human
Complete the below
@not_implemented_for('directed')
@not_implemented_for('multigraph')
def jaccard_coefficient(G, ebunch=None):

    ''''Compute the Jaccard coefficient of all node pairs in ebunch.
    Jaccard coefficient of nodes `u` and `v` is defined as
    .. math::
    \frac{|\Gamma(u) \cap \Gamma(v)|}{|\Gamma(u) \cup \Gamma(v)|}
    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)
    Jaccard coefficient 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 Jaccard coefficient.
    Examples
    >>> import networkx as nx
    >>> G = nx.complete_graph(5)
    >>> preds = nx.jaccard_coefficient(G, [(0, 1), (2, 3)])
    >>> for u, v, p in preds:
    ...     \'(%d, %d) -> %.8f\' % (u, v, p)
    \'(0, 1) -> 0.60000000\'
    \'(2, 3) -> 0.60000000\'
    References
    .. [1] D. Liben-Nowell, J. Kleinberg.
    The Link Prediction Problem for Social Networks (2004).
    http://www.cs.cornell.edu/home/kleinber/link-pred.pdf'
    '''
 ... What is a Python program to compute the Jaccard coefficient of all node pairs in ebunch?
assistant
#Here is Python program to compute the Jaccard coefficient of all node pairs in ebunch.
    def predict(u, v):
        union_size = len((set(G[u]) | set(G[v])))
        if (union_size == 0):
            return 0
        return (len(list(nx.common_neighbors(G, u, v))) / union_size)
    return _apply_prediction(G, predict, ebunch)