docstr: Test if we can pass catalog to algorithm config and it ends up inside
  RLModule and is used to build models there.
docstr_tok: ["Test","if","we","can","pass","catalog","to","algorithm","config","and","it","ends","up","inside","RLModule","and","is","used","to","build","models","there","."]
code: def test_customize_catalog_from_algorithm_config(self):
      """Test if we can pass catalog to algorithm config and it ends up inside
      RLModule and is used to build models there."""
  
      class MyCatalog(PPOCatalog):
          def build_vf_head(self, framework):
              return torch.nn.Linear(self.latent_dims[0], 1)
  
      config = (
          PPOConfig()
          .experimental(_enable_new_api_stack=True)
          .rl_module(
              rl_module_spec=SingleAgentRLModuleSpec(
                  catalog_class=MyCatalog
              ),
          )
          .framework("torch")
      )
  
      algo = config.build(env="CartPole-v0")
      self.assertEqual(
          algo.get_policy("default_policy").model.config.catalog_class,
          MyCatalog,
      )
  
      # Test if we can pass custom catalog to algorithm config and train with it.
  
      config = (
          PPOConfig()
          .rl_module(
              rl_module_spec=SingleAgentRLModuleSpec(
                  module_class=PPOTorchRLModule, catalog_class=MyCatalog
              )
          )
          .framework("torch")
      )
  
      algo = config.build(env="CartPole-v0")
      algo.train()
  
imports: from collections import namedtuple
  import functools
  import itertools
  import unittest
  import gymnasium
  from gymnasium.spaces import Box, Discrete, Dict, Tuple, MultiDiscrete
  import numpy
  import tree
  from ray.rllib.algorithms.ppo.ppo import PPOConfig
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.core.models.base import Encoder, STATE_IN, ENCODER_OUT, STATE_OUT
  from ray.rllib.core.models.catalog import Catalog, _multi_action_dist_partial_helper, _multi_categorical_dist_partial_helper
  from ray.rllib.core.models.configs import MLPEncoderConfig, ModelConfig, CNNEncoderConfig
  from ray.rllib.core.models.torch.base import TorchModel
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.models import MODEL_DEFAULTS
  from ray.rllib.models.tf.tf_distributions import TfCategorical, TfDiagGaussian, TfMultiCategorical, TfMultiDistribution
  from ray.rllib.models.torch.torch_distributions import TorchCategorical, TorchDiagGaussian, TorchMultiCategorical, TorchMultiDistribution
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.test_utils import framework_iterator
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 4 for calling the TestCatalog obj's underlying member method code with example usage: obj.test_customize_catalog_from_algorithm_config() and returns: torch