class_docstr: G-BFS Tuner
  Based on paper Compiler-Level Matrix Multiplication Optimization for Deep Learning
  
  Parameters
  ----------
  optimize_mode: str, 'maximize' or 'minimize'
  num_samples: int,
      The random selection parameter rho
class_docstr_tok: ["G-BFS","Tuner","Based","on","paper","Compiler-Level","Matrix","Multiplication","Optimization","for","Deep","Learning","Parameters","--","--","--","--","--","optimize_mode",":","str",",","'maximize","'","or","'minimize'","num_samples",":","int",",","The","random","selection","parameter","rho"]
code: def __init__(self, optimize_mode="maximize", num_samples=5):
      self.logger = logging.getLogger(
          self.__module__ + "." + self.__class__.__name__
      )
      self.logger.setLevel("DEBUG")
  
      self.opt_mode = optimize_mode
      self.num_samples = num_samples
  
      self.request_list = []
      self.serve_list = []
      self.wait_dict = {}
  
imports: import math
  import random
  import logging
  import copy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 1 to create a new G_BFS object from inherited base classes: Tuner with example: obj = G_BFS(optimize_mode, num_samples)