code: def test_knn_train_val(self) -> None:
      torch.manual_seed(0)
      dataset_train = LightlyDataset.from_torch_dataset(
          FakeData(
              size=10,
              image_size=(3, 32, 32),
              num_classes=2,
              transform=ToTensor(),
          )
      )
      dataloader_train = DataLoader(dataset_train, batch_size=2)
      dataset_val = LightlyDataset.from_torch_dataset(
          FakeData(
              size=10,
              image_size=(3, 32, 32),
              num_classes=2,
              transform=ToTensor(),
              random_offset=10,
          )
      )
      dataloader_val = DataLoader(dataset_val, batch_size=2)
  
      model = _DummyModel(dataloader_kNN=dataloader_train, knn_k=3)
      trainer = Trainer(max_epochs=2)
      trainer.fit(
          model,
          train_dataloaders=dataloader_train,
          val_dataloaders=dataloader_val,
      )
      assert model.max_accuracy < 1.0
  
file_path: lightly/tests/utils/benchmarking/test_benchmark_module.py
imports: import unittest
  import torch
  from pytorch_lightning import Trainer
  from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
  from torch.optim import SGD
  from torch.utils.data import DataLoader
  from torchvision.datasets import FakeData
  from torchvision.transforms import ToTensor
  from lightly.data import LightlyDataset
  from lightly.utils.benchmarking import BenchmarkModule
label_desc: Use image node_id 3 for calling the TestBenchmarkModule obj's underlying member method code with example usage: obj.test_knn_train_val() without return types