class_docstr: PPOTuner, the implementation inherits the main logic of the implementation
  `ppo2 from openai <https://github.com/openai/baselines/tree/master/baselines/ppo2>`__ and is adapted for NAS scenario.
  It uses ``lstm`` for its policy network and value network, policy and value share the same network.
  
  Parameters
  ----------
  optimize_mode : str
      maximize or minimize
  trials_per_update : int
      Number of trials to have for each model update
  epochs_per_update : int
      Number of epochs to run for each model update
  minibatch_size : int
      Minibatch size (number of trials) for the update
  ent_coef : float
      Policy entropy coefficient in the optimization objective
  lr : float
      Learning rate of the model (lstm network), constant
  vf_coef : float
      Value function loss coefficient in the optimization objective
  max_grad_norm : float
      Gradient norm clipping coefficient
  gamma : float
      Discounting factor
  lam : float
      Advantage estimation discounting factor (lambda in the paper)
  cliprange : float
      Cliprange in the PPO algorithm, constant
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docstr: Import additional data for tuning, not supported yet.
  
  Parameters
  ----------
  data : list
      A list of dictionarys, each of which has at least two keys, ``parameter`` and ``value``
docstr_tok: ["Import","additional","data","for","tuning",",","not","supported","yet",".","Parameters","--","--","--","--","--","data",":","list","A","list","of","dictionarys",",","each","of","which","has","at","least","two","keys",",","``","parameter","``","and","``","value","``"]
code: def import_data(self, data):
      """
      Import additional data for tuning, not supported yet.
  
      Parameters
      ----------
      data : list
          A list of dictionarys, each of which has at least two keys, ``parameter`` and ``value``
      """
      logger.warning("PPOTuner cannot leverage imported data.")
  
imports: import copy
  import logging
  import numpy
  from gym import spaces
  from schema import Schema, Optional
  import nni
  from nni import ClassArgsValidator
  from nni.tuner import Tuner
  from nni.utils import OptimizeMode, extract_scalar_reward
  from .model import Model
  from .util import set_global_seeds
  from .policy import build_lstm_policy
label_desc: Use image node_id 11 for calling the PPOTuner obj's underlying member method code with example usage: obj.import_data(data) without return types