code: def _create_model(self, is_even_sharded_model, device_mesh=None):
      dummy_model = (
          TestDummyModel()
          if is_even_sharded_model
          else TestDummyModelUneven()
      )
  
      model = FSDP(dummy_model.cuda(), device_mesh=device_mesh)
      optim = torch.optim.Adam(model.parameters(), lr=0.1)
      model(model.get_input()).sum().backward()
      optim.step()
  
      return model, optim
  
imports: import io
  from copy import deepcopy
  import torch
  import torch.nn
  from torch.distributed._shard.sharded_tensor import ShardedTensor
  from torch.distributed._tensor import DTensor, Shard
  from torch.distributed.device_mesh import init_device_mesh
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.api import ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictType
  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
label_desc: Use image node_id 1 for calling the TestFSDPWithDeviceMeshAndDTensor obj's underlying member method code with example usage: obj._create_model(is_even_sharded_model, device_mesh) and returns: model,  optim