human
Solve the following python programming problem given the following code:
@ not_implemented_for ('directed') 
@ not_implemented_for ('multigraph') 
def within_inter_cluster(G, ebunch = None, delta = 0.001, community = 'community') : 
    "Compute the ratio of within- and inter-cluster common neighbors
    of all node pairs in ebunch.

    For two nodes `u` and `v`, if a common neighbor `w` belongs to the
    same community as them, `w` is considered as within-cluster common
    neighbor of `u` and `v`. Otherwise, it is considered as
    inter-cluster common neighbor of `u` and `v`. The ratio between the
    size of the set of within- and inter-cluster common neighbors is
    defined as the WIC measure. [1]_

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

    ebunch : iterable of node pairs, optional (default = None)
        The WIC measure 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.

    delta : float, optional (default = 0.001)
        Value to prevent division by zero in case there is no
        inter-cluster common neighbor between two nodes. See [1]_ for
        details. Default value: 0.001.

    community : string, optional (default = 'community')
        Nodes attribute name containing the community information.
        G[u][community] identifies which community u belongs to. Each
        node belongs to at most one community. Default value: 'community'.

    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 WIC measure.

    Examples
    --------
    >>> import networkx as nx
    >>> G = nx.Graph()
    >>> G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 4), (2, 4), (3, 4)])
    >>> G.node[0]['community'] = 0
    >>> G.node[1]['community'] = 1
    >>> G.node[2]['community'] = 0
    >>> G.node[3]['community'] = 0
    >>>...

    
    

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

return _apply_prediction (G, def predict(, ebunch)