              3: 0.3308409978164495,
              4: 1,
          },
      }
      # Use the importance_factor from the paper to get the same numbers.
      actual = simrank_similarity(G, importance_factor=0.8)
      for k, v in expected.items():
          assert v == pytest.approx(actual[k], abs=1e-2)
  
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 28 for calling the TestSimilarity obj's underlying member method code with example usage: obj.test_simrank_no_source_no_target(simrank_similarity) without return types