code: def hash_dataset(
      dataset: "Dataset", *, hash_engine=None, num_workers=0
  ) -> str:
      if hash_engine is None:
          hash_engine = hashlib.sha256()
  
      data_loader = DataLoader(
          dataset,
          collate_fn=lambda batch: tuple(zip(*batch)),
          num_workers=num_workers,
      )
      for loaded_elem in data_loader:
          example = tuple(
              tuple_element[0] for tuple_element in loaded_elem
          )
  
          # https://stackoverflow.com/a/63880190
          buff = io.BytesIO()
          torch.save(example, buff)
          buff.seek(0)
          hash_engine.update(buff.read())
      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 2 for calling a global function with example usage: hash_dataset(dataset) and returns: hash_engine