def losses(self, s_outputs, t_outputs, gt_labels):
        """
        Compute loss from modeling's outputs, the loss function input arguments
        must be the same as the outputs of the model forwarding.
        """
        loss_dict = super().losses(s_outputs, gt_labels)

        s_logits = s_outputs['pred_class_logits']
        loss_jsdiv = 0.
        for t_output in t_outputs:
            t_logits = t_output['pred_class_logits'].detach()
            loss_jsdiv += self.jsdiv_loss(s_logits, t_logits)

        loss_dict["loss_jsdiv"] = loss_jsdiv / len(t_outputs)

        return loss_dict

        Compute loss from modeling's outputs, the loss function input arguments
        must be the same as the outputs of the model forwarding.
        
fastreid/modeling/meta_arch/distiller.py