For the encoder component, we utilize a bidirectional RNN with GRU cells. In place of the GRU cell, we could also use an LSTM. The reader may experiment with an LSTM to look at the differences in the model's performance:

def get_cell(csize,dprob):
    rnc = GRUCell(csize)
    rnc = DropoutWrapper(rnc, input_keep_prob = dprob)
    return rnc

def encoding_layer(csize, len_s, nl, rinp, dprob):
    for l in range(nl):
        with tf.variable_scope('encoding_l_{}'.format(l)):
            rnn_frnt = get_cell(csize,dprob)
            rnn_bkwd = get_cell(csize,dprob)
            eop, est = tf.nn.bidirectional_dynamic_rnn(rnn_frnt, rnn_bkwd, 
                                                                    rinp,
                                                                    len_s,
                                                                    dtype=tf.float32)
    eop = tf.concat(eop,2)
    return eop, est