code: def test_2d_ddp_integration_functionality(self) -> None:
      model, twod_model, dp_pg = init_model(self.device_type)
      optim = torch.optim.Adam(model.parameters(), lr=LR)
      twod_optim = torch.optim.Adam(twod_model.parameters(), lr=LR)
  
      # Create Input
      input_seed = dist.get_rank(dp_pg)
      torch.manual_seed(input_seed + 1)
      input = torch.rand(4, 10, device=self.device_type)
  
      output = model(input)
      twod_output = twod_model(input)
      self.assertEqual(output, twod_output)
  
      output.sum().backward()
      twod_output.sum().backward()
      self._check_module(model, twod_model, check_grad=True)
  
      optim.step()
      twod_optim.step()
      self._check_module(model, twod_model)
  
      torch.manual_seed(input_seed + 1004)
      input = torch.rand(16, 10, device=self.device_type)
  
      output = model(input)
      twod_output = twod_model(input)
      self.assertEqual(output, twod_output)
  
file_path: pytorch/test/distributed/tensor/parallel/test_ddp_2d_parallel.py
imports: import torch
  import torch.distributed
  from torch.distributed._tensor import DeviceMesh, DTensor, Replicate
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, RowwiseParallel
  from torch.distributed.tensor.parallel.ddp import _pre_dp_module_transform
  from torch.nn.parallel import DistributedDataParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  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 2 for calling the Test2dParallelIntegration obj's underlying member method code with example usage: obj.test_2d_ddp_integration_functionality() without return types