code: def test_modules_w_meta_dtensor(self):
      model = DummyMLP("meta")
      device_mesh = self.build_device_mesh()
      parallelize_plan = {
          "net1": ColwiseParallel(),
          "net2": RowwiseParallel(),
      }
      model_tp = parallelize_module(
          model, device_mesh, parallelize_plan
      )
      model_tp.to_empty(device=self.device_type)
      model_tp.reset_parameters()
      optim = torch.optim.SGD(model_tp.parameters(), lr=0.1)
      model_regular = DummyMLP(self.device_type)
      model_regular_tp = parallelize_module(
          model_regular, device_mesh, parallelize_plan
      )
      optim_regular = torch.optim.SGD(
          model_regular_tp.parameters(), lr=0.1
      )
      model_regular_tp.reset_parameters()
      torch.manual_seed(0)
      inp = torch.randn(20, 5, device=self.device_type)
  
      output = model_tp(inp)
      output_regular = model_regular_tp(inp)
      self.assertEqual(output, output_regular)
  
      output.sum().backward()
      output_regular.sum().backward()
  
      optim.step()
      optim_regular.step()
  
      torch.manual_seed(1)
      inp = torch.randn(20, 5, device=self.device_type)
      self.assertEqual(model_tp(inp), model_regular_tp(inp))
  
imports: import torch
  import torch.nn.functional
  from numpy.testing import assert_array_equal
  from torch.distributed._functional_collectives import AsyncCollectiveTensor
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor, init_device_mesh
  from torch.distributed._tensor.placement_types import _Partial, Replicate, Shard
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, 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 3 for calling the DTensorTest obj's underlying member method code with example usage: obj.test_modules_w_meta_dtensor() without return types