code: def trpo_swimmer(ctxt=None, seed=1, batch_size=4000):
      """Train TRPO with Swimmer-v2 environment.
  
      Args:
          ctxt (garage.experiment.ExperimentContext): The experiment
              configuration used by Trainer to create the snapshotter.
          seed (int): Used to seed the random number generator to produce
              determinism.
          batch_size (int): Number of timesteps to use in each training step.
  
      """
      set_seed(seed)
      with TFTrainer(ctxt) as trainer:
          env = GymEnv("Swimmer-v2")
  
          policy = GaussianMLPPolicy(
              env_spec=env.spec, hidden_sizes=(32, 32)
          )
  
          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 = TRPO(
              env_spec=env.spec,
              policy=policy,
              baseline=baseline,
              sampler=sampler,
              discount=0.99,
              max_kl_step=0.01,
          )
  
          trainer.setup(algo, env)
          trainer.train(n_epochs=40, batch_size=batch_size)
  
file_path: garage/src/garage/examples/tf/trpo_swimmer.py
imports: from garage import wrap_experiment
  from garage.envs import GymEnv
  from garage.experiment.deterministic import set_seed
  from garage.np.baselines import LinearFeatureBaseline
  from garage.sampler import RaySampler
  from garage.tf.algos import TRPO
  from garage.tf.policies import GaussianMLPPolicy
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: trpo_swimmer(ctxt, seed, batch_size) without return types