code: def update(self, labels, preds):
          
          batch_sum_metric = 0
          batch_num_inst = 0
          for label, pred_outcaps in zip(labels[0], preds[0]):
              label_np = int(label.asnumpy())
              pred_label = int(np.argmax(pred_outcaps.asnumpy()))
              batch_sum_metric += int(label_np == pred_label)
              batch_num_inst += 1
          batch_loss = preds[1].asnumpy()
          recon_loss = preds[2].asnumpy()
          self.sum_metric += batch_sum_metric
          self.num_inst += batch_num_inst
          self.loss += batch_loss
          self.recon_loss += recon_loss
          self.batch_sum_metric = batch_sum_metric
          self.batch_num_inst = batch_num_inst
          self.batch_loss = batch_loss
          self.n_batch += 1
docs: Update the hyper-parameters and loss of CapsNet