How to use the nengo.Lowpass function in nengo

# account for continuous lowpass filter
        A = tau * A + np.eye(2)
        B = tau * B
    else:
        # discretize state matrices
        Ad = expm(A*dt)
        Bd = np.dot(np.linalg.inv(A), np.dot((Ad - np.eye(2)), B))
        # account for discrete lowpass filter
        a = np.exp(-dt/tau)
        A = 1.0 / (1.0 - a) * (Ad - a * np.eye(2))
        B = 1.0 / (1.0 - a) * Bd

    if net is None:
        net = nengo.Network(label='Point Attractor')
    config = nengo.Config(nengo.Connection, nengo.Ensemble)
    config[nengo.Connection].synapse = nengo.Lowpass(tau)

    with config, net:
        net.ydy = nengo.Ensemble(n_neurons=n_neurons, dimensions=2,
            # set it up so neurons are tuned to one dimensions only
            encoders=nengo.dists.Choice([[1, 0], [-1, 0], [0, 1], [0, -1]]))
        # set up Ax part of point attractor
        nengo.Connection(net.ydy, net.ydy, transform=A)

        # hook up input
        net.input = nengo.Node(size_in=1, size_out=1)
        # set up Bu part of point attractor
        nengo.Connection(net.input, net.ydy, transform=B)

        # hook up output
        net.output = nengo.Node(size_in=1, size_out=1)
        # add in forcing function

def generate(net=None, n_neurons=200, alpha=1000.0, beta=1000.0/4.0,
             dt=0.001, analog=False):
    tau = 0.1  # synaptic time constant
    synapse = nengo.Lowpass(tau)

    # the A matrix for our point attractor
    A = np.array([[0.0, 1.0],
                  [-alpha*beta, -alpha]])

    # the B matrix for our point attractor
    B = np.array([[0.0], [alpha*beta]])

    # if you have the nengolib library you can do it this way
    # from nengolib.synapses import ss2sim
    # C = np.eye(2)
    # D = np.zeros((2, 2))
    # linsys = ss2sim((A, B, C, D), synapse=synapse, dt=None if analog else dt)
    # A = linsys.A
    # B = linsys.B