class_docstr: Wrapper for multiple stateful objects. Necessary because we might have multiple nn.Modules or multiple optimizers,
  but save/load_checkpoint APIs may only accepts one stateful object.
  
  Stores state_dict as a dict of state_dicts.
class_docstr_tok: ["Wrapper","for","multiple","stateful","objects",".","Necessary","because","we","might","have","multiple","nn.Modules","or","multiple","optimizers",",","but","save\/load_checkpoint","APIs","may","only","accepts","one","stateful","object",".","Stores","state_dict","as","a","dict","of","state_dicts","."]
code: def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
      for k in state_dict:
          self.stateful_objs[k].load_state_dict(state_dict[k])
  
file_path: tnt/torchtnt/utils/stateful.py
imports: from typing import Any, Dict, Union
  import torch
  from torchtnt.utils.lr_scheduler import TLRScheduler
  from torchtnt.utils.prepare_module import FSDPOptimizerWrapper
  from torchtnt.utils.progress import Progress
  from typing_extensions import Protocol, runtime_checkable
label_desc: Use image node_id 3 for calling the MultiStateful obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types