code: def _check_module(self, m1, m2, check_grad=False):
      named_parameters = dict(m1.named_parameters())
      for name, param_m2 in m2.named_parameters():
          if name not in named_parameters:
              print(name, named_parameters.keys())
          self.assertTrue(name in named_parameters)
          param_m1 = named_parameters[name]
          if check_grad:
              param_m2 = param_m2.grad
              param_m1 = param_m1.grad
          if isinstance(param_m2, DTensor):
              replicate = [Replicate()]
              param_m2 = param_m2.redistribute(
                  device_mesh=param_m2.device_mesh, placements=replicate
              ).to_local()
          self.assertEqual(param_m2, param_m1)
  
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 1 for calling the Test2dParallelIntegration obj's underlying member method code with example usage: obj._check_module(m1, m2, check_grad) without return types