human
Solve the following python programming problem given the following code:
@ not_implemented_for ('directed') 
@ not_implemented_for ('multigraph') 
def resource_allocation_index(G, ebunch = None) : 
    "Compute the resource allocation index of all node pairs in ebunch.

    Resource allocation index of `u` and `v` is defined as

    .. math::

        \\sum_{w \\in \\Gamma(u) \\cap \\Gamma(v)} \\frac{1}{|\\Gamma(w)|}

    where :math:`\\Gamma(u)` denotes the set of neighbors of `u`.

    Parameters
    ----------
    G : graph
        A NetworkX undirected graph.

    ebunch : iterable of node pairs, optional (default = None)
        Resource allocation index will be computed for each pair of
        nodes given in the iterable. The pairs must be given as
        2-tuples (u, v) where u and v are nodes in the graph. If ebunch
        is None then all non-existent edges in the graph will be used.
        Default value: None.

    Returns
    -------
    piter : iterator
        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
        pair of nodes and p is their resource allocation index.

    Examples
    --------
    >>> import networkx as nx
    >>> G = nx.complete_graph(5)
    >>> preds = nx.resource_allocation_index(G, [(0, 1), (2, 3)])
    >>> for u, v, p in preds:
    ...     '(%d, %d) -> %.8f' % (u, v, p)
    ...
    '(0, 1) -> 0.75000000'
    '(2, 3) -> 0.75000000'

    References
    ----------
    .. [1] T. Zhou, L. Lu, Y.-C. Zhang.
       Predicting missing links via local information.
       Eur. Phys. J. B 71 (2009) 623.
       http://arxiv.org/pdf/0901.0553.pdf
    " 
    def predict(u, v) : 
        return sum (((1 / G.degree (w)) for w in nx.common_neighbors (G, u, v))) 
return _apply_prediction (G, ebunch, ebunch) 

    
    

    Fix the buggy line: return _apply_prediction (G, ebunch, ebunch)
assistant
#Here is Python program to fix the buggy line: return _apply_prediction (G, ebunch, ebunch)
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return _apply_prediction (G, def predict(, ebunch)