code: def forward(self, pred, batch):
      shrink_maps = pred[:, 0, :, :]
      threshold_maps = pred[:, 1, :, :]
      binary_maps = pred[:, 2, :, :]
      loss_shrink_maps = self.bce_loss(
          shrink_maps, batch["shrink_map"], batch["shrink_mask"]
      )
      loss_threshold_maps = self.l1_loss(
          threshold_maps,
          batch["threshold_map"],
          batch["threshold_mask"],
      )
      metrics = dict(
          loss_shrink_maps=loss_shrink_maps,
          loss_threshold_maps=loss_threshold_maps,
      )
      if pred.shape[1] > 2:
          loss_binary_maps = self.dice_loss(
              binary_maps, batch["shrink_map"], batch["shrink_mask"]
          )
          metrics["loss_binary_maps"] = loss_binary_maps
          loss_all = (
              self.alpha * loss_shrink_maps
              + self.beta * loss_threshold_maps
              + loss_binary_maps
          )
          metrics["loss"] = loss_all
      else:
          metrics["loss"] = loss_shrink_maps
      return metrics
  
imports: import paddle
  from models.losses.basic_loss import BalanceCrossEntropyLoss, MaskL1Loss, DiceLoss
label_desc: Use image node_id 2 for calling the DBLoss obj's underlying member method code with example usage: obj.forward(pred, batch) and returns: metrics