code: def test_2d_fsdp_tp_compile(self):
      data_parallel_size = 2
      model = SimpleModel(self.device_type)
      model_copy = copy.deepcopy(model)
  
      # 2-D mesh is [dp, tp]
      twod_mesh = init_device_mesh(
          "cuda",
          (data_parallel_size, self.world_size // data_parallel_size),
          mesh_dim_names=["dp", "tp"],
      )
  
      fsdp_pg = twod_mesh.get_group(mesh_dim=0)
  
      inp = torch.rand(20, 10, device=self.device_type)
      parallelize_plan = {
          "mlp_0.net1": ColwiseParallel(),
          "mlp_0.net2": RowwiseParallel(),
          "mlp_1.net1": ColwiseParallel(),
          "mlp_1.net2": RowwiseParallel(),
      }
      tp_model = parallelize_module(
          model, twod_mesh["tp"], parallelize_plan
      )
      eager_2d = FSDP(
          tp_model,
          device_id=self.rank,
          use_orig_params=True,
          device_mesh=twod_mesh["dp"],
      )
      out = eager_2d(inp)
      tp_model2 = parallelize_module(
          model_copy,
          twod_mesh["tp"],
          parallelize_plan,
      )
      fsdp_2d = FSDP(
          tp_model2,
          device_id=self.rank,
          use_orig_params=True,
          device_mesh=twod_mesh["dp"],
      )
  
      # TODO: once aot autograd support is ready we can just use default backend
      compiled_2d = torch.compile(fsdp_2d, backend="aot_eager")
      compiled_output = compiled_2d(inp)
  
      self.assertEqual(out, compiled_output)
  
imports: import copy
  import functools
  import unittest
  from unittest.mock import patch
  import torch
  import torch._dynamo
  import torch.distributed
  import torch.nn
  from torch._C import FileCheck
  from torch._inductor.utils import run_and_get_triton_code
  from torch.distributed._tensor import DeviceMesh, DTensor, init_device_mesh, Replicate, Shard
  from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import checkpoint_wrapper, CheckpointImpl
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, PrepareModuleInput, PrepareModuleOutput, RowwiseParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, MLPModule, with_comms
  from torch.testing._internal.distributed.fake_pg import FakeStore
  from torch.utils._triton import has_triton
  from torch.utils.checkpoint import checkpoint
  from functorch.compile import min_cut_rematerialization_partition
  from torch._dynamo.backends.common import aot_autograd
label_desc: Use image node_id 3 for calling the TestDTensorCompileE2E obj's underlying member method code with example usage: obj.test_2d_fsdp_tp_compile() without return types