'See SequenceLayerBase.get_train_input for details.'

def get_train_input(self, prev, i):

    if (i == 0):
        prev = self._zero_label
    else:
        prev = self._labels_one_hot[:, (i - 1), :]
    image_feature = self.get_image_feature(i)
    return tf.concat([image_feature, prev], 1)
