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

    ''''Return a Waxman random graph.
    The Waxman random graph model places `n` nodes uniformly at random
    in a rectangular domain. Each pair of nodes at distance `d` is
    joined by an edge with probability
    .. math::
    p = \beta \exp(-d / \alpha L).
    This function implements both Waxman models, using the `L` keyword
    argument.
    * Waxman-1: if `L` is not specified, it is set to be the maximum distance
    between any pair of nodes.
    * Waxman-2: if `L` is specified, the distance between a pair of nodes is
    chosen uniformly at random from the interval `[0, L]`.
    Parameters
    n : int or iterable
    Number of nodes or iterable of nodes
    beta: float
    Model parameter
    alpha: float
    Model parameter
    L : float, optional
    Maximum distance between nodes.  If not specified, the actual distance
    is calculated.
    domain : four-tuple of numbers, optional
    Domain size, given as a tuple of the form `(x_min, y_min, x_max,
    y_max)`.
    metric : function
    A metric on vectors of numbers (represented as lists or
    tuples). This must be a function that accepts two lists (or
    tuples) as input and yields a number as output. The function
    must also satisfy the four requirements of a `metric`_.
    Specifically, if *d* is the function and *x*, *y*,
    and *z* are vectors in the graph, then *d* must satisfy
    1. *d*(*x*, *y*) â¥ 0,
    2. *d*(*x*, *y*) = 0 if and only if *x* = *y*,
    3. *d*(*x*, *y*) = *d*(*y*, *x*),
    4. *d*(*x*, *z*) â€ *d*(*x*, *y*) + *d*(*y*, *z*).
    If this argument is not specified, the Euclidean distance metric is
    used.
    .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
    Returns
    Graph
    A random Waxman graph, undirected and without self-loops. Each
    node has a node attribute ``\'pos\'`` that stores the position of
    that node in Euclidean space as generated by this function.
    Examples
    Specify an alternate distance metric using the ``metric`` keyword
    argument. For example, to use the...'''

=====
Write me a the Python code you requested?
assistant
#Here is the Python code you requested.
    (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