code: def test_update_multigpu(self):
      return
  
      fws = ["torch", "tf2"]
      scaling_modes = ["multi-gpu-ddp", "remote-gpu"]
      test_iterator = itertools.product(fws, scaling_modes)
  
      for fw, scaling_mode in test_iterator:
          print(
              f"Testing framework: {fw}, scaling mode: {scaling_mode}."
          )
          env = gym.make("CartPole-v1")
  
          config_overrides = REMOTE_CONFIGS[scaling_mode]
          config = BaseTestingAlgorithmConfig().update_from_dict(
              config_overrides
          )
          learner_group = config.build_learner_group(env=env)
          reader = get_cartpole_dataset_reader(batch_size=1024)
  
          min_loss = float("inf")
          for iter_i in range(1000):
              batch = reader.next()
              results = learner_group.update_from_batch(
                  batch=batch.as_multi_agent(), reduce_fn=None
              )
  
              loss = np.mean(
                  [
                      res[ALL_MODULES][Learner.TOTAL_LOSS_KEY]
                      for res in results
                  ]
              )
              min_loss = min(loss, min_loss)
              print(
                  f"[iter = {iter_i}] Loss: {loss:.3f}, Min Loss: {min_loss:.3f}"
              )
              # The loss is initially around 0.69 (ln2). When it gets to around
              # 0.57 the return of the policy gets to around 100.
              if min_loss < 0.57:
                  break
  
              for res1, res2 in zip(results, results[1:]):
                  self.assertEqual(
                      res1[DEFAULT_POLICY_ID]["mean_weight"],
                      res2[DEFAULT_POLICY_ID]["mean_weight"],
                  )
  
          self.assertLess(min_loss, 0.57)
  
          # Make sure the learner_group resources are freed up so that we don't
          # autoscale.
          learner_group.shutdown()
          del learner_group
  
imports: import gymnasium
  import itertools
  import numpy
  from typing import Any, Dict, List
  import tempfile
  import unittest
  import ray
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.ppo.tests.test_ppo_learner import FAKE_BATCH
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.core.testing.tf.bc_learner import BCTfLearner
  from ray.rllib.core.testing.tf.bc_module import DiscreteBCTFModule
  from ray.rllib.core.testing.utils import add_module_to_learner_or_learner_group
  from ray.rllib.core.testing.testing_learner import BaseTestingAlgorithmConfig
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch, MultiAgentBatch
  from ray.rllib.utils.test_utils import check, get_cartpole_dataset_reader
  from ray.rllib.utils.metrics import ALL_MODULES
  from ray.util.timer import _Timer
label_desc: Use image node_id 5 for calling the TestLearnerGroupSyncUpdate obj's underlying member method code with example usage: obj.test_update_multigpu() without return types