code: def bbox_loss(self, s_bbox, t_bbox, weight_targets=None):
      # [x,y,w,h]
      if weight_targets is not None:
          loss = paddle.sum(
              self.loss_bbox(s_bbox, t_bbox) * weight_targets
          )
          avg_factor = weight_targets.sum()
          loss = loss / avg_factor
      else:
          loss = paddle.mean(self.loss_bbox(s_bbox, t_bbox))
      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 3 for calling the DistillPPYOLOELoss obj's underlying member method code with example usage: obj.bbox_loss(s_bbox, t_bbox, weight_targets) and returns: loss