code: def validate_save_restore(
      trainable_cls: Type,
      config: Optional[Dict] = None,
      num_gpus: int = 0,
  ):
      """Helper method to check if your Trainable class will resume correctly.
  
      Args:
          trainable_cls: Trainable class for evaluation.
          config: Config to pass to Trainable when testing.
          num_gpus: GPU resources to allocate when testing.
          use_object_store: Whether to save and restore to Ray's object
              store. Recommended to set this to True if planning to use
              algorithms that pause training (i.e., PBT, HyperBand).
      """
      assert ray.is_initialized(), "Need Ray to be initialized."
  
      remote_cls = ray.remote(num_gpus=num_gpus)(trainable_cls)
      trainable_1 = remote_cls.remote(config=config)
      trainable_2 = remote_cls.remote(config=config)
  
      from ray.air.constants import TRAINING_ITERATION
  
      for _ in range(3):
          res = ray.get(trainable_1.train.remote())
  
      assert res.get(TRAINING_ITERATION), (
          "Validation will not pass because it requires `training_iteration` "
          "to be returned."
      )
  
      ray.get(trainable_2.restore.remote(trainable_1.save.remote()))
  
      res = ray.get(trainable_2.train.remote())
      assert res[TRAINING_ITERATION] == 4
  
      res = ray.get(trainable_2.train.remote())
      assert res[TRAINING_ITERATION] == 5
      return True
  
imports: import copy
  import glob
  import inspect
  import logging
  import os
  import threading
  import time
  from collections import defaultdict
  from datetime import datetime
  from numbers import Number
  from threading import Thread
  from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union
  import numpy
  import psutil
  import ray
  from ray.util.annotations import DeveloperAPI, PublicAPI
  from ray.air._internal.json import SafeFallbackEncoder
  from ray.air._internal.util import is_nan, is_nan_or_inf
  from ray._private.dict import merge_dicts, deep_update, flatten_dict, unflatten_dict, unflatten_list_dict, unflattened_lookup
label_desc: Use image node_id 10 for calling a global function with example usage: validate_save_restore(trainable_cls, config, num_gpus) and returns: True