code: def __call__(self, p1, p2, reduction="mean"):
      p1 = F.softmax(p1, axis=-1)
      p2 = F.softmax(p2, axis=-1)
  
      loss = paddle.multiply(
          p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)
      )
  
      if self.mode.lower() == "js":
          loss += paddle.multiply(
              p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)
          )
          loss *= 0.5
      if reduction == "mean":
          loss = paddle.mean(loss)
      elif reduction == "none" or reduction is None:
          return loss
      else:
          loss = paddle.sum(loss)
      return loss
  
imports: import paddle
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the KLJSLoss obj's underlying member method code with example usage: obj.__call__(p1, p2, reduction) and returns: loss, loss