code: def _test_train_step(
      self,
      train_step,
      num_iters,
      batch_size,
      layers,
      dim,
      use_fused_optimizer=False,
  ):
      def _ddp_train_step(model, optim, batch):
          model(batch).sum().backward()
          with torch.no_grad():
              for p in model.parameters():
                  p.grad *= self.world_size
          optim.step()
          optim.zero_grad()
  
      model, optim, ddp_model, ddp_optim = self._init(
          batch_size,
          layers,
          dim,
          foreach=(not use_fused_optimizer),
          fused=use_fused_optimizer,
      )
      for i in range(num_iters):
          batch = torch.randn(batch_size, dim).cuda()
          kwargs = (
              {} if i < num_iters - 1 else {"last_train_step": True}
          )
          out = train_step(model, optim, batch, **kwargs)
          ddp_out = _ddp_train_step(ddp_model, ddp_optim, batch)
      self.assertEqual(
          list(ddp_model.parameters()), list(model.parameters())
      )
  
imports: import unittest
  from copy import deepcopy
  from functools import wraps
  from unittest.mock import MagicMock
  import torch
  import torch.nn
  from torch.distributed._spmd.api import compile
  from torch.distributed._spmd.gm_transformation import GraphModuleTransformation
  from torch.distributed._spmd.graph_optimization import _optimized_func, comm_fusion_with_concat, find_all_descendants, get_all_fused_optimizer_blocks, graph_optimization_pass, iter_move_grads_and_optimizers, remove_copy_from_optimizer, schedule_comm_wait, split_fused_optimizer
  from torch.distributed._spmd.graph_utils import find_node
  from torch.distributed._spmd.iter_graph_module import IterGraphModule
  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, skipIfRocm
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
  from torch.utils._triton import has_triton
label_desc: Use image node_id 3 for calling the TransformationTest obj's underlying member method code with example usage: obj._test_train_step(train_step, num_iters, batch_size, layers, dim, use_fused_optimizer) without return types