code: def benchmark_fsdp(rank, args, world_size):
      """Benchmark a given model using a single process and multiple devices."""
  
      init_method_pgroup = "tcp://localhost:{}".format(RPC_PORT)
      torch.distributed.init_process_group(
          backend="nccl",
          rank=rank,
          world_size=world_size,
          init_method=init_method_pgroup,
      )
  
      torch.cuda.set_device(rank)
      init_random_seed(0)
  
      benchmark_config = create_benchmark_config(args.model_name)
      model_specs = get_model_specs(args.model_name)
      model_config = create_model_config(
          args,
          benchmark_config=benchmark_config,
          model_specs=model_specs,
      )
      model = model_config["model"]
      config = {}
  
      if args.full_fp16:
          config["compute_dtype"] = torch.float16
          config["mixed_precision"] = False
  
      if args.enable_auto_wrap:
          with enable_wrap(wrapper_cls=FSDP, **config):
              fsdp_model = auto_wrap(
                  model, auto_wrap_policy=default_auto_wrap_policy
              )
              fsdp_model = FSDP(fsdp_model, **config)
      else:
          fsdp_model = FSDP(model, **config)
  
      if args.full_fp16:
          fsdp_model = fsdp_model.half()
      print(f"param dtype {[p.dtype for p in fsdp_model.parameters()]}")
      if args.dry_run:
          train(
              model_config,
              fsdp_model,
              benchmark_config,
              model_specs,
              args,
          )
      else:
          benchmark_language_model(
              model_config,
              fsdp_model,
              benchmark_config,
              model_specs,
              args,
          )
  
imports: import argparse
  from collections import defaultdict
  from functools import reduce
  import gc
  import logging
  import math
  import operator
  import time
  from datasets.wikitext2_data import get_real_dataloaders
  from datasets.wikitext2_data import get_synthetic_dataloaders
  from models import transformer_lm
  import numpy
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from benchmarks.golden_configs.lm_wikitext2 import FSDP
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 19 for calling a global function with example usage: benchmark_fsdp(rank, args, world_size) without return types