code: def forward(self, predicts, batch):
      if isinstance(predicts, dict) and self.key is not None:
          predicts = predicts[self.key]
      labels = batch[5]
      attention_mask = batch[2]
      if attention_mask is not None:
          active_loss = (
              attention_mask.reshape(
                  [
                      -1,
                  ]
              )
              == 1
          )
          active_output = predicts.reshape([-1, self.num_classes])[
              active_loss
          ]
          active_label = labels.reshape(
              [
                  -1,
              ]
          )[active_loss]
          loss = self.loss_class(active_output, active_label)
      else:
          loss = self.loss_class(
              predicts.reshape([-1, self.num_classes]),
              labels.reshape(
                  [
                      -1,
                  ]
              ),
          )
      return {"loss": loss}
  
file_path: PaddleOCR/ppocr/losses/vqa_token_layoutlm_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from paddle import nn
  from ppocr.losses.basic_loss import DMLLoss
label_desc: Use image node_id 2 for calling the VQASerTokenLayoutLMLoss obj's underlying member method code with example usage: obj.forward(predicts, batch) and returns: dict