code: def test_two_optimizers():
      # Check that the ShardedDDP wrapper accepts tuple(tensors) as inputs
      world_size = 2
      backend = "gloo"
      device = "cpu"
      with temp_files_ctx(num=1) as temp_files:
          mp.spawn(
              run_test_two_optimizers,
              args=(world_size, backend, device, temp_files[0]),
              nprocs=world_size,
              join=True,
          )
  
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 20 for calling a global function with example usage: test_two_optimizers() without return types