code: def profile_online_avl(
      benchmark,
      device,
      strategy="naive",
      use_interactive_logger: bool = True,
  ):
      """
      Online strategy using Avalanche.
      """
      print("=" * 30)
      print(f"Profiling online {strategy} strategy using Avalanche ...")
  
      experience_0 = benchmark.train_stream[0]
  
      # Create list of loggers to be used
      loggers = []
      if use_interactive_logger:
          interactive_logger = InteractiveLogger()
          loggers.append(interactive_logger)
  
      # Evaluation plugin
      eval_plugin = EvaluationPlugin(
          loss_metrics(
              minibatch=True, epoch=True, experience=True, stream=True
          ),
          loggers=loggers,
      )
  
      with cProfile.Profile() as pr:
          # Model
          model = SimpleMLP(num_classes=10)
  
          plugins = []
          if strategy == "er":
              # CREATE THE STRATEGY INSTANCE (ONLINE-REPLAY)
              storage_policy = ReservoirSamplingBuffer(max_size=100)
              replay_plugin = ReplayPlugin(
                  mem_size=100,
                  batch_size=1,
                  storage_policy=storage_policy,
              )
              plugins.append(replay_plugin)
  
          # Create OnlineNaive strategy
          cl_strategy = OnlineNaive(
              model,
              torch.optim.SGD(model.parameters(), lr=0.01),
              CrossEntropyLoss(),
              train_passes=1,
              train_mb_size=1,
              device=device,
              evaluator=eval_plugin,
              plugins=plugins,
          )
          online_cl_scenario = OnlineCLScenario(
              benchmark.streams.values(), experience_0
          )
  
          # Train on the first experience only
          cl_strategy.train(online_cl_scenario.train_stream)
  
      stats = pstats.Stats(pr)
      stats.sort_stats("tottime").print_stats(40)
  
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 3 for calling a global function with example usage: profile_online_avl(benchmark, device, strategy, use_interactive_logger) without return types