code: def test_diabetes(mocked_model):
      train_dataset = DiabetesDataset(train=True)
      test_dataset = DiabetesDataset(train=False)
      lightning = pl.Regression(
          optimizer=torch.optim.SGD,
          train_dataloader=pl.DataLoader(train_dataset, batch_size=20),
          val_dataloaders=pl.DataLoader(test_dataset, batch_size=20),
          max_epochs=100,
          enable_progress_bar=enable_progress_bar,
      )
      lightning.evaluate(FCNet(train_dataset.x.shape[1], 1))
      assert mocked_model.metric < 2e4
      assert len(mocked_model.metrics.intermediates) == 100
  
imports: import pytest
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.nn.functional
  from sklearn.datasets import load_diabetes
  from torch.utils.data import Dataset
  from torchvision import transforms
  from torchvision.datasets import MNIST
  import nni
  import nni.nas.evaluator.pytorch.lightning
  from nni.nas.evaluator import FunctionalEvaluator, Evaluator
  from nni.nas.space import RawFormatModelSpace
label_desc: Use image node_id 4 for calling a global function with example usage: test_diabetes(mocked_model) without return types