code: def test_torch_compile_no_memory_leak_gpu(self):
      assert torch.cuda.is_available()
  
      def get_memory_usage_cuda():
          torch.cuda.empty_cache()
          return torch.cuda.memory_allocated()
  
      compile_cfg = TorchCompileConfig()
  
      env = gym.make("CartPole-v1")
  
      memory_before_create = get_memory_usage_cuda()
  
      torch_rl_module = DiscreteBCTorchModule(
          config=RLModuleConfig(
              env.observation_space,
              env.action_space,
              model_config_dict={"fcnet_hiddens": [32]},
          )
      )
  
      torch_rl_module.cuda()
  
      torch_rl_module.compile(compile_cfg)
  
      memory_after_create = get_memory_usage_cuda()
      memory_diff_create = memory_after_create - memory_before_create
      print("memory_diff_create: ", memory_diff_create)
      # Sanity check that we actually allocated memory.
      assert memory_diff_create > 0
  
      del torch_rl_module
      gc.collect()
      memory_after_delete = get_memory_usage_cuda()
      memory_diff_delete = memory_after_delete - memory_after_create
      print("memory_diff_delete: ", memory_diff_delete)
  
      # Memory should be released after deleting the module.
      check(memory_before_create, memory_after_delete)
  
imports: import tempfile
  import unittest
  from typing import Mapping
  import gc
  import gymnasium
  import torch
  from ray.rllib.utils.torch_utils import _dynamo_is_available
  from ray.rllib.core.rl_module.rl_module import RLModuleConfig
  from ray.rllib.core.rl_module.torch import TorchRLModule
  from ray.rllib.core.rl_module.torch.torch_compile_config import TorchCompileConfig
  from ray.rllib.core.testing.torch.bc_module import DiscreteBCTorchModule
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 1 for calling the TestRLModuleGPU obj's underlying member method code with example usage: obj.test_torch_compile_no_memory_leak_gpu() and returns: torch