code: def test_training_and_can_resume_normally(self, state_dict_type):
      output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
  
      sharding_strategy = "full_shard"
      use_accelerate = state_dict_type == "SHARDED_STATE_DICT"
      launcher = get_launcher(True, use_accelerate=use_accelerate)
      args = self.get_base_args(output_dir, 2, 25).split()
      script = [
          f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"
      ]
      logs = self.run_cmd_and_get_logs(
          use_accelerate,
          sharding_strategy,
          launcher,
          script,
          args,
          output_dir,
      )
  
      # resume from ckpt
      checkpoint = os.path.join(output_dir, "checkpoint-115")
      resume_args = (
          args + f"--resume_from_checkpoint {checkpoint}".split()
      )
  
      is_fsdp_ckpt = os.path.isdir(checkpoint) and (
          # this checks the FSDP state dict when `SHARDED_STATE_DICT` is used
          any(
              FSDP_MODEL_NAME in folder_name
              for folder_name in os.listdir(checkpoint)
              if os.path.isdir(os.path.join(checkpoint, folder_name))
          )
          # this checks the FSDP state dict when `FULL_STATE_DICT` is used
          or os.path.isfile(
              os.path.join(checkpoint, f"{FSDP_MODEL_NAME}.bin")
          )
      )
      self.assertTrue(is_fsdp_ckpt)
  
      logs_resume = self.run_cmd_and_get_logs(
          use_accelerate,
          sharding_strategy,
          launcher,
          script,
          resume_args,
          output_dir,
      )
  
      for log, log1 in zip(logs, logs_resume):
          if "learning_rate" in log:
              self.assertAlmostEqual(
                  log["learning_rate"],
                  log1["learning_rate"],
                  delta=1e-5,
              )
  
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 6 for calling the TrainerIntegrationFSDP obj's underlying member method code with example usage: obj.test_training_and_can_resume_normally(state_dict_type) without return types