class_docstr: Simple Vanilla Policy Gradient.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (garage.tf.policies.StochasticPolicy): Policy.
      sampler (garage.sampler.Sampler): Sampler.
code: def __init__(self, env_spec, policy, sampler):
      self.env_spec = env_spec
      self.policy = policy
      self._sampler = sampler
      self.max_episode_length = env_spec.max_episode_length
  
      self._discount = 0.99
      self._policy_opt = torch.optim.Adam(
          self.policy.parameters(), lr=1e-3
      )
  
file_path: garage/src/garage/examples/torch/tutorial_vpg.py
imports: import numpy
  import torch
  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.torch.policies import GaussianMLPPolicy
  from garage.trainer import Trainer
label_desc: Use image node_id 1 to create a new SimpleVPG object  with example: obj = SimpleVPG(env_spec, policy, sampler)