human
Can you provide a the Python program you requested given the following:
Complete the below
def eigenvector_centrality(graph, normalized=True, reversed=True, rating={}, iterations=100, tolerance=0.0001):

    ''''Eigenvector centrality for nodes in the graph (cfr. Google\'s PageRank).
    Eigenvector centrality is a measure of the importance of a node in a directed network.
    It rewards nodes with a high potential of (indirectly) connecting to high-scoring nodes.
    Nodes with no incoming connections have a score of zero.
    If you want to measure outgoing connections, reversed should be False.'
    '''
assistant
#Here is the Python program you requested.
    def normalize(vector):
        w = (1.0 / (sum(vector.values()) or 1))
        for node in vector:
            vector[node] *= w
        return vector
    G = adjacency(graph, directed=True, reversed=reversed)
    v = normalize(dict([(n, random()) for n in graph]))
    for i in range(iterations):
        v0 = v
        v = dict.fromkeys(v0.keys(), 0)
        for n1 in v:
            for n2 in G[n1]:
                v[n1] += (0.01 + ((v0[n2] * G[n1][n2]) * rating.get(n1, 1)))
        normalize(v)
        e = sum([abs((v[n] - v0[n])) for n in v])
        if (e < (len(G) * tolerance)):
            m = ((normalized and max(v.values())) or 1)
            v = dict(((id, (w / m)) for (id, w) in v.items()))
            return v
    warn('node    weight    is    0    because    eigenvector_centrality()    did    not    converge.', Warning)
    return dict(((n, 0) for n in G))