code: def forward(self, teacher_model, student_model):
      teacher_distill_pairs = teacher_model.yolo_head.distill_pairs
      student_distill_pairs = student_model.yolo_head.distill_pairs
      if self.logits_distill and self.loss_weight_logits > 0:
          distill_bbox_loss, distill_dfl_loss, distill_cls_loss = (
              [],
              [],
              [],
          )
  
          distill_cls_loss.append(
              self.quality_focal_loss(
                  student_distill_pairs["pred_cls_scores"].reshape(
                      (
                          -1,
                          student_distill_pairs[
                              "pred_cls_scores"
                          ].shape[-1],
                      )
                  ),
                  teacher_distill_pairs["pred_cls_scores"]
                  .detach()
                  .reshape(
                      (
                          -1,
                          teacher_distill_pairs[
                              "pred_cls_scores"
                          ].shape[-1],
                      )
                  ),
                  num_total_pos=student_distill_pairs["pos_num"],
                  use_sigmoid=False,
              )
          )
  
          distill_bbox_loss.append(
              self.bbox_loss(
                  student_distill_pairs["pred_bboxes_pos"],
                  teacher_distill_pairs["pred_bboxes_pos"].detach(),
                  weight_targets=student_distill_pairs["bbox_weight"],
              )
              if "pred_bboxes_pos" in student_distill_pairs
              and "pred_bboxes_pos" in teacher_distill_pairs
              and "bbox_weight" in student_distill_pairs
              else paddle.zeros([1])
          )
  
          distill_dfl_loss.append(
              self.distribution_focal_loss(
                  student_distill_pairs["pred_dist_pos"].reshape(
                      (
                          -1,
                          student_distill_pairs["pred_dist_pos"].shape[
                              -1
                          ],
                      )
                  ),
                  teacher_distill_pairs["pred_dist_pos"]
                  .detach()
                  .reshape(
                      (
                          -1,
                          teacher_distill_pairs["pred_dist_pos"].shape[
                              -1
                          ],
                      )
                  ),
                  weight_targets=student_distill_pairs["bbox_weight"],
              )
              if "pred_dist_pos" in student_distill_pairs
              and "pred_dist_pos" in teacher_distill_pairs
              and "bbox_weight" in student_distill_pairs
              else paddle.zeros([1])
          )
  
          distill_cls_loss = paddle.add_n(distill_cls_loss)
          distill_bbox_loss = paddle.add_n(distill_bbox_loss)
          distill_dfl_loss = paddle.add_n(distill_dfl_loss)
          logits_loss = (
              distill_bbox_loss * self.bbox_loss_weight
              + distill_cls_loss * self.qfl_loss_weight
              + distill_dfl_loss * self.dfl_loss_weight
          )
  
          if self.logits_ld_distill:
              loss_kd = self.main_kd(
                  student_distill_pairs["mask_positive_select"],
                  student_distill_pairs["pred_cls_scores"],
                  teacher_distill_pairs["pred_cls_scores"],
                  student_model.yolo_head.num_classes,
              )
              logits_loss += loss_kd
      else:
          logits_loss = paddle.zeros([1])
  
      if self.feat_distill and self.loss_weight_feat > 0:
          feat_loss_list = []
          inputs = student_model.inputs
          assert "gt_bbox" in inputs
          assert self.feat_distill_place in student_distill_pairs
          assert self.feat_distill_place in teacher_distill_pairs
          stu_feats = student_distill_pairs[self.feat_distill_place]
          tea_feats = teacher_distill_pairs[self.feat_distill_place]
          for i, loss_module in enumerate(
              self.distill_feat_loss_modules
          ):
              feat_loss_list.append(
                  loss_module(stu_feats[i], tea_feats[i], inputs)
              )
          feat_loss = paddle.add_n(feat_loss_list)
      else:
          feat_loss = paddle.zeros([1])
  
      student_model.yolo_head.distill_pairs.clear()
      teacher_model.yolo_head.distill_pairs.clear()
      return (
          logits_loss * self.loss_weight_logits,
          feat_loss * self.loss_weight_feat,
      )
  
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 6 for calling the DistillPPYOLOELoss obj's underlying member method code with example usage: obj.forward(teacher_model, student_model) and returns: unknown,  unknown