code: def __call__(self, p1, p2, reduction="mean", eps=1e-5):
      if self.mode.lower() == "kl":
          loss = paddle.multiply(
              p2, paddle.log((p2 + eps) / (p1 + eps) + eps)
          )
          loss += paddle.multiply(
              p1, paddle.log((p1 + eps) / (p2 + eps) + eps)
          )
          loss *= 0.5
      elif self.mode.lower() == "js":
          loss = paddle.multiply(
              p2, paddle.log((2 * p2 + eps) / (p1 + p2 + eps) + eps)
          )
          loss += paddle.multiply(
              p1, paddle.log((2 * p1 + eps) / (p1 + p2 + eps) + eps)
          )
          loss *= 0.5
      else:
          raise ValueError(
              "The mode.lower() if KLJSLoss should be one of ['kl', 'js']"
          )
  
      if reduction == "mean":
          loss = paddle.mean(loss, axis=[1, 2])
      elif reduction == "none" or reduction is None:
          return loss
      else:
          loss = paddle.sum(loss, axis=[1, 2])
  
      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 2 for calling the KLJSLoss obj's underlying member method code with example usage: obj.__call__(p1, p2, reduction, eps) and returns: loss, loss