code: def node_redundancy(G, nodes=None):
      r"""Computes the node redundancy coefficients for the nodes in the bipartite
      graph `G`.
  
      The redundancy coefficient of a node `v` is the fraction of pairs of
      neighbors of `v` that are both linked to other nodes. In a one-mode
      projection these nodes would be linked together even if `v` were
      not there.
  
      More formally, for any vertex `v`, the *redundancy coefficient of `v`* is
      defined by
  
      .. math::
  
          rc(v) = \frac{|\{\{u, w\} \subseteq N(v),
          \: \exists v' \neq  v,\: (v',u) \in E\:
          \mathrm{and}\: (v',w) \in E\}|}{ \frac{|N(v)|(|N(v)|-1)}{2}},
  
      where `N(v)` is the set of neighbors of `v` in `G`.
  
      Parameters
      ----------
      G : graph
          A bipartite graph
  
      nodes : list or iterable (optional)
          Compute redundancy for these nodes. The default is all nodes in G.
  
      Returns
      -------
      redundancy : dictionary
          A dictionary keyed by node with the node redundancy value.
  
      Examples
      --------
      Compute the redundancy coefficient of each node in a graph::
  
          >>> from networkx.algorithms import bipartite
          >>> G = nx.cycle_graph(4)
          >>> rc = bipartite.node_redundancy(G)
          >>> rc[0]
          1.0
  
      Compute the average redundancy for the graph::
  
          >>> from networkx.algorithms import bipartite
          >>> G = nx.cycle_graph(4)
          >>> rc = bipartite.node_redundancy(G)
          >>> sum(rc.values()) / len(G)
          1.0
  
      Compute the average redundancy for a set of nodes::
  
          >>> from networkx.algorithms import bipartite
          >>> G = nx.cycle_graph(4)
          >>> rc = bipartite.node_redundancy(G)
          >>> nodes = [0, 2]
          >>> sum(rc[n] for n in nodes) / len(nodes)
          1.0
  
      Raises
      ------
      NetworkXError
          If any of the nodes in the graph (or in `nodes`, if specified) has
          (out-)degree less than two (which would result in division by zero,
          according to the definition of the redundancy coefficient).
  
      References
      ----------
      .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008).
         Basic notions for the analysis of large two-mode networks.
         Social Networks 30(1), 31--48.
  
      """
      if nodes is None:
          nodes = G
      if any(len(G[v]) < 2 for v in nodes):
          raise NetworkXError(
              "Cannot compute redundancy coefficient for a node"
              " that has fewer than two neighbors."
          )
      # TODO This can be trivially parallelized.
      return {v: _node_redundancy(G, v) for v in nodes}
  
imports: from itertools import combinations
  import networkx
  from networkx import NetworkXError
label_desc: Use image node_id 1 for calling a global function with example usage: node_redundancy(G, nodes) and returns: unknown