class_docstr: A :class:`allennlp.nn.parallel.ddp_accelerator.DdpAccelerator` for FairScale's [`FullyShardedDataParallel`]
  (https://fairscale.readthedocs.io/en/latest/api/nn/fsdp.html).
  
  To save memory while initializing a model, you should call [`.wrap_module()`](#wrap_module) on submodules
  as they're created.
  
  See the :class:`allennlp.modules.transformer.t5.T5` class for an example of how to use this.
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code: def wrap_module(self, module: torch.nn.Module) -> torch.nn.Module:
      with enable_wrap(wrapper_cls=_FSDP, **self._fsdp_kwargs):
          wrapped_module = wrap(module)
      return wrapped_module
  
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 3 for calling the FairScaleFsdpAccelerator obj's underlying member method code with example usage: obj.wrap_module(module) and returns: wrapped_module