code: def test_parallelize_mlp_with_module_api(self):
      inp_size = [12, 10]
      model = MLPModule(self.device_type)
      model_tp = MLPModule(self.device_type)
  
      # Ensure model are initialized the same way.
      self.assertEqual(model.net1.weight, model_tp.net1.weight)
      self.assertEqual(model.net1.bias, model_tp.net1.bias)
      self.assertEqual(model.net2.weight, model_tp.net2.weight)
      self.assertEqual(model.net2.bias, model_tp.net2.bias)
  
      # Parallelize module.
      device_mesh = DeviceMesh(
          self.device_type, torch.arange(self.world_size)
      )
      model_tp = parallelize_module(
          model_tp,
          device_mesh,
          {
              "net1": ColwiseParallel(output_layouts=Replicate()),
              "net2": ColwiseParallel(output_layouts=Replicate()),
          },
      )
      self._compare_module(model, model_tp, inp_size, rank0_only=False)
  
imports: from collections import OrderedDict
  import torch
  from torch.distributed._tensor import DeviceMesh, DTensor, Replicate, Shard
  from torch.distributed.tensor.parallel._utils import _create_1d_device_mesh
  from torch.distributed.tensor.parallel.api import parallelize_module
  from torch.distributed.tensor.parallel.style import ColwiseParallel, PrepareModuleInput, PrepareModuleOutput, RowwiseParallel
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, MLPModule, with_comms
label_desc: Use image node_id 6 for calling the TensorParallelAPITests obj's underlying member method code with example usage: obj.test_parallelize_mlp_with_module_api() without return types