code: def _create_model(self, compile, model_type, state_dict_options=None):
      dummy_model = TestDummyModel().cuda()
  
      assert model_type in ModelType, f"{model_type} is not supported."
      if model_type == ModelType.FSDP:
          device_mesh = init_device_mesh(
              self.device_type, (self.world_size,)
          )
          model = FSDP(
              dummy_model,
              device_mesh=device_mesh,
              use_orig_params=True,
          )
      elif model_type == ModelType.HSDP:
          device_mesh = init_device_mesh(
              self.device_type, (2, self.world_size // 2)
          )
          model = FSDP(
              dummy_model,
              device_mesh=device_mesh,
              use_orig_params=True,
              sharding_strategy=ShardingStrategy.HYBRID_SHARD,
          )
      elif model_type == ModelType.FSDP_TP:
          mesh_2d = init_device_mesh(
              self.device_type,
              (2, self.world_size // 2),
              mesh_dim_names=("dp", "tp"),
          )
          tp_mesh = mesh_2d["tp"]
          dp_mesh = mesh_2d["dp"]
          parallelize_plan = {
              "net1": ColwiseParallel(),
              "net2": RowwiseParallel(),
          }
          model = parallelize_module(
              dummy_model, tp_mesh, parallelize_plan
          )
          model = FSDP(model, device_mesh=dp_mesh, use_orig_params=True)
      else:
          model = dummy_model
  
      if compile:
          # TODO: enable dynamic=True when dynamic shape support is enabled.
          # model = torch.compile(model)
          model = torch.compile(model, dynamic=False)
  
      optim = self._optim(model)
      if model_type is not ModelType.NONE:
          _patch_model_state_dict(model, options=state_dict_options)
          _patch_optimizer_state_dict(
              model, optimizers=optim, options=state_dict_options
          )
  
      return model, optim
  
imports: import time
  from enum import auto, Enum
  import torch
  import torch.distributed
  import torch.distributed.checkpoint
  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, get_state_dict
  from torch.distributed.distributed_c10d import ReduceOp
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.api import ShardingStrategy
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, RowwiseParallel
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, skip_if_lt_x_gpu, with_comms
  from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
  from torch.testing._internal.distributed.common_state_dict import VerifyStateDictMixin
label_desc: Use image node_id 2 for calling the TestE2ESaveAndLoad obj's underlying member method code with example usage: obj._create_model(compile, model_type, state_dict_options) and returns: model,  optim