import tensorflow as tf


		if model_io_config.fix_lm == True:
			scope = model_config.scope + "_finetuning"
		else:
			scope = model_config.scope

		with tf.variable_scope(scope, reuse=model_reuse):
			(loss, 
				per_example_loss, 
				logits) = classifier.classifier(model_config,
											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, 
				tgt_per_example_loss, 
				tgt_logits) = classifier.classifier(model_config,
											features["distillation_feature"],
											num_labels,
											label_ids,
											dropout_prob)

		if mode == tf.estimator.ModeKeys.TRAIN:

			distillation_api = distill.KnowledgeDistillation(kargs.get("disitllation_config", Bunch({
														"logits_ratio_decay":"constant",
														"logits_ratio":0.5,
