code: def forward(self, predicts, batch):
      loss = predicts
      if self.key is not None and isinstance(predicts, dict):
          loss = loss[self.key]
      if self.reduction == "mean":
          loss = paddle.mean(loss)
      elif self.reduction == "sum":
          loss = paddle.sum(loss)
      return {"loss": 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 LossFromOutput obj's underlying member method code with example usage: obj.forward(predicts, batch) and returns: dict