code: def test_make_sampler_ray_sampler(self, ray_session_fixture):
      del ray_session_fixture
      assert ray.is_initialized()
      with TFTrainer(snapshot_config) as trainer:
          env = GymEnv("CartPole-v1")
  
          policy = CategoricalMLPPolicy(
              name="policy", env_spec=env.spec, hidden_sizes=(8, 8)
          )
  
          baseline = LinearFeatureBaseline(env_spec=env.spec)
  
          sampler = RaySampler(
              agents=policy,
              envs=env,
              max_episode_length=env.spec.max_episode_length,
              is_tf_worker=True,
          )
  
          algo = VPG(
              env_spec=env.spec,
              policy=policy,
              baseline=baseline,
              sampler=sampler,
              discount=0.99,
              optimizer_args=dict(
                  learning_rate=0.01,
              ),
          )
  
          trainer.setup(algo, env)
          assert isinstance(trainer._sampler, RaySampler)
          trainer.train(n_epochs=1, batch_size=10)
  
imports: import pytest
  import ray
  import tensorflow
  from garage.envs import GymEnv
  from garage.experiment import deterministic
  from garage.np.baselines import LinearFeatureBaseline
  from garage.sampler import LocalSampler, RaySampler
  from garage.tf.algos import VPG
  from garage.tf.plotter import Plotter
  from garage.tf.policies import CategoricalMLPPolicy
  from garage.trainer import TFTrainer
  from tests.fixtures import snapshot_config, TfGraphTestCase
  from tests.fixtures.sampler import ray_session_fixture
label_desc: Use image node_id 7 for calling the TestTrainer obj's underlying member method code with example usage: obj.test_make_sampler_ray_sampler(ray_session_fixture) without return types