code: def forward(self, predicts, batch):
      # structure_loss
      structure_probs = predicts["structure_probs"]
      structure_targets = batch[1]
      structure_targets = structure_targets[:, 1:]
      structure_probs = structure_probs.reshape(
          [-1, structure_probs.shape[-1]]
      )
      structure_targets = structure_targets.reshape([-1])
  
      structure_loss = self.structure_loss(
          structure_probs, structure_targets
      )
      structure_loss = structure_loss.mean()
      losses = dict(structure_loss=structure_loss)
  
      # box loss
      bboxes_preds = predicts["loc_preds"]
      bboxes_targets = batch[2][:, 1:, :]
      bbox_masks = batch[3][:, 1:]
      # mask empty-bbox or non-bbox structure token's bbox.
  
      masked_bboxes_preds = bboxes_preds * bbox_masks
      masked_bboxes_targets = bboxes_targets * bbox_masks
  
      # horizon loss (x and width)
      horizon_sum_loss = self.box_loss(
          masked_bboxes_preds[:, :, 0::2],
          masked_bboxes_targets[:, :, 0::2],
      )
      horizon_loss = horizon_sum_loss / (bbox_masks.sum() + self.eps)
      # vertical loss (y and height)
      vertical_sum_loss = self.box_loss(
          masked_bboxes_preds[:, :, 1::2],
          masked_bboxes_targets[:, :, 1::2],
      )
      vertical_loss = vertical_sum_loss / (bbox_masks.sum() + self.eps)
  
      horizon_loss = horizon_loss.mean()
      vertical_loss = vertical_loss.mean()
      all_loss = structure_loss + horizon_loss + vertical_loss
      losses.update(
          {
              "loss": all_loss,
              "horizon_bbox_loss": horizon_loss,
              "vertical_bbox_loss": vertical_loss,
          }
      )
      return losses
  
imports: import paddle
  from paddle import nn
label_desc: Use image node_id 2 for calling the TableMasterLoss obj's underlying member method code with example usage: obj.forward(predicts, batch) and returns: losses