class_docstr: Implement seed alignment by broadcasting a new seed from the main process.
  
  This is usually slower than using :class:`RollingSeedContext`.
class_docstr_tok: ["Implement","seed","alignment","by","broadcasting","a","new","seed","from","the","main","process",".","This","is","usually","slower","than","using",":","class",":","`","RollingSeedContext","`","."]
code: def __enter__(self):
      pass
  
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 2 for calling the BroadcastSeedContext obj's underlying member method code with example usage: obj.__enter__() without return types