code: def test_fsdp_config(self, sharding_strategy, dtype):
      output_dir = self.get_auto_remove_tmp_dir()
      kwargs = {
          "output_dir": output_dir,
          "train_len": 128,
          "save_steps": 5,
          "learning_rate": 0.1,
          "fsdp": f"{sharding_strategy} offload auto_wrap",
          "fsdp_config": self.fsdp_config,
      }
      kwargs[dtype] = True
      with mockenv_context(**self.dist_env_1_gpu):
          trainer = get_regression_trainer(**kwargs)
          self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
          self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
          self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
          for k, v in trainer.args.fsdp_config.items():
              self.assertEqual(v, self.fsdp_config[k])
          self.assertEqual(
              os.environ.get("ACCELERATE_USE_FSDP", "false"), "true"
          )
  
imports: import itertools
  import os
  import unittest
  from functools import partial
  from parameterized import parameterized
  import tests.trainer.test_trainer
  from tests.trainer.test_trainer import TrainerIntegrationCommon
  from transformers import is_torch_available
  from transformers.testing_utils import TestCasePlus, backend_device_count, execute_subprocess_async, mockenv_context, require_accelerate, require_fsdp, require_torch_accelerator, require_torch_multi_accelerator, slow, torch_device
  from transformers.trainer_callback import TrainerState
  from transformers.trainer_utils import FSDPOption, set_seed
  from transformers.utils import is_accelerate_available, is_torch_bf16_available_on_device
label_desc: Use image node_id 3 for calling the TrainerIntegrationFSDP obj's underlying member method code with example usage: obj.test_fsdp_config(sharding_strategy, dtype) without return types