code: def test_save_restore(self) -> None:
      input_dim = 2
      dataset_len = 10
      batch_size = 2
      max_epochs = 2
      expected_steps_per_epoch = math.ceil(dataset_len / batch_size)
      save_every_n_train_steps = 2
  
      my_unit = DummyTrainUnit(input_dim=input_dim)
      dataloader = generate_random_dataloader(
          dataset_len, input_dim, batch_size
      )
      expected_paths: List[str] = []
      with tempfile.TemporaryDirectory() as temp_dir:
          cumulative_steps = 0
          for epoch in range(max_epochs):
              for _ in range(
                  save_every_n_train_steps,
                  expected_steps_per_epoch + 1,
                  save_every_n_train_steps,
              ):
                  cumulative_steps += save_every_n_train_steps
                  expected_paths.append(
                      os.path.join(
                          temp_dir,
                          f"epoch_{epoch}_step_{cumulative_steps}",
                      )
                  )
          dcp_cb = DistributedCheckpointSaver(
              temp_dir,
              save_every_n_train_steps=save_every_n_train_steps,
          )
          train(
              my_unit,
              dataloader,
              max_epochs=max_epochs,
              callbacks=[dcp_cb],
          )
  
          end_num_steps_completed = (
              my_unit.train_progress.num_steps_completed
          )
          self.assertGreater(len(expected_paths), 0)
          dcp_cb.restore(expected_paths[0], my_unit)
          restored_num_steps_completed = (
              my_unit.train_progress.num_steps_completed
          )
          # A snapshot is saved every n steps
          # so the first snapshot's progress will be equal to save_every_n_train_steps
          self.assertNotEqual(
              restored_num_steps_completed, end_num_steps_completed
          )
          self.assertEqual(
              restored_num_steps_completed, save_every_n_train_steps
          )
  
imports: import math
  import os
  import shutil
  import tempfile
  import unittest
  from typing import Any, Dict, Iterator, List
  from unittest import mock
  from unittest.mock import MagicMock, patch
  import torch
  from torch import nn
  from torch.utils.data import DataLoader
  from torchsnapshot.test_utils import assert_state_dict_eq, check_state_dict_eq
  from torchtnt.framework._test_utils import DummyAutoUnit, DummyTrainUnit, generate_random_dataloader
  from torchtnt.framework.callbacks.checkpointer_types import RestoreOptions
  from torchtnt.framework.callbacks.dcp_saver import DistributedCheckpointSaver
  from torchtnt.framework.train import train
  from torchtnt.utils.distributed import get_global_rank
  from torchtnt.utils.env import seed
  from torchtnt.utils.test_utils import spawn_multi_process
label_desc: Use image node_id 1 for calling the DistributedCheckpointSaverTest obj's underlying member method code with example usage: obj.test_save_restore() without return types