code: def average_clustering(G, trials=1000, seed=None):
      r"""Estimates the average clustering coefficient of G.
  
      The local clustering of each node in `G` is the fraction of triangles
      that actually exist over all possible triangles in its neighborhood.
      The average clustering coefficient of a graph `G` is the mean of
      local clusterings.
  
      This function finds an approximate average clustering coefficient
      for G by repeating `n` times (defined in `trials`) the following
      experiment: choose a node at random, choose two of its neighbors
      at random, and check if they are connected. The approximate
      coefficient is the fraction of triangles found over the number
      of trials [1]_.
  
      Parameters
      ----------
      G : NetworkX graph
  
      trials : integer
          Number of trials to perform (default 1000).
  
      seed : integer, random_state, or None (default)
          Indicator of random number generation state.
          See :ref:`Randomness<randomness>`.
  
      Returns
      -------
      c : float
          Approximated average clustering coefficient.
  
      Examples
      --------
      >>> from networkx.algorithms import approximation
      >>> G = nx.erdos_renyi_graph(10, 0.2, seed=10)
      >>> approximation.average_clustering(G, trials=1000, seed=10)
      0.214
  
      References
      ----------
      .. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering
         coefficient and transitivity. Universität Karlsruhe, Fakultät für
         Informatik, 2004.
         https://doi.org/10.5445/IR/1000001239
  
      """
      n = len(G)
      triangles = 0
      nodes = list(G)
      for i in [int(seed.random() * n) for i in range(trials)]:
          nbrs = list(G[nodes[i]])
          if len(nbrs) < 2:
              continue
          u, v = seed.sample(nbrs, 2)
          if u in G[v]:
              triangles += 1
      return triangles / trials
  
label_desc: Use image node_id 1 for calling a global function with example usage: average_clustering(G, trials, seed) and returns: unknown