code: def run(rank, world_size):
      # Set up world pg
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "12355"
  
      dist.init_process_group(
          "cpu:gloo,cuda:nccl", rank=rank, world_size=world_size
      )
      torch.cuda.set_device(rank)
  
      model, optim = _init_model(rank, world_size)
      state_dict = {"model": model, "optim": optim}
      checkpointer = _FileSystemCheckpointer(CHECKPOINT_DIR)
      loss_calc = torch.nn.BCELoss()
  
      f = None
      for epoch in range(NUM_EPOCHS):
          try:
              torch.manual_seed(epoch)
              x, y = _input()
  
              loss = loss_calc(model(x), y)
  
              _print(f"{epoch=} {loss=}")
  
              loss.backward()
              optim.step()
              optim.zero_grad()
  
              if epoch % SAVE_PERIOD == 0:
                  if f is not None:
                      f.result()
                  f = checkpointer.async_save(state_dict)
  
              if FAULT_PERIOD > 0 and epoch % FAULT_PERIOD == 0:
                  raise InjectedException("Fault injection!")
  
          except InjectedException as e:
              dist.barrier()
  
              _print("Trainer encountered exception:")
              traceback.print_tb(e.__traceback__)
  
              _print("Reloading model from last checkpoint!")
              if f is not None:
                  f.result()
              checkpointer.load(state_dict)
  
imports: import os
  import shutil
  import traceback
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  from torch.distributed._tensor.device_mesh import init_device_mesh
  from torch.distributed.checkpoint.filesystem import _FileSystemCheckpointer
  from torch.distributed.checkpoint.state_dict import _patch_model_state_dict, _patch_optimizer_state_dict
  from torch.distributed.fsdp import FullyShardedDataParallel
label_desc: Use image node_id 4 for calling a global function with example usage: run(rank, world_size) without return types