code: def test_fully_shard_non_0_batch_dim(self):
      class WrapperModule(nn.Module):
          def __init__(self):
              super().__init__()
              self.mlp = SimpleMLP()
  
          def forward(self, x):
              reshaped_x = x.t().contiguous()
              return self.mlp(reshaped_x).t()
  
      mod = WrapperModule().cuda(self.rank)
      opt = torch.optim.Adam(mod.parameters(), lr=0.1, fused=True)
  
      train_batch = (
          torch.randn((50, 128), device=torch.device(self.rank)),
          torch.randn((8, 128), device=torch.device(self.rank)),
      )
  
      ddp_mod = DDP(deepcopy(mod), device_ids=[self.rank])
      ddp_opt = torch.optim.Adam(
          ddp_mod.parameters(), lr=0.1, fused=True
      )
  
      self._test_data_parallel(
          mod,
          ddp_mod,
          opt,
          ddp_opt,
          train_batch,
          train_step,
          "fully_shard",
          {"input_batch_dim": 1},
      )
  
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 8 for calling the TestDataParallel obj's underlying member method code with example usage: obj.test_fully_shard_non_0_batch_dim() and returns: self