code: def main(args):
      # Compute device
      device = (
          "cuda"
          if args.cuda >= 0 and torch.cuda.is_available()
          else "cpu"
      )
      print("Using ", device)
  
      # Benchmark
      benchmark = SplitMNIST(
          n_experiences=5,
          dataset_root=expanduser("~") + "/.avalanche/data/mnist/",
      )
  
      # Profilers:
      profile_online_naive_no_avl(benchmark, device)
  
      profile_online_naive_lazy_stream(benchmark, device)
  
      profile_online_avl(benchmark, device, strategy="naive")
  
      profile_online_avl(benchmark, device, strategy="er")
  
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 4 for calling a global function with example usage: main(args) without return types