code: def strongly_connected_components_recursive(G):
      """Generate nodes in strongly connected components of graph.
  
      .. deprecated:: 3.2
  
         This function is deprecated and will be removed in a future version of
         NetworkX. Use `strongly_connected_components` instead.
  
      Recursive version of algorithm.
  
      Parameters
      ----------
      G : NetworkX Graph
          A directed graph.
  
      Returns
      -------
      comp : generator of sets
          A generator of sets of nodes, one for each strongly connected
          component of G.
  
      Raises
      ------
      NetworkXNotImplemented
          If G is undirected.
  
      Examples
      --------
      Generate a sorted list of strongly connected components, largest first.
  
      >>> G = nx.cycle_graph(4, create_using=nx.DiGraph())
      >>> nx.add_cycle(G, [10, 11, 12])
      >>> [
      ...     len(c)
      ...     for c in sorted(
      ...         nx.strongly_connected_components_recursive(G), key=len, reverse=True
      ...     )
      ... ]
      [4, 3]
  
      If you only want the largest component, it's more efficient to
      use max instead of sort.
  
      >>> largest = max(nx.strongly_connected_components_recursive(G), key=len)
  
      To create the induced subgraph of the components use:
      >>> S = [G.subgraph(c).copy() for c in nx.weakly_connected_components(G)]
  
      See Also
      --------
      connected_components
  
      Notes
      -----
      Uses Tarjan's algorithm[1]_ with Nuutila's modifications[2]_.
  
      References
      ----------
      .. [1] Depth-first search and linear graph algorithms, R. Tarjan
         SIAM Journal of Computing 1(2):146-160, (1972).
  
      .. [2] On finding the strongly connected components in a directed graph.
         E. Nuutila and E. Soisalon-Soinen
         Information Processing Letters 49(1): 9-14, (1994)..
  
      """
      import warnings
  
      warnings.warn(
          (
              "\n\nstrongly_connected_components_recursive is deprecated and will be\n"
              "removed in the future. Use strongly_connected_components instead."
          ),
          category=DeprecationWarning,
          stacklevel=2,
      )
  
      yield from strongly_connected_components(G)
  
file_path: networkx/networkx/algorithms/components/strongly_connected.py
imports: import networkx
  from networkx.utils.decorators import not_implemented_for
label_desc: Use image node_id 3 for calling a global function with example usage: strongly_connected_components_recursive(G) without return types