class_docstr: The wrapped model type returned from [`FairScaleFsdpWrappedModel.wrap_model`](#wrap_model).
class_docstr_tok: ["The","wrapped","model","type","returned","from","[","`","FairScaleFsdpWrappedModel.wrap_model","`","]","(","#","wrap_model",")","."]
code: def load_state_dict(
      self, state_dict: StateDictType, strict: bool = True
  ) -> LoadStateDictReturnType:
      return self.model.load_local_state_dict(
          state_dict["weights"], strict=strict
      )
  
imports: import os
  from typing import Tuple, Union, Optional, TYPE_CHECKING, List, Any, Dict, Sequence
  from fairscale.nn import FullyShardedDataParallel
  from fairscale.nn.wrap import enable_wrap, wrap
  from fairscale.nn.misc import FlattenParamsWrapper
  from fairscale.optim.grad_scaler import GradScaler
  import torch
  from torch.cuda import amp
  from allennlp.nn.parallel.sharded_module_mixin import ShardedModuleMixin
  from allennlp.nn.parallel.ddp_accelerator import DdpAccelerator, DdpWrappedModel, StateDictType, LoadStateDictReturnType
label_desc: Use image node_id 2 for calling the FairScaleFsdpWrappedModel obj's underlying member method code with example usage: obj.load_state_dict(state_dict, strict) and returns: self