code: def rl2_trpo_halfcheetah(
      ctxt,
      seed,
      max_episode_length,
      meta_batch_size,
      n_epochs,
      episode_per_task,
  ):
      """Train TRPO with HalfCheetah environment.
  
      Args:
          ctxt (ExperimentContext): The experiment configuration used by
              :class:`~Trainer` to create the :class:`~Snapshotter`.
          seed (int): Used to seed the random number generator to produce
              determinism.
          max_episode_length (int): Maximum length of a single episode.
          meta_batch_size (int): Meta batch size.
          n_epochs (int): Total number of epochs for training.
          episode_per_task (int): Number of training episode per task.
  
  
      """
      set_seed(seed)
      with TFTrainer(snapshot_config=ctxt) as trainer:
          tasks = task_sampler.SetTaskSampler(
              HalfCheetahVelEnv,
              wrapper=lambda env, _: RL2Env(
                  GymEnv(env, max_episode_length=max_episode_length)
              ),
          )
  
          env_spec = RL2Env(
              GymEnv(
                  HalfCheetahVelEnv(),
                  max_episode_length=max_episode_length,
              )
          ).spec
          policy = GaussianGRUPolicy(
              name="policy",
              hidden_dim=64,
              env_spec=env_spec,
              state_include_action=False,
          )
  
          baseline = LinearFeatureBaseline(env_spec=env_spec)
  
          envs = tasks.sample(meta_batch_size)
          sampler = LocalSampler(
              agents=policy,
              envs=envs,
              max_episode_length=env_spec.max_episode_length,
              is_tf_worker=True,
              n_workers=meta_batch_size,
              worker_class=RL2Worker,
              worker_args=dict(n_episodes_per_trial=episode_per_task),
          )
  
          algo = RL2TRPO(
              meta_batch_size=meta_batch_size,
              task_sampler=tasks,
              env_spec=env_spec,
              policy=policy,
              baseline=baseline,
              sampler=sampler,
              episodes_per_trial=episode_per_task,
              discount=0.99,
              max_kl_step=0.01,
              optimizer=ConjugateGradientOptimizer,
              optimizer_args=dict(
                  hvp_approach=FiniteDifferenceHVP(base_eps=1e-5)
              ),
          )
  
          trainer.setup(algo, envs)
  
          trainer.train(
              n_epochs=n_epochs,
              batch_size=episode_per_task
              * max_episode_length
              * meta_batch_size,
          )
  
file_path: garage/src/garage/examples/tf/rl2_trpo_halfcheetah.py
imports: import click
  from garage import wrap_experiment
  from garage.envs import GymEnv
  from garage.envs.mujoco.half_cheetah_vel_env import HalfCheetahVelEnv
  from garage.experiment import task_sampler
  from garage.experiment.deterministic import set_seed
  from garage.np.baselines import LinearFeatureBaseline
  from garage.sampler import LocalSampler
  from garage.tf.algos import RL2TRPO
  from garage.tf.algos.rl2 import RL2Env, RL2Worker
  from garage.tf.optimizers import ConjugateGradientOptimizer, FiniteDifferenceHVP
  from garage.tf.policies import GaussianGRUPolicy
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: rl2_trpo_halfcheetah(ctxt, seed, max_episode_length, meta_batch_size, n_epochs, episode_per_task) without return types