import tensorflow as tf

											model.get_pooled_output(),
											num_labels,
											label_ids,
											dropout_prob)
			label_loss = tf.reduce_sum(per_example_loss * features["label_ratio"]) / (1e-10+tf.reduce_sum(features["label_ratio"]))
			tf.get_variable_scope().reuse_variables()

			(tgt_loss, 
