docstr: Test if torch.compile() can be applied and used on the learner.
  
  Also tests if we can update with the compiled update method without errors.
docstr_tok: ["Test","if","torch.compile","(",")","can","be","applied","and","used","on","the","learner",".","Also","tests","if","we","can","update","with","the","compiled","update","method","without","errors","."]
code: def test_torch_compile(self):
      """Test if torch.compile() can be applied and used on the learner.
  
      Also tests if we can update with the compiled update method without errors.
      """
  
      env = gym.make("CartPole-v1")
      is_multi_agents = [False, True]
      what_to_compiles = [
          TorchCompileWhatToCompile.FORWARD_TRAIN,
          TorchCompileWhatToCompile.COMPLETE_UPDATE,
      ]
  
      for is_multi_agent, what_to_compile in itertools.product(
          is_multi_agents, what_to_compiles
      ):
          print(
              f"Testing is_multi_agent={is_multi_agent},"
              f"what_to_compile={what_to_compile}"
          )
          config = BaseTestingAlgorithmConfig().framework(
              torch_compile_learner=True,
              torch_compile_learner_what_to_compile=what_to_compile,
          )
          learner = config.build_learner(env=env)
  
          learner.build()
  
          reader = get_cartpole_dataset_reader(batch_size=512)
  
          for iter_i in range(10):
              batch = reader.next()
              learner.update(batch.as_multi_agent())
  
          rl_module_spec = config.get_default_rl_module_spec()
          rl_module_spec.observation_space = env.observation_space
          rl_module_spec.action_space = env.action_space
          learner.add_module(
              module_id="another_module", module_spec=rl_module_spec
          )
  
          for iter_i in range(10):
              batch = MultiAgentBatch(
                  {
                      "another_module": reader.next(),
                      "default_policy": reader.next(),
                  },
                  0,
              )
              learner.update(batch)
  
          learner.remove_module(module_id="another_module")
  
file_path: ray/rllib/core/learner/torch/tests/test_torch_learner_compile.py
imports: import itertools
  import unittest
  import gymnasium
  import ray
  from ray.rllib.algorithms.algorithm_config import TorchCompileWhatToCompile
  from ray.rllib.core.testing.testing_learner import BaseTestingAlgorithmConfig
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.test_utils import get_cartpole_dataset_reader
  from ray.rllib.utils.torch_utils import _dynamo_is_available
label_desc: Use image node_id 3 for calling the TestLearner obj's underlying member method code with example usage: obj.test_torch_compile() without return types