code: def fixture_exp(snapshot_config, sess):
      """Dummy fixture experiment function.
  
      Args:
          snapshot_config (garage.experiment.SnapshotConfig): The snapshot
              configuration used by Trainer to create the snapshotter.
              If None, it will create one with default settings.
          sess (tf.Session): An optional TensorFlow session.
                A new session will be created immediately if not provided.
  
      Returns:
          np.ndarray: Values of the parameters evaluated in
              the current session
  
      """
      with TFTrainer(
          snapshot_config=snapshot_config, sess=sess
      ) as trainer:
          env = GymEnv("CartPole-v1", max_episode_length=100)
  
          policy = CategoricalMLPPolicy(
              name="policy", env_spec=env.spec, hidden_sizes=(8, 8)
          )
  
          baseline = LinearFeatureBaseline(env_spec=env.spec)
  
          sampler = LocalSampler(
              agents=policy,
              envs=env,
              max_episode_length=env.spec.max_episode_length,
          )
  
          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)
          trainer.train(n_epochs=5, batch_size=100)
  
          return policy.get_param_values()
  
imports: from garage.envs import GymEnv
  from garage.np.baselines import LinearFeatureBaseline
  from garage.sampler import LocalSampler
  from garage.tf.algos import VPG
  from garage.tf.policies import CategoricalMLPPolicy
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: fixture_exp(snapshot_config, sess) and returns: policy