code: def _test(self, accelerator: str) -> None:
      dataset = FakeData(
          size=10,
          image_size=(3, 8, 8),
          num_classes=5,
          transform=ToTensor(),
      )
      train_dataloader = DataLoader(dataset, batch_size=2)
      val_dataloader = DataLoader(dataset, batch_size=2)
      model = _DummyModule()
      trainer = Trainer(
          max_epochs=1,
          accelerator=accelerator,
          devices=1,
          log_every_n_steps=1,
      )
      trainer.fit(
          model=model,
          train_dataloaders=train_dataloader,
          val_dataloaders=val_dataloader,
      )
      assert (
          trainer.callback_metrics["train_online_cls_loss"].item() >= 0
      )
      assert (
          trainer.callback_metrics["train_online_cls_top1"].item() >= 0
      )
      assert (
          trainer.callback_metrics["train_online_cls_top5"].item()
          >= trainer.callback_metrics["train_online_cls_top1"].item()
      )
      assert (
          trainer.callback_metrics["train_online_cls_top5"].item() <= 1
      )
      assert trainer.callback_metrics["val_online_cls_loss"].item() >= 0
      assert trainer.callback_metrics["val_online_cls_top1"].item() >= 0
      assert (
          trainer.callback_metrics["val_online_cls_top5"].item()
          >= trainer.callback_metrics["val_online_cls_top1"].item()
      )
      assert trainer.callback_metrics["val_online_cls_top5"].item() <= 1
  
file_path: lightly/tests/utils/benchmarking/test_online_linear_classifier.py
imports: import pytest
  import torch
  from pytorch_lightning import LightningModule, Trainer
  from torch import Tensor, nn
  from torch.optim import SGD
  from torch.utils.data import DataLoader
  from torchvision.datasets import FakeData
  from torchvision.transforms import ToTensor
  from lightly.utils.benchmarking import OnlineLinearClassifier
label_desc: Use image node_id 3 for calling the TestOnlineLinearClassifier obj's underlying member method code with example usage: obj._test(accelerator) without return types