code: def _get_validation_metrics(self):
      # TODO: split metric of multiple models?
      if len(self.metrics) == 1:
          metric_name = next(iter(self.metrics))
          ret = []
          for idx in range(self.n_models):
              ret.append(
                  self.trainer.callback_metrics[
                      f"val_{idx}_" + metric_name
                  ].item()
              )
          return ret
      else:
          warnings.warn(
              'Multiple metrics without "default" is not supported by current framework.'
          )
          return {
              name: self.trainer.callback_metrics["val_" + name].item()
              for name in self.metrics
          }
  
imports: import warnings
  from typing import Dict, List, Optional, Union, Type
  import torch.nn
  import torch.optim
  import torchmetrics
  from torch.utils.data import DataLoader
  import nni
  from ..lightning import LightningModule, _AccuracyWithLogits, Lightning
  from .trainer import Trainer
label_desc: Use image node_id 10 for calling the _MultiModelSupervisedLearningModule obj's underlying member method code with example usage: obj._get_validation_metrics() and returns: ret, unknown