class_docstr: Wrapper for FSDP optimizer to call specific FSDP optimizer state checkpointing APIs.
class_docstr_tok: ["Wrapper","for","FSDP","optimizer","to","call","specific","FSDP","optimizer","state","checkpointing","APIs","."]
code: def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
      optim_state_dict = FSDP.optim_state_dict_to_load(
          self.module, self.optimizer, state_dict
      )
      self.optimizer.load_state_dict(optim_state_dict)
  
imports: from dataclasses import asdict, dataclass
  from functools import partial
  from typing import Any, Callable, Dict, Iterable, Optional, Union
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from torch.distributed.fsdp import FullyShardedDataParallel, StateDictType
  from torch.distributed.fsdp.api import OptimStateDictConfig, StateDictConfig
  from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, CPUOffload, MixedPrecision, ShardingStrategy
  from torch.nn.parallel import DistributedDataParallel
  from torchtnt.utils.rank_zero_log import rank_zero_warn
  from torchtnt.utils.version import is_torch_version_geq_1_12, is_torch_version_geq_2_0
label_desc: Use image node_id 3 for calling the FSDPOptimizerWrapper obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types