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: Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
  
  Parameters
  ----------
  parameter_id_list : list of int
      Unique identifiers for each set of requested hyper-parameters.
      These will later be used in :meth:`receive_trial_result`.
  **kwargs
      Not used
  
  Returns
  -------
  list
      A list of newly generated configurations
docstr_tok: ["Returns","multiple","sets","of","trial","(","hyper-",")","parameters",",","as","iterable","of","serializable","objects",".","Parameters","--","--","--","--","--","parameter_id_list",":","list","of","int","Unique","identifiers","for","each","set","of","requested","hyper-parameters",".","These","will","later","be","used","in",":","meth",":","`","receive_trial_result","`",".","*","*","kwargs","Not","used","Returns","--","--","--","-","list","A","list","of","newly","generated","configurations"]
code: def generate_multiple_parameters(self, parameter_id_list, **kwargs):
      """
      Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
  
      Parameters
      ----------
      parameter_id_list : list of int
          Unique identifiers for each set of requested hyper-parameters.
          These will later be used in :meth:`receive_trial_result`.
      **kwargs
          Not used
  
      Returns
      -------
      list
          A list of newly generated configurations
      """
      result = []
      self.send_trial_callback = kwargs["st_callback"]
      for parameter_id in parameter_id_list:
          had_exception = False
          try:
              logger.debug("generating param for %s", parameter_id)
              res = self.generate_parameters(parameter_id, **kwargs)
          except nni.NoMoreTrialError:
              had_exception = True
          if not had_exception:
              result.append(res)
      return result
  
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 6 for calling the PPOTuner obj's underlying member method code with example usage: obj.generate_multiple_parameters(parameter_id_list) and returns: result