code: def _test_data_parallel(
      self,
      mod,
      ddp_mod,
      opt,
      ddp_opt,
      inp,
      train_step,
      data_parallel_mode,
      data_parallel_options=None,
  ):
      ddp_inp = deepcopy(inp)
  
      # need one step to warm up optimizers
      train_step(mod, opt, inp)
      opt.zero_grad()
      # DDP run full train step once to align with the warmup
      train_step(ddp_mod, ddp_opt, ddp_inp)
      ddp_opt.zero_grad()
  
      # train a DDP model once manually as DDP grads are different
      torch.sum(ddp_mod(ddp_inp[0]) - ddp_inp[1]).backward()
      ddp_opt.step()
  
      # compile it with replicate and run step once
      data_parallel_options = data_parallel_options or {}
      compiled_fn = compile(
          parallel_mode=DataParallel(
              data_parallel_mode, **data_parallel_options
          )
      )(train_step)
      compiled_fn(mod, opt, inp)
  
      for p1, p2 in zip(mod.parameters(), ddp_mod.parameters()):
          # mod parameters are DTensors, convert to local tensor before compare
          if data_parallel_mode == "fully_shard":
              # gather the shards for comparison
              p1_replica = p1.redistribute(placements=[Replicate()])
              p1_local_param = p1_replica.to_local()
          else:
              p1_local_param = p1.to_local()
          self.assertEqual(p1_local_param, p2)
  
imports: from copy import deepcopy
  import torch
  import torch.nn
  from torch.distributed._spmd.api import compile
  from torch.distributed._spmd.parallel_mode import DataParallel
  from torch.distributed._tensor import Replicate
  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, with_comms
label_desc: Use image node_id 2 for calling the TestDataParallel obj's underlying member method code with example usage: obj._test_data_parallel(mod, ddp_mod, opt, ddp_opt, inp, train_step, data_parallel_mode, data_parallel_options) without return types