code: def test_consolidate_optimizer_diff_world_size(
      self, optim_fn, transformer
  ):
      if torch.cuda.device_count() < 4:
          raise unittest.SkipTest("This test requires at least 4 GPUs.")
      config = {"mixed_precision": True, "flatten_parameters": True}
      config["compute_dtype"] = torch.float32
      test_fn = functools.partial(
          self._test_consolidated_optimizer,
          config,
          optim_fn=Adam,
          transformer=transformer,
      )
  
      spawn_and_init_multiple_groups(
          test_fn, world_sizes=[min(torch.cuda.device_count(), 4)]
      )
  
imports: import copy
  import functools
  from time import time
  import unittest
  from parameterized import parameterized
  import torch
  from torch import nn
  from torch.optim import SGD, Adadelta, Adam
  from fairscale.fair_dev.testing.testing import dist_init, objects_are_equal, spawn_for_all_world_sizes
  from fairscale.internal.params import recursive_copy_to_device
  from fairscale.nn.data_parallel import FullyShardedDataParallel, get_fsdp_instances
  from fairscale.nn.data_parallel.fsdp_optim_utils import is_singleton_tensor
  from .test_fsdp import DistributedTest, DummyProcessGroup, MixtureOfExperts, TransformerWithSharedParams, rename_test, spawn_and_init
label_desc: Use image node_id 2 for calling the TestOptimizerUtils obj's underlying member method code with example usage: obj.test_consolidate_optimizer_diff_world_size(optim_fn, transformer) without return types