x = tf.placeholder(tf.int32, [batch_size, num_steps], name='input_placeholder')
    y = tf.placeholder(tf.int32, [batch_size, num_steps], name='labels_placeholder')

    embeddings = tf.get_variable('embedding_matrix', [num_classes, state_size])
    rnn_inputs = [tf.squeeze(i) for i in tf.split(1,
                            num_steps, tf.nn.embedding_lookup(embeddings, x))]

with tf.variable_scope('softmax'):
         W = tf.get_variable('W', [state_size, num_classes])
         b = tf.get_variable('b', [num_classes], initializer=tf.constant_initializer(0.0))
 logits = [tf.matmul(rnn_output, W) + b for rnn_output in rnn_outputs]

 y_as_list = [tf.squeeze(i, squeeze_dims=[1]) for i in tf.split(1, num_steps, y)]