code: def test_ddp_parity_two_optim(reduce_buffer_size):
      world_size = 2
      backend = dist.Backend.NCCL
      with temp_files_ctx(num=1) as temp_files:
          mp.spawn(
              run_ddp_parity_two_optim,
              args=(
                  world_size,
                  backend,
                  temp_files[0],
                  reduce_buffer_size,
              ),
              nprocs=world_size,
              join=True,
          )
  
file_path: fairscale/tests/nn/data_parallel/test_sharded_ddp_pytorch_parity.py
imports: from contextlib import suppress
  import copy
  import numpy
  import pytest
  import torch
  from torch.cuda.amp import GradScaler
  import torch.distributed
  import torch.multiprocessing
  from torch.nn import Linear, Sequential
  from torch.nn.parallel import DistributedDataParallel
  from fairscale.fair_dev.testing.testing import check_same_model_params, skip_if_no_cuda, skip_if_single_gpu, temp_files_ctx
  from fairscale.internal import torch_version
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS
label_desc: Use image node_id 6 for calling a global function with example usage: test_ddp_parity_two_optim(reduce_buffer_size) without return types