code: def test_multi_agent_spec(self):
      env = gym.make("CartPole-v1")
      num_agents = 2
      for module_class in MODULES:
          module_specs = {}
          for i in range(num_agents):
              module_specs[f"module_{i}"] = SingleAgentRLModuleSpec(
                  module_class=module_class,
                  observation_space=env.observation_space,
                  action_space=env.action_space,
                  model_config_dict={"fcnet_hiddens": [32 * (i + 1)]},
              )
  
          spec = MultiAgentRLModuleSpec(module_specs=module_specs)
          module = spec.build()
          self.assertIsInstance(module, MultiAgentRLModule)
  
file_path: ray/python/ray/rllib/core/rl_module/tests/test_rl_module_specs.py
imports: import unittest
  import gymnasium
  import torch
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule, MultiAgentRLModuleSpec
  from ray.rllib.core.testing.torch.bc_module import DiscreteBCTorchModule, BCTorchRLModuleWithSharedGlobalEncoder, BCTorchMultiAgentModuleWithSharedEncoder
  from ray.rllib.core.testing.tf.bc_module import DiscreteBCTFModule, BCTfRLModuleWithSharedGlobalEncoder, BCTfMultiAgentModuleWithSharedEncoder
label_desc: Use image node_id 2 for calling the TestRLModuleSpecs obj's underlying member method code with example usage: obj.test_multi_agent_spec() without return types