code: def hash_model(
      model: "Module", include_buffers=True, *, hash_engine=None
  ) -> str:
      if hash_engine is None:
          hash_engine = hashlib.sha256()
  
      for name, param in model.named_parameters():
          hash_engine.update(name.encode())
          buff = io.BytesIO()
          torch.save(param.detach().cpu(), buff)
          buff.seek(0)
          hash_engine.update(buff.read())
  
      if include_buffers:
          for name, model_buffer in model.named_buffers():
              hash_engine.update(name.encode())
              buff = io.BytesIO()
              torch.save(model_buffer.detach().cpu(), 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 5 for calling a global function with example usage: hash_model(model, include_buffers) and returns: hash_engine