class_docstr: The lightning module for a merged "multi-model".
  
  The output of the multi-model is expected to be a tuple of tensors.
  The tensors will be each passed to a criterion and a metric.
  The loss will be added up for back propagation, and the metrics will be logged.
  
  The reported metric will be a list of metrics, one for each model.
  
  Parameters
  ----------
  criterion
      Loss function.
  metric
      Metric function.
  n_models
      Number of models in the multi-model.
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code: def _get_validation_metrics(self):
      # TODO: split metric of multiple models?
      assert self.n_models is not None
      return [
          self.trainer.callback_metrics[f"val_metric_{idx}"].item()
          for idx in range(self.n_models)
      ]
  
imports: from __future__ import annotations
  import pytorch_lightning
  import torch
  from pytorch_lightning.strategies import SingleDeviceStrategy
  from torch import nn
  from torchmetrics import Metric
  import nni
  from nni.nas.evaluator.pytorch.lightning import LightningModule
label_desc: Use image node_id 11 for calling the MultiModelLightningModule obj's underlying member method code with example usage: obj._get_validation_metrics() and returns: unknown