code: def run_test_slowmo_with_slowmo_freq_1(
      rank: int,
      world_size: int,
      tempfile: str,
      _filename_rpc: str,
      slowmo_init_dict: Dict[Any, Any],
  ) -> None:
      """
      Note: we pass down `device_ids` all the way to SlowMoDistributedDataParallel
      as part of the test. Below you find tests that either use a list of
      integers, a list of `torch.Device` instances, or an empty list.
      The `devices` argument is used to control placement of the model and
      must always be specified as list of `torch.Device` instances.
      """
  
      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])
      local_batch_size = len(devices)
      global_batch_size = world_size * local_batch_size
  
      (
          model,
          slowmo_model,
          input,
          target,
      ) = _prepare_single_device_module(
          rank,
          world_size,
          tempfile,
          devices,
          slowmo_init_dict,
          global_batch_size,
      )
      model_optimizer = torch.optim.SGD(
          model.parameters(),
          lr=slowmo_model.slowmo_lr,
          momentum=slowmo_model.slowmo_momentum,
      )
      slowmo_model_optimizer = torch.optim.SGD(
          slowmo_model.module.parameters(), lr=1, momentum=0
      )
      slowmo_model._init_global_momentum_buffers(slowmo_model_optimizer)
  
      # check two model parameters over 3 iterations
      for iteration in range(3):
          # single cpu/gpu training
          step_model(model, input, target)
  
          # SlowMo training, SlowMo scatters subsets of input_cpu to nodes/GPUs
          step_model(
              slowmo_model,
              input[
                  rank
                  * local_batch_size : (rank + 1)
                  * local_batch_size
              ],
              target[
                  rank
                  * local_batch_size : (rank + 1)
                  * local_batch_size
              ],
          )
  
          # Update weights and run a second iteration to shake out errors
          update_parameters(model_optimizer)
          update_parameters(slowmo_model_optimizer)
          slowmo_model.perform_slowmo(slowmo_model_optimizer)
  
          for a, b in zip(
              model.parameters(), slowmo_model.module.parameters()
          ):
              assert torch.allclose(a, b)
  
          # Shuffle the input so that DDP input is different
          torch.manual_seed(1337 + iteration)
          input = input[torch.randperm(global_batch_size)]
  
      if torch.distributed.is_initialized():
          torch.distributed.destroy_process_group()
  
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 6 for calling a global function with example usage: run_test_slowmo_with_slowmo_freq_1(rank, world_size, tempfile, _filename_rpc, slowmo_init_dict) without return types