code: def obj_weighted_cls(self, scls, tcls, tobj):
      loss = ops.sigmoid_cross_entropy_with_logits(
          scls, F.sigmoid(tcls)
      )
      weighted_loss = paddle.mean(
          paddle.multiply(loss, F.sigmoid(tobj))
      )
      return weighted_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 DistillYOLOv3Loss obj's underlying member method code with example usage: obj.obj_weighted_cls(scls, tcls, tobj) and returns: weighted_loss