human
Give me a the Python code you requested given the following:
Complete the below
def build_rbm(v, W, bv, bh, k):

    ''''Construct a k-step Gibbs chain starting at v for an RBM.
    v : Theano vector or matrix
    If a matrix, multiple chains will be run in parallel (batch).
    W : Theano matrix
    Weight matrix of the RBM.
    bv : Theano vector
    Visible bias vector of the RBM.
    bh : Theano vector
    Hidden bias vector of the RBM.
    k : scalar or Theano scalar
    Length of the Gibbs chain.
    Return a (v_sample, cost, monitor, updates) tuple:
    v_sample : Theano vector or matrix with the same shape as `v`
    Corresponds to the generated sample(s).
    cost : Theano scalar
    Expression whose gradient with respect to W, bv, bh is the CD-k
    approximation to the log-likelihood of `v` (training example) under the
    RBM. The cost is averaged in the batch case.
    monitor: Theano scalar
    Pseudo log-likelihood (also averaged in the batch case).
    updates: dictionary of Theano variable -> Theano variable
    The `updates` object returned by scan.'
    '''
assistant
#Here is the Python code you requested.
    def gibbs_step(v):
        mean_h = T.nnet.sigmoid((T.dot(v, W) + bh))
        h = rng.binomial(size=mean_h.shape, n=1, p=mean_h, dtype=theano.config.floatX)
        mean_v = T.nnet.sigmoid((T.dot(h, W.T) + bv))
        v = rng.binomial(size=mean_v.shape, n=1, p=mean_v, dtype=theano.config.floatX)
        return (mean_v, v)
    (chain, updates) = theano.scan((lambda v: gibbs_step(v)[1]), outputs_info=[v], n_steps=k)
    v_sample = chain[(-1)]
    mean_v = gibbs_step(v_sample)[0]
    monitor = (T.xlogx.xlogy0(v, mean_v) + T.xlogx.xlogy0((1 - v), (1 - mean_v)))
    monitor = (monitor.sum() / v.shape[0])
    def free_energy(v):
        return ((- (v * bv).sum()) - T.log((1 + T.exp((T.dot(v, W) + bh)))).sum())
    cost = ((free_energy(v) - free_energy(v_sample)) / v.shape[0])
    return (v_sample, cost, monitor, updates)