code: def test_data_parallel_batch_dim_analysis(self):
      # test batch dim analysis by adding a few ops that changes
      # the batch dim in non-trival ways
  
      class WrapperModule(nn.Module):
          def __init__(self, *args, **kwargs) -> None:
              super().__init__(*args, **kwargs)
              self.mlp = SimpleMLP()
  
          def forward(self, x):
              output = self.mlp(x)
              new_output = output.clone().view(-1)
              squeezed_out = new_output.squeeze()
              unsqueezed_out = squeezed_out.unsqueeze(0)
              output = output + 0.1 * unsqueezed_out.view(
                  output.shape[0], -1
              )
  
              # test factory ops with data parallel expansion
              arange = torch.arange(
                  output.shape[1], device=output.device
              )
              ones = torch.ones(output.shape, device=output.device)
              added_arange = arange.unsqueeze(0) + ones
  
              # test repeat logic
              zeros = torch.zeros(output.shape[1], device=output.device)
              repeated_zeros = zeros.unsqueeze(0).repeat(
                  output.shape[0], 1
              )
  
              output = output + added_arange + repeated_zeros
  
              return output
  
      for parallel_mode in ["replicate", "fully_shard"]:
          mod = WrapperModule().cuda(self.rank)
          opt = torch.optim.SGD(mod.parameters(), lr=0.1)
  
          train_batch = (
              torch.randn((128, 50), device=torch.device(self.rank)),
              torch.randn((128, 8), device=torch.device(self.rank)),
          )
  
          ddp_mod = DDP(deepcopy(mod), device_ids=[self.rank])
          ddp_opt = torch.optim.SGD(ddp_mod.parameters(), lr=0.1)
          self._test_data_parallel(
              mod,
              ddp_mod,
              opt,
              ddp_opt,
              train_batch,
              train_step,
              parallel_mode,
          )
  
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 7 for calling the TestDataParallel obj's underlying member method code with example usage: obj.test_data_parallel_batch_dim_analysis() and returns: output