code: def test_save_load_state(self):
      fws = ["tf2", "torch"]
      scaling_modes = REMOTE_SCALING_CONFIGS.keys()
      test_iterator = itertools.product(fws, scaling_modes)
  
      batch = SampleBatch(FAKE_BATCH)
      for fw, scaling_mode in test_iterator:
          print(
              f"Testing framework: {fw}, scaling mode: {scaling_mode}."
          )
          env = gym.make("CartPole-v1")
  
          scaling_config = REMOTE_SCALING_CONFIGS[scaling_mode]
          initial_learner_group = get_learner_group(
              fw, env, scaling_config, eager_tracing=True
          )
  
          # checkpoint the initial learner state for later comparison
          initial_learner_checkpoint_dir = (
              tempfile.TemporaryDirectory().name
          )
          initial_learner_group.save_state(
              initial_learner_checkpoint_dir
          )
          initial_learner_group_weights = (
              initial_learner_group.get_weights()
          )
  
          # do a single update
          initial_learner_group.update(
              batch.as_multi_agent(), reduce_fn=None
          )
  
          # checkpoint the learner state after 1 update for later comparison
          learner_after_1_update_checkpoint_dir = (
              tempfile.TemporaryDirectory().name
          )
          initial_learner_group.save_state(
              learner_after_1_update_checkpoint_dir
          )
  
          # remove that learner, construct a new one, and load the state of the old
          # learner into the new one
          initial_learner_group.shutdown()
          del initial_learner_group
          new_learner_group = get_learner_group(
              fw, env, scaling_config, eager_tracing=True
          )
          new_learner_group.load_state(
              learner_after_1_update_checkpoint_dir
          )
  
          # do another update
          results_with_break = new_learner_group.update(
              batch.as_multi_agent(), reduce_fn=None
          )
          weights_after_1_update_with_break = (
              new_learner_group.get_weights()
          )
          new_learner_group.shutdown()
          del new_learner_group
  
          # construct a new learner group and load the initial state of the learner
          learner_group = get_learner_group(
              fw, env, scaling_config, eager_tracing=True
          )
          learner_group.load_state(initial_learner_checkpoint_dir)
          check(
              learner_group.get_weights(), initial_learner_group_weights
          )
          learner_group.update(batch.as_multi_agent(), reduce_fn=None)
          results_without_break = learner_group.update(
              batch.as_multi_agent(), reduce_fn=None
          )
          weights_after_1_update_without_break = (
              learner_group.get_weights()
          )
          learner_group.shutdown()
          del learner_group
  
          # compare the results of the two updates
          check(results_with_break, results_without_break)
          check(
              weights_after_1_update_with_break,
              weights_after_1_update_without_break,
          )
  
file_path: ray/release/rllib_tests/checkpointing_tests/test_learner_group_checkpointing.py
imports: import gymnasium
  import itertools
  import numpy
  import tempfile
  import unittest
  import ray
  from ray.rllib.core.learner.scaling_config import LearnerGroupScalingConfig
  from ray.rllib.core.testing.utils import get_learner_group
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 3 for calling the TestLearnerGroupCheckpointing obj's underlying member method code with example usage: obj.test_save_load_state() without return types