docstr: Tests whether the get_marl_module_spec() method works properly.
docstr_tok: ["Tests","whether","the","get_marl_module_spec","(",")","method","works","properly","."]
code: def test_get_marl_module_spec(self):
      """Tests whether the get_marl_module_spec() method works properly."""
      from ray.rllib.core.testing.torch.bc_module import (
          DiscreteBCTorchModule,
      )
  
      class CustomRLModule1(DiscreteBCTorchModule):
          pass
  
      class CustomRLModule2(DiscreteBCTorchModule):
          pass
  
      class CustomRLModule3(DiscreteBCTorchModule):
          pass
  
      class CustomMARLModule1(MultiAgentRLModule):
          pass
  
      ########################################
      # single agent
      class SingleAgentAlgoConfig(AlgorithmConfig):
          def get_default_rl_module_spec(self):
              return SingleAgentRLModuleSpec(
                  module_class=DiscreteBCTorchModule
              )
  
      # multi-agent
      class MultiAgentAlgoConfigWithNoSingleAgentSpec(AlgorithmConfig):
          def get_default_rl_module_spec(self):
              return MultiAgentRLModuleSpec(
                  marl_module_class=CustomMARLModule1
              )
  
      class MultiAgentAlgoConfig(AlgorithmConfig):
          def get_default_rl_module_spec(self):
              return MultiAgentRLModuleSpec(
                  marl_module_class=CustomMARLModule1,
                  module_specs=SingleAgentRLModuleSpec(
                      module_class=DiscreteBCTorchModule
                  ),
              )
  
      ########################################
      # This is the simplest case where we have to construct the marl module based on
      # the default specs only.
      config = SingleAgentAlgoConfig().experimental(
          _enable_new_api_stack=True
      )
  
      spec, expected = self._get_expected_marl_spec(
          config, DiscreteBCTorchModule
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      # expected module should become the passed module if we pass it in.
      spec, expected = self._get_expected_marl_spec(
          config, CustomRLModule2, passed_module_class=CustomRLModule2
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      ########################################
      # This is the case where we pass in a multi-agent RLModuleSpec that asks the
      # algorithm to assign a specific type of RLModule class to certain module_ids.
      config = (
          SingleAgentAlgoConfig()
          .experimental(_enable_new_api_stack=True)
          .rl_module(
              rl_module_spec=MultiAgentRLModuleSpec(
                  module_specs={
                      "p1": SingleAgentRLModuleSpec(
                          module_class=CustomRLModule1
                      ),
                      "p2": SingleAgentRLModuleSpec(
                          module_class=CustomRLModule1
                      ),
                  },
              ),
          )
      )
  
      spec, expected = self._get_expected_marl_spec(
          config, CustomRLModule1
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      ########################################
      # This is the case where we ask the algorithm to assign a specific type of
      # RLModule class to ALL module_ids.
      config = (
          SingleAgentAlgoConfig()
          .experimental(_enable_new_api_stack=True)
          .rl_module(
              rl_module_spec=SingleAgentRLModuleSpec(
                  module_class=CustomRLModule1
              ),
          )
      )
  
      spec, expected = self._get_expected_marl_spec(
          config, CustomRLModule1
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      # expected module should become the passed module if we pass it in.
      spec, expected = self._get_expected_marl_spec(
          config, CustomRLModule2, passed_module_class=CustomRLModule2
      )
      self._assertEqualMARLSpecs(spec, expected)
      ########################################
      # This is an alternative way to ask the algorithm to assign a specific type of
      # RLModule class to ALL module_ids.
      config = (
          SingleAgentAlgoConfig()
          .experimental(_enable_new_api_stack=True)
          .rl_module(
              rl_module_spec=MultiAgentRLModuleSpec(
                  module_specs=SingleAgentRLModuleSpec(
                      module_class=CustomRLModule1
                  )
              ),
          )
      )
  
      spec, expected = self._get_expected_marl_spec(
          config, CustomRLModule1
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      # expected module should become the passed module if we pass it in.
      spec, expected = self._get_expected_marl_spec(
          config, CustomRLModule2, passed_module_class=CustomRLModule2
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      ########################################
      # This is not only assigning a specific type of RLModule class to EACH
      # module_id, but also defining a new custom MultiAgentRLModule class to be used
      # in the multi-agent scenario.
      config = (
          SingleAgentAlgoConfig()
          .experimental(_enable_new_api_stack=True)
          .rl_module(
              rl_module_spec=MultiAgentRLModuleSpec(
                  marl_module_class=CustomMARLModule1,
                  module_specs={
                      "p1": SingleAgentRLModuleSpec(
                          module_class=CustomRLModule1
                      ),
                      "p2": SingleAgentRLModuleSpec(
                          module_class=CustomRLModule1
                      ),
                  },
              ),
          )
      )
  
      spec, expected = self._get_expected_marl_spec(
          config,
          CustomRLModule1,
          expected_marl_module_class=CustomMARLModule1,
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      # This is expected to return CustomRLModule1 instead of CustomRLModule3 which
      # is passed in. Because the default for p1, p2 is to use CustomRLModule1. The
      # passed module_spec only sets a default to fall back onto in case the
      # module_id is not specified in the original MultiAgentRLModuleSpec. Since P1
      # and P2 are both assigned to CustomeRLModule1, the passed module_spec will not
      # be used. This is the expected behavior for adding a new modules to a
      # multi-agent RLModule that is not defined in the original
      # MultiAgentRLModuleSpec.
      spec, expected = self._get_expected_marl_spec(
          config,
          CustomRLModule1,
          passed_module_class=CustomRLModule3,
          expected_marl_module_class=CustomMARLModule1,
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      ########################################
      # This is the case where we ask the algorithm to use its default
      # MultiAgentRLModuleSpec, but the MultiAgentRLModuleSpec has not defined its
      # SingleAgentRLmoduleSpecs.
      config = MultiAgentAlgoConfigWithNoSingleAgentSpec().experimental(
          _enable_new_api_stack=True
      )
  
      self.assertRaisesRegex(
          ValueError,
          "Module_specs cannot be None",
          lambda: config.rl_module_spec,
      )
  
      ########################################
      # This is the case where we ask the algorithm to use its default
      # MultiAgentRLModuleSpec, and the MultiAgentRLModuleSpec has defined its
      # SingleAgentRLmoduleSpecs.
      config = MultiAgentAlgoConfig().experimental(
          _enable_new_api_stack=True
      )
  
      spec, expected = self._get_expected_marl_spec(
          config,
          DiscreteBCTorchModule,
          expected_marl_module_class=CustomMARLModule1,
      )
      self._assertEqualMARLSpecs(spec, expected)
  
      spec, expected = self._get_expected_marl_spec(
          config,
          CustomRLModule1,
          passed_module_class=CustomRLModule1,
          expected_marl_module_class=CustomMARLModule1,
      )
      self._assertEqualMARLSpecs(spec, expected)
  
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 13 for calling the TestAlgorithmConfig obj's underlying member method code with example usage: obj.test_get_marl_module_spec() and returns: SingleAgentRLModuleSpec, MultiAgentRLModuleSpec, MultiAgentRLModuleSpec