human
Complete the below
def girvan_newman(G, most_valuable_edge=None):

    ''''Finds communities in a graph using the GirvanâNewman method.
    Parameters
    G : NetworkX graph
    most_valuable_edge : function
    Function that takes a graph as input and outputs an edge. The
    edge returned by this function will be recomputed and removed at
    each iteration of the algorithm.
    If not specified, the edge with the highest
    :func:`networkx.edge_betweenness_centrality` will be used.
    Returns
    iterator
    Iterator over tuples of sets of nodes in `G`. Each set of node
    is a community, each tuple is a sequence of communities at a
    particular level of the algorithm.
    Examples
    To get the first pair of communities::
    >>> G = nx.path_graph(10)
    >>> comp = girvan_newman(G)
    >>> tuple(sorted(c) for c in next(comp))
    ([0, 1, 2, 3, 4], [5, 6, 7, 8, 9])
    To get only the first *k* tuples of communities, use
    :func:`itertools.islice`::
    >>> import itertools
    >>> G = nx.path_graph(8)
    >>> k = 2
    >>> comp = girvan_newman(G)
    >>> for communities in itertools.islice(comp, k):
    ...     print(tuple(sorted(c) for c in communities)) # doctest: +SKIP
    ([0, 1, 2, 3], [4, 5, 6, 7])
    ([0, 1], [2, 3], [4, 5, 6, 7])
    To stop getting tuples of communities once the number of communities
    is greater than *k*, use :func:`itertools.takewhile`::
    >>> import itertools
    >>> G = nx.path_graph(8)
    >>> k = 4
    >>> comp = girvan_newman(G)
    >>> limited = itertools.takewhile(lambda c: len(c) <= k, comp)
    >>> for communities in limited:
    ...     print(tuple(sorted(c) for c in communities)) # doctest: +SKIP
    ([0, 1, 2, 3], [4, 5, 6, 7])
    ([0, 1], [2, 3], [4, 5, 6, 7])
    ([0, 1], [2, 3], [4, 5], [6, 7])
    To just choose an edge to remove based on the weight::
    >>> from operator import itemgetter
    >>> G = nx.path_graph(10)
    >>> edges = G.edges()
    >>> nx.set_edge_attributes(G, {(u, v): v for u, v in edges}, \'weight\')
    >>> def heaviest(G):
    ...     u, v, w = max(G.edges(data=\'weight\'), key=itemgetter(2))
 ...'''

Can you provide a the Python code you requested?
assistant
#Here is the Python code you requested.
    if (G.number_of_edges() == 0):
        (yield tuple(nx.connected_components(G)))
        return
    if (most_valuable_edge is None):
        def most_valuable_edge(G):
            'Returns    the    edge    with    the    highest    betweenness    centrality\n                                                in    the    graph    `G`.\n\n                                                '
            betweenness = nx.edge_betweenness_centrality(G)
            return max(betweenness, key=betweenness.get)
    g = G.copy().to_undirected()
    g.remove_edges_from(g.selfloop_edges())
    while (g.number_of_edges() > 0):
        (yield _without_most_central_edges(g, most_valuable_edge))