class_docstr: N-A2C Tuner
  Based on paper Compiler-Level Matrix Multiplication Optimization for Deep Learning
  
  Parameters
  ----------
  optimize_mode: str, 'maximize' or 'minimize'
  n_states: int,
      The maximum search steps Tau
  n_steps: int
      number of steps to train the policy and critic networks each iteration
  hidden_size: int,
      number of hidden size of the policy and critic networks
  lr: float,
      learning rate of the policy and critic networks
class_docstr_tok: ["N-A2C","Tuner","Based","on","paper","Compiler-Level","Matrix","Multiplication","Optimization","for","Deep","Learning","Parameters","--","--","--","--","--","optimize_mode",":","str",",","'maximize","'","or","'minimize'","n_states",":","int",",","The","maximum","search","steps","Tau","n_steps",":","int","number","of","steps","to","train","the","policy","and","critic","networks","each","iteration","hidden_size",":","int",",","number","of","hidden","size","of","the","policy","and","critic","networks","lr",":","float",",","learning","rate","of","the","policy","and","critic","networks"]
docstr: Returns multiple sets of trial (hyper-)parameters,
  as iterable of serializable objects.
docstr_tok: ["Returns","multiple","sets","of","trial","(","hyper-",")","parameters",",","as","iterable","of","serializable","objects","."]
code: def generate_multiple_parameters(self, parameter_id_list, **kwargs):
      """Returns multiple sets of trial (hyper-)parameters,
      as iterable of serializable objects.
      """
      result = []
      self.send_trial_callback = kwargs["st_callback"]
      for parameter_id in parameter_id_list:
          had_exception = False
          try:
              self.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 math
  import random
  import logging
  import copy
  import torch
  from torch import optim
  from torch import nn
  import torch.nn.functional
  import numpy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 3 for calling the N_A2C obj's underlying member method code with example usage: obj.generate_multiple_parameters(parameter_id_list) and returns: result