code: def test_distributed_loading_and_training(
      self, mixed_precision, flatten_parameters
  ):
      run_distributed_test(
          [0, 1],
          func=_dist_load_and_train,
          test_dir=self.TEST_DIR,
          mixed_precision=mixed_precision,
          flatten_parameters=flatten_parameters,
      )
  
      # Now make sure the sharded saved state is exactly the same as the original state when consolidated.
      original_state = torch.load(
          self.TEST_DIR / "state.pt", map_location="cpu"
      )
      consolidated_state = (
          FairScaleFsdpWrappedModel.consolidate_sharded_state(
              [
                  self.TEST_DIR / "state_worker0.pt",
                  self.TEST_DIR / "state_worker1.pt",
              ]
          )
      )
  
      assert set(original_state.keys()) - set(
          consolidated_state.keys()
      ) == {
          "decoder.linear.weight"  # won't be in the state dict since param is tied to embedding.weight
      }
  
      for key, tensor0 in original_state.items():
          if key not in consolidated_state:
              continue
          # Need to give extra tolerance for buffers when `mixed_precision` is `True`.
          tolerance = (
              None
              if not mixed_precision or "buffer" not in key
              else 1e-3
          )
          tensor1 = consolidated_state[key]
          assert_allclose(
              tensor0,
              tensor1,
              msg=f"{key} is off in consolidated state.\nExpected:\n{tensor0}\nGot:\n{tensor1}",
              atol=tolerance,
              rtol=tolerance,
          )
  
imports: import os
  from os import PathLike
  from typing import Union, Dict, Optional
  import torch
  from torch.cuda import amp
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.testing import AllenNlpTestCase, run_distributed_test, requires_multi_gpu
  from allennlp.nn.util import load_state_dict_distributed
  from allennlp.nn.parallel import FairScaleFsdpAccelerator, FairScaleFsdpWrappedModel, ShardedModuleMixin
label_desc: Use image node_id 1 for calling the TestFairScaleFsdpAccelerator obj's underlying member method code with example usage: obj.test_distributed_loading_and_training(mixed_precision, flatten_parameters) without return types