code: def forward(self, x, label):
      loss_dict = {}
      if self.epsilon is not None:
          class_num = x.shape[-1]
          label = self._labelsmoothing(label, class_num)
          x = -F.log_softmax(x, axis=-1)
          loss = paddle.sum(x * label, axis=-1)
      else:
          if label.shape[-1] == x.shape[-1]:
              label = F.softmax(label, axis=-1)
              soft_label = True
          else:
              soft_label = False
          loss = F.cross_entropy(x, label=label, soft_label=soft_label)
      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 CELoss obj's underlying member method code with example usage: obj.forward(x, label) and returns: loss