def METHOD_NAME(mocked_model):
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
    train_dataset = nni.trace(MNIST)(root='data/mnist', train=True, download=True, transform=transform)
    test_dataset = nni.trace(MNIST)(root='data/mnist', train=False, download=True, transform=transform)
    lightning = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                                  val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
                                  max_epochs=2, limit_train_batches=0.25,  # for faster training
                                  enable_progress_bar=enable_progress_bar,
                                  num_classes=10)
    lightning.evaluate(MNISTModel())
    assert mocked_model.metric > 0.7
    assert len(mocked_model.metrics.intermediates) == 2