code: def test_step(self, batch, batch_idx):
      x, y = batch
      multi_y_hat = self(x)
      if isinstance(multi_y_hat, tuple):
          assert len(multi_y_hat) == self.n_models
      else:
          assert self.n_models == 1
          multi_y_hat = [multi_y_hat]
      for idx, y_hat in enumerate(multi_y_hat):
          self.log(
              f"test_loss_{idx}",
              self.criterion(y_hat.to("cpu"), y.to("cpu")),
              prog_bar=True,
          )
          for name, metric in self.metrics.items():
              self.log(
                  f"test_{idx}_" + name,
                  metric(y_hat.to("cpu"), y.to("cpu")),
                  prog_bar=True,
              )
  
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 6 for calling the _MultiModelSupervisedLearningModule obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) without return types