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: Given actions, to generate the corresponding trial configuration
docstr_tok: ["Given","actions",",","to","generate","the","corresponding","trial","configuration"]
code: def _actions_to_config(self, actions):
      """
      Given actions, to generate the corresponding trial configuration
      """
      chosen_arch = copy.deepcopy(self.chosen_arch_template)
      for cnt, act in enumerate(actions):
          act_name = self.full_act_space[act]
          (_key, _type) = self.actions_to_config[cnt]
          if _type == "input_choice":
              if act_name == "None":
                  chosen_arch[_key] = {"_value": [], "_idx": []}
              else:
                  candidates = self.search_space[_key]["_value"][
                      "candidates"
                  ]
                  idx = candidates.index(act_name)
                  chosen_arch[_key] = {
                      "_value": [act_name],
                      "_idx": [idx],
                  }
          elif _type == "layer_choice":
              idx = self.search_space[_key]["_value"].index(act_name)
              chosen_arch[_key] = {"_value": act_name, "_idx": idx}
          else:
              raise ValueError("unrecognized key: {0}".format(_type))
      return chosen_arch
  
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 5 for calling the PPOTuner obj's underlying member method code with example usage: obj._actions_to_config(actions) and returns: chosen_arch