code: def test_consolidate_weights(self, transformer):
      config = {
          "mixed_precision": True,
          "flatten_parameters": True,
          "compute_dtype": torch.float32,
      }
      world_size = min(torch.cuda.device_count(), 4)
      if USE_TEMPFILE:
          with tempfile.TemporaryDirectory() as d:
              paths = [
                  os.path.join(d, f"checkpoint_{rank}.pt")
                  for rank in range(world_size)
              ]
              test_fn = functools.partial(
                  self._test_consolidate_weights,
                  config,
                  transformer=transformer,
                  paths=paths,
              )
              spawn_and_init(test_fn, world_sizes=[world_size])
      else:
          paths = [
              f"checkpoint_{rank}.pt" for rank in range(world_size)
          ]
          test_fn = functools.partial(
              self._test_consolidate_weights,
              config,
              transformer=transformer,
              paths=paths,
          )
          spawn_and_init(test_fn, world_sizes=[world_size])
  
imports: import functools
  import os
  import tempfile
  from parameterized import parameterized
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.optim import Adam
  from fairscale.fair_dev.testing.testing import in_temporary_directory, skip_if_single_gpu, temp_files_ctx
  from fairscale.nn import FullyShardedDataParallel
  from tests.nn.data_parallel.test_fsdp import DistributedTest, MixtureOfExperts, rename_test, spawn_and_init
label_desc: Use image node_id 1 for calling the TestConsolidatedWeights obj's underlying member method code with example usage: obj.test_consolidate_weights(transformer) without return types