code: def train_step(model, optim, train_batch):
      def loss_fn(out, labels):
          return (out - labels).sum()
  
      optim.zero_grad()
      inputs, labels = train_batch
  
      out = model(inputs)
      loss = loss_fn(out, labels)
      loss.backward()
      optim.step()
      return loss
  
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 1 for calling a global function with example usage: train_step(model, optim, train_batch) and returns: loss, unknown