class_docstr: Proximal Policy Optimization algorithm (GPU version).
  Paper: https://arxiv.org/abs/1707.06347
  
  :param policy: (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...)
  :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
  :param gamma: (float) Discount factor
  :param n_steps: (int) The number of steps to run for each environment per update
      (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
  :param ent_coef: (float) Entropy coefficient for the loss calculation
  :param learning_rate: (float or callable) The learning rate, it can be a function
  :param vf_coef: (float) Value function coefficient for the loss calculation
  :param max_grad_norm: (float) The maximum value for the gradient clipping
  :param lam: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
  :param nminibatches: (int) Number of training minibatches per update. For recurrent policies,
      the number of environments run in parallel should be a multiple of nminibatches.
  :param noptepochs: (int) Number of epoch when optimizing the surrogate
  :param cliprange: (float or callable) Clipping parameter, it can be a function
  :param cliprange_vf: (float or callable) Clipping parameter for the value function, it can be a function.
      This is a parameter specific to the OpenAI implementation. If None is passed (default),
      then `cliprange` (that is used for the policy) will be used.
      IMPORTANT: this clipping depends on the reward scaling.
      To deactivate value function clipping (and recover the original PPO implementation),
      you have to pass a negative value (e.g. -1).
  :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
  :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
  :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
  :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
  :param full_tensorboard_log: (bool) enable additional logging when using tensorboard
      WARNING: this logging can take a lot of space quickly
  :param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
      If None (default), use random seed. Note that if you want completely deterministic
      results, you must set `n_cpu_tf_sess` to 1.
  :param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
      If None, the number of cpu of the current machine will be used.
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code: def _make_runner(self):
      return Runner(
          env=self.env,
          model=self,
          n_steps=self.n_steps,
          gamma=self.gamma,
          lam=self.lam,
      )
  
imports: import time
  import gym
  import numpy
  import tensorflow
  from stable_baselines import logger
  from stable_baselines.common import explained_variance, ActorCriticRLModel, tf_util, SetVerbosity, TensorboardWriter
  from stable_baselines.common.runners import AbstractEnvRunner
  from stable_baselines.common.policies import ActorCriticPolicy, RecurrentActorCriticPolicy
  from stable_baselines.common.schedules import get_schedule_fn
  from stable_baselines.common.tf_util import total_episode_reward_logger
  from stable_baselines.common.math_util import safe_mean
label_desc: Use image node_id 2 for calling the PPO2 obj's underlying member method code with example usage: obj._make_runner() and returns: Runner