import tensorflow as tf

                                          state_keep_prob=encoder.rnn_state_keep_prob,
                                          variational_recurrent=encoder.pervasive_dropout,
                                          dtype=tf.float32, input_size=input_size)
                return cell

            batch_size = tf.shape(encoder_inputs_)[0]
            time_steps = tf.shape(encoder_inputs_)[1]

            if embeddings is not None:
                flat_inputs = tf.reshape(encoder_inputs_, [tf.multiply(batch_size, time_steps)])
                flat_inputs = tf.nn.embedding_lookup(embeddings, flat_inputs)
                encoder_inputs_ = tf.reshape(flat_inputs,
