code: def run_test_two_optimizers(
      rank, world_size, backend, device, temp_file_name
  ):
      dist.init_process_group(
          init_method="file://" + temp_file_name,
          backend=backend,
          rank=rank,
          world_size=world_size,
      )
      if device == torch.device("cuda"):
          torch.cuda.set_device(rank)
  
      torch.manual_seed(rank)
      np.random.seed(rank)
      model = _DoubleInput().to(device)
  
      parameters = list(model.parameters())
      optimizer_1 = OSS(
          params=parameters[:-10],
          optim=torch.optim.SGD,
          lr=1e-3,
          momentum=0.99,
      )
      optimizer_2 = OSS(
          params=parameters[-10:],
          optim=torch.optim.SGD,
          lr=1e-3,
          momentum=0.99,
      )
      ddp_model = ShardedDataParallel(model, [optimizer_1, optimizer_2])
  
      # Optim loop
      def closure():
          input_tensor = torch.rand((64, 2)).to(device)
          loss = ddp_model(input_tensor, input_tensor).abs().sum()
          loss.backward()
          return loss
  
      for i in range(5):
          optimizer_1.zero_grad()
          optimizer_2.zero_grad()
  
          _ = optimizer_1.step(closure=closure)
          _ = optimizer_2.step(closure=closure)
  
      dist.destroy_process_group()
  
imports: from contextlib import suppress
  import numpy
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn import Linear, Sequential
  from fairscale.fair_dev.testing.testing import GPT2, SGDWithPausingCompute, available_devices, check_same_models_across_ranks, skip_if_less_than_four_gpu, skip_if_no_cuda, skip_if_single_gpu, temp_files_ctx
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS
label_desc: Use image node_id 19 for calling a global function with example usage: run_test_two_optimizers(rank, world_size, backend, device, temp_file_name) and returns: loss