code: def forward(self, teacher_model, student_model):
      teacher_distill_pairs = teacher_model.yolo_head.loss.distill_pairs
      student_distill_pairs = student_model.yolo_head.loss.distill_pairs
      distill_reg_loss, distill_cls_loss, distill_obj_loss = [], [], []
      for s_pair, t_pair in zip(
          student_distill_pairs, teacher_distill_pairs
      ):
          distill_reg_loss.append(
              self.obj_weighted_reg(
                  s_pair[0],
                  s_pair[1],
                  s_pair[2],
                  s_pair[3],
                  t_pair[0],
                  t_pair[1],
                  t_pair[2],
                  t_pair[3],
                  t_pair[4],
              )
          )
          distill_cls_loss.append(
              self.obj_weighted_cls(s_pair[5], t_pair[5], t_pair[4])
          )
          distill_obj_loss.append(self.obj_loss(s_pair[4], t_pair[4]))
      distill_reg_loss = paddle.add_n(distill_reg_loss)
      distill_cls_loss = paddle.add_n(distill_cls_loss)
      distill_obj_loss = paddle.add_n(distill_obj_loss)
      loss = (
          distill_reg_loss + distill_cls_loss + distill_obj_loss
      ) * self.loss_weight
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ppdet.modeling import ops
  from ppdet.modeling.losses.iou_loss import GIoULoss
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 5 for calling the DistillYOLOv3Loss obj's underlying member method code with example usage: obj.forward(teacher_model, student_model) and returns: loss