code: def obj_loss(self, sobj, tobj):
      obj_mask = paddle.cast(tobj > 0.0, dtype="float32")
      obj_mask.stop_gradient = True
      loss = paddle.mean(
          ops.sigmoid_cross_entropy_with_logits(sobj, obj_mask)
      )
      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 4 for calling the DistillYOLOv3Loss obj's underlying member method code with example usage: obj.obj_loss(sobj, tobj) and returns: loss