code: def run_one_step(
      rank,
      world_size,
      backend,
      device,
      temp_file_name,
      broadcast_buffers,
      grad_accumulation,
      reduce_buffer_size,
      optimizer_type,
      reduce_fp16=False,
  ):
      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)
  
      # Any model works. Add one different buffer per rank
      model = _get_mlp()
      model.register_buffer("test_buffer", torch.ones(1) * rank)
      model.to(device)
  
      next(
          model.parameters()
      ).requires_grad = False  # Test non-trainable parameters
  
      optimizer_settings = {"lr": 1e-3, "momentum": 0.99}
      if optimizer_type == SGDWithPausingCompute:
          optimizer_settings["rank"] = rank
  
      optimizer = OSS(
          params=model.parameters(),
          optim=optimizer_type,
          **optimizer_settings,
      )
      ddp_model = ShardedDataParallel(
          model,
          optimizer,
          broadcast_buffers=broadcast_buffers,
          reduce_buffer_size=reduce_buffer_size,
          reduce_fp16=reduce_fp16,
      )
  
      # The model should be synchronized in between the ranks at ShardedDataParallel construction time, check that
      check_same_models_across_ranks(
          ddp_model,
          dist.group.WORLD,
          params_should_be_equal=True,
          check_broadcast_buffers=broadcast_buffers,
      )
  
      # Optim loop
      def closure():
          ddp_model.zero_grad(set_to_none=True)
  
          with ddp_model.no_sync() if grad_accumulation else suppress():
              input_tensor = torch.rand((64, 2)).to(device)
              loss = ddp_model(input_tensor).abs().sum()
  
              # If grad_accumulation, we can check after the forward that the models are different
              # (not synced)
              if grad_accumulation:
                  check_same_models_across_ranks(
                      ddp_model,
                      dist.group.WORLD,
                      params_should_be_equal=False,
                      check_broadcast_buffers=True,
                  )
  
              loss.backward()
          return loss
  
      # The models should stay the same in between the ranks
      for i in range(5):
          _ = optimizer.step(closure=closure)
  
          # For a sync of all the streams
          if device.type == torch.device("cuda").type:
              torch.cuda.synchronize(device=device)
  
          # when running on cpu/gloo the "nodes" are not really different
          same_params = (
              device == torch.device("cpu") or not grad_accumulation
          )
          check_same_models_across_ranks(
              ddp_model,
              dist.group.WORLD,
              params_should_be_equal=same_params,
              check_broadcast_buffers=broadcast_buffers,
          )
  
      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 2 for calling a global function with example usage: run_one_step(rank, world_size, backend, device, temp_file_name, broadcast_buffers, grad_accumulation, reduce_buffer_size, optimizer_type, reduce_fp16) and returns: loss