human
Complete the below
def build_rnnrbm(n_visible, n_hidden, n_hidden_recurrent):

    ''''Construct a symbolic RNN-RBM and initialize parameters.
    n_visible : integer
    Number of visible units.
    n_hidden : integer
    Number of hidden units of the conditional RBMs.
    n_hidden_recurrent : integer
    Number of hidden units of the RNN.
    Return a (v, v_sample, cost, monitor, params, updates_train, v_t,
    updates_generate) tuple:
    v : Theano matrix
    Symbolic variable holding an input sequence (used during training)
    v_sample : Theano matrix
    Symbolic variable holding the negative particles for CD log-likelihood
    gradient estimation (used during training)
    cost : Theano scalar
    Expression whose gradient (considering v_sample constant) corresponds
    to the LL gradient of the RNN-RBM (used during training)
    monitor : Theano scalar
    Frame-level pseudo-likelihood (useful for monitoring during training)
    params : tuple of Theano shared variables
    The parameters of the model to be optimized during training.
    updates_train : dictionary of Theano variable -> Theano variable
    Update object that should be passed to theano.function when compiling
    the training function.
    v_t : Theano matrix
    Symbolic variable holding a generated sequence (used during sampling)
    updates_generate : dictionary of Theano variable -> Theano variable
    Update object that should be passed to theano.function when compiling
    the generation function.'
    '''

Write me a the Python program you requested?
assistant
#Here is the Python program you requested.
    W = shared_normal(n_visible, n_hidden, 0.01)
    bv = shared_zeros(n_visible)
    bh = shared_zeros(n_hidden)
    Wuh = shared_normal(n_hidden_recurrent, n_hidden, 0.0001)
    Wuv = shared_normal(n_hidden_recurrent, n_visible, 0.0001)
    Wvu = shared_normal(n_visible, n_hidden_recurrent, 0.0001)
    Wuu = shared_normal(n_hidden_recurrent, n_hidden_recurrent, 0.0001)
    bu = shared_zeros(n_hidden_recurrent)
    params = (W, bv, bh, Wuh, Wuv, Wvu, Wuu, bu)
    v = T.matrix()
    u0 = T.zeros((n_hidden_recurrent,))
    def recurrence(v_t, u_tm1):
        bv_t = (bv + T.dot(u_tm1, Wuv))
        bh_t = (bh + T.dot(u_tm1, Wuh))
        generate = (v_t is None)
        if generate:
            (v_t, _, _, updates) = build_rbm(T.zeros((n_visible,)), W, bv_t, bh_t, k=25)
        u_t = T.tanh(((bu + T.dot(v_t, Wvu)) + T.dot(u_tm1, Wuu)))
        return (([v_t, u_t], updates) if generate else [u_t, bv_t, bh_t])
    ((u_t, bv_t, bh_t), updates_train) = theano.scan((lambda v_t, u_tm1, *_: recurrence(v_t, u_tm1)), sequences=v, outputs_info=[u0, None, None], non_sequences=params)
    (v_sample, cost, monitor, updates_rbm) = build_rbm(v, W, bv_t[:], bh_t[:], k=15)
    updates_train.update(updates_rbm)
    ((v_t, u_t), updates_generate) = theano.scan((lambda u_tm1, *_: recurrence(None, u_tm1)), outputs_info=[None, u0], non_sequences=params, n_steps=200)
    return (v, v_sample, cost, monitor, params, updates_train, v_t, updates_generate)