code: def _distributed_worker(
      gpu_id,
      world_size,
      with_model2,
      with_sync_bn,
      with_fsdp,
      with_checkpoint,
      files,
      mixed_precision,
      flatten,
      wrap_bn,
      fp32_reduce_scatter,
      bucket_cap_mb,
  ):
      filename, filename_rpc = files[:2]
      filename_loss = files[2:]
  
      torch.cuda.set_device(gpu_id)
  
      rank = gpu_id
      result = dist_init(rank, world_size, filename, filename_rpc)
      assert result, "Dist init failed"
  
      # use False below to debug since error msg is not as good with cudnn.
      torch.backends.cudnn.enabled = True
  
      # these make things deterministic.
      torch.manual_seed(0)
      torch.backends.cudnn.deterministic = True
      torch.backends.cudnn.benchmark = False
  
      # Ensure we have multiple forward passes.
      batch = [
          torch.randn(size=(2, 3, 16, 16)).cuda(),
          torch.randn(size=(2, 3, 9, 9)).cuda(),
          torch.randn(size=(2, 3, 9, 9)).cuda(),
      ]
  
      if mixed_precision and not with_fsdp:
          batch = [x.half() for x in batch]
  
      model = _create_model(
          with_model2,
          with_sync_bn,
          with_fsdp,
          with_checkpoint,
          mixed_precision,
          flatten,
          wrap_bn,
          fp32_reduce_scatter,
          bucket_cap_mb,
      )
      model = model.cuda()
  
      if with_fsdp:
          model = FSDP(
              model,
              flatten_parameters=flatten,
              mixed_precision=mixed_precision,
              compute_dtype=torch.float32,
              fp32_reduce_scatter=fp32_reduce_scatter,
              bucket_cap_mb=bucket_cap_mb,
          )
          model.set_gradient_divide_factors(1.0, 2.0, True)
          no_sync_context = contextlib.suppress()
      else:
          # With DDP, we need no_sync and manual gradient reduction below because
          # it can't handle multiple forward pass + checkpointing otherwise.
          model = DistributedDataParallel(model, device_ids=[gpu_id])
          no_sync_context = model.no_sync()
  
      mp_context = contextlib.suppress()
      if mixed_precision:
          mp_context = torch.cuda.amp.autocast(enabled=True)
  
      if gpu_id == 0:
          print(model)
  
      target = torch.tensor([0, 1, 2, 3, 4, 5], dtype=torch.long).cuda()
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
  
      losses = {}
      i = 0
      with no_sync_context:
          for iteration in range(3):
              with mp_context:
                  out = model(batch)
                  loss = criterion(out, target)
                  print("Loss", iteration, ":", loss.item())
                  losses[f"iter_{i}"] = loss
                  i += 1
                  optimizer.zero_grad()
                  loss.backward()
              # Manual grad reduction, no autocast.
              if not with_fsdp:
                  for p in model.parameters():
                      dist.all_reduce(p.grad.data)
                      p.grad.data.div_(2.0)
              # Stepping, no autocast
              optimizer.step()
  
      # Due to dist.all_reduce code block above with ddp.no_sync, we seem to hit a bug
      # in DDP where tensor.cpu() and torch.save() calls both hang. FSDP is not affected.
      # Therefore, we have to compare losses here instead of states.
      with open(filename_loss[rank], "wb") as f:
          pickle.dump(losses, f)
  
      teardown()
  
imports: import contextlib
  import pickle
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  import torch.optim
  from fairscale.fair_dev.testing.testing import dist_init, skip_if_single_gpu, teardown, temp_files_ctx
  from fairscale.internal import torch_version
  from fairscale.nn import checkpoint_wrapper
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from fairscale.nn.data_parallel import auto_wrap_bn
  from fairscale.nn.wrap import enable_wrap, wrap
label_desc: Use image node_id 2 for calling a global function with example usage: _distributed_worker(gpu_id, world_size, with_model2, with_sync_bn, with_fsdp, with_checkpoint, files, mixed_precision, flatten, wrap_bn, fp32_reduce_scatter, bucket_cap_mb) without return types