code: def run_test_memory_usage_localsgd_with_slowmo(
      rank: int,
      world_size: int,
      tempfile: str,
      slowmo_init_dict: Dict[Any, Any],
      use_gossip_data_parallel: bool = False,
      *_,
      **__,
  ) -> int:
      int_devices = get_gpus_for_rank(world_size)[rank][:1]
      devices = [torch.device("cuda:" + str(i)) for i in int_devices]
  
      torch.cuda.set_device(devices[0])
      torch.cuda.reset_peak_memory_stats(devices[0])
      initial_max_memory = torch.cuda.max_memory_allocated(devices[0])
  
      local_batch_size = len(devices)
      global_batch_size = world_size * local_batch_size
  
      if not torch.distributed.is_initialized():
          torch.distributed.init_process_group(
              "nccl",
              init_method=f"file://{tempfile}",
              rank=rank,
              world_size=world_size,
          )
      if use_gossip_data_parallel:
          model: nn.Module = gossip.SlowMoDistributedDataParallel(
              LargeNet().to(devices[0]),
              comm_device=devices[0],
              process_rank=rank,
              process_world_size=world_size,
              **slowmo_init_dict,
          )
      else:
          model = LargeNet().to(devices[0])
  
      input = torch.randn(global_batch_size, 2).to(devices[0])
      target = torch.randn(global_batch_size, 4).to(devices[0])
  
      model_optimizer = torch.optim.SGD(
          model.parameters(), lr=1, momentum=0.5
      )
  
      # check two model parameters over 3 iterations
      for iteration in range(3):
          step_model(
              model,
              input[
                  rank
                  * local_batch_size : (rank + 1)
                  * local_batch_size
              ],
              target[
                  rank
                  * local_batch_size : (rank + 1)
                  * local_batch_size
              ],
          )
  
          update_parameters(model_optimizer)
          if hasattr(model, "perform_slowmo"):
              model.perform_slowmo(model_optimizer)  # type: ignore
  
          # Shuffle the input so that distributed input is different
          torch.manual_seed(1337 + iteration)
          input = input[torch.randperm(global_batch_size)]
  
      torch.cuda.synchronize(devices[0])
      final_max_memory = torch.cuda.max_memory_allocated(devices[0])
      # print(f"{initial_max_memory}, {final_max_memory}")
  
      if torch.distributed.is_initialized():
          torch.distributed.destroy_process_group()
  
      return final_max_memory - initial_max_memory
  
imports: import copy
  import os
  import tempfile
  from typing import Any, Dict, List, Tuple, Type
  import unittest
  import pytest
  import torch
  from torch import nn
  import torch.distributed
  import torch.nn.functional
  import fairscale.experimental.nn.data_parallel.gossip
  from fairscale.fair_dev.testing.testing import skip_if_single_gpu, spawn_for_all_world_sizes
label_desc: Use image node_id 9 for calling a global function with example usage: run_test_memory_usage_localsgd_with_slowmo(rank, world_size, tempfile, slowmo_init_dict, use_gossip_data_parallel) and returns: unknown