code: def test_tp_transform_e2e(self):
      torch.manual_seed(0)
      model = MLPListModule(2).to(device=self.device_type)
      inputs = (torch.randn((10, 12)).to(device=self.device_type),)
      parallel_strategies: Dict[str, ParallelStyle] = {
          "mlps.0.0": ColwiseParallel,
          "mlps.0.2": RowwiseParallel,
          "mlps.1.0": ColwiseParallel,
          "mlps.1.2": RowwiseParallel,
      }
  
      with torch.inference_mode():
          res = model(*inputs)
      exported_program = torch._export.export(
          model,
          inputs,
          constraints=None,
      )
      tp_exported_program = tensor_parallel_transformation(
          exported_program,
          self.rank,
          self.world_size,
          self.device_type,
          parallel_strategies,
      )
      tp_model = tp_exported_program.module()
      with torch.inference_mode():
          tp_res = tp_model(*inputs)
      self.assertEqual(res, tp_res)
      # Expect all_reduce to be inserted at the end of each MLP
      self.assert_has_c10d_ops(
          tp_exported_program.graph_module,
          {
              "c10d_functional.all_reduce.default": 2,
              "c10d_functional.wait_tensor.default": 2,
          },
      )
  
imports: from collections import defaultdict
  from typing import Dict
  import torch
  from torch.distributed._tensor.experimental.tp_transform import tensor_parallel_transformation
  from torch.distributed.tensor.parallel.style import ColwiseParallel, ParallelStyle, RowwiseParallel
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
label_desc: Use image node_id 4 for calling the TensorParallelTest obj's underlying member method code with example usage: obj.test_tp_transform_e2e() without return types