code: def tutorial_vpg(ctxt=None):
      """Train VPG with PointEnv environment.
  
      Args:
          ctxt (ExperimentContext): The experiment configuration used by
              :class:`~Trainer` to create the :class:`~Snapshotter`.
  
      """
      set_seed(100)
      with TFTrainer(ctxt) as trainer:
          env = PointEnv(max_episode_length=200)
          policy = GaussianMLPPolicy(env.spec)
          sampler = LocalSampler(
              agents=policy,
              envs=env,
              max_episode_length=env.spec.max_episode_length,
              is_tf_worker=True,
          )
          algo = SimpleVPG(env.spec, policy, sampler)
          trainer.setup(algo, env)
          trainer.train(n_epochs=200, batch_size=4000)
  
file_path: garage/src/garage/examples/tf/tutorial_vpg.py
imports: import numpy
  import tensorflow
  from garage import EpisodeBatch, log_performance, wrap_experiment
  from garage.envs import PointEnv
  from garage.experiment.deterministic import set_seed
  from garage.np import discount_cumsum
  from garage.sampler import LocalSampler
  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: tutorial_vpg(ctxt) without return types