code: def _distributed_worker(
      gpu_id,
      world_size,
      with_fsdp,
      with_nested_trunk,
      freezing_method,
      freeze_after_wrap_fsdp,
      tempfile_name,
      unused,
      rank_0_output,
      expected_state,
  ):
      torch.cuda.set_device(gpu_id)
  
      rank = gpu_id
      result = dist_init(rank, world_size, tempfile_name, unused)
      assert result, "Dist init failed"
  
      torch.manual_seed(0)
      torch.backends.cudnn.deterministic = True
      batch = torch.randn(size=(2, 3, 224, 224)).cuda()
  
      model = _create_model(
          with_fsdp, with_nested_trunk, freeze_after_wrap_fsdp
      )
      model = model.cuda()
  
      # freezing the trunk using requires_grad.
      if freezing_method == FreezingMethod.RequiresGrad:
          for param in model.trunk.parameters():
              param.requires_grad = False
  
      if with_fsdp:
          if not freeze_after_wrap_fsdp:
              model.fsdp_wrap()
          model = FSDP(model)
      else:
          model = DistributedDataParallel(model, device_ids=[gpu_id])
  
      if gpu_id == 0:
          print(model)
  
      target = torch.tensor([0, 1], dtype=torch.long).cuda()
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
  
      for iteration in range(3):
          out = model(batch)
          fake_loss = criterion(out, target)
          print("Loss", iteration, ":", fake_loss.item())
          optimizer.zero_grad()
          fake_loss.backward()
          if freezing_method == FreezingMethod.GradToNone:
              for param in model.trunk.parameters():
                  param.grad = None
          optimizer.step()
  
      if with_fsdp:
          fsdp_state = model.state_dict()
          # Move tensors to CPU to compare numerics.
          for k, v in fsdp_state.items():
              fsdp_state[k] = v.cpu()
          assert objects_are_equal(
              expected_state, fsdp_state, raise_exception=True
          )
      elif rank == 0:
          state_after = model.module.cpu().state_dict()
          torch.save(state_after, rank_0_output)
  
      teardown()
  
imports: from enum import Enum
  from itertools import product
  import tempfile
  import pytest
  import torch
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  import torch.optim
  from fairscale.fair_dev.testing.testing import dist_init, objects_are_equal, rmf, skip_if_single_gpu, teardown
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 4 for calling a global function with example usage: _distributed_worker(gpu_id, world_size, with_fsdp, with_nested_trunk, freezing_method, freeze_after_wrap_fsdp, tempfile_name, unused, rank_0_output, expected_state) without return types