code: def _test__accelerator(
      self, accelerator: str, expected_device: str
  ) -> None:
      torch.manual_seed(0)
      linear = nn.Linear(3, 2)
      batch_norm = nn.BatchNorm1d(2)
      model = nn.Sequential(linear, batch_norm)
      initial_weights = linear.weight.clone()
      initial_bn_weights = batch_norm.weight.clone()
      classifier = KNNClassifier(model, num_classes=10, knn_k=20)
      trainer = Trainer(
          max_epochs=1, accelerator=accelerator, devices=1
      )
      train_features = torch.randn(40, 3)
      train_targets = torch.randint(0, 10, (40,))
      train_dataset = _FeaturesDataset(
          features=train_features, targets=train_targets
      )
      val_features = torch.randn(10, 3)
      val_targets = torch.randint(0, 10, (10,))
      val_dataset = _FeaturesDataset(
          features=val_features, targets=val_targets
      )
      train_dataloader = DataLoader(train_dataset, batch_size=3)
      val_dataloader = DataLoader(val_dataset, batch_size=3)
      trainer.fit(
          model=classifier,
          train_dataloaders=train_dataloader,
          val_dataloaders=val_dataloader,
      )
      assert trainer.callback_metrics["val_top1"].item() >= 0.0
      assert (
          trainer.callback_metrics["val_top5"].item()
          >= trainer.callback_metrics["val_top1"].item()
      )
      assert trainer.callback_metrics["val_top5"].item() <= 1.0
      assert classifier._train_features == []
      assert classifier._train_targets == []
      assert classifier._train_features_tensor is not None
      assert classifier._train_targets_tensor is not None
      assert classifier._train_features_tensor.shape == (2, 40)
      assert classifier._train_targets_tensor.shape == (40,)
      assert classifier._train_features_tensor.dtype == torch.float32
      assert (
          classifier._train_features_tensor.device.type
          == expected_device
      )
      assert (
          classifier._train_targets_tensor.device.type
          == expected_device
      )
  
      # Verify that model weights were not updated.
      assert torch.all(torch.eq(initial_weights, linear.weight))
      assert torch.all(torch.eq(initial_bn_weights, batch_norm.weight))
      # Verify that batch norm statistics were not updated. Note that even though the
      # running mean was not updated, it is still initialized to zero.
      assert batch_norm.running_mean is not None
      assert torch.all(
          torch.eq(
              batch_norm.running_mean,
              torch.zeros_like(batch_norm.running_mean),
          )
      )
  
imports: from typing import Tuple
  import pytest
  import torch
  from pytest_mock import MockerFixture
  from pytorch_lightning import Trainer
  from torch import Tensor, nn
  from torch.utils.data import DataLoader, Dataset
  from lightly.utils.benchmarking import KNNClassifier
  from lightly.utils.benchmarking.knn_classifier import F
label_desc: Use image node_id 4 for calling the TestKNNClassifier obj's underlying member method code with example usage: obj._test__accelerator(accelerator, expected_device) without return types