class_docstr: Takes a dict of parameters and exposes state_dict by parameter key.
  
  Convenience wrapper to take in optim_factory callable to create KeyedOptimizer
class_docstr_tok: ["Takes","a","dict","of","parameters","and","exposes","state_dict","by","parameter","key",".","Convenience","wrapper","to","take","in","optim_factory","callable","to","create","KeyedOptimizer"]
code: def step(self, closure: Any = None) -> None:
      self._optimizer.step(closure=closure)
  
imports: import json
  from copy import deepcopy
  from typing import Any, Callable, Collection, Dict, List, Mapping, Optional, OrderedDict, Set, Tuple, Union
  import torch
  from torch import optim
  from torchrec.distributed.types import ShardedTensor
label_desc: Use image node_id 3 for calling the KeyedOptimizerWrapper obj's underlying member method code with example usage: obj.step(closure) without return types