class_docstr: DMLLoss
code: def forward(self, out1, out2):
      if self.act is not None:
          out1 = self.act(out1) + 1e-10
          out2 = self.act(out2) + 1e-10
      if self.use_log:
          # for recognition distillation, log is needed for feature map
          log_out1 = paddle.log(out1)
          log_out2 = paddle.log(out2)
          loss = (
              self._kldiv(log_out1, out2) + self._kldiv(log_out2, out1)
          ) / 2.0
      else:
          # for detection distillation log is not needed
          loss = self.jskl_loss(out1, out2)
      return loss
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import L1Loss
  from paddle.nn import MSELoss
  from paddle.nn import SmoothL1Loss
label_desc: Use image node_id 3 for calling the DMLLoss obj's underlying member method code with example usage: obj.forward(out1, out2) and returns: loss