code: def test_tp_compile_fullgraph(self, is_seq_parallel):
      mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
  
      model = SimpleModel(self.device_type)
  
      colwise_style = (
          ColwiseParallel(input_layouts=Shard(0))
          if is_seq_parallel
          else ColwiseParallel()
      )
      rowwise_style = (
          RowwiseParallel(output_layouts=Shard(0))
          if is_seq_parallel
          else RowwiseParallel()
      )
  
      if is_seq_parallel:
          # use input preparation to test out the compile of it
          prepare_module_input = PrepareModuleInput(
              input_layouts=Shard(0),
              desired_input_layouts=Replicate(),
          )
          prepare_module_out = PrepareModuleOutput(
              output_layouts=Replicate(),
              desired_output_layouts=Shard(0),
          )
          plan = {
              "mlp_0": prepare_module_input,
              "mlp_0.net1": ColwiseParallel(),
              "mlp_0.net2": rowwise_style,
              "mlp_1.net1": colwise_style,
              "mlp_1.net2": RowwiseParallel(),
              "mlp_1": prepare_module_out,
          }
      else:
          plan = {
              "mlp_0.net1": colwise_style,
              "mlp_0.net2": rowwise_style,
              "mlp_1.net1": colwise_style,
              "mlp_1.net2": rowwise_style,
          }
  
      model = parallelize_module(
          model,
          mesh,
          parallelize_plan=plan,
      )
      rng_seed = self.rank if is_seq_parallel else 0
      torch.manual_seed(rng_seed)
      inp = torch.rand(20, 10, device=self.device_type)
      out = model(inp)
      compiled_mod = torch.compile(
          model, backend="aot_eager", fullgraph=True
      )
      compiled_out = compiled_mod(inp)
      self.assertEqual(compiled_out, out)
  
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 2 for calling the TestDTensorCompileE2E obj's underlying member method code with example usage: obj.test_tp_compile_fullgraph(is_seq_parallel) without return types