code: def hash_benchmark(
      benchmark: "DatasetScenario", *, hash_engine=None, num_workers=0
  ) -> str:
      if hash_engine is None:
          hash_engine = hashlib.sha256()
  
      for stream_name in sorted(benchmark.streams.keys()):
          stream = benchmark.streams[stream_name]
          hash_engine.update(stream_name.encode())
          for experience in stream:
              exp_dataset = experience.dataset
              hash_dataset(
                  exp_dataset,
                  hash_engine=hash_engine,
                  num_workers=num_workers,
              )
      return hash_engine.hexdigest()
  
file_path: avalanche/avalanche/distributed/distributed_consistency_verification.py
imports: import hashlib
  import io
  from typing import Tuple, TYPE_CHECKING
  import torch
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: hash_benchmark(benchmark) and returns: hash_engine