class_docstr: Integration of DiceLoss on both binary
      prediction and thresh prediction.
code: def forward(self, pred, batch):
      assert isinstance(pred, dict)
      assert "binary" in pred
      assert "thresh_binary" in pred
  
      binary = pred["binary"]
      thresh_binary = pred["thresh_binary"]
      gt = batch["gt"]
      mask = batch["mask"]
      main_loss = self.main_loss(binary, gt, mask)
      thresh_loss = self.thresh_loss(thresh_binary, gt, mask)
      loss = main_loss + thresh_loss
      return loss, dict(main_loss=main_loss, thresh_loss=thresh_loss)
  
imports: import sys
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the AdaptiveDiceLoss obj's underlying member method code with example usage: obj.forward(pred, batch) and returns: loss,  dict