human
Solve the following python programming problem given the following code:
def __init__(self, nh, nc, ne, de, cs) : 
    '
        nh :: dimension of the hidden layer
        nc :: number of classes
        ne :: number of word embeddings in the vocabulary
        de :: dimension of the word embeddings
        cs :: word window context size
        ' 
    self.emb = theano.shared (name = 'embeddings', value = (0.2 * numpy.random.uniform ((- 1.0), 1.0, ((ne + 1), de)).astype (theano.config.floatX))) 
    self.wx = theano.shared (name = 'wx', value = (0.2 * numpy.random.uniform ((- 1.0), 1.0, ((de * cs), nh)).astype (theano.config.floatX))) 
    self.wh = theano.shared (name = 'wh', value = (0.2 * numpy.random.uniform ((- 1.0), 1.0, (nh, nh)).astype (theano.config.floatX))) 
    self.w = theano.shared (name = 'w', value = (0.2 * numpy.random.uniform ((- 1.0), 1.0, (nh, nc)).astype (theano.config.floatX))) 
    self.bh = theano.shared (name = 'bh', value = numpy.zeros (nh, dtype = theano.config.floatX)) 
    self.b = theano.shared (name = 'b', value = numpy.zeros (nc, dtype = theano.config.floatX)) 
    self.h0 = theano.shared (name = 'h0', value = numpy.zeros (nh, dtype = theano.config.floatX)) 
    self.params = [self.emb, self.wx, self.wh, self.w, self.bh, self.b, self.h0] 
    idxs = T.imatrix () 
    x = self.emb [idxs].reshape ((idxs.shape [0], (de * cs))) 
    y_sentence = T.ivector ('y_sentence') 
    def recurrence(x_t, h_tm1) : 
        h_t = T.nnet.sigmoid (((T.dot (x_t, self.wx) + T.dot (h_tm1, self.wh)) + self.bh)) 
        s_t = T.nnet.softmax ((T.dot (h_t, self.w) + self.b)) 
        return [h_t, s_t] 
([h, s], _) = theano.scan (fn = recurrence, sequences = x, outputs_info = [s.h0, None], n_steps = x.shape [0]) 
    p_y_given_x_sentence = s [:, 0, :] 
    y_pred = T.argmax (p_y_given_x_sentence, axis = 1) 
    lr = T.scalar ('lr') 
    sentence_nll = (- T.mean (T.log (p_y_given_x_sentence) [(T.arange (x.shape [0]), y_sentence)])) 
    sentence_gradients = T.grad (sentence_nll, self.params) 
    sentence_updates = OrderedDict (((p, (p - (lr * g))) for (p, g) in zip...

    
    

    Fix the buggy line: ([h, s], _) = theano.scan (fn = recurrence, sequences = x, outputs_info = [s.h0, None], n_steps = x.shape [0])
assistant
#Here is Python program to fix the buggy line: ([h, s], _) = theano.

([h, s], _) = theano.scan (fn = recurrence, sequences = x, outputs_info = [self.h0, None], n_steps = x.shape [0])