code: def profile_online_naive_no_avl(benchmark, device):
      """
      Online naive strategy without Avalanche.
      """
      print("=" * 30)
      print("Profiling online naive strategy without Avalanche ...")
  
      experience_0 = benchmark.train_stream[0]
  
      with cProfile.Profile() as pr:
          # Initialize model, optimizer and criterion
          model = SimpleMLP(num_classes=10)
          model.train()
          optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
          criterion = torch.nn.CrossEntropyLoss()
  
          # Iterate over the dataset and train the model
          dataloader = DataLoader(experience_0.dataset, batch_size=1)
          pbar = tqdm(dataloader)
          for x, y, _ in pbar:
              x, y = x.to(device), y.to(device)
              optimizer.zero_grad()
              pred = model(x)
              loss = criterion(pred, y)
              loss.backward()
              optimizer.step()
              pbar.set_description(f"Loss: {loss.item():0.4f}")
  
      stats = pstats.Stats(pr)
      stats.sort_stats("tottime").print_stats(15)
  
imports: from os.path import expanduser
  import argparse
  import torch
  from torch.nn import CrossEntropyLoss
  import torch.optim.lr_scheduler
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  import cProfile
  import pstats
  from avalanche.benchmarks import SplitMNIST
  from avalanche.benchmarks.scenarios.online import FixedSizeExperienceSplitter
  from avalanche.models import SimpleMLP
  from avalanche.training.supervised.strategy_wrappers_online import OnlineNaive
  from avalanche.benchmarks.scenarios import OnlineCLScenario
  from avalanche.training.plugins import ReplayPlugin
  from avalanche.training.storage_policy import ReservoirSamplingBuffer
  from avalanche.evaluation.metrics import loss_metrics
  from avalanche.logging import InteractiveLogger
  from avalanche.training.plugins import EvaluationPlugin
label_desc: Use image node_id 1 for calling a global function with example usage: profile_online_naive_no_avl(benchmark, device) without return types