code: def greedy_color(G, strategy="largest_first", interchange=False):
      """Color a graph using various strategies of greedy graph coloring.
  
      Attempts to color a graph using as few colors as possible, where no
      neighbors of a node can have same color as the node itself. The
      given strategy determines the order in which nodes are colored.
  
      The strategies are described in [1]_, and smallest-last is based on
      [2]_.
  
      Parameters
      ----------
      G : NetworkX graph
  
      strategy : string or function(G, colors)
         A function (or a string representing a function) that provides
         the coloring strategy, by returning nodes in the ordering they
         should be colored. ``G`` is the graph, and ``colors`` is a
         dictionary of the currently assigned colors, keyed by nodes. The
         function must return an iterable over all the nodes in ``G``.
  
         If the strategy function is an iterator generator (that is, a
         function with ``yield`` statements), keep in mind that the
         ``colors`` dictionary will be updated after each ``yield``, since
         this function chooses colors greedily.
  
         If ``strategy`` is a string, it must be one of the following,
         each of which represents one of the built-in strategy functions.
  
         * ``'largest_first'``
         * ``'random_sequential'``
         * ``'smallest_last'``
         * ``'independent_set'``
         * ``'connected_sequential_bfs'``
         * ``'connected_sequential_dfs'``
         * ``'connected_sequential'`` (alias for the previous strategy)
         * ``'saturation_largest_first'``
         * ``'DSATUR'`` (alias for the previous strategy)
  
      interchange: bool
         Will use the color interchange algorithm described by [3]_ if set
         to ``True``.
  
         Note that ``saturation_largest_first`` and ``independent_set``
         do not work with interchange. Furthermore, if you use
         interchange with your own strategy function, you cannot rely
         on the values in the ``colors`` argument.
  
      Returns
      -------
      A dictionary with keys representing nodes and values representing
      corresponding coloring.
  
      Examples
      --------
      >>> G = nx.cycle_graph(4)
      >>> d = nx.coloring.greedy_color(G, strategy="largest_first")
      >>> d in [{0: 0, 1: 1, 2: 0, 3: 1}, {0: 1, 1: 0, 2: 1, 3: 0}]
      True
  
      Raises
      ------
      NetworkXPointlessConcept
          If ``strategy`` is ``saturation_largest_first`` or
          ``independent_set`` and ``interchange`` is ``True``.
  
      References
      ----------
      .. [1] Adrian Kosowski, and Krzysztof Manuszewski,
         Classical Coloring of Graphs, Graph Colorings, 2-19, 2004.
         ISBN 0-8218-3458-4.
      .. [2] David W. Matula, and Leland L. Beck, "Smallest-last
         ordering and clustering and graph coloring algorithms." *J. ACM* 30,
         3 (July 1983), 417–427. <https://doi.org/10.1145/2402.322385>
      .. [3] Maciej M. Sysło, Narsingh Deo, Janusz S. Kowalik,
         Discrete Optimization Algorithms with Pascal Programs, 415-424, 1983.
         ISBN 0-486-45353-7.
  
      """
      if len(G) == 0:
          return {}
      # Determine the strategy provided by the caller.
      strategy = STRATEGIES.get(strategy, strategy)
      if not callable(strategy):
          raise nx.NetworkXError(
              f"strategy must be callable or a valid string. {strategy} not valid."
          )
      # Perform some validation on the arguments before executing any
      # strategy functions.
      if interchange:
          if strategy is strategy_independent_set:
              msg = "interchange cannot be used with independent_set"
              raise nx.NetworkXPointlessConcept(msg)
          if strategy is strategy_saturation_largest_first:
              msg = (
                  "interchange cannot be used with"
                  " saturation_largest_first"
              )
              raise nx.NetworkXPointlessConcept(msg)
      colors = {}
      nodes = strategy(G, colors)
      if interchange:
          return _greedy_coloring_with_interchange(G, nodes)
      for u in nodes:
          # Set to keep track of colors of neighbors
          nbr_colors = {colors[v] for v in G[u] if v in colors}
          # Find the first unused color.
          for color in itertools.count():
              if color not in nbr_colors:
                  break
          # Assign the new color to the current node.
          colors[u] = color
      return colors
  
imports: import itertools
  from collections import defaultdict, deque
  import networkx
  from networkx.utils import arbitrary_element, py_random_state
label_desc: Use image node_id 10 for calling a global function with example usage: greedy_color(G, strategy, interchange) and returns: colors, dict, _greedy_coloring_with_interchange