code: def _auto_wrap_smoke_test(self, enable_mixed_precision):
      device = torch.device("cuda")
      torch.cuda.set_device(0)
  
      # Random port in case the next test run quickly, same port would cause conflict.
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = str(random.randint(2000, 3000))
      torch.distributed.init_process_group(
          backend="nccl", rank=0, world_size=1
      )
  
      try:
          with enable_wrap(
              wrapper_cls=FSDP, mixed_precision=enable_mixed_precision
          ):
              sequential = nn.Sequential(
                  nn.Linear(5, 5),
                  nn.Linear(5, 5),
                  nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5)),
              )
              my_auto_wrap_policy = functools.partial(
                  default_auto_wrap_policy, min_num_params=40
              )
              model = auto_wrap(
                  sequential, auto_wrap_policy=my_auto_wrap_policy
              )
          model.to(device)
          input = torch.rand((1, 5), dtype=torch.float).to(device)
  
          with autocast(enabled=enable_mixed_precision):
              output = model(input)
              loss = F.mse_loss(input, output)
          loss.backward()
      finally:
          torch.distributed.destroy_process_group()
          del os.environ["MASTER_ADDR"]
          del os.environ["MASTER_PORT"]
  
imports: import functools
  import os
  import random
  import unittest
  import torch
  import torch.nn
  import torch.nn.functional
  from fairscale.fair_dev.testing.testing import DummyProcessGroup
  from fairscale.nn import FullyShardedDataParallel
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap, wrap
label_desc: Use image node_id 12 for calling the TestAutoWrap obj's underlying member method code with example usage: obj._auto_wrap_smoke_test(enable_mixed_precision) without return types