docstr: Tests if torch.compile() does encounter too many breaks.
  
  torch.compile() should ideally not encounter any breaks when compiling the
  update method of the learner. This method tests if we encounter only a given
  number of breaks.
docstr_tok: ["Tests","if","torch.compile","(",")","does","encounter","too","many","breaks",".","torch.compile","(",")","should","ideally","not","encounter","any","breaks","when","compiling","the","update","method","of","the","learner",".","This","method","tests","if","we","encounter","only","a","given","number","of","breaks","."]
code: def test_torch_compile_no_breaks(self):
      """Tests if torch.compile() does encounter too many breaks.
  
      torch.compile() should ideally not encounter any breaks when compiling the
      update method of the learner. This method tests if we encounter only a given
      number of breaks.
      """
  
      env = gym.make("CartPole-v1")
  
      config = BaseTestingAlgorithmConfig().framework(
          torch_compile_learner=True
      )
      learner = config.build_learner(env=env)
  
      import torch._dynamo as dynamo
  
      reader = get_cartpole_dataset_reader(batch_size=512)
  
      batch = reader.next().as_multi_agent()
      batch = learner._convert_batch_type(batch)
  
      # The followingcall to dynamo.explain() breaks depending on the torch version.
      # It works for torch==2.0.0.
      # TODO(Artur): Fit this to to the correct torch version once it is enabled on
      #  CI.
      # This is a helper method of dynamo to analyze where breaks occur.
      (
          explanation,
          out_guards,
          graphs,
          ops_per_graph,
          break_reasons,
          explanation_verbose,
      ) = dynamo.explain(learner._update, batch)
  
      print(explanation_verbose)
  
      # There should be only one break reason - `return_value` - since inputs and
      # outputs are not checked
      # TODO(Artur): Attempt bringing breaks down to 1. (This may not be possible)
      # Note: This test is skipped on CI if torch dynamo is available.
      self.assertEquals(len(break_reasons), 3)
  
file_path: ray/python/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 4 for calling the TestLearner obj's underlying member method code with example usage: obj.test_torch_compile_no_breaks() without return types