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Complete this python program:
def generate_data(rnn, T, E, x0s=None, P_sxn=None, input_magnitude=0.0, input_times=None):
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#Here is the Python code you requested.

    """Generates data from an randomly initialized RNN.
    Args:
    rnn: the rnn
    T: Time in seconds to run (divided by rnn[\dt\] to get steps, rounded down.
    E: total number of examples
    S: number of samples (subsampling N)
    Returns:
    A list of length E of NxT tensors of the network being run.
    """
    N = rnn['N']
    def run_rnn(rnn, x0, ntime_steps, input_time=None):
        rs = np.zeros([N, ntime_steps])
        x_tm1 = x0
        r_tm1 = np.tanh(x0)
        tau = rnn['tau']
        dt = rnn['dt']
        alpha = (1.0 - (dt / tau))
        W = (((dt / tau) * rnn['W']) * rnn['g'])
        Bin = ((dt / tau) * rnn['Bin'])
        Bin2 = ((dt / tau) * rnn['Bin2'])
        b = ((dt / tau) * rnn['b'])
        us = np.zeros([1, ntime_steps])
        for t in range(ntime_steps):
            x_t = (((alpha * x_tm1) + np.dot(W, r_tm1)) + b)
            if ((input_time is not None) and (t == input_time)):
                us[(0, t)] = input_magnitude
                x_t += (Bin * us[(0, t)])
            r_t = np.tanh(x_t)
            x_tm1 = x_t
            r_tm1 = r_t
            rs[:, t] = r_t
        return (rs, us)
    if (P_sxn is None):
        P_sxn = np.eye(N)
    ntime_steps = int((T / rnn['dt']))
    data_e = []
    inputs_e = []
    for e in range(E):
        input_time = (input_times[e] if (input_times is not None) else None)
        (r_nxt, u_uxt) = run_rnn(rnn, x0s[:, e], ntime_steps, input_time)
        r_sxt = np.dot(P_sxn, r_nxt)
        inputs_e.append(u_uxt)
        data_e.append(r_sxt)
    S = P_sxn.shape[0]
    data_e = normalize_rates(data_e, E, S)
    return (data_e, x0s, inputs_e)