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"]
docstr: Method invoked when a trial is completed or terminated.
  
  Override of the abstract method in :class:`~nni.tuner.Tuner`.
docstr_tok: ["Method","invoked","when","a","trial","is","completed","or","terminated",".","Override","of","the","abstract","method","in",":","class",":","`","~nni.tuner.Tuner","`","."]
code: def trial_end(self, parameter_id, success, **kwargs):
      """Method invoked when a trial is completed or terminated.
  
      Override of the abstract method in :class:`~nni.tuner.Tuner`.
      """
      if not success:
          self.population.append(self.wait_dict[parameter_id], 0.0)
          del self.wait_dict[parameter_id]
  
imports: import math
  import random
  import logging
  import copy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 6 for calling the G_BFS obj's underlying member method code with example usage: obj.trial_end(parameter_id, success) without return types