code: def run_worker(rank, world_size, args):
      if args.world_size != 0:
          world_size = args.world_size
      dist_init(rank + args.rank_base, world_size, hostname=args.host)
      initialize_model_parallel(1, world_size)
      utils.init_random_seed(0)
      run_mp_worker(args, world_size)
  
      rpc.shutdown()
      torch.distributed.destroy_process_group()
  
imports: from collections import defaultdict
  import gc
  import logging
  import math
  import time
  import torch
  import torch.distributed
  from torch.distributed import rpc
  from torch.nn.parallel import DistributedDataParallel
  import utils
  from benchmarks.golden_configs.lm_wikitext2 import Pipe
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import Pipe
  from fairscale.nn.model_parallel import initialize_model_parallel
label_desc: Use image node_id 13 for calling a global function with example usage: run_worker(rank, world_size, args) without return types