import tensorflow as tf

						correct = tf.equal(
							tf.cast(tf.zeros_like(label_ids, dtype=tf.int32), tf.int32),
							tf.cast(pred_label, tf.int32)
						)
						te_accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
					except:
						te_accuracy = tf.constant(0.0)
						st_accuracy = tf.constant(0.0)

					try:
						st_accuracy = tf.reduce_mean(distillation_loss["src_f1_prob"])						
						te_accuracy = tf.reduce_mean(distillation_loss["tgt_f1_prob"])
					except:
						te_accuracy = tf.constant(0.0)
						st_accuracy = tf.constant(0.0)

					return {
						"train":{
										"loss":loss, 
										"logits":logits,
										"train_op":train_op,
										"cross_entropy":label_loss,
										"distillation_loss":distillation_loss["distillation_loss"],
										"kd_num":tf.reduce_sum(features["distillation_ratio"]),
										"ce_num":tf.reduce_sum(features["label_ratio"]),
										"teacher_logit":teacher_logit,
										"student_logit":student_logit,
										"label_ratio":features["label_ratio"],
