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"]
code: def __init__(
      self,
      optimize_mode="maximize",
      n_states=6,
      n_steps=3,
      hidden_size=128,
      lr=1e-3,
  ):
      self.logger = logging.getLogger(
          self.__module__ + "." + self.__class__.__name__
      )
      self.logger.setLevel("DEBUG")
  
      self.opt_mode = optimize_mode
      self.n_states = n_states
      self.n_steps = n_steps
      self.hidden_size = 128
      self.lr = lr
  
      self.request_list = []
      self.serve_list = []
      self.wait_dict = {}
  
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 1 to create a new N_A2C object from inherited base classes: Tuner with example: obj = N_A2C(optimize_mode, n_states, n_steps, hidden_size, lr)