def METHOD_NAME(G, heuristic=None):
    """Generate an ordering (permutation) of the graph nodes to make
    a sparse matrix.
    Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
    Parameters
    ----------
    G : graph
      A NetworkX graph
    heuristic : function, optional
      Function to choose starting node for RCM algorithm.  If None
      a node from a pseudo-peripheral pair is used.  A user-defined function
      can be supplied that takes a graph object and returns a single node.
    Returns
    -------
    nodes : generator
       Generator of nodes in Cuthill-McKee ordering.
    Examples
    --------
    >>> from networkx.utils import cuthill_mckee_ordering
    >>> G = nx.path_graph(4)
    >>> rcm = list(cuthill_mckee_ordering(G))
    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
    Smallest degree node as heuristic function:
    >>> def smallest_degree(G):
    ...     return min(G, key=G.degree)
    >>> rcm = list(cuthill_mckee_ordering(G, heuristic=smallest_degree))
    See Also
    --------
    reverse_cuthill_mckee_ordering
    Notes
    -----
    The optimal solution the bandwidth reduction is NP-complete [2]_.
    References
    ----------
    .. [1] E. Cuthill and J. McKee.
       Reducing the bandwidth of sparse symmetric matrices,
       In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
       http://doi.acm.org/10.1145/800195.805928
    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
       Springer-Verlag New York, Inc., New York, NY, USA.
    """
    for c in nx.connected_components(G):
        yield from connected_cuthill_mckee_ordering(G.subgraph(c), heuristic)