code: def maybe_fsdp_wrap(opt):
      """
      Context manager for enabling wrapping in FullyShardedDataParallel.
      """
      if not should_use_fsdp(opt):
          # make a no-op
          yield
          return
  
      mixed_precision = opt["fp16"] and opt["fp16_impl"] == "safe"
  
      # settings as of pytorch 1.13
      # There is a warning in pytorch 1.13 for FSDP that is unavoidable;
      # at the risk of suppressing valid warnings, just going to suppress that one.
      import warnings
  
      warnings.filterwarnings("ignore")
  
      # sharding strategy determines zero2 or zero3
      sharding_strategy = (
          ShardingStrategy.FULL_SHARD
          if opt["ddp_backend"] == "zero3"
          else ShardingStrategy.SHARD_GRAD_OP
      )
  
      # mp determines how to mix precision
      if mixed_precision:
          mp_strategy = MixedPrecision(
              reduce_dtype=torch.float16,
              param_dtype=torch.float16,
              buffer_dtype=torch.float16,
          )
      else:
          mp_strategy = None
  
      # autowrap policy.
      auto_wrap_policy = None
      ignored_modules = None
      if "hugging_face" not in opt["model"]:
          from parlai.agents.transformer.modules.encoder import (
              TransformerEncoderLayer,
          )
          from parlai.agents.transformer.modules.decoder import (
              TransformerDecoderLayer,
          )
  
          auto_wrap_policy = functools.partial(
              transformer_auto_wrap_policy,
              transformer_layer_cls={
                  TransformerEncoderLayer,
                  TransformerDecoderLayer,
              },
          )
  
      # backward prefetch; determines when to fetch the parameters during backward pass
      # set to BACKWARD_PRE to increase throughput, at the cost of memory
      backward_prefetch = BackwardPrefetch.BACKWARD_POST
  
      # CPU offloading; this can offload parameters to the CPU
      cpu_offload = None
  
      fsdp_args = dict(
          process_group=get_dist_group(),
          sharding_strategy=sharding_strategy,
          cpu_offload=cpu_offload,
          auto_wrap_policy=auto_wrap_policy,
          backward_prefetch=backward_prefetch,
          mixed_precision=mp_strategy,
          ignored_modules=ignored_modules,
          param_init_fn=None,
          device_id=opt["gpu"],
          sync_module_states=False,  # need this for syncing the first call; specify False because we do it manually after cuda
          forward_prefetch=False,  # specify true for CPU-heavy workload
          limit_all_gathers=False,  # specifying the default here
      )
      with enable_wrap(wrapper_cls=FSDP, **fsdp_args):
          yield
  
imports: import contextlib
  import functools
  import torch
  import torch.distributed
  from torch.distributed.algorithms.join import Join, Joinable, JoinHook
  import torch.nn
  from parlai.scripts.eval_model import Evaluator
  from parlai.scripts.train_model import TrainLoop
  from parlai.utils.distributed import is_distributed, get_dist_group
label_desc: Use image node_id 3 for calling a global function with example usage: maybe_fsdp_wrap(opt) without return types