code: def _dist_train(
      self,
      with_nested_trunk,
      freezing_method,
      freeze_after_wrap_fsdp,
      with_fsdp,
      disable_autograd,
      forward_prefetch,
  ):
      torch.manual_seed(0)
      batch = torch.randn(size=(2, 3, 224, 224)).cuda()
  
      fsdp_kwargs = {
          "device_id": self.rank,
          "forward_prefetch": forward_prefetch,
      }
  
      ddp_kwargs = {
          "device_ids": [self.rank],
          "find_unused_parameters": True if disable_autograd else False,
      }
  
      model = self._create_model(
          with_fsdp,
          with_nested_trunk,
          freeze_after_wrap_fsdp,
          disable_autograd,
          fsdp_kwargs,
      )
      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(fsdp_kwargs)
          model = FSDP(model, **fsdp_kwargs)
      else:
          model = DistributedDataParallel(model, **ddp_kwargs)
  
      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)
          optimizer.zero_grad()
          fake_loss.backward()
          if freezing_method == FreezingMethod.GradToNone:
              for param in model.module.trunk.parameters():
                  param.grad = None
          optimizer.step()
  
      if with_fsdp:
          return get_full_params(model)
  
      return list(model.parameters())
  
imports: import contextlib
  import sys
  from enum import Enum
  import torch
  import torch.nn
  import torch.optim
  from torch import distributed
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.nn.parallel import DistributedDataParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import FSDPTest, get_full_params
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 2 for calling the TestFreezingWeights obj's underlying member method code with example usage: obj._dist_train(with_nested_trunk, freezing_method, freeze_after_wrap_fsdp, with_fsdp, disable_autograd, forward_prefetch) and returns: list, get_full_params