code: def test_automatic_loading(self):
      with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42)
          model = DummyModel()
          optimizer = torch.optim.Adam(
              params=model.parameters(), lr=1e-3
          )
          scheduler = torch.optim.lr_scheduler.StepLR(
              optimizer, step_size=1, gamma=0.99
          )
          train_dataloader, valid_dataloader = dummy_dataloaders()
          project_config = ProjectConfiguration(
              automatic_checkpoint_naming=True
          )
          # Train baseline
          accelerator = Accelerator(
              project_dir=tmpdir, project_config=project_config
          )
          (
              model,
              optimizer,
              train_dataloader,
              valid_dataloader,
              scheduler,
          ) = accelerator.prepare(
              model,
              optimizer,
              train_dataloader,
              valid_dataloader,
              scheduler,
          )
          # Save initial
          accelerator.save_state(
              safe_serialization=self.use_safetensors
          )
          train(
              2,
              model,
              train_dataloader,
              optimizer,
              accelerator,
              scheduler,
          )
          (a2, b2) = model.a.item(), model.b.item()
          # Save a first time
          accelerator.save_state(
              safe_serialization=self.use_safetensors
          )
          train(
              1,
              model,
              train_dataloader,
              optimizer,
              accelerator,
              scheduler,
          )
          (a3, b3) = model.a.item(), model.b.item()
  
          # Load back in the last saved checkpoint, should point to a2, b2
          accelerator.load_state()
          self.assertNotEqual(a3, model.a.item())
          self.assertNotEqual(b3, model.b.item())
          self.assertEqual(a2, model.a.item())
          self.assertEqual(b2, model.b.item())
  
imports: import inspect
  import logging
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import uuid
  from contextlib import contextmanager
  import pytest
  import torch
  from parameterized import parameterized_class
  from torch import nn
  from torch.utils.data import DataLoader, TensorDataset
  from accelerate import Accelerator
  from accelerate.test_utils import device_count, execute_subprocess_async, require_non_cpu
  from accelerate.utils import ProjectConfiguration, set_seed
label_desc: Use image node_id 7 for calling the CheckpointTest obj's underlying member method code with example usage: obj.test_automatic_loading() without return types