code: def test_dataloader_with_multiple_workers(self):
      device = torch.device("cpu")
      # initialize a simple model
      model = SimpleMLP(num_classes=10, input_size=6)
  
      # initialize a benchmark
      benchmark = get_fast_benchmark()
      train_stream = benchmark.train_stream
  
      # Prepare for training & testing
      optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)
      criterion = CrossEntropyLoss()
  
      # Continual learning strategy
      cl_strategy = Naive(
          model,
          optimizer,
          criterion,
          train_mb_size=32,
          train_epochs=1,
          eval_mb_size=32,
          device=device,
      )
  
      # test training for one experience
      train_exp = train_stream[0]
      # setting num_workers > 0 should not fail
      cl_strategy.train(train_exp, num_workers=4)
  
imports: import unittest
  import torch
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from torch.optim import SGD
  from torch.nn import CrossEntropyLoss
  from torch.utils.data import TensorDataset, DataLoader
  from avalanche.benchmarks.utils import _concat_taskaware_classification_datasets
  from avalanche.benchmarks.utils.utils import concat_datasets
  from avalanche.models import SimpleMLP
  from avalanche.training.plugins import ReplayPlugin
  from avalanche.training.supervised import Naive
  from avalanche.benchmarks import nc_benchmark
  from avalanche.benchmarks.utils.data_loader import ReplayDataLoader, TaskBalancedDataLoader, GroupBalancedDataLoader, GroupBalancedInfiniteDataLoader
label_desc: Use image node_id 4 for calling the DataLoaderTests obj's underlying member method code with example usage: obj.test_dataloader_with_multiple_workers() without return types