docstr: Test if logic around TorchCompileConfig works as intended.
docstr_tok: ["Test","if","logic","around","TorchCompileConfig","works","as","intended","."]
code: def test_torch_compile_forwards(self):
      """Test if logic around TorchCompileConfig works as intended."""
  
      spec = SingleAgentRLModuleSpec(
          module_class=PPOTorchRLModule,
          observation_space=gym.spaces.Box(low=0, high=1, shape=(32,)),
          action_space=gym.spaces.Box(low=0, high=1, shape=(1,)),
          model_config_dict={},
          catalog_class=PPOCatalog,
      )
      torch_module = spec.build()
  
      compile_config = TorchCompileConfig()
  
      torch_module.compile(compile_config)
  
      # We should still be able to call the forward methods
      torch_module._forward_train({"obs": torch.randn(1, 32)})
      torch_module._forward_inference({"obs": torch.randn(1, 32)})
      torch_module._forward_exploration({"obs": torch.randn(1, 32)})
  
imports: import unittest
  from dataclasses import dataclass
  import gymnasium
  from ray.rllib.core.models.configs import ModelConfig
  from ray.rllib.core.models.specs.checker import SpecCheckingError
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.core.models.tf.base import TfModel
  from ray.rllib.core.models.torch.base import TorchModel
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.core.rl_module.torch.torch_compile_config import TorchCompileConfig
  from ray.rllib.utils.torch_utils import _dynamo_is_available
label_desc: Use image node_id 4 for calling the TestModelBase obj's underlying member method code with example usage: obj.test_torch_compile_forwards() without return types