import tensorflow as tf

        self.g_sum = tf.summary.merge([self.loss_GABA_sum,self.loss_GBAB_sum,self.g_total_loss_sum])
        
        self.loss_db_sum = tf.summary.scalar("db_loss", self.D_B_loss)
        self.loss_da_sum = tf.summary.scalar("da_loss", self.D_A_loss)
        self.loss_d_sum = tf.summary.scalar("d_loss",self.discriminator_loss)
        
        self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.D_B_loss_real)
        self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.D_B_loss_fake)
        self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.D_A_loss_real)
        self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.D_A_loss_fake)
        self.d_sum = tf.summary.merge(
            [self.loss_da_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
             self.loss_db_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
             self.loss_d_sum]
        )

        
        trainable_variables = tf.trainable_variables()
        
        self.d_variables = [var for var in trainable_variables if 'discriminator' in var.name]
