code: def greedy_branching(
      G, attr="weight", default=1, kind="max", seed=None
  ):
      """
      Returns a branching obtained through a greedy algorithm.
  
      This algorithm is wrong, and cannot give a proper optimal branching.
      However, we include it for pedagogical reasons, as it can be helpful to
      see what its outputs are.
  
      The output is a branching, and possibly, a spanning arborescence. However,
      it is not guaranteed to be optimal in either case.
  
      Parameters
      ----------
      G : DiGraph
          The directed graph to scan.
      attr : str
          The attribute to use as weights. If None, then each edge will be
          treated equally with a weight of 1.
      default : float
          When `attr` is not None, then if an edge does not have that attribute,
          `default` specifies what value it should take.
      kind : str
          The type of optimum to search for: 'min' or 'max' greedy branching.
      seed : integer, random_state, or None (default)
          Indicator of random number generation state.
          See :ref:`Randomness<randomness>`.
  
      Returns
      -------
      B : directed graph
          The greedily obtained branching.
  
      """
      if kind not in KINDS:
          raise nx.NetworkXException("Unknown value for `kind`.")
  
      if kind == "min":
          reverse = False
      else:
          reverse = True
  
      if attr is None:
          # Generate a random string the graph probably won't have.
          attr = random_string(seed=seed)
  
      edges = [
          (u, v, data.get(attr, default))
          for (u, v, data) in G.edges(data=True)
      ]
  
      # We sort by weight, but also by nodes to normalize behavior across runs.
      try:
          edges.sort(key=itemgetter(2, 0, 1), reverse=reverse)
      except TypeError:
          # This will fail in Python 3.x if the nodes are of varying types.
          # In that case, we use the arbitrary order.
          edges.sort(key=itemgetter(2), reverse=reverse)
  
      # The branching begins with a forest of no edges.
      B = nx.DiGraph()
      B.add_nodes_from(G)
  
      # Now we add edges greedily so long we maintain the branching.
      uf = nx.utils.UnionFind()
      for i, (u, v, w) in enumerate(edges):
          if uf[u] == uf[v]:
              # Adding this edge would form a directed cycle.
              continue
          elif B.in_degree(v) == 1:
              # The edge would increase the degree to be greater than one.
              continue
          else:
              # If attr was None, then don't insert weights...
              data = {}
              if attr is not None:
                  data[attr] = w
              B.add_edge(u, v, **data)
              uf.union(u, v)
  
      return B
  
imports: import string
  from dataclasses import dataclass, field
  from enum import Enum
  from operator import itemgetter
  from queue import PriorityQueue
  import networkx
  from networkx.utils import py_random_state
  from .recognition import is_arborescence, is_branching
label_desc: Use image node_id 5 for calling a global function with example usage: greedy_branching(G, attr, default, kind, seed) and returns: B