human
I wrote the below code

    (n_name, nodes) = n
    G = nx.Graph()
    G.add_nodes_from(nodes)
    (xmin, ymin, xmax, ymax) = domain
    pos = {v: (uniform(xmin, xmax), uniform(ymin, ymax)) for v in G}
    nx.set_node_attributes(G, pos, 'pos')
    if (metric is None):
        metric = euclidean
    if (L is None):
        L = max((metric(x, y) for (x, y) in combinations(pos.values(), 2)))
        dist = (lambda u, v: metric(pos[u], pos[v]))
    else:
        dist = (lambda u, v: (random.random() * L))
    def should_join(pair):
        return (random.random() < (beta * math.exp(((- dist(*pair)) / (alpha * L)))))
    G.add_edges_from(filter(should_join, combinations(G, 2)))
    return G

What's a good function header?
 ... Here's a good function header?
assistant
#Here's a good function header.

@nodes_or_number(0)
def waxman_graph(n, beta=0.4, alpha=0.1, L=None, domain=(0, 0, 1, 1), metric=None):