code: def prepare_ddp(
      module: torch.nn.Module,
      device: torch.device,
      strategy: Optional[DDPStrategy] = None,
  ) -> DDP:
      """
      Utility to move a module to device and wrap in `DistributedDataParallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`_.
  
      Args:
          module: module to be wrapped in DDP
          device: device to which module will be moved
          strategy: an instance of :class:`~torchtnt.utils.prepare_module.DDPStrategy` which defines the settings of DDP APIs
  
      Examples::
          strategy = DDPStrategy(find_unused_parameters=True, gradient_as_bucket_view=True)
          module = nn.Linear(1, 1)
          device = torch.device("cuda")
          ddp_module = prepare_ddp(module, device, strategy)
      """
      strategy = strategy if strategy is not None else DDPStrategy()
      # wrap module in DDP
      device_ids = None
      if device.type == "cuda":
          device_ids = [device.index]
      params_dict = asdict(strategy)
      # remove ddp comm hook variables from params dict
      del params_dict["comm_state"]
      del params_dict["comm_hook"]
      module = module.to(device)
  
      # remove sync batch norm from params dict before converting module
      del params_dict["sync_batchnorm"]
      if strategy.sync_batchnorm:
          if device.type == "cuda":
              module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
                  module
              )
          else:
              rank_zero_warn(
                  f"SyncBatchNorm layers only work with GPU modules. Skipping the conversion because the device type is {device.type}."
              )
  
      module = DDP(module, device_ids=device_ids, **params_dict)
      if strategy.comm_hook:
          module.register_comm_hook(
              state=strategy.comm_state, hook=strategy.comm_hook
          )
      return module
  
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 1 for calling a global function with example usage: prepare_ddp(module, device, strategy) and returns: module