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'):
assistant
#Here is the Python code you requested.

    """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
    >>> G.node[4][\community\] = 0
    >>> preds = nx.within_inter_cluster(G, [(0, 4)])
    >>> for u, v, p in preds:
    ...     \(%d, %d) -> %.8f\ % (u, v, p)
    \(0, 4) -> 1.99800200\
    >>> preds = nx.within_inter_cluster(G, [(0, 4)], delta=0.5)
    >>> for u, v, p in..."""
    if (delta <= 0):
        raise nx.NetworkXAlgorithmError('Delta    must    be    greater    than    zero')
    def predict(u, v):
        Cu = _community(G, u, community)
        Cv = _community(G, v, community)
        if (Cu != Cv):
            return 0
        cnbors = set(nx.common_neighbors(G, u, v))
        within = set((w for w in cnbors if (_community(G, w, community) == Cu)))
        inter = (cnbors - within)
        return (len(within) / (len(inter) + delta))
    return _apply_prediction(G, predict, ebunch)