code: def stochastic_graph(G, copy=True, weight="weight"):
      """Returns a right-stochastic representation of directed graph `G`.
  
      A right-stochastic graph is a weighted digraph in which for each
      node, the sum of the weights of all the out-edges of that node is
      1. If the graph is already weighted (for example, via a 'weight'
      edge attribute), the reweighting takes that into account.
  
      Parameters
      ----------
      G : directed graph
          A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.
  
      copy : boolean, optional
          If this is True, then this function returns a new graph with
          the stochastic reweighting. Otherwise, the original graph is
          modified in-place (and also returned, for convenience).
  
      weight : edge attribute key (optional, default='weight')
          Edge attribute key used for reading the existing weight and
          setting the new weight.  If no attribute with this key is found
          for an edge, then the edge weight is assumed to be 1. If an edge
          has a weight, it must be a positive number.
  
      """
      if copy:
          G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
      # There is a tradeoff here: the dictionary of node degrees may
      # require a lot of memory, whereas making a call to `G.out_degree`
      # inside the loop may be costly in computation time.
      degree = dict(G.out_degree(weight=weight))
      for u, v, d in G.edges(data=True):
          if degree[u] == 0:
              d[weight] = 0
          else:
              d[weight] = d.get(weight, 1) / degree[u]
      return G
  
imports: import networkx
  from networkx.classes import DiGraph, MultiDiGraph
  from networkx.utils import not_implemented_for
label_desc: Use image node_id 1 for calling a global function with example usage: stochastic_graph(G, copy, weight) and returns: G