docstr: This is a utility function that retrieves the expected marl specs.
  
  Args:
      config: The algorithm config.
      expected_module_class: This is the expected RLModule class that is going to
          be reference in the SingleAgentRLModuleSpec parts of the
          MultiAgentRLModuleSpec.
      passed_module_class: This is the RLModule class that is passed into the
          module_spec argument of get_marl_module_spec. The function is
          designed so that it will use the passed in module_spec for the
          SingleAgentRLModuleSpec parts of the MultiAgentRLModuleSpec.
      expected_marl_module_class: This is the expected MultiAgentRLModule class
          that is going to be reference in the MultiAgentRLModuleSpec.
  
  Returns:
      Tuple of the returned MultiAgentRLModuleSpec from config.
      get_marl_module_spec() and the expected MultiAgentRLModuleSpec.
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code: def _get_expected_marl_spec(
      self,
      config: AlgorithmConfig,
      expected_module_class: Type[RLModule],
      passed_module_class: Type[RLModule] = None,
      expected_marl_module_class: Type[MultiAgentRLModule] = None,
  ):
      """This is a utility function that retrieves the expected marl specs.
  
      Args:
          config: The algorithm config.
          expected_module_class: This is the expected RLModule class that is going to
              be reference in the SingleAgentRLModuleSpec parts of the
              MultiAgentRLModuleSpec.
          passed_module_class: This is the RLModule class that is passed into the
              module_spec argument of get_marl_module_spec. The function is
              designed so that it will use the passed in module_spec for the
              SingleAgentRLModuleSpec parts of the MultiAgentRLModuleSpec.
          expected_marl_module_class: This is the expected MultiAgentRLModule class
              that is going to be reference in the MultiAgentRLModuleSpec.
  
      Returns:
          Tuple of the returned MultiAgentRLModuleSpec from config.
          get_marl_module_spec() and the expected MultiAgentRLModuleSpec.
      """
      from ray.rllib.policy.policy import PolicySpec
  
      if expected_marl_module_class is None:
          expected_marl_module_class = MultiAgentRLModule
  
      env = gym.make("CartPole-v1")
      policy_spec_ph = PolicySpec(
          observation_space=env.observation_space,
          action_space=env.action_space,
          config=AlgorithmConfig(),
      )
  
      marl_spec = config.get_marl_module_spec(
          policy_dict={"p1": policy_spec_ph, "p2": policy_spec_ph},
          single_agent_rl_module_spec=SingleAgentRLModuleSpec(
              module_class=passed_module_class
          )
          if passed_module_class
          else None,
      )
  
      expected_marl_spec = MultiAgentRLModuleSpec(
          marl_module_class=expected_marl_module_class,
          module_specs={
              "p1": SingleAgentRLModuleSpec(
                  module_class=expected_module_class,
                  observation_space=env.observation_space,
                  action_space=env.action_space,
                  model_config_dict=AlgorithmConfig().model,
              ),
              "p2": SingleAgentRLModuleSpec(
                  module_class=expected_module_class,
                  observation_space=env.observation_space,
                  action_space=env.action_space,
                  model_config_dict=AlgorithmConfig().model,
              ),
          },
      )
  
      return marl_spec, expected_marl_spec
  
imports: import gymnasium
  from typing import Type
  import unittest
  import ray
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.callbacks import make_multi_callbacks
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
  from ray.rllib.algorithms.ppo.tf.ppo_tf_learner import PPOTfLearner
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec, RLModule
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModuleSpec, MultiAgentRLModule
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 12 for calling the TestAlgorithmConfig obj's underlying member method code with example usage: obj._get_expected_marl_spec(config, expected_module_class, passed_module_class, expected_marl_module_class) and returns: marl_spec,  expected_marl_spec