class_docstr: Implement seed alignment by storing the state of random number generators.
  
  Doesn't require a distributed communication (even broadcast), which makes
  this the best choices when wrapping sections that (may) both:
    - behave differently depending on the rank
    - change the global state of random number generators
class_docstr_tok: ["Implement","seed","alignment","by","storing","the","state","of","random","number","generators",".","Does","n't","require","a","distributed","communication","(","even","broadcast",")",",","which","makes","this","the","best","choices","when","wrapping","sections","that","(","may",")","both",":","-","behave","differently","depending","on","the","rank","-","change","the","global","state","of","random","number","generators"]
code: def __enter__(self):
      self.save_generators_state()
  
imports: import os
  import pickle
  import warnings
  from io import BytesIO
  from typing import ContextManager, Optional, List, Any, Iterable, Dict, TypeVar
  import torch
  from torch import Tensor
  from torch.nn.modules import Module
  from torch.nn.parallel import DistributedDataParallel
  from typing_extensions import Literal
  from torch.distributed import init_process_group, broadcast_object_list
  from avalanche.distributed.distributed_consistency_verification import hash_tensor
label_desc: Use image node_id 5 for calling the RollingSeedContext obj's underlying member method code with example usage: obj.__enter__() without return types