code: def _greedy_coloring_with_interchange(G, nodes):
      """Return a coloring for `original_graph` using interchange approach
  
      This procedure is an adaption of the algorithm described by [1]_,
      and is an implementation of coloring with interchange. Please be
      advised, that the datastructures used are rather complex because
      they are optimized to minimize the time spent identifying
      subcomponents of the graph, which are possible candidates for color
      interchange.
  
      Parameters
      ----------
      G : NetworkX graph
          The graph to be colored
  
      nodes : list
          nodes ordered using the strategy of choice
  
      Returns
      -------
      dict :
          A dictionary keyed by node to a color value
  
      References
      ----------
      .. [1] Maciej M. Syslo, Narsingh Deo, Janusz S. Kowalik,
         Discrete Optimization Algorithms with Pascal Programs, 415-424, 1983.
         ISBN 0-486-45353-7.
      """
      n = len(G)
  
      graph = {node: _Node(node, n) for node in G}
  
      for node1, node2 in G.edges():
          adj_entry1 = _AdjEntry(node2)
          adj_entry2 = _AdjEntry(node1)
          adj_entry1.mate = adj_entry2
          adj_entry2.mate = adj_entry1
          node1_head = graph[node1].adj_list
          adj_entry1.next = node1_head
          graph[node1].adj_list = adj_entry1
          node2_head = graph[node2].adj_list
          adj_entry2.next = node2_head
          graph[node2].adj_list = adj_entry2
  
      k = 0
      for node in nodes:
          # Find the smallest possible, unused color
          neighbors = graph[node].iter_neighbors()
          col_used = {
              graph[adj_node.node_id].color for adj_node in neighbors
          }
          col_used.discard(-1)
          k1 = next(
              itertools.dropwhile(
                  lambda x: x in col_used, itertools.count()
              )
          )
  
          # k1 is now the lowest available color
          if k1 > k:
              connected = True
              visited = set()
              col1 = -1
              col2 = -1
              while connected and col1 < k:
                  col1 += 1
                  neighbor_cols = graph[node].iter_neighbors_color(col1)
                  col1_adj = list(neighbor_cols)
  
                  col2 = col1
                  while connected and col2 < k:
                      col2 += 1
                      visited = set(col1_adj)
                      frontier = list(col1_adj)
                      i = 0
                      while i < len(frontier):
                          search_node = frontier[i]
                          i += 1
                          col_opp = (
                              col2
                              if graph[search_node].color == col1
                              else col1
                          )
                          neighbor_cols = graph[
                              search_node
                          ].iter_neighbors_color(col_opp)
  
                          for neighbor in neighbor_cols:
                              if neighbor not in visited:
                                  visited.add(neighbor)
                                  frontier.append(neighbor)
  
                      # Search if node is not adj to any col2 vertex
                      connected = (
                          len(
                              visited.intersection(
                                  graph[node].iter_neighbors_color(col2)
                              )
                          )
                          > 0
                      )
  
              # If connected is false then we can swap !!!
              if not connected:
                  # Update all the nodes in the component
                  for search_node in visited:
                      graph[search_node].color = (
                          col2
                          if graph[search_node].color == col1
                          else col1
                      )
                      col2_adj = graph[search_node].adj_color[col2]
                      graph[search_node].adj_color[col2] = graph[
                          search_node
                      ].adj_color[col1]
                      graph[search_node].adj_color[col1] = col2_adj
  
                  # Update all the neighboring nodes
                  for search_node in visited:
                      col = graph[search_node].color
                      col_opp = col1 if col == col2 else col2
                      for adj_node in graph[
                          search_node
                      ].iter_neighbors():
                          if graph[adj_node.node_id].color != col_opp:
                              # Direct reference to entry
                              adj_mate = adj_node.mate
                              graph[adj_node.node_id].clear_color(
                                  adj_mate, col_opp
                              )
                              graph[adj_node.node_id].assign_color(
                                  adj_mate, col
                              )
                  k1 = col1
  
          # We can color this node color k1
          graph[node].color = k1
          k = max(k1, k)
  
          # Update the neighbors of this node
          for adj_node in graph[node].iter_neighbors():
              adj_mate = adj_node.mate
              graph[adj_node.node_id].assign_color(adj_mate, k1)
  
      return {node.node_id: node.color for node in graph.values()}
  
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 11 for calling a global function with example usage: _greedy_coloring_with_interchange(G, nodes) and returns: unknown