code: def _simrank_similarity_python(
      G,
      source=None,
      target=None,
      importance_factor=0.9,
      max_iterations=1000,
      tolerance=1e-4,
  ):
      """Returns the SimRank similarity of nodes in the graph ``G``.
  
      This pure Python version is provided for pedagogical purposes.
  
      Examples
      --------
      >>> G = nx.cycle_graph(2)
      >>> nx.similarity._simrank_similarity_python(G)
      {0: {0: 1, 1: 0.0}, 1: {0: 0.0, 1: 1}}
      >>> nx.similarity._simrank_similarity_python(G, source=0)
      {0: 1, 1: 0.0}
      >>> nx.similarity._simrank_similarity_python(G, source=0, target=0)
      1
      """
      # build up our similarity adjacency dictionary output
      newsim = {u: {v: 1 if u == v else 0 for v in G} for u in G}
  
      # These functions compute the update to the similarity value of the nodes
      # `u` and `v` with respect to the previous similarity values.
      def avg_sim(s):
          return (
              sum(newsim[w][x] for (w, x) in s) / len(s) if s else 0.0
          )
  
      Gadj = G.pred if G.is_directed() else G.adj
  
      def sim(u, v):
          return importance_factor * avg_sim(
              list(product(Gadj[u], Gadj[v]))
          )
  
      for its in range(max_iterations):
          oldsim = newsim
          newsim = {
              u: {v: sim(u, v) if u != v else 1 for v in G} for u in G
          }
          is_close = all(
              all(
                  abs(newsim[u][v] - old) <= tolerance * (1 + abs(old))
                  for v, old in nbrs.items()
              )
              for u, nbrs in oldsim.items()
          )
          if is_close:
              break
  
      if its + 1 == max_iterations:
          raise nx.ExceededMaxIterations(
              f"simrank did not converge after {max_iterations} iterations."
          )
  
      if source is not None and target is not None:
          return newsim[source][target]
      if source is not None:
          return newsim[source]
      return newsim
  
file_path: networkx/networkx/algorithms/similarity.py
imports: import math
  import time
  import warnings
  from dataclasses import dataclass
  from itertools import product
  import networkx
label_desc: Use image node_id 7 for calling a global function with example usage: _simrank_similarity_python(G, source, target, importance_factor, max_iterations, tolerance) and returns: newsim, unknown, unknown, newsim, newsim