code: def forward(self, pred, batch):
      if isinstance(pred, dict):  # for ABINet
          loss = {}
          loss_sum = []
          for name, logits in pred.items():
              if isinstance(logits, list):
                  logit_num = len(logits)
                  all_tgt = paddle.concat([batch[1]] * logit_num, 0)
                  all_logits = paddle.concat(logits, 0)
                  flt_logtis = all_logits.reshape(
                      [-1, all_logits.shape[2]]
                  )
                  flt_tgt = all_tgt.reshape([-1])
              else:
                  flt_logtis = logits.reshape([-1, logits.shape[2]])
                  flt_tgt = batch[1].reshape([-1])
              loss[name + "_loss"] = self.loss_func(flt_logtis, flt_tgt)
              loss_sum.append(loss[name + "_loss"])
          loss["loss"] = sum(loss_sum)
          return loss
      else:
          if self.with_all:  # for ViTSTR
              tgt = batch[1]
              pred = pred.reshape([-1, pred.shape[2]])
              tgt = tgt.reshape([-1])
              loss = self.loss_func(pred, tgt)
              return {"loss": loss}
          else:  # for NRTR
              max_len = batch[2].max()
              tgt = batch[1][:, 1 : 2 + max_len]
              pred = pred.reshape([-1, pred.shape[2]])
              tgt = tgt.reshape([-1])
              if self.smoothing:
                  eps = 0.1
                  n_class = pred.shape[1]
                  one_hot = F.one_hot(tgt, pred.shape[1])
                  one_hot = one_hot * (1 - eps) + (
                      1 - one_hot
                  ) * eps / (n_class - 1)
                  log_prb = F.log_softmax(pred, axis=1)
                  non_pad_mask = paddle.not_equal(
                      tgt, paddle.zeros(tgt.shape, dtype=tgt.dtype)
                  )
                  loss = -(one_hot * log_prb).sum(axis=1)
                  loss = loss.masked_select(non_pad_mask).mean()
              else:
                  loss = self.loss_func(pred, tgt)
              return {"loss": loss}
  
imports: import paddle
  from paddle import nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the CELoss obj's underlying member method code with example usage: obj.forward(pred, batch) and returns: loss, dict, dict